From fe90848a6b5925ce7735f7b61e03c8c9290e772f Mon Sep 17 00:00:00 2001 From: Cecile Hannay Date: Mon, 22 Jul 2024 14:22:26 -0600 Subject: [PATCH 1/5] review cesm code/workflow section --- .../basics/cesm_workflow/create_clone.ipynb | 10 +- .../basics/cesm_workflow/create_newcase.ipynb | 2 + notebooks/basics/code/git_download_cesm.ipynb | 12 +- notebooks_copy/basics/basics_overview.ipynb | 109 ++ notebooks_copy/basics/cesm_workflow.ipynb | 92 ++ .../basics/cesm_workflow/case_build.ipynb | 258 ++++ .../basics/cesm_workflow/case_setup.ipynb | 154 ++ .../basics/cesm_workflow/case_submit.ipynb | 222 +++ .../checking_jobs_and_status.ipynb | 201 +++ .../basics/cesm_workflow/create_clone.ipynb | 109 ++ .../basics/cesm_workflow/create_newcase.ipynb | 533 +++++++ .../basics/cesm_workflow/model_output.ipynb | 78 ++ notebooks_copy/basics/cesm_workspaces.ipynb | 180 +++ .../basics/code/cesm_code_explore.ipynb | 347 +++++ .../basics/code/git_download_cesm.ipynb | 355 +++++ 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notebooks_copy/sourcemods/overview.ipynb create mode 100644 notebooks_copy/sourcemods/sourcemods.ipynb create mode 100644 notebooks_copy/troubleshooting/debugging_flag.ipynb create mode 100644 notebooks_copy/troubleshooting/exercises/troubleshooting_cam.ipynb create mode 100644 notebooks_copy/troubleshooting/exercises_overview.ipynb create mode 100644 notebooks_copy/troubleshooting/log_files.ipynb create mode 100644 notebooks_copy/troubleshooting/troubleshooting.ipynb diff --git a/notebooks/basics/cesm_workflow/create_clone.ipynb b/notebooks/basics/cesm_workflow/create_clone.ipynb index e53a335a3..49133f88a 100644 --- a/notebooks/basics/cesm_workflow/create_clone.ipynb +++ b/notebooks/basics/cesm_workflow/create_clone.ipynb @@ -1,11 +1,19 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "d6d5e586-3e55-40e2-89ac-43cf2a986a92", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", "metadata": {}, "source": [ - "# Create Clone" + "# Create Clone (Advanced)" ] }, { diff --git a/notebooks/basics/cesm_workflow/create_newcase.ipynb b/notebooks/basics/cesm_workflow/create_newcase.ipynb index 648ec7edc..cd9985017 100644 --- a/notebooks/basics/cesm_workflow/create_newcase.ipynb +++ b/notebooks/basics/cesm_workflow/create_newcase.ipynb @@ -45,6 +45,7 @@ "id": "557d4028-7176-4e2a-8a90-7febf2de37ef", "metadata": {}, "source": [ + "___\n", "## Command Syntax" ] }, @@ -153,6 +154,7 @@ "id": "d7064891-5a5a-41f2-9b3b-d4acb231a2ee", "metadata": {}, "source": [ + "___\n", "## Command Arguments" ] }, diff --git a/notebooks/basics/code/git_download_cesm.ipynb b/notebooks/basics/code/git_download_cesm.ipynb index 1b05f161d..36422ad2d 100644 --- a/notebooks/basics/code/git_download_cesm.ipynb +++ b/notebooks/basics/code/git_download_cesm.ipynb @@ -36,11 +36,9 @@ ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "88f98dea-ada2-4186-bdf6-97c383755fdc", + "cell_type": "markdown", + "id": "40257a4e-aa6d-4f12-b825-b9986cdf4daf", "metadata": {}, - "outputs": [], "source": [ "## Step 1: Create CESM Code Directory" ] @@ -315,13 +313,15 @@ "/glade/work/$USER/code/my_cesm_code\n", "```\n", "\n", - "To navigate to that directory, use the command:\n", + "To navigate to that directory, use the UNIX command `cd`:\n", "```\n", "cd /glade/work/$USER/code/my_cesm_code\n", "```\n", "\n", + "If you are not familiar with UNIX commands, please review the [UNIX chapter](https://ncar.github.io/CESM-Tutorial/notebooks/resources/unix.html) of this documentation. \n", "\n", - "" + "\n", + "\n" ] }, { diff --git a/notebooks_copy/basics/basics_overview.ipynb b/notebooks_copy/basics/basics_overview.ipynb new file mode 100644 index 000000000..1ba5a527e --- /dev/null +++ b/notebooks_copy/basics/basics_overview.ipynb @@ -0,0 +1,109 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b2cd974a-dfbb-4937-8ed0-ab87ab60f1ef", + "metadata": {}, + "source": [ + "# Basics" + ] + }, + { + "cell_type": "markdown", + "id": "9db9d54e-ef78-44ef-a003-4ddd8a416317", + "metadata": {}, + "source": [ + "The CESM Model is developed around a structured workflow. The first step of this workflow is to set up your workspace, which is usually a one-time task. After setting up your workspace, a basic CESM simulation can be run with only four commands. This section will cover these steps and enable you to complete your first CESM run." + ] + }, + { + "cell_type": "markdown", + "id": "e2abf5cb-c507-408e-8f2a-2fd12934873d", + "metadata": {}, + "source": [ + "## Workflow Elements" + ] + }, + { + "cell_type": "markdown", + "id": "acfe09a5-e3ee-457a-a328-bde665cfe761", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "
\n", + "\n", + "- Set up workspace (one time setup)\n", + " - **``Download``** the CESM code\n", + " - **``Create or Locate ``** an **``Input Data``** Root Directory\n", + " - Possibly **``Porting``** if not on a setup machine\n", + "- Creating, Running and Reviewing a Case\n", + " - **``Create``** a new case\n", + " - **``Invoke``** `case.setup`\n", + " - **``Build``** the executable with `case.build`\n", + " - **``Run``** the model with `case.submit`\n", + " - **``Review``** output data\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "0c07b51d-f999-4737-9739-3ea989f20802", + "metadata": {}, + "source": [ + "## Setting up your environment" + ] + }, + { + "cell_type": "markdown", + "id": "9d6954eb-3505-4a58-b6fa-bea7047c63cf", + "metadata": {}, + "source": [ + "Every time you log onto the NCAR HPC you want to ensure you have the correct modules loaded. Please refer to the [NCAR HPC environment](https://ncar.github.io/CESM-Tutorial/notebooks/resources/profile.html#setting-up-your-ncar-hpc-environment) section to make sure you are setting up your environment properly. \n", + "\n", + "**YOU SHOULD ENSURE YOU CHECK THIS EVERY TIME YOU ARE BUILDING A NEW CASE**\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "523165e7-728b-4305-b9f7-4e6ee530a27d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "715674b2-a858-49ae-b044-7f7fc61a750c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "toc-showcode": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/cesm_workflow.ipynb b/notebooks_copy/basics/cesm_workflow.ipynb new file mode 100644 index 000000000..b31e36592 --- /dev/null +++ b/notebooks_copy/basics/cesm_workflow.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CESM Workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CESM has been designed to be easy to use. Once you have downloaded the CESM code, a CESM `case` can be run with a set of 4 commands. \n", + "\n", + "*Note: In CESM jargon, a case refers to a specific instance of a model simulation.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "1) Create a new case using `create_newcase`\n", + "2) Set up the case by invoking `case.setup`\n", + "3) Build the executable using `case.build`\n", + "4) Run your case using `case.submit`\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following sections will go into more detail about these 4 commands and also provide information about how to check your job status or create a clone of a previous case." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More information" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More detailed information on the CESM2 Workflow is documented in the following links and can be read in detail at a later time." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "\n", + "[CESM2 release](https://escomp.github.io/CESM/release-cesm2/)\n", + " \n", + "[CIME](https://esmci.github.io/cime/versions/master/html/index.html)\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks_copy/basics/cesm_workflow/case_build.ipynb b/notebooks_copy/basics/cesm_workflow/case_build.ipynb new file mode 100644 index 000000000..d9832e576 --- /dev/null +++ b/notebooks_copy/basics/cesm_workflow/case_build.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f9dac1fe-df62-4560-adf5-2779d9ed8f2d", + "metadata": { + "tags": [] + }, + "source": [ + "# Case Build\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "4f42d8e1-ac3a-4f2d-b9fb-5664e1625fb4", + "metadata": {}, + "source": [ + "After a new case is setup, the tool that builds the new case by compiling the code is `case.build`. This tool is located in the `$CASEROOT` directory.\n", + "\n", + "Running this script results in the following actions:\n", + "- Checks and creates final component namelists\n", + "- Builds individual model component libraries\n", + "- Builds the final CESM model executable" + ] + }, + { + "cell_type": "markdown", + "id": "0d8b2ab4-7976-48b7-81e4-e2ae0f28cd97", + "metadata": {}, + "source": [ + "![CESM case.build](../../../images/basics/CESM2_case_build.png)\n", + "*

Figure: Detailed view of the location of case.build

*\n", + "\n", + "\n", + "For the current tutorial on derecho, the paths are:\n", + "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", + "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", + "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", + "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` " + ] + }, + { + "cell_type": "markdown", + "id": "1892841c-9c0c-4baf-b73e-413b74798e59", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "35ab1731-c0c2-4a39-a6e1-31f86d6f8cdd", + "metadata": {}, + "source": [ + "## Command Syntax" + ] + }, + { + "cell_type": "markdown", + "id": "f2581dbe-4077-43c1-b2bb-1d26c1af544e", + "metadata": {}, + "source": [ + "
\n", + "\n", + "You should still be in the `CASEROOT` directory after running `case.setup`\n", + "```\n", + "cd /glade/work/$USER/cases/CASE\n", + "```\n", + " \n", + "
\n", + " \n", + "Example `case.build` command:
\n", + " \n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + " \n", + "
\n", + "\n", + "**NOTE:** Do not enter the example above at the command line. You will create your first case in the Exercise at the end of this section.
" + ] + }, + { + "cell_type": "markdown", + "id": "8835e60d-6412-4b21-8b50-f122ce6b167d", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + " \n", + "```\n", + "Building case in directory /glade/work/$USER/cases/b.day1.0\n", + "sharedlib_only is False\n", + "model_only is False\n", + "Generating component namelists as part of build\n", + "- Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.rof\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.atm\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.tavg.5\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.pop.ro.0301-01-01-00000\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.mosart.r.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cpl.r.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.pop.r.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cam.r.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cice.r.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cam.rs.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.clm2.r.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cam.i.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cism.r.0301-01-01-00000.nc\n", + "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.ww3.r.0301-01-01-00000\n", + "Creating component namelists\n", + "Calling /glade/u/home/$USER/my_cesm_code/components/cam//cime_config/buildnml\n", + "...calling cam buildcpp to set build time options\n", + "CAM namelist copy: file1 /glade/work/$USER/cases/b.day1.0/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b.day1.0/run/atm_in\n", + "Calling /glade/u/home/$USER/my_cesm_code/components/clm//cime_config/buildnml\n", + "Calling /glade/u/home/$USER/my_cesm_code/components/cice//cime_config/buildnml\n", + "...buildnml calling cice buildcpp to set build time options\n", + "Calling /glade/u/home/$USER/my_cesm_code/components/pop//cime_config/buildnml\n", + "... buildnml: calling pop buildcpp to set build time options\n", + "given is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2666.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2667.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2668.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2675.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2676.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2677.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2678.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2679.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2680.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2681.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2682.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2683.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2684.\n", + "Calling /glade/u/home/$USER/my_cesm_code/components/mosart//cime_config/buildnml\n", + "Running /glade/u/home/$USER/my_cesm_code/components/cism//cime_config/buildnml\n", + "Calling /glade/u/home/$USER/my_cesm_code/components/ww3//cime_config/buildnml\n", + "Calling /glade/u/home/$USER/my_cesm_code/cime/src/components/stub_comps/sesp/cime_config/buildnml\n", + "Calling /glade/u/home/$USER/my_cesm_code/cime/src/drivers/mct/cime_config/buildnml\n", + "Finished creating component namelists\n", + "Building gptl with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/gptl.bldlog.240606-134217\n", + "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.gptl\n", + "Component gptl build complete with 2 warnings\n", + "Building mct with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/mct.bldlog.240606-134217\n", + "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.mct\n", + "Component mct build complete with 55 warnings\n", + "Building pio with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/pio.bldlog.240606-134217\n", + "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.pio\n", + "Component pio build complete with 28 warnings\n", + "Building csm_share with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/csm_share.bldlog.240606-134217\n", + "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.csm_share\n", + "Component csm_share build complete with 85 warnings\n", + "- Building clm4_5/clm5_0 Library\n", + "Building lnd with output to /glade/derecho/scratch/$USER/b.day1.0/bld/lnd.bldlog.240606-134217\n", + "\n", + "Component lnd build complete with 292 warnings\n", + "clm built in 109.479993 seconds\n", + "Building atm with output to /glade/derecho/scratch/$USER/b.day1.0/bld/atm.bldlog.240606-134217\n", + "Building ice with output to /glade/derecho/scratch/$USER/b.day1.0/bld/ice.bldlog.240606-134217\n", + "Building ocn with output to /glade/derecho/scratch/$USER/b.day1.0/bld/ocn.bldlog.240606-134217\n", + "Building rof with output to /glade/derecho/scratch/$USER/b.day1.0/bld/rof.bldlog.240606-134217\n", + "Building glc with output to /glade/derecho/scratch/$USER/b.day1.0/bld/glc.bldlog.240606-134217\n", + "Building wav with output to /glade/derecho/scratch/$USER/b.day1.0/bld/wav.bldlog.240606-134217\n", + "Building esp with output to /glade/derecho/scratch/$USER/b.day1.0/bld/esp.bldlog.240606-134217\n", + "Component esp build complete with 1 warnings\n", + "sesp built in 4.413109 seconds\n", + "Component wav build complete with 35 warnings\n", + "ww built in 33.126202 seconds\n", + "Component rof build complete with 14 warnings\n", + "mosart built in 33.278138 seconds\n", + "Component ice build complete with 74 warnings\n", + "cice built in 51.958399 seconds\n", + "Component ocn build complete with 207 warnings\n", + "pop built in 86.837194 seconds\n", + "Component atm build complete with 528 warnings\n", + "cam built in 105.142718 seconds\n", + "Component glc build complete with 150 warnings\n", + "cism built in 133.173952 seconds\n", + "Building cesm with output to /glade/derecho/scratch/$USER/b.day1.0/bld/cesm.bldlog.240606-134217\n", + "Component cesm exe build complete with 27 warnings\n", + "Time spent not building: 130.716460 sec\n", + "Time spent building: 389.249592 sec\n", + "MODEL BUILD HAS FINISHED SUCCESSFULLY\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e677c5f-2b85-459b-ad9e-2f4b42904589", + "metadata": {}, + "source": [ + "**Notes:**\n", + "- Notice the `./` before any command run in the CASEROOT. \n", + "- On NCAR HPC machines you must call the `case.build` using `qcmd` because it compiles the model on a compute node rather than a login node. This reduces the load on login nodes and prevents a timeout while you are building the model.\n", + "- The output tells you if it was successful at the end: `MODEL BUILD HAS FINISHED SUCCESSFULLY`" + ] + }, + { + "cell_type": "markdown", + "id": "f6edbb1c-0ca4-49f9-b238-974c1c19de89", + "metadata": {}, + "source": [ + "## Tips" + ] + }, + { + "cell_type": "markdown", + "id": "9b750904-678a-40d2-b5fa-e87c52d5c8eb", + "metadata": {}, + "source": [ + "- To completely rebuild a case, run `./case.build --clean-all` first before building the model.\n", + "- If you want to make any source code modifications in `SourceMods`, do this before building the model. We will cover SourceMods in a later section.\n", + "- If you want to make any modifications to `env_build.xml`, do this before building the model.\n", + "- If any input data is missing the build will abort but provide a list of missing files. To acquire missing data run `./check_input_data --download`. This will download the required data and put it in the `inputdata` directory defined by the XML variable `DIN_LOC_ROOT`. After you have done these steps you can re-run the `case.build` script." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48056ea8-6b71-4485-ad55-d91c8ade8ba0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/cesm_workflow/case_setup.ipynb b/notebooks_copy/basics/cesm_workflow/case_setup.ipynb new file mode 100644 index 000000000..93c5935fd --- /dev/null +++ b/notebooks_copy/basics/cesm_workflow/case_setup.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f9dac1fe-df62-4560-adf5-2779d9ed8f2d", + "metadata": {}, + "source": [ + "# Case Setup\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "69d76269-97de-4fed-9816-22f66e13866e", + "metadata": {}, + "source": [ + "After a new case is created, the tool that sets up the new case is `case.setup`. This tool is located in the `$CASEROOT` directory.\n", + "\n", + "After running `case.setup` the following actions occur:\n", + "- The `$RUNDIR` and `$EXEROOT` directories are created as shown on the figure." + ] + }, + { + "cell_type": "markdown", + "id": "93aec491-6938-4d83-adb0-c059c8c8b87e", + "metadata": {}, + "source": [ + "![CESM case.setup](../../../images/basics/CESM2_case_setup.png)\n", + "*

Figure: Detailed view of the location of case.setup

*\n", + "\n", + "\n", + "For the current tutorial on derecho, the paths are:\n", + "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", + "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", + "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", + "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` " + ] + }, + { + "cell_type": "markdown", + "id": "831f1ad8-627d-4c5f-a692-02e16ce0112e", + "metadata": {}, + "source": [ + "Note that other files and directories are created at `case.setup`. This will be covered in more detail in later sections (i.e. [Namelist modifications](https://ncar.github.io/CESM-Tutorial/notebooks/namelist/namelist.html))\n", + "- `user_nl_xxx` files, where the user can customize component namelist files\n", + "- The `case.run` and `case.st_archive` scripts and `Macros.make` file\n", + "- Hidden files like `env_mach_specific.*`\n", + "- The `CaseDocs` directory, which should not be edited" + ] + }, + { + "cell_type": "markdown", + "id": "07a1da51-7e51-4f3d-b63c-d8492fd1f171", + "metadata": {}, + "source": [ + "## Command Syntax" + ] + }, + { + "cell_type": "markdown", + "id": "d630ea67-4c16-495d-8bdd-3d5fb254b530", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Go to the `$CASEROOT` directory \n", + "```\n", + "cd /glade/work/$USER/cases/CASE\n", + "```\n", + " \n", + "
\n", + " \n", + "Example `case.setup` command:
\n", + " \n", + "```\n", + "./case.setup\n", + "```\n", + " \n", + "
\n", + " \n", + "NOTE: Do not enter the example above at the command line. You will create your first case in the Exercise at the end of this section.
" + ] + }, + { + "cell_type": "markdown", + "id": "b68a6c25-55ee-42a6-bff2-93004c84abd5", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + " \n", + "```\n", + "/glade/work/$USER/cases/b.day1.0/env_mach_specific.xml already exists, delete to replace\n", + "job is case.run USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", + "Creating batch scripts\n", + "Writing case.run script from input template /glade/work/$USERrun\n", + "Writing case.st_archive script from input template /glade/work/$USER/code/my_cesm_code/cime/config/cesm/machines/template.st_archive\n", + "Creating file case.st_archive\n", + "Creating user_nl_xxx files for components and cpl\n", + "If an old case build already exists, might want to run 'case.build --clean' before building\n", + "You can now run './preview_run' to get more info on how your case will be run\n", + "\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "f12bff84-04f0-4d28-8da9-af706f874933", + "metadata": {}, + "source": [ + "**Notes:**\n", + "- Notice the `./` before any command run in the CASEROOT. \n", + "- You can run the script name followed by the `--h` or `--help` argument to see help documentation for that script and a list of all command line arguments for that script.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bae850c-b342-41d3-9237-12e4c5ef3fc0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/cesm_workflow/case_submit.ipynb b/notebooks_copy/basics/cesm_workflow/case_submit.ipynb new file mode 100644 index 000000000..4d66a87a2 --- /dev/null +++ b/notebooks_copy/basics/cesm_workflow/case_submit.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "683d9e77-d700-4f75-b3f0-eb813657351b", + "metadata": { + "tags": [] + }, + "source": [ + "# Case Submit\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae2803f9-270d-4dd6-803a-e4158cbf7b86", + "metadata": {}, + "source": [ + "After a new case is built, the tool that submits the experiment to start it running is `case.submit`. This tool is located in the `$CASEROOT` directory.\n", + "\n", + "Running this script results in the following actions:\n", + "- Checking archive and run options\n", + "- Checking in namelists that need to be rebuilt\n", + "- Checking input data\n", + "- Submitting the `case.run` script to the NCAR HPC batch job scheduler\n", + "- Submitting the `case.st_archive` script to archive the model output. This step is dependent on the successful completion of `case.run`" + ] + }, + { + "cell_type": "markdown", + "id": "fd09cd4e-8e74-4867-aaf7-bb480d679e3d", + "metadata": {}, + "source": [ + "![CESM case.submit](../../../images/basics/CESM2_case_submit.png)\n", + "*

Figure: Detailed view of the location of case.submit

*\n", + "\n", + "For the current tutorial on derecho, the paths are:\n", + "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", + "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", + "- `$CIME_OUTPUT_ROOT` = `/glade/derecho/scratch/$USER`\n", + "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", + "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` " + ] + }, + { + "cell_type": "markdown", + "id": "579cb4c8-03f1-4c9f-913f-de2ffeccfbc5", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "d8e6525b-31d9-450d-a3b9-e3b435fc231a", + "metadata": {}, + "source": [ + "## Command Syntax" + ] + }, + { + "cell_type": "markdown", + "id": "12524d65-0037-4652-a77d-3bfea021869e", + "metadata": {}, + "source": [ + "
\n", + "\n", + "You should still be in the `CASEROOT` directory after running `case.build`\n", + "```\n", + "cd /glade/work/$USER/cases/CASE\n", + "```\n", + " \n", + "
\n", + " \n", + "Example `case.submit` command:
\n", + " \n", + "```\n", + "./case.submit\n", + "```\n", + " \n", + "
\n", + "\n", + "NOTE: Do not enter the example above at the command line. You will create your first case in the Exercise at the end of this section.
" + ] + }, + { + "cell_type": "markdown", + "id": "630dfd32-c4fc-40a9-812c-4c62ff6c4862", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + " \n", + "```\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.rof\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.atm\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.tavg.5\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", + "Creating component namelists\n", + " Calling /glade/u/home/$USER/my_cesm_code/components/cam//cime_config/buildnml\n", + "CAM namelist copy: file1 /glade/work/$USER/cases/b.day1.0/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b.day1.0/run/atm_in \n", + " Calling /glade/u/home/$USER/my_cesm_code/components/clm//cime_config/buildnml\n", + " Calling /glade/u/home/$USER/my_cesm_code/components/cice//cime_config/buildnml\n", + " Calling /glade/u/home/$USER/my_cesm_code/components/pop//cime_config/buildnml\n", + " given is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2666.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2667.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2668.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2675.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2676.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2677.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2678.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2679.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2680.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2681.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2682.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2683.\n", + "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2684.\n", + " Calling /glade/u/home/$USER/my_cesm_code/components/mosart//cime_config/buildnml\n", + " Running /glade/u/home/$USER/my_cesm_code/components/cism//cime_config/buildnml \n", + " Calling /glade/u/home/$USER/my_cesm_code/components/ww3//cime_config/buildnml\n", + " Calling /glade/u/home/$USER/my_cesm_code/cime/src/components/stub_comps/sesp/cime_config/buildnml\n", + " Calling /glade/u/home/$USER/my_cesm_code/cime/src/drivers/mct/cime_config/buildnml\n", + "Finished creating component namelists\n", + "Checking that inputdata is available as part of case submission\n", + "Loading input file list: 'Buildconf/clm.input_data_list'\n", + "Loading input file list: 'Buildconf/cpl.input_data_list'\n", + "Loading input file list: 'Buildconf/pop.input_data_list'\n", + "Loading input file list: 'Buildconf/ww3.input_data_list'\n", + "Loading input file list: 'Buildconf/cice.input_data_list'\n", + "Loading input file list: 'Buildconf/cism.input_data_list'\n", + "Loading input file list: 'Buildconf/mosart.input_data_list'\n", + "Loading input file list: 'Buildconf/cam.input_data_list'\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.rof\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.atm\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.tavg.5\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.rof\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.atm\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.tavg.5\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", + "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", + "Creating component namelists\n", + "Finished creating component namelists\n", + "Check case OK\n", + "submit_jobs case.run\n", + "Submit job case.run\n", + "Submitting job script qsub -q main -l walltime=12:00:00 -A $PROJECT -l job_priority=regular -v ARGS_FOR_SCRIPT='--resubmit' .case.run\n", + "Submitted job id is 4743615.desched1\n", + "Submit job case.st_archive\n", + "Submitting job script qsub -q main -l walltime=0:20:00 -A $PROJECT -l job_priority=regular -W depend=afterok:4743615.desched1 -v ARGS_FOR_SCRIPT='--resubmit' case.st_archive\n", + "Submitted job id is 4743616.desched1\n", + "Submitted job case.run with id 4743615.desched1\n", + "Submitted job case.st_archive with id 4743616.desched1\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "138df653-4e57-4668-ab27-cabe6806076a", + "metadata": {}, + "source": [ + "**Notes:**\n", + "- `case.submit` coordinates all the tasks of running CESM.\n", + "- `case.run` is submitted to the batch job scheduler by `case.submit`. Do not try to submit `case.run` separately.\n", + "- CESM runs in the Build/Run Directory.\n", + "- `case.st_archive` moves the history files to the archive directory runs only after `case.run` completes successfully. If a model run was unsuccessful the output remains in the Run Directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2463ac2-7ada-48bd-8662-9e4115f7e8f5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/cesm_workflow/checking_jobs_and_status.ipynb b/notebooks_copy/basics/cesm_workflow/checking_jobs_and_status.ipynb new file mode 100644 index 000000000..fe99cc33c --- /dev/null +++ b/notebooks_copy/basics/cesm_workflow/checking_jobs_and_status.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Checking Your Run\n" + ] + }, + { + "cell_type": "markdown", + "id": "169d4dc6-f655-4d60-b8c8-46bab0524c91", + "metadata": {}, + "source": [ + "## Batch Job Scheduler Monitoring" + ] + }, + { + "cell_type": "markdown", + "id": "e70a2387-d1d3-41b7-b0df-4811d2246566", + "metadata": {}, + "source": [ + "Once CESM has been submitted the status of the run on derecho can be followed through the qstat command. Qstat accesses the information in the Batch Job Scheduler to see the status of all jobs running on derecho. To simplify the list down the -u option can be specified for a particular user.\n", + "\n", + "
\n", + "Result of running qstat with an active job running:

\n", + "\n", + "```\n", + "qstat -u $USER\n", + "```\n", + "
\n", + " \n", + "Output:
\n", + "\n", + "```\n", + " Req'd Req'd Elap\n", + "Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time\n", + "--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----\n", + "4743615.desche* $USER cpu run.b.day* -- 6 768 1410g 12:00 Q -- \n", + "4743616.desche* $USER cpu st_archiv* -- 1 1 235gb 00:20 H -- \n", + "```\n", + "\n", + "
\n", + "\n", + "Note, there may be a slight delay from submitting CESM until the jobs appear in the queue. Once the jobs have completed they will disappear from the qstat command." + ] + }, + { + "cell_type": "markdown", + "id": "5e079b70-b679-44ff-b13f-0089b0a9e3b1", + "metadata": {}, + "source": [ + "## Monitoring Files in the Run and Archive Directories\n" + ] + }, + { + "cell_type": "markdown", + "id": "a213054b-be89-464b-a596-b6e67fad52db", + "metadata": {}, + "source": [ + "\n", + "As the CESM job runs it will update files in Build / Run Directory. \n", + "Once the job has completed successfully the archiver will transfer history files to the Archive Directory.\n", + "One way to keep track of the progress of the run is to monitor the files in these directories.\n", + "- Check the files in the `$RUNDIR` as the model is running and once it is finished\n", + "- Check the files in the `$DOUT_S_ROOT` after `case.st_archive` runs." + ] + }, + { + "cell_type": "markdown", + "id": "6d99a83a-9b78-4b02-9aa0-e8dbf1cb8d0b", + "metadata": {}, + "source": [ + "## CaseStatus file" + ] + }, + { + "cell_type": "markdown", + "id": "c93c592b-bda0-45a5-97a0-747c600807e1", + "metadata": {}, + "source": [ + "\n", + "All activities for the case are recorded in the CaseStatus file in the CASEROOT directory. By looking through the file the successful or otherwise outcome of each step of the run can be tracked.\n", + "\n", + "
\n", + "Status of the case recorded in the `$CASEROOT` CaseStatus file:

\n", + "\n", + "```\n", + "cd /glade/work/$USER/cases/CASE\n", + "more CaseStatus\n", + "```\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "462ea73c-e73c-49cd-a74c-1261ebda445d", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + " \n", + "```\n", + "2024-06-06 13:41:19: case.setup starting \n", + " ---------------------------------------------------\n", + "2024-06-06 13:41:22: case.setup success \n", + " ---------------------------------------------------\n", + "2024-06-06 13:42:17: case.build starting \n", + " ---------------------------------------------------\n", + "CESM version is release-cesm2.1.5\n", + "Processing externals description file : Externals.cfg (/glade/u/home/$USER/my_cesm_code)\n", + "Processing externals description file : Externals_CAM.cfg (/glade/u/home/$USER/my_cesm_code/components/cam)\n", + "Processing externals description file : Externals_CISM.cfg (/glade/u/home/$USER/my_cesm_code/components/cism)\n", + "Processing externals description file : Externals_CLM.cfg (/glade/u/home/$USER/my_cesm_code/components/clm)\n", + "Processing externals description file : Externals_POP.cfg (/glade/u/home/$USER/my_cesm_code/components/pop)\n", + "Checking local status of required & optional components: cam, chem_proc, carma, clubb, cosp2, cice, cime, cism, source_cism, clm, \n", + "fates, mosart, pop, cvmix, marbl, rtm, ww3, \n", + " ./cime\n", + " clean sandbox, on cime5.6.49\n", + " ./components/cam\n", + " clean sandbox, on cam_cesm2_1_rel_60\n", + " ./components/cam/chem_proc\n", + " clean sandbox, on tools/proc_atm/chem_proc/release_tags/chem_proc5_0_03_rel\n", + " ./components/cam/src/physics/carma/base\n", + " clean sandbox, on carma/release_tags/carma3_49_rel\n", + " ./components/cam/src/physics/clubb\n", + " clean sandbox, on vendor_clubb_r8099_n03\n", + " ./components/cam/src/physics/cosp2/src\n", + " clean sandbox, on v2.1.4cesm\n", + " ./components/cice\n", + " clean sandbox, on cice5_cesm2_1_1_20231220\n", + " ./components/cism\n", + " clean sandbox, on cism-release-cesm2.1.2_04\n", + " ./components/cism/source_cism\n", + " clean sandbox, on release-cism2.1.04\n", + " ./components/clm\n", + " clean sandbox, on release-clm5.0.37\n", + " ./components/clm/src/fates\n", + " clean sandbox, on sci.1.30.0_api.8.0.0\n", + " ./components/mosart\n", + " clean sandbox, on release-cesm2.0.04\n", + " ./components/pop\n", + " clean sandbox, on pop2_cesm2_1_rel_n15\n", + " ./components/pop/externals/CVMix\n", + " clean sandbox, on v0.93-beta\n", + " ./components/pop/externals/MARBL\n", + " clean sandbox, on cesm2.1-n00\n", + " ./components/rtm\n", + " clean sandbox, on release-cesm2.0.04\n", + " ./components/ww3\n", + " clean sandbox, on ww3_181001\n", + "2024-06-06 13:50:57: case.build success \n", + " ---------------------------------------------------\n", + "2024-06-06 13:53:31: case.submit starting \n", + " ---------------------------------------------------\n", + "2024-06-06 13:53:46: case.submit success case.run:4743615.desched1, case.st_archive:4743616.desched1\n", + " ---------------------------------------------------\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1189f3e-7266-492a-aa42-664a968227e3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/cesm_workflow/create_clone.ipynb b/notebooks_copy/basics/cesm_workflow/create_clone.ipynb new file mode 100644 index 000000000..e53a335a3 --- /dev/null +++ b/notebooks_copy/basics/cesm_workflow/create_clone.ipynb @@ -0,0 +1,109 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Create Clone" + ] + }, + { + "cell_type": "markdown", + "id": "0848a34c-67f8-4727-b8e5-8e9246927d2b", + "metadata": {}, + "source": [ + "The tool that clones a new case based on an existing case is `create_clone`. This tool is located in the `$SRCROOT` directory under the `cime/scripts` directory." + ] + }, + { + "cell_type": "markdown", + "id": "aa0ffc44-9f53-4b02-bdaf-4035ff768cdd", + "metadata": {}, + "source": [ + "Things that **are** copied into the new case are:\n", + "- Most `env_*.xml` settings (not all!)\n", + "- user_nl_xxx files\n", + "- Macros\n", + "- SourceMods\n", + "- Batch system files\n", + "- README.case\n", + "\n", + "Things that **are not** copied into the new case are:\n", + "- Logs\n", + "- Timing files" + ] + }, + { + "cell_type": "markdown", + "id": "76ae1e16-f343-4149-a82b-a75f5bb94fa9", + "metadata": {}, + "source": [ + "## Command Syntax" + ] + }, + { + "cell_type": "markdown", + "id": "404c5149-3a66-4677-9dc5-19f4f2d0a08b", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Go to the `cime/scripts` directory in the CESM code\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "```\n", + " \n", + "
\n", + " \n", + "Example of basic `./create_clone` syntax to create CASE2:
\n", + " \n", + "```\n", + "./create_clone --clone /glade/work/$USER/cases/CASE1 --case /glade/work/$USER/cases/CASE2\n", + "```\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "d5d86794-b242-4f85-9b9c-4e1fcf235d46", + "metadata": {}, + "source": [ + "**Notes:**\n", + "- Do not use `cp` or `cp -R` to copy case directories to new experiments. \n", + "- Document changes in CaseStatus and README.case to keep track of the changes you make.\n", + "- If you are making many cases with only a small changes, you can script the steps into a python or shell script of your choice to make the process more automated and less prone to human error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "839f45aa-3c6b-4170-b913-a6eb9da8feb4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb b/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb new file mode 100644 index 000000000..648ec7edc --- /dev/null +++ b/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb @@ -0,0 +1,533 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Create New Case" + ] + }, + { + "cell_type": "markdown", + "id": "efcaf0cc-2b3f-49de-8c4e-953464c295ef", + "metadata": {}, + "source": [ + "The tool that generates a new case is `create_newcase`. This tool is located in the `$SRCROOT` directory under the `cime/scripts` directory. \n", + "\n", + "After running `create_newcase`, a `$CASEROOT` directory is created. The directory `$CASEROOT` contains the scripts needed for the following steps (`case.setup`, `case.build` and `case.submit`)" + ] + }, + { + "cell_type": "markdown", + "id": "25ef9ed6-7dea-493f-8ef5-3f1de625a31c", + "metadata": {}, + "source": [ + "![CESM create_newcase](../../../images/basics/CESM2_create_newcase.png)\n", + "*

Figure: Detailed view of the location of create_newcase

*\n", + "\n", + "For the current tutorial on derecho, the paths are:\n", + "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", + "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "af956bea-9166-48bf-99df-02f744bd792d", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "557d4028-7176-4e2a-8a90-7febf2de37ef", + "metadata": {}, + "source": [ + "## Command Syntax" + ] + }, + { + "cell_type": "markdown", + "id": "40696acf-8c56-4c1b-8fab-f9d1b1d3b2d1", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Go to the `cime/scripts` directory in the CESM code
\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "```\n", + " \n", + "
\n", + " \n", + "Example of basic `./create_newcase` syntax:
\n", + " \n", + "```\n", + "./create_newcase --case /glade/work/$USER/cases/CASE --res RES --compset COMPSET\n", + "```\n", + " \n", + "
\n", + "\n", + "NOTE: Do not enter the example above at the command line. You will create your first case in the Exercise at the end of this section.
" + ] + }, + { + "cell_type": "markdown", + "id": "3923b808-2496-4f4d-b5a1-8b4cd44466ba", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + " \n", + "```\n", + "Compset longname is 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + "Compset specification file is /glade/work/$USER/code/cesm2.1.1/cime/../cime_config/config_compsets.xml\n", + "Compset forcing is 1850\n", + "Com forcing is Biogeochemistry intercomponent with diagnostic CO2\n", + "ATM component is CAM cam6 physics:\n", + "LND component is clm5.0:BGC (vert. resol. CN and methane) with prognostic crop:\n", + "ICE component is Sea ICE (cice) model version 5\n", + "OCN component is POP2 EcosystemAbiotic DIC/DIC14\n", + "ROF component is MOSART: MOdel for Scale Adaptive River Transport\n", + "GLC component is cism2 (default, higher-order, can run in parallel):cism ice evolution turned off (this is the standard configuration unless you're explicitly interested in ice evolution):\n", + "WAV component is Wave Watch\n", + "ESP component is \n", + "Pes specification file is /glade/work/$USER/code/cesm2.1.1/cime/../cime_config/config_pes.xml\n", + "Compset specific settings: name is RUN_STARTDATE and value is 0001-01-01\n", + "Compset specific settings: name is RUN_REFDATE and value is 0301-01-01\n", + "Compset specific settings: name is RUN_TYPE and value is hybrid\n", + "Compset specific settings: name is RUN_REFCASE and value is b.e20.B1850.f19_g17.release_cesm2_1_0.020\n", + "Compset specific settings: name is CLM_NAMELIST_OPTS and value is use_init_interp=.true.\n", + "Machine is derecho\n", + "Pes setting: grid match is a%1.9x2.5.+l%1.9x2.5.+oi%gx1 \n", + "Pes setting: machine match is derecho \n", + "Pes setting: compset_match is CAM.+CLM.+CICE.+POP.+ \n", + "Pes setting: grid is a%1.9x2.5_l%1.9x2.5_oi%gx1v7_r%r05_g%gland4_w%ww3a_m%gx1v7 \n", + "Pes setting: compset is 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD \n", + "Pes setting: tasks is {'NTASKS_ATM': 288, 'NTASKS_ICE': 108, 'NTASKS_CPL': 288, 'NTASKS_LND': 144, 'NTASKS_WAV': 36, 'NTASKS_ROF': 40, 'NTASKS_OCN': 288, 'NTASKS_GLC': 36} \n", + "Pes setting: threads is {'NTHRDS_ICE': 1, 'NTHRDS_ATM': 1, 'NTHRDS_ROF': 1, 'NTHRDS_LND': 1, 'NTHRDS_WAV': 1, 'NTHRDS_OCN': 1, 'NTHRDS_CPL': 1, 'NTHRDS_GLC': 1} \n", + "Pes setting: rootpe is {'ROOTPE_OCN': 288, 'ROOTPE_LND': 0, 'ROOTPE_ATM': 0, 'ROOTPE_ICE': 144, 'ROOTPE_WAV': 252, 'ROOTPE_CPL': 0, 'ROOTPE_ROF': 0, 'ROOTPE_GLC': 0} \n", + "Pes setting: pstrid is {} \n", + "Pes other settings: {}\n", + "Pes comments: about 12ypd expected\n", + " Compset is: 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD \n", + " Grid is: a%1.9x2.5_l%1.9x2.5_oi%gx1v7_r%r05_g%gland4_w%ww3a_m%gx1v7 \n", + " Components in compset are: ['cam', 'clm', 'cice', 'pop', 'mosart', 'cism', 'ww3', 'sesp', 'drv', 'dart'] \n", + "\n", + "This is a CESM scientifically supported compset at this resolution.\n", + "\n", + "Using project from .cesm_proj: P93300641\n", + "No charge_account info available, using value from PROJECT\n", + "Using project from .cime/config: P93300641\n", + "cesm model version found: cesm2.1_tutorial2022\n", + "Batch_system_type is pbs\n", + "job is case.run USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", + "job is case.st_archive USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", + " Creating Case directory /glade/work/$USER/cases/b.day1.0\n", + "\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "87ae84f9-449b-4051-9dc9-9c53bcb8ac57", + "metadata": {}, + "source": [ + "**Notes:**\n", + "- For all user scripts you can run the script name followed by the `--h` or `--help` argument to see help documentation for that script and a list of all command line arguments for that script.\n", + "- Double dashes `--` are required with command line arguments\n" + ] + }, + { + "cell_type": "markdown", + "id": "d7064891-5a5a-41f2-9b3b-d4acb231a2ee", + "metadata": {}, + "source": [ + "## Command Arguments" + ] + }, + { + "cell_type": "markdown", + "id": "b89a4484-e32c-413a-bfec-4051d666a8a8", + "metadata": {}, + "source": [ + "![Create New Case Command Image](../../../images/basics/CESM2_Create_Newcase_Command.png)" + ] + }, + { + "cell_type": "markdown", + "id": "122cb2ea-b34d-472b-8790-9d3950d8eea4", + "metadata": {}, + "source": [ + "### Casename" + ] + }, + { + "cell_type": "markdown", + "id": "0ea36beb-fe98-4289-a15e-37c21ff2f4ad", + "metadata": {}, + "source": [ + "`--case` is the argument that specifies the **name** and **location** of the case being created. In the example above, the casename is `CASE` and the location of the case is `/glade/work/$USER/cases/`. Note that if a path preceding the casename is not specified, then the case is created as a subdirectory of the `$CIMEROOT/scripts` directory.\n" + ] + }, + { + "cell_type": "markdown", + "id": "bb90ccd2-b4d4-4ab7-96ec-749a596f25ae", + "metadata": {}, + "source": [ + "- The path `/glade/work/$USER/cases/CASE` is your workspace `CASEROOT`, as described in the [workspaces section](https://ncar.github.io/CESM-Tutorial/notebooks/basics/cesm_workspaces.html#workspace-overview)." + ] + }, + { + "cell_type": "markdown", + "id": "8f080dc7-3bfb-4e61-b112-af418f52018a", + "metadata": {}, + "source": [ + "In this tutorial we will use fairly simple names for the cases you create, build, and run. However, most CESM experiments have much longer casenames that provide information about the experiment at a glance. Knowing the CESM case naming conventions will help you navigate CESM community experiments. Information about the CESM naming is available at:" + ] + }, + { + "cell_type": "markdown", + "id": "2216d55d-6705-4d1d-9734-eddfc225420d", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM case naming conventions](https://www.cesm.ucar.edu/models/cesm2/naming-conventions)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "83b752d0-34c8-42d0-ba5b-efee17ab3386", + "metadata": {}, + "source": [ + "### Resolution" + ] + }, + { + "cell_type": "markdown", + "id": "6e3f9220-e60f-456b-93a2-09c4d52f5e22", + "metadata": {}, + "source": [ + "`--res` is the argument that specifies the **model resolution or grid**. In the example above, the resolution is `RES`. Each model resolution can be specified with the `--res` argument by its alias or its full long name. However, the long name conventions are generally quite lengthy, so it is useful if you become familiar with using the alias version. Because the atmosphere and land share a grid and the ocean and sea ice share a grid only two grids are specified by the alias using the following format: `atm/lnd_ocn/ice`." + ] + }, + { + "cell_type": "markdown", + "id": "4c69afd3-925c-42c8-a54e-017336ef03c6", + "metadata": {}, + "source": [ + "![Create New Case Resolution Image](../../../images/basics/CESM2_Create_Newcase_Resolution.png)" + ] + }, + { + "cell_type": "markdown", + "id": "8143f8da-726e-4db9-99af-ce75e19c4810", + "metadata": {}, + "source": [ + "#### CESM2 Supported Grid Definitions" + ] + }, + { + "cell_type": "markdown", + "id": "83f7274d-95fe-4be3-bf08-a6964eebef10", + "metadata": {}, + "source": [ + " Link for CESM Supported Grids: \n", + "
\n", + "[http://www.cesm.ucar.edu/models/cesm2/config/grids.html](http://www.cesm.ucar.edu/models/cesm2/config/grids.html)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0ffff371-8ee8-45c8-b452-b225a4464b21", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Cime Tool for listing configurations:

\n", + "\n", + "```\n", + "CIMEROOT/scripts/query_config --grids --long\n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "885ef623-f80d-4173-a8f8-cf4232217549", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + " \n", + "```\n", + " =========================================\n", + " GRID naming convention\n", + " =========================================\n", + " The notation for the grid longname is\n", + " a%name_l%name_oi%name_r%name_m%mask_g%name_w%name\n", + " where\n", + " a% => atm, l% => lnd, oi% => ocn/ice, r% => river, m% => mask, g% => glc, w% => wav\n", + "\n", + " Supported out of the box grid configurations are given via alias specification in\n", + " the file \"config_grids.xml\". Each grid alias can also be associated with the\n", + " following optional attributes\n", + "\n", + " compset (Regular expression for compset matches that are required for this grid)\n", + " not_compset (Regular expression for compset matches that are not permitted this grid)\n", + "\n", + " Using the alias and the optional \"compset\" and \"not_compset\" attributes a grid longname is created\n", + " Note that the mask is for information only - and is not an attribute of the grid\n", + " By default, if the mask is not specified below, it will be set to the ocnice grid\n", + " And if there is no ocnice grid (such as for single column, the mask is null since it does not mean anything)\n", + " \n", + " -------------------------------------------------------------\n", + " default component grids:\n", + "\n", + " component compset value \n", + " -------------------------------------------------------------\n", + " atm SATM null \n", + " lnd SLND null \n", + " ocnice SOCN null \n", + " rof SROF null \n", + " rof DWAV rx1 \n", + " rof RTM r05 \n", + " rof MOSART r05 \n", + " rof DROF rx1 \n", + " rof DROF%CPLHIST r05 \n", + " rof XROF r05 \n", + " glc SGLC null \n", + " glc CISM1 gland5UM \n", + " glc CISM2 gland4 \n", + " glc XGLC gland4 \n", + " wav SWAV null \n", + " wav DWAV ww3a \n", + " wav WW3 ww3a \n", + " wav XWAV ww3a \n", + " -------------------------------------------------------------\n", + "\n", + " alias: g16_g16 (only for compsets that are DATM.+DROF )\n", + " non-default grids are: atm:gx1v6 lnd:gx1v6 ocnice:gx1v6 \n", + " \n", + " gx1v6 is displaced Greenland pole v6 1-deg grid: with domain file(s): \n", + " $DIN_LOC_ROOT/share/domains/domain.ocn.gx1v6.090206.nc (only for grid match: atm|lnd)\n", + " $DIN_LOC_ROOT/share/domains/domain.ocn.gx1v6.090206.nc (only for grid match: ocnice) \n", + "\n", + " alias: g17_g17 (only for compsets that are DATM.+DROF )\n", + " non-default grids are: atm:gx1v7 lnd:gx1v7 ocnice:gx1v7 \n", + " \n", + " gx1v7 is displaced Greenland pole 1-deg grid with Caspian as a land feature: with domain file(s): \n", + " $DIN_LOC_ROOT/share/domains/domain.ocn.gx1v7.151008.nc (only for grid match: atm|lnd)\n", + " $DIN_LOC_ROOT/share/domains/domain.ocn.gx1v7.151008.nc (only for grid match: ocnice) \n", + "\n", + "...\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "82faa8c6-88d6-41ba-a063-52939e664391", + "metadata": {}, + "source": [ + "### Compset" + ] + }, + { + "cell_type": "markdown", + "id": "0ccf426a-57d0-4447-9eb7-c53c18544148", + "metadata": {}, + "source": [ + "`--compset` is the argument that specifies the **component set**. In the example above, the compset is `COMPSET`. The compset specifies the component models and if they are active or not, forcing scenarios, and physics options for the models. As above, the `--compset` can be specified by its alias or its full long name. However, the long name conventions are generally quite lengthy, so it is useful if you become familiar with using the alias version." + ] + }, + { + "cell_type": "markdown", + "id": "160dd746-6ec4-4ea2-85f1-92bbbdd5647d", + "metadata": {}, + "source": [ + "![Create New Case Compset Image](../../../images/basics/CESM2_Create_Newcase_Compset.png)" + ] + }, + { + "cell_type": "markdown", + "id": "ee1cde34-5742-48c3-964b-6362d32288d0", + "metadata": {}, + "source": [ + "**Note**:\n", + "- Compsets are defined by different model components (active or data) and cime.\n", + "- Some compsets are scientifically supported and/or tested, while some are only defined. The CESM project will not provide comprehensive support for compsets that are only defined.\n", + "- Compsets determine which grid is required." + ] + }, + { + "cell_type": "markdown", + "id": "0d522ed3-098c-4194-be32-f219be984248", + "metadata": {}, + "source": [ + "#### CESM2 Supported Compsets" + ] + }, + { + "cell_type": "markdown", + "id": "e8b7943e-0be5-4feb-9b3f-cb8d4fe4aac0", + "metadata": {}, + "source": [ + " Link for CESM2 Supported Compsets: \n", + "
\n", + "[http://www.cesm.ucar.edu/models/cesm2/config/compsets.html](http://www.cesm.ucar.edu/models/cesm2/config/compsets.html)" + ] + }, + { + "cell_type": "markdown", + "id": "e91dff67-7992-48d2-afba-c0acb5528fb8", + "metadata": {}, + "source": [ + "
\n", + "Cime Tool for listing configurations:

\n", + "\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./query_config --compsets\n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "65d58f32-51ff-4dfa-a52d-edc182e6cae4", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + " \n", + "```\n", + "Active component: allactive\n", + " --------------------------------------\n", + " Compset Alias: Compset Long Name \n", + " --------------------------------------\n", + " B1850 : 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " B1850cmip6 : 1850_CAM60_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BMH6kcmip6 : midH_CAM60_CLM50%BGC_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BLIG127kcmip6 : 127ka_CAM60_CLM50%BGC_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BCO2x4cmip6 : 1850_CAM60%4xCO2_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " B1PCTcmip6 : 1850_CAM60%1PCT_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BW1850 : 1850_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BW1850cmip6 : 1850_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWsc1850 : 1850_CAM60%WCSC_CLM50%BGC-CROP_CICE_POP2%ECO_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWsc1850smyle : 1850_CAM60%WCSC%SMYLE_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BWscHIST : HIST_CAM60%WCSC_CLM50%BGC-CROP_CICE_POP2%ECO_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWCO2x4cmip6 : 1850_CAM60%WCTS%4xCO2_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BW1PCTcmip6 : 1850_CAM60%WCTS%1PCT_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWmaCO2x4cmip6 : 1850_CAM60%WCCM%4xCO2_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWma1PCTcmip6 : 1850_CAM60%WCCM%1PCT_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP126cmip6 : SSP126_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP245cmip6 : SSP245_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP370cmip6 : SSP370_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP585cmip6 : SSP585_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP534oscmip6 : SSP534_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP126 : SSP126_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP245 : SSP245_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP370 : SSP370_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP585 : SSP585_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP534os : SSP534_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWHIST : HIST_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP585extcmip6 : SSP585EXT_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWSSP534osextcmip6 : SSP534EXT_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWHISTcmip6 : HIST_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWma1850 : 1850_CAM60%WCCM_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BWmaHIST : HIST_CAM60%WCCM_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", + " BHIST : HIST_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BHISTcmip6 : HIST_CAM60_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BHISTsmbb : HIST_CAM60%SMBB_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BSSP370smbb : SSP370_CAM60%SMBB_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BSSP370smbbext : SSP370EXT_CAM60%SMBB_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BSSP245smbb : SSP245_CAM60%SMBB_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BC5L45BGC : 2000_CAM50_CLM45%BGC_CICE_POP2_MOSART_SGLC_SWAV\n", + " B1850L45BGCR : 1850_CAM60_CLM45%BGC_CICE_POP2_RTM_SGLC_SWAV\n", + " B1850C5L45BGC : 1850_CAM50_CLM45%BGC_CICE_POP2_MOSART_SGLC_SWAV\n", + " BSSP585 : SSP585_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BSSP126 : SSP126_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BSSP245 : SSP245_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BSSP370 : SSP370_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + " BSSP585cmip6 : SSP585_CAM60_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", + "\n", + "...\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "61581a4e-86f4-44e4-92d9-d29aefa9ed8b", + "metadata": {}, + "source": [ + "### Machines" + ] + }, + { + "cell_type": "markdown", + "id": "5e4e5466-8955-4f61-84f7-6228b1aac32b", + "metadata": {}, + "source": [ + "The argument `--mach` is not required on CESM supported machines, but is required on other machines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94b79d8f-2da5-4a3f-b9b9-0cbb45896c0e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/cesm_workflow/model_output.ipynb b/notebooks_copy/basics/cesm_workflow/model_output.ipynb new file mode 100644 index 000000000..feebad7b9 --- /dev/null +++ b/notebooks_copy/basics/cesm_workflow/model_output.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "683d9e77-d700-4f75-b3f0-eb813657351b", + "metadata": { + "tags": [] + }, + "source": [ + "# Model Output\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d8e6525b-31d9-450d-a3b9-e3b435fc231a", + "metadata": {}, + "source": [ + "If the model run is successful, the CESM netcdf output history files are automatically moved to a short term archive (`$DOUT_S_ROOT`) by `case.st_archive`." + ] + }, + { + "cell_type": "markdown", + "id": "d62c7e4b-86e0-40ec-8dfa-5744c3884bc0", + "metadata": {}, + "source": [ + "![Case Submit Image](../../../images/basics/CESM2_short_term_archive.png)\n", + "\n", + "\n", + "For the current tutorial on derecho, the paths are:\n", + "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", + "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", + "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", + "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` \n", + "- `$DOUT_S_ROOT` =`/glade/derecho/scratch/$USER/archive/$CASE` " + ] + }, + { + "cell_type": "markdown", + "id": "9be4ca8e-8483-48b1-80e7-821b8627eead", + "metadata": {}, + "source": [ + "**Notes:**\n", + "- If a model run was unsuccessful the output remains in the Run Directory (`$RUNDIR`) and the short term archive is not created.\n", + "- Both `$RUNDIR` and `$DOUT_S_ROOT` are in the NCAR HPC scratch space. This space is scrubbed and files deleted after a number of days. Thus, it is a good idea to move your model output files from the short term archive to a more permanent location as soon as you are able." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b026c623-617e-42d1-a3d1-4f6fce55bd5f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/cesm_workspaces.ipynb b/notebooks_copy/basics/cesm_workspaces.ipynb new file mode 100644 index 000000000..2c00a02dc --- /dev/null +++ b/notebooks_copy/basics/cesm_workspaces.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Workspaces" + ] + }, + { + "cell_type": "markdown", + "id": "15d62f62-74a4-4936-8283-4d0226dcedab", + "metadata": {}, + "source": [ + "Utilizing the CESM model requires knowledge of the locations of the different parts of the model. The CESM model consists of five main directory tree areas that make up the **CESM Workspace**. \n", + "\n", + "One of the key elements of the Tutorial will be learning where each of these workspace elements are located and how to use them to setup, build, run and analyze the output of model simulations.\n", + "\n", + "\n", + "There are 5 main directories (see figure, below) you will need to navigate:\n", + "1) The path to your **CESM source code**. This is referred to as `$SRCROOT` (or `$CESMCODE`) and contains the `$CIMEROOT`.\n", + "2) The path to the **input data**, referred to as `$CESMDATA`.\n", + "3) The path to your experiment **case directories**. This is referred to as `$CASEROOT`.\n", + "4) The path to your **build** and **run directories**, referred to as the `$EXEROOT` and `$RUNDIR`, respectively.\n", + "5) The path to your **archived model output**, referred to as `$DOUT_S_ROOT`.\n", + "\n", + "We will investigate these in more detail in the remainder of the Tutorial.\n", + "\n", + "**Note on Terminology**
\n", + "- Paths are the directions to the location of different pieces of your workspace.\n", + "- Roots are saved paths that point to a specific piece of the model." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6b1d1b2e-9c93-4e47-8f4d-d51b96cdab7b", + "metadata": {}, + "source": [ + "## Workspace Overview\n", + "\n", + "![CESM workspace](../../images/basics/CESM2_Workspaces_Detail.png)\n", + "*

Figure: Overview of the CESM2 Workspace Directories

*\n", + "\n", + "For the current tutorial on derecho, the paths are:\n", + "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", + "- `$DIN_LOC_ROOT` = `/glade/campaign/cesm/cesmdata/cseg/inputdata`\n", + "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", + "- `$CIME_OUTPUT_ROOT` = `/glade/derecho/scratch/$USER`\n", + "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", + "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` \n", + "- `$DOUT_S_ROOT` =`/glade/derecho/scratch/$USER/archive/$CASE` \n" + ] + }, + { + "cell_type": "markdown", + "id": "40962c56-fb4d-4862-95a1-8d5c98b82720", + "metadata": {}, + "source": [ + "Each of these directories contains sub-directories. You will need to learn to navigate between these directories." + ] + }, + { + "cell_type": "markdown", + "id": "19acc3aa-fb98-44af-966f-16ca49710135", + "metadata": {}, + "source": [ + "____\n", + "\n", + "## Setting your workspaces\n", + "\n", + "You will need to create a directory for the CESM Code and a directory when you will keep all your cases. \n", + "In the CESM jargon, a `case` refers to a specific instance of a model simulation." + ] + }, + { + "cell_type": "markdown", + "id": "1c6bda59-5302-4454-88bb-dfe5c4bd3209", + "metadata": {}, + "source": [ + "- ### Create CESM Code Directory\n", + "Create a workspace location where you can put your CESM code on the derecho glade file system. In all exercises in this tutorial `$USER` is a placeholder and you should use your NCAR HPC login name when completing the exercise.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b281cda4-e1c0-458b-98df-a1cf34c45a72", + "metadata": {}, + "source": [ + "
\n", + "\n", + "```\n", + "cd /glade/work/$USER\n", + "mkdir code\n", + "```\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "76f14c71-ffae-4d67-b496-d6b075d4652d", + "metadata": {}, + "source": [ + "- ### Create Cases Workspace Location\n" + ] + }, + { + "cell_type": "markdown", + "id": "667ca15d-2ef7-40b1-be89-3abac566d472", + "metadata": {}, + "source": [ + "Create a workspace location where all your cases from this tutorial will be located on the derecho glade file system." + ] + }, + { + "cell_type": "markdown", + "id": "37b8b7c6-9152-47d8-af76-7814006a5718", + "metadata": {}, + "source": [ + "
\n", + "\n", + "```\n", + "cd /glade/work/$USER\n", + "mkdir cases\n", + "```\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "3a779263-cb21-443e-a3d8-0bd4a0e267e8", + "metadata": {}, + "source": [ + "- ### Other Workspace Locations\n" + ] + }, + { + "cell_type": "markdown", + "id": "4e972986-5267-43a8-917b-1e36c7117323", + "metadata": {}, + "source": [ + "The `INPUTDATA` directory does not need to be set up directly on NCAR HPC as these data are in a common directory.\n", + "\n", + "We will not directly set up your `Build/Run` or `Archive` workspace locations. These directories will be automatically created when you create a CESM case." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0ddf488-72e4-40f8-a797-cc2d1b958208", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/code/cesm_code_explore.ipynb b/notebooks_copy/basics/code/cesm_code_explore.ipynb new file mode 100644 index 000000000..fe18a4f87 --- /dev/null +++ b/notebooks_copy/basics/code/cesm_code_explore.ipynb @@ -0,0 +1,347 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Explore CESM Code\n" + ] + }, + { + "cell_type": "markdown", + "id": "285f4d12-6411-4f57-aa81-09ca1106a380", + "metadata": {}, + "source": [ + "Having downloaded the CESM Code component to your workspace we are now going to briefly locate and explore the different code directories within it. To get some familiarity with this area we are going to use the terminal window to explore the directories illustrated below this top level location. Specifically in this exercise we will:\n", + "\n", + "- Step 1. Locate your CESM Code area on the glade file system.\n", + "- Step 2. Examine the CIME Directory area.\n", + "- Step 3. Examine the Model Components area.\n", + "- Step 4. Look at the Community Atmosphere Model (CAM) Component Model.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8a064b52-fc3a-4efd-93b8-ac7d96538e36", + "metadata": {}, + "source": [ + "![CESM directories](../../../images/basics/CESM2_Workspaces_Code.png)\n", + "*

Figure: Detailed view of the CESM2 Code Directory

*\n", + "\n", + "For the current tutorial on derecho, the paths are: \n", + "- `$SRCROOT = /glade/work/$USER/code/my_cesm_code` " + ] + }, + { + "cell_type": "markdown", + "id": "44f8925f-4703-44d9-baa7-5ac59da0ede1", + "metadata": {}, + "source": [ + "## Step 1. Locate Your Code Workspace Area\n", + "\n", + "Returning to your terminal window we will now locate your `$SRCROOT` location on the NCAR HPC file system.\n", + "\n", + "
\n", + "Locate your current directory:
\n", + "\n", + "```\n", + "pwd\n", + "```\n", + "
\n", + " \n", + "Expected output:
\n", + "\n", + "```\n", + "/glade/work/$USER/code/my_cesm_code\n", + "```\n", + "
\n", + "\n", + " \n", + "If you have not done anything since the last exercise then you will already be in the `$SRCROOT` location. If your output doesn't match the path above, then change the current directory to your Workspace Code directory:
\n", + "\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code\n", + "```\n", + "
\n", + " \n", + "List the contents of the Workspace Code directory with the ls long option:
\n", + "\n", + "```\n", + "ls -l\n", + "```\n", + "
\n", + " \n", + "Expected output:
\n", + "\n", + "```\n", + "total 2119\n", + "-rw-r--r-- 1 $USER ncar 2116708 Jun 3 15:46 ChangeLog\n", + "-rw-r--r-- 1 $USER ncar 2433 Jun 3 15:46 ChangeLog_template\n", + "drwxr-xr-x 10 $USER ncar 4096 Jun 4 09:32 cime\n", + "drwxr-xr-x 4 $USER ncar 4096 Jun 3 15:46 cime_config\n", + "drwxr-xr-x 10 $USER ncar 4096 Jun 4 09:32 components\n", + "-rwxr-xr-x 1 $USER ncar 2475 Jun 3 15:46 describe_version\n", + "drwxr-xr-x 3 $USER ncar 4096 Jun 3 15:46 doc\n", + "-rw-r--r-- 1 $USER ncar 1409 Jun 3 15:46 Externals.cfg\n", + "-rw-r--r-- 1 $USER ncar 5498 Jun 3 15:46 LICENSE.txt\n", + "drwxr-xr-x 5 $USER ncar 4096 Jun 3 15:46 manage_externals\n", + "-rw-r--r-- 1 $USER ncar 8904 Jun 3 15:46 README.rst\n", + "```\n", + "
\n", + "\n", + " \n", + "
\n", + "\n", + "**If the output of these commands does not match for your CESM Workspace then you will need to ask for help in configuring them. This area will be used for the remainder of the Tutorial so it is important to get this step right.**" + ] + }, + { + "cell_type": "markdown", + "id": "d3d8846b-cfa4-4b89-b262-39892ebdb52a", + "metadata": {}, + "source": [ + "## Step 2. Examine the CIME Directory\n", + "\n", + "In your terminal window we will now move to the CIME directory and examine the contents. Remember that CIME is the Common Infrastructure for Modeling the Earth. It is a python-based framework that integrates the various components of the CESM into a single modeling framework. The CIME Scripts directory will be the location where you will start the Workflow of creating a new case through the rest of the Tutorial.\n", + "\n", + "
\n", + "Change into the CIME Directory:
\n", + "\n", + "```\n", + "cd cime\n", + "```\n", + "
\n", + " \n", + "List the contents of the CIME Directory:
\n", + "\n", + "```\n", + "ls -l\n", + "```\n", + "
\n", + " \n", + "Expected output:
\n", + "\n", + "```\n", + "total 648\n", + "-rw-r--r-- 1 $USER ncar 618865 Jun 4 09:32 ChangeLog\n", + "-rw-r--r-- 1 $USER ncar 421 Jun 4 09:32 ChangeLog_template\n", + "-rw-r--r-- 1 $USER ncar 4000 Jun 4 09:32 CMakeLists.txt\n", + "drwxr-xr-x 5 $USER ncar 4096 Jun 4 09:32 config\n", + "-rw-r--r-- 1 $USER ncar 4670 Jun 4 09:32 CONTRIBUTING.md\n", + "drwxr-xr-x 3 $USER ncar 4096 Jun 4 09:32 doc\n", + "-rw-r--r-- 1 $USER ncar 444 Jun 4 09:32 index.html\n", + "-rw-r--r-- 1 $USER ncar 2461 Jun 4 09:32 LICENSE.TXT\n", + "-rw-r--r-- 1 $USER ncar 1747 Jun 4 09:32 README.md\n", + "drwxr-xr-x 7 $USER ncar 4096 Jun 4 09:32 scripts\n", + "drwxr-xr-x 8 $USER ncar 4096 Jun 4 09:32 src\n", + "drwxr-xr-x 7 $USER ncar 4096 Jun 4 09:32 tools\n", + "drwxr-xr-x 3 $USER ncar 4096 Jun 4 09:32 utils\n", + "```\n", + "
\n", + " \n", + "Change into the CIME Scripts directory:
\n", + "\n", + "```\n", + "cd scripts\n", + "```\n", + "
\n", + " \n", + "List the contents of the CIME Scripts directory:
\n", + "\n", + "```\n", + "ls -l\n", + "```\n", + "
\n", + " \n", + "Expected output:
\n", + "\n", + "```\n", + "total 101\n", + "-rwxr-xr-x 1 $USER ncar 5128 Jun 4 09:32 create_clone\n", + "-rwxr-xr-x 1 $USER ncar 11117 Jun 4 09:32 create_newcase\n", + "-rwxr-xr-x 1 $USER ncar 29859 Jun 4 09:32 create_test\n", + "drwxr-xr-x 2 $USER ncar 4096 Jun 4 09:32 data_assimilation\n", + "drwxr-xr-x 4 $USER ncar 4096 Jun 4 09:32 fortran_unit_testing\n", + "drwxr-xr-x 3 $USER ncar 4096 Jun 4 09:32 lib\n", + "-rwxr-xr-x 1 $USER ncar 14663 Jun 4 09:32 query_config\n", + "-rwxr-xr-x 1 $USER ncar 9067 Jun 4 09:32 query_testlists\n", + "drwxr-xr-x 5 $USER ncar 4096 Jun 4 09:32 tests\n", + "drwxr-xr-x 3 $USER ncar 4096 Jun 4 09:32 Tools\n", + "```\n", + "
\n", + "\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "37af6f2c-34ce-4de6-a13e-593e61a90f52", + "metadata": {}, + "source": [ + "## Step 3. Examine the CESM Components Area\n", + "\n", + "In your terminal window we will now move to the Components Directory and examine the contents of the individual sub models. \n", + "\n", + "![CESM directories](../../../images/basics/CESM2_Code_Components_List.png)\n", + "*

Figure: CESM2 Code Components

*\n", + "\n", + "
\n", + "Change back to the Code Workspace Location:
\n", + "\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code\n", + "```\n", + "OR\n", + "Alternatively, you can use the `cd ../..` to move up two levels in the directory hierarchy.\n", + "```\n", + "cd ../..\n", + "```\n", + "\n", + "
\n", + " \n", + "Change into the Components area of the Code Workspace:
\n", + "\n", + "```\n", + "cd components\n", + "```\n", + "
\n", + "\n", + "List the contents of the Components Directory:
\n", + "\n", + "```\n", + "ls -l\n", + "```\n", + "
\n", + " \n", + "Expected Output:
\n", + "\n", + "```\n", + "total 8\n", + "drwxr-xr-x 12 $USER ncar 4096 Jun 4 09:34 cam\n", + "drwxr-xr-x 7 $USER ncar 4096 Jun 4 09:32 cice\n", + "drwxr-xr-x 14 $USER ncar 4096 Jun 4 09:32 cism\n", + "drwxr-xr-x 12 $USER ncar 4096 Jun 4 09:31 clm\n", + "drwxr-xr-x 6 $USER ncar 4096 Jun 4 09:31 mosart\n", + "drwxr-xr-x 15 $USER ncar 4096 Jun 4 09:32 pop\n", + "drwxr-xr-x 6 $USER ncar 4096 Jun 4 09:32 rtm\n", + "drwxr-xr-x 6 $USER ncar 4096 Jun 4 09:31 ww3\n", + "```\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "aa9fb22c-de5f-41d7-ac25-07ba585ad2a9", + "metadata": {}, + "source": [ + "## Step 4. Look at the Community Atmosphere Model (CAM) Component Model\n", + "\n", + "As a final task in this exercise we are going to have a quick look at the Community Atmosphere Model (CAM) component model. At this point we are just exploring the Workspace. \n", + "\n", + "
\n", + "Change into the CAM Directory of the Components area:
\n", + "\n", + "```\n", + "cd cam\n", + "```\n", + "
\n", + "\n", + "List the contents of the CAM Directory:
\n", + "\n", + "```\n", + "ls -l\n", + "```\n", + "
\n", + " \n", + "Expected Output:
\n", + "\n", + "```\n", + "total 26\n", + "drwxr-xr-x 6 $USER ncar 4096 Jun 4 09:32 bld\n", + "drwxr-xr-x 10 $USER ncar 4096 Jun 4 09:34 chem_proc\n", + "drwxr-xr-x 4 $USER ncar 4096 Jun 4 09:32 cime_config\n", + "-rw-r--r-- 1 $USER ncar 7501 Jun 4 09:32 CODE_OF_CONDUCT.md\n", + "drwxr-xr-x 2 $USER ncar 4096 Jun 4 09:32 doc\n", + "-rw-r--r-- 1 $USER ncar 685 Jun 4 09:32 Externals_CAM.cfg\n", + "-rw-r--r-- 1 $USER ncar 1155 Jun 4 09:32 Externals.cfg\n", + "drwxr-xr-x 5 $USER ncar 4096 Jun 4 09:32 manage_externals\n", + "-rw-r--r-- 1 $USER ncar 1925 Jun 4 09:32 README_EXTERNALS\n", + "-rw-r--r-- 1 $USER ncar 282 Jun 4 09:32 README.md\n", + "drwxr-xr-x 11 $USER ncar 4096 Jun 4 09:32 src\n", + "drwxr-xr-x 4 $USER ncar 4096 Jun 4 09:32 test\n", + "drwxr-xr-x 11 $USER ncar 4096 Jun 4 09:32 tools\n", + "```\n", + "\n", + "
\n", + "\n", + "**Congratulations, you have now completed the CESM code exploration exercises!!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50898f29-f9dd-470c-9918-a05df415b4a8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80d816ff-6217-4a2b-a9d0-4e5ae5e7ab21", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab049642-5bfe-4d46-92c7-c3c2ec6570fc", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18a9a811-0883-410d-bfb1-d3eb16e3fd75", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5191d62a-76ee-4f90-b46b-f70ec0af9909", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/code/git_download_cesm.ipynb b/notebooks_copy/basics/code/git_download_cesm.ipynb new file mode 100644 index 000000000..dd90e7451 --- /dev/null +++ b/notebooks_copy/basics/code/git_download_cesm.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Download CESM" + ] + }, + { + "cell_type": "markdown", + "id": "9d121236-47a7-48ef-8b67-40ef3b827178", + "metadata": {}, + "source": [ + "You are going to download the CESM code from github into your own personal CESM workspace. Follow the three steps, below, to setup your CESM Code workspace.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "458e826c-60cb-4c5a-980f-b89f47cd06d1", + "metadata": {}, + "source": [ + "
\n", + "We will use the CESM release version `release-cesm2.1.5` for use in this tutorial.

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c029d6bc-bd76-471a-8126-e888616e70d0", + "metadata": {}, + "source": [ + "Downloading the CESM code is an important step as this will be the code workspace we will be using throughout the rest of the Tutorial. **If you have problems here please ask for help**." + ] + }, + { + "cell_type": "markdown", + "id": "40257a4e-aa6d-4f12-b825-b9986cdf4daf", + "metadata": {}, + "source": [ + "## Step 1: Create CESM Code Directory" + ] + }, + { + "cell_type": "markdown", + "id": "986d1b83-f863-4294-b65b-5c9c65a8ac58", + "metadata": {}, + "source": [ + "\n", + "Create a workspace location where you can put your CESM code on the derecho glade file system. In all exercises in this tutorial `$USER` is a placeholder and you should use your NCAR HPC login name when completing the exercise." + ] + }, + { + "cell_type": "markdown", + "id": "d95a81b5-16b9-4291-9490-4058f98dcb3f", + "metadata": {}, + "source": [ + "
\n", + "\n", + "```\n", + "cd /glade/work/$USER\n", + "mkdir code\n", + "```\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e980a755-b914-46eb-b217-3fe15781aeb3", + "metadata": {}, + "source": [ + "## Step 2. Download CESM Code with Git Clone" + ] + }, + { + "cell_type": "markdown", + "id": "76ef78ca-bb75-4340-996f-04f95233c84f", + "metadata": {}, + "source": [ + "First we will change into the `Code` Workspace location then we will use the `git clone` command to download the CESM code.\n", + "\n", + "
\n", + "Change the current directory to the code workspace directory:
\n", + "\n", + "```\n", + "cd /glade/work/$USER/code\n", + "```\n", + "
\n", + " \n", + "Download the cesm code to your code workspace directory as `my_cesm_code`:
\n", + "```\n", + "git clone https://github.com/ESCOMP/CESM.git my_cesm_code\n", + "cd my_cesm_code\n", + "git checkout release-cesm2.1.5\n", + "```\n", + " \n", + "Note that while we named the downloaded code `my_cesm_code`, you can name it anything you want and for future downloads outside this tutorial it can be helpful to label the code for the release you are using for your experiments.\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b9211e68-8991-4f08-a6fd-ebd009cafe75", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + " \n", + "```\n", + "Note: switching to 'release-cesm2.1.5'.\n", + "\n", + "You are in 'detached HEAD' state. You can look around, make experimental\n", + "changes and commit them, and you can discard any commits you make in this\n", + "state without impacting any branches by switching back to a branch.\n", + "\n", + "If you want to create a new branch to retain commits you create, you may\n", + "do so (now or later) by using -c with the switch command. Example:\n", + "\n", + " git switch -c \n", + "\n", + "Or undo this operation with:\n", + "\n", + " git switch -\n", + "\n", + "Turn off this advice by setting config variable advice.detachedHead to false\n", + "\n", + "HEAD is now at 7a6c5b0 Update for cesm2.1.5-rc.01\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "97b726d5-99e6-450d-b494-fd9ddc917284", + "metadata": {}, + "source": [ + "### Versions" + ] + }, + { + "cell_type": "markdown", + "id": "cdeef63a-d107-46d2-a89b-d233c8ed13c2", + "metadata": {}, + "source": [ + "One thing you will need to determine is what version of the CESM code you should use. Some things to consider when making that decision include: \n", + "- What versions of the code are supported scientifically or technically?\n", + "- Things you may want to consider are what sort of model features do you need and what versions of the model are these available in? \n", + "- If you want to compare your sensitivity experiment against a community experiment (e.g. CESM2-LE) then you should use the same version of the code.\n", + "\n", + "\n", + "More information can be found on the [CESM Models website](https://www.cesm.ucar.edu/models)." + ] + }, + { + "cell_type": "markdown", + "id": "d5db54cd-0018-4187-afa7-6da675ea55ba", + "metadata": {}, + "source": [ + "
\n", + " \n", + "If you want to check which other release versions are available, you can use the command:\n", + "```\n", + "git tag --list 'release-cesm2*' \n", + "``` \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "195d4a4b-514b-446e-9d2b-4c7aeae60aa2", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + " \n", + "```\n", + "release-cesm2.0.0\n", + "release-cesm2.0.1\n", + "release-cesm2.1.0\n", + "release-cesm2.1.1\n", + "release-cesm2.1.2\n", + "release-cesm2.1.3\n", + "release-cesm2.1.4\n", + "release-cesm2.1.5\n", + "release-cesm2.2.0\n", + "release-cesm2.2.1\n", + "release-cesm2.2.2\n", + "\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "88bd0742-e990-4fcf-9b40-5c8b21a9ec43", + "metadata": {}, + "source": [ + "## Step 3. Download the Component Models with checkout_externals" + ] + }, + { + "cell_type": "markdown", + "id": "615c0e7b-5e6c-4352-b1a2-3f8ac03b4b6b", + "metadata": {}, + "source": [ + "\n", + "The CESM code downloaded with the branch tain the previous step does not include all of the code and support for the component models. These are developed and maintained in other Github repositories. To include all of the component models into your code workspace you need to download them as an additional step. The checkout_externals tool is located in manage_externals directory in the `my_cesm_code` code directory.\n", + "\n", + "
\n", + "Exercise: Run the `checkout_externals` command to download all the component models:
\n", + "\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code\n", + "./manage_externals/checkout_externals\n", + "```\n", + "
\n", + "*Note: If you get a message about accepting a certificate permanently or temporarily, accept the certificate permanently. If you do not get this message, do not worry, you are still on track!\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "8341952a-2c3b-4892-a7ea-6b0407ecb78c", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for example output \n", + "\n", + "
\n", + "\n", + " where `$USER` will be replaced by your user name\n", + "```\n", + "Processing externals description file : Externals.cfg (/glade/work/$USER/code/my_cesm_code)\n", + "Checking local status of required & optional components: cam, cice, cime, cism, clm, mosart, pop, rtm, ww3, \n", + "Checking out externals: cime, cam, Processing externals description file : Externals_CAM.cfg (/glade/work/$USER/code/my_cesm_code/components/cam)\n", + "Checking out externals: chem_proc, carma, clubb, cosp2, \n", + "cice, cism, Processing externals description file : Externals_CISM.cfg (/glade/work/$USER/code/my_cesm_code/components/cism)\n", + "Checking out externals: source_cism, \n", + "clm, Processing externals description file : Externals_CLM.cfg (/glade/work/$USER/code/my_cesm_code/components/clm)\n", + "Checking out externals: fates, ptclm, \n", + "mosart, pop, Processing externals description file : Externals_POP.cfg (/glade/work/$USER/code/my_cesm_code/components/pop)\n", + "Checking out externals: cvmix, marbl, \n", + "rtm, ww3, \n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "74d14003-78cc-48e6-bcbc-622f0150dfed", + "metadata": {}, + "source": [ + "- The path `/glade/work/$USER/code/my_cesm_code` is your workspace `$SRCROOT`, as described in the [workspaces section](https://ncar.github.io/CESM-Tutorial/notebooks/basics/cesm_workspaces.html#workspace-overview)." + ] + }, + { + "cell_type": "markdown", + "id": "ff11dd07-faa2-42a2-8692-83d0895bc9f3", + "metadata": {}, + "source": [ + "**Congratulations, you have now downloaded the CESM code component of your workspace!!**" + ] + }, + { + "cell_type": "markdown", + "id": "a9a323ca-575d-4d80-ae5d-6e5402cccc75", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "You downloaded the CESM code with the instructions above. In which directory is the CESM code? How do you navigate to that directory?\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "4e18eab9-75d1-4bf0-8d60-454f0cb6ce7d", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + "Click here for the solution
\n", + "If you download CESM with the instructions above, the CESM code is located in:\n", + "\n", + "```\n", + "/glade/work/$USER/code/my_cesm_code\n", + "```\n", + "\n", + "To navigate to that directory, use the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code\n", + "```\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9630dee5-2e36-4c2f-81b6-f8abd736a73f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/code_overview.ipynb b/notebooks_copy/basics/code_overview.ipynb new file mode 100644 index 000000000..b22cf4e8c --- /dev/null +++ b/notebooks_copy/basics/code_overview.ipynb @@ -0,0 +1,80 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c2d8431-0e66-4597-96bb-858874da9235", + "metadata": {}, + "source": [ + "# CESM code" + ] + }, + { + "cell_type": "markdown", + "id": "56f53601-ba7c-41b0-8d9e-d56f67689734", + "metadata": {}, + "source": [ + "Before you can start running the CESM model you must first download CESM from Github and set up your Workspace. This exercise is estimated to take around 30-60 minutes, depending on your previous experience with NCAR HPC and Github." + ] + }, + { + "cell_type": "markdown", + "id": "9dcb3c4e-df54-4e99-892c-8ce99ea35410", + "metadata": {}, + "source": [ + "## Learning Objectives" + ] + }, + { + "cell_type": "markdown", + "id": "f7a8cc2d-e2eb-4f06-a173-9ca19a1dc383", + "metadata": {}, + "source": [ + "
\n", + " \n", + "- Download CESM to your workspace with Git commands.\n", + "- Explore your CESM workspace for CESM Code.\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "89b36bf1-182b-4460-af55-6f05b7a4a086", + "metadata": {}, + "source": [ + "If you are unfamiliar with logging into NCAR HPC resources, please go back to this [section](https://ncar.github.io/CESM-Tutorial/notebooks/prereqs/prereqs_overview.html#ncar-high-performance-computing-hpc) to learn more about logging into derecho.\n", + "\n", + "Github and Git software are used as development and management tools for supporting and distributing CESM. This tutorial is not intended to be a Github or Git software tutorial, so we will provide the basic git commands needed to download CESM. If you want to learn more about using these tools go to this [section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/github.html#github).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "729b1035-407c-4a7a-8da2-97402230fc75", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/exercises/examine_history_B1850.ipynb b/notebooks_copy/basics/exercises/examine_history_B1850.ipynb new file mode 100644 index 000000000..c31b7875e --- /dev/null +++ b/notebooks_copy/basics/exercises/examine_history_B1850.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Examine History Files\n" + ] + }, + { + "cell_type": "markdown", + "id": "0f737e0e-3ba8-40e4-8a82-8484830e002a", + "metadata": {}, + "source": [ + "Having successfully completed your first month of the new CESM B1850 case you will now examine the history files that have been transfered to the Archive directory. You will be using the NetCDF viewer ncview to look at the monthly average values for the single month. For this exercise you will be looking at the output from the Community Atmospheric Model (CAM) component of CESM, however the ncview tool can be applied to other components such as the Community Land Model (CLM). In this exercise we will:\n", + "\n", + "- Step 1. Explore the b.day1.0 Archive directory.\n", + "- Step 2. Open the b.day1.0 cam h0 in ncview.\n", + "- Step 3. Examine Average Monthly Surface Temperature.\n", + "- Step 4. Examine Average Monthly Precipitation." + ] + }, + { + "cell_type": "markdown", + "id": "736b22e8-4b55-4c33-bff9-1ab1a026931c", + "metadata": {}, + "source": [ + "## Step 1. Explore the b.day1.0 Archive directory" + ] + }, + { + "cell_type": "markdown", + "id": "0c778f29-eacc-4e41-b729-1cb6e45cc65f", + "metadata": {}, + "source": [ + "The short term archive directory has a range of model component directories along with the restart (rest) and logs directories. First change directory into the archive directory and then list the contents of the restart and logs directories. " + ] + }, + { + "cell_type": "markdown", + "id": "27929215-8026-42f7-acf7-84c259f944e2", + "metadata": {}, + "source": [ + "
\n", + "Change into the b.day1.0 short term archive directory:

\n", + "\n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "```\n", + "
\n", + "\n", + "List the logs directory:
\n", + "\n", + "```\n", + "ls -l logs\n", + "```\n", + "
\n", + " \n", + "This is where all the log files from the model run are kept.
\n", + "\n", + "List the logs directory:
\n", + "\n", + "```\n", + "ls -l rest/0001-02-01-00000\n", + "```\n", + "
\n", + "\n", + "These are the files that CESM will use to restart the model.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f1fad1f2-8f0a-4555-a40b-c226877d9c06", + "metadata": {}, + "source": [ + "## Step 2. Open the CAM h0 monthly history file" + ] + }, + { + "cell_type": "markdown", + "id": "b7a357a4-fec5-44df-8a2d-db27dd57d9fe", + "metadata": {}, + "source": [ + "You will now use `ncview` to look at the b.day1.0 case CAM monthly average values for the single month you ran.\n", + "\n", + "If you are unfamiliar with `ncview` you can learn more at [this link](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#ncview). \n", + "\n", + "**Note**: you must load the `ncview` module to your environment. If you loaded the tutorial `.profile` or `.tcshrc` file it should already be loaded. Check that it is in your environment.\n", + "\n", + "```\n", + "module list\n", + "```\n", + "\n", + "If you don't see `ncview` in the list of modules, you can load it by doing:\n", + "\n", + "```\n", + "module load ncview\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "e4952d0c-ce4b-4274-9850-1f6e9e8fe7b6", + "metadata": {}, + "source": [ + "
\n", + "Change into the CAM history directory:

\n", + "\n", + "```\n", + "cd atm/hist\n", + "```\n", + "
\n", + "\n", + "List the files in the directory:
\n", + "\n", + "```\n", + "ls -l\n", + "```\n", + "
\n", + " \n", + "There should be a single file, b.day1.0.cam.h0.0001-01.nc.
\n", + "\n", + "Open the file using `ncview`:
\n", + "```\n", + "ncview b.day1.0.cam.h0.0001-01.nc &\n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "3c1fc1c9-910a-40e6-9a5e-77306885925d", + "metadata": {}, + "source": [ + "If your X11 environment is correctly set up then the ncview tool should appear as shown below on the left. Note the ` &` at the end of the command. This puts the command into the background allowing other tasks to be run from the derecho terminal window. The view on the right shows the ncview application once the Surface Temperature variable has been selected.\n", + "\n", + "
\n", + "\n", + "![NCView App Window](../../../images/basics/NCVIEW_App_Window.png)\n", + "*

Figure: NCView App Window

*" + ] + }, + { + "cell_type": "markdown", + "id": "24a515d6-95e5-48be-b7c4-b335ec0ad88a", + "metadata": {}, + "source": [ + "## Step 3. Examine Average Monthly Surface Temperature" + ] + }, + { + "cell_type": "markdown", + "id": "0b5d30af-109a-40a8-a49f-01ac21734dfb", + "metadata": {}, + "source": [ + "Using ncview with the b.day1.0 January 0001 CAM monthly average values file loaded, examine the Surface Temperature variable. Update the color palette used to plot values and change the size of the plot.\n", + "\n", + "
\n", + "Select the Surface Temperature Variable:

\n", + "\n", + "Click on the **(244) 2d vars** button shown above. This will pull up a list of all 2d vars on the b.day1.0 January 0001 CAM history file. This is a long list but it is in alphabetical order. You are looking for the `TS` variable. \n", + "
\n", + "\n", + "Change the color selection from detail to bright:
\n", + "\n", + "Click on the color button marked with **detail**. This will change the color selection to **3gauss**. Clicking again will change this to **ssec** and finally **bright**.\n", + " \n", + "
\n", + " \n", + "Increase the map plot size:
\n", + "\n", + "Click on the magnify button marked with **M X3**. This will change the magnification to \"M X4\". Clicking again will change this to \"M X5\" and finally \"M X6\"\n", + " \n", + "
\n", + "\n", + "![NCView TS](../../../images/basics/NCVIEW_Jan0001_TS.png)\n", + "*

Figure: NCView b.day1.0 TS Jan 0001

*\n", + "\n", + "The Surface Temperature variable is in Kelvin. A value of 273.15K represents 0°C and 32°F. There are many options to better view and explore data with ncview. Some of these will come up over the next few days of the tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "14ba2172-2188-4bca-b861-deb6bb45731a", + "metadata": {}, + "source": [ + "## Step 4. Examine Average Monthly Precipitation" + ] + }, + { + "cell_type": "markdown", + "id": "25c204c4-d1f2-4af8-93c1-d9296b5cba9c", + "metadata": {}, + "source": [ + "Using ncview with the b.day1.0 January 0001 CAM monthly average values file loaded, examine the the two variables representing Convective Precipitation and Large Scale Precipitation.\n", + "\n", + "
\n", + "Exercise: Select the Convective Precipitation Variable:

\n", + "\n", + "Click on the \"(244) 2d vars\" button shown above. Find and select the PRECC variable. \n", + "
\n", + "\n", + "
\n", + "\n", + "![NCView PRECC](../../../images/basics/NCVIEW_Jan0001_PRECC.png)\n", + "*

Figure: NCView b.day1.0 PRECC Jan 0001

*\n", + "\n", + "
\n", + "Exercise: Select the Convective Precipitation Variable:

\n", + "\n", + "Click on the \"(244) 2d vars\" button shown above. Find and select the PRECL variable. \n", + " \n", + "
\n", + "\n", + " \n", + "
\n", + "\n", + "![NCView PRECL](../../../images/basics/NCVIEW_Jan0001_PRECL.png)\n", + "*

Figure: NCView b.day1.0 PRECL Jan 0001

*\n", + "\n", + "Precipitation in the model is recorded in m/s. To convert to mm/day we can multiply the values by 1000 mm in a meter and 86,400 seconds in a day. Can you see the differences in Convective and Large Scale Precipitation?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aefd9392-3406-466a-939e-3382755675ad", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/exercises/extra.ipynb b/notebooks_copy/basics/exercises/extra.ipynb new file mode 100644 index 000000000..fcb8145f4 --- /dev/null +++ b/notebooks_copy/basics/exercises/extra.ipynb @@ -0,0 +1,205 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Explore Further\n" + ] + }, + { + "cell_type": "markdown", + "id": "0f737e0e-3ba8-40e4-8a82-8484830e002a", + "metadata": {}, + "source": [ + "The focus of this exercise has been to get the model running. We will learn more about how to modify runs in this section, and we will cover details in further sections of this tutorial. For now, if you have extra time, try the following actions to better familiarize yourself with the CESM code." + ] + }, + { + "cell_type": "markdown", + "id": "b86bda30-feb3-4cc4-aa4e-90bde99a8d1d", + "metadata": {}, + "source": [ + "## Explore the `CASE` directory" + ] + }, + { + "cell_type": "markdown", + "id": "19338922-d825-435d-bc21-4b6bf78c2ad9", + "metadata": {}, + "source": [ + "
\n", + " \n", + "```\n", + "cd /glade/work/$USER/cases/b.day1.0/\n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b349d98b-d239-4760-bc52-54300f47d6c9", + "metadata": { + "tags": [] + }, + "source": [ + "Look around your case directory to become familiar with the structure. Note there are scripts (e.g. case.setup, case.submit, etc.), xml files, etc. We will delve into these in more detail in future sections of the tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "33e05339-2796-427a-867d-5891dba3803e", + "metadata": { + "tags": [] + }, + "source": [ + "### Check the CaseStatus file" + ] + }, + { + "cell_type": "markdown", + "id": "be1faf19-5e5b-48fa-ad77-89cdc01a6fc5", + "metadata": {}, + "source": [ + "All activities for the case are recorded in the CaseStatus file. By looking through the file the successful or otherwise outcome of each step of the run can be tracked." + ] + }, + { + "cell_type": "markdown", + "id": "972ea62f-d33d-49c4-a615-bb4efd603946", + "metadata": {}, + "source": [ + "
\n", + " \n", + "```\n", + "more CaseStatus \n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e8455397-9f25-4108-ba7d-5636dd6f14df", + "metadata": {}, + "source": [ + "### Check the Job Queue and Project" + ] + }, + { + "cell_type": "markdown", + "id": "67bef6ac-3a5a-4594-9fb4-bb0aadfe9fb8", + "metadata": {}, + "source": [ + "
\n", + " \n", + "```\n", + "./xmlquery JOB_QUEUE\n", + "./xmlquery PROJECT\n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "bc61ce2c-016d-42a1-807f-d9429b60c8b2", + "metadata": { + "tags": [] + }, + "source": [ + "## Explore the `cime/scripts` directory" + ] + }, + { + "cell_type": "markdown", + "id": "6d59eac7-637a-4ab5-aa13-e58c76402ffb", + "metadata": {}, + "source": [ + "
\n", + " \n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "0bf92c97-a6f3-42d1-81dc-3afb5df626e6", + "metadata": {}, + "source": [ + "### Check the CESM compset definitions" + ] + }, + { + "cell_type": "markdown", + "id": "0fea9ec7-4dfe-4901-be41-2815b8d0408c", + "metadata": {}, + "source": [ + "
\n", + " \n", + "```\n", + "./query_config --compsets | more\n", + "```\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b20b8542-59e8-4f7f-9a47-daf1d324794f", + "metadata": {}, + "source": [ + "Note the ` | more` after the command sends the output of the query_config script to the `more` tool so you can scroll through it more easily. You can use the spacebar to move to the next page and the q key to quit out of the tool." + ] + }, + { + "cell_type": "markdown", + "id": "ca441693-1d6a-47d9-add7-cba4754fe18a", + "metadata": {}, + "source": [ + "### Check the CESM grid resolutions" + ] + }, + { + "cell_type": "markdown", + "id": "8407b1f3-5e5f-4b85-aee5-b2b197b8c6b1", + "metadata": {}, + "source": [ + "
\n", + " \n", + "```\n", + "./query_config --grids --long | more\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cff6ac2f-b73d-4585-805d-79267555b707", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/exercises/first_B1850.ipynb b/notebooks_copy/basics/exercises/first_B1850.ipynb new file mode 100644 index 000000000..2c7a44637 --- /dev/null +++ b/notebooks_copy/basics/exercises/first_B1850.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7013ccab-46dc-4a38-8039-7ec464251982", + "metadata": {}, + "source": [ + "# Your first CESM run" + ] + }, + { + "cell_type": "markdown", + "id": "30c6953e-8197-4fea-b398-5a090db1a762", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Run a basic, out-of-the-box (that is, no user changes are implemented) CESM experiment

\n", + " \n", + "Create a case called **b.day1.0** using the compset ``B1850`` at ``f19_g17`` resolution. \n", + "\n", + "Check the status of the run after submitting.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "73e4cb02-1717-420c-8a28-f56c0078956c", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + " \n", + "**Is my workspace set up?**\n", + "\n", + "Did you already create a `cases` directory where you will put all new cases\n", + "```\n", + "cd /glade/work/$USER\n", + "mkdir cases\n", + "```\n", + " \n", + "**Did you create a new case?**\n", + "\n", + "You can create a new case with the command:\n", + "```\n", + "./create_newcase --case /glade/work/$USER/cases/CASE --res RES --compset COMPSET\n", + "```\n", + "Make sure you use the correct information for the CASE, RES, and COMPSET inputs.\n", + "\n", + "**Did you set up the case?**\n", + "\n", + "You can invoke case set up as follows:\n", + "```\n", + "./case.setup\n", + "```\n", + "**How do I compile?**\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "**How do I check my job status?**\n", + "\n", + "You can check job status using the command:\n", + "```\n", + "qstat -u $USER\n", + "```\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "f7c918d3-6475-463d-a10e-2badb6728cb5", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Create a new case b.day1.0 with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", + "./create_newcase --case /glade/work/$USER/cases/b.day1.0 --res f19_g17 --compset B1850\n", + "```\n", + "\n", + "Case setup:\n", + "``` \n", + "cd /glade/work/$USER/cases/b.day1.0 \n", + "./case.setup\n", + "```\n", + " \n", + "Do not make any other changes since we are running an out-of-the-box simulation.\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + " \n", + "Remember that `qcmd` is used on derecho only.\n", + "\n", + "Check the status:\n", + "```\n", + "qstat -u $USER\n", + "``` \n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "b.day1.0. \n", + " \n", + "Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "ls \n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9c66385f-055c-4b74-b6fe-6fc189c02690", + "metadata": { + "tags": [] + }, + "source": [ + "## Test your understanding" + ] + }, + { + "cell_type": "markdown", + "id": "8218314c-f299-4171-b0fe-bac655c2d4f5", + "metadata": { + "tags": [] + }, + "source": [ + "- Did your run complete successfully?\n", + "- What sorts of files did you get as output?\n", + "- How long did the run go for?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a7b563b-7afd-4699-954b-8a3cc1e7bc4e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/exercises/review_questions.ipynb b/notebooks_copy/basics/exercises/review_questions.ipynb new file mode 100644 index 000000000..5742e8c53 --- /dev/null +++ b/notebooks_copy/basics/exercises/review_questions.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Review Questions\n", + "\n", + "If time allows here are some review questions to go back over the material in the [Introduction](https://ncar.github.io/CESM-Tutorial/notebooks/intro.html#introduction) and [Basics](https://ncar.github.io/CESM-Tutorial/notebooks/basics.html#basics) sections." + ] + }, + { + "cell_type": "markdown", + "id": "400791b9-2296-448b-a246-3eddd1ee828f", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Question 1: What is the CESM Workspace? How many separate directory areas are there?

\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "41bfb932-b6bc-4bce-adb9-6bb568abc0c5", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "The CESM model consists of five main directory tree areas that make up the CESM Workspace. The individual directories represent different elements including the source code, your experiment cases, the area where the model runs, the location of input data, and the location where the output data from the model will be archived for later use.\n", + " \n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1d27e05-48e4-476f-8714-abd94ff8b38c", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Question 2: Where is the CESM Code Development Managed?

\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c081edba-611f-4f08-88c5-57d4ce94ea5b", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "The CESM Project is hosted on Github under under the Earth System Community Modeling Portal (ESCOMP) which is managed by the University Corporation for Atmospheric Research (UCAR). Code can be directly downloaded from Github using Git commands.\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "9b6dd2e6-d422-4460-acf5-08843d505872", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Question 3: Where do you find the create_newcase command? What does it do?

\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "740a99b4-14df-4167-9dfd-75b464a91ef3", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "The create_newcase script is located in the CIME Scripts Directory. The create_newcase script creates a new case in your CASE Directory. The command takes the new casename, compset, and resolution as values.\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7fe2eb40-6373-4a71-a488-a3b98b8e1dc8", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Question 4: What are the three commands required to start running a new case?

\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7c13abe3-e7f2-4371-a43a-f3a82856f40a", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "After create_newcase you need to change to the new case directory where you need to run case.setup, case.build and case.submit. As an additional step you can run the script preview_namelists.\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "30282de3-e5e8-4886-88c8-409c74ca9506", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Question 5: How can you monitor your CESM submission while it is running?

\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "4392489e-96c9-44d7-8897-a6560891c6e9", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "There are three main ways to monitor your CESM job while it is running. The first is with the qstat command, the second is with the build/run directory files, and the last is with the archive directory files. \n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "1a83b705-7035-4614-a13b-1ecf153a5f1c", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Question 6: What is the main way to understand how a CESM simulation ran?

\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "197fefcd-c57a-431b-b517-5e2aacc4948c", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "The main output from your CESM job is the history files. These can be specified to output the status of the component models at various levels of detail and for different time averaging periods. \n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "69723db1-9640-4744-94e1-78f327581c0f", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Question 7: What is an XML file? What is it used for in the Case Directory?

\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "70243d30-f462-4254-957e-e40ef33118ad", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "The XML files are Markup Language files that contain variables used by the CESM scripts. In the Case Directory there are seven xml files:\n", + " \n", + "- env_archive.xml specifies rules for short-term archival script case.st_archive\n", + "- env_batch.xml set by create_newcase to define batch specific settings used by script case.submit\n", + "- env_build.xml specifies build information used by script case.build\n", + "- env_case.xml set by create_newcase and cannot be modified\n", + "- env_mach_pes.xml specifies PE layout of components used by script case.run\n", + "- env_mach_specific.xml specifies machine specific information used by script case.build\n", + "- env_run.xml sets run time information (such as length of run, frequency of\n", + "restarts, …) User interacts with this file most frequently\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "12cb104f-bc23-4464-b531-a89934b9bb54", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Question 8: What are the main ways to investigate and modify XML values in the Case Directory?

\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "b030bacf-4dfb-42aa-8c56-8dc199840fd2", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "While the values in XML files can be modified directly with an editor, CESM provides tools for querying and changing values. The xmlquery and xmlchange tools ensure that the values found and updated are valid for the files they belong to and that any changes made are recorded in the CaseStatus file in the CASE Directory.\n", + " \n", + "
\n", + "
\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/exercises/second_B1850.ipynb b/notebooks_copy/basics/exercises/second_B1850.ipynb new file mode 100644 index 000000000..be5f5cb55 --- /dev/null +++ b/notebooks_copy/basics/exercises/second_B1850.ipynb @@ -0,0 +1,163 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7013ccab-46dc-4a38-8039-7ec464251982", + "metadata": {}, + "source": [ + "# Extending your run" + ] + }, + { + "cell_type": "markdown", + "id": "30c6953e-8197-4fea-b398-5a090db1a762", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Run the same case, but for longer

\n", + "\n", + "The default value for a new case run length is 5 days. The run length is set by the XML variables STOP_OPTION and STOP_N. As the 5 days is too short to produce a monthly history file we will change these to run for a single month. In the next section you will learn more about `xml` files, but for now we will just provide the commands.\n", + " \n", + "From your `CASE` directory, extend the run b.day1.0 with the command:\n", + "```\n", + "./xmlchange STOP_OPTION=nmonths\n", + "./xmlchange STOP_N=1\n", + "```\n", + "\n", + "After you have changed these variables, submit the job again and check its status.\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "73e4cb02-1717-420c-8a28-f56c0078956c", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + " \n", + "**Submit:**\n", + "```\n", + "./case.submit\n", + "```\n", + "\n", + "**Check the Status:**\n", + "```\n", + "qstat -u $USER\n", + "``` \n", + " \n", + "**List the contents of the Run directory**\n", + "\n", + "```\n", + "ls -lt /glade/derecho/scratch/$USER/b.day1.0/run\n", + "```\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "f7c918d3-6475-463d-a10e-2badb6728cb5", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Go to your b.day1.0 case directory:\n", + "```\n", + "cd /glade/work/$USER/cases/b.day1.0\n", + "```\n", + "\n", + "Changing xml files:\n", + "``` \n", + "./xmlchange STOP_OPTION=nmonths\n", + "./xmlchange STOP_N=1\n", + "```\n", + " \n", + "Do not make any other changes since we are running an out-of-the-box simulation.\n", + "\n", + "Submit:\n", + "```\n", + "./case.submit\n", + "```\n", + "\n", + "Check the job status:\n", + "```\n", + "./qstat -u $USER\n", + "``` \n", + " \n", + " \n", + "When the run is completed, look into the archive directory for: \n", + "b.day1.0. \n", + " \n", + "Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "ls \n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9c66385f-055c-4b74-b6fe-6fc189c02690", + "metadata": { + "tags": [] + }, + "source": [ + "## Test your understanding" + ] + }, + { + "cell_type": "markdown", + "id": "8218314c-f299-4171-b0fe-bac655c2d4f5", + "metadata": { + "tags": [] + }, + "source": [ + "- Did your run complete successfully?\n", + "- What sorts of files did you get as output and how are these different from before?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a7b563b-7afd-4699-954b-8a3cc1e7bc4e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/basics/exercises_overview.ipynb b/notebooks_copy/basics/exercises_overview.ipynb new file mode 100644 index 000000000..93c3f11be --- /dev/null +++ b/notebooks_copy/basics/exercises_overview.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c2d8431-0e66-4597-96bb-858874da9235", + "metadata": {}, + "source": [ + "# Exercise Overview \n", + "\n", + "\n", + "## Learning Goals:\n", + "\n", + "- The student will successfully set up and run their first (short) CESM simulation.\n", + "- This lab exercise is designed to build off the one-time setup section and uses the CESM workflow information presented. \n", + "\n", + "## Exercise overview:\n", + "\n", + "By then end of this exercise you will have:\n", + "- created, setup, updated, built, run, and examined the output of a standard B1850 control case\n", + "\n", + "**You should not proceed in working on this exercise until you have successfully completed the [one-time set up exercise](https://ncar.github.io/CESM-Tutorial/notebooks/basics/basics_overview.html). You must have successfully downloaded CESM using git and have a `cases` directory to begin this exercise.**\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e062147c-f8f3-4a21-877e-b1c5755c6cad", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/bgc/bgc.ipynb b/notebooks_copy/challenge/bgc/bgc.ipynb new file mode 100644 index 000000000..6e85a9dda --- /dev/null +++ b/notebooks_copy/challenge/bgc/bgc.ipynb @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Biogeochemistry" + ] + }, + { + "cell_type": "markdown", + "id": "c0590b2a-ae3f-48b2-b287-8fa40eae213d", + "metadata": {}, + "source": [ + "## Biogeochemistry in CESM" + ] + }, + { + "cell_type": "markdown", + "id": "34516fd8-ed12-44c0-ba57-b684c1c279cf", + "metadata": {}, + "source": [ + "Biogeochemistry features have been available since CESM1.0 and are on by default in CESM2.0. \n", + "\n", + "Three components of CESM have biogeochemistry features:\n", + "\n", + "**CAM**\n", + "- CO$_{2}$ constituents in CAM that use the land and ocean fluxes of CO$_{2}$ as surface boundary conditions.\n", + "- CAM passes CO$_{2}$ to the coupler for land and ocean flux computations. \n", + "- The CO$_{2}$ consitutients are coupled with radiation computations.\n", + "\n", + "**POP**\n", + "- Includes the MARBL ecosystem library in CESM2.0\n", + "\n", + "**CLM**\n", + "- Features covered elsewhere [LINK]" + ] + }, + { + "cell_type": "markdown", + "id": "f6f495f2-b20f-441e-877e-b0f5e9ec5755", + "metadata": {}, + "source": [ + "## Coupled BGC Compsets" + ] + }, + { + "cell_type": "markdown", + "id": "ef4e3af3-cc84-4dda-b483-daf8bf2b1b30", + "metadata": {}, + "source": [ + "**Terminology**\n", + "- BGC CO$_{2}$: what is used by surface components\n", + "- RAD CO$_{2}$: what is used by the atmospheric radiative code\n", + "- Diagnostic CO$_{2}$: prescribed atmospheric CO$_{2}$ concentrations. (E.g. constant, read from file, 1% ramp, etc.)\n", + "- Prognostic CO$_{2}$: predicted atmospheric CO$_{2}$ concentrations. Atmospheric consitutent is computed from surface CO$_{2}$ fluxes.\n", + "\n", + "\n", + "**B1850, BHIST**\n", + "- Compset long name has BCG%BDRD\n", + "- Coupled model, BGC & RAD CO$_{2}$ are diagnostic\n", + "\n", + "**B1850_BPRP, BHIST_BPRP**\n", + "- Shortnames introduced in CESM2.1.1\n", + "- Compset long name has BGC%BPRP\n", + "- Coupled model, BGC & RAD CO$_{2}$ are prognostic" + ] + }, + { + "cell_type": "markdown", + "id": "0a62ca85-30f9-409d-86d4-8bc2c1da2bcd", + "metadata": {}, + "source": [ + "## Ocean Specific BGC Compsets" + ] + }, + { + "cell_type": "markdown", + "id": "64720908-da20-491c-9c03-dea85f6f770f", + "metadata": {}, + "source": [ + "**C1850ECO**\n", + "- Ocean alone, 1850 aerosols, normal year forcing\n", + "\n", + "**G1850ECO**\n", + "- Ocean and sea ice, 1850 aerosols, normal year forcing\n", + "\n", + "**G1850ECOIAF**\n", + "- Ocean and sea ice, 1850 aerosols, interannually varying forcing\n", + "\n", + "These can be found by using the following command in the same directory as `create_newcase`\n", + "```\n", + "./query_config --compsets pop\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "acd0ff2e-fceb-4245-a8fb-0b7ff86aeb8c", + "metadata": {}, + "source": [ + "## BGC Initial Conditions" + ] + }, + { + "cell_type": "markdown", + "id": "56ffb1dc-1d35-4859-85f7-b3a47e9b567d", + "metadata": {}, + "source": [ + "**Coupled BDRD compsets**\n", + "- BDRD is default RUN_REFCASE set for f09_g17 or f19_g17 resolution experiments\n", + "- Provided initial conditions were spun-up with f09_g17 resolution model. *Note that the carbon cycle is not as well balanced in f19_g17 resolution experiments as it is with f09_g17 resolution.*\n", + "\n", + "**Coupled BPRP compsets**\n", + "- PBRP is the default RUN_REFCASE set for f09_g17\n", + "\n", + "**Ocean alone or Ocean-ice compsets**\n", + "- Initial conditions are provided but are not spun up." + ] + }, + { + "cell_type": "markdown", + "id": "d52161c7-d87e-455d-848f-6be823d6d110", + "metadata": {}, + "source": [ + "## BGC env*xml variables" + ] + }, + { + "cell_type": "markdown", + "id": "644683f9-5b69-4756-8643-84af9fdf7e6c", + "metadata": {}, + "source": [ + "**CCSM_BGC**\n", + "- Controls which CO$_{2}$ fields are exchanged between CESM components.\n", + "\n", + "![bgc_settings](../../../images/challenge/bgc_table_1.png)\n", + "\n", + "*

Figure: CCSM_BGC settings.

*\n", + " - CO2A: land only or ocean only runs.\n", + " - CO2B: atmosphere-land runs. Ocean and fossil fuel CO$_{2}$ fluxes are read for m a file.\n", + " - CO2C: fully coupled runs.\n", + "\n", + "**CCSM_CO2_PPMV**\n", + "- Constant CO$_{2}$ reference value used in some configurations.\n", + "\n", + "**OCN_CO2_TYPE or LND_CO2_TYPE**\n", + "- Controls CO$_{2}$ used by ocean and land components.\n", + "- Constant, prognistic, diagnostic.\n", + "\n", + "**OCN_TRACER_MODULES**\n", + "- Controls which ocean tracers are used.\n", + "- Ocean ecosystem model is called ecosys." + ] + }, + { + "cell_type": "markdown", + "id": "387d8807-75a6-40d2-97b2-eb20a7f7930a", + "metadata": {}, + "source": [ + "## BGC units and sign conventions" + ] + }, + { + "cell_type": "markdown", + "id": "255b3eff-e3f2-4a8d-9733-f4ba018588c7", + "metadata": {}, + "source": [ + "![bgc_units](../../../images/challenge/bgc_table_2.png)\n", + "\n", + "*

Figure: BGC units and sign conventions.

*" + ] + }, + { + "cell_type": "markdown", + "id": "a150934b-18e6-48da-98f4-b1a464e74343", + "metadata": {}, + "source": [ + "- CAM variables CO2, CO2_LND, CO2_OCN, CO2_FF all have units of (kg CO2)/(kg dry air). Note that these are not typical units for carbon cycle modelers. To convert to ppmv multipy by 1.0e6*28.966/44.0 . 28.966 is the molecular weight of dry air and 44.0 is the molecular weight of CO$_{2}$." + ] + }, + { + "cell_type": "markdown", + "id": "7e7c19a6-9a75-42a3-8338-59ffb4061acb", + "metadata": {}, + "source": [ + "* Same quantity in different CESM component output has: different names, different units, different sign conventions (for fluxes)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/bgc/bgc_exercise_1.ipynb b/notebooks_copy/challenge/bgc/bgc_exercise_1.ipynb new file mode 100644 index 000000000..e3e910b22 --- /dev/null +++ b/notebooks_copy/challenge/bgc/bgc_exercise_1.ipynb @@ -0,0 +1,81 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 1: Set up a BGC case" + ] + }, + { + "cell_type": "markdown", + "id": "f4238578-b8ac-4e94-aa01-2aac2c42a9dc", + "metadata": {}, + "source": [ + "We will create two different cases and compare the case directories. We want to check if the differences make sense. You will **NOT** run the model in this challenge exercise." + ] + }, + { + "cell_type": "markdown", + "id": "6457c1d2-0530-435d-ae27-d0f1eeabe583", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Set up a BGC control case

\n", + " \n", + "Create a case called **b_1850** using the compset ``B1850`` at ``f19_g17`` resolution. \n", + "\n", + "Create a case called **b_1850_bprp** using the compset ``B1850_BPRP`` at ``f19_g17`` resolution.\n", + "- What happens when you run create_newcase? Follow the instructions to get this working, but don't do it for real unless you know what you're doing.\n", + "\n", + "Run case.setup and preview_namelists for each case.\n", + "- What changes occur when the carbon cycle is made prognostic?\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "5efef9de-221c-411d-b209-23e53668a101", + "metadata": {}, + "source": [ + "## POP BGC Specific Output" + ] + }, + { + "cell_type": "markdown", + "id": "5d25903c-cd61-42fe-9631-04517ac35103", + "metadata": {}, + "source": [ + "**ocn/hist/$CASE.pop.h.ecosys.nday1.????-??-??.nc**\n", + "- selected ocean ecosys variables at daily resolution\n", + "- surface flux related, productivity, and functional group vertical integrals.\n", + "\n", + "**ocn/hist/$CASE.pop.h.ecosys.nyear1.????.nc**\n", + "- selected three dimensional ocean ecosys tracer budget terms." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam-chem_waccm/cam-chem_waccm.ipynb b/notebooks_copy/challenge/cam-chem_waccm/cam-chem_waccm.ipynb new file mode 100644 index 000000000..8d2b202b4 --- /dev/null +++ b/notebooks_copy/challenge/cam-chem_waccm/cam-chem_waccm.ipynb @@ -0,0 +1,130 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "curious-gibraltar", + "metadata": {}, + "source": [ + "# Atmospheric chemistry" + ] + }, + { + "cell_type": "markdown", + "id": "available-federation", + "metadata": { + "tags": [] + }, + "source": [ + "## Learning Goals\n", + "\n", + "- Student will learn about different chemistry (CAM-chem/WACCM) compsets and how to run one.\n", + "- Student will learn how to modify chemistry and change output settings.\n", + "- Student will learn how to change emissions.\n" + ] + }, + { + "cell_type": "markdown", + "id": "brave-copyright", + "metadata": {}, + "source": [ + "## CAM-chem and WACCM component sets\n", + "\n", + "The CESM2 components can be combined in numerous ways to carry out various scientific or software experiments. A particular mix of components, along with component-specific configuration and/or namelist settings, is called a component set or ``compset``. \n", + "\n", + "**WACCM** (Whole Atmosphere Community Climate Model) and **CAM-chem** (Community Atmosphere Model with chemistry) are atmospheric components of CESM, like CAM. They can be run as ``F`` and ``B`` compsets, as well as with nudged meteorology (for example towards reanalysis). They can be run using various dycores and resolutions.\n", + "In contrast to CAM, WACCM and CAM-chem run with different possibilities of chemistry complexity, whereby CAM uses simplified chemistry. CAM and CAM-chem use the same vertical resolution and model top.\n", + "Different chemical mechanisms for CAM-chem with more or less complexity have been developed based on the MOZART-TS1 and TS2 (currently the most comprehensive) chemical mechanism.\n", + "WACCM has a model top at about 150km and therefore covers the Troposphere, Stratosphere, Mesosphere and Lower-Thermosphere. The most comprehensive WACCM mechanism is therefore called the TSMLT chemical mechanism.\n", + "WACCM can also run with Middle Atmosphere (MA) chemistry (e.g., FWmaHIST, which includes very simplified chemistry in the troposphere, and with Specified Chemistry (e.g., FWscHIST), similar to the CAM chemistry.\n" + ] + }, + { + "cell_type": "markdown", + "id": "weekly-brass", + "metadata": {}, + "source": [ + "## CAM-chem and WACCM tested F component sets\n", + "\n", + "In CESM2.2 only the CAM-chem F compsets have been scientifically tested and no PI-control or transient history simulation has been scientifically validated. In contrast, WACCM has been used for CMIP6 simulations.\n", + "Two different nudging approaches exist using CAM-chem and WACCM, both using MERRA2 or GEOS5 meteorological analysis fields. The ``nudged`` approach is the recommended approach that uses meteorological fields (U, V, T) on CESM model levels.\n", + "The older specified dynamics (SD) approach requires the model to run on the vertical MERRA2 grids.\n", + "\n", + "An overview of different compsets can be found in the diagrams below.\n", + "\n", + "\n", + "![CAMChem tested F compsets](../../../images/challenge/CAMchem_compsets.png)\n", + "

Figure 1: CAM-chem tested F compsets.

\n", + "\n", + "![WACCM scientifically supported F compsets](../../../images/challenge/WACCM_compsets1.png)\n", + "

Figure 2: WACCM scientifically supported F compsets.

\n", + "\n", + "![WACCM tested F compsets](../../../images/challenge/WACCM_compsets2.png)\n", + "

Figure 3: WACCM tested F compsets.

" + ] + }, + { + "cell_type": "markdown", + "id": "unsigned-cooling", + "metadata": {}, + "source": [ + "## Overview of the Challenge Exercises for CAM-chem/WACCM\n", + "Start running one control case and choose between two options:\n", + "\n", + "a) Run a CAM-chem compset with TS1 chemistry, historical SSTs and 0.9x1.25 degrees horizontal resolution
\n", + "b) Run a WACCM with TSMLT1 chemistry, historical SSTs and 0.9x1.25 degrees horizontal resolution\n", + "\n", + "Once a control case has been configured and run, perform two test cases - one changing chemistry and one changing emissions." + ] + }, + { + "cell_type": "markdown", + "id": "adolescent-plaintiff", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "\n", + "\n", + "### Finding more information CAM-chem and WACCM\n", + "\n", + "More explanation (and some more options) can be found in the docs:
\n", + "https://wiki.ucar.edu/display/camchem/Home
\n", + "https://www.cesm.ucar.edu/models/cesm2/config/compsets.html
\n", + "https://ncar.github.io/CAM/doc/build/html/users_guide/atmospheric-configurations.html\n", + "\n", + "**Discussion board:**
\n", + "CAM-chem: https://bb.cgd.ucar.edu/cesm/forums/cam-chem.154/
\n", + "WACCM: https://bb.cgd.ucar.edu/cesm/forums/waccm.155/
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "equal-ethics", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam-chem_waccm/exercise_1.ipynb b/notebooks_copy/challenge/cam-chem_waccm/exercise_1.ipynb new file mode 100644 index 000000000..718e5881d --- /dev/null +++ b/notebooks_copy/challenge/cam-chem_waccm/exercise_1.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "arctic-workstation", + "metadata": {}, + "source": [ + "# 1: Control case: running with chemistry" + ] + }, + { + "cell_type": "markdown", + "id": "urban-jason", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise 1: Run a present-day compset that includes chemistry

\n", + " \n", + "Create, configure, build and run a case for one of these following configurations: \n", + "- **CAM-chem:** ``f.e21.FCHIST.f09_f09_mg17.tutorial.test1`` with the ``FCHIST`` compset; or \n", + "- **WACCM:** ``f.e21.FWHIST.f09_f09_mg17.tutorial.test1`` with the ``FWHIST`` compset. \n", + " \n", + "Run a simulation for 5 days, with daily instantaneous output of the variables: ‘PS’,’Z3’,’T’,’U’,’V’,’O3’.\n", + "You are also welcome to output your own additional variables.\n", + "Remember, F compsets have data ocean and data sea ice. \n", + "Check your model output in your run directory.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "animal-crash", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**How do I output daily instantaneous variables?**\n", + "\n", + "- Modify history stream 2 with namelist variable ``fincl2``\n", + "- Use namelist variables: ``nhtfrq``, ``mfilt``\n", + "- For more information, look at the chapter:
\n", + " **NAMELIST MODIFICATIONS** -> [Changing CAM namelist options](https://ncar.github.io/CAM/doc/build/html/users_guide/customizing-compsets.html#changing-cam-namelist-options)\n", + "- The default set up for these namelist variables can be found in ``CaseDocs/atm_in``\n", + "\n", + "**What is the resolution for FCHIST and FWHIST?**\n", + "\n", + "- Use resolution: f09_f09_mg17\n", + " \n", + "**Important! Check the newly generated namelist prior run**\n", + "\n", + "``ls CaseDocs/*``\n", + " \n", + "``atm_in`` \n", + "``atm_modelio.nml`` \n", + "``chem_mech.doc`` \n", + "``chem_mech.in`` \n", + "``cism.config`` \n", + "``cism_in`` \n", + "``cpl_modelio.nml`` \n", + "``docn_in ice_in`` \n", + "``docn.streams.txt.prescribed``\n", + "``drv_flds_in``\n", + "``drv_in`` \n", + "``esp_modelio.nml`` \n", + "``glc_modelio.nml``\n", + "``ice_modelio.nml``\n", + "``lnd_in``\n", + "``lnd_modelio.nml``\n", + "``mosart_in``\n", + "``ocn_modelio.nml``\n", + "``rof_modelio.nml``\n", + "``seq_maps.rc``\n", + "``wav_modelio.nml``\n", + " \n", + "- atm_in: atmospheric namelist variables\n", + "- chem_mech.in: chemical mechanism file\n", + "- drv_flds_in: dry deposition variables, MEGAN variables (if used)\n", + "- lnd_in: land namelist variables\n", + "- …\n", + "\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "synthetic-reproduction", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " \n", + "**# Set environment variables**\n", + " \n", + "Use the following commands
\n", + "for **CAM-chem**:\n", + "```\n", + "set CASENAME = f.e21.FCHIST.f09_f09_mg17.tutorial.test1\n", + "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET = FCHIST\n", + "set RESOLUTION = f09_f09_mg17\n", + "```\n", + "\n", + "or for **WACCM**\n", + " \n", + "```\n", + "set CASENAME = f.e21.FWHIST.f09_f09_mg17.tutorial.test1\n", + "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET = FWHIST\n", + "set RESOLUTION = f09_f09_mg17\n", + "```\n", + "\n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET\n", + "```\n", + "You may need to add ``--project \"UESM0013\"`` when creating a new case.\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "**If needed**, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the tutorial project number ``UESM0013``\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "From within the case directory, invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + " \n", + "**# Build**\n", + " \n", + "Perform an initial build within the case directory\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "namelists (``atm_in``, ``ice_in``, ``lnd_in``, ``docn_in``) will appear in the CaseDocs\n", + "subdirectory, as well as in your $rundir\n", + "\n", + "**# Customize namelists**\n", + "\n", + "Edit the file ``user_nl_cam``. First open the ``CaseDocs/atm_in`` and find the lines for avgflag_pertap, mfilt, nhtfrq and copy and paste them into ``user_nl_cam``. Then make changes to those lines in ``user_nl_cam`` and add a line to write out a second history file, ``fincl2``. The namelist lines should look something like:\n", + "```\n", + "&cam_history_nl\n", + " avgflag_pertape = 'A','I','A','A','A','A','A','A','I'\n", + " mfilt = 1,30,365,240,240,480,365,73,30\n", + " nhtfrq = 0,-24,-24,-3,-1,1,-24,-120,-240\n", + " fincl2 = 'PS','Z3','T','U','V','O3'\n", + "/\n", + "``` \n", + " \n", + "You can do this with a text editor.\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "\n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Submit:**\n", + "```\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the run files and history files. \n", + " \n", + "(1) Find your model output in your run directory (``$run_dir``) after finished:\n", + "\n", + "```\n", + "ls /glade/derecho/scratch/$USER/$CASENAME/run\n", + "```\n", + "\n", + "![rundir example](../../../images/challenge/chemistry-rundir-example.png)\n", + "\n", + " \n", + "(2) Check that your archive directory on derecho (the path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "(3) Look at the contents of the ``h1`` files using ``ncdump``.\n", + "\n", + "```\n", + "ncdump –h f.e21.FCHIST.f09_f09_mg17.tutorial.test1.cam.h1.1995-01-01-00000.nc\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "northern-agency", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam-chem_waccm/exercise_2.ipynb b/notebooks_copy/challenge/cam-chem_waccm/exercise_2.ipynb new file mode 100644 index 000000000..52681d6c2 --- /dev/null +++ b/notebooks_copy/challenge/cam-chem_waccm/exercise_2.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "prepared-adaptation", + "metadata": {}, + "source": [ + "# 2: Test case: changing the chemistry" + ] + }, + { + "cell_type": "markdown", + "id": "sound-recovery", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise 2: Building the model with new chemistry

\n", + " \n", + "Create, configure, build a new case as done in exercise 1, for one of these following configurations: \n", + "- **CAM-chem:** ``f.e21.FCHIST.f09_f09_mg17.tutorial.test2`` with the ``FCHIST`` compset; or \n", + "- **WACCM:** ``f.e21.FWHIST.f09_f09_mg17.tutorial.test2`` with the ``FWHIST`` compset. \n", + "\n", + "Set up this simulation to run for 5 days, with daily instantaneous output of the variables: ‘PS’,’Z3’,’T’,’U’,’V’,’O3’. \n", + " \n", + "**To change the chemistry**, copy your chemistry preprocessor file chem_mech.in from CaseDocs to your case directory as my_chem_mech.in and change reaction rate for the O1D reaction with O2 to 1.65e-12. Point the simulation to your new mechanism file using ``CAM_CONFIG_OPTS`` and re-build and submit. Once the simulation is complete, compare the 5th day output to the results from exercise 1.\n", + "\n", + "\n", + "**Extra information:**\n", + " \n", + "The chemistry preprocessor generates CAM Fortran source code to solve chemistry. The input is an ASCII file listing chemical reactions and rates. The chemistry preprocessor input file used in your previous run is in your\n", + "``$CASEROOT/CaseDocs/chem_mech.in``\n", + "\n", + "An example of a simple chemistry mechanism file content can be seen below:\n", + "![chemistry mechanism example](../../../images/challenge/chemistry-mechanism-example.png)\n", + "\n", + "Additional input files for default chemical mechanisms are in each source code subdirectory for mechanisms under:\n", + "``$SRCROOT/components/cam/src/chemistry/pp_*``\n", + "(i.e. ``pp_waccm_tsmlt_mam4``). You do not need to change these additional files for this exercise.\n", + "\n", + "Below is an overview of the different types of reactions and rates that can be defined in the mechanism file.\n", + "![chemistry preprocessor options example](../../../images/challenge/chemistry-preprocessor-info.png)\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "daily-acting", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**Creating a new chemistry input file**\n", + "``` \n", + "less CaseDocs/chem_mech.in\n", + "cp CaseDocs/chem_mech.in my_chem_mech.in\n", + "```\n", + "Open your copied new chemistry mechanism file with a text editor and change the reaction rate:\n", + " \n", + "![chemistry ozone reaction](../../../images/challenge/chemistry-ozone-reaction.png)\n", + "\n", + "**How to tell CESM to read your new chemistry mechanism file**\n", + "\n", + "- First look at the ``CAM_CONFIG_OPTS`` default setting for CAM-chem
\n", + "``./xmlquery CAM_CONFIG_OPTS``
\n", + "``CAM_CONFIG_OPTS: -phys cam6 -chem trop_strat_mam4_vbs -age_of_air_trcs``
\n", + "(note this will be different for WACCM)\n", + " \n", + "- Use xmlchange to append a pointer to your user mechanism:
\n", + "``--usr_mech_infile `pwd`/my_chem_mech.in``\n", + " \n", + "- Check the ``CAM_CONFIG_OPTS`` has been changed
\n", + "``./xmlquery CAM_CONFIG_OPTS``
\n", + "``CAM_CONFIG_OPTS: -phys cam6 -chem trop_strat_mam4_vbs -age_of_air_trcs --usr_mech_infile /glade/u/home/$USER/cases/$CASENAME/my_chem_mech.in``\n", + " \n", + "- You will need to reset the case setup, and rebuild before submitting\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "political-wages", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution \n", + "
\n", + " \n", + "**# Set environment variables**
\n", + "Use the commands
\n", + "for **CAM-chem**\n", + "```\n", + "set CASENAME = f.e21.FCHIST.f09_f09_mg17.tutorial.test2\n", + "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET = FCHIST\n", + "set RESOLUTION = f09_f09_mg17\n", + "```\n", + "\n", + "or for **WACCM** \n", + "```\n", + "set CASENAME = f.e21.FWHIST.f09_f09_mg17.tutorial.test2\n", + "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET = FWHIST\n", + "set RESOLUTION = f09_f09_mg17\n", + "```\n", + "\n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET\n", + "```\n", + "You may need to add ``--project \"UESM0013\"`` when creating a new case.\n", + " \n", + "**# Change the job queue and account number**\n", + "\n", + "**If needed**, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the project number ``UESM0013``\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "From within the case directory, invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + " \n", + "**# Build**\n", + " \n", + "Perform an initial build within the case directory\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "namelists (``atm_in``, ``ice_in``, ``lnd_in``, ``docn_in``) will appear in the CaseDocs\n", + "subdirectory, as well as in your $rundir\n", + "\n", + "**# Customize namelists**\n", + "\n", + "As before, edit the file ``user_nl_cam``. First open the ``CaseDocs/atm_in`` and find the lines for avgflag_pertap, mfilt, nhtfrq and copy and paste them into ``user_nl_cam``. Then make changes to those lines in ``user_nl_cam`` and add a line to write out a second history file, ``fincl2``. The namelist lines should look something like:\n", + "```\n", + "&cam_history_nl\n", + " avgflag_pertape = 'A','I','A','A','A','A','A','A','I'\n", + " mfilt = 1,30,365,240,240,480,365,73,30\n", + " nhtfrq = 0,-24,-24,-3,-1,1,-24,-120,-240\n", + " fincl2 = 'PS','Z3','T','U','V','O3'\n", + "/\n", + "``` \n", + "You can do this with a text editor.\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "**# Change the chemistry**\n", + " \n", + "Copy the default mechanism to your own user defined file \n", + "``` \n", + "cp CaseDocs/chem_mech.in my_chem_mech.in\n", + "```\n", + "\n", + "Open your mechanism file with a text editor (e.g. nedit) and change the reaction rate for O1D with O2\n", + "```\n", + "nedit my_chem_mech.in &\n", + "```\n", + " In CAM-chem\n", + "![chemistry ozone reaction](../../../images/challenge/chemistry-ozone-reaction.png)\n", + "\n", + " In WACCM:\n", + "![chemistry ozone reaction](../../../images/challenge/chemistry-ozone-reaction_orig.png)\n", + "\n", + "\n", + "Append a pointer in the to the user mechanism:\n", + "```\n", + "./xmlchange --append CAM_CONFIG_OPTS=\"--usr_mech_infile `pwd`/my_chem_mech.in\"\n", + "```\n", + "\n", + "**# Re-build the model**\n", + "```\n", + "./case.setup --reset\n", + "./case.build --clean\n", + "qcmd -- ./case.build\n", + "```\n", + " \n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Submit:**\n", + "```\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the run files and history files. \n", + " \n", + "(1) Find your model output in your run directory (``$run_dir``) after finished:\n", + "\n", + "```\n", + "ls /glade/derecho/scratch/$USER/$CASENAME/run\n", + "```\n", + " \n", + "(2) Check that your archive directory on derecho (the path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "(3) Look at the contents of the ``h1`` files using ``ncdump``.\n", + "\n", + "```\n", + "ncdump –h f.e21.FCHIST.f09_f09_mg17.tutorial.test2.cam.h1.1995-01-01-00000.nc\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "alpha-politics", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam-chem_waccm/exercise_3.ipynb b/notebooks_copy/challenge/cam-chem_waccm/exercise_3.ipynb new file mode 100644 index 000000000..3fc955e6e --- /dev/null +++ b/notebooks_copy/challenge/cam-chem_waccm/exercise_3.ipynb @@ -0,0 +1,365 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "expensive-beatles", + "metadata": {}, + "source": [ + "# 3: Test case: changing the emissions" + ] + }, + { + "cell_type": "markdown", + "id": "brave-confirmation", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise 3: Using different emissions

\n", + " \n", + "Create, configure, build a new case as done in exercise 1 or 2, for one of these following configurations: \n", + "- **CAM-chem:** ``f.e21.FCHIST.f09_f09_mg17.tutorial.test3`` with the ``FCHIST`` compset; or \n", + "- **WACCM:** ``f.e21.FWHIST.f09_f09_mg17.tutorial.test3`` with the ``FWHIST`` compset. \n", + "\n", + "Set up this simulation to run for 5 days, with daily instantaneous output of the variables: ‘PS’,’Z3’,’T’,’U’,’V’,’O3’. \n", + " \n", + "Change the namelist variables to use daily fire emissions from QFED. This will also require a change of model STARTDATE. Once the simulation is complete, compare the 5th day output to the previous results.\n", + " \n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "checked-arrest", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**Pointing to new emission files**\n", + "\n", + "Point to new emission files in your ``user_nl_cam`` file. Note that you will also need to include pointers to emissions that you are not changing.\n", + "\n", + "The location of qfed fire emissions on glade is described on the wiki: https://wiki.ucar.edu/display/camchem/Emission+Inventories\n", + " \n", + "**Make sure your model start date is covered by the emissions files**\n", + "For this model set up will also need to double check the following are available:\n", + "- The SST file covers your selected dates (``env_run.xml`` file)\n", + "- Lower boundary condition file must include your selected dates (``atm_in`` file)\n", + "- Consider that the initialization file will be for the default 1995. If you don't change your initialization file, you will need to run your own model spin up.\n", + "\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "three-taiwan", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " \n", + "**# Set environment variables**\n", + "Use the commands:\n", + " \n", + "**CAM-chem**\n", + "```\n", + "set CASENAME = f.e21.FCHIST.f09_f09_mg17.tutorial.test3\n", + "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET = FCHIST\n", + "set RESOLUTION = f09_f09_mg17\n", + "```\n", + "\n", + "or for **WACCM**\n", + " \n", + "```\n", + "set CASENAME = f.e21.FWHIST.f09_f09_mg17.tutorial.test3\n", + "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET = FWHIST\n", + "set RESOLUTION = f09_f09_mg17\n", + "```\n", + "\n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET\n", + "```\n", + "You may need to add ``--project \"UESM0013\"`` when creating a new case.\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "**If needed**, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the project number ``UESM0013``\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "From within the case directory, invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + " \n", + "**# Change the dates to cover emission files**
\n", + "Here, we choose 2010\n", + "```\n", + "./xmlchange RUN_STARTDATE=2010-01-01\n", + "```\n", + "\n", + "Make sure the sea surface temperature and ice (SSTICE) data file covers your dates of interest\n", + "```\n", + "./xmlquery SSTICE_DATA_FILENAME\n", + "```\n", + " \n", + "should return: \n", + "```\n", + "SSTICE_DATA_FILENAME: /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/sst/sst_HadOIBl_bc_0.9x1.25_1850_2017_c180507.nc\n", + "```\n", + "\n", + " \n", + "**# Build**\n", + " \n", + "Perform an initial build within the case directory\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "namelists (``atm_in``, ``ice_in``, ``lnd_in``, ``docn_in``) will appear in the CaseDocs\n", + "subdirectory, as well as in your $rundir\n", + "\n", + "**# Customize namelists**\n", + "\n", + "As before, edit the file ``user_nl_cam``. First open the ``CaseDocs/atm_in`` and find the lines for avgflag_pertap, mfilt, nhtfrq and copy and paste them into ``user_nl_cam``. Then make changes to those lines in ``user_nl_cam`` and add a line to write out a second history file, ``fincl2``. The namelist lines should look something like:\n", + "```\n", + "&cam_history_nl\n", + " avgflag_pertape = 'A','I','A','A','A','A','A','A','I'\n", + " mfilt = 1,30,365,240,240,480,365,73,30\n", + " nhtfrq = 0,-24,-24,-3,-1,1,-24,-120,-240\n", + " fincl2 = 'PS','Z3','T','U','V','O3'\n", + "/\n", + "``` \n", + "You can do this with a text editor.\n", + "\n", + "**# Add in different emissions**\n", + "Also add in the namelist pointers to emission files, replacing the fire emissions (every file with 'bb' in the name) with qfed files found at ``/glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/``\n", + "\n", + "```\n", + "&chem_inparm\n", + " srf_emis_specifier\t\t= 'BENZENE -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_BENZENE_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'BENZENE -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_BENZENE_0.9x1.25_mol_2000_2022.nc',\n", + " 'BIGALK -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_BIGALK_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'BIGALK -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_BIGALK_0.9x1.25_mol_2000_2022.nc',\n", + " 'BIGENE -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_BIGENE_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'BIGENE -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_BIGENE_0.9x1.25_mol_2000_2022.nc',\n", + " 'C2H2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H2_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'C2H2 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C2H2_0.9x1.25_mol_2000_2022.nc',\n", + " 'C2H4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'C2H4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C2H4_0.9x1.25_mol_2000_2022.nc',\n", + " 'C2H4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H4_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", + " 'C2H5OH -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H5OH_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'C2H5OH -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C2H5OH_0.9x1.25_mol_2000_2022.nc',\n", + " 'C2H6 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H6_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'C2H6 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C2H6_0.9x1.25_mol_2000_2022.nc',\n", + " 'C2H6 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H6_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", + " 'C3H6 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C3H6_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'C3H6 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C3H6_0.9x1.25_mol_2000_2022.nc',\n", + " 'C3H6 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C3H6_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", + " 'C3H8 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C3H8_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'C3H8 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C3H8_0.9x1.25_mol_2000_2022.nc',\n", + " 'C3H8 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C3H8_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", + " 'CH2O -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH2O_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'CH2O -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH2O_0.9x1.25_mol_2000_2022.nc',\n", + " 'CH3CHO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3CHO_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'CH3CHO -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3CHO_0.9x1.25_mol_2000_2022.nc',\n", + " 'CH3CN -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3CN_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'CH3CN -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3CN_0.9x1.25_mol_2000_2022.nc',\n", + " 'CH3COCH3 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3COCH3_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'CH3COCH3 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3COCH3_0.9x1.25_mol_2000_2022.nc',\n", + " 'CH3COCHO -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3COCHO_0.9x1.25_mol_2000_2022.nc',\n", + " 'CH3COOH -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3COOH_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'CH3COOH -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3COOH_0.9x1.25_mol_2000_2022.nc',\n", + " 'CH3OH -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3OH_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'CH3OH -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3OH_0.9x1.25_mol_2000_2022.nc',\n", + " 'CO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CO_anthro_surface_1750-2015_0.9x1.25_c20180504.nc',\n", + " 'CO -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CO_0.9x1.25_mol_2000_2022.nc',\n", + " 'CO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CO_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", + " 'E90 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions_E90global_surface_1750-2100_0.9x1.25_c20170322.nc',\n", + " 'GLYALD -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_GLYALD_0.9x1.25_mol_2000_2022.nc',\n", + " 'HCN -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_HCN_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'HCN -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_HCN_0.9x1.25_mol_2000_2022.nc',\n", + " 'HCOOH -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_HCOOH_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'HCOOH -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_HCOOH_0.9x1.25_mol_2000_2022.nc',\n", + " 'ISOP -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_ISOP_0.9x1.25_mol_2000_2022.nc',\n", + " 'IVOC -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_IVOC_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'IVOC -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_IVOC_0.9x1.25_mol_2000_2022.nc',\n", + " 'MEK -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_MEK_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'MEK -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_MEK_0.9x1.25_mol_2000_2022.nc',\n", + " 'MTERP -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_TERPENES_0.9x1.25_mol_2000_2022.nc ',\n", + " 'NH3 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_NH3_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'NH3 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_NH3_0.9x1.25_mol_2000_2022.nc ',\n", + " 'NH3 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_NH3_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", + " 'NO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_NO_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'NO -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_NO_0.9x1.25_mol_2000_2022.nc ',\n", + " 'NO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_NO_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", + " 'SVOC -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_SVOC_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'SVOC -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_SVOC_0.9x1.25_mol_2000_2022.nc ',\n", + " 'TOLUENE -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_TOLUENE_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'TOLUENE -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_TOLUENE_0.9x1.25_mol_2000_2022.nc ',\n", + " 'XYLENES -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_XYLENES_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'XYLENES -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_XYLENES_0.9x1.25_mol_2000_2022.nc ',\n", + " 'bc_a4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_bc_a4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'bc_a4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_bc_a4_0.9x1.25_mol_2000_2022.nc ',\n", + " 'DMS -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_DMS_0.9x1.25_mol_2000_2022.nc ',\n", + " 'DMS -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_DMS_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", + " 'num_a1 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_num_so4_a1_0.9x1.25_mol_2000_2022.nc ',\n", + " 'num_a1 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_num_so4_a1_anthro-ag-ship_surface_1750-2015_0.9x1.25_c20170616.nc',\n", + " 'num_a2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_num_so4_a2_anthro-res_surface_1750-2015_0.9x1.25_c20170616.nc',\n", + " 'num_a4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_num_bc_a4_0.9x1.25_mol_2000_2022.nc ',\n", + " 'num_a4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_num_bc_a4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'num_a4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_num_pom_a4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'num_a4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_num_pom_a4_0.9x1.25_mol_2000_2022.nc ',\n", + " 'pom_a4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_pom_a4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", + " 'pom_a4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_pom_a4_0.9x1.25_mol_2000_2022.nc ',\n", + " 'SO2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_SO2_anthro-ag-ship-res_surface_1750-2015_0.9x1.25_c20170616.nc',\n", + " 'SO2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_SO2_anthro-ene_surface_1750-2015_0.9x1.25_c20170616.nc',\n", + " 'SO2 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_SO2_0.9x1.25_mol_2000_2022.nc',\n", + " 'so4_a1 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_so4_a1_anthro-ag-ship_surface_1750-2015_0.9x1.25_c20170616.nc',\n", + " 'so4_a1 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_so4_a1_0.9x1.25_mol_2000_2022.nc ',\n", + " 'so4_a2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_so4_a2_anthro-res_surface_1750-2015_0.9x1.25_c20170616.nc'\n", + "/\n", + "```\n", + "\n", + "**# Lower boundary condition file check**\n", + "\n", + "Make sure the LBC files cover your dates of interest. In ``CaseDocs/atm_in`` you should see:\n", + "```\n", + " flbc_file = '/glade/campaign/cesm/cesmdata/cseg/inputdata/atm/waccm/lb/LBC_1750-2014_CMIP6_0p5degLat_c170126.nc'\n", + "```\n", + "\n", + "**# Initialization files for your new startdate**\n", + "\n", + "Note: Below are initialization files for 2010. For scientifically valid simulations you need to point to an initialization file for the start date you choose. If one is not available, you will need to spin up the model.\n", + "\n", + "For **CAM-chem**:\n", + "atmospheric initialization files are redefined in ``user_nl_cam``\n", + "\n", + "```\n", + "&cam_initfiles_nl\n", + " ncdata = '/glade/campaign/cesm/acom/acom-climate/tilmes/inputdata/init/camchem/FCnudged_MAM4_f09.carma_trop_strat09.aqchem.2001_2020.cam.i.2010-01-01-00000.nc'\n", + "/\n", + "```\n", + "\n", + "\n", + "For **WACCM**:\n", + "atmospheric initialization files are redefined in ``user_nl_cam``\n", + "\n", + "```\n", + "&cam_initfiles_nl\n", + " ncdata = '/glade/campaign/cesm/acom/acom-climate/tilmes/inputdata/init/waccm/f.e21.FWHISTBgcCrop.f09_f09_mg17.CMIP6-AMIP-WACCM.001.cam.i.2010-01-01-00000.nc'\n", + "/\n", + "``` \n", + "\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "\n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Submit:**\n", + "```\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the run files and history files. \n", + " \n", + "(1) Find your model output in your run directory (``$run_dir``) after finished:\n", + "\n", + "```\n", + "ls /glade/derecho/scratch/$USER/$CASENAME/run\n", + "```\n", + " \n", + "(2) Check that your archive directory on derecho (the path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "(3) Look at the contents of the ``h1`` files using ``ncdump``.\n", + "\n", + "```\n", + "ncdump –h f.e21.FCHIST.f09_f09_mg17.tutorial.test3.cam.h1.2010-01-01-00000.nc\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "affected-donna", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam-chem_waccm/visualization.ipynb b/notebooks_copy/challenge/cam-chem_waccm/visualization.ipynb new file mode 100644 index 000000000..7cab6a1ac --- /dev/null +++ b/notebooks_copy/challenge/cam-chem_waccm/visualization.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "straight-opposition", + "metadata": {}, + "source": [ + "# 4: Visualization option with GEOV" + ] + }, + { + "cell_type": "markdown", + "id": "industrial-enhancement", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Visualization with GEOV

\n", + " \n", + "CESM history files are in standard netCDF format, and may be analyzed with standard\n", + "analysis tools, including Matlab, Python, IDL, NCL, and NCO and ncview.\n", + " \n", + "GEOV is an IDL-based viewer with a graphical user interface (GUI) for geophysical history files created by NCAR's CAM, WACCM and MOZART models. GEOV can be downloaded from the WACCM webpage
\n", + "http://www.cesm.ucar.edu/working_groups/Whole-Atmosphere/code-release.html
\n", + "It is useful for quick viewing and comparisons.\n", + " \n", + "**#To use GEOV on derecho/casper:**\n", + " \n", + "Add the idl module:
\n", + "``module load idl``
\n", + " \n", + "Then go to the location of GEOV and start it\n", + "```\n", + "cd /glade/u/home/fvitt/geov4.8e\n", + "idl geov\n", + "```\n", + " \n", + "Then you can use the GEOV interface to find files and explore the model output. Some examples of using the GEOV interface are below:\n", + "\n", + "![GEOV opening](../../../images/challenge/geov-openingfile.png)\n", + "

Figure 1: Example using GEOV to browse to a file to open.

\n", + " \n", + "![GEOV 2 files](../../../images/challenge/geov-2files.png)\n", + "

Figure 2: Example using GEOV to plot surface layer ozone for two files.

\n", + " \n", + "![GEOV difference](../../../images/challenge/geov-difference.png)\n", + "

Figure 3: Example using GEOV to look at zonal average difference in model ozone (left).

\n", + " \n", + " \n", + " \n", + "If you quit GEOV, you will need to type ``exit`` to also exit IDL.\n", + "\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "weekly-kennedy", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam/cam.ipynb b/notebooks_copy/challenge/cam/cam.ipynb new file mode 100644 index 000000000..e6ae0430b --- /dev/null +++ b/notebooks_copy/challenge/cam/cam.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Atmosphere" + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "## Learning Goals\n", + "\n", + "- Student will learn what a F compset is, the types of forcing available to run one, and how to run one.\n", + "- Student will learn how to modify datasets using nco operators." + ] + }, + { + "cell_type": "markdown", + "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", + "metadata": {}, + "source": [ + "## What is a F compset ?\n", + "\n", + "The CESM2 components can be combined in numerous ways to carry out various scientific or software experiments. A particular mix of components, along with component-specific configuration and/or namelist settings is called a component set or ``compset``. \n", + "\n", + "In the previous chapter, we have run experiments with the ``B`` compset. In this chapter we will run the experiments with the ``F`` compset. \n", + "The ``F`` compsets use prescribed ocean (observed sea-surface temperature data) and prescribed sea-ice (observed sea-ice thickness and area)\n", + "\n", + "- ``F2000climo`` uses climatological forcings from around year 2000\n", + "- ``F2010climo`` uses climatological forcings from around year 2010\n", + "- ``FHIST`` use time varying forcing\n", + "\n", + "![B compsets versus F compsets](../../../images/challenge/Fcompset.png)\n", + "\n", + "*

Figure: Differences between a F and a B compset.

*" + ] + }, + { + "cell_type": "markdown", + "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", + "metadata": {}, + "source": [ + "## Overview of CAM Challenge Exercises\n", + "Start running a control case with the compset ``F2000climo``\n", + "\n", + "Then choose one or more exercises to try:\n", + "- Use historical SSTs/forcings instead of fixed (compset change)\n", + "- Try running starting 1850 with spun-up pre-industrial model\n", + "- Increase orographic height over the western US (dataset change)\n", + "- Modify sea surface temperature in the tropics \n", + "- Increase the triggering threshold for deep convection over land (code change--simple)\n", + "- Add a (fake) physics parameterization (code change--advanced)\n", + "Compare your test exercise(s) to your control\n" + ] + }, + { + "cell_type": "markdown", + "id": "f01e969b-ab1a-466a-9cf0-5da2a137c35f", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "\n", + "\n", + "### Finding more information about compsets\n", + "\n", + "If you want to run a different configuration from what you’ve learned here, it is important to learn how to find and/or modify a compset.\n", + "\n", + "The tool `query compsets` allows you to find more information about the available. This tool is located in the same directory as `create_newcase` in `/glade/work/$USER/code/my_cesm_code/cime/scripts/`:\n", + "\n", + "The command is:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./query_config --compsets\n", + "```\n", + "\n", + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "Using the tool above and/or web searches below, find a CESM compset with an active atmosphere version cam6.0, that uses historical forcing data including sea surface temperatures. If you find several candidates, look at the components option and/or webpage to decide. Is it scientifically validated? For what resolutions?\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "Click here for the solution
\n", + "\n", + "The command gives a list of all the compsets available, and what components are included. \n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./query_config --compsets\n", + "```\n", + "This can show exactly what you ran before, and what is possible. To narrow down your choices:\n", + "```\n", + " ./query_config --compsets | grep -i hist\n", + "```\n", + "
\n", + "
\n", + "\n", + "\n", + "If you want to build your own, you can see all your options, perhaps modify one of the above with changes\u000b", + "\n", + "```\n", + "./query_config --components\n", + "```\n", + "More explanation about `compsets` can be found in the docs?|:\n", + "https://www.cesm.ucar.edu/models/cesm2/config/compsets.html\n", + "\n", + "\n", + "There are a number of atmospheric models which can run within CESM. While CAM is the basic atmospheric model within CESM, there are several models with significant extensions to CAM which may also be run within CESM. The available atmospheric models in CESM2 are:\n", + "\n", + "- CAM: Community Atmosphere Model\n", + "- CAM-chem: Community Atmosphere Model with Chemistry\n", + "- WACCM: Whole Atmosphere Community Climate Model\n", + "- WACCM-X: Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension\n", + "\n", + "https://ncar.github.io/CAM/doc/build/html/users_guide/atmospheric-configurations.html\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam/exercise_1.ipynb b/notebooks_copy/challenge/cam/exercise_1.ipynb new file mode 100644 index 000000000..c24986518 --- /dev/null +++ b/notebooks_copy/challenge/cam/exercise_1.ipynb @@ -0,0 +1,263 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 1: Control case: F2000climo" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Customize your CAM history files

\n", + " \n", + "Create, configure, build and run a case called ``f2000_control`` with the ``F2000climo`` compset (data ocean and climatological forcings from around year 2000). \n", + "\n", + "Run for 5 days, with 3-hourly instantaneous output of the variables: TS, PS, Z500, U850, U200, T850, T500, T200, CLDLOW, PRECT, LHFLX, SHFLX, FLNT, FLNS.\n", + "You are also welcome to output your own variables\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**How do I output 3 hourly instantaneous variables?**\n", + "\n", + "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", + "- For more information, look at the chapter:
\n", + "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", + "\n", + "**What is the resolution for F2000climo?**\n", + "\n", + "- Use resolution: f09_f09_mg17\n", + "\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " \n", + "**# Set environment variables** \n", + "\n", + "Set environment variables with the commands:\n", + " \n", + "**For tcsh users** \n", + " \n", + "```\n", + "set CASENAME=f2000_control\n", + "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET=F2000climo\n", + "set RESOLUTION=f19_f19_mg17\n", + "```\n", + "**For bash users** \n", + " \n", + "```\n", + "export CASENAME=f2000_control\n", + "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "export COMPSET=F2000climo\n", + "export RESOLUTION=f19_f19_mg17\n", + "```\n", + "\n", + "**# Make a case directory**\n", + "\n", + "If needed create a directory `cases` into your home directory:\n", + " \n", + "```\n", + "mkdir /glade/u/home/$USER/cases/\n", + "```\n", + " \n", + "\n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET\n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "Invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + "\n", + "**# Customize namelists**\n", + "\n", + "Edit the file ``user_nl_cam`` and add the lines:\n", + "```\n", + "nhtfrq(2) = -3\n", + "mfilt(2) = 240\n", + "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", + "```\n", + "You can do this with a text editor. Alternatively, you can use the echo command:\n", + "```\n", + "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", + "echo \"mfilt(2) = 240\">> user_nl_cam\n", + "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", + "echo \"\">> user_nl_cam \n", + "```\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "\n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Build and submit**:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the history files into the archive directory. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", + "\n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ncdump -h f2000_control.cam.h1.0001-01-01-00000.nc\n", + "```\n", + "\n", + "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", + "```\n", + " float FLNS(time, lat, lon) ;\n", + " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNS:units = \"W/m2\" ;\n", + " FLNS:long_name = \"Net longwave flux at surface\" ;\n", + " float FLNT(time, lat, lon) ;\n", + " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNT:units = \"W/m2\" ;\n", + " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", + " float LHFLX(time, lat, lon) ;\n", + " LHFLX:units = \"W/m2\" ;\n", + " LHFLX:long_name = \"Surface latent heat flux\" ;\n", + " float PRECT(time, lat, lon) ;\n", + " PRECT:units = \"m/s\" ;\n", + " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", + " float PS(time, lat, lon) ;\n", + " PS:units = \"Pa\" ;\n", + " PS:long_name = \"Surface pressure\" ;\n", + " float SHFLX(time, lat, lon) ;\n", + " SHFLX:units = \"W/m2\" ;\n", + " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", + " float T850(time, lat, lon) ;\n", + " T850:units = \"K\" ;\n", + " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", + " float TS(time, lat, lon) ;\n", + " TS:units = \"K\" ;\n", + " TS:long_name = \"Surface temperature (radiative)\" ;\n", + " float U850(time, lat, lon) ;\n", + " U850:units = \"m/s\" ;\n", + " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", + "```\n", + "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "472131c7-88f9-4863-a2bc-d7364333542d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam/exercise_2.ipynb b/notebooks_copy/challenge/cam/exercise_2.ipynb new file mode 100644 index 000000000..473bad878 --- /dev/null +++ b/notebooks_copy/challenge/cam/exercise_2.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 2: Historical compset: FHIST" + ] + }, + { + "cell_type": "markdown", + "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", + "metadata": {}, + "source": [ + "CAM is capable of running over historical periods with time-varying sea surface temperatures (SSTs) as well as anthropogenic and natural forcings. This is called the AMIP protocol.\n", + "\n", + "\n", + "
\n", + "\n", + "For more information about the [AMIP protocol](https://pcmdi.llnl.gov/mips/amip/) and the [HadSST data sets](https://climatedataguide.ucar.edu/climate-data/sst-data-hadisst-v11)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Customize your CAM history files

\n", + " \n", + "Create, configure, build and run a case called ``fhist`` with the compset ``FHIST`` at the resolution ``f09_f09_mg17`` using the same history file output as in the control.\n", + "Run for 5 days. \n", + "\n", + "- How can you check that there is a difference between the set up of this and your control?\n", + "- How can you check that it is running the way you intended: using ssts & ghg forcings from time-varying, historical files?\n", + "- What year is the model running?\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**How do I output 3 hourly instantaneous variables?**\n", + "\n", + "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", + "- For more information, look at the chapter:
\n", + "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", + "\n", + "**I am getting an error: ``This compset and grid combination is untested in CESM``**\n", + "\n", + "- overide this error by adding ``--run-unsupported`` to the create_newcase command\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " \n", + "**# Set environment variables** \n", + " \n", + "Set environment variables with the commands:\n", + "\n", + "**tcsh user**\n", + "```\n", + "set CASENAME=fhist\n", + "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET=FHIST\n", + "set RESOLUTION=f19_f19_mg17\n", + "```\n", + "**bash user**\n", + "```\n", + "export CASENAME=fhist\n", + "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "export COMPSET=FHIST\n", + "export RESOLUTION=f19_f19_mg17\n", + "``` \n", + " \n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET --run-unsupported\n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "Invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + "\n", + "**# Customize namelists**\n", + "\n", + "Edit the file ``user_nl_cam`` and add the lines:\n", + "```\n", + "nhtfrq(2) = -3\n", + "mfilt(2) = 240\n", + "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", + "```\n", + "You can do this with a text editor. Alternatively, you can use the echo command:\n", + "```\n", + "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", + "echo \"mfilt(2) = 240\">> user_nl_cam\n", + "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", + "echo \"\">> user_nl_cam \n", + "```\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "\n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Build and submit**:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the history files into the archive directory. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "Note that by default the starting year for a `FHIST` compset is `1950` instead of the year `0001` with a `F2000climo` compset.\n", + "\n", + "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", + " \n", + "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", + "```\n", + " float FLNS(time, lat, lon) ;\n", + " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNS:units = \"W/m2\" ;\n", + " FLNS:long_name = \"Net longwave flux at surface\" ;\n", + " float FLNT(time, lat, lon) ;\n", + " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNT:units = \"W/m2\" ;\n", + " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", + " float LHFLX(time, lat, lon) ;\n", + " LHFLX:units = \"W/m2\" ;\n", + " LHFLX:long_name = \"Surface latent heat flux\" ;\n", + " float PRECT(time, lat, lon) ;\n", + " PRECT:units = \"m/s\" ;\n", + " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", + " float PS(time, lat, lon) ;\n", + " PS:units = \"Pa\" ;\n", + " PS:long_name = \"Surface pressure\" ;\n", + " float SHFLX(time, lat, lon) ;\n", + " SHFLX:units = \"W/m2\" ;\n", + " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", + " float T850(time, lat, lon) ;\n", + " T850:units = \"K\" ;\n", + " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", + " float TS(time, lat, lon) ;\n", + " TS:units = \"K\" ;\n", + " TS:long_name = \"Surface temperature (radiative)\" ;\n", + " float U850(time, lat, lon) ;\n", + " U850:units = \"m/s\" ;\n", + " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", + "```\n", + "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", + "\n", + "
\n", + "
\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam/exercise_3.ipynb b/notebooks_copy/challenge/cam/exercise_3.ipynb new file mode 100644 index 000000000..511ee4664 --- /dev/null +++ b/notebooks_copy/challenge/cam/exercise_3.ipynb @@ -0,0 +1,264 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 3: Starting FHIST from spunup state in 1850" + ] + }, + { + "cell_type": "markdown", + "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", + "metadata": {}, + "source": [ + "By default the FHIST starts from a default initial condition in 1979, but you can start from another year and use initial conditions coming from a previous run. " + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Customize your CAM history files

\n", + " \n", + "Create, configure, build and run a case called `fhist.1850` with the compset FHIST at the resolution `f09_f09_mg17` using the same history file output as in the control. \n", + "- Use initialization datasets from a previous case.
For instance, use the data from the run: `b.e21.B1850.f19_g17.CMIP6-piControl-2deg.001` at year `321`\n", + "- Start model in `1850`. \n", + "- Run for 5 days. \n", + "\n", + "Check your solution:\n", + "- How can you check that there is a difference between the set up of this and your control?\n", + "- How can you check that it is running the way you intended: using ssts & ghg forcings from time-varying, historical files?\n", + "- What year is the model running?\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + " \n", + "**How do I start my run using initialization datasets from a previous run?**\n", + "\n", + "- Look at chapter `Modifify the run type`. In that chapter, you learned different ways to start up the model, and how to use initial conditions/restart files from a different case to start up a hybrid case. Use that method for the current exercise. \n", + "\n", + "- Data from spun-up model runs can be found in inputdata (DIN_LOC_ROOT) . \n", + " \n", + "**How do I start my run in 1850?**\n", + "\n", + "- Look at the description of the xml variable `RUN_STARTDATE`\n", + " \n", + " \n", + "**How do I output 3 hourly instantaneous variables?**\n", + "\n", + "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", + "- For more information, look at the chapter:
\n", + "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", + "\n", + "**I am getting an error: ``This compset and grid combination is untested in CESM``**\n", + "\n", + "- overide this error by adding ``--run-unsupported`` to the create_newcase command\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " \n", + "**# Set environment variables** \n", + "\n", + "Set environment variables with the commands:\n", + "\n", + "**tcsh**\n", + "```\n", + "set CASENAME=fhist.1850\n", + "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET=FHIST\n", + "set RESOLUTION=f19_f19_mg17\n", + "```\n", + "**bash**\n", + "```\n", + "export CASENAME=fhist.1850\n", + "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "export COMPSET=FHIST\n", + "export RESOLUTION=f19_f19_mg17\n", + "```\n", + " \n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET --run-unsupported\n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the project number ``UESM0013``\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "Invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + "\n", + "**# Customize namelists**\n", + "\n", + "Edit the file ``user_nl_cam`` and add the lines:\n", + "```\n", + "nhtfrq(2) = -3\n", + "mfilt(2) = 240\n", + "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", + "```\n", + "You can do this with a text editor. Alternatively, you can use the echo command:\n", + "```\n", + "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", + "echo \"mfilt(2) = 240\">> user_nl_cam\n", + "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", + "echo \"\">> user_nl_cam \n", + "```\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "**# change starting date and ref case**\n", + "\n", + "``` \n", + "./xmlchange RUN_STARTDATE=1850-01-01 \n", + "./xmlchange RUN_REFCASE=b.e21.B1850.f19_g17.CMIP6-piControl-2deg.001\n", + "./xmlchange RUN_REFDATE=0321-01-01\n", + "```\n", + "\n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Build and submit**:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the history files into the archive directory. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "Notice that the start year is `1850`.\n", + "\n", + "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", + "\n", + "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", + "```\n", + " float FLNS(time, lat, lon) ;\n", + " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNS:units = \"W/m2\" ;\n", + " FLNS:long_name = \"Net longwave flux at surface\" ;\n", + " float FLNT(time, lat, lon) ;\n", + " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNT:units = \"W/m2\" ;\n", + " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", + " float LHFLX(time, lat, lon) ;\n", + " LHFLX:units = \"W/m2\" ;\n", + " LHFLX:long_name = \"Surface latent heat flux\" ;\n", + " float PRECT(time, lat, lon) ;\n", + " PRECT:units = \"m/s\" ;\n", + " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", + " float PS(time, lat, lon) ;\n", + " PS:units = \"Pa\" ;\n", + " PS:long_name = \"Surface pressure\" ;\n", + " float SHFLX(time, lat, lon) ;\n", + " SHFLX:units = \"W/m2\" ;\n", + " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", + " float T850(time, lat, lon) ;\n", + " T850:units = \"K\" ;\n", + " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", + " float TS(time, lat, lon) ;\n", + " TS:units = \"K\" ;\n", + " TS:long_name = \"Surface temperature (radiative)\" ;\n", + " float U850(time, lat, lon) ;\n", + " U850:units = \"m/s\" ;\n", + " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", + "```\n", + "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", + "\n", + "
\n", + "
\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam/exercise_4.ipynb b/notebooks_copy/challenge/cam/exercise_4.ipynb new file mode 100644 index 000000000..da445e6be --- /dev/null +++ b/notebooks_copy/challenge/cam/exercise_4.ipynb @@ -0,0 +1,291 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 4: Increase orographic height over the western US" + ] + }, + { + "cell_type": "markdown", + "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", + "metadata": {}, + "source": [ + "Change input boundary datasets by increasing surface geopotential height by 50% in the western USA" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Increase orographic height over the western US

\n", + "\n", + "Create a case similar to control case but change input boundary datasets by increasing surface geopotential height by 50% in the western USA.\n", + "\n", + "\n", + "![Increase orographic height over the western US](../../../images/challenge/cam_topo_diff.png)\n", + "\n", + "*

Figure: Increase orographic height over the western US.

*\n", + " \n", + "Create, configure, build and run a case called ``f2000.topo`` with the compset ``F2000climo`` at the resolution ``f09_f09_mg17`` using the same history file output as in the control. Change the topography input datasets\n", + "Run for 5 days. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**How do I output 3 hourly instantaneous variables?**\n", + "\n", + "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", + "- For more information, look at the chapter:
\n", + "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", + "\n", + "\n", + "**Where do I change the topography dataset?**\n", + "\n", + "- Look at the namelist definition for the variable `bnd_topo` \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "\n", + "**# Set environment variables** \n", + " \n", + "Set environment variables with the commands:\n", + "\n", + "**tcsh user**\n", + "```\n", + "set CASENAME=f2000.topo\n", + "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET=F2000climo\n", + "set RESOLUTION=f19_f19_mg17\n", + "```\n", + "**bash user**\n", + "```\n", + "export CASENAME=f2000.topo\n", + "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "export COMPSET=F2000climo\n", + "export RESOLUTION=f19_f19_mg17\n", + "```\n", + " \n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET \n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "Invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + "\n", + "**# Modify orography**\n", + "\n", + "Copy the topography file into your case directory and modify it over there. \n", + "\n", + "Here we use the `nco` operators but feel free to use any other tool that you familiar with.\n", + "```\n", + "cd $CASEDIR\n", + "cp \\\n", + "/glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/topo/fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.nc .\n", + "\n", + "ncap2 -O -s 'lat2d[lat,lon]=lat ; lon2d[lat,lon]=lon' \\\n", + "-s 'omask=(lat2d >= 30. && lat2d <= 50.) && (lon2d >= 235. && lon2d <= 260.)' \\\n", + "-s 'PHIS=(PHIS*(1.+omask*0.5))' \\\n", + "fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.nc \\\n", + "fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.topo50.nc\n", + "``` \n", + "\n", + "**# Visualize your modification to orography**\n", + "\n", + "You could create a file `diff_topo.nc` with the differences of topography `ncdiff`and then visualize the that file with `ncview`. You also welcome to use your own tools to visualize your mods to orography.\n", + "\n", + "- Create a file `diff_topo.nc` with the differences in topographic:\n", + "```\n", + "ncdiff \\\n", + "fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.nc \\\n", + "fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.topo50.nc \\\n", + "diff_topo.nc\n", + "```\n", + "\n", + "- Look at the `diff_topo.nc` with `ncview`:\n", + "```\n", + "ncview diff_topo.nc\n", + "```\n", + "\n", + "![Increase orographic height over the western US](../../../images/challenge/cam_topo_diff.png)\n", + "\n", + "*

Figure: View the increase in orographic height over the western US with ncview.

*\n", + "\n", + "\n", + "**# Customize namelists**\n", + "\n", + "Edit the file ``user_nl_cam`` and add the lines:\n", + "```\n", + "nhtfrq(2) = -3\n", + "mfilt(2) = 240\n", + "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", + "bnd_topo = '$CASEDIR/fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.topo50.nc'\n", + "```\n", + "You can do this with a text editor. Alternatively, you can use the echo command:\n", + "```\n", + "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", + "echo \"mfilt(2) = 240\">> user_nl_cam\n", + "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", + "echo \"bnd_topo = '$CASEDIR/fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.topo50.nc' \" >> user_nl_cam\n", + "echo \"\">> user_nl_cam \n", + "```\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Build and submit**:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the history files into the archive directory. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", + "\n", + "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", + "```\n", + " float FLNS(time, lat, lon) ;\n", + " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNS:units = \"W/m2\" ;\n", + " FLNS:long_name = \"Net longwave flux at surface\" ;\n", + " float FLNT(time, lat, lon) ;\n", + " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNT:units = \"W/m2\" ;\n", + " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", + " float LHFLX(time, lat, lon) ;\n", + " LHFLX:units = \"W/m2\" ;\n", + " LHFLX:long_name = \"Surface latent heat flux\" ;\n", + " float PRECT(time, lat, lon) ;\n", + " PRECT:units = \"m/s\" ;\n", + " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", + " float PS(time, lat, lon) ;\n", + " PS:units = \"Pa\" ;\n", + " PS:long_name = \"Surface pressure\" ;\n", + " float SHFLX(time, lat, lon) ;\n", + " SHFLX:units = \"W/m2\" ;\n", + " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", + " float T850(time, lat, lon) ;\n", + " T850:units = \"K\" ;\n", + " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", + " float TS(time, lat, lon) ;\n", + " TS:units = \"K\" ;\n", + " TS:long_name = \"Surface temperature (radiative)\" ;\n", + " float U850(time, lat, lon) ;\n", + " U850:units = \"m/s\" ;\n", + " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", + "```\n", + "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam/exercise_5.ipynb b/notebooks_copy/challenge/cam/exercise_5.ipynb new file mode 100644 index 000000000..d1174970d --- /dev/null +++ b/notebooks_copy/challenge/cam/exercise_5.ipynb @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 5: Modify sea surface temperature in the tropics" + ] + }, + { + "cell_type": "markdown", + "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", + "metadata": {}, + "source": [ + "Change input boundary datasets (Sea Surface Temperature) by increasing its value by 2K in the tropical Central Pacific. " + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Modify sea surface temperature in the tropics

\n", + "\n", + "Create a case similar to control case but change input boundary datasets (Sea Surface Temperature) by increasing its value by 2K in the tropical Central Pacific. \n", + "\n", + "\n", + "![Increase orographic height over the western US](../../../images/challenge/cam_SST_diff.png)\n", + "\n", + "*

Figure: Increase orographic height over the western US.

*\n", + " \n", + "Create, configure, build and run a case called ``f2000.sst`` with the compset ``F2000climo`` at the resolution ``f09_f09_mg17`` using the same history file output as in the control. Change the SST input datasets\n", + "Run for 5 days. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**How do I output 3 hourly instantaneous variables?**\n", + "\n", + "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", + "- For more information, look at the chapter:
\n", + "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", + "\n", + "\n", + "**Where do I change the SST dataset?**\n", + "\n", + "- Look at the xml variable `SSTICE_DATA_FILENAME` \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "\n", + "**# Set environment variables** \n", + " \n", + "Set environment variables with the commands:\n", + "\n", + "**tcsh user**\n", + "```\n", + "set CASENAME=f2000.sst\n", + "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET=F2000climo\n", + "set RESOLUTION=f19_f19_mg17\n", + "```\n", + "\n", + "\n", + "**bash user**\n", + "```\n", + "export CASENAME=f2000.sst\n", + "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "export COMPSET=F2000climo\n", + "export RESOLUTION=f19_f19_mg17\n", + "```\n", + " \n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET \n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "Invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + "\n", + "**# Modify orography**\n", + "\n", + "Copy the SST file into your case directory and modify it over there. \n", + "\n", + "Here we use the `nco` operators but feel free to use any other tool that you familiar with.\n", + "```\n", + "cd $CASEDIR\n", + "cp /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/sst/sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc .\n", + "\n", + "ncap2 -O -s 'lat2d[lat,lon]=lat ; lon2d[lat,lon]=lon' \\\n", + " -s 'omask=(lat2d >= -10. && lat2d <= 10.) && (lon2d >= 180. && lon2d <= 240.)'\\\n", + " -s 'SST_cpl=(SST_cpl+omask*2.)' sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc \\\n", + " sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc.warmtcp.nc\n", + "``` \n", + "\n", + "**# Visualize your modification to orography**\n", + "\n", + "You could create a file `diff_sst.nc` with the differences of SST `ncdiff`and then visualize the that file with `ncview`. You also welcome to use your own tools to visualize your mods to orography.\n", + "\n", + "- Create a file `diff_sst.nc` with the differences in SSTs:\n", + "```\n", + "ncdiff \\\n", + "sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc \\\n", + "sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc.warmtcp.nc \\\n", + "diff_sst.nc\n", + "```\n", + "\n", + "- Look at the `diff_sst.nc` with `ncview`:\n", + "```\n", + "ncview diff_sst.nc\n", + "```\n", + "\n", + "![Increase orographic height over the western US](../../../images/challenge/cam_SST_diff.png)\n", + "\n", + "*

Figure: View the increase in orographic height over the western US with ncview.

*\n", + "\n", + "**# Point to the new SST file**\n", + "\n", + "```\n", + "./xmlchange SSTICE_DATA_FILENAME=\"$CASEDIR/sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc.warmtcp.nc\"\n", + "```\n", + "\n", + "**# Customize namelists**\n", + "\n", + "Edit the file ``user_nl_cam`` and add the lines:\n", + "```\n", + "nhtfrq(2) = -3\n", + "mfilt(2) = 240\n", + "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", + "```\n", + "You can do this with a text editor. Alternatively, you can use the echo command:\n", + "```\n", + "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", + "echo \"mfilt(2) = 240\">> user_nl_cam\n", + "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", + "echo \"\">> user_nl_cam \n", + "```\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Build and submit**:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the history files into the archive directory. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", + "\n", + "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", + "```\n", + " float FLNS(time, lat, lon) ;\n", + " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNS:units = \"W/m2\" ;\n", + " FLNS:long_name = \"Net longwave flux at surface\" ;\n", + " float FLNT(time, lat, lon) ;\n", + " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNT:units = \"W/m2\" ;\n", + " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", + " float LHFLX(time, lat, lon) ;\n", + " LHFLX:units = \"W/m2\" ;\n", + " LHFLX:long_name = \"Surface latent heat flux\" ;\n", + " float PRECT(time, lat, lon) ;\n", + " PRECT:units = \"m/s\" ;\n", + " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", + " float PS(time, lat, lon) ;\n", + " PS:units = \"Pa\" ;\n", + " PS:long_name = \"Surface pressure\" ;\n", + " float SHFLX(time, lat, lon) ;\n", + " SHFLX:units = \"W/m2\" ;\n", + " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", + " float T850(time, lat, lon) ;\n", + " T850:units = \"K\" ;\n", + " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", + " float TS(time, lat, lon) ;\n", + " TS:units = \"K\" ;\n", + " TS:long_name = \"Surface temperature (radiative)\" ;\n", + " float U850(time, lat, lon) ;\n", + " U850:units = \"m/s\" ;\n", + " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", + "```\n", + "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54e459f8-1ebc-4746-b667-4fd6bd2ce867", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cam/exercise_6.ipynb b/notebooks_copy/challenge/cam/exercise_6.ipynb new file mode 100644 index 000000000..8f21ceced --- /dev/null +++ b/notebooks_copy/challenge/cam/exercise_6.ipynb @@ -0,0 +1,295 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 6: Adjust threshold for deep convection over land" + ] + }, + { + "cell_type": "markdown", + "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", + "metadata": {}, + "source": [ + "The purpose of this exercise is to add a code change to the deep convection to delay the initiation of convection. This is done by increasing the minimum required convective available potential energy (CAPE) to initiate convection over land. " + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Effects of delaying the initiation of convection

\n", + "\n", + "Create a case similar to control case but add code to delay the initiation of convection over land by increasing the minimum required convective available potential energy (CAPE) to initiate convection. \n", + "\n", + "\n", + "![Increase orographic height over the western US](../../../images/challenge/cape.png)\n", + "\n", + "*

Figure: CAPE.

*\n", + " \n", + "Create, configure, build and run a case called ``f2000.cape`` with the compset ``F2000climo`` at the resolution ``f09_f09_mg17`` using the same history file output as in the control. \n", + "Run for 5 days. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**How do I output 3 hourly instantaneous variables?**\n", + "\n", + "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", + "- For more information, look at the chapter:
\n", + "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", + "\n", + "\n", + "**Where do I change threshold for CAPE?**\n", + "\n", + "- Look for the file `zm_conv.F90` into the cesm code\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "\n", + "**# Set environment variables** \n", + " \n", + "Set environment variables with the commands:\n", + "\n", + "**Tcsh user**\n", + "```\n", + "set CASENAME=f2000.cape\n", + "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET=F2000climo\n", + "set RESOLUTION=f19_f19_mg17\n", + "```\n", + "**bash user**\n", + "```\n", + "export CASENAME=f2000.cape\n", + "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "export COMPSET=F2000climo\n", + "export RESOLUTION=f19_f19_mg17\n", + "```\n", + " \n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET \n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change ``job queue`` and ``account number``.
\n", + "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "Invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + "
\n", + "\n", + "**# Modify code**\n", + "\n", + "- copy `zm_conv.F90` into the `SourceMods/src.cam` directory and modify it\n", + "```\n", + "cp /glade/work/$USER/code/my_cesm_code/components/cam/src/physics/cam/zm_conv.F90 SourceMods/src.cam\n", + "```\n", + "\n", + "- In the subroutine `zm_convr`\n", + "\n", + "Replace the code:\n", + "```\n", + "if (cape(i) > capelmt) then \n", + " lengath = lengath + 1 \n", + " ideep(lengath) = i \n", + " end if\n", + "```\n", + "by\n", + "```\n", + "if (landfrac(i) > 0.5_r8) then\n", + " capelmt_mask = 10._r8*capelmt\n", + "else\n", + " capelmt_mask = capelmt\n", + "end if\n", + "\n", + "if (cape(i) > capelmt_mask) then \n", + " lengath = lengath + 1 \n", + " deep(lengath) = i \n", + "end if\n", + "\n", + "write(iulog,*) 'HELLO WORLD'\n", + "\n", + "```\n", + "\n", + "- Near the top of subroutine `zm_convr`:\n", + "\n", + "Add the line\n", + "```\n", + "real(r8) :: capelmt_mask\n", + "```\n", + "right after:\n", + "```\n", + "real(r8) pblt(pcols) \t\n", + "```\n", + "\n", + "**# Customize namelists**\n", + "\n", + "Edit the file ``user_nl_cam`` and add the lines:\n", + "```\n", + "nhtfrq(2) = -3\n", + "mfilt(2) = 240\n", + "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", + "```\n", + "You can do this with a text editor. Alternatively, you can use the echo command:\n", + "```\n", + "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", + "echo \"mfilt(2) = 240\">> user_nl_cam\n", + "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", + "echo \"\">> user_nl_cam \n", + "```\n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "**# Set run length**\n", + "\n", + "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", + "``` \n", + "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", + "```\n", + "\n", + "\n", + "**# Build and submit**:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the history files into the archive directory. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is only 5-day, there should be no monthly file (``h0``)\n", + "\n", + "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", + "\n", + "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", + "```\n", + " float FLNS(time, lat, lon) ;\n", + " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNS:units = \"W/m2\" ;\n", + " FLNS:long_name = \"Net longwave flux at surface\" ;\n", + " float FLNT(time, lat, lon) ;\n", + " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", + " FLNT:units = \"W/m2\" ;\n", + " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", + " float LHFLX(time, lat, lon) ;\n", + " LHFLX:units = \"W/m2\" ;\n", + " LHFLX:long_name = \"Surface latent heat flux\" ;\n", + " float PRECT(time, lat, lon) ;\n", + " PRECT:units = \"m/s\" ;\n", + " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", + " float PS(time, lat, lon) ;\n", + " PS:units = \"Pa\" ;\n", + " PS:long_name = \"Surface pressure\" ;\n", + " float SHFLX(time, lat, lon) ;\n", + " SHFLX:units = \"W/m2\" ;\n", + " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", + " float T850(time, lat, lon) ;\n", + " T850:units = \"K\" ;\n", + " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", + " float TS(time, lat, lon) ;\n", + " TS:units = \"K\" ;\n", + " TS:long_name = \"Surface temperature (radiative)\" ;\n", + " float U850(time, lat, lon) ;\n", + " U850:units = \"m/s\" ;\n", + " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", + "```\n", + "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc170590-11bc-40fa-a209-4ed1d1f64849", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/challenge.ipynb b/notebooks_copy/challenge/challenge.ipynb new file mode 100644 index 000000000..0cd561b2d --- /dev/null +++ b/notebooks_copy/challenge/challenge.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0959db1d-1790-4519-a34e-7026444af537", + "metadata": {}, + "source": [ + "# Challenge Exercises" + ] + }, + { + "cell_type": "markdown", + "id": "33e420f8-dca3-47ed-9999-17da427c6443", + "metadata": {}, + "source": [ + "This section of the CESM tutorial is designed to test your understanding of the CESM model that you have learned about in previous sections. \n", + "\n", + "We provide challenge exercises for the individual model components for you to test yourself. \n", + "\n", + "Feel free to try all the challenge exercises or just the one(s) that are relevant for the CESM components of interest to you." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cice/cice.ipynb b/notebooks_copy/challenge/cice/cice.ipynb new file mode 100644 index 000000000..3f01f39f1 --- /dev/null +++ b/notebooks_copy/challenge/cice/cice.ipynb @@ -0,0 +1,218 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Sea Ice\n", + "\n", + "The sea ice component of CESM is CICE. This is developed by the [CICE Consortium](https://github.com/CICE-Consortium/CICE). Note that CESM2 uses CICE version 5. In version 6 of CICE, the vertical thermodynamics was separated into a submodule known as Icepack. CICE6 will be the sea ice component in CESM3. \n", + "\n", + "It can be useful for people interested in sea ice science to run simulations with only active sea ice and ocean components and atmospheric forcing. In this exercise, you will learn how to run one of these ice-ocean simulations.\n", + "\n", + "This exercise was created by David Bailey and Alice DuVivier." + ] + }, + { + "cell_type": "markdown", + "id": "ea051c12-642e-4194-8291-2c1924d2e1e8", + "metadata": {}, + "source": [ + "## Learning Goals" + ] + }, + { + "cell_type": "markdown", + "id": "346cbd7b-3b8e-41f0-b120-b369ab20f6cc", + "metadata": {}, + "source": [ + "- Student will learn what a G compset is, the types of forcing available to run one, and how to run one.\n", + "- Student will learn how to make a namelist modification that changes snow albedo and compare results with a control experiment.\n", + "- Student will learn how to make a source code modification that changes the conductivity through snow (ksno) and compare results with a control experiment.\n" + ] + }, + { + "cell_type": "markdown", + "id": "47c3af32-d018-441e-bcb0-c74e86ef4272", + "metadata": {}, + "source": [ + "## Exercise Details" + ] + }, + { + "cell_type": "markdown", + "id": "df216c41-3ec4-43e0-b7bd-be2b1e4294bd", + "metadata": {}, + "source": [ + "- This exercise uses the same code base as the rest of the tutorial. \n", + "- You will be using the G compset at the T62_g37 resolution.\n", + "- You will run a control simulation and two experimental simulations. Each simulation will be run for one year. \n", + "- You will use simple, command line netcdf tools to evaluate how the experiments differ from the control simulation." + ] + }, + { + "cell_type": "markdown", + "id": "fd2ff959-6ec0-4534-8713-f5dfdb13b955", + "metadata": {}, + "source": [ + "## Useful CICE references" + ] + }, + { + "cell_type": "markdown", + "id": "baa2afd1-4289-4baf-8bdf-83de7592d76b", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM CICE User's Guide](https://www.cesm.ucar.edu/models/cesm2/sea-ice)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "523878ee-2bfa-4363-a8ec-5ff151b15d57", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "\n", + "[CESM CICE Discussion Forum](https://bb.cgd.ucar.edu/cesm/forums/cesm-cice.137/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "cb8a0e10-a920-41dd-8bc4-5c03395d231e", + "metadata": {}, + "source": [ + "## What is a G case?" + ] + }, + { + "cell_type": "markdown", + "id": "69988cae-fd26-4fda-8fee-6ab52e79f42d", + "metadata": {}, + "source": [ + "The G compset has active and coupled ocean and sea-ice components. The G compset requires boundary forcing from the atmosphere. The G compset is forced with atmospheric data that does not change interactively as the ocean and sea-ice evolve in time. The land and land ice are not active during a G compset experiment run and the runoff is specified. " + ] + }, + { + "cell_type": "markdown", + "id": "68ca54e2-d8ad-41bc-be8f-31a85eec6e65", + "metadata": {}, + "source": [ + "![gcase](../../../images/challenge/gcase.png)\n", + "\n", + "*

Figure: G Compset definition.

*" + ] + }, + { + "cell_type": "markdown", + "id": "c93817fd-8031-4917-bf45-eb0f442578f9", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Compset Definitions](https://www2.cesm.ucar.edu/models/cesm2/config/compsets.html)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "5cbebcff-7d58-4d34-8613-6c1613ab438e", + "metadata": {}, + "source": [ + "## G Compset forcing data" + ] + }, + { + "cell_type": "markdown", + "id": "6deea789-4c3f-475d-9fc9-a1153fb3061b", + "metadata": {}, + "source": [ + "There are two types of temporal forcing for G compsets:\n", + "- Normal Year Forcing (NYF) is 12 months of atmospheric data (like a climatology) that repeats every year. NYF is the default forcing.\n", + "- Interannual varying forcing (GIAF) is forcing that varies by year over the time period (1948-2017). \n", + "\n", + "There are two datasets that can be used for G compsets:\n", + "- JRA55-do atmospheric data \\([Tsujino et al. 2018](https://doi.org/10.1016/j.ocemod.2018.07.002)\\)\n", + "- Coordinated Ocean-ice Reference Experiments (CORE) version 2 atmospheric data \\([Large and Yeager 2009](http://doi.org/10.1007/s00382-008-0441-3)\\).\n", + "\n", + "In these exercises we will use the CORE NYF." + ] + }, + { + "cell_type": "markdown", + "id": "da3d9215-30c1-4152-abc0-ccb1e709cde3", + "metadata": {}, + "source": [ + "## Post processing and viewing your output\n", + "\n", + "You will use [ncview](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#ncview) and [NCO operator](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#netcdf-operators-nco) tools to evaluate how the experiments differ from the control simulation.\n", + "\n", + "These modules should already be loaded into your environment, so verify that they are in your environment and it not load them using the NCAR HPC [modules](https://ncar.github.io/CESM-Tutorial/notebooks/tools/unix/modules.html).\n", + "\n", + "```\n", + "module list\n", + "```\n", + "\n", + "If they are not listed, then you can load the modules:\n", + "\n", + "```\n", + "module load ncview\n", + "module load nco\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "828e538d-3558-4747-9b61-3b29da04e294", + "metadata": {}, + "source": [ + "1) You can create an annual average of the first year's data for each simulationg using the `ncra` (netCDF averager) command from the netCDF operators package \\([NCO](https://nco.sourceforge.net/)\\). \n", + "```\n", + "ncra $OUTPUT_DIR/*.cice.h.*nc $CASENAME.cice.h.0001.nc\n", + "```\n", + "\n", + "2) Create a file that contains differences between each of the experiments and the control simulation\n", + "```\n", + "ncdiff $CASENAME.cice.h.0001.nc $CONTROLCASE.cice.h.0001.nc $CASENAME_diff.nc\n", + "```\n", + "\n", + "3) Examine variables within each annual mean and the difference files using `ncview`\n", + "```\n", + "ncview $CASENAME_diff.nc\n", + "```\n", + "\n", + "4) You can also look at other monthly-mean outputs or component log files." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cice/cice_exercise_1.ipynb b/notebooks_copy/challenge/cice/cice_exercise_1.ipynb new file mode 100644 index 000000000..41990385a --- /dev/null +++ b/notebooks_copy/challenge/cice/cice_exercise_1.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 1: Control case" + ] + }, + { + "cell_type": "markdown", + "id": "e2bfe3d7-3e5f-46ea-a30c-65fc1743c69e", + "metadata": {}, + "source": [ + "**NOTE:** Building the control case for the CICE challenge exercises is idential to building the control case in the POP challenge exercises. If you have already completed the POP challenge exercises you can skip this step." + ] + }, + { + "cell_type": "markdown", + "id": "6457c1d2-0530-435d-ae27-d0f1eeabe583", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Run a control case

\n", + " \n", + "Create a case called **g_control** using the compset `G` at `T62_g37` resolution. \n", + " \n", + "Set the run length to **1 year**. \n", + "\n", + "Build and run the model. Since this is a control case, we want to build it \"out of the box\" without any modifications. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "
\n", + " \n", + "**How do I compile?**\n", + "
\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "
\n", + " \n", + "**How do I control the output?**\n", + "
\n", + "\n", + "Use namelist variables: `histfreq`, `histfreq_n`, and `f_var`. \n", + "\n", + "```\n", + "histfreq = 'm','d','x','x','x'\n", + "histfreq_n = 1,1,1,1,1\n", + "\n", + "f_aice = 'mdxxx'\n", + "f_hi = 'mdxxx'\n", + "```\n", + "
\n", + "\n", + "[CICE History](https://cesmcice.readthedocs.io/en/latest/users_guide/ice_history.html#history-files)\n", + "
\n", + "\n", + "**How do I check my solution?**\n", + "
\n", + "\n", + "When your run is completed, go to the archive directory. \n", + "\n", + "(1) Check that your archive directory contains the files:\n", + "\n", + "- `h` files\n", + "```\n", + "g_control.cice.h.0001-01.nc\n", + "```\n", + "- `h1` files\n", + "```\n", + "g_control.cice.h1.0001-01-01.nc\n", + "g_control.cice.h1.0001-01-02.nc\n", + "```\n", + "
\n", + "\n", + "(2) Compare the contents of the `h` and `h1` files using `ncdump`.\n", + "```\n", + "ncdump -h g_control.cice.h.0001-01.nc\n", + "ncdump -h g_control.cice.h1.0001-01-01.nc\n", + "```\n", + "
\n", + "\n", + "Look at the sizes of the files. \n", + "```\n", + "ls -l g_control.cice.h.0001-01.nc\n", + "ls -l g_control.cice.h1.0001*.nc\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Create a new case g_control with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case /glade/work/$USER/cases/g_control --compset G --res T62_g37 \n", + "```\n", + "
\n", + "\n", + "Case setup:\n", + "``` \n", + "cd ~/cases/g_control \n", + "./case.setup\n", + "```\n", + "
\n", + "\n", + "Change the run length:\n", + "``` \n", + "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", + "```\n", + "
\n", + "\n", + "If needed, change job queue \n", + "and account number. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "
\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "
\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "g_control. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/g_control/ice/hist\n", + "\n", + "ls \n", + "```\n", + "
\n", + "\n", + "(2) Compare the contents of the `h` and `h1` files using `ncdump`.\n", + "\n", + "(3) Check the number of timesteps / files for the `h` and the `h1` files. \n", + "\n", + "- `h1` has 31 time samples / files.\n", + " \n", + "- Check the size of the files\n", + "```\n", + "du –ks –h /glade/derecho/scratch/$USER/archive/g_control/ice/hist/*\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17cf7e19-1211-45f2-97cd-fe2badc69dac", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cice/cice_exercise_2.ipynb b/notebooks_copy/challenge/cice/cice_exercise_2.ipynb new file mode 100644 index 000000000..d46c199c4 --- /dev/null +++ b/notebooks_copy/challenge/cice/cice_exercise_2.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 2: Tune the albedo" + ] + }, + { + "cell_type": "markdown", + "id": "0037b73f-f174-48e7-8e4f-0744d7d23fe0", + "metadata": {}, + "source": [ + "One of the most common changes to the cice model involves changing the albedo of the snow on the surface of the sea ice using the delta-Eddington radiation scheme. To tune the albedo, you must actually change the inherent optical properties of the snow, bare ice, or ponds.\n", + "\n", + "Here we will experiment with changing the snow properties using the `r_snw` parameter. \n", + "\n", + "`r_snw` specifies the number of **standard deviations** away from the base optical properties of the shortwave radiative transfer code. `r_snw` is used to determine the non-melting snow grain radius using the following equation:" + ] + }, + { + "cell_type": "markdown", + "id": "d49c7e65-cc71-4838-91a9-0bbc27393e24", + "metadata": {}, + "source": [ + "$$\n", + " {rsnw}_{nonmelt} = 500 - r_{snw} * 250\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "737f543b-8c17-42a2-98a0-da3b5de9368e", + "metadata": {}, + "source": [ + "This is in microns $(\\mu m)$ and `rsnw_nonmelt` has a minimum value of 100 and a maximum value of `rsnw_mlt`. As can be seen, when `r_snw` is larger then there is a lower value of `rsnw_nonmelt`. When `rsnw_nonmelt` is lower then the albedos are higher and vice versa. This is because smaller grains lead to higher albedos. Hence the sign of `r_snw` is positive for higher albedos and negative for lower albedos." + ] + }, + { + "cell_type": "markdown", + "id": "67bee263-c16d-43d3-989b-03959c0b31dd", + "metadata": {}, + "source": [ + "The figure below shows how this works. The x-axis is temperature and the y-axis is effective snow grain radius." + ] + }, + { + "cell_type": "markdown", + "id": "1ce8051e-1439-4c94-8d10-07712dd9c9d3", + "metadata": {}, + "source": [ + "![rsnw](../../../images/challenge/cice_rsnw2.png)\n", + "\n", + "*

Figure: r_snw parameter plot.

*" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Change snow albedo

\n", + " \n", + "Create a case called **g_snowalbedo** by cloning the control experiment case. \n", + " \n", + "Verify that the run length is set to **1 year**. \n", + "\n", + "In user_nl_cice make the following modifications:`r_snw = 2.0`.\n", + "\n", + "Build and run the model for one year. \n", + "\n", + "Provide info about how to compare the simulations using ncview/ncdiff, etc.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", + "metadata": {}, + "source": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + " \n", + "**How do I compile?**\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "
\n", + "\n", + "**How do I control the output?**\n", + "\n", + "Use namelist variables: `histfreq`,`histfreq_n`, and `f_var`.\n", + "\n", + "Look at the online documentation for these variables.\n", + "\n", + "**How do I check my solution?**\n", + "\n", + "When your run is completed, go to the archive directory. \n", + "\n", + "(1) Check that your archive directory contains the files:\n", + "\n", + "- `h` files\n", + "```\n", + "g_snowalbedo.cice.h.0001-01.nc\n", + "```\n", + "- `h1` files\n", + "```\n", + "g_snowalbedo.cice.h1.0001-01-01-00000.nc\n", + "g_snowalbedo.cice.h1.0001-02-01-00000.nc\n", + "```\n", + "
\n", + "\n", + "(2) Compare the contents of the `h` and `h1` files using `ncdump`.\n", + "\n", + "```\n", + "ncdump -h g_snowalbedo.cice.h.0001-01-01-00000.nc\n", + "ncdump -h g_snowalbedo.cice.h1.0001-01-01-00000.nc\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Clone a new case g_snowalbedo from your control experiment with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_clone --case /glade/work/$USER/cases/g_snowalbedo --clone /glade/work/$USER/cases/g_control\n", + "```\n", + "
\n", + "\n", + "Case setup:\n", + "``` \n", + "cd /glade/work/$USER/cases/g_snowalbedo\n", + "./case.setup\n", + "```\n", + "
\n", + "\n", + "Verify that the run length is 1 year:\n", + "``` \n", + "./xmlquery STOP_N\n", + "./xmlquery STOP_OPTION\n", + "```\n", + "
\n", + "\n", + "Edit the file user_nl_cice and add the lines:\n", + "```\n", + " r_snw = 2.0\n", + "```\n", + "
\n", + "\n", + "If needed, change job queue \n", + "and account number. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "
\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "
\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "g_snowalbedo. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/g_snowalbedo/ice/hist\n", + "ls \n", + "```\n", + "
\n", + "\n", + "(2) Compare to control run:\n", + "```\n", + "ncdiff g_snowalbedo.cice.h.0001-01.nc /glade/derecho/scratch/$USER/archive/g_control/ice/hist/g_control.cice.h.0001-01.nc g_diff.nc\n", + "\n", + "ncview g_diff.nc\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ff6921a7-bb56-4617-8e13-1d9169c9dd8f", + "metadata": { + "tags": [] + }, + "source": [ + "## Test your understanding" + ] + }, + { + "cell_type": "markdown", + "id": "e46ea9f9-937c-48f8-bbec-7b16dc7b2266", + "metadata": { + "tags": [] + }, + "source": [ + "- What changes do you see from the control case with an increased snow albedo? Try other values of `r_snw`.\n", + "- What time of year did you start your run and which season do you expect to see the biggest impact for shortwave changes?\n", + "- How did changes in the Arctic vs. the Antarctic compare?\n", + "- Are the modified `r_snw` values physically realistic? Why or why not? Why do you think this parameter is sometimes used to tune ESMs?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57e44486-5057-4394-9e00-46ed9c2623d9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cice/cice_exercise_3.ipynb b/notebooks_copy/challenge/cice/cice_exercise_3.ipynb new file mode 100644 index 000000000..6318ca3b7 --- /dev/null +++ b/notebooks_copy/challenge/cice/cice_exercise_3.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 3: Modify the snow conductivity" + ] + }, + { + "cell_type": "markdown", + "id": "63f3e4ed-fcf6-4b6e-bdfd-2eccb16b640d", + "metadata": {}, + "source": [ + "One of the more sensitive sea ice parameters is the snow thermal conductivity or `ksno` parameter \\([Urrego-Blanco et al. 2016](https://doi.org/10.1002/2015JC011558)\\). Thermal conductivity determines the ability of the snow to conduct heat through it.\n", + "\n", + "$$\n", + "F_{cond} = - k_{sno} * dT / dz\n", + "$$\n", + "\n", + "Here you will see how this impacts a simulation by multiplying this value by a factor of three. In this version of the CICE model, this is actually a hard coded parameter in a Fortran module. It was found that this parameter was important enough that it was included in the namelist in future versions of CICE.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "ee251754-9068-46b7-a36c-78784d7fc6c9", + "metadata": {}, + "source": [ + "
\n", + "Exercise 3: Something with source code

\n", + " \n", + "Create a case (clone) called **g_ksno** using the compset `G` at `T62_g37` resolution. \n", + " \n", + "Set the run length to **1 year**. \n", + "\n", + "Go to the main source directory under /glade/work/$USER/code/my_cesm_code/components/cice/src and search for the variable `ksno`.\n", + " \n", + "Copy the source module from the main CESM code directory into $CASE/SourceMods/src.cice.\n", + "\n", + "Edit the fortran code in SourceMods/src.cice.\n", + " \n", + "Find the variable \"ksno\" and change this to 0.9.\n", + " \n", + "Build and run the model for one year. \n", + "\n", + "Provide info about how to compare the simulations using ncview/ncdiff, etc.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "604924a5-bf6e-4102-a019-8f50cbf35c35", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + " \n", + "**How do I compile?**\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "
\n", + "\n", + "**How do I control the output?**\n", + "\n", + "Use namelist variables: `histfreq`,`histfreq_n`, and `f_var`.\n", + "\n", + "Look at the online documentation for these variables.\n", + "\n", + "**How do I check my solution?**\n", + "\n", + "When your run is completed, go to the archive directory. \n", + "\n", + "(1) Check that your archive directory contains the files:\n", + "\n", + "- `h` files\n", + "```\n", + "g_ksno.cice.h.0001-01.nc\n", + "```\n", + "- `h1` files\n", + "```\n", + "g_ksno.cice.h1.0001-01-01-00000.nc\n", + "g_ksno.cice.h1.0001-02-01-00000.nc\n", + "```\n", + "
\n", + "\n", + "(2) Compare the contents of the `h` and `h1` files using `ncdump`.\n", + "\n", + "```\n", + "ncdump -h g_ksno.cice.h.0001-01-01-00000.nc\n", + "ncdump -h g_ksno.cice.h1.0001-01-01-00000.nc\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "031b65a2-ab0f-4784-bab3-1b4824e6e029", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " Clone a new case g_ksno from your control experiment with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_clone --case /glade/work/$USER/cases/g_ksno --clone /glade/work/$USER/cases/g_control\n", + "```\n", + "
\n", + "\n", + "Case setup:\n", + "``` \n", + "cd /glade/work/$USER/cases/g_ksno\n", + "./case.setup\n", + "```\n", + "
\n", + "\n", + "Verify that the run length is 1 year:\n", + "``` \n", + "./xmlquery STOP_N\n", + "./xmlquery STOP_OPTION\n", + "```\n", + "
\n", + "\n", + "Copy the file from the $CODEROOT directory:\n", + "```\n", + "cp /glade/work/$USER/code/my_cesm_code/components/cice/src/drivers/cesm/ice_constants.F90 /glade/work/$USER/cases/g_ksno/SourceMods/src.cice\n", + "\n", + "vi /glade/work/$USER/cases/g_ksno/SourceMods/src.cice/ice_constants.F90\n", + "```\n", + "
\n", + "\n", + "Change the following line to a value of `0.90_dbl_kind`:\n", + "```\n", + " ksno = 0.30_dbl_kind ,&! thermal conductivity of snow (W/m/deg)\n", + "```\n", + "
\n", + "\n", + "If needed, change job queue \n", + "and account number. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "
\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "
\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "g_ksno. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/g_ksno/ice/hist\n", + "ls \n", + "```\n", + "
\n", + "\n", + "(2) Compare to control run:\n", + "```\n", + "ncdiff g_ksno.cice.h.0001-01.nc /glade/derecho/scratch/$USER/archive/g_control/ice/hist/g_control.cice.h.0001-01.nc g_diff.nc\n", + "\n", + "ncview g_diff.nc\n", + "``` \n", + " \n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "928576e7-dc1e-4b85-88e7-1b6e9b09f0d8", + "metadata": { + "tags": [] + }, + "source": [ + "## Test your understanding" + ] + }, + { + "cell_type": "markdown", + "id": "617f643f-50e3-4867-996a-0a72080d1d6e", + "metadata": { + "tags": [] + }, + "source": [ + "- What changes do you see in the ice state from the control case with increased thermal conductivity? Are these changes in line with what you expect?\n", + "- How did changes in the Arctic vs. the Antarctic compare?\n", + "- How does the magnitude of the changes compare to the snow albedo changes done in exercise 2? Do these results agree with those of [Urrego-Blanco et al. 2016](https://doi.org/10.1002/2015JC011558)?\n", + "- Are the modified `ksno` values physically realistic? Why or why not? Could you imagine this being used as a tuning parameter for ESMs?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e311bb4-822f-4150-ad15-0572864365dc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cism/cism.ipynb b/notebooks_copy/challenge/cism/cism.ipynb new file mode 100755 index 000000000..72044ffdb --- /dev/null +++ b/notebooks_copy/challenge/cism/cism.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cd674360-c487-4160-92f8-67a283d8104d", + "metadata": {}, + "source": [ + "# Land Ice\n", + "\n", + "The land ice component of CESM is the Community Ice Sheet Model (CISM). Running CESM with a fully evolving ice sheet and 2-way coupling is relatively new and is not the default CESM fully-coupled setup. \n", + "\n", + "However, it can be useful for people interested in land ice science to investigate the impact of atmospheric forcing on the ice sheet. In this exercise, you will learn how to evolve CISM in an uncoupled configuration using an existing CESM forcing dataset.\n", + "\n", + "This exercise was created by Gunter Leguy and Kate Thayer-Calder." + ] + }, + { + "cell_type": "markdown", + "id": "e220126d-bde0-4f98-a9ed-889932480f54", + "metadata": {}, + "source": [ + "## Learning Goals" + ] + }, + { + "cell_type": "markdown", + "id": "edf500e6-fbd0-42ca-88ea-e0e94ceb5813", + "metadata": {}, + "source": [ + "- Student will learn what a T compset is, the types of forcing available to run one, and how to run one.\n", + "- Student will learn how to make XML variable modifications to force CISM with forcing data from an existing run.\n", + "- Student will learn how to look at 2D and time series CISM history datasets.\n", + "- Student will learn how to compute the sea level change due ice sheet evolution. " + ] + }, + { + "cell_type": "markdown", + "id": "3caefecd-731a-496d-9245-1586cdec05ba", + "metadata": { + "tags": [] + }, + "source": [ + "## Exercise Details" + ] + }, + { + "cell_type": "markdown", + "id": "01b99858-0566-4d36-a5b5-0f0d7550d6a0", + "metadata": {}, + "source": [ + "- This exercise uses the same codebase as the rest of the tutorial. \n", + "- You will be using the T compset at the f19_g17_gl4 resolution.\n", + "- You will run an experimental simulation for 86 years. \n", + "- You will then use jupyter notebooks to look at your simulation." + ] + }, + { + "cell_type": "markdown", + "id": "cefcfbc6-e5c7-4323-b292-5bba542ddabb", + "metadata": {}, + "source": [ + "## Useful CISM references" + ] + }, + { + "cell_type": "markdown", + "id": "2b884ee2-d45d-4b76-ab55-f69be8acbe64", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM CISM User's Guide](https://www.cesm.ucar.edu/models/cesm2/land-ice)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1c520b9a-5e96-4aee-927d-5b9124a45ac7", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "\n", + "[CESM CISM Discussion Forum](https://bb.cgd.ucar.edu/cesm/forums/cism.135/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "d720b227-8b14-4bcf-8b99-04acf4e2a94b", + "metadata": {}, + "source": [ + "## What is a T case?" + ] + }, + { + "cell_type": "markdown", + "id": "3b6cd7b4-1086-4397-bb58-f9ab8f475e5f", + "metadata": {}, + "source": [ + "The CESM T compset runs only the ice sheet component (here CISM). Within a T compset case, CISM can run in Evolve or Non-Evolve mode and is forced by output from a previous CESM run. A \"plain\" T compset (like T1850) runs CISM in Non-Evolve mode and a \"TG\" compset (like T1850G) runs CISM in Evolve mode. All other components of a T compset are stubs. Before running a T compset, you must have coupler history files from a previous run that included CLM (version 4.5 or later). You can run with either existing forcing data or with your own forcing data.\\\n", + "Typically, coupler history files for CISM are yearly averages downscaled onto the CISM grid. For now, downscaled variables include surface temperature and glacier ice flux available at each elevation class. (In the future, ocean data will also be available.)" + ] + }, + { + "cell_type": "markdown", + "id": "e0126170-58d9-4f81-82d7-c5b1406a196a", + "metadata": {}, + "source": [ + "![Tcase](../../images/challenge/Tcompset.png)\n", + "\n", + "*

Figure: TG compset definition.

*" + ] + }, + { + "cell_type": "markdown", + "id": "94de8f70-6391-4201-83fe-3a61c514750a", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[T Compset definition](https://www2.cesm.ucar.edu/models/cesm2/config/compsets.html)\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CMIP6 2019.10", + "language": "python", + "name": "cmip6-201910" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cism/cism_exercise_1.ipynb b/notebooks_copy/challenge/cism/cism_exercise_1.ipynb new file mode 100755 index 000000000..b0e19e070 --- /dev/null +++ b/notebooks_copy/challenge/cism/cism_exercise_1.ipynb @@ -0,0 +1,197 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a76577f4-b549-4ae4-afd4-5e58e83375be", + "metadata": {}, + "source": [ + "# CISM Challenge Exercise" + ] + }, + { + "cell_type": "markdown", + "id": "bcd30c0c-7ae2-47ef-b228-b32b5bdbb0a0", + "metadata": {}, + "source": [ + "In this challenge, you will set up and run a T compset forced with existing output from a B compset simulation (fully coupled climate simulation with no evolving ice sheet). The experiment will force the Greenland ice sheet (GrIS) with atmospheric forcing spanning 2015-2100 that were created from a 2 degree fully-coupled SSP5-8.5 scenario experiment. \n", + "\n", + "**WARNING**\\\n", + "The forcing data you will use in this exercise has not been scientifically validated yet, or ever been looked at carefully. You are entering the world of research that we deal with daily at NCAR :)." + ] + }, + { + "cell_type": "markdown", + "id": "f3d1fec9-fad1-48ab-ba75-ac1c0177aeb4", + "metadata": {}, + "source": [ + "
\n", + "Step 1: Create your T compset experiment

\n", + " \n", + "Create a case called **T_GrIS_SSP585_2015_2100** using the compset ``T1850G`` at ``f19_g17_gl4`` resolution. \n", + "\n", + "**Note1:** The 2 deg grid we are using in this exercise is not scientifically validated. \n", + "**Note2:** In the new version of CESM, the compset names will be different and T1850G is replaced by T1850Gg (evolving GrIS) and the grid resolution f19_g17_gl4 will be replaced by f19_g17_gris4. \n", + "**Note3:** The grid resolution for this case and the forcing data are both f19_g17_gl4. For a T compset, the forcing data and experiment must be at the same grid resolution.\n", + "\n", + "Step 2: Make the changes to the case XML variables

\n", + "\n", + "**Note1:** The run length is **86 years**. \\\n", + "**Note2:** The forcing data for this case can be found here: **/glade/u/home/gunterl/workshops/CESM-Tutorial/data/cpl_SSP585**\n", + "\n", + "Step 3: Build and run the model

\n", + " \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "860bdf2f-c173-4e46-859c-dc45c9ca60a0", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "\n", + "**How to I create a case a case that is not scientifically supported?**\n", + "\n", + "You need to add the option \n", + "```\n", + "--run-unsupported\n", + "```\n", + "at the end of your call to **./create_newcase**\n", + " \n", + " \n", + "**How do I make changes to case xml variables?**\n", + "\n", + "Make your changes to xml variables using the command:\n", + "```\n", + "./xmlchange\n", + "```\n", + " \n", + " \n", + "**How do I compile?**\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "cdaee5ee-b575-4484-a78e-23a88c140ffd", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Set up your tutorial run queue if you have not done this yet. For csh:\n", + "```\n", + "setenv TUTORIAL_QUEUE regular\n", + "```\n", + "And for bash:\n", + "```\n", + "export TUTORIAL_QUEUE=\"regular\"\n", + "```\n", + " \n", + " \n", + "Create a new case T_GrIS_SSP585_2015_2100 with the command:\n", + "```\n", + "cd /glade/campaign/cesm/development/cross-wg/tutorial/cesm2.1_tutorial2022/cime/scripts\n", + "./create_newcase --case ~/cases/T_GrIS_SSP585_2015_2100 --compset T1850G --res f19_g17_gl4 --run-unsupported\n", + "```\n", + "\n", + "Case setup:\n", + "``` \n", + "cd ~/cases/T_GrIS_SSP585_2015_2100\n", + "./case.setup\n", + "```\n", + " \n", + "Change the run length:\n", + "``` \n", + "./xmlchange STOP_N=86,STOP_OPTION=nyears\n", + "```\n", + "\n", + "If you are completing this exercise outside of the tutorial, change job queue \n", + "and account number. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "Modify the environment variables:\n", + "```\n", + "./xmlchange DLND_CPLHIST_DIR=/glade/u/home/gunterl/workshops/CESM-Tutorial/data/cpl_SSP585\n", + "./xmlchange DLND_CPLHIST_CASE=b.e21.BSSP585cmip6.f19_g17.CMIP6-SSP5-8.5.001\n", + "./xmlchange DLND_CPLHIST_YR_START=2015\n", + "./xmlchange DLND_CPLHIST_YR_END=2100\n", + "./xmlchange RUN_STARTDATE=2015-01-01\n", + "./xmlchange DLND_CPLHIST_YR_ALIGN=2015\n", + "```\n", + "\n", + "Confirm that you have set up the paths and file names correctly by running:\n", + "```\n", + "./preview_namelists\n", + "and examine the generated file, CaseDocs/dlnd.streams.txt.sno.cplhist.\n", + "```\n", + "\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "T_GrIS_SSP585_2015_2100. \n", + " \n", + "(1) Check your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/T_GrIS_SSP585_2015_2100/glc/hist\n", + "ls \n", + "```\n", + " \n", + "(2) Take a look at the contents of a file using ``ncdump``.\n", + "```\n", + "ncdump -h T_GrIS_SSP585_2015_2100.cism.h.2016-01-01-00000.nc\n", + "```\n", + "\n", + "You will notice that CISM outputs are written yearly (by default) even though the time step is 0.1 year. Changes in ice sheet variables that are typically looked at (such as changes in ice mass or ice thickness) will be captured at this output frequency. \n", + "\n", + "\n", + "
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CMIP6 2019.10", + "language": "python", + "name": "cmip6-201910" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cism/cism_exercise_2.ipynb b/notebooks_copy/challenge/cism/cism_exercise_2.ipynb new file mode 100644 index 000000000..8ac04912c --- /dev/null +++ b/notebooks_copy/challenge/cism/cism_exercise_2.ipynb @@ -0,0 +1,649 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b94bf6b6-ccac-40e8-ad8f-1b5d993ec48d", + "metadata": {}, + "source": [ + "# Looking at the simulation\n", + "\n", + "Here we will plot a few diagnostics to see what happens to the ice sheet during the simulation. We will focus on comparing the last time slice to the first time slice.\n", + "\n", + "\n", + "First, and for convenience, we will rename the first output file corresponding to year 2015." + ] + }, + { + "cell_type": "markdown", + "id": "49386dc4-979d-4919-b72d-f8a7b6757b9e", + "metadata": {}, + "source": [ + "
\n", + " \n", + " Find your history output on derecho (descibed above as well):

\n", + "``cd /glade/derecho/scratch/$USER/archive/T_GrIS_SSP585_2015_2100/glc/hist``
\n", + "``ls``
\n", + " \n", + " Rename the initial_hist file to the start date and time:

\n", + "\n", + "``\n", + "mv T_GrIS_SSP585_2015_2100.cism.initial_hist.2015-01-01-00000.nc T_GrIS_SSP585_2015_2100.cism.h.2015-01-01-00000.nc\n", + "``\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "916e2e9b-fd7e-4fc3-83f9-4f69c052a16b", + "metadata": {}, + "source": [ + "### Loading packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e78178bd-6f9f-420c-9a5d-1da6201d6fab", + "metadata": {}, + "outputs": [], + "source": [ + "#import xarray as xr \n", + "import numpy as np \n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt \n", + "import matplotlib.colors as mplc\n", + "import matplotlib.cm as mcm\n", + "from netCDF4 import Dataset\n", + "import netCDF4\n", + "\n", + "# to display figures in notebook after executing the code.\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "id": "071fa04b-c689-4c76-8da6-eed739ca6398", + "metadata": {}, + "source": [ + "### Set your user name " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "988024ed-5662-4f7c-bc78-670f27710d4f", + "metadata": {}, + "outputs": [], + "source": [ + "User = ''" + ] + }, + { + "cell_type": "markdown", + "id": "177b05f1-ec2b-4086-9f26-2234c19d18c6", + "metadata": {}, + "source": [ + "### Set the 2 years you would like to compare" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "74fd7507-61c7-4b62-8f93-2a555c58c9ca", + "metadata": {}, + "outputs": [], + "source": [ + "year1 = '2016'\n", + "year2 = '2101'" + ] + }, + { + "cell_type": "markdown", + "id": "e57243eb-3968-4a6d-9e6a-b477507507ed", + "metadata": {}, + "source": [ + "### Defining the path and filenames " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "12cf918c-9dee-4595-84da-a1522ea9e1a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Defining the path\n", + "path_to_file = '/glade/derecho/scratch/' + User + '/archive/T_GrIS_SSP585_2015_2100/glc/hist/'\n", + "\n", + "# Defining the files to compare\n", + "file1 = path_to_file + 'T_GrIS_SSP585_2015_2100.cism.h.' + year1 + '-01-01-00000.nc'\n", + "file2 = path_to_file + 'T_GrIS_SSP585_2015_2100.cism.h.' + year2 + '-01-01-00000.nc'\n", + "\n", + "# Loading the data\n", + "ncfile = Dataset(file1,'r')\n", + "thk1 = np.squeeze(ncfile.variables[\"thk\"][0,:,:]) # ice thickness\n", + "artm1 = np.squeeze(ncfile.variables[\"artm\"][0,:,:]) # air temperature\n", + "smb1 = np.squeeze(ncfile.variables[\"smb\"][0,:,:])/1000. # surface mass balance, switching the units from mm/yr w.e. to m/yr w.e\n", + "ncfile.close()\n", + "\n", + "ncfile = Dataset(file2,'r')\n", + "thk2 = np.squeeze(ncfile.variables[\"thk\"][0,:,:])\n", + "artm2 = np.squeeze(ncfile.variables[\"artm\"][0,:,:])\n", + "smb2 = np.squeeze(ncfile.variables[\"smb\"][0,:,:])/1000. # switching the units from mm/yr w.e. to m/yr w.e\n", + "ncfile.close()\n", + "\n", + "# Computing the differences in the different variables\n", + "diff_thk = thk2 - thk1 # thickness difference\n", + "diff_artm = artm2 - artm1 # air temperature difference\n", + "diff_smb = smb2 - smb1 # SMB difference" + ] + }, + { + "cell_type": "markdown", + "id": "918d524b-c3f9-40cd-84dd-e1924ff15ea9", + "metadata": {}, + "source": [ + "## Looking at 2D plots\n", + "\n", + "**Note:**\n", + "In these plots, you can adjust the range of the colorbars by adjusting the \"vmin\" and \"vmax\" values of each subplot." + ] + }, + { + "cell_type": "markdown", + "id": "8630fc1d-8656-4b52-b51c-c00f1ca43d51", + "metadata": {}, + "source": [ + "### Air temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cbb7265a-095b-4bf5-b700-523fef9fe1a9", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "labelsize=15\n", + "\n", + "my_cmap = mcm.get_cmap('Spectral_r')\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 3, sharey=True, figsize=[21, 9])\n", + "\n", + "\n", + "# Plotting air temperature for year1\n", + "\n", + "vmin = -20\n", + "vmax = 20\n", + "\n", + "last_panel0 = ax[0].imshow(artm1, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title0 = 'Air temperature (deg C), ' + year1 \n", + "ax[0].set_title(title0, fontsize=labelsize)\n", + "\n", + "fig.subplots_adjust(right=0.8)\n", + "\n", + "pos = ax[0].get_position()\n", + "cax = fig.add_axes([0.32, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "cbar = fig.colorbar(last_panel0,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "cbar.ax.get_yaxis().labelpad = 15\n", + "\n", + "\n", + "\n", + "# Plotting air temperature for year2\n", + "\n", + "last_panel1 = ax[1].imshow(artm2, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title1 = 'Air temperature (deg C), ' + year2 \n", + "\n", + "ax[1].set_title(title1, fontsize=labelsize)\n", + "\n", + "pos = ax[1].get_position()\n", + "cax = fig.add_axes([0.56, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "cbar = fig.colorbar(last_panel1,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "cbar.ax.get_yaxis().labelpad = 15\n", + "\n", + "\n", + "\n", + "# Plotting the difference in air temperature\n", + "\n", + "\n", + "vmin = -15\n", + "vmax = 15\n", + "\n", + "last_panel2 = ax[2].imshow(diff_artm, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title2 = 'Difference (deg C), ' + year2 + '-' + year1 \n", + "ax[2].set_title(title2, fontsize=labelsize)\n", + "pos = ax[2].get_position()\n", + "cax = fig.add_axes([0.8, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "cbar = fig.colorbar(last_panel2,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "\n", + "\n", + "for i in range(len(ax)):\n", + " ax[i].invert_yaxis()\n", + " ax[i].set_xlabel('')\n", + " ax[i].set_ylabel('')\n", + " ax[i].set_xticklabels('')\n", + " ax[i].set_yticklabels('')\n", + " ax[i].set_xticks([])\n", + " ax[i].set_yticks([])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e352d623-3525-4b67-b86c-23e25ceb4608", + "metadata": {}, + "source": [ + "### Surface mass balance" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "053ef542-d289-4dd7-8881-8a1a6b324ac0", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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xINSsRBOGeS6TqTKbwZ8D/wu1JMa0/0UR3Eg/2J7Gfdh4JGweHmq7EO/N9X7UTJcXnmM+voN61vpMLEFp7285yoDHkBBCbJdSntLSZlGbtxmNLSnld4QQfxt/H6qzx1HaS+nvbGyi3xZCHEYZpWeN2NB7AvCjhsvvQW0W9a+o6HXeG9EsXI+KiP4ucGMSHBBCfCtOWwW+bin7UZQxv1qgTfejlMctOflM+H+odXVPMExrQ0p5mxDiJtQUxQ/F/Dvx7w8MU5GU8nahduV+PGojwwSPB26N3w6UKFHCglI/l/p5k/gA8JNCiH1SyiRw/VTUrBH97WuJEuc14jf+r0b1qY9sAclh+rOOj6FmEkxIKT+8BXz8lhDih6SU/2W4fh1qH5/dUkpTQDgL32WLdHSMpwCv0xOllJ8TQtyIWoJ4MWpGxaYgpVyI9fGvo5ZBfE1KKYUQn0FtouphX3Y4zHPZH/+9aVge4+D7C4Efk1LaeCmCDwAvE0I8JqEjhHgIKig9rK39ZQuvu1B2/K3ATxgCUVvKB/BfqE13H0e8sbhQpzU+GLXXX4ktRBnwGB7XCyH+HfgfVAR0H/AS1Pnd1qOFpJS/WJD+/tRu/LOona4ngDfllPssqpNsBZKpeJ/UL0gpW0KIf0Lt5P0vUh1/tilIKSMhxGdRA0Pawf90TP/DUsoAQAjxJtSGWZfFeT6MUkwfFkK8GhXVHkdtOFiTUqb3vHgIcIOUsncagRDircBjpZT7bfwJIf4/1E7hrwIi7ZSEb6fWRb4S+Eehjnn7LOqkhMtRu5Wn6SUOykFgJPX7k6mZIC8D3i2EeC1Kxh4LPJtNHAlWosQFiFI/l/oZUvpZCHElanpwMh37IUKIVeC0lDJpw3ehgjjvEUK8DPVM/xy1YWn6KOEitEqUuCvhpWR/DKVnfgl1ytKTLE7bsBimPw9ASvldIcRfA/8az7T7Mqr/XIXa1P15m+Djn4UQv486lWsXcK2U8heklItCHa37f4UQ+1BLPhyUzfU4KeXTM2h/FhXkPGsIIS6L6/xvS5Y3oWZeXHcWMx4SfAqljz+UetafjunfnOzXJIR4OfByKaUHQz+Xh6CWvvSWR8bt/AoppXWfGCHET6L2VHorcFTT0bemguj76M+6qABXxvbxmpTyAzG/1wkhPgS8XQjxElSQ/NXAZ6SUvaBeEVoWXuuogMUUav+Mq0X/4LC2VPt9bSkfUsovx/bKm4QQv4PaK+W3UDMp/8rGa4lNQp4HO6fekz4oxfI/qB3sW6hdqf8ZuI+WTwIvyKBT5BSAOdTaw8cV4OtHUbv91lNpj0XbbdvGG2oN2bvi73+OYRfiVN7kBJInGK716BRsz9+Oaf1kKu1ZcdrLU2lvRTvZBPX27fdQbzE69NdCPkXL9w027tr9b6j17Vm8fcLwTJLPY7W8z4/5aKMG4ccb6BWl9dOoqW6dmOYv3t1yX37Kzz3hU+rnUj/rOhUVkDbl+YRGby9qSvIqaur8XwEjWp5CtMpP+bkrPpo8RqgZWV8G/gjtJCbO4pSWOD23P6OdCpVKF6gTV25A2UinUQHbn0nl2aCbTLwAddTb7yMxrUPAH2nlfhq1D0ITtefCF0idaGVpy4fEbXhxVpvZ7jPdlsCLgK9n1HVZTPd5hmsbnkkO34k+/v9SaQ+P096sy8qwzyXO9++kTqqJ016D5ZQu7V5sOvo5qXzPseS5XaM3idrgdRF1ksk/AzNankK0DLzut5Q7p3ygNi19I2rMaaJmZGWe/FZ+NvdJjq8rcQ9HPM35COrow3eeJa3keK6/s1x/DUrJHpDqBIHzFkKIK1DK/DIp5e2p9DtQBrv1rW+JEjYIdSrHd1E7wo/JcrlRiQyU+tmMUj+XONcodXWJohBCfB34Jymlbd+LonT+B3XizO9arv8yKmCwW5pPUDlvEC+xOIkKoH8mlf5J1PHpv3e3MVfiHo941suLgStQOvoO4B+A18jNbUBuRbmk5V4CKWUnXgbxIuCsDGop5UFTemycXomaLvl757sxHePXgX/UjOndgA/8y93FVIl7PF6LehPcuLsZKXH+o9TPVpT6ucS5RqmrSxTFHwGvFUL8uYyX620GUsrvN6ULIfajlrr8f8Bbz/dgR4xfAj6vBTs84H6Y95EqUWIYbEPNlH0tasbMw1CzkXayxUfzlgGPexf+ErU3xIRMrYfeQvwNaqrcfwB/cQ7obymEWoB3CM1wllIeA3bfLUyVuMdDCPE9qI0S/xj7DuglSugo9XMKpX4uca5R6uoSQ+JdqM0n9zB46t5W4ZWovd0+Sf9ksPMdS8CvphPiYNC2u4edEvcmSCn/Rkv6uBBiHPgVIcQL5RYuQymXtJQoUaJEQcTHYH4FtTZ1EbWOs5wmXaJEiRLnEUpdXaJEiRL3PAghXgz8IdDYyoCHs1WESpQoUeICwC+idjIvd9AuUaJEifMXpa4uUaJEiXsAhBCuEGJECPEY1IyiN25lsAPKJS0lSpQoUQhCiG3AHwA/LaXspo4sK1GiRIkS5wlKXV2iRIkS9yisoU6CAng78JtbXcFQAY+ZmQm5f//OreahRIkSJQqh/ZU5VuieE9p3sHID6ijTBH8rpfzb1O8/Ar4gpfzvc8LAWaLUzyVKlLg7cfvtJ5ibWxIA9xfbZKmr+yj1c4kSJe5OpPVzGudKVxfQ02k8ChhBbVr6ctSeZ7+8lfwMFfDYv38nX/rSX6OOEk63WTLrRKR+F42op/Om6ejfTbTz6tFnw2yGp2HzJ9/TdWe1lYlW+nqaXlYZvQ1NOJu3HEXbxPYM8+iVMlTKUDpfUtfgvVzi/C9+z33YWfBgx3PCj7aklA8xXRNCXAX8HHCtEGIyTh6J/04IIUIpZfOcMFYQSj+/sWBuU/8YFsOUs8l2ke95NLL4MukhW//S+0lWPlu9CbJ4HwZb8Wzy7mmzdZUyVMrQIB760F/qfV+lW+rqFPr2cxomOyjPHtmsXaH/LkIrz2bAcN1Wpoj9YaK51W0yDPJsrSxeithdRegMm7eUoY00SxkCeOhDf9GY81zp6iw9rUNK+dX462eEEHPA24QQr5NS3rpV/GxySYve8Hm/iwpbOk+ew1wkT5bxkOVwZ3WUooKnGydFDUNbW+r09LbSywzbybJg4tfUDqa0rGc3zO9ShkoZultxOeqozOsM144AbwKed5dylAmbs1fE8c2jWcTxMtWh/01/13kxpZuuJ3mStCwnzVZeL2vr0zoNG/10/x+272bB1CdNtExtYOJlmPr0tFKGShk6b3EP0tWbsXuGcX6y8g1jgwxzbdj09LU8GyyNzbbNVrWfiabJpjrX9kspQ4PXShm6FyAJfhwA7u6AR9bD1o2Z5AHaymQ5gqZyJtrp36b6TfXYrtvqK8KTLX+6nClPGiZDK52eVz6Ll6y60mlZCtTW7qb6dMMsrx7TtVKGShmSAz8dN4elzSLMvPoZ4HFa2pOA3waeDNx2bpjaDNLP3eSY2JwVUz8wlctzUocpk9ST5WDa7iNdh64jTM6aiU46zeawmvg36QLbb9s1233ZeNqMw2trP5tu1tuwlKFShoaVof7PUldvBlnjoS3tXPJR1LkbNv9meEljWFuwCM0i5Uww9fUivGTpCZsdWgSlDNlpp3GhyZC96nOiq7P1dBYeHf89tDWMKGzRDA+TgaQbDzZDKM+5MtHV8xZxOm086teH4dM22JvKmHjLawNT/rxOZKu7aJmsDmbqRMMYdVnppQyVMqTnwXL9roeUcg74RDpNCLE//vrp8+uoQ5uc2ga8vOsmxyaLVpKnSPk8pyuPr4SGKY/NibS1ia2v25B1zdY3peV7kTbNqiOr3mHaJv27lKFShs5Ghu4e3LN0dQLb+KnDZiMUHd9tdpeNlzR9U3kTbVOedD369yz+8tKLlDHZlKbr6XJFbSBbu+u082xNG9+mvzaeShnKpl/K0PkIIcQHgY8AN6BCJI8GfgN4x1YuZ4GzPqXFJoBp5AluEZq2TmgalG1CMMxvnU7aGDIJvQlZisMk1DbkCbaNdhHHNsuYSa7bjLmsOkz3bstbypAdpQzpbSoAxylyDxcysvpL+m8Rx0mXYZPM63Kt09MdvyxDoIgDV5RWFo82vvPqzqojzU8WbRMfWc6zrf1M+UzP10Q3z6EtZSifx1KGzHz3U0tdbYKtD+Tl0dPz7II8G8z2u8gzK8KfjS9bX4GNMmWr25Qny77Lyp/XBll2UJ4NOswzsn0v2s6lDPXL6/lNdZjyXEgyNEjtbtTVXwKeA+wHAtQMvJcC+oZHZw3n7EmkH0p6gNSFMUtodFp5+YpezzLITAZEerDX85gEL8+Y0YVS/24zNk158mAz3kzPxGY8mZC+7yIG4mY6TSlDxfgrZeh8gpTyrVJKcf6+MUw7RAmGadt0f0w/t7w+YDNibLT1PEUNlyL9z4Zhy5qMLb1vZvXBdFmbEWJySov0F1s9tmt6m+cZnqUMbU3ZC1GGzg+cX7pad9ZMzyrPOdLL5NkJJppZz9dWtzR83wzNog63rWyeo2pqR5v+MNVtarcs28fEo63eNJ2s55xna5UyZObJlqZfv9Bl6PyAlPJlUsr7SSlHpZSTUsoHSSnfIKXc8mNjznKGB5gNIZPgFIk0beZaliGW9dvEn41/PT3vXmy0Td9tsLVf+ne6Q9p4KqoMsp6ZXqaIoihyj1m0Sxky077AZUiAe67Whd8rkDdIpp+xLgN6Odt1U51ZRkxWHTY5MvFt4sdmXNh4LjLwZzmdpvrzjDKb8Zhcz3oWRWhlOaObQSlDpQxtgYFc6moD9PEfBvuTLb8NWWNzEdtLr8NWn2281vugqZ/l2XG6XZPFX1EUtVmy7C+b/TKsPaTTKmpn2fKVMlTK0NnK0MZsF4Ku3sQMjyIDY55BMWzUKa9OE71haGcJyVYajzaFkGdgmmDr3HobmJ6Dni/doUx5bXzYDE5TJxv2HksZ2kgr/f1Cl6ESZtgcxbTcpJ9pltNogm1QNtWdrt9WXucnK5+JVrqszYiwyb8uWzZjSecj/T1dt4leXt/Xn0VW/VntmIes/qT/LWUou35bHaUMlciD/jyzxsQkv37NJMdZ9Gx50rT0+my0TY6XrZ+hXUuXP1ubKE1Tr0eHzWbJew6mNi7avsM+m2HqKWWolCFbmWHrubBwljM8bA6NLsCmDmEamG2Dtc0Z0n8XcY6yHEUThnW4dMPQ1kb6NZvzaaszq32z6Gc5uVkGYhFeddjKp1HKUDb9UobS5QXluvB8mAbwrDQbNjNYm/Jn8WGjV8SI0/uJqV8MY7zZkG6HYYxEPU0vO2z7pvNnGaymcib6WXWXMlTKkF7ORN9ettTVNuTZMem0LGdy2LY19V9dLk08ZvFqQp5dUcQ+K3ItT0+Y0m33WNQ+zbq3onrJVNZWTxF7tpQhe1retQtZhgZzXgi6ehMzPLIGyKJCbBtoiwy+0pBnGOPBJsy2crJAnjRfRY2kIoaLjccswTblKWLYFFVOtntMt5PeaU38ljJk56uUoXwZKmGG3s4mWTwbZyvrtyk9ef7DyGkRw8XUp9Kw3WdCY1iDK0lLZDdNI08fmOiYfufxN4xjq+crohNN91PKkJ1GKUObo1ViEFnOWxF7xkZLH1ttMmMrY8Mw/S7L6Spqh9mu6TKep7ey6tDbKks3FO1zWXZvUXvmbPKVMpRfbylDFxK24FhavROYrtkEuEheW/02A83Eh4lvW7p+L+m6dB71evKMkDwesxzMYdrFVocpWJDn6NqMJlvH3EwnLGXIXDarPhuP93IZEuBswVbL914UMTb0Nh/G6SlSly4/tutFypvk0ZR/WKMuq/6sevQ6bXl1ftK/t8pxzLtn03VTWpqvou1YylB2/Vn16HXa8t4TZWgwW6mrNwP9mdvGQ5uNotNI6KT/2sb1POdyGB5M9etpNj6zaGTxVuQ+suw6my1qsxWzbFcdRdtqK1DKUDaNUoYGcIHo6i24RduDTa4NY5hkXbcJtc1Q0K9JQ96iA32eUWDq+FmOcZ4CAnPHsOXPcjRNhpzuqOq85fG11R1Of17peyplyEy3lKESJuhta2rn5HlvxsHT6xrmmq2uIgN8nswUNfCSvHnGXFYb5ukTEz8mFDVybND7ue16Xpp+vZShUoZMPGSlldg6pGXVFqjL6xsm+bWVHyY4ZtMNNrsoj/ZmZNZUp87XMHKb5tXWZ9N1FumXOorwVwSbeValDNnLXIgydOHiLPbwMD3YrEHX1HFMHVKnneW06fXlDfrD5DWVTRt5Nnq6g1tEqdh4LMKnqQOYOllePXmwKaUitLLaQUsPA4gC8GtxlhCEo/KGwWBe16OUIZ2G6fe9R4YEAsfdzAB3oSAvwKYPuno+/RmZ6BR5jsMMxsM4znn0i/BbxDnejDOf7mdZRqbJkNsq46UI7zZjT+erlCF7mVKGzHylf5W6+uyRbmfT+GuzJRLkXbfVkWdD6P1Ap5FOz7Kxijh+ev4itE20bM5iXvuY6jWlp3+bnleRZ5EHezlJRCRDIhniO7VemsDpXetREQ4OroHXUobu/TK0EReKrt5EwGMYYTDls6WbHKy8cnkOWFpoigpiFj0bdJ70jqzTNxkSNkNqGCHMU1bDltVpFDH2TPXalIT2u9tSwQ7hqO9eRf31axB0QEbqkyCKxTcJjpQyZKnz3iZDJYrDJCP6YJ+XP0HRZ5bOa3ruJufN5tCZeMzqKyYHzUbbhqz8tr6ZvmbTA6b7NNVpKpfHr07fxLOeR1jym+ib6rPxV8pQKUMlzg5ZTk8apj5ism9sdIqOsVk2hanutHzoPOTZaaa6sxzWonwWSc9rxyz6efbN2dhW+ehGbSIZIoSgG7XwnAqdqInv1AiiDpIIKft91BEuAtELjpQyVITPIun3XBm6t2MTAY8sIdUbvoiTM6wzlVWXKa+pHtv3LOQZhGmeNtNhsnjI49F0PctIHRYmY+csO1jQidlKBTHSf2UEnQCqo7TCNSp+nXa4Rj10+/mieMZHGIDrF7wPKGWo6PXzUIbEhbHW8OxhMyCKOEh5dCFbHk0Okc3pynMkizqiw8hSngOo34OpPv27nkevo2h7FOUxL286zeaQmujr5UoZyuYx7/qFKEP94qWu3gxMzpjJXkkjy+Es4qieDfS6bX5Anq01TB1nC1uf30w9puc1rF9ThH6fZiA7SBkhkUiUPZwEM6SUhASEYUDNHYWVk1Tqk7C2SGdssjfTI/nryC6u8A33YLvHIunDopShu0aG7CQuBF29iVvMEjI9uCEs33WDSqebVadet16fKT0r8GLrxKbB30Q/TaOogVHE+dTrTvOV/ujlbO0xbCe38VCUVtrgS31ktHFpSq+IWRwjGRBEHerumDmf62FvkwSlDA3ydU+RIRutEvlIy6zNYTEhT2+naafzm+jbHKassSPPSbTBlkfnLy+f3i9NsmqS5TxHdlhHNf23SF+3GYg2g0z/buI7T4ZMOqLI2J+mbeLdxFMpQ/cMGSqRj6znbGtPm52Rl2azsYd1yGzf8+jqtoeep+jYnnWvw9oHReovStPWjpvRRXY7NQluhLJr5kJY+AgD6KzDxC4k0YZ8KtiRx0cpQxtxz5OhCx1nMcNDN1bSg2PeQ7Y5UCanMsuxS1/T86WNtLyBQq8nzzBI32eWIWCrs4jw2e4tq6ytPdPfdR50oyiPx6IdZ7B9u1ELX1esMmI+OsO0mNKqcOjN4uisMyJdRS5YZ2BJS5I3QWcd3EocAEnzW8rQPVOGzLgQ1hpuHWwDpc2ASV+3OafDOFp6WlLeJEM6jSy6+t8sJ9B0zcRfllFhag9THlt9JpomXtLX85xfvWyeUZTnVGfxrK4FsksQdfCdKkI43LJ0iG4kuGrqUkCwHiziCI+a27DcVylDG9P1PLb6TDRNvKSv39UyNIhSV+swjY1Z42he+5mu22wV3UHL+q2nmewigZmGbfy39dMiyLJlsuyzrOumeytKOw82m9LWNqb2TF1rLkN9DIlECAcpQySSytEbae852MvZjVrK1nZqCCF43+1LtELBMw7UIGqy3DmNI1wanrK5HeH2yrajNTxR0WZ7mHgs+ltPK2VoOGylDOXjQtDVZzGJRR/gTcJsg+la+kEl3/MExTZomGjq9evX8gwxk9FiEsIsFBVGG29Z+W3paUPORtt2vcg9ZRlKyZWIM+07Ido4u2Pa2TaY2UkFK9LBjCjAGOyojCh+w/h60NLuo5ShjfXY8tvS704Zsin3EsVhk99hdbReJsupsqXlOVTJb5txIbU8WY5XVt+yOeJ6OVv5JJ8u6zZHNc+oMkHvQ8OUNV2ztb+5r3aiFqvB/GBauM5Kd45O1KQbtWj4EVdNXcJasMB6sIhExsEOG2+lDG2kf2+SoRLZyBq/bc4d5OtrMq6bZMbUD/L4yXMyi9pCpjbI69ObdRh1J3cY5NmOw4yh6f5nc1RNFJTNLG/9em9DUhnbwa1whZMz4wP52+Eah1c7tMM1OmGT8UrI0/aPs9A+xlLnJJEMB4IdFacOQCi7SCnpRu2Meyll6J4oQyX6OItTWsAu8OkB2zRI6w5fXmfRH7Q+eOvl8xzLPGdNNziKOI9ZhkSewKfzmIwdU1vmtbGJlq3z24wivU1tz9JEI5VDSrbXL4FQ7c2xEJ1hyt+hNiTVkQ5q6AGONByvv1lp2E0FOuhvdGp0vEsZumfIkKktQQhwL4C1hmcH/ZmadHPy3VTORIOMcnr+dB6bHGLIhyHdVE9y3SSLNtqmemz3n9U/snhMlzelJ2VMtIrQN5VJp2fxmoV+3ogIKSNuXDzM7kadujtOIDu9De7q3jgj3kSv7j0jlwNQdRsIRPx2sJShC1mGepRLXW2Azc5IkLZP8uyFzdSn11WEP1v5LHuhCIrYQ6axS8+bVd+wbWizB/NoJ9Btl3TePPvHVIND5Eqc+zwCIRy6YZPa3FE+0BnhiskWk5U6nbCJI1wc4TLqb+O+U6K3n8ejdig6SmfT26BUD3Z0ozbJiS7JDBHzPQ3bBnnlSxnaiLOVIVu9dn1+oejqswx4bGZgTFBEiGyGTNbgX0Qo8owPk7Cly9lgqk/nM6s+U902nmx/02VsNG0o0umH6cxJEQeR4mmqskttWprM4DAFNtLLWhyPgdkebvJbbAx2gJoN0tvI1GTQJt9LGbLzenfK0DCDUYmNkNpfPX0YGkXSs2RU7ydFHFcT7WH7sW1cyuq3m+HblkfnPUvfpMsWHU+z7iGvvF3XnW7ezloQcdXU5dywcCu7G+BGXmwgjwBQGTCGFbxC68Cz0ksZMvN/T5OhEtmwjW228ZbUddt4biqTduxs+U31pstm2SJk5DGhqHzo95lFO6uvD1vXsLD1dZt9aHvOet6N/Di44IKUEd9eOE1XVHni3h2899AcD5xZp+6t4wqhNigF6t44QvSDHq7w8Lz+TBBHuPGRtJJQBr1gh+IkIpRBvJGpZ7knE/9F76mUIXvZrZIhG0r9vAUzPNJ/N+skJchzFpM8wpDHJOCbdaxMndU04Og0dEPJxKetfptwo+WxXbPRzUNWh81ScDrv6V8y5qKfJ5Ihjusiwo55ZocJwlEzNfRNSsMAoviUl8iyAWrCVxio7YeFs4HPjfdSypCZbh62QoaKXgPHKRV3MWQ5UnkOkymfzRm0GTBZTqRpcM6jrfOJJV+ROvTvJpj6k6kds8qbYDP6TGVsPNrKF4G6r2Tzu2RWRkTEdSdr7B9rE8gOl4zPstQ5wY76ZbTDdXynyuHVW9le3xFPjZ401D0oQxFhbFyXMmTPf0+Vobx6S11dHFl2A1qaST/b6Onfs/Kl67GVyxvLTXyn6erlbfmzbYCNPNnazcRfUuZsZNNm9+m/Tf3URiv+K0OIIrpCnaLiORVE0EH4Ff7xlioPmYH7Ta/x+D0R31nwedDsFM1gmarb4FPHl3jwzBwRIZOVXXhOJda/fYSyS0cq+7kbtQb28dD5CmU3nt3nWHi33XspQ8XqLPJ7EzK0CVwIuvosAx46bMJigkm4hjEe8jqHiS9bnqwOZiqTx4MpT1Z9ehuk03Waen5dseQZhnmdTO/MWc/J/LwEArWfRhuECjYolRrGVUT9WRv6kbQJks1Hgw6EncG8RRF04jJeznytUobM1+4qGTLxoN+Xui4EOLbxuQTZOlWXYZuuzXLIijiB6XzJ9zwnNIuvvHEhnU/nMeuebTzofKevZ7VnFs+2MkXvzYYihpmZr9dff5pdI/A9u1bZUb8ISYQjJHMtDykj6u449bp6M1hzR1kNzjBbm6XujqfoZ8vQl07dycO37ye7HTfyZr6XUobM6XefDGXVW+pqG4qM3VlOjW2sT1/Lshts9PV6iiDPFjBdy+I/j46pbJG05HeevaGXNT0rnWZWHj1vAQgX6Qp+9F+b7LlolWdf1uH+28aQ3TVcAbcuq2wTlR08bLvavLThT7HQPsaDZnxG/Rk8p4InKnSjFh3ZHJjxkcbbbmryv68YNbLRjfovJhNfWBTe9rGUobtehvJsaDsuFF29xQEPsDtytger58sT9rzyebwVSc8bNGy86OlFO72t/ry6TWV1mlmdWK/TlM9UXi9jQBQHJ5JAhUz9Tj4D7DqDeWSkZoJEwca8RZCuMzkK1zHUu+FeShnaSHOrZcjEs61Pm66XsCPtiBSRZZvzmVeHXkZ3gEw0izh5tvqKlMviJw9FecriJZ1mM2qK8FBUb+TxY0I/z88c7FB1RwiiEdrhGp5T4bG7K71TWPRxfNSbNvCm/i52juMKnzF/ZqCe+01PZPCxUYba0TrrwRJTld0b+N3c/Q5brpShfJxNcKWEfbw3XTPZ0kUcwmHtARtMMpSVVsQpO5ux3FTWlpblFBdpw7xyw9Ae7p4lkj/8/jl2jMBqN2Stu4jnVPi1+zdph4KaO6uo9uxmh6nq7rgmZeN2oxahVDOgj64do+pKtlV3D9TzjANrwGDAQxIhU31cItUMbeGyFiyw2p1ne/0AxYMfpQzlXxtWhorqhbNtq3sXtjDgYYuQoaXlCXKSJytooteXLmP6LdjIU1YHzKvfFnTRfxfNr6OIgGYJd1Y76Xn1cib6xTtNRMRKZ45RfxoXD1yPUAa4osKGGRqRZe8Or9I/dWUzwQ4wL3WJKLAzTylDd60MZbWRufyFMPVu8zA5MuT8PhsHRu8jesBlsw5qUibNX56jaKrXRiudLrV0E5/D3ouN96xnovOe12eyrudBMl6Z5dPHz/DonZO9WRs1d3TA2M2ToVa4ytfm5pishtxn8gDNcBlHuFSdBiB768qz0b/nqtMgckNWunOM+dsoZShN63yToTSf5vylrs5Dnu40jflFHcM0bLJtGuNNNo+JlinNNGZnOY06it6TzVdI08iyt7LopX+n04rWfxYIu3D6Fpxt+zkwPsHbb2rzc1eMU3HVBqPJKS3qaNqN9rPAwXMqKh+S9WCRt9/kcPHoCE+6qMFKd27gWNqGN0USuEj28RA4veWOAnUEbrI8ve6NE8mQhfYxpqt7M26klKG7RoaKlCtG+0LQ1ZsIeGQ5KCZkpZsUusmZLOI46TRt+TZrNJnoDTvY2GDrbKZrWeWK1JenaEyOORRtMweHicp29SM+17kTNam7Y1iXpSTp6WUsesBC3+A0a8PTBMm1MFBBlKSezCBKKUP59Q0rQ1n00vmyeCxRDEUcKlu6Ta/bHLx0Xls9m3EG8/g0pevjRtZYktdGNl5MhgwZ6ZtxenUjyZY3Ty/n6ShB1WnwhD0N0s9HxP/yaCx1ThLIDt2oyeUTVWZql7IanKEVrFLzRplrHaXujvCt+ZBrd+3L4W9QhrpRi/VgMZ4tUspQNu27V4ZKDIssG6CobWDLX8SxyatrC533DXUVGSuGsYdMfdNWXxEfwuZz2Oo3fT/L9nN92HlfAEZlhV++0oGlEzAxiiRSG4kKkFq/VCdkVXCESygDjq/fznLH5UzL47G7O1w5tYuF9jGOrwt2jnT5/Kl5dtS7/NcddV5wvwr6bA2BEwdNwrheHyEcBIJO1OTO1S7TVdtNlDJkplk0r422iZeidWzlM7nnYhMBD9ND0p0b0wBrUs5Z9LMGhiznLg9ZA7gpIjdsJ7LVs5mgkO5wFB0QsxxN2/2k022OziYgQ+pOIz41pYC4dVvmIIaelrefRzow4mYtZdkMShkqLkN5yAsuDdYvUCuTSuRhM303K7Chp9l0vomm3g9M+sWm0/Puw+bE2fi1lbOl6465CbY+n0WjyL3lOfg6nSK8punlPUM7jUCqZS+TlZ0sd09z28rN7BvdTyRDOmGThjdGN2pzzcxIBs/m+sf9Wcb9We1eShm6Z8jQINVSV+soMv7mOTu29GHK2bCZMbvIGJ5H26QL8mz8LHp59kRWXXn1b8YO2wSEQ0QEE9uR8eahxmzxbA8hnN6JK8sdl/FKyL6xSY6tLfDRo3Ncu2uabbXTLLRdLh5tM9/y+LFL1xCMD9BTJ7WoI2qFEDj4CERch8t0dQ/bqnkvDfNQytDwMpTVPnn3mv1MLhRdvUW3aBOkrEE0y9GypdkG+jyhNfFpM1T06zajv0g9RYTbRCc9gGU5lzqyBj49jyk959mEWSeibEQgO4REhET2YEc6EBHP6lhkGWOAYphNS/XlMuk9PawoIpOlDA0vQ8MYzjb+8miUGB62wT95Flm6OC+f/vxNMmminaaXp+OHcfiKOvO2vlMURdrMhLw6iuqmvDK2fjlc/9pW3UszWCGQHcb8GZY7Lt2oje/U6ERNXOEz6k+nZmnYeC5lyF6fLe3eIUMXLoq2WdYzz7Oj88rZ+lwR6DZCns1QtN/oDprp/vP6homWqb3znMEsxzjPdjNdjXrLRAqjs47TaeF0O/FJWhpH8YyLBEF8cmH1+G0cnNjDsbUK3ajNrpFJTjZdWuEqNbfCfMuj6kr2jQlma7Opu8p2B2X8b2tQylAxHvJoJuWGGV8uXGzRHh42pyxLCGxOTpahYCtrql//m2csZTmWWZEzU5rOu6m8TUhNZU2ddlgDqkhHGcwTyC6eQdEWhScqqariE1qyTluJ0xveFAThpusF+uFKfQlLFKkwX+bmpaUMbcTmZGjjdRPvtns1DFACHLdU7ptDUeeriJGQJ3umdBOdvGs2noYxvEzjRtZYoufLGqtsaVnGkqmczpOJF1vdpv6dDhBsfFZqX6Wiul3EZZTB3gzVMQHbahexHixSdRvsHAlxhYfrqI1Lb1teYOeIo5YyljJkyJPFxz1DhgbzmpNLXW1Cno2sX7ONocMgKVNk7LfJrq3uPH5MtlBWmaK2hol+lu2Vx+ew19N9x9Am7VWojsU5HHqnFBZAN2rhV5LlhpDsr5FAIHrBBynVJqPJqSrRzgMstY9y5dQEy915RrxRLh/v4okanlvh0ok5PnZ0lIMTbe43bXcBJRFIB0SEiM9ZTGZ5SCKyAySlDBW7niVDRenn3U8BXCC6epMzPGyGgkmI9Wumgdj2sLMEVmp5sgwEW/1Z+fRrNsPAxGeRASltUCT00gaGTiurY+cZbiInn9T+KgRRpzeroxu1iy1JSaEVroIM1UZM+oyLyD7jwg+lOrb2bGHaFDUTpQwVq8tGgwLXs5wBG19FB4MSdujtrctJluNp0uMY0tNyrQ/kWXRMaVlOgU1u8gwN/a9JVk35wMx7nsGVrqOoMZPF08a2XwsW40BEns4Z/H1o5Q46UZOV7tzA8YMmdKImgezEpSU1dwwHl3a4RjNYoRM2CaXEd2o4uNS9cTxHUnVHKGXI9Dddz90vQ2Y+8mUov84S+chrW9hoL2TZ2TYMM37qY0Je/zDZMzqK2Dv67yyn01a2iP1mg6578srn2CXdFvPtI0q/tleGCDDDdSdPQtDmTPsIa8EikRwMliSbl4Kys3vBDhkSyi6+U6UZLnNktcJ6sEo7ElTdRu8kLd+BHSPFZ5xIZK++YrM8ShnaEhnaNLLGgAsXmwx4FH1AeYKtC5btgeidIe0E2YwBUxkbTzbkGU9S+5jy5tEsogjyrhe5J729NtYRaFPuWuEKyAiCjlo/mBzvmlNPKAMIOkji2RWOo4IlwuVk8zaVLQrURz+u1oZhlrPYyicfa7Blq2XIpPh0Wbl3yZB9gCnqVJjKDqa4zrn53PtgGrizBj2T06M/N9sAnhXoyht8TfKfpq1/z6KTlydNK493G3Q9kGUgmdLT7WrrL3o95rYPZTc2QCUREXOtwwY6gv4bQpV+2fhlVBy187/YoHcFX527naXOKbpRm4pTxxM+najJerCIJyqsdM/QDJb57qKHI1z2jBwEYC1YYKlzkkvGd1JxRuJ6z2cZKuo83ntlyF6v6bnZ6i119eaQbtes5362TlER/X8ukdfPTP3IxrMpAGQLKg5zv7psD9HmMoTOGgPPcfkUnaipghGORyA73LJ8i7F41Jv9IaHb5FE7d0FzEUe4uMJXm4ZKCd/+LBLJP9x8mmPrh+JlKqNUnDqtcJXl7jyeU+H2lS4n1+EzJyp4wuVRO6YBtdn00bUzPH5PRN2tEslBe77Ispa7D/dyGcq1zRKaWXmzfGs7L+dKV59v2AKWihicOtJKXk8vUtZWn+5YmujlCb4pr20wSgv0sANSVkc05ctKy8pT3AD0RIVu1KIdrQEw6m8jcAUIp79hkmlnmziQ0AxXAIkrfLqOVNOZuy116kq3BUGb2fp+jR0LPdspLcMgvawlF3oQK/k+rAxpstJehbX5fht0W7A612uPwbrv+TKUXzZt3BUx9s/W0LtQkcjkZoJzOg3T9ywaOh+mMnnOp2lcSdPS+dTryTI2svjUaZig6wrdqSxCe1g5N/fPcX97b+mgg8u22kWG/JJvnLldS1dHFp5uLeAKj8XOiTjgrfjYP+bTDJcIZIf59hEAqm6DhjdFIDvUvTF8p8Zjdu1lsXM6phhxfP0Mk5WdVJ0GNXc0NqDPXxnqRq3e7BUQzLePstw9zcnmbcy3j26Szyye9et3vwwVy5/nRNydjtA9CTY9m/7o9maek2NybvQ6s+Qx6zkWfdY2Z1OXbxt/aT71PpDVH0z92pQvi/esevLKxNeFA5URaC4r+w5g28XsdHZSdRvgqRNUDoztM5SO+I/bT8DKScWDX8NrNVmqwhdPhQghuGP1ZtbCZcTBhwHwmJ3rHFqu0omaHFu/EyEcRrwJpirb6YRNZutdJqsBv3hlnRsW/F49152scdHodka8SRr+FI7InkktiZBSqr9xO5iOw91aGerXvdg5zpn2nQSyQydqctPSLVw/fxtfPn2IO1ZvoR/IvxfIUGbf1MeLLF1vGheGDdzcO7EFAQ9daIoa2bpyzyuTridLMNOGfpbiz0rT6zQZITbjYZigj+l70bJ57T2ME93n2Xdqvc2PPOGrPTxcD4HYGHRIZnyEAW3ZSm3KJPnGmeO0wjVCzxvI341anOkc69OKgs0FM84W1jq3SIZkqIIcQUe1U3tVfW+vqt+tZWguqjSjkVDoJizfi5Y9NzKUXzaLnt7mWnsIgeOem8+9C1nPVg8cFDEO0nSyHMnk+zB61lSnKdhicgJIXdP7bZZznc6r82cy0Ex86bxkOSp5TktWnVnPcPBZCO05hDJgLVjkmm2XbCg74k1wydhldKM2H7xT8t3FO3vBjWQKdd0dZ7q6B1C79AdSzfYb8SaZqGzHwWXPyOUxVYfLxi9N8ZClO+9+GQpkly+eOsli+zhfnbudZrjMSrdJM1jho0frvOjjVc6077TwkqZ5T5ch/bspT54TYChf6moDioypWfJWhH6BZ2O9njVWFKFns8l1Ojo9XT6H8SPONYZov/q4epmFUPt3VMfUUhbh4uAOnLQiiWhHa8y3j/K02Rq013v05If+g4mOww9sn6YdrvGqr4/yoTvXWZZLOIe/TjcSuELtd7e3sb9HU20Y7TFZmWZ7fQ+eU+HRO7chhEDg8KSLJkhmceTN5hgOWydDaq5iSDNY5g3fkty4EPHGG+ZY7BznG2dGOLLm86pPTfE7zx/j5qXbUvTu6TJksv1t/SkP+QGlwUsXhp7e4lNabA8jb/AvQt+W32b46tfSvNkMimFg6rxZ954HWwTc1hH0um1OYlEHup+n4U3Zs6UDBclRs16FqqwwKsZJZlNcM7OHUHZxNRGrygpT1d1YAw5ZwY+tCoz0ZqmYZPZsZEhtBgiiv2xGpGaZhB02oLUct9m9S4byrxU1ms4/pXnPwDBGrx4cyHLGkt8mZ1T/rtPKkr9hZCcr3zD9x1ZvXluZdIbJqE+n24IvJj1ikn9Tf86iodKT6dBH128y8Kv2Zvro0TkWO/DhI3X+54hDK1yl4U/xldMjah+mGK7wqToNHNzBDal7NO9ZMhREHX7tHy7ipV+osLvRpeaOsm/0MnbUL+EZB8Z55MVdnvPuSRY6xzZR771HhjZeT/Nl+11icxjWDi1SRmqfotfOhicYrm8MG6RJYxi+TXJfRG/Y2i5NN+Z5aq+VWhJkUKe2BFScOjV3lOtWVwi37SWZsSB+8GnQbdF0Q177jYCFM1Xe9oUxfvdLAa09V7B7ZJT/POyzFiz0aHuiQt0bx3V8fKe2sW5RrE0FzsBn4I7j428H07dKhiR0mwip9gvpRE1u/YOIV79ijH1jEVWnwZMuqvDonVX+4gdPcer7Jvjj59RSY9u9SIYKwzTO2nyaEmfpQaadQ9PAmOU8pvMOEUHN7Vg67aL1bTbwYaOT/p1FU28bk9DrBo+tHtt3W/sO2xEGaZ0OjhPIrlrK4nrg+r2NQj1cVrqn4xkMfXREQCtYUT8cb3Bz0rtqpkevnq2VoWQQC2RHtYfjgV/beF/p3zKC9UW1sWs/g85wDq/nswzp7bUZhT5YxnHOzefehSw9bHNibHJmM5KTMrb6i+iWrLFjM7Rt10xGgKlsEdjKZjmHCQ/D0C36PPRr6b4maYdrzNT2DJSbbx8hlAE3Lh5jthawbzTkmpmAJ+4NWe6eou6O8ZSL91BzGym6JsPtnitDkog9Ny1w4/XbCCWkZ8e4wuPScTXL5aVftOnVC0WGitRlrqfU1UWQfuZFA3xp5NkHSZ6sIFvWGH82TljRQOSwdnfRdtHL6Dohz7bJ6vu2wG5+ewkc3Fu/imiv4QqfB2ybxMXpzaw7Gh5HnjnKBw6vcPEoHNzT4jH3XeX3H6L21hv1p/nDh84wUdmxga7AKRzcGBb6UbhbKkMy6i37doTbC6pMn1onkmJgdozveFxxpQr2/Pxvbo9nl99bZMjGo+m7yW/dnExeCHr6LFlKN2Ty4GxOkw6TMaIbONLwyetYet22wcRUVxYvWchT2kWVj+nes+iaytnqNBmsuqKSBLLDiebNvbxzrcP9zUzDQaUy5s/gCk+tUURAt0lXhIQyQArBzvpBWk5qd2mhFHrdGx/csHQYnE1QpDfjQpehtHwNVJa6nvy1y1CioEPZJUrWFha5x2QD14G6dNxzZKiYwWMz6PN4KFEMJj08rAzoz9H0PYtmlvFjo53Fj03v6+OQjVaW06iPY6b0PN5M/KTrtPFgo53dttJ6P4P6Kll68vrrj/c2Na24dQLZ4eDkDlYDl/1jHR6762K2VffyrXmPV37leDwO6Dzee2To169bo1tx2b5rnTF/EoCIiOXuaW5cPMYXT7kszlcJwzSte5MM2erWy+v863mLONwlNkJ/jqb2LRrgynJ8NmM7DHNNh0lus/LqvsCwQZ6zGT9sdaVtvTz9lLreWUN+65OA2ohU3v6leDNTUku+45I7DkBlhBpVPKfK09+5ykL7GHSbjPmTtA5cyTMO7GaxDQ+ekfza/aeZaru855DkCX8T8oWTxzfspyFynvXZBkIEAn3WR3HkPNfEtu+2EGHID7xmW+/SFZPqFJpAdrhzdYV33FLjy59UwR43iOJA0b1EhjZcM9Vl0sG2MiUSbOLsT5tysgmCMHzHkNeWbjO0TAGTLF5NxoiJt80IS5ZBk9WR9Ppt924aFEz5dNqmZ2NzPtX19WCRnZX9armKjJip7o2XZ8SzFpKggYzwnWqsAAURIStymQlnhwqMyC7IiBruQE01pw7ddj8QkNCLaSp2U9+Nt2vZ6DQrv3DUjIuBo3WLKMXiMhSh9igRCDXlO+xAZ50N0Gd4uBU1E2SAnqn+LN7OHxkyl9fzFB2cBh0HIcBxSoWeDVO769f1a7ZB1zRgm56tTjcLNqM8i67J0cuSQ5PDaRtDigQGhwnImfpZViAgq93tdA6v3srexn7WgoXekYPehqMPVX7fqfFr99+NJOJE8xZCKXnnraP8zMGIR+2Y5T2HFtg3ugjA0TWPgxOw2nXoVFtUnJqBx3u+DC0tVnjEc9d5/n3XGPcvpxu1+OzJU7z7kMfhwzMq2DEveNqBtDF975Ihs3OaVU9eXamUUldnwNameeM0OdfT5YexG7Ck2eSniK1vG+PzZMJkR6RpZDmIOvIcWpvNUtQf0PhbOEJ01aNAdvn0ieM86uL7sdg5Tq07SsUZwXHVzAVJhKiNg3DBdamE8N5rb0fWruDrZ25nrevymk/UeN2Tb+Xn7zvGb38x4tpdc4iqw2ePOVx23wUWOy7rwRIj3gTJHnvmDUW1OzYEPaS0t2ey/4crvNTRupuVMdP1GGGglnjHM6N33tHiWw/fzR/81FH2NMZoh2u8+hs+33jHdnYfWuJKjgOw72db+M60znUGL8n181SGrGVs91CUftYzvjB09SZCdaaHaxtQ0+l5g3eSxzYYC+16+prJKbPVYxKQrDJ5nSLrepYAmdrGZNQn6cMIY1ZHE73P4FGFqo5xfzu4Hqe7RwmTc4XCjpqBkN6EM31yCyAQ+E4Vus3hZm+c7XGzPQYc86cyooIJXkUtuSms6LJkQFdQ/eueU0EkbWO6N7cS81LpL+npzTox3lgGj3e/DJmVr94PiwQ3su4/a2AtMYgiTqRMfbKCB+l0mwGblhNp+BQ1bk3Ooi5jOv86L+nfaX5t+fMMkyyjQ+ddp5Nn4Bcdg9J/+zT/5OvHqLoghIMnKiy0j7HWXWA9WMqoS/LxY0d4/fUNPOHiCvjmfMTnT57i6Bqcbp3GdXz2NAK21UI+cnSEd922gH1cvefK0HUnb+fEkQY/d59VdtYvAxlypn0n95ls8ev3X+MVTzjDfa+e5x9//XaefPFu7o0yZE8zIesZFHUcSiiY9FMaeeOlTX7StE19IX1dp5POa6KfVafpuumeitRn41WnUcQmGNZ2MN2rqf3iPDLUrgnYcQWu8Hnzd09z6bh6+SVwuGF+nTPtY5xp3amCHTjKDkyjOsqffuMEv/DWfVTdCM+PeO+hEd7y3ZA7bx/jm/PqJePlUxF7RiVv/UqDX/uMdprhJiGE2PBxhEvFreM7NXynagh2ZFI0fM/yB1H2cmUEgPccXWRyrsnLf+IY37NrBEe4fOm0ww/sDXjBi5Z4xB87HDswwZP+1OFVD6tm8HGey9CGvLYx0lRH8j1P/2bZ8hcWNjHDI4GuUE2NmdUxstJNBotezhZsybumGygmPrMUsH49T/nbYArc2Mpm8arTzDNM+vXNt4+yrbqXgZkVcbvN1C5G4MQbjyZFopSDDkJKWtE6NbeBRMYbdmYon6Qev7Zhb4/+rebM7rDBOIOjd9H+O72xqJmwJW1j1NTBpeo01AyX5srGo3X9GlRHVZ3JpqYySs3uuOfJUH6/MfGWRSOfH8c15ywB5sHPZHzaDGL9et6AurEfmOvWn7fNOc0KZAyDIvybHMI8YwPsMm0aE3WH0iT3pjzqeii7uMInIsJJzZb7zQfM4oiddKM2oQyoug2EEDTDFTynSsWpG/lph4JrtsH2+gH2NI7zpVMeO0eg7sH7Dzf4xStrPG53f9M9aZWTe64MBbLL185UeOSD55mu7uylz9T24Yr4RLIxwf97TIjDzoK8na8yZKvXxp+NRt6zNN9nqauLwCYvaRvW1gf07+m/UvtrQ5Y9nFVvFi+mvioM37Ngk9Ui96TzZaNjyp93Pf5+59cJLro/rvAIok68UajK89z77MBhN6HsEsous3VVbr7dpeGvUnfHkUIgVk/D6Kwi2W2x3G3wwIef4v7btnPR7iU+9vUJdl+8wkijy199fox/eVqD33zAuFo2/sAIUC/YNi5tUbNIiiI5QcbdMENQbzM9NerVZ0eWDKXgVWB0hma4wlv+W3DmgTUetv0QUAfg0TurjPrbEAgiGfK//7SDYCoe62w4z2Uo039Fy4slfTj72YYLQVdvYoZHuuHTn/T1osKUppeGja7NISxiJGelZwl0ul6bkk4r8GLCtTFvFm1TWp4xY8nTi0oLFezottRUsmTmRlxO4BDIDsvd04R+hYFlJ4lyDYNY4QkcXEa8CZXu1wePo00jjIMAXiXe6NQz724zzF4dyZKQ3gwOg+zIEIK2eXNQa4Alyxg055dEahmLHuwA1dbt+NSD+HSbwWDHXSlDWTT1cragxjB1pXnLo2Hjp8TwGKatE5j0qU3nDAObY5vFo0lmbLSLptvGrrz8yfUieiHP6NlIPyLiTHwsrJ7/VPO2gRTlmDtUnDpj/gyjnprSe8tS2Nv4zkTn4dsFj929hsBhuSu4dFyy2IYf3rfKz10xjoPalC7552wwEe75MvTRoyfxHXjOwfV4lqIA4dAO1/jW/O29fP17P99lyJbHRs8UxDCNJSac7fMrYZbf9DWT7Wv6ni6TvmZzjnTotq8tf55cFLEJznYcSsoN41vo7aLTsdVptlUC2eWO1Zv76Rc/mHa4RigDvjp3goj+0jcHFzpruEe/zU53NztHpnAFfOzYCEGkXvaFMojtYanswam9/OzBdV58/zWqkcfCisdlly+xOF/jVx6xwt8+pQmo07L8OKitO/vpTUVNp6xsbCEnnsFRi4Mdugwlv9T+fuoeN4Miz1+AjPi9rygb+ed//DQCtzeezbWW+KebTxLJsHdiWHawQ3G+kY+7T4bMtPPSTQHIdHpR37bU3Ztc0lKkgfV8RZx0MHe4PKc/LQy2wSLLacviq4gTaesUWWVsv02Dlux/wiA+59tEW6MXJktLUunC2fhbRsr5ToIMcVDCEz5T7ixuqAUEktkZrkfdafR2SHaFx3fWjrHSncON6NNLZl94lY3T+NLIOs0kCbjov/1aPKsj9QxkGH/6uz4TdAxLbYoqgKRtbcZBP10E3X6wwxRI6bbUqSwbnuG5liFTXSbaRR3HLIPNdt3mBOWhnydZa3guPhcWsgbjooE0Xe+a6GU5ajZD3ZbfRC+PP1OfldpHz1+Ej2H6qX0sO7p+E52oCUgcXHVkdyrPfPsooGYftKM1ljone0Z3M1xmrnUH7WiNm5aO0o3aNEOH9WDRytV0dY9awgHcsACHVwU/fRAuahyk5o7m3JOOe6YMnWq6PGZnk+31BlIqQ14iWe3Oc/18jV+77hg3LNya4vn8lqHsPDq/NsMZ7frZo9TVWbCNk6YxWA+u2Z5TnoOlXxumHYvaBaZrebaNrV8M6yjaIGKqEaHs0o1amGc/DPb1UHb58ulD8dHcEk9U2NvYP1Di9pV5IhlyzcwsrWCFudYdfOr4HQCsiCa3jDdYFmt84HCbhbbLWhdOtxYAcISLnNgB3RYSCX6dS798PQfbktB1uOmGaY4cafBnP3Ca79m5i8nKTsNx4Gnuze2iHzOrwtguDi6+Ux2YOQhqqbt6dRfSiZp0oibdqE0kQ8PmoEVQ/Pl3ZZdDx2s8/cErPHhGPadu1CaIOty2XOW/vl3jib/T5Z23HdOe4V0jQ8Xy6f6nyVfNgs3mMY1FpqCH7jtCnm4/V7r6fMMmt9stKkR5Bo9NudvKph+giZ7+gG1ptjpNBowJeQOOKb8tqGIx3sKgfypKmNo/I3Gme0tC0oaM1mZuPD0tDPoBgIF6RS8IEZC6npRD9DcqHWDbYS1Y5GPHjxIQDkyDu+/kJYx5U2rWRnqZidtfCpOLooEOv67RTLVVEuTQ6cqI9CyXfJ5sinSjDK0G80jPZyDQYyQZzwIxbWi6ZTKUR8uELONeH7RMRr2ND1OdNuPMdo8lNoc8p8n0XGzOko22PsDreUyyMkyaSYenr5vSbbyYyurXdaeiiPFUdDwYbKs9IweV4RvXMWh8SmZr+1S6cGkFq0xUdrBv9DJAUHfHmKldDMDuRp1Ihlw5FahTsArU/3NXNPmhfet8bS7vhKh7jwx9/cwhbl2GZuAQRB060TrNYJm51mGuO1nj0AosrHi8/GPJCQHnvwwVv67zZMpnsp9s9E0BoRLFYZLrYYMDWXnzrul1Fs2jO1E2/VBUNrL8Br0+U7qeGhERqlNS4iBHIDsEUYdIhvSXX28smf7u4vCg2YtjGgFRz9bt57tq6lI8p0JVVlgN5pmp7ePaXUonj/nbuGz8MqSUPGz7GkEkeNzuNlPVBszfQfSWNyPaa+BVSGZiiO97OnL7pQjh8PIfPc6vPHqJ9x5qDBzTamxBzdbUgx/JjL0EyawOkZrF1g8ItXtBhg0tJKOBQENSvtjymRwZmjvEx46d5tbvTLLcUZtmL7QdzrTafP1Ml3+6scptN00ydXKN9/9GRDtcZ3AfQltden2mdBOK9J2sMrpflh4DbX0GzGOYKZ9Nl5vs9hJwVnt46NAH980oujy6pjJppWur32YAmPgcVkCGue+MThimZh+k97FInPZU0CH0PFzhqXID+28M1iGJEFHUDz4YeYaQSCnDMIhnYAxebzkhQgiqIt57QzhUnQYPnQ37fKTvUbhKEbnugELcEIFO36fjqH0tbLDu7RE/+7A7uIxk2ONrM/fySOTLpqzUfVUdtZdJpgQlQSDHUW2ZzHwx0LPzMsC4IU2XR/1vURm3BNIy6zX1R51vU9kiA48qcyGsNTx3sA2mOmyOW/qaKb+tLtN32/UitLPq0tNsDngeDZujbcqfxaOJD4XV4Aw1d8xgzA7mdXBxHY9u1FKbQ6dQdRpUKyNERNyydIhx37QUQ9dhcN/JSwHJ7pEla73Z95OH80+GPnykxie/NM2DZubYMbJOKAPWgogvnmrwodtdbvnOFO1ll3/8lUPAZEY9RfhN8pxbGbLXXfQ52fLa+lNxp6HU1TpMTsowNqhpHDXlH8YuNQW3smhn8Wsb14vwWSTdXG8ouyRBY0H6yG427G9RCSJkpRrXomYzqECzVl8UsRCcYNSf1va2GGxXp6tsYoFDM1ym7g4GnCfaMOFM051s8D9H5rhswoOxKbynPBa8GojBILdAIKTkB3fvBNfj4dvPIKS0Nod+v6Y9PfT86fpAqICOdSnkIHJt3EIwUGhM8/YvdrnfF45x/cM9Lhn3WAsc5loe//6dKp13RVy1dAyAp7xO7S/Y3zvFzOkwMjTctSJlsvqFrW8O2+9s5Ye3+S8EXX0We3joaTan0BbJyhs4bbSyyulK3saHyfDS70Gvy/bXZnCZjAWtThkNnnwCIBxatOk4EVF6H4zUzIZIhnSiFkgVxx6E1j69YEda6AeF3xW+ihB7VTZ2DsGRtRMqMi6EctCjAC+UjPrTKUN9sJxAvUFLIu2Fz+7WZ0fER1QNzJzwKgw8q7Ry12eEWGGQ4/T+JLlLXzY+T9+p4nS0pSrpE2Pie1HLcCpqA9PeJqublKFchV3EmDb1u6w+V8SAsl036Yo0v7b+e/ZD7IUBez83G9qbbVfTs7LVYXrWOnQaWQO+SH1Mul8vYxr09e+68aAbJCZn0kZHh70fJ5vF2Xnu0xj1tmnG3WDdDi57Gtt43+3LBLLDxmeQbrc+RrxJSz69DgbSW+GakZ4Jc63DrAZnONG8OV4CmdC6a2Wo5sLoeJcgEgSRoBVGHFmt8MXTcNOnpnjsI+Z4yy/dzsGJyxjUPeevDJnTs4xhGx2hfWz0TDyejS65EGGzj01j72bK2GTIRNtkV+TVb/uu0zDpaROfWX6CnpakRGrmRrwkTdUmcNcW8ZpreGGclrIHfacGYYBYX1J2abe9gW6vLkcdxaqWkWTon3gfttnaPuru2Ab+5de+AN0WvvB55A74rS+ErIs2zF5isP9iCEfNAA46jImxDTatzZ5OBzPWggVc4VFx6vjS7e3v4TnJ/ShEhBxauYPFzgmunz+e0s+DdDcumYn3rOv9inqzagb3+jC120a5kfUx1ldVYCnoOgSRYKHtccMCnPx0nbGlNjc/dgdP+3PB0w/MUHNH43sx1VNMhgZ5yetfWXKfZTfb+rPOg16njYd0/iI2kMmGvzCxiRkeeYYoDDo1eppexpQXS1oaJgPEpMCFJd2ULy+/npYlQDbDKCWc6Y1AU6i4dZIt40CbeiejeDdltdQk+WuqO5mS5gt9Y8z0X4VkKnXvyKwUr5eNX0Yyrc9xXRw8cH0kIcQKb6lzAs+pxJvnqfrVm0jTgJZm1TJzw1MR+EB2iKIWFRnfp1/r856US594kszUsNEdCIhINavFSQVZgk5/EAo6WnAFNQi58RG3YbcfUAraaoZJmHFMmOulNillwyBmYNbyPatfpK+b+l+WbOfRNdE2OQV6/nR9eY6UiY/+dyHAPQ/XBp5fyBrgbUaBLc1GK/2MbAZtGmk5zBt4h+VR56EonfQ1Wz8xlTXVpdOy8T3YZr2Nnq30zPraVlfDm+R/7Xe1mXdZRhLaNf23+T4+cOcRHrnDo+Y2jNf134dXI3Y3znBoucK2anpm4HAyFKHWkXvW0wRMPPS/uw5MzzTZP9ah4VVohm1GvIhxH7wg4iVXu0xULrHwcH7KUD5/w8lQv0zWM8mXoVJXm2ALiunf837bbGtTGZtdbKJto5OmNWywzaavi8CeT8QbLEudfn1S/XU9iJ33JOgREeJ4FbUcmrD/d0OdElqrTDICvtNPM/Y/0Z+lu2GmsEBc+8PKLuysM+3t4NUPm1fLDoOuyus4cPxGdSTrzIHUfaTHBnM7pE9jSc/s+Ksb5vmpyzu43Q4Eq4T1UaSM8Jz+8hn1IlLR/eKpEfY2unxrocLByS6+UzPOEhHCSfkKknSgSeDQjZpEMqTqjsSzERN7XaEdreGJCq7wCWW3R68btZRujwTLUzWu3dVhRz3gdMtjsiJYnVD+wHt/4U5ma/t7914MebJWxC9MkOVLSu13kl+XF4sM5dLK4zErLdu2v1B09VkuackKVqT/6tdNCrpIcKGowjXxaIOJbpay16/bOovBEZRh3zlPFJ3rDTjJoQOtYJmq2+hFqAcQBkrdiNhRz5i6FhGqY1I33K9+T4q/iHBgDXkUL0NJjoFyhaeOw3IEnXC5Fwl2cKk49Q07OKu1kl2kjBDC0YxUtTlSKAM8DHOpumpHak84hK6n2i19okkyO8bxiFwHcHA6gQpGbNicNF1tSknqeZINYbuounpLTbTnkAREZBTHe+I9OZIyxr05UPxWRtgY6BhChjYYuFlGtg1ZwQW0ayb+7DJkLps2lrPqshnegzAd6lMiQZ5TZkKWYZrOYxuEswb6rGdfNHCRld+UnuXgme51GMcxj4+ssUYfQ2xtqNc1XJuot4w2Wum/6b6bTVO/9oQ9s/HsvSg+zSS7bfaNeSx1Akb9KPXGdXgZum35EP9zZIRnX15hzN9mqc/eXg+bbTNbg/tObcMRLn6wij+6xA9dDI0fm6fS27j1wpah/HvOkyGFUlfbkKUPi9iraZgCDEVs76K28Vb8zrPlsxxI9V0iextnOqJ/jGp6RoLb7cDqHDSmCbSlf4Aq77k4sbOf2KyDL/qSzAE0pjWebPyTekEW50lenglH0fJqEHaYEOOwdFLZicJRZerj8fL1FO0wgKClbF/Xg0ralpeIMISwCRX1gjQd+HnufSqEUtByJTVRQyI3BDvCqKvaMIx4wt4WXznts3tEndBoWxKjZiMqHiWyZ9uDWlb0X3fM85brxnnTM25ne+0SI41keVCyNCaUXTpRE9+p8fQHLfHxaY8fv2wVgctkNcB3qjzzKSd5X2MHVWdee07DyZC5z9meaxF/z1afTlsvn+U7Z/GUpNnuyXYtP5hxIejqswx4FIkI2QwSU76s9Lxgh42GjeYwvBQZiPSyWl4Z9vfjSGYfRKhFRan9K1a784x4E2oanYgVeRIgMWwe2rtuuJ+NwY50h9nIp6MFHsIoNZA4PgLR2yXaEWoDqIpTRwiHTrjeWwe5FizQ8KaIZEgrXKHijMS19BVCN2rhOr4KdvSWr9BX7tBrLzdicKZFvJTHBYgConjxmZMEkWSqrfQ2SmiHgRqEhINxZdeGNpWD9JD9YFWo1nDiePZgR5pe0FFlqolxbTMUTPJmUr5FAgpZg4Lpu+m3XmcRPnWlm3eveQNSiXyYBnpbHptDlzX46/Jmc/SyHEKbMWLKM8z92H6beNLTirRJEfoiI7/JQSxiqKXzqd/rwRKtcJXp6t4U7TQPeQ54Vptmt7kQDgObP+fI0HR1D6vdW7lqel88TmxOhkb9iMftbrIeNBnzZww8mtq9T+shswe4ZqbTC777lRqVsI47Ns+TLmrhCd3BuTfLkI12moezkaESZuSN0TbYxk7TczPRzLOfs+oy8VfUlhiWt422QEREEHVIZiQkwQ9HMLhnxdztMLUXqqOIeGNS5VhvnAmiHGfbC0MBjW32a6Z79NIBFknHiVjrnmQqbKgXXa7f9yq9+MVcbVzZkeuL/WDJ/B0wfbG6vjIHI5Mb26i9Cl6NqFLrHZ8t4+CNI1x8p0YUqr331J5//f30IkKcbofQjV9kRiHbvT1U3ZM8YY/tnhUSHyEJmKhlMvTadrwS8lMPX+HIqmC7YWuNtI+RvEDthOsIBK7weOYldZ66b42Kq/iouh221VZo+DUaTzxN1R3vbUCrThUrLkNmPWizU/WyNmTJdFb5rDqyeMr6buNlmH5/78YWbVqa5aTYBk3diTE5QyZ6pvSsoEiW4rY5ilkGv00ITWXj/MlshAKY8GcgiuLZFglJh8BRx2Ilm4b2FGdqCl0gu7jCY3BJSnrmht72YGr/RJl5ToW51mEa3qSaepZaa151Gkqpxkav6/hIKWmGy0gi2tEaUko8ES9NiToEdKi6IwhEfy26SLVRgjj4E3oe3agdl0nlCwMiR+LG9+1FotdOvdkypiNu0zMz0vWly/gqCj+IlPz6tX6bJ0GTMFDBi8764Mapev1pRPGRub5pw6U8IwZDuknmiyq8rLqy+qspT9YAYPqdVb+BlgDHLZX35mFyltLpGP4m103XbM8+S46y6jc5ZDanqqhMp2noZfU69bz6tSKw5TU5hXlGkd3hfN031/nRS9pMV233IAhkh9uWDwMwXfUJZcBcvM1Q3YuIpAoi7KwfzOB7I1+e8A3LSuwyJHC4ePTSeGwaToZC2WU9WIoDHIKdI3Vje5jKR4QcX7+VPSOXA/Dl04c4ONlg3J8FlAFedRu0wlUa/pp22sG9X4YG0212kalMAbqlrjbANH4m6Zt1UHQbOCtfHn39OZryDmNPFK3fPv7LONjRy5nqo/1gRvx7932VEx901Po1QCDUzI9Kg0D2gyaDtTvQWVP2WM/OjakGnVQwwxRk0u49npnxE/8V8paneio44apl4CQzJBrTUB1Fej7fOHM7I17EjpExgvYR1qttFucPMV4JoQZVcZqd7QlFZ2Ra0YlnCittmjo1JQ7++E6VWuTC+irUK7226M3sCDrgqnuKKmr5yqN32oMdQjjxMkR6ywoTtMM1VrpzTFf3MlkJuXi0Q9XVdYNqK9+pkcymSYIdnz6xwhP27KQTNfnrbzd58sVtLh5VL2td4VNzxxj3W0xXPYRQdCIZpJbM9J+iXp/ZRzTeofXeN2KY/pnVf4rQGaauYXzqjUUvBF29yYBHXiPqwpbn9OQ1dJZTp9eXvlZ0cM9Szum/WYKaRyf5qW3KqSOKkK5LJ1yn5tR7zr+HSydqURG2R9ZfdtIjRUQkQy0amjgS8XcZ9ZVwElaI1/q1wlUmKztJorfJOjvfqfXW3bWCVWreKGotYYiUUW/Kmiu83oCUfO9GbSqxkloPFql744MDkOPQlV3a0RqjTOKS2khVhmowcT0EIb3nm7GsZ6DdkzZPToOJN2DtXZdR/wjdzjob12Om0FuW5EG9NnjKTha6LTVYDZzM0iNKIRnakJ7nCA6h+DL7pq1fFQlW2NKKBkRs10oMooiDYwsq2HRmkfY3OXpFxgk9+JFnjOu868aMKVBiq9PkiGY5rjovw8AWtLG1tz7ebMQzDjSZqoapMgn9Pp3F9nF+94XjLM3UEfd3WTpaYfdtiyzOjrA8VaOx0sHbL3n29x7jafsFk5VdBr5M7TO8DJkcDT2PqW41U3CVUX+anXV1JO96sEggu5YNBfvPViCYrMz2rlw6UaUdroOvXhCsB4u0wlUW2xH7x/Ya6N27ZSibHz2AZaNXlH6JzY3BtnSRca0IfZNdUGT8L0pfx9kFZdL6w6ZL1IwFoLmIGJvpzfyIKjWc9QVEfdRYDqSyzdwkOCBUoCMKVKBhbLvx3qxLYSojvOoJh1jpRoxXZpVt2F5RddTGVQCltUx3ZJTXP0/ZgscOjDBzNKLSGRkgtzxV44Ev6PDrV1cZbS33Z54sHoOx7QjXHwh6uK11WJsnmrkY6U2mlpAof8B1fHD6s7eTmdxuxr5I/T0F6bWpI9yef3FyHSYrIQ+e3YdEMte6g3a0Fs8y19tMAmovQs+p8rDt6yTLYx6/p8V8y+Oi0YhO2ORks83xdZ/Dq1WecnEHgYMnKnhG+7nPbVKT2XfEcD39u6jdosuyzf7N62N59WwGpnG6tJ+38JSWLMO6KJ0izp5t0E/S0kJoUq56J9AFdrMGf8a1IY9IFVIOBDtwfVbiHZftkFScWjwQqPtwcPFEhZo7qjb/JFR89hxzQSedHtNJZokk+3Isd07xnYUjRKglLA5uvDeHRAiBlDKeRlfFd2p4oqL+OhU8p6KWwwi1uVI3atGJWnSjVm/XaFBH40YCIgHH1u/ksyfW+YdbTnJD/HYSJAiHwBVqOUvcFoEreuskI89L7XqtEHmeGlzSS1n0wITj9QMdybNyK6nnZgla9TZJdVUAozKyMV/62QtH0d2ALKWcRpYhrsv+sI6nqR5bYEPvM/p1m5K29WVT3eZyAjW56Vx87n2wDfJ6G6eDBUVoDHPd5Iymv2fJVBb/et68gF/yN69f2MYAEy19rLHVn04vYsDojqb5+lVTl8QzM5KckutO3s4LP3eME82bOdM+wq3LEieSzG9v0Gq6hFWHI5dN4d8HHnntCa568jwXX7LMO784zo+8bdKwQ3//XtaDJW5YuJXnPP0If/DVY5xLGQpSfPhOldnavpSTI6l5Y6nghF2GBA6N+BSaCGVE+06VZEr8p493+MiRGrP1EdaDxQye7o0ylFeXySHQ6dnHrlJXD4NhnJs8+corq9dbpJ4859FURkeWnNn4S0qqssJiSwvh9D7SdYlSwQ5XeDinbhuYTatmhWjLlhvb1OyJhE+vosqMzqigR7zHhiqnnpdYX1Ib1qfp+HUQDgfru9gTTSFP3IT85IcgDHjfXIdHvsHlzrWbWKpE3Bwfswqw7fgalY4KYC9P1Tj0xFluffx2Dh+c5nNvbfCsF7nK3lxfgNZqbE+qYIfoNGkFK1x34gjP+ckV7qgFtF/6pzgf/Y8UZw5et6uO0a2MIISDH4R4TkUFH0K54cQwL5R4uL1gSHL8bydcj5+WpOFPcdX0voHj0sf97VQc80w8B7enfwGmKrvwRAVHeMy3PSarAWEUIJG84YY6f/fVBtdsa3GqGWqnsqSRL0Mbr2Xp2Dzk+apZPmg6T15QvEifyaKbR6d/9ULQ05uc4WEzBhKYghK6sizq5Ol085RtVsRuWMdvMzzodOL8Tuo0lbTSjgyzAlJpoQMukjFnYnC5oTWIMsjbyeatjFdmY4VSoRM1Y0Wk+AplFxEJnN4hzP3y3ahNzR3l68unuWQ82U25TTNYpuFPUXNHe0ouUZSOUMrMwYWwq6i5fm9zKIGgE60TRgHV1A7/a8ECoQyoOiPM1mbZtyu+lj4mNoroyHUiQuruOC5qOUvoRLiur6J3YWrwEQ4rnTkqbj0+G1322y7Zv6M3y8PrbwKbbCTVQ1wuHSzp7cGRam99/44kgOJ4/Xpdn+GVrimanCfPRQzaNG1bWVuerHSRcz19P1nRb4H53kvYkTfwmwbDPAMhyxg/2ze7WQO+XndeQM1GKytImDVWme7d1h62dtfzFnFssvi01/WV03fwx6+cZNuJNX7mwE4WdjVozLdpP9jnvvc9w+R4l5oLp5Y8tk8EPHqHxHckNy7C5xYDFs9U+YebT/Pki1tsrx0YoB/ILi/8dIvwL6r4v+bysgftzOFZvzaMDAla4QqjnnqT2d/Zv2+uOJlLY8x1N4Nlblt2uWamQTNYZqV7hqdcfCnm9rwwZchOp8j9lMhHkbG0iJ0qC+TT6Q8D07icZbfo47QuN1mwOYrJN6c3m0D9FhuWtQxyEgc7haOce98nnN3fpycc0qebmKHuR373OsSuS9XLrIoHa/OI9Eam6Y1H9Xttr8LYDuT/fABx9UE+sbTC+37d4QpO8LJPjaB2x1f28633m8XZBZ4fsXbSp7Gjy7UPPEPNhW8fr3B0qcGOO5d5w+0hz7lCMHbmxOCGqs1lnv9ZH//vXE7/yCR7Gzvgj19MiESdhxUpu3x9UQVuJnaC5xL6FdxuG9ergOcRpQLNAgGnblZ7iEzu6bWXQLAazFN1GzjCZblzipo7RtVVL/wc3HhGeZ8SqGBJ8gxDGag8qSY70z7GF0+N8pwr2qwGSxxeqfBnj5iKgxxjqDkmRfyeLN8yy24+Gx1mC3xn+aMY/haFia7peqmb0ziLY2mHcbayDJ08gcirxxZ5zotI2wZyU515hrYNGYZekfBXFC8PkdqxWem9KFI8KqUW0Q7XaIWrTFV2M16Z7W0sCiravdKdi9dDQ8WpE8pg4ISWZNfkilMnkB2u3jaNlOrM84pTp+aNqs1Gwy5uby8RSbIsREQRkDom1unv/yGFVHt7OBUC2WY1aMfR3AhPVNQxjenAUBxkaEVNqu4IVdnoBVWSCFCyczeAnzwrr8rHjh3m2l0747Xmss9Pb7PSIeE40IqXuugbjnZbG/O7lXjtZ+r5y1ANksbTWkzICtaZ0m35bQaIrW8VMWyHCfxl5dF51vtu6rcA4Zytk30hwKZ3TUEEm3OUNqwxlLHVYdLhRfjL46tI/UWcQZ1e1neddtb1YerIa7si9am868ESP/JPPt7nJ1kbr7I4M8L0VW2u3HcCgJ112DkCNRcqjsTZ0aXuRczWAhwh6UZVTuxVwdq/fM9O/q4ZcPDaU7zl8Tt6df/w27vMndrOh99xkonKDgOf6vuZ9hHe+l2XF95vKl66KHrr5gePyjU/w/7mhH3dqIL1hpO8DG2YvDlUdQ3y6AiXHSPqyMXs5TUXngwNz4OuG8ykS12tI89GtY3nWXSGqbfo8yjqvNnyD8Nflo3fv2ad3cHghqQDv/3ahsCG3vd7+wole7mtzqnP7itVsKM+rl5aCUfZbadvg5n96ndjWtlzvX0+pLIF1xdVufYKzjN+hu/93RYHvj3Xq3N5qsad953GcSS7L1rl/nvOMB6vKHEEjPuwp6FOxG2FHeYOjnDCmeBLrzjDV6IWp3/kIt7/pNPQuhXRbfHId1zBzE0r/P0/t5muejjxJqWh7CrbflUFak7XurzkUw3+/gmpVvAq/RMKa2pWhodL8KY3wbN/Aqpj8ZNQS9Lb4dpAW1acesbMi1Q7C4d2uEwYBYz60wPXulELVwguHVc+iSNcDk4GWuBE7R/SCZvxvn5Z+y1R8FpeHpseHbb/5QUL9Tq2KkiRFeAZzHYh6OqzmHSSJVzp77rhm/Xg89LT9HXaeh6bMZwF3RjTDf00jSzh0OikZwYUdbSjqDddLfmenicUEREQQuz8q0DHOuvBIjV3lMnKTkLZVTMhhN9bhhLJUJ0BHsMVfm+ZSsL3cvc0v/qZNf7zjlO8/aY5bl85TStcpRM2cYRLNXT66xvDQH1PPslGnumjYcMAJ47Se45a7uIKHzXdeArfqaqgiwwJ4521m9Ea68ESAHes3cZPvS/g2wuHaIdrCCnpRq2BpSqB7ODL2Cj2Knz2xB186rjHXOuO+CSVoD9zJj3rIs4/EIDSNzVN0pKNZ11t6YwM1f2ml8R4FXA91oKFgenZ6lqtgByYFGuOgWk0oNLpwyhRkzFTpP/YnFRTX0rTtznnReot0UfeoJlnyCZp6Wvp5y+wP0tdN5rkwWQ4mNJMQRad5yLBjiJBuSzDfZjxK2s8K9r/TG1g409dH/EmeczVS2w7scbyVI2rfmCea69c5RHb4an7Ah63u8N9JzvsHukyUwu4fKLF5RMtJqvqmNjxSsieEbho9zp79q8CcP2XZjnRvKlX3/pNLiONLpOVnWxYs55qo23Vi/iZg51egOI7i7fy59+c49DKHVpbmGUoOeK84U2RtLdaYmlqg40y1A7XmW8fNdax1DnJ4VX1RtUVHiPxUpdBXJgydHYGfPK71NPDYdigwlbWOWyQy0ZnM2WHuSYGNsnUZ3eoEhuDnlJGOBKQamaDK+IgQDy7g6CNaK8pvZLsDbc+D/OH1RKWnfdRAYCJXeo4WK+qfiezIxIe/JoKenip5XUnb+L6p7yD9v/9e9Z/5w3Ij72Xpz5rvsffoStnCL+/wiMefYIffcJJfvgBazxkBvaMqM9DZiQPnAnZNRIw5kdMVmD7rjW8/ZJjByYAmPr3ZRbGq+o0l5FJdnx7icWZEXa4O9US8qWTsHhUtdkXP8KHrnoL8qPvY7bl87JHLOPHS1U+fuw4z//UKe5oHSasqRkanqgQ/ds/cOJDS8ibv0oou/ES9ijWmxNsq17Uu5+aNzYQkJZEvRnd6bQg6rAeLHHz0vqG/IHscNNihW/GzVR1G0xVd6sTazprA7R8p2oYgwZrG4RNx+p2jimwbCqbZVfZAtK2crqNlRd4yfJJ9fQsehcmtmCVjUx9YOPAmvVbRxFjo4jzlc6T9WYjjze9riyjy1SOfgAgjSKzOxw15W61O99bBtGVXRXkQCiFLlwWO8c52bwl5iRi1NuGQLDanacZLgMMKJ9kj42sjjFV2c3Ddkjee5PPxaMBO+pVVrpNw/puBgMDpkBBkh4GOLi989M9pzKwYakrPCpuvTdtueaOUvfGOdo+xJ9+s8H0TJNPn6jz7YUFOjLZsVuCjPCF348Eux53rt3Md5d8Lh2HnZX9g7zowY40kgh/FPW/p8slp+NURvqDXzp/enlSFEFHHbfVjVqDdHrBkoIy1PtdpP8k9GyKtyhMRrMtj4kHU3qW8s3qf4N1JY9iqz/3DuQ5O1mBJT1vck03BPIMCB15A/IwxneejBR1CtOOd979mcakLKNEaJ/0GJnXf00BAd1hBp3O6VjFzO5qcvW05KGzXe4/3eTS8TZ7RztMVQO21QKmqiF1L2LEk4x4EkdI6m7EzhHYPwq79q4xeXkHvxrxwTuTvZ8img2f0fGuxkOf776OU0fQJseX/8PNdT5/ywh+6u1RK1zdGATeoLs21nGyeRugZjImgfHBMlBzG8zW9sWp/Xa7c+0mBA476l3a4Ronmjf3xsdShmx8ZY1BJv439uNSV9swjF7dDO08ZD27ImP/MPRN92nT+4N8JaeK5NcmGNjUVDjKBpu7PV4GIRDtNZxOi8RuxK3AsW8jr/9UXFkEs5coe3v+MCyfUnmTPTpkpGZtVJKNOC19Ye8DuPTBa3zzX0Iq992GuHw/lVhmVyaqbH9Ik8ddtcrjdkkeOBNw2XiXHfWIi0Yls3V1tOu4H9LwIqpuxHgFZie77NyzRnt/hdO7R/GCiFd/HToiAK9Ct+LiRBJZiffNGJ2BiZ04X/skt778Bi6/n8vqe29EfuoTXFbfEy/FcXnVf89y8p+rLLRVAMIVHhz+Cp3vnGHbgZDoqkcNtHNivw+0MyBlxNfm7gDUcpX0LBD1fNQLz3F/Ow+e3Uc3atONWiQbqX7s6DKugIMTsBas89kTi8y3jihbfX2xX39qP5GiMrQRuv7L8u/S3019xEQ3i5dhg+m2fLb+tXkdciHo6S04ltYmDCYjYhjYBoA82vrvzQ4awwpiWpGnBop0ACCWgJAIt2CsaczfpiLQwiEiJAibnOneyXwbLhvfw3hlO35QI9m9uBWu4giXEW+CIA4M/NutJ/nxS3fjCp9QdjWFoQeH1O/n3mcHP335Gt1IbVZX91wmKjsQ3TabQjzjw3E9HFEhmWYYyC5O/LatE63HJ9FUEFLyH4ePsx6Msn8UXCFZ6cBkNWSxc5xRb5rIidQeJMLBxaNFm2Orx/nK6VGmq5IfvHhyo34JO/3piT3e4mUuyb4dMjKfoiKj/lKUZFlMGAdBklNevEo/EBIGjLgjhgBLlsNokCGrYZxlmJv6hK2vmNIxpJvS7DJkR5HrOm+b7ccXKoYxWm0yp383vV1I09Cv2dLy+MkKnOg8mAIopvx6vVnBIJNRkhdISl+ztY+pDXVHP69c+nf/+8EJePPDd/ND9zvBwYmAg5NtfKfeCzxUnCZhPB4I+kbriNdhshoCHaaqLpNVl/2jy9y6tMIHvlPjb97v8JCHn+SybwjuHN/Gcvd070jXBAudY/z6syJe+y8BFafOH34t5PcfMkrdHedlDxrlC3sX2dvYzz/cfJwzLfj6yyPu8wqH37lmF4MQ8fjkDaSpsUKyvX4AUAa1OWjfx2pwhhvmV7jPZJ2JynbG/QkOraxx8WhFLfHE5VTzJJOVJhOV7VrpC1OGNvZXmyGt64oSxZEez7J0ZRGnrejYr9O3Bbr066ax3fa9iA2u07Px2//dm9kRb3Cf7Msh/Wqce/D0p2Q5i0Aou2z20p7+EEEH2qvIE4fhxBzikd8HOw4i6uPqJVRlBFZOqUDI5O7enmyt338Dtd9/Sf9Fl19nEBuf2cif/RYPXZ2D1jLy5DEuGt/OV4D5R4zxAwfmeehsl9l6l4av7OD5lsdqV9mkviMHjnWdrUnCCcH2Woex8TOc2VPjO7ft4vC72/z8q86w8KxxZo8tE7mC9aDJiDdB5DoEr/t/REtt9j9jjOd94Cm86WEfRDzkGv7oWwv87gN3QdDmQ89zee/jW1y9bR9/cf1JrpkJeOuLdvCn//KjzISj2jwNFaxO9u5IEEZq2cwDtu0F1DG1I94EOiIZ9paizLeP8L7bGzx9f5PZ+n6umGzxkaMjfN/udUb9UTwn4HMnXR62fZ0d9Ustkl5MhszlbPaOrVxRvzKvP2TRN13P0rVFdMVmfO97LzYR8MgaqG3IU7amvLYyW+EEmYQoy6k0pad5QTm5vbf3qWBHsglmsmFl2KUZLlN1lNLYEPiIj10FEHLQgPGdGl+fO83e0ZBLxy8ikB1c+kdO+cJXG7xRxRVefCQURKnb7BuT2YOQg8tffmuVTgRP2ONz9bZp3LDAsatZiGd6ICJEHCRwhYcb369ad6h4WAuXuXi0w+4GfG3OZ7njsrfRYVtthHa4RigDWsEKVbdBKAOWOic5ttblTLvK0TV4+oF1qlI3ZBkMdiQzbfSTW2zBjigAUevP5pBB/zSXykhqJ++YRrK0Z+AMd+hETSRR7/lkK1NdgW4moGALSJiUsKl/5/W/LIWeZfgMcy/9vELcPYa2EOJHgRcDVwAN4A7gH4DXSNmbdnSeIc95K/IcTM6QrY5hrpn4Sn7rZYs4WCaHNMuBNNVhSrc5uaayJj5N/cfGQxYN/T7U/a0FC3z4Ww0e8piTXDoOu0a6VN1RKk4d31Fr2J3Q7QXAIdkpP0JEgvFKG1eoDUx9R1JzPRqe5HCtxdypOt991xi1yS57PzvPp58a8OSLlUPxuL9uM/O5ZRorSnf+5k94fP9rQ07/SUT4TvVWtu6O8727Rvn5T56k+/q+jt0Y7FD3ok9PB7WhtYMbL0GRlmCHYDU4w4g3iYNLO1znm/MVrppWY8uoP80123YQyA5SRszW97PcOWXYG2QrZMhkX2yFDNnK6fTzaNjuT+eNjHSdromHUlcbOCPbfi0a6Chq++rjZ55jmOXUZdnkNhpF0uM9HBy/t4+csmjV2/9k9oYrPDWza+UUoj4ZBykGgw+hDHo2biQAkvIO1MaJPvR+xCV7EY94bH8vNQAZIetjiLl5qMV2YWNapffsXmEIdljuy/WYe9HbCDoOO17yIP7kH3fgXh1xnyvmOTgh2dXoMOb7vZNM3PoyDT/EjUlVXUk3UjZxNxL4jkvDc3CFZMRrUqmGHGuM0j7sMfUONVNtx50rvO0mh1+47yju4gmef/9nId7YYe32Cg06PPd9T+KZj4Obfz+C90SwOofXmOaZe8Z4/idP0X19xNdjf2TbN28kfOj3Ge5UbNCZi50TCATTNRXwGPEm0JecSCLm23cyU9uPQHBszeND1zd45iUhDg4Xje7i2Zd36EQgZcQjtk9zePUEIDfQYn1B2dWjM+a2T9WaL4tFghh5dpLet4bxjfPy2Gz3InSHC3bcXbr6rsQWbVpqatisAdIUAUvTT5e1GeZFDHcbX1mGRTpPHk1Jb2+HZNlKGDu/pvvuNkFGjLrjymn2HBAuA5uSpveGSBSLVMtYHBzGKyEn1332jNSQVFkLFhj1pgFY6p6m6jZ6yztOtW7j+e+bYXrW5ScvM/Ce0VZLnZMcWoWXXL3GkdWKmhViOlFmWCRBIKkCHsLtby7n4dKVXZaD05xprbN/rEE7XOfAWJvVrkvdi1jtqjXmnaipdu53fI6uHSOSgoYvaYeCJ1/cYd/o5RAapkImy1LCgN6KrvSeHLZ5WMlSmPZq/8Qdv6bKJc+/Pq7yddbVtW4rPgpN7bIiwhDaq1RGptQU8Hjmjh1Zyqq4IssOSNhomyLgOq0i/c3ER5YSt5U7L7AN+DjwWmAReBjwSmAn8IK7jasB2HRalq7DcM3m+OTVaXKe8njQ68kLquTdi05P5W+GK1ScejzDrZ8eyC4r3Tm+cjrg8ok2s7VZKu5IvNlxUZid3oXOMb42F3D/6YjZ2v4C95FldJnuTfKp46t0PjnGyLMlDU9S9yJ8p0rVbfRmeLjCJ4g6KsgR671Qdnvf3UqbUCq6nlBBD9cRLB5c5HZnkhMnRxifb/KmV7Z458Ixms8Z4ZEPbnH7xwf5/Z/fVGPE50+ucfHoAkfWKrz9VwZ13P95m2GD52QMED56u6j9PEhtrG2WoTAKmGvdwZg/w02LIT912RhVt0EzXGGpc4LZ2gE8USGki0AwXtnee4Oc8HCieRM765cb2tkUnDCl513LQhZ9W/Bka2Ro8Hqeca/TOm/0cxrnqa7OGzOLOlVZec+m/iK0i47baVrpsukram+HUAYstU5S98bj/i7i3Gq53ELnFG/97igPmgl55I4KvhNR92o4CCIi0kujEyQnekRECBmC6yMmx+DISbhmCmRIV3Z50wmX7/du45K1QAU5Ykda3vgZ/uuxn2H3RRUe/EcFbzW53xM3cvr2Gvf5kyvoXvNo7vMHJ/jWw3czUVd7Jo36aha276ijcl3Ho+r2Z08LBJ7o4NdUWtWVhNIjlA6+A67oEEVrHLpqhkuuP907zvaLvxvxRU4y/dsue6YCjgGNlX58750vUn+vX7iDHSPw+WNneM+vDXL/1vfsArEHZLBhv5SapzYwjWTYC3xMVXcDaqaHbfNSgUM3gq/PHeXAuMdHj1b526cuMFHZzULnON+e7/LQ7dsZ8SZYD5aouHX2NmbwkqNuZUgk4MNHjvEDe/dCcxl9dk+6tsG/huczlOxm6cEifXIr+imG3zofOrL88wsTZ7GkJSsYYDKSs5yuohExk9GRR6dINHoYgyHOn8xWCLWXBb1ZHS4DC5mClJGXfA/oL4FInGjX/EiSpSgHJy7m+vk7+fqZQxycmGaUUeisE1VqeE5lYFfjSIasrvhMzzYN96U/C/U3IuQDh4/TjQQrKxVGfZcD421Wu2eY9nZs3I9ks0gfN5ssBwkD/Hg5TljpstRZxRESzxHM1rs4Alwh8J0ajnBphsucaS/TDh1ma55a8hOFXDZxgEB28By3H6RJ1xNhbedcRBFE2jOP4o1bk0BXJZ654ffPIZdINatFRrB2Bl844IVacMymOG0KNC/omKBIQEGnpwclTAaXWYaKBzFsQUWd5/53Ibjb1gZKKf9GS/q4EGIc+BUhxAullOeB5W9ztEwOShEnLsu5Mjlj6Wu2ek08DwMb/3184dTtbKsFXDaujh3tRC0+fGSZx+8JafQ2q1RllzonedmXXX7uYMhFo5cA0ApW8LxJOlGTIGrHU3Szgjk6f+r+X/5lZYTP1lrM1mz5s4I9ZmdaIvmBF6/xzJ9f4bMvFcyyyunFKuF2FeR1ha+CBzKEKMJzfVzXG3DwHenGG/kJnMhlqto/VrsThYz5HrMjkvVda0xOt5ifq7H83RrjCy3qb13nGGDbm/8fX+AA/Rs+9MRZfukHzvDBl0TM1BqpnPkyJBCsBYt4ToWqM2KpUTJR2U4guyx1ThJJQSgDJJK17gITlZ2xgS57TlH/bWW/7snKbprhSrzXlT9AfyPPerqeNy/wWESGivaN4WUou+7037xApflaqatNGMb2zbJ188ZYm91bdIwexsnLw8a8/3zLMbbVQq7dNQGopRJ//HWf337APHVvXO33hkskQo6tz/G8V13Ej//MAtfuUvbterCE79QIojbNcIWp6u6eE95zgmObTyDUy8bKCOJ7noT86H8i//tfEY/+Xh79V9txou3c57nHuKRWg7EdfSaDkIUzAXv3pWfomoKgqfsL2nT+8u8glJz+l6dzW9flnT9yBoDJuXW6EYRS9A8LiIMGNXcM36kRyRAZ28aOcHEil221JlVXEkmIpAoIrwWwONahPhNwYv8E9dUOs8dWe2zMvzr75eTrnjuouW941G5e/+w7+btfclnonmRKjuFUGyQz7sKo2zsdUZ/l4QiXpc7J3mkttg1Fd9QvYtxf5tbldULp9k5XPLLa4tKJsFc2CXAPbCodReA6PGBbixPNW6i6DUYiLzVL+lwh/Yz172nY0mFjn86RoQ1lkryma0X6YX6A8u7U1XclNnGLpoa3DXq2B5MeUIuMOzqdtPDlGdHDCEQRSPXmvrNuPopUxLM2YnptJ+MI1Cg+Csp2gkvYJZn94cZHZ7k4HF2rcNtylZo3RuR5RJUaDm5PUfTvyiEK1f13oqbxHpe7p/iHm4+xFiwAanfr99/pcOVUk2v3d/n8yQrdSGw4HmrLoG/4GQVUZYXxynamq1NMVbexrTrOqD/KmD9J3R2n7qoosyt8qq5ktqYM2HYYxANDqDZYTWZzuH5/WUmSZtqYNA/p5yOcePYGg8tkuk0I2rTCVbW7dHNJZcEdlBfXo+UWeYtiMj5NfSkrSJeHLPnXFXQePVMQNF1POk03qNMfU/nzEmew+313E4YJ4Gb9NuW3yZ7p2dqCdsMYy0XybzQ07jc9weHVCnes3sINC7exFizwxL3jVN0GOi9v+JbL/abgsomaOk1KEgc4JL5TVSdUxRuspcstdk4Y61ZQ7bDadLl6Gq6a3oe9ve3Pqxu1Weqc6qVLIm5buYXV7jy7Dy3x2ZdG1H5DGZ+tpkc3giASPaO5p1fDACFl7EaoT7Lkpeo2qLh1Ks4Io77a1NRz1DG222tw0UyHPTubXHJwEXmFy633m6Vd2xgwjhzFd+ANjmM3Pngnb/+Zw7z79/pLFztRiyheW9+/50EZWg8W481Q1fgzaNyaDUspI6ruCAcnJWP+DJ7wqXvjfOHUogr2yIiblm4FBLcs3zpQVu0HJai6DW0fER1FggUmmAKGyd/NOpj5MmTmIc/AzoPe34ctf7fgPNTVWUjbuqZrCfICa6Z8RWxefSwuGujIHlc6UZPv27PGZ064fOzoMu+6bZUT601eek2HujfWm5kB4DXX+KV37mHqgR2+f+9a75S/8cpsTx+fanZoBssxhyKuUXKidWvPYVebeQqkX6V74yk6XzsO49uJHMH3PPEU1+7aA1N7B9lOTkSMpFpGMXA/cfstnSB619vUaSKgAiy+Q+VZ1/LmX3Z554vgqlcqOiMrHVpdQRDFgdzYZhQiPsFQqP2FlD4eoeaqGWp1d5zxisOOkS7T1YBxX9LwYKwqmdnRpHuRx8mLxjl05UyBZ7MRxw5M8IlfX+KNL1QBXk9UoDraC447OPjxTIvkpBuAxfZxdWoigoY3Zdy3Iw2BOnFntt7lcbs7bK8foOLU2TXi8nffaRBG6jSYDx25E1ZO8h93HGE9WFSFXY/1YBFHuNTdcarOCNW1Va2GYYPDeTAFfG16elj9Z7KN8sYOk+1v8hFs5Uts8jW3/vZBD0AUbdyijpb+W4+mpR96ViTbxpvuYFn4l1E/QGFb3iGjOLigzpOuilp2cyQzD8JkD5AaK90zarNSV+0u3VvyIiPWwmVONh3+9xU7EQia4UocADDzPTIaMHdyhI8fm1NTwVL3udA5zpHVFk/cG9DwdgOS1e481+5UOf7X/hY3L7q0QwdHeAzsc7EVMB39ChAFVICKM9GbiSHjf8kGSZ6sIByB51R66z6TNaBJlL8TtXCEy0rnJJ2wyY7a/n5djsOGeF9eiDN9PX3SjutBOiAkI2r4EMaysr6oZnKk7zeKqGF7W9mrMCFo+a73H1sfyeoDer48fkz1ZfWrrL63OSfYuZvPCxdCuEAVeBDwq8Abz4/ZHWlkGcR5A2yeQ5cVuNJppP/mBamz6kzo2+ofHA8a3hSP3KGMo3Tepc4pJiqpt3jAC67qMt/uMlm5jGRGhNrYX70dnK7uYTWYRyDiKcjqHiYru1K0hfZXpb/l8TtSeXTY+lz/fufbR/nHm+v8xtUqpRWu8vs/Uwda/MQbJP/yQkHrdWp8CLoOy13oxgb1Bv2qj1kOeMLvva2TUlJzA9puQNVRU69bYRxMCaFblfhXLnBHfZzbJmcRTUngOQOrMqvNgNGlFtMn16m2VED5sm+c4qXPHaXRUTPj/uuOgFH/DE/c2w9em2RoJDUTRwUg8mWoGS7jiQpVd4TV4Ay+U6PhTfHYXVMEskNbttheV4GTyYqf2iRVjX2+U82YRZKG6dnlOaEb5cN8LUuvF+EjTTvvus63CZsvX+rqLGQ912Ht6LzyeXZCUbqb4ScJQkR0ozaRDBn3Z/nV+y3S8CbjDUmVnj62fpzxbjyzamQKOTLB3/zYrXx9boTdI7sQxDM2kqV4js8l4ztZaB8jOaWQUzfD9kvZWT9IYjkm9Yv5OwnuWGbkT38ThMuXf+k4TOy23snIqMuROzpc/bZ/xf+lX0rdj4Qj34TGNO978BN4xnRMo7PO8z/yVPiIWrr3hz9b44ZXKr3rt0NWVyq0w/ikGBhYMjLwnf6JKL1DBvw1pqohzdCh4btMVWBpsk3QdWiNeXS2OdzsbefAt+fwguK2+u5DS/zKM/vO4Ou+2WF7/Ti/cN9ZdarNqVuQy8s4Vz6a9DKSZCkL0AuIZMF1fBbaa4x4asZ2MitkuraX332QmkWy0p3jPpMtuiOz7G4dV75UABV3hFPNeaqupBHvsReOTjO4m0gRmS56Tc+X1ReGqVe33c8mEGELOtuCM9lq7+7W1XcFNhnw0IMKUMzpyaKVhTzhyxMyU4DERs8UyKGfZpoVkCyViFFxavHbK0C4dKMWmavBtWUdah1czEdiScazRhY7p1loq+irK7yUwajfs1JCV16uIt+jXrThPk+sNwGBJ6oEssPtK4dZ7TrsG3VY7rh0o4jZepfxyhYGOkxBBX2GSyrwkSyhEcJRkXC3RjdqUXUbJFPrhHDi6HCAEP0jeyWSbtRi1JtG+DHtxOjXjwYuMp/Ltpynt2ypM7hpaWrTWun5iKR88qx7POQFDvLk0+QQbsaIyQqqmPr8MEpb5zNdT1Yk/WwGhHOGNZQRDfB24DfvRl4syHsjYYLuHG2FM5XOZ6Jte/5ZfJnoyNRVSTNYou6N89W5Qzxo5gB3rN7C9voOJirbWQ8WcR2/N2NgqrqbmZqvjh9048BuGOvkeHPlUW8aieSzJ+7godu3997I6UGXF3/+GJeNwy9fuTuVrt9DX/6b4UrvCL8xf6a3v0goAyIZ8mNv2ctHflGVC2SHa/9slKtQOv1fXjj4bFdXKqx112gGjjLyhQNk6O4oAiIcx+mtJY9kyKi/zGo3ohk6TFcjfMdhpQvLHdhWBfeSZWa2r+O4kqDrEARqJmGn7TI/V2OVGqHn4AYR7bqPE0mcMGJkpUO77vF3H6nwgAfM8727mnFAqpgMBbJDKLupgMRGGWoGKwQyZLZ2EUBqyYqgFa6w1l3sLYcMZJuF9jG21S5CIJio7Oi/UdTqtjv4RWTTRiMrKJiuR+Zc0+vWadt+mwzkPKPXlCdL19ztOA91dd4YTc41G72s8nl2xbmHRBLKrlpq3Z1n1J/mn25Z52cOTvCRIws8dHuH3SO7WI7WqbljKCtYcEn9YnZd3MZzKkgZKTsQtcFxJFUgI1nS8ubvHuPnLtlLEHUG9pMQcXu//qjPr736RcqWc32Y0DdP7kOMjfK4pwtGZsD7nisHTjgMZYBz4iQ/fWoP//Sg09AeR97wBZ5208OYisv/4c8OrmEcW2pzZnmC5W67v3QlfcRr6nkkQRpHuPhOlWQ2y3RtiXYoONNyGfFgx2hEtbLG0lKF1eUKnVmX2+4/y+iimk3sBhFeN8INItxuxNhS/imL3/lzl08+ZoKfvnyB8WYI2/YjtntKC8mICGmcAdeJmoRRd3ApSgpB1OHIaoVm6PCw7W5vj6nkheWZzp3csOBQdXzG/DnOtD26C23uv62CFwn2NHZzqnm01z76/iLFYAoC2Gzbs6Wdd83WR4cNhBTxw208XFg4i01L07ANrhjSTYGHLIVtG4DzhKLIg7dE9JIARJKezOgIDEtYqqMsdk4wWd2pNrQEaK/iVEZichI/EmrZQ5H9L6KAulNXy1lcn0goRbHcPsV4ZTuecNlehztWb+eSscss96z+OsLl4oak5sH1Cz5Xb5uj6jZYCxYIoy5BJGj4IRV3lHa4xnS1xlRVsh40aXgj1NzR3n4ZrvDVco1zgSKKq7fZaYTv+qmTVVSbOsLBJ9lYNQAHHFetE3Sc1Gk4CR2nYg++2PhxPCAOaFT0t4BCBTXio8x6x9YCePG6xmTj08qImtETBuC52IMLpu8m49L2Pd3f8t7qZDmjWcEKm9Gcp+BtfT9f6Z/DtYYzQogvp37/rZTybw35HgWMoDbCeznwl8AvnzOuzho2GUpfg43PcFhkBb31uvRrecgKrgxek0ja0TpBt8OehjIUd47sVsdru+OsBYuMeBOx4yxYD5aUrvNq9DYSTvZmkvGGxrHQTdcC3vzdBbZV4Xt2rdHwptRsvJiH73x3gvn3LfC8dzepODWNz8H7mG8f5cU/3s/xlNed5lE7V/GEy3P+bSenj9e53xeO8djuDl7zrMP87TfrXPW5wf2DViaqVNohx/dPsGOmSSuE9cChHa6ptfDJ8j3AdEQ6AFE/6BE4HXxZY6qqxrqm6+A5LpF0aQVqmcv+UWA0GctCWiGsdNUsEM+PWG1U6HRcvDpE7SSu69B1quzeu8pl06tEEl5yXcQfPuxWdtQv6bNCSBh1N7wxlEQcXr2DS8YuZyP6z/6GBZ+PH6vz29es9zbzTjDiTVJ11Lr0z588xWzdxXe6NPzV3kygujfORpj6ix6ssAU0bME+E2yBCJ2PvMCkjY/kWh7vNt6yeDbxW+pqM0zjaxp5wS29nM2ezhqHTTq5iKOl59lYV3pjZFB9Wko1uwNUUHW+Da1wju/dJaieWOBxu/dwx+oZRr2AmxbXuHQioBIvQzkVHO31T1f4COjNShMke/XEAYGq5NkfXWV2ssvz79NkT2OM8a6PPHETYudBPvyNcb7+8gXe8qrbEfd5TOadRhddzW8szMIC8DJ46p+d4qHb15ip7eSH3uxTqTyaHe9b4AmXXsQf/OYcf3nimt6JKSYsztRpNT0W27DUCRj1OzjSNTru6aU5Qji901yqUYvxSpedIwGO8Gj4UG9CJLt02i4jjYDaTDBAqdWssrxYIWwKdt6xRGO5Q7UVEDkCJ3WEY+A5nNg3jnuVQ4WIH/jLaT74K2eYEJXeMcDdqE0g2xuWz0skXz41xyN3asuCUnCEy4eP+nzw4zv54K8eTc2+VBS21S7i6unjRFLyphsd9jRgoe1y+cQqo7VpRBQwVd1GzR0lkJ3UxqkmG9ImpxtbunjAdjOB3aw+lRdEt9lLeTZWnu1uxoWwh8dZbFqabkSbAtZRVLnqtEyDcd5DzxMyPY8hvwzj/SUCspaxTFR2DM78cJJmjevSZoBkQkZ9YzsKEH6VUHbxnRrrwSK+U2VPI2Ct68bLWcZZ7p6iFa6yvZYYjqrNfKfGZROrNAOHUApuXV7mwFjIjQsdLhkP2T+mdomuuHWcMKLhjYLrMVWN1DrKoBO/IIxAnmWwY5jelNVeyak4pvQ0ogBHOupMchmyEiwop8b17bwEnezNTN1UsALU8iavQhKlF8lM2eSElqQeN7Vs2K+pNwuuviFeUWVsMn5MNIYNkOQFI2wBSf13nh6wvd2yBT70POcUc1LKh+RlklJ+Nf76GSHEHPA2IcTrpJS3ZpW7e2DTnSanx+ac5TlG2U6PmZ88eTLRLkJTxt+E0on1AwBUnQah0+X9h4/wPbtG4hkNKv+YP9M7KloksyJ0HSQjhHC5dHw3u0eWWQ2W+NyJBkfXOuxpHONpB3bSDJap1VWff/5HVnjzEwd1iXoz2dc9nzg2eG+ukLz++lFO/0nEDhbYwQIAO7+2yIuDvTiOxLk6wokkbhAR7nFxHEmr6bF91zrjk8qhCOINO9VGc5a5hXpgN4pwXA/PqeBGHlUXxishkRTUo4iJiqATqfwNH0Y8qDiSUApWurBdqhkgs/ubnGk36UbgCuKNpqEbwWQF9jRgW1XS8COumnK4ft5nx56+7nBwcCzTo9WYt5ya1bhRZh6zc5a9jaNUnWmWu6epOCPx/lNqf6qFzjGmKru5cgpGvG18e2GB3SPJc06OQMwLCJr6hy04YUrP61NF5b1IfhsfRRzjInWcbZB007iH6+qsMVK3fU1jZl45PY/NjkhfL+LI2fT2xrKSSOkh2UVK2QtKCKHyHlvzefiOi2A0ourAtto8/+fL6/zWAwKqTt+hnqntY7V7Jp5doI6ZVf8dkmNnied8PG63xwNnFjm0UuXvbqyz0g559K6A50zNDNh2f9a5hN9YXyCsqyNcWTmFvPiBiFQ7/9utJwfu59pdAb/31VFWXrvMRan0vbcu8je/6OFjOn1KIfAcFrY3qDkR3QjaodozCEzH3KZbV/SCHp5Q+5fUvTbjfhhvYurRDaETSdrjyi4daXRpNAJqLkQSFlY8ZnY4rC5XWN9R5/j8JG47InIE0hc4jkQ0JdXpkN0Xr7JjW5vJCix34d9ug+dfCuL0IRidoVofo6otxRY4SEJONj1WOnMblowm8ESF37mmzoNnjlN3xzjRvImGN82Yr/YeCaIO35oPeeBMg2t3rbJvrMu/3tLAdfxeW9TcMVzh95f5xFf6yAsKoqUXlfui+XRkldF15rA+tI1Wlh64sLGJgIdJ+ZqcLbA3tD7Qmpy4IhEuGx2dli2PhbfE2A069GYE2NBeRQCLYpVJRhk4mSUthDKi6/v4XcNRqVZWIkSnSd2tU/HrtMJV1oMl9o91GfFkvKEb8W7HATcs3MqexkhvenIrXGWyEjJZUev+Fjsep1srXDbhsa22HycINp46EgWb2cnWjs2GDfVym9k/REaIoIvwPEa8CTUNL6ajlru0B9/Cul5vN2gr0kfY+tob3CTo1W2pwIhXZUCh+YNTHPvIks/NBg9tBmiRfqUbXCZlaku3BVlsMAVyMsoJEOfXWsPEoD4AnCcBj7znpufT08lIM13PC4KY6BWtb5hnLVnqnKLmjdKJmnz+ZIUf2icRwFqwwHUnV9hRDxn39/byJ1jpzuGJCt840+LRO3fj6sfrCeUIe06FmjdKRMjlEy3Wghrv/W6F//uBgFc8bYXfeWzEqz48xuQbmzz3jScJPKe3nvrmq7fzql88wrW79nHL8i3oxu5/vHjQcT52YILFmRGmdrV58MGTjPuw2BF0u8pI7bRd1td89WZvtMtYVbJrBBpehCu84af8yqi3cV4kQ/CaA6e6VN2I9apDEAk8RzLiRYRSMF4B35F4QuI7kvm218tfcSTN0GGtq45Ur7uSqhvR8FWbnGz6HF2/iXF/Op4pM9DovfYQCO43vR+ZI0MVp8bBiUsB8JyZgQBG8lawGS4zWVVT2Wfrp3vj5SBN3dlMXzf1LxP063pwxBT4sOlXG82suk3I0q8mXk16Q79uCvhQ6upMmNo2K4hhKmejabMHTPmTa5t1isz8JMfNqtkd/WDH0bVTTFWrLLZd/vnWCvfftognqtQ6kjd8q8Z9J1WAI71kQsqIo2srXDQ6yV99a51fvLJClUYvGJDUJ4TAd6qM+XX2Nlo8ZMbjw3c4/OPfNvifOwU///oF3vwjXX7hk5Nc/4omz2ENteJJADu49fEBf/1Td3IVFU7UWlzUqEBql4iX/ISLbYngzVdvJxh3qSwG+J2Q0HOorXUYXWxTaYesTlZZG6uwf88c22rqmFnTaSZJWnqD7OQ+Xcen4tZpyC7TNfUCshsJQql4bHghi2NNOpGaiTfuQyhhW1UFP2oXd/BdOBxPRG946rPShYV4ueK4rwLa01W1XOjwquCm9aPsmpxk1O8vnxdSqr3pGkpnO7g8df9sf7NsCxreFE/br4JZNW8sDjApGWqFKwRScLK5yP2mt1Fx6jx8+wka3g5ApPR0Vh/IsyOz9Fhe3iJjQdFAiq3P5wWhi9x7Fn/2oueZrj4n2IIlLaYHNGzELM9Byks3CYjNmC8w4JtmENg2KY0xKeNNKzfMThDKQXYcVrsnmULfc4P8oEDYwY0cGv4krvDxnRXcePd5Lz7equaO0fBarAdLnGqusdp1We06VGN9PVkJqboRY76g4U2qYMdWb0I6zD3dVYgCnDDhJQ5IuGqNeydaHwx4CCc72DEAgwyl2zOMZ4sEnXgWSDKQSSIZ4AYBoefFA/vZRGDTMp7Xn4aNbOcZUEWi6bb683gyfT+bdjpneHT899DdysUAbIGIYQe0YZwrkxOXly/NU9bgbPu9sa7VYIlPHu/y6J0+T923p0e74U3ymJ2+dXO1bdW9rAWLvOf2kGZ4gmu2dZiu7tHyCwT9IwVn66vsaQQc3A7ra01e+i972HHTEtNzallbs+HTrntMzinj9PJvnuLNv+zy6h9ZZfbdNToVhwqhgRs4vm8ceZXH/S85zSWTERePKiP2TFuy0pGsBXDGV/oseaO3rQoztYipaoDnjKjpvlmPzzDLw3U9Ku6IepMqHARNHBFQdR1Wuy6OgGbg4DkSz4GGE+II2FYLGPXVPlEH6JAYqFJKAtmKN5J24rea/b494rX5zkKNuVaHJ+49wrbqRQZG062fnoGRLUNi4JrAQfHQDNTMSNfxGa804mc8THDO1L9MfcD2O01Hv7aZoKAJmQ8+I18WP6bgxz3KSD6PdLVuq0KxYFTWGJhlDxfhw1ZvXp2DeUIZIGWENMjGrctV/uuwy3Ov6PKah4/GNUS0PIcX3a/bm5Gb3qTYFT5XTO7Haa7yvveMsdyd48kXrXLV9Eh86pZCJJXO8p0aY36b3Y2Ay6bhtkt8uqdc3vrLIVBlEvNs5Us/eorXfrSK/2su3dfXOXLpJHtZzGydwwenEZcJ7nPZPNVKxJm5GqsrFVaXXVbm6khHICLJ6kSNqdk2uyYDdtQjRn05MEtBaG2sZk1stM99p0bkhoz5AdDFESrY3PBcFjsC34HFDlQc8B2Y9NX3XSPqxafvSB4wLfAdyXglpBsJVrsuVTfCFbDadYikCmgDjFcc/vOOEe5c6/Dr97+VvY39iu+wDc3lXsAD4tNdhjDRHG3LUbVJKxxerTBb61B1G+wY6Q7sxULYhSig6wr8UIJvmyEzjC85TF6TnWuye/P6oClwYtIJtnJF+ncWfxcmzmKGR4JhG/NsFbEtkjbsw9d5ipG8pc+b2QH9TSfTQY7YiLzu1J08YsfFJBHbufYRAJacdcYqM2pqc/pI2jzEJ8TUvHrPCHcjIGrhotY21rw6DX+KyWqLVrDKarDEyXWfSNJTcF58FCHhOQx2nAsMsyxIL5feLDTelNCJNyLcQgb7Mzi6rf5RtUFH1V9Vg7uQEre1DjLC9Sfp73qdFQjISrMF+9JlbMiqo2jU2mZkZeW16YD8AUNw98XShBAfBD4C3ACEKAP6N4B33P1TpNPICxgkyMuT99vkyOUFMNJlTLJn4ifL4Rx0wpqBwyN2dJiobFxLXHMbvOFbx3nY9g6XjgtmahcPlB3xJrh0rMlrXz9F9Rr44QfP83NXTMVB0WQKsjLM1PGIdXbUO1w+4dHZt47nRdzOBOtjFZxI0ql6dCsuC9sb+O2AkZUO9bUus+9eBKDSMQc7AFam61yxf55H7Io4MBYwXVVTmKeqLqeaHmda6u2dPx7gCvV27uJRmK0FNPwoXuteIGClBz1khCfUhq4CJz71qonvqJcAyZJvR0DViai6klE/ZMwX1L1Jau5Yz0DVjXiJJIg6BFE73qsjwHc63H+6gxABtyw5VKfP8IHDbX7w4mqsn7dOhjynghCCUW86HkNFbzNa81KWrLbTrxcJkKTL5qFIsNEWaDQFJmxlTQEOk/7Q6Zlol7r67JDlYA3rrNjGWpPDNixPZt4k5iBHGmtdh5++rMOBsX6gwhEuUkpG/W38xLtDnnz1As8fOQ57ru4t/RW3fRnpOPz4M/fwlZdF/OlEg8qPOrzx+5epe+NIKQf0c8WtM1trcum4x8Gr5vl2d4Yd31hifMG+7CRB9/VKL++9dTEz37EDE+z4niZX7m6zf0zp4xONFrcutZk7OcJcVGe+0kC4MD3T4sDlS+wfUy8fPVHpLe1JoM/4SAc9klkejlCbfap9UlYZ8dq9mXSO8AilIJJKPzd8NWNjohKxox6wu6E2FPWd2oa6QD2/TtikG7UIZUggQ5Y7LmN+hCskHz3a4H/tP8Frv+Hy29fA1MyB3LbMR18eG94Uk5Uldo6EjHjbcYXHVLWh9qOK9/uQrkuLJlEU4vtT8V6HRV8a2uzOIulpWR/Why1iZ+fxrOv2YQIfaVp2Ts+Xd9TnEpuc4ZHnjGVd1wfPLGW+0aDNj4SZBClPoJKvmkOddpbTm7/pMDjjy103NqQUxivb8Z0qS52TnGndSd0bY9SbVM5xUcRBD1c/zST5HgU4wqEqPCqVWRzh0q4u0AyS6cRVqm6RI/fOEueq52w26JEORsUGfuAKPOGzFiwq50CIgscRGiugJ2N+PTVDSEAtnvkTBnREQCWM+fAqdKN2vDbTZ6OM5gUEDHVnBhlMyApcmvpOHu2svGeTTwwk6Qfs3IX4EvAcYD9qutBtwEuBv77bODJis46S6betrMkR1cubAhYmJ8tWdxGnb1DmLxu/NJPv+28LaIaOpgfVNYHg+/Y0+cwJF/c/I952ejtP238bO+uXExGqnfWReKLSW/oxU+uwb7RDK6zQClt02i6LjSrtwKFSDRlrtPG8iE7HZWm5wfE1n6nja0ydWqe+Zl7euDZWIfAcqtWIvY2Q/WMdRv2I9UDgCGXM1lyXhi9U0MOByYpkshoyWQ2pOJXiAQ8YDHrER/L6TlXtOSLcnmEe+V0iKXpvAH1HLU/x/n/23jtQluSu7/1UdZp05qR7br5370attIpIKBKEQSAkBJYIEhiEAmDAYIJ4RsYWD2xAJGEwwQaB0SMLAcaYYLJABoRAICShuHn35nTyhO6uen9U90xPT3WYc84Nqz2/u2dnprpSd1f96lff+gU59pgvhTPhp2TyDQh82UyiDmiUjglVn0gN0SieuAjvOrvNuW0HV6SaNXs3hgSCg43b+IdLD3DH/JCut0IvXkfEcmRHPtnG9B0UAwOzjt26wIKNioDGMiBoFhCk7D5s91zwLvZ5tYXqbHDI5JkV6Mi3Y9uwzVpHEY2vpWBH3qQhHwH4c09OazhLHOJE+/b5T9hidQD6zMOIY08dt3T4Lmh2eeXgId6Lb6KN/By8/5kxzzkESkfEiaxuwOiA5cYmt3SGPHXZp/+EVT7cW+bOf7owCpcNxrdG7Eq8YTzhwLMODZoeTziywbNWjPbEeujgJE402/4Wne4QpQR+ELPYjjnWgqMtNTp4zGs42KgI9Gg4cwgkQ9VjubGF0jGRFoTKJVRmTWi70EhMCD1p3qUjvCS6mJ0c1yPQbZSOUMQ0nR6LQY9Ya453Qn70Ax3uO91k7pMTwOrKQ+agT7oT2h71SWS+SZ6+fBtv/cgZXnbLwxxu3kmkhmypq/iyidPbJGoYc0tPNqC3ZhiMk8jYVrmkSNYs2h/a0svqyVN+/hblr7PnTfOVyfizzOeSvDeWV1832gMNj5TqvOA6g6mozqL2qwZINl9JuyVOSSfINiqyGgQZHx5/d+ERnnPwFFvRKg9vXkFpWB24dH3FqTltN52pQ2Wb/mRTL0JF21sk0kPabg8p/CTEVSPR8LiBEVeuN+U1cLRCOCbomSv9UZSGWEfWcFt2KjpN0wbkSGz+03DCDNbxW4sQbpCGH3b8BrEKkypmGbNFzLpsvuQ/68y/fB1Vv6sWHJtwX9bXm4u01m8C3nSj+1FN+U1KXpCzbY5sG7Ci+vJ1lm3usuPMtnm1tV12mlFUfxmN6376spc4PnOnrl0ePMoPvS7gc74/5nIflrcuMOetAEZdOo0GILWDI9zRSdvJuVUAfOnTdre4cGwLpQUNRzOXqBQD9GK4uC0488gc959d4dQHLtHemFwDem2PqwdbzK0MWWloDjUj5n3T15Y7xBGR8YHhKpYaEokBHpquouUqur7Gky2jwZAHxOtSAnq4wgMJWitiGRE4IU1XESevwUtAD08aDQmBTEItlo0HAyyZkOIS6TgGmE9C8z73kMPZbejF60jhsDY8z7f9ZYuf/axuzvRk9jGkEvOhe5YOkp5wttwF41QRRaqRsja8QNPt5qLs2Db6RWBHvn/5PEXXyuasre4y8KGoTBmVlasCWWqCa9eJbl5eXcWv9mrdKwK8bFRHpi/LV9QDkZQq5z2RHhr+IQRfcvsW834H/T/X0P/rV5GveDVceQj9/vchnng33/iGBd78C+v88sfb/MHvrnD34iOAMaOJtOGlQhtzuqYzz8m5NQaJs2XXu8i9nSXkWdOfMHAQ8+AHhi/0ey7Nc0NOfuxKrfuLDjqcmoMjLQNcKy1wWsa8ZLkhWG+HxrTEMQ6b5zzFgUbEUiPCl01jMlhDXi7T9BAIlI7p+n22I0noxyOfHg3H8Gaj/aEn6ylqVxv/T45w0RizGxk79OMNYg2vfcIWb7rgsz68QNtbZKvt8G/+2OVXX+SbKI6FJibVlGrofNkdTaToACZUeKQG9OJ1Oq0l4niLhzevcKgZsOStJAfGeTmhDMiwARJZspWx8bi6e9ai/EUA5E721kV7h5tPhr7RtMMoLWWIU1EaJWWqNjr59ooW2rKBXvPlp6ddNpOWuhCYkNw53+fDV83kb7uL9KJV/vxMwGce6/PExeO4YUiRA6Q9Ia2Q4ZAF/7CJGZ6cvGm0UQO7FnQzgh0F5EQRypNorRPNm3Nc7Lk8ZenWnVcaDY2jUpFD7uMkRO1gY2wypSKkBmRWu8MGShQJ9mCfR/l8ZUAFlt9ZqtrEFqVnP8vmZlX5fB9MfULcPIL1zUdFYyZPRZsr21iss/nK/q6zuauqu6yOsvZt5ce04B+29MHQcnCCn/+fiqHq8arfiPh3n34VXxqzCgN4JBtjZTbqvmwmdscOt8+vsRD0ONj0uNBzGSpNwzGRTFIHnZESbISS04vr3HfrOu+Vhzh47zreIEY5gtB32OoG6BOSu+66wlOXYKUZ4sk5Yh0BPk1XETjxKIKKFBpPioS/e3iyQcPtjB0020B8W+htiz8PA3r4KGnAnliEdLw0tGRi1uIofOkkTlLFdJ1Tr2WyXYmDxIQ9VzrGl01edPwiP/XPHe69so7+CZdhQ/HAcx4dOSPd2RiCj64+yOFWk0X/yCjNFR5XB2cYqiHHWneiUAzUFk26U+UzN2bpg73NcVr+2qxAQRGostM6y+aWDbS28Ygq2ufV02Rbv/NpUPyMi951mTxe1X6ZzJ1tr7yN1G+H2ZQzAhizZHfKKUefR1snjFbZLYcI33M/PsDSSdj8a658x2/xa6++k7jz+Xzoh67wpf/hIg2nMzJniXU4crLsCI/AaSOFw90LV+l6McfaPv+8eIkH1yRRKGm2jE+L5cBEkurFcGUgOP3wPKsf9rn9gxcLn+KDdy/zSZ98kVNzEYtBzHZkIsY0XTECoyMFroSmowgcY/7X9WM67jy+0xoBQnnTP9sYKAI9PNkg1hFNN0zWhEmzw6ZjgPDA0fXAldTXnDYAcBoRRQoXiOh4Dq9/+jav/KUV1s77POnvIKDH4CUN/D/5PcRnf1FlG0X0fx45z7NWBhxqpnxe48smD26c5oGNgE874hGqPud7HgcaEcp1kTQol4PJfS8a67ZrReN9FiChTh07mbO2NvJpVbxkso3HA6/eA6eldTZMNgZbdM1Wh22gZNPLytXdTKZVSZQjkbjj8KMpSXdSMFTRRDkypia3zt3OR1dPo4jZDC8TKsFX3NXHFT5b4VUTumm4Zen3HpJWiHCAKySk6Pe1bfHa0258eeROPGU4pOF1GCiBKwIWgsEOOpQZQ66fS0+edjycDGebUthH+u1c/iJh2fY7L+wUXZuF8ZW1nVJe+K0S8vNpZXPd1u5jftReR6raFKVUtIEq+11Vfhaygdhlbc+6sSvLm/8cb5z/6NHT/Ke3HuLWD63xK8d97pw/z3LjRBIJKwE9hEYIY57hCp+Wu4AvWzSdLZYb66wOhgxiSayh46lEpVgwiGE7khxtuRxpefDc83y0u8j2psfC0gClBCeObHJsKeTpy3DnfJ+2a5yPKh0bJ6QJOOBJI/hK4SQCqYNAjDX4dLF/EHPr9UEPR5hwtUrHaKXpeMOR2rTASU4EPSbswsvCiqeUaME50sNJtGeUjhnEktPb8MCfdFmZ22DQdHn1j5/iL7+jn9O6qB5DkQ75nQcv8o+XBd/4ZDECvC4PHqHpzNFyFxAIut5SMhJMfQ9vnuaO7h1pR5llDE3+zuaZ7NvkPZQBDPk68nw/f73OvK0zd4rKfuILxteOqjZhVWvdrOWL5N6ifGV122XqrO8OgcCJFTjOCHxI+UKqNZBu2LMRoLJgqUYjnvPZiPf+NEQDuPwgehiy+P98Kv944Bjf+9o15voRv/aOg7zwmx7l9q6fmMdFDGJBy+0hEEZLy2niCJdGt8fR9gYnOx6PLvlsR0ZTbd5XhkdLTS8WrA8dHp7f4J+PCT7CyhTocfFoh0u3zfHM557neQfheHtI4GhCNZ7PTRcgxhFG886TYsSjfdkcaWZkacw78/O4GvRwpY+nGywGmyOwI1QiMTk0PpaarkJS7CQ173tFCDkCPVzp4yqfCKPpuBn6DO8XPOlDZ0f5X/Ztij/8hmKH00UUqj7f+49X+NsPzvPjL++zkoSRz4asHcSSZx4wfqlC+kg0f/yozxfe1idwW0yP5Tr7yDr502v5+VNEdduwySZlVFf+zcvP+3Jznnao4ZElG8qcpTobnqI6qhZkm3Ahcml1B1WOnATwSJ1PJj4yRqBHkUCXAB8ijnnGgT7f9u7zKA1D5bMcwKtu77EQbOM7TZo1bPh2TdcyEkuWrqd2x05Bj3x5rSAOGYX80lF1WRulTkqdTGzw0eZBmGtgNEDSkLZxZP50jNEIqRqfRSjvLCBhnfrL5opNaCoSpGZZYMoAmen6HkOKRDeA8huZIuDLlqesrnyesk1PVV1Fddfd8JVtJIv6UgagjK/90gcb3Pn+C5y9pcvH/6bN3x4/y2ccXUejifSQWIVoqRFK4AofRxjXnr5s4MsmTbfLnNcnVH201ok9eWMkoIaqz8HmJkfb2ywGAU+cv8pWBMfasBQoDjVDFoKYed9lzjOO2wZqKwE70p7LBOhwkQkYMfKdEUfTGnxFpi11QA/MRsVEXElUwZVAOrGJeiKkATykm/j7mH1ySqQBVOKICOOv5KlL8NA9MdvrPitnNlm6sI3SC7mS1WPIFT6feczlhUdDWu5B7lu/n1NzJzO+BjRNt0vD6RDpEFd4NJwOPbmWfSi5T1v7ewX+ZX8XzaN823XBxzrzPN8v26et/YIW93l1joredVmeumVt5Wxrel52qNuHfP7pPpr5bzankR6OgFiNHmkap6Fqi0ggUALcT7+Hez/vJwGIhpL24gX+6E1fZvx3YKJe/cLHG/y7p62iUWyFku1IEilF1+/ha6OB50iPpjSmjO2FPrd2e/TjHkpD4LjGP0SiYTZUPS71Y+6cD3j3F1zmg0+ZY3vL4+jJDY50Y14wt83R1ibH2kNumWvgiC79eJPAifCkeR6u1LgJEO1K3/yJIOGT3tS9llM56EESlUbpGK0Vy41tpDDahFIYwMOALqZtkWp5W565aW18LQU9HGHC4aooJtQRy0HMwWf14EPj8kcfWEO0FzK9ntbgsZEnG3z1Ezd57RO2WGms8DfnHuXZBw8xiAVNxwBiR1oOHW+ZregqTafLLXNrXOq7xCpESZX4jNrJPCgjm7xSp0xZH2x1F5Wps58uq7dOP3KlHwe8eg8Aj7KNV120q87AmLXebPk8s8+lZVV/pSRWITKxg8P1iVE4UXJdWTbFWe2OlKQRSkNlvDf70qjNPbgRcCgKaThrNN3D06f+14Jsgu1e0mNtpmSFfhXR8DpshpfpeEtcHjzK1cEgCbEIh5t31qszDUNbtJmX0mh5uMZ3CHFo0ibMX8oEj4oxPEFFG8gqZmnrRx5cKUKRi08mqtPr8oN9qqYqkDhL6fssGit1AQ4blW1G8/Vl02w8v2xDV7XZq9rMjfNdHjyC89OCl75FEjjr/NY3r/OjC8s8+ZWPMu93Ej8TGtTYqWe+Tld4uI43CpeY+qtIyZcNGk6HjjdkubHGMw5ssh2Z07iO18EVcwSOCZUqEGaTkGh1GD8TciQ4G3VjFxHHoDN+pCa6VAEMV4EeGdMWLVVid276lWqY5LVMalOmnfSe4jii4czxWce28CW8PVxg4y8CGl8iRpFVxsJ0NrpK0RjSdH3jh0UgR05tm24XN4l21nYXjeZO4n+k663Q9VaYHr9TN2BJy86pbNos+fN5y4CVnYCXeaoqXyVI7/Ps2Si/ptbJl81bBDjMIm9X9aFMZqh412Gf2DHz2pMBYtgjdI2coyyaZ0KYcNMCMclTXQcVCZQSeA3Fwed6fOS7FW/5Nc29azH/7V+7XPx+xT/8uMOd8yEXez6xNnI2xGRNZUw7EDhtc9jopBFd3AkwuUmXltvnSKvPExa2OHMspBdLPKk52RnS8TQtdx5fLuAIl6HqJRp38eh5uSIYAR2ebOAm/vPGb0An/RIT/Sufn8WgR6o5Yu5RshhsMYjJmDu6CTjvTq1HU+8CMQWIiOSeHDnA0UOOd4a85plb/ErQwHurCboweH0T2gH6wb+Dc5eg1QApEU/+9OQOVCHwsRwcRemIlrvACw4vAND1zXsCWAiOJI5KAxpOh1s6d3K8HRqn2tZD47r7w5TqjvNZAZMiqgtU7oRsc3ufP6e0B4BHSjt5yGUbvCKmXnaiY6snL2xYNo5Cks7FvuoR6xDPXQAp2Y438GQDRytwfDvgMapuckKvNE7x5Xc8zP96yMeX0HDg42uStWHAExebDMSQwBZxZZ+qaSdaHrYyWsFwi6XGca70H+XKIKTra3zZQqM537ufS324Z/F2Chck6cJwO60QEJNAhuubmOV+Rv3O8XL11QEZyKUXkU2YqWLe1ad11SCL7XudsnWQ9EQ4ECDkrJvvxzPVBbyKAIm6YIMN1CjazBXVlW0337eidqvqLlobJumTf0hxz1+bfL/3BoX3zQ4Q033XJr/3vDZPWgxpOh4tV7HSHKB0M9kg24W4okglwMgpXJDwgzQCzNh7vk74lE4ipbiJEDoGFxzhJmHJEzO8/Dpi69dO15hM5BapHQRy5GDOCNzuCPiYWcMjeYZGqPaJhfGTcri1yMtvHXBq7hK/eDLg3ncu8JHnPYwU8AOvDUbFf/hX40x4YfsYSoEjTYQrPDRG88ZsEkzei/0HCZw2i/5RWyepM4aK08vAjzogXxEgUTTXyuZg2Zysms9l9zWdZ59XF1HZWlwERNjS62yciuTpfB359b+srsnfZiNr5rHcuGRMdZtzeKKBWD0LzS4KEz0l5RtZKtoIiyc8jzu+/n4e+f8u8qGf+BL+8781bb/hVYKNL19gjk0AfuZv53juHQ5zvgnDCrDUiChyliqQI7BzMt3U78smvmzS6ixwoqOIVUisI3xneQTMZMukPBmSkOXCRIkJnHbiHHucP+scuezep6kY9ABwpEeQRNWSOLiiP+pPFpDOa5hMt5LjnYmWRwqqRGpI4Ax51sqAJ3xKn7cfafEXvzjH0Z9b48qLYPHUM3ntt5wZlX/br/fQf/H7iM96RSHo4TtNtsKrDFUPXzZRxASyRSOJvuIIl/977hx3zvfpuMtJWl5+zj8rmB6/+TxV86woX11Zquh3EbixU9k7T7ODJ48XXr2HgEcdcCOfrwxpLhLSbcy5DhJXNljFaDPc0B5ox5gfqIjAa+NEY0eTVsoKeZnvAsFzDp7iPRfO0I8N4LHcMHaDD25c4Wi7SeCsGKFPuqb+awV8XCstj8eadkcByWGfpcZxmu76aHGQwiFSQ9regNEmxHa/jpuAGVAoGPutSZMXKxO0gXQ20K+IdsIU6ywOtjJ18tfdbNdtf5/qUdEmJZ9WB2gqK1O24bFt0spAlDqgSlHdtvL5DaMNONRoNM/+Ibjnr89MlA5/1JxEzq0N+IXfOsxzP/MCT12CU3NR4pNjMFtUpxK+LjHOO4v8bmRPB1ObbRlFk9HB8rzJBuyW9W0Gfx7CkcRqbI8vxPh01py47UxwMieljZEv74bT4ZkHeqw8Y4vv2fR489c0caPJ+3jtt3T5uf/yAAcbt5a0azYl/XgT1/G40HuAtrcwEYb8YPO2xMylCFwoHkPT38uoztjNtpe/Xve7Ddgo63+2jlnBln2qT3txoFAkF+xETiiSoW3rdvEaYTbgMWF7HphH6IhYhwStBYauAURsYMe45kk+kppryFe8mr98yhn++Js1MjPe5n5pc+L7rz//KHc/5TJ3HwppuS7Lg4i2OzSmeCXaBdn2R99FqhkC4CClM/J+kQd0U57nSgNWp+Z/gdPOgN7j55h12jrZp8xcKpTV7aBH3ompQOJIF631SHvGaN+ZSFo7MTtM/UMFTsgwNn1Y8OHVd21y69fDb35PwLe+agBMrqOv+ZLL/Pivfzpzo/V2+l1IHJpul160ji+b3L/+AMuNFi0WRvf5/EMrTGumFO376sip+fmUptWZU1Uy7U7nZRHVAR/rlNmnHexWbacDdRbNoo1b0aJvW3CzdRWdUmTrrLqWW8yFNKfxXmP06UTR2AcDFG/ws+m5aC4nOpqFAFaaxondnBcbr8kIs1l2ffNpM43ZS/pE0iK5BvciwyFtx4Q/TAEPo/7YZaC2iSxex0fkZKOt5EnkwI4krVRgLBJ4yaXZBOiiMV5E+blatBGuK+CW1ZGtpwhsKW8nG/15L/8+sanOKUKd8lXXihbnbF4bOJEXrOsCcmXrgK3NyXo1egrsyNMd/3SBv333Qe5dhws9l8t9l+2ol4Tqiyz9z3cxa0Kniv+sPU9PEN2RmrSMC3xI1XEUWqePFWkSmThHNarbqdNSmZqXzMqbM/40UjVwmQBJrvQ52vZ45VN7PPTpB1g90CRyx5N1+dwWX/HGA0Q6pGwMCQRNpwvAYnCUjruU7QAyMRUqpuIxNP09X65uffkyO5Vxivpj2xDUobK5WM5X9nl1nmYFnW3vaFYenpclZj0UKapzMp/EbLhTnuDJBqHrTMyr/Ga3CohQxPzpv1NIVT5W7/nrM9z/0QUe2RKc2Rac3fbYijaI1BCtzUa7WOMjASOEHIMumX+p5lr2Wrb/jnRHWh3TYIedSsGO9NPKR+3PIe2TAT0CfNnCd5ojJ9au8Ee+nkagkmUi2cxdsvnSetNnEjguT1vucfRrisGsb/ySPkQDxLBHXjMlJVf4zHkHAM3x9uEpTTs/dcZtpSo+ms9XJX+keYrG3F6ACVUHUlXA+N4fID4e+PQOupQfHPmNUtFCPutpQNXCXGfhrhq0Je3GkXFGqdXIz4byG7lIHEy+Vet3zYuOtzjejjnaCjneDjnaDpn33UR9K/VwLez17DV9IoEeO6GyZ6sVhH0cpLG7zIRMFEge3HgYVRlKuIqRTXQmdz0vlOTHb53TuTp9KgNJ0nm1E2E7j5SXbXzrINT5MvtUj4oWy6KTiar3nS2fL5fn90VAnu1a0cYuu5bYqAwgKeq77bsxPXn1T5bPaTdSdB/a5oHzAae34MrA5WLPY224yna0ZsIhEqOTf1Pjv8i8JPsHVvAj9dlhHN8lYEeZWeUsmh1VlPZbykkQXyvQMULr0R86LhHSZ2gSgSO9Ca0WV/icmhtyz9Mvc/Vgm7UDTdYXG6wvNtiYD2ivD/mJD17ije85w0BlTQvtY8hEetm7MVRORYc9ZfytjkBdBQRScr2s/3keYZvPtmexf6JYj4oO/bI8sGqzY/tdt71Zx0NR3uL5IpBJ1JQ+ipgoiRAYKHekCTGZP6PVkSOJw2t+qh5PWfjYFmcfbfPoFjy65fLR1QYb4UUiPTAOLpNIW0rHEwDIhKPOPPiR/BX5JzImLAbo8GXLhCq3+pWosQ+ZBcSGif6n95A6Y06B4xEAJT2r2eEs2h4p+GMA6fE9djzFMw+YUL1F9K1/f5lfeXiNoepVtpKasqR3Wc1byvaZZbJulYxbBILU5XNVfDaf1wYkF5XZyZzdp12atNiQ4ypEOv9iswJBlpnWRe2yaUWDpuxkoqBuxzVCXmyES+W6hKo/oQY7EghL4SyDCB9qRnR9Ex7Klz4Np4PvtJLY5SCERjpuErkjo9J2LQCKa+3E9LFO0XDynToungw41FwYq55PPb8iwZaCNJtQaQMfbPXY2irbHNrAyCqy5S27jzoLQdF9FbVtqVOAfBzYGu4t5QXpOmOhjHemv8vGcFk/svnL2szms60NtnqL0rL9m6wrqDGe3FCxue5zeTDgkS3jV2MzlNy9uG5CH+KNBHdNPBacyawRWpk1pUCbY0QTGoKJJqBWoMJ668FOfBvZvsuC9CxAk6X8vckZ1pjMmiQTXyXGwaHRqltuRJxoa/75iGQrCgzwA0ilcSLF3/+nIb1XtZDPSiNeXd8xVHFzVM+LMgC7rI2iPpa1lc9nu6+yzbetXUvaPq8uIZvsnP+ezZu/ViRT15F1y+TknQBo03WnZsGRGiKExI019NeRnRWy2g1ZzYR8WlqfJ+qNodbGkHOXFnigE+KIAasDlwXf49buwES0IjXrEKM+CHQSEleOeJBAmJCsGZ6nMzw17+ciBRKqnTbPOBemZEyR1KIm+qCv4yFmCnqYqFpjjecjrRjnNuAj9nJXfkDx3z7zIF/09aZMuZlRnXFe3MPi8vl6quaFre6i/LaydYCRfF/y8nSRfF1vHtbuxw3k1UKILwa+AngmMA98FPhhrfWv7nVbOwQ88gtz9oXVZZhljLmugFDVx3zdRQt8wSZVOIABIGQ4JEBC3B8xok1hTpM6dBgIYy8oNYQ6ZBj3RkjoQG1zsiMRibO6FHmdCAWlFQgPhCVs4D7ocX0pe1KZPH/huLS9RRSxYdO1n9/k2OrF64lqddkYLNqQVpWZbq+cUebL5OsuE3CrwBlb20Vku6ei+vapmmwbl2x6/rNsc1UEPhVtjIreeRXIUiWY2Oqvmif535Ppq8Oz/NlpxR+d9qHEXO3i0Q6Xj3SYHww5vylpOorVgcPtXYfDrZC2O0QLPXIYl9qfG8oIdhOgR64RmQXNx4KtATySaxJTToyF89pkW0OqgI5Z14c8AKJUMehRCuhrHOESixChBUIIOl6D5xyMePApl/lguELcE7iRQsQaqTSh73D0V9bof36UU32+tmOovFy2jSpeXtanWfieDcjJy1z5ftjK2srUAYD2qZyqNi5V6/dON1ZVfanavM1aB6T+IpSOGUqg1YHMCb9/5RwA0fIx5NYqBB2U6yJ6G9BbZWOuzc98eMiHLgdAiVZbQm6o4LzmUrPF+mrAnbevc2rT51i7N2GmLIVjNNMQydCVidyd46s5kMMcTmpGmhVJaF0hBFoLw5oRCKFLgY/pjX4yd6z8VmRyZTQ6CgCYvEPV/PWdUuq81NQrE0BriNQOrow43h7ywmde5S8+usDx+1atddz+pxfo/2s/ibZVBHpUjL21szB/uCSPTZYsk2uz5YrqKupXFQBRBF6W9aXo96xzvGxve9PRtwIPAN8CXAJeAvyKEOKA1vrH97KhHQIeZQto0cYre71o0OXrz5cpQsPy38H+wuswdHPtA1fu58y2hyc1bVcROIqWayb87d1bcKKIRzZX2Qolz1rsEGgfYiPgaTS9aJ1Q9gmcNi13fhSOK3UelHdEN/6RE4avJXr7WAY9dgMEzVJWRaP80vXpqx5CCAJhsyc0Y+d87z4ONW9nepxpQtUnUsPEXrFonJcBG2UMsE5aGahQt26bEFxUpoof5NutmtOP3SF7/ahoY1METhQBwfmyRcBIGYCSXs/3y7YprLqXIvDF0LnevXzXezv85AsOZLy420Geb/6bs3z47R2OPLROGdhx+XCby0c6NA9E+EHM9pbHaW/InKdZGMBmaE4xkWOBVAoHcieEE2TTjEjypgJ1VqBNBWghHHAS3lWHfWV5XJbn5fu1F2CHjWbR8ICJ9UgYrxpooRFJCNyTnQFPXXaJnnqZKxebrK/69Hsuw0jSaxsfSf/ve9f54efGOeepdcdQHqCwj53i+WGbE/n6q+ZeUX5bH22/i9rK5sVyrah/RX2pB3bs8+o8Fa11WapaU235Z9ng2Nb5ou9V67d9jOg/+k2ij19ABC7egSY0AsSnfiY/ev+Ab37KEUI1QH/w/bC6Dl/wSuJWd8wvteKN93qce3M/qbea2Q19h17Ho7UxYON0k+15l4tLPhsHhvRjBQxGpoEAUgDIkrvKAg1jsGNkDpP8HmmLCEYgimDszNlOkyaL5XN4vK5k+5J+z1Pap/y97CXokY1Mo0SMI8CTmqctK/7h2TFnoy4rpzenHEwDvP53Pd7x0h7Kb0z1M+2t/tC7EE/6FKw8Z7AJF+6Fg3cW9TL3WSbvYsljq2unwEFd+X0vQcyd13EDefXLtNaXMr//TAhxFAOE3GjAo87LL2PKOx04tnrzIEgZolZW9/SkuDpw+JPTDm3XeCNuuHCwoTnUjAicB2m5Hl0/ZjGIiR3j7QFACRhEW7S9xZFan0CgxZhRZb1RmxYTppl66a+j9rxXtFvQ47EMmtSlVOMjgsBrsRVdxXE8XI313leap5JvZmxFOsTFgTii62btHPNzIvmcMJkpm0v570V5sjTL/KgSpOqCm1X15uffbnjFPhmqu4GybbjKNndF9RTVVYeKNl2zkOC73tuh/5aY17/lPF/8Y9B0Fc88EBDrkKXgOACxDrlv/SH+7v+c5M6HLhTW1mt7XD3Y4uqRNksH+rTaIX5g+HNv26ExFxHGsBnKkV2yI13SE6sx6JEBs3Xin2MKbB3P9awwPVKrHvHY5ERMyDHwkaX8mpEHdm18uhTssPGKPaYS8FkKJ3EKawT5tqe4ZynCc+DcwiYX+nD1asDmus/6qs8qLR78mT6v+4GzfO8vb3OsVSQUT1Kkh6OQwKvDsyz4h5MrNlAg/7sMHMxfs9VjA2Bs4GK+jioQsgywLJKjqgDNsv7sU32qI5Nmv88KZMzSVlW+srLFGzV1YY1Hfr+H31K0Fy7j+or7PvlZvO87fV7Dad7wc0MOPufJHBo0WBueZzE4mpQWnBOXOPfmZo0+G9qYD+i3PZRj2vZ7Eb22TxRKVgdGll8MYgJniNAmUolGg1BonZPDc7J5Flww/j+i0XfAREFJNT3Qo98p8JHWO3o6Qo7AB5Hwe7tpR32wI29+Y6OdgB5l9U4e2gpiLej6imfducX7vCYPnT6APKtYPrtJ92p/VK79C1u85he2+M5f6HHr3O3W+xd3PZv8HFDExCrk8pyJqFV2t0YLe9y36XFaBEJMPQEm5dBZgY+6cnOdPtapd9Z+3ByUAztS+kfgC/a6rR0AHnZE11DZgy0Snoo2YEWbrbKTB1s+m8CRH8DTff6UI8fZCM/yV+fN1PIlBI6m68c0HMMUAxmg0awNz9N0u7jCR+mYtreYtCJIvRLrBHuFDOCRDVtlC0l4rcxZ9mlnpBVCaxrOHJEasq22abkLuMJEaPnY2r0cas4x7x+aKOP2e+Y9+q0KcCgZj2GfyPNGQvjENWB6/O6EodUBFerM5TIApahvdpDR3qfJ8gIQNW16H99UZ1NWlD/7uwgQseUVJdfLrpX1q6yPk2n9t4x56Du+CUDyE4sOkefx6z+1ScPp8KMfuMhvv+MESPjIMw8TBxIRahYubbN8doth4HD1UJuNAw0WlgYcn9ug1TFgh4oFSglcT+ElmINCEOshsXYRWqCIRirOQjiJkKnGQm/WCz9MgAt5sGNs6mgBPdBMqV5nNSpUpv6iNaTIX8e1JpuGyVQfdfJ/NXI0CHCoGRIqwYLvcHAAj/oDVhcHnHXbZoOz0qJ7tc/X/NZRvuJ5Z3nV7Ues9WapF62jUXS9gxmwowzwK6pvt3ypqHxZvWXz0la+6n5sgM3O5uw+ry6i/LpXN/+sZOO7RWtr1cFGERiSb8N8d1715Ry/9FbO/YXZ7DpNyY9+1Vieecvr/Ykyb/stoL/JN79vi9UfrAd2DBou60sNBk2XMDBbGZGYt7muQjqaoYL1oUPgaKSIkSIc8WKJA8LwGJkEnp1yoCokSoWkTk4ntTwMsCEFaC2Sx5Ezj0nqSOuuA07YnrVVmyNr1rLHoMcIWKmoV6MJlWaQhKq9vQvhyR4LS30uHWxxemmRK5eGHL/36oTGx2u//wTf9FVnecXRLgRzpqZ3/g7i7rvg8BNH+RSK01v3MYglp+ZOcrh556jlorEaqSF+pMBvY9+f7nTeVYEXRUBKGcBSBUJX5a0zZ4tkr8nWbjJe/XzgQ3td6S41PKpeZN3NVPZF5l9SndOEMmS6bNAWI3cSh2PtIduRMV1ouRA4iqarEnVpA1z4SSiqFO0MZMs4vUwFVCERMhMCakJIjcvtnLNC4LXUpHisanlcD7OWbL7khFY6xg50I1zlXWeHnOwMuTxw6UUBt3eXMoX12Amt45q/sA/RkJ4vaCoPvOzinoxBv4UzNdbrCBxV+aGY6VeBlTZma5s/VYtEGWpePB/3aSdUZ8NSVi5fpgw4qQK36lAZfy/uTy/e4Ou+aN1aMj1detXXah69vcWRk3M89/Mu03AgVLAVwZVVH6UEKvbpb/jM+yG3LG/QbMT40qg/hwpCZe6t4WjarlkTZPJ8RxEARGw0liWIvMq0yvARi3lJ1tP+6PsIJs/myYAe2TpuBDi+K96fea8WB31Kx6MIOOb5Gq2dQ82QwNE0HOOg1BGwtTBg9UrAoOEyaLis/OYqP7l6kBe89uO03CbLwQls40slgMp969s8/YA5GcxqfNQDNGzj0zY3bFSVp6zuunOrCBQp4g/5vpXRTSUkP0ZpVqAhf73OWplfW6s2gmVruq3eXBuOi3fXAYb/5wzNkwGf3vomnnjf2cJaXvMKc219scHpT1mk0YyIQsmg57B0YQsAbxDT2AoJA4e1Ay225nyEA66riCIDXAOIALqdodHKS0DpQSzwpMSTMREmaowQYsRfNQopMqAHqYlh4mRTpyC0GoEdRjvBMd9FCk4zAXpkQY7U3CT/u4iKwujOoqlRFAZ2L0jpmEgNGcSSSAmUhnk/5tScY9ZI37y3M3S4ut5i5czmqOytH7rET/7JMi9/0QcRzS7x/3kX8doA/wWfneQw428Y99gMHf7mfJNTczBQW/SidRYGkmG7iy/z4JgwaV7M9PyxzYFZwYM8Fcm3RfOnbP+8E2CkiGaR768pHRBC/H3m989orX+mrIAQ4jMx2h2v2+vO7DJKS9lDnXVDZaP8S55FeC7rk21QTvbvY2v38t8/3GYp0DRcmPMUS0FMx9MTKnDmMwljKhwDdoSJCldWRdjmaC5Nz4IeNlOWwlPBPaTHg2nKXpA2i5xA0PWW+OSD6/SimO1IEik5AsMAMxaGSZhEP4nu4/ow3KY5kBAUnWSU+/rO5y3pLNPje1bGXXW9aA5VATHZ/pS1latD7A/T+lS2KSpLL0orA0FsGypK0qvqzvaxfEP2sx/eLLg+puZmiHDg2MlN7lmEBd+U7ceCKwtD1ofQdmGo+oTKfAcDiGxFBvCItU7slWHOg4ZjtP5iDVJkQx5qhJbJCaLx2WTV4Bvd5lgonVBVToANndHymDxtm3E9vJbrR54qNUbK13SNItYRsYqI1BBNPAKcWq4iUgYA2Y5c8+7aEX6g2Ao81pcarJzZ5PY/vcCb/rTF6oEmv/1W+3MaxFtI4XCoFdKL1vFlk0e2HuFE+8TIud50v/Pzqu47sAHH+Tpsm9F8HUXXitoqIlubs4ArFW3s8+oSmgXor3uoUbSRq6I6def7US7P67/4XT7yg+dozWv8N30zT3x5MdiRpfMnutz2hFUWFwcADIaS1SsNtjc9/KYiUgFxJDnQ7HOAPsOBY/z4DCRRKJGOptGM6S4MaAWKhgtKQ6QEoRKESiFFhJRO0lMDboyituQGbBZ8Nr9TIHrsuFSIlFfbwtHuPdk0NWYJL5ulfISXCe1CiiVRpU244aGK2QxdtiM54s8LviZUgljDxkqP9TWfrW4wAXgAnPj9y7z294+wMR/wG/8yQA/izAGgIFR9VodnCRx46tKA9eEFGk6HP35U85nHQuZFyfZ1ZG4zC1Aw6/60LF+dumyHQ3XqhnJ5vi6QM1n9NeLVl7TWz6qbWQhxCvgV4H9prd+2153ZwS2WLbi2xTr9q9qY1W2vTEC21Vu0UOc/x+3cu34vP/WhFkuB5mADjrYUR1ohK81wBGykoZkc4SVhrRLmUBRLW0Xj73nNDTBAh8ql5/Pmr+3T7mZp3bK5kIzZeORNp0vHa3C8M2QzdPjLsw/Ri9cZjTnXh2Y3Abecyfocl+mxaWPQ+U1fXUG7TDip2qBWtV8EVBQJ7nX6VrZx3qd6JMjzM/tvXXCtDGDI1133HRf1K22viKr4NlwdnuW9b6rmhasrTboLAxqOAS06XsxSEHG4FXF7N+buBc1KE0524NQcrDSN3yZPGmCj4RgQpO1C14O2ZzbegaNQmuTEb9qp3cR92Ph2QejDXVF+HSmi7DWrz6iyjXdBPVUkxk76yk4eYx0R6zD5GzKIBZESDGJJrI0auRRG26bhgCNMWD3tCTYXGmzNjdXnFy71eM3LT7MVrZIfQ8O4hyt9jrbuoOl2CVWfvzrXph9vUj5eiwC6LIhrK5OXX4qE1qK0ovk5q4BdJcdl27PlKUrbp/pUBmZlfxe92yqwrAykKhp3RX3LUxHv1+j/+7vc9/330V6MOPbSDq+rCXYoKdCOQErNgg8n23DbguIJJ7e54841Dh3d5sjxLU6c2mDlUI9WO8R1FX4Q02jGdLohnbmQVtv8tRP+HSZgR8o/kl5O+uFI/hU/jUngw57HBtxOp+UPTG01mesVJirCDtLsFY1M8TPmLSZdE+kBoerTiyRboUMvlvQi86e04ceOgMBXtNoRsVvcx7m1Aa/9/z4DeaBJ/Es/B6unR9cGMSwHXZ65cgsLgXF2+xsfaLA67E0eLmZ6PUll47zuXtLGA4v4cNWaWSZf1elDSrOAI0V9ublICLEE/AHwMPDl16KNPdDwKNpM1WWoZZO+aAEoA1DyDL9Ou6a+1eFZ7l/v8/Mfa3GwYYTeoy3F8bYBOxqOP2IujnBHDklHDnziaAxsWLuW06KYss/OpNu+2+ob3eoeML3daHk8XjREtII4wnE8o7YuBE6CNJ+aG3Cp7/KHj2zwgsNXWPAP47ktJsaakBB0IB6mCUyOZ9sYLwL9bEJrvnzZ5jVfh+13WZ6ivhbPMXvdNn5RPNfFDYoX/tigIgALJjdMZZunbN5sHVUAh62dbD+KheTJcVC0oZu8/rG1+/i+19iiJU2ScUDa5tjSJi3XCGIAHc/wz4EUeFLjJadUjoahMqYSfsLSUtMWX8KcbwCQjqdwhBGoPWm0O1LTxrGfpqQxG6BQaXM96UCvlLLAeT7N9r2szonr12aujTVacuY5JAK1GpqIVnrIdiToZU4QlTa/B7FgmJwkArieQkrNoOly8dgc6uwmc2uDUb3/4yM9vvHJC2TH0tjfkgn/23IXeNktPc73VpNIWkUghS09z/eKNqC2eTi++/Gn7VrZHCr6nS9XthHO57fdg61ee7/2ebWNyuTXOtdTsq3hdeTpOnJ0kUxfQmf+Gf0P7+fe/3qOzlLE4Tc+i9d+913lZTK0vtQgbkukNKD0QmB49VYITQfWQ51o2kE/NH1JHUn7fox0NK6raHVCOs2YBd+UH2t3CFwlCHR6h2MeaOOxY99707w7NRVJDztHZXJaeLYwsdnvE378SijV6shGSkl/5/MV9bUujSOFaRCp2U7WeaoiVAN6kdHu2Iwk60OHfmzejdKwPjSakYOhRCmBVNV84MOf8QKe8I4/YfDf34H/Rc/FO/ZEbumMfXYIJPP+IX7kRR/jfZda3DY32es6dzaWM8v2rXmaZZ7VARTrzO0iKurjLHO5INcN5NVCiBbwu4APvFRrvXUt2tkl4JFf9LODqShflnayyavqxywbtvHvzegyP/nPmn4c8MQFo7I87xuwY7kRETjuSLPDEW6i6eGOND0EwoAdVdFV8iBH9jObZ9bTvuuprvyJRLP6Aclo6zjJmIiFhyLmSGsViJACzm0rfLmG7xgVvabTHdfheDlgzAZS1AE5Jm6kIP9OqQhAKWu7DOgo689u+7pP9ajo9KFoE0Pu+6ztFG3mysoU5dHJ/xWDeJuv/Motmlv1wI5zt8wzdyRkYanPom+0Anyp8aRGCo1RdEx4QGZjLUWi5ZF0KTVn6XqapqtouyopP0lZFWnT6Sr+Uv6M6zm6yxeaATRP686aVhaBInughZI/Lc3PfqMuPSDWEaGKGMQOg9gAHEoLQi3oRYLN0GEjhO1ovAECiAPJtvTRcg43UjS3QgDe+ybFyw6v8nP/5SqL/lFe90cb/I/Pnhut68Z5oWTBP8yCX7aRrwICq+ZLHcCxqJ4iAKUq/yx9spXL97cI7NmncqqzYdnL9bCujF23npRy4+7Sg5z7tt8jCgUnni/5wju+npXvXq1de+RK1peadLpD2p2ItmtAZT/HglITQzAaXa5nnJNKaf78IGauoZjzoOsbfg3GjweAIzRS5MEHO2/NR0LJU5UGho3sfLz4ECALVhSBHuP+5MAX9ExveGzOMvZRgpYmNHimpliFhKrPdiTZDI1Wx1Zk+PAwhn4M6yGsDmFz3Wdz3cOJSkw6E/qh1wU88KSv5J2v/yc4d4kv+HvB/3rFFmiFDtoJf3Y41rqLg8f7udJF47NovNu+VwEgNsDbNp/rPPWyum15ZpH/be3sxZ7g2pAQwgXeAdwJvEBrXRw+b5e0hxoeVQ/TBo7kr9mEDFvdRb9tg6aqL7AeXuCRzQ1Wh01u78KJtmLBj2l7MSvNCE+6SMZghyM8HOkhcZJPOXJGOUEp+JG3Z64CO7Kfs9Jj1QHpbmi30WzqlJ9yXmpWXeG4OMKl5S7gyQZtr48rArwkgk+o+gzjHr1oHUd4+E6TWIe0vAXLclnE1OqAfVWgYlG5svQqRDtfR922ynhBnqavXc+AEo89Ktqo2RZzCvLZ+KgNBKmziarqX9nGcrrs6/70AvonQur48d+a8zl3yzzuMc3hY5scnVMsN6DtKVquwpU6EYCNgzmlNaE09sepdkeqDeJJ8KXGlZpWAnYEjjKaIVIjMGuD0chI/1Lgo0TYk2MV68K7LuTHSZm8dkcdsCPL5/OgR9qvOiD6LLw3MWdJTX5GXRFZdXLjrFRrTaxDBrEYqUr3YnNK24sk66FgOzIniVeHsL3lMRw45jRRamRT08OEFpZntwj6hl8vn9vi332pD1zCAT73T5o0t3osfEHEow/O8Ydfo62O8JKeFtxYEehQNP6L0qrHvz1fGe+3ze0i0MTGL7KARjbNJqdN0z6vzpONz+3VRqSK39tk6bL1t+zdZtLOfwz9gQ/S33Bwfu1lfO2Xt1j5p9W6nQbg8pE2WwcDTh1cY8E3TqF9acBpMFpcoYJenJhLOEa7w0209DzP8OGGY0wOFxItPEeAKzWe0AROyvPFCOSUCc+evvOxGUfqt6MO2Xh1PtTtVIRGmOShU1obdtBjqpyFisPe2kEcA3roMY8WJCYq7uh6rCOUjhnEDluRZCMUbAwNwNGLYSOE1XWP7S3POJK+4nBwtd6B/a0fusRr33AMOMYi67zm7Sa995qQR/5pjr/+90NYPYO3dLKipjIQw5ZeVodNVhK5NJs8W3VIWBfkKCNbG7MAJWO6gbz6p4CXAN8ELAkhnpu59o9a64G92Oy0S8AD6jPQoo1R1aDIU9EgseUpq9O0uzY8zy98LGaomjzzABxoRCz4MV0/pu0pfOmPNDhSfx0G+PCMgKu1MU/Igx1ZsgEfZWDHbmlf2+PaUPq+cu9JaLMAebKBK3yzOMQR2nERskngtAHjIO9S/xwdr5MsJlknV1WCRxXDqgM8FOWvA37Y0mZF1fNtz34qKITeV5Mupd2cuOaF8Vk2eHWAi+y1os8032TdH1u7t5b5Skrriw0uH+kgj8DC0oC5uZAF32juNRPBd6ThocCTmoFIARBtws5mhq4rNYGT/ElF4Jjyph4y2n8GCB+ZOaKLtf5K+HNeCM+GS7Rqj+TBjvxaYhOoi0CPtD5ZIVRLWa3RmKOsavVkJJqxWUuqUq10PAI7NpPPUAk2QgN0rCZq02s9yfamx/aWa3zDpr7CA9iaC1BSMH+pN2HektLx+1bNl5+G6MldlI6oNwfqzhXbmE/Ts2Xr8MkyOccGHubno22jXcYvqgBOW/uZ2vd5dQHZNkR1ZWJbHtuBYVH9dQ8pqq4ldZ37CFff9NssfMU9vO5JX8fJL79SUWaatuZ8em2f7sKQTnfIXOIjqeEYbYzUj9KWyALQ5hqMHUln/Sy1R06lDa9uumrEv52MH768SUpdGvvtk4kPkHEo2+xvm8lJKdiR/Z3xdzSOGjMOcZ7V9thLmgyPHpuwu+hMu6a9XiTZDB1WB3B5YLQ6+jGsrfmsrwasr/r0Vl0WrmzTWd3dnrX5tm3uYhvOXTTPRUWJD7y6768I8JuVbPO0aD7Nsg+eZb4X1Ve3LkuJG8ur09A8P2a5divw4F41tAvAYzcvoGgjleadFQSZhUw7Q9Xn7fdd5ULPqL4dCzRLjZiVRkjHUzRdB4nR5Ej9dTjCG326woc4NGBHXOK3I0tZ4VDKaUZ3s9BOtTxupHbI9dDyyOYF8+5H96wQjmNCm0UD0ArheKPY1lorYh0SKsED6wPuWRomTpeSsT6qp0oYmupMQXq9E7h6cy3fl3yZPOMvEq6KhK18H+sIfPtUTmUbsrKxUFSHbUNV1XZVH4pAjnH62vACf3dxwFv/fokm2yVtjmljPuDisTncY5qFpT5LKz2ONGG5AV0/TpyNGsACAKkJlR4J0cbhmrkmRSp0a1xh8jRdNfpsuUaAdqU/1gBM1wwcRtFZZuBNI5Xl7IngFNih7WBHGdCRTSszV7Fpe8xCFfnHwrSpX5FuEswYSP2gxFoziCWhNmElN0JBqAzYsREaAXutJ+n3XKJIomKBGymUEtA0J8DRgmSt02K7GxCe3iDoRShpbMrbG5OHFL/1H87ScG6hfGxi+W6jPO+1gQV1AYd8vUV5djJHi8gGatrKV93DPk1S1cEGJdezVAfAKFpf94C2r9L7rp/GOdph8c2v4k8GDic/NjvYMWi4rC81GBz0OHRgnZW2ouuZkN9u4ssDwJdipHGXOiz2GJsZ+pKRk9JuxsdSx1N03DgBPBSeNBq5o4ADJWYp+QgtKY38d+TAjlSjLwt+mPwF/BumeTjk+Ofku8sDH0WgR3b9qOu/Y2zOMum7RGkSf4WGL6dA/kBJ+okJy1YEV7cchgNnBHYMrjh0V3s0tkL8YbVJSxX9xDua6PecgSvriBcezUR0gTJNFsudMrusWUcuL5J3q+TgnfRnr/PdONJan7pebe2BhkfZol4kONiYctUCnG8vX342enjzNKe3Wtw1rznQiBI/HZqOZ1RajVaHNFocCSNLhVmJNJvaeDjzCdeIChncHtJj0TzlZichQSbTRkUTmwSRjuEUPAl7CC8YLUqRGqK0IHCyYyYZv0qBI3PpVQzTJsjYwIcqwdf2uw7IUoWalzH1srby5SbT9od0HaqzmbGd2trqyOetAjPKNkC2du0bq6Hq802vDAFZG+y4fLjN5SMdgoMxC0sDDhzaZqVlIrCsNFJTRTVSbwaI43HUj0BqcMfzUwrwhB5phHhyrBqdBTsc4eLJxgQoXhqKtoDyKstZYXlMObAjr9VRB1zJaxjanGbbfHtYO113QibmLBmwQyf3krcTN+utAZzSsJL92Dgw7MdGdTpUEIUmHKVSAtdTaCWRSjMcOLieotE0hxGqLTjnztNZG+ANItxQIZUe+fcAeMv7fb7/2aZXuYfF7HJGFQhQV94pqncn7dYBJ4ry5Odovb7v8+o8FYET+d9VAPUsJ9Zlbc42tkehsv/hr4h7MV/3F/8S/qJ28Qka+g5XDrW4erDNkSNbHFzpczgBplNQGkjCfgu8JFpWSin44SfaHQ13HFGr5SqarqLrGX7f8WIazphXp9raZZFZbCSEMKFoi8COjMZI1pRlDHbkgA6Y3kOMgGYyE2ga+KgCPUwpPdM9ZsPupqYtUiTpIu2BRODgifH7CRX0ey7bm94oVLAbxnjDGG+we7AD4Dv/fsBblIaGP8VYlI5xSplNFShYRnXy2+ZRXr7Nz1nbHK4jSxflK+tDer2YHg+8eoeARxHKld+cVZ0SUHCt/kvaidAwVH3+77kWT1pUnOwMWWlG+NLHk8FIaAUmGNpIiI1DUIkJy16Zjuz0JK0O7YMe146kOwY90s2D4yaq3oCKECHErlGfbLpdOt4mocqppYNxZDpFtjlWhjCnafmNqm0OzoIOlzHhMtAln7fod5nQVWdjvk+TlAfL8unkrhfx7VlPn7Pt1C1n7+NWtMq/+eL6jro35gPWDjRZX2rSXgo5eGSbxeUBKw3NkZaJtnWoGbIQmNM+R5hxHetUJVqjkhCzrtGVHjm4kyIFOsafAmdCeHalPwpZPtbcojYgLhCQcUY3Sp/w+J8IzEVgR5m/DpheB/ImLaOGCnx72NanumYtubbH5izGf8rYxE+P1luBQ+AYzRoYC9Zh0pwjksgsQxOdwfUUA+ngJGCGigVuU9NoRkip6XRDNtc91tcbdNYGKEfgDWLcyFR47s2Kj73tPu6av51i3ln3YMc2/yafQD3ZqIqK2sjzzTJekO9X0fW6B1r7tDsqWw9nATrq0A7q2b5K/KHTfMOjX7njVgcNl8tH2lw90ubwsS2Ondjk9jk42dF0/Zg0iukgNv3zpabtCpSGdjr/ZQ7wSExWmq4ikJq2Z7T52p7R7PBkMOLVhr8UOyu1RzsRgESIMfCTBztSU0a7z44c765DU7x7PD9toAcwEUa2rrNrnfwbfR/58dAoYhRRJiql4c+Bo5jzNJ40PlZULBgOHIYDE2lHO4LYlcSeJHLliM/mSUlB6Ds4kSrMA3DlBxSD33ghwV/88dQ1e4ja6bucDbCok140H2eVgev0qaovswCmj0/aIeBR9HJtg6AI1cptlIbb4Ld31p0ZaSu6ypMXBxxqhbTdFoGzOPLJYez6xmjpCJ3VGsLeJNCR0l6BCtdD66Mu7Zu1FJOKDNhh2ySkGwOR/EVDXNkCxyGQLeb9Qwzi85nKihiTDUjI/i6YR4XfbRvbIipqu26ZMiad33Db2ikDQBmZCe1TERXx46rNV93T6Kq60jx1ytvrefX/EsxZr0zS6oEmqwda9Nse3oJiZaFHd2HA4uKAI0041oZDTcWRVsihVoifCLvGxCzCETERKajBtHaH1CPAw/jqcEdrxehTemOfTum91XGCnE9KQI982pQZi82EZafOrqs0PPaYnxsngKl2xzj0YRrSN/WVlZoHeTIcqbVnfaqkquyBr1CZiFfDgUO4avqbhqlsNCP8IDagx9yQ7QWP9VWfq1eMvHHokY1R+e97TYO3/c+JHufvIPksAxWzecvmiq3+LJUByrbvdeZlWRtl7dYBL6fz7PPqulS0fubJLiMM1BaBbFNvfd8lXXmYH372y+APdiZrmTDhLdaOtTh5ap2jR7Z54gLc3o3pegaQjpRgO5KJeaFAiSLtjrH2XeBomo5KTA3Npy8dXNnEHWnieVN+O4pAgYkIKDkwOjXvyIMdKSAwrdWR4911yOZAGhJ+bN5zHvTYLRkNvOlILUrESTtJoAbh0HSHBI55xo4w/FalEc6kZth06IU+QmlkrOmsDazhaXttj+05n8iT+IMYvxdZ/S0BfO0XrfLz/76VW5PK5cXq9DpligCO/PVsubpydJFsnq+7SK621Z/n29Vj4/HAq/fApAWmH3zR5ssiGMQhRH3wW0yfeuSBkt32T7M2vMDl/ja3dgN8OUfT7Rp/HPm+pQ4ldWzMV1RU7qtjrzf6e6n1sa/lsfeUDSubRm3RKjF3cceAB5jx7RiApOnMcazdyIy5KiZsmxO2a3nKM9sy5pgvVwetrjppKrpeBerYNhT7NDvlF7qq01nb5qVoA1V2Cmwrb8tjG9vm86X/Zo2VM5sF9aYhDBtsz/msLzVxmpp2J2RhaUBnbshyN+JIE1aacKgZc6gZJeD2HI7wTNhThkZI1MZWHCVwHD2yG099dzjCCLfZkORjp9XeKCy5xBnfw6y+gGBCcJ18Sjk16CKtjt36g6rS8IBq05aqJiYclk4K1AizwUjvP9WWabqDkcNYT4rEUeEY8Gi7QMOoTLuuQkrNeuCzue6PwlQ2mhGNZkTb10BIPx7QXfC40m6yOWjQWR2M/HlEbv7+ZgECKfg9qwxjmzdVJ4a2eVYFtuT7lO9nkbBfxBv2efXuqOygwJ7f+AUb4DvNUVr98nVpPB7EuY/yuT94agIkrEtKCtaXGlw+bJxJnzyyzokjPe7oGrDjZGdIy1WESrAZyhHAmYLR5lMghR5HX5GaIAE9UgelxhwGXBHgSG8EdmRNTqr8PVh9d1iip0ybtVjAjlmBjqkHl9sLWJyaFt5HwsPrmrWkUWlGGh5oYJyGMPftyyaBM6DtKhquY0II+zF+EBsNjyRU8KCZRHeRgkHTpXulP4qYBWZMDJouvY7PoOESBg5SaeYvbXPkoXVrH8XTng9B/jhkFrm26HsZ2eTRuqBDnbqr8heBIWV56tb9+KE9AjyKTnHBjopl8mgFbsMyefcG+OjHWwziLRQxLXeeSA850GjT9Q8iowj6PXBCJnwzpP1KN7J1nZJeC2DhWpq7fCLS9dLyKHIOmAIhrm8E+VTlOxlDyoFQ9XEdj+JNfdn8KQM9bAy4LkMvyzcL4FJUTxGYqS1p+XbHdQixPxWqKX+6YNugVG1eyOUtqreo7TpgiPl+dXiWt98Hh5uaJy/1WDkzHY1la85ncyHg8uEOzYUI11P4vmLF6eH7MY1WxPzCkMNNzXIAh1uw0ow41AxZCqDtLePJBkrHhKqP0ONx7AiN42SE+pxachboGIciH4MghVodMwq500J47mTQps1xLRxf59exonUt5ZUzRmvJbyYm/HokZi2u9Gm6XWId0fVjBrFxXNqPjVp7P4amY9TZDfAR049j2p2ITnfI5roPwMJyn04zHtn2pw5p170hrqtYX11kfaMxAjzWXl6kV5Qf10U8ugggzPO7usJtGXBoazPPU7Pp+TRb34vkuHxaNbiyz6vrUNnBQNkGK03ReDKYBFwL1/4dgCAb51FzB/jlj58n1vCKW5d3BHakJixrx1osHehz+Pgmx+dj7pqHE52IW+cGtF2fWEeEGS0AV6Z+fExa6mcp1bpLQY80CosrHFzZmNDAk0lI1Z1GZIEsIC0n6sqDHZOmMnsAdmTJuhfQo77NQnZ/Hyr3W48AExM1KxppMfpOk8WgRy8KWWm4bIWCfhzR74UMB+aw2HUVUSCJmg6rzRZBL6LX8WmvD5i70kcqzeUjbbbnAgZNF90UBEGM6yo255qckYKjD6xN9Ony4Tb01nOARxUPy+8f8zJmHZ6cbyeb38ZnJ5/kdD3562XztAiILupfPk/1vH+88OpdAh5FzJqCtPz1hCG4PiBQxBnGXYRMVS3IY+rHW6wNz9PxlnClEYDa7iIN2YTt9bEgl92sjoCOHQqT10qbYpena/taHteRVAQRjLU8xmNJa+NgaW+Q4TpIdZXwk2eQZW0XMfy0vnw9RXmrkOp8//bR6fpUtJiWLYRVwIT5VMSkKrvFG75s2fz1ybo1mlAN+MpvbLB0wTgl/W3GYEfqgNRZ0hw42GOhFXG4eYkgMFFSQiWIE39oc4EeRWI51FQcaESsNEPm/QZNZ46GO4fSMbEKR47ZDLDB+LcQJqSskGOAQ4xDzE6YPRY5oLM+xqp1pABE2mvzlVmoar1Ir9UBiGusO+PTxKQIEkd4eDKg420wiAWRFgyVS14rug0MlQFBQqWZWwzZmgsJI0Hb17TcySgODdeMk1hHPNqKCAOXXtuDL/b4jZfZQKey32laXdAQ7POxTjtF9drqKKovD54UfdraqduPfSqmos2MjarWW4HSMZ4MSGVpJUhk6N3IFQltXYb1C7zu1WPHvu9kaM069B02F8zGddA0/hT8QTRyWLm50IBjguOHNjh6ZJtTHTjZgVvnBhzvRLTceaN5Fw9H/ZRC4wFx0vXUafQ4RLhKwswaoMEVxmTFke6Ij+cBismnUe955B2VjstPAuO78tUxC6V7gT2W6bMRYLI0itoizJqd3qcrfLp+n4UgZjl0TcSW5QFRZMKEG4pRShB1BP1tl42hx+Z8wPpSE28Q0Wv7RF0HKTXtzhDfV7Q6IVJqHhzOTwAeVw62+J8/1YarNs2PIlkxL//OIiNPPIUa5cvKlVHVHrcuvyi6vs+XU9ol4FEXRZpOj/TQqPVrZfxi9NfH51tew2h9pL4Qai0K0+RKn8XgKL7wIexn/G8MJpmFUoBFsBw1MSNT2StnpnnarbbHrAzysejH43pRlaCvojHgIeVI88NxW/iyaQH3isCLMoZXxZyz3+vPzclyZe0X9T3NV2chKet7vr3kVOXGxQt/DFDVyWt+c1a9kbnYf4h3nvF560/M0bst4Fde+yBKC27p3Il981TVJ0M/9oFzvO87FUuZtMiVnLltgcVnD7n96DbPbfToetD2zOleero/jE3YUjCn9gsBLAWK5SBmIYg40ICG06XlzuNJA6LEeiy4CyERWiBwQDDS5Eg1N9KTwfS7EDJ3iqor5r/lWl3gI++U9FprdBR2Z2/XMbNh0OPbTEIgGvv4RIVapGY9RsvDlT5NJ2C5MUBpo92htBFbhnGisSET0CMJkRhrA24MlR45NGy7BujwJHQ9cyJ86xycvbTNxy8v4t0Nv/DZZwnk7UyO6dHDKL2z6nlUd85VgRd5sCTfV1vdWR5tA2eqAMyd89t9Xp2nsjWxCgTL0GATgg4AH1l9gO/563m8t/YB+PKfUPhS86lHTjChbVC5dk/S179vwPYPLxZ24eG7lhgc9eguDOl0hzSlxosUclMxHErC2CPEw/U0B5d6LK/0OdHWHGvDyU5k/Co1AwJnAYBIDUf9SR1JxxqCTGjwFOQIHCZ9KVlAjpSmfHZYzFmqwI880GHX6tBMROa6FmBHtu7cPiDvxyMFJwyPdSbzFkR1MeuiRBOPgA5Tr/GzlPrxSLU8mu4WK40QpSHWLrHWhEt948NjYIAM6RgH0o1mzHDgEIWCYduhH3m4rqLTGtJqRyNfS3PdkAVfc+fxHh+8tzU6DPkP/3kdVyxBc8G4Qphy9G/jc3Wu2fLYrhXRLIdzRXnKZOi8DG87vKpD5bz48cCr98ikBWZDocwp90BvEcSRUVHKUrwJchscH5rdDOhRZxCbl6bRRGpIQzkwXC0WFOsIkTfbxn+32h6f6LRbs5bdUj6smJDGpCV5Z7GOkNrJgXl1AYkiQcnGGMvK1AUj6lzbCapeVVede9gnO80CaBSVNc/6ka2P8W1/coAnn+jzktdusBVt8G1/coBH/6HDcz73DN/x9JADjZM1+zKZ3sisPuuLDVZXWjh3CT73WVd5xoGIA41oZNM9iCWboaQXS3ParwSxNuYobU/RcRVLjYjFIMaXLZpul8BpmxDiyf2kwqAwbvaRIut4Luss05vw1TEGN2qE18sLuXXN4/LRUrJgx/XQ6KiitI97sPZI4aB0+kYyoAfjE0QwJ9W+bKGcGI2m60eEShCqmFg79KWJ2OLJjBNDxwAfoYKGNmBH14eWa6I4SGFs/A80Igax5GnHB8BVAO5dcznUHI/9912+n+VGxIn2nUnPbWADBb+zaWXCNblyRTIOBXnrUBEvsAEmZeCN7X520p/HM9k2Lzs40Igj2LzEn17t8/Z/G3DfMxdwXqiJNwS/9A3nAHjTk4b8/Bsf4ba5Oy3tVcgdOuZgAx60XPr4Cw/xnOdc5JmdVRYCM9fWh7A6hPUQNgYmWkcaKnq+FbPSSB1IG59KR9sRTaebaKeAIp7YrEuhcSW4GH8dY6BjMhS4K3wryAHlQMfI+WiRw9LEuXIamSXVeigHOzL8+VqCHdk2JJD0wWbWMgYsqsPTppobUjhoFBInATkyEVwy4Wkd4dFwOiw3VpM1WtCPHbY6CqWGbG96RJE0EbSaCj+MGfoOUSSJwpgokvhBTHdhQKsdEvjKHF74cOucCUH+3uc2WH+XwokU//ec5NkHFSLxifcHZ85xqBnxSQdunbiLyU9qXjNPqx6wkf9dxBdt87xIri2SodPvNhBkFtqXn2FPAY/8yUH5w/VjQPoQFTinUwpU3zCR1gLGttcOeoyZ0TgNNA2nDb1LJV22OGory7tT0KNO/bPSPuhx7aguYFKUL7VpV8ocP6Z5pAtxRIPAKBTJ1GY9S3mk18ZEi/Jn06AaFS5jglUodBHVBVKK6q64R3FtMMRPLNrpafTkJmwrWuUVP3UHP/Tlj/L8Qysjgan35HX+5pnn+e9/3eVf/Zrg9/+Lse0tqgdMKPCrgzNI4dBy52m7Cyz4xrb74087yInbNrjn+CrPOwj/4tgmi8ExA4rHWwxVj80wInAkg1gSJp7gHWHUmzueYs4TBE4rcabWzgEVoKeEXzE69Ro7JDUO7lIfHVMndmW0I40OJvl4EdhxI4GOOiSkEbwrhfxUHTqNZCCQuKRRAcCZ8OMBGl82RxsL2MDYCmLGwDAFkBNTFWcyZK3nQNeDxSCm6ZpxKBlHdLhlzud8TxHrAfevSs5se5zv3c+//eoOX/o9EX91vsH3fPJ8epPMBjzkhet8njwIkc2X5YU2kKKozSrK12k7LCoDX6qAk+nkfV6dJ9vGZpaDAVMmbDTZCC/x9n8rWPp2yW8/7X4EZqN6Zsvlv/1zg1t/9BL/6dVN3vqb/ZGWm7WNOGQoIh7cOI0UsNKYY947QD/H+s7e0mXhMyK+4xnneeZKj1hrLvZcrgxcLvRcGi7MhaCamlhHeNKYkq004WAzYqURsdKMaDhzeLIxATpoFY00DozDaIGXnDiPzVdcPNlITFfc2kCHuevZB2JeC8LunNQCdsDMfo12R5PztEh7o4pGIE4yjkzNmTV0xKeT/AgCp41AcqS1BqQalA6hirkE9HsuKhYjB9LAyKFpqxOysNRnpa2YS5Q1HMHI39LzDvU424O/GSzR+NCA+zYiPnjlQd7yep8T/2GNd777EH/0df0dPaOCJ1CRtwqUqJu3rM0yDZU69RXVU3HvjxNevUc+PLIvpw7T1iBdrsYXWSS/4ctRNDQaIK0FpjZwceJzw01my3DbpLk+wvWN2lPtW6kBaOxGY+NaAB87AT32zVquD2U3MWkI22gIvjvaxIRCIIiTiC0p2ZhcHkmGaqZom5dlwnMZkGJDsmsCFNb0Mr5RttDsnyBWUx1AoyjP5ObLkw3+5Wed4+6FAb5sjq57coXPPq659XPu5/9RB3n3+TO84PDJqXoiHfITH7xEw4W/PeOgfyKg1/bof57DN37qIzy44bL64jk+64nnuHsBnrgw5J6lOTruUTSKoeqNgIi2qwmcOPF/Yza4AhMhxZdNfKeJJxuJRgdTQIU5oxqf0qXRPrL+OVzpJ2aWMaXaHHUE2SKnxlX5bzaw4xqZtUgADUrEoMe249PREbR5r8IxZi/+OkpH9GI50vKJE42OcVQHkIkZy3IjZjmIaCbhhkM1Vpd/dLPPnfOaQ02H73r/Cn8k1/mMoy5nnrvIT/3XAX/4I23EFO+py4vKtD3K5p7t9zSAWJ9sALcNrLEdWNn6UPS5TzunMpnZfughhcPStuZDn3yU7zh8jsDpjMbqbV34/udGvOP7HN79HYq3vP8K3/70w8n1cV29eJ0v+03NoaPbvO+dK9z9XgOKDBqa53zHeT7wsS4rrHLfk1dYumfAp9y1yStv3+Zwq4vWHr14fRQK1mjjSZRO1w5jNtZ2FSvNkI7nEMh2Ei1lDHSkpDL8Op2fYMCPwNH40seTwYgXVJmtjJ/s7OYrRfXkwY4xKH7zANK28LRGu6MeGbBDm3sToLUmOV8w33WMFpNaeIHTRgqHw61LKC0IlWBjKOnHMVEoGcZGq0Om7zQBP1rtkKNzimNtA0yDMUn0JLRczd9fbPLsFc1d/+IK7/xDxX3v6NL8T8a85ZHvVbzr1wYQO0xvIatkV1u+fF4bXUstiar986xt7/NpG+0A8CjbpNTZDCX5HY9FtQh+ZICKMiEyHhofHF6CUkdDY8OYMBqRZzhRDvXby433bjfyew187Gt63LyUqhw6SZjaOHH4lXx63hyTaoi2ebMbJluECpcBJXXbywMpZfWlaUVl6rSZyePs9Hk8HmknmyWTz5dNXnjkAm96T4f/+inrNJ3uCEgQQnLH/K285SUP8N1/PU/wzAd5xoHjDFWP7WiNb/jjFq12yIWzcxw7aTzyL/8nSduLabtGq+8zjg757OMXONoOaTrztNzDyUnkWIXWER5IcISLT3pK7+BIN3Fq2Rh5jjddt6wjQqL1ONKWUdkdC2yuDPBkYOqwaXTMelI3qwCcrf9mADmKKG96U3cNm8ibqliT2ItrNPHEhmU6PK/GER6+00Sj6PqrbEcRvUiwFRlpN1QQC0YOTVONj6ZjTJ0CR6M09CLTD6XhZKdNqPo8YeE4b/uSB41NurPMqz/rEm//2S4/8v5zfPNTVnLaS6Y/e0NFfLIO+FEEYBfVU4cH2LQ78m3ZPkv48T6vrkl1TpXHz9wRHsP5A7zy887ztu9t8NQfucKC30w02wy//KJbhzhv9viz75b8/Hed4yvuXKIXr3Oht853fnWb7a7PwqUeZw62cA4rHnjRCq12RKsTct/GNs990jqNp0hesXCRk50hR9sQyDkEkpghAkngKBYCo4Wx4McmikpiftLxFL5s4coWjvAm5nQW7JjURhAjkAPAkwJPNvFlcyLiyrienQMdaR/qmHpkv6egh+n8DebVqRYx5p6LorXomvw663xVaG3C7uoUoBKkWh4igzIIBJ5s0HS6HGqt0Yskh1uSq0PY9BT9nstw4OB6CpWgJ66raLZiuh6caCsCRxFrwVZo+hgqwQsOb9GLJE9YOMHTf/ZROt4FDjUX2Hq1Q/sXtvhXfzrgl16ylHt7RbJoncP5Kl5VdohXRjYwuYrqADR1y1WBKQk9Dnj1DgCP/EPJb2iq0KTMy/AagCD2GzibV4oFS6Wgv54AI9Fkvjqb/bogQ10hbi+0F/YS+JgV9Hi8a1/UoVnMWqA4bz7SwiBjwhX2jCbSaErlAYFs2kSjBddsCLWtnrLrNlBilhOoonptTLisfpsmyz5CXU15IClL6TMs2yhN1vWCw7fwvMMxj2zez+GWQyBbo6sSyW3dW/nyJ5/hP//pMl/5nAu8/f1NPuduh6975ia3zA3peCGeDEamIqkn/ek+T3+XiQaHo72RYDt2KuqN82pV0P/s/RmBUCTzWmb8dYy1OjJ11AU56vroqFvXjXJQeh0pNVFROh59T0lrNXGCOCZj3mKiUjRpuQOarsaTJECGyWXCzhrAI/2dnhDHOkR6KpHrjEmNK30u9h7kby8E/NzfdBgOBBfPHqbZDPmrH/V5/ptP87xDp9gZDyoSWin5nS9XJkfVTcvy2DL+kG87X0f2exE4sk+7o6J1f3qd9WTAG556hDf8jOIX7x3wL45t0nbH4Tpd6fOKWyPe87WCv3yj4q/+7Srxf404/y+P8MRvWeMZywNOdBTLwSpt7zItV+OKIAGdMxta4QL+FIgghUPbhZarWDKuOIwT0YTfj3wkFQAd4zub1L7zpErq8gq1OmYxW7EBGkW+O6zlhZz4Pq31VUDXy6xlJM/vzYY11e4ArABK1o9HSgJBw+0Q65ClxoDLA4dFX3Ix0awbDsdaHlJqcJMoPBLanqLrxfQiOdLscYXx3dLxFB+++ii/+UCDR75XAX3aSZveW/v86l3n+LI7jk70xE67eTZlsuysh4O2uot4bV5GrloLZgVwHn+0C5OWqs1Q/lTAtpCaa+d6DzDfXKazZQ95BSQ+ESzXZ4lcUgdkuJ6gR90+1aF9TY9JutGOS2H8PmTitNRCWggu9u/nYOO2NCX53AmzsjHOOkx2lkWiLlOtIwCX3WPB4iJAyH1GXk5Fm5EqAMS+0ZJIbuncwb3r93GicwRPNghVn0Aa0eOTVwT/5aXnCRzN537eHK5YyoR0LQOzbJcMXxUIA5BMCNo6AwjU8IZv4YdSOJBEYxn56shrdRTVNys/qesMe1bzl53QXgD0exwCMZ3GqWO8UVMTfjyyZQS+bBLKPoHTN+eZehydBYzPgFiAoxKtDw1OAmw42iXSw6QNzUa4OgI//vi05J7bN7kyEPR7Dr3ITfqWl2Pq32F5uTx/tM3R/PX897J5WwZI2OSwfPtlwEzN57DPq2tSnZPbIhLof/xzvuLk7bz14Q4vPrFB0wnoxQM6noni8hV3Dbn3+z1aruJ5v9ij4QxMGFfpI/AQBNO1VkQ2EYiReUoWjKgLcmRpBHaMzEScUZhZV/pWE5ayvmX7aE3PAhgVz92mBTIZ+ebmJZt5S61yQiKTIZlGaoHxeyrSIhFIGk6HxWCblqvwpEQp48C23zPjwnU1rqeQjmYwlIRK4QlNx4sJHMVmaPJFSnDfeoArNItBzB/+0SHEp8DCI1sceWgc5MKToD/yfxF3f0rJHZXvP9PeF6cXybxF9ZVdz9Zta3eWtmxldshLHie8eoeAR9kAsi3M+QV5crE/1DyFqwT9zoCG9ow2R8EGsZBmBT72AvTYS7pWzk33aedUF/hK89o2KloZzSSZmLXk8gkkK41T+YbTwpTPL3N9I7xEx1vKLfx1wIT0exGTLpvnlOS31V224NSpe5/qU5a/2t5HERVtlkzZ27q38gsfO8+7L6wjheZ7nnUVISQdb5mloGkpW4MK5kxq7oCq8KdRpwlSZ3gOWotMCFpZDZzsFHyYRasj/3mjgdo85dfDFFzPp890opk4MBVpHB1dy9FeqpUTOEaxeiuCjRC2IuO7I5RGq0NpY9IyVIJQaRqOcVLr4BGqPutDwXbkEiUq1p92GC4PjCPcy4d6PNqb466vHfK8w8frP6dKXjl5/3ZBtWp+1jxdnpK1yoRsW30zApX7vHpGKjo1zo8B28GFSUvXe3HPc1BBi6+e2+b1fy258OsBQmm+9bs2CaTiroUhT1n2ktY6E72YObqJEGhtTGqESEwfKsANWxujzbNWRmMgvSMxDgteFIElHyJ2sq3iMVgFvBRdnzJjmWWNux5aHhmzlpRSx6V1QY98vhT00CIFO/TIXLBICy89pHCET9PRhMo4Ld3c8Nhc90e+O9zIlBsOHPqxoheLxBRKo7RifehwvueyHooEyPZ41qef5+KFJg+qeVZOb+JGio9+2mH+x20CEa9Y7simhVHAny/eD0snc2Fu68jA+TmZT7Ptd+1zubitKsWCPO3L0mW0Rz48bAtx0eCYBj9c4RHLCBVH4LZ3t+mvC3zslVnKXgMUu6lzFi2PHdtgz0CfaKYzRfdSJKjbzF2khHgS3JoGKuowufGcc4RLqFLHktPXp8tNdZRyJjsLeGLv4+R1Gw+pqjvTJ+cTaExdEyo6nc2fCBcJQrbxYDQ9XnPXEc5tn+XBTcGlfp8z2z7PPRiX1JXvSm6uFAqDO9z4l/CbNDLLSPMk6yi0qp+zXp+F8qYsNzOlPD0PeuxQo04gxmWzdv1TfjzG+VOTqEjB6sCEwoy1O+7dvQABAABJREFUATlCOTZnuTqABd9hfegQOD082UBrRag025HD6tBlEJs2Yg1h4vxUKUE4MBooIye4lWAEFIMWZRul/Jwsq7uonTJNElsfqvJl85SdKlbxk31eXU5Fa2fZmppP0xDMIYdb0Fvn5z7jGM97X8SRD6zyoasuH16Fb3tqSKOmfX4KJozAFAv4IcQYWBBWGWZ2ymp8pdodebCjTpjZqXor1oPyPo3vMfVrYSgzzuvwvesBeow0IyUkzp+zoMdOKdXYUTqaND3M+fFISSATn0cxQwXrqwGb6z5xKMzZHw5R4qdjfTXg6kLI1YHLgh/T9WNCJdiMJKtDwYWeAbJTikKJCDUyQV5cTyHxEh5TJGvWIK9hTM1HgTEm76i8vp3IskXptn21rZ2ytKL0qufx+ODVe+DDI00rUw2qeNjDbZw4oqVitKsRfss4Jk2dPeb9dtShOpv/sg35LKYtsLcb+93UuW/asreUvoP8u8guclmBvcBpImC0PFQ0+q1cF6WHuMJjNmY9Oad8p0U0Ze5lQ6WrhNj8tbLyRd9tZcs0P/JUxNj3aTYq2nhVbXjAvtCO0156ss8/X21wqDnHbz0QE8hLPOMAJgz4qFgBsJFN34n/iyKeWORMMw21lwypqTCC+XWlSEtrLyjP123aHTcr2dakIk2PGSgV0FPeOVafzp8giiTdOM0LlXFsFyfDcmMgcBwYKo0vx+FpLw8EF/segTNk3h+iUXiJFsggFlwdjJ2eXujDA2cbPHx/l+7VPqsTLDWvIVHF/7LX6oDPu+FxtrleBIaUAStF97UboGafimnW09tsmZQEQ9UjdmKidsBcHPJNn3+R3zzV5FMP9/nlPz7Bz/rwVXdvMu93MqUmAQ0DZGS+Z9qYNAEp1rao7LmeHifpfDfaIk6ieedMARVFWh1FjkiraNZyuzZjua5has39ZUEPwIAfO+DTqSbPBH/O+fFI/bBobb6FWqC0AY+l1PhbEbEnUVuCgTs2bzl/xefB5pBWEmkz1iYcccMxZooX+yYKl1KC1SsBx++9OgI8pNRGe9pvUW/fmdegSN5ls2vqqQVq5Hm9pT6r3FumKVJERXuCLN+o02a23OOb9iAsLdiZdh1mnlzz24y8/yNRno9sLYCQhFLj9bbs/juqaLeb/+uhBXEt6twHPQzt1o9HFuzIP89stWUOS4UExzflhQOxHNWrtaIfb9Bxl9MC1GOG+ZMOYYkkMJ2vWtgtSrMJ9DYAo6hsjVOqwsUglyaAx4Gt4e4pu1mpC3SU1TGmpyzdylOWzPc3Ph0u9h9ibXieRvO2yeJpiFWwAx17scGvGTVkDHQUmLBcSz8atrrLANSbmfLARxb02AWNIreUgBypPXmkhoRKoLQJMZuCHr3tJBKAp4m1EZpDBasDh67n0HSHBDJACMlSY0jgKDqux7vOeSwE8FV3b/HeZc2vKoH7DwMuX2pQDibkv2d/58vV4btFoEo+rW7dRfUUka3OuuCopZ19Xl1CZc+lDPSfLufLJgqfwGkDklfdsswX3qoRos1ffc0271yN+KfLTT7tyGQ5KZwpkCOvsVHly2Py2jjvhE+ekY8OYQU9UrCjTh9GaTsAOmbVdKgbxaU2XUvQY2TWokmjtdhAj1lIjEBoM1YUjPh8NoR4CnQoHaGICVWfSAXE2kRjUUqgpMDvRcSueabGiWlEFEou9GGlL2l7Dgt+hCM0tKDhOCxswp9+YI4Dh7b5oS88y298UsDHfypgbm2AOgusnYOVVO6waVvY0nKypnTBbczyZAp+59sUue+2slWHkHn+mt9T29qs6qeFHie8epeAR/4UoAjNKjrhzXzPqEtJJLg+xBGeErsTpqo2/3vpz+NaaXtcK7OQTzSTk70kIcufTbp4ZfMUaHeEIgYdI1E4jguOh0YRqwF+JuqFnSFVa2U4wstFvrAJxbb5WDVPbXXl68x/tyHgtrpsZYsWqVlQ8X2afqdFqH/RhspWBwXlBGvDCCk0kQ4TbSWSeWEBPKrMSKr40YT1V2ajndeyKoueVKXVcT2ciF7Leq8HFb2rmYH2sYBuwgXrxK5foUQKhGjj2FQrFDEaNQI6pDC+Oi4PHIZDJwl7GOMFZoz2IujHYhSOFowJYNNxabmS27oHeMryKt/13oivfeKd3NKBFx59lK/63w3O3t8lVH08GWCfB1A8h8p47OT9F5e3padlygCRsvrLgOlsHbMAJbZ698lOVaeu+Y1SNr2YjLPPpIwb4KIMMBgEPLLpEioYxNsETmuiXBZkyAIMRcDCRNkquRkDfKR15Z1dpuYWqWmETZMkb2KT71OZ3426ZPU7kgE7dmuuc90ow5PzoMduSGL4shTmnG/EnxPfWhqF0jFKx8Q6BMCVMFTG7KTVDlFXIOiFhIGLkoI4SICPgWP8MGXOs5uuoukqjrRCPvvEAl/9xIgv+40mz5rv8Kxntzn3lI/zxi9rcvzeq3DgVKanRQdrNvknk9drJBFDi6j+PJzMU0duLZL5bfJz1Z7aVj5f7+Ob9kDDY9aFMaWCzVI0NOpFQoLjovwGMj1h3yk6WuXXY6+dmO418LGT+m4WLY9PFFBFSCbGulbVY1JIgx5johCkC0+sw2QBcZJTS8WkymRubsQhPXo0lWfqdH0mmSrTZSY+i0CIos9smaI6i6jsehmwUVbH5G/xOIgXvjsqOp3diw3MZHqsQ/78TIuGA7/Tv8RrniCY9w9NCpw285ZZTVuAke+IlGQm3UZlc7MOqHEtAYmd3P+NpjwvH4FX7HKtyZ9KjimN4KK1ItYRShujbhN9BRaDcYQWFYspiSZUxrdHrI0Jiy8NH47UkMXgKK7w8WWLNz97zJM+uhrRvdqn+5fn+LMv1nzO8bzj0jLwogjgJZffBi7m82Sv2erK96FE28JKZfdQVb4IHM3l2ufVOSpa24rWWzLXy9Z88zvSQwbxFqkDSV82+fW/m8N1FW/9/YP8yJc9yhMWXKQwsy4bfjYFHfOaFqbm+u8x7z9i0hnmWMtjymylwF+HDXzZad8m2qs0Y6lZ7yyaxNday0MCo0g5kmwEnJ1oeeRJIEEwkluBCbAjTvjzIBY0HVhY6tPvuaxLj9iVKCnQjjFzAYgiidKCWGt6kaDjGq299aHDPUs+LXcBpWN++1UuDLbQKN5zwfiqC/oRv3jveb7ijkNkD8zzcyXWEc76JYZzCyP/MJN5899nOWTbzWFc0XqRl4/LDpzyZcv4dnk/Hw+8epeAR0pFDyp5AWEfvMYE2mvIwsQd19hkuWZQyt66+e52IeqbuuLo2oAKewl67LTM9azvsdL2jSaZgh0wGtPpIpd1QmojFeFJl6GIknCLjFQFpXAYqG1cfKzIM0AcwnAbNwhAhWbsu1mGPQviPAuVMddZyMas64InFhI8LlTvdk82gbmKbAts0XeT3xEeX333Ec717iVUmlD5KB2PTayymlIpz06UP6wRSfK+cWa5ZnF+OVWuKn0W4OPxyA+L3sEeAuypcJ6asKSnianKtBGwBW1PsRALLvTMePQDg3ykwnQaljbWEGnBIJa0XHMCOVQxH109S9ePOdI6zqNbj/LgRsCnHF7hgQ2f1GbRRHGZTethLOfYNDWm7rbuU2F6Lu7moKmsbJHgn98c5PuRq3OfV89AxQCH0TJqGFnAcUvzSuEQOO1kU6fZjtZ4xytdPrJ6mUv9NQZKEmccUBrTBEDHGWBBJ8BH2rPZ3mHelMKmZTAGOO0gR5rH1v4spiY78VlRUSO7lomuuT+P8fzNOzEtLmG/p8l3mLwjbd5dyp+zYEesQ8LEx8aCD0stxSVXoaRg0EycTbsSR2pULBKNPMOre5FkEJv+bkaS339Yc7h1mmcfbHNm6yofvNLkc09q/uFSKkAY7T5Lr0d3pVAM4i1afguljbmN47qZcVe2dy1+WlP7AStIsruDvWI5uawuGyhegx4nvHoPTFryZHnIQkLY56q6zFJwPJcvt9ETEtxMXHDX5/7+ozCE4+3j+DQgWs80N8NGfC98eqR9nLXMrOXK6vtEAVGuB+3Gj8eo3KRK5ohZVqnPp5u9RGD3ZIBKFoo07GZDNhORMeukLzOHHBeaXTyAIJ0XNuZapcY2y4lRfm4WIc1FlM1XxoCL+pBP3wMh43FDRae2+Wt1NDiKvmd/m883/PkiP/s5PpvhlTHgISWQAzxS7Q4RjUOP77WGwywmKbOAIbPkuZl46rU2iUw/betr7bantTyyDkyVjlHExCpC6RhPajpuTM8VeNKh3Y4Io7EAHWujUp0eWg1iwSA2IWqRQ3qR5ONrAR9eFbzg8EWOtgTf/ZMHaW0MOPTI+L2+45tg44fP8JnHhqw0bpnor40iPWQzvMyCf4RivlWkmVGWr+rkrqy+onrKTg7zfNpW3z5P3hnVWbsntRmGqod/5iNw4hmW/OPvEpmAGSaPJxu47/lzflk8jTc/+wDvu3SaWId4BGitRyfyo/JJFKuJUN5MhyDdCY3md6LlkXWUml6fzF+t0bH3gMZ0G9eEriXoMZJJzRjIgh6jLJm1K59ebCY0Nj1UOiLrwyMFPUIVMYglgaNZCEwErUYzQi1IhpGJsqI9wz+UEvi+Aan7cWJ6GAtC7bAVSt5zET78kQN83iev8jkn4He+VfM7rE/06ffeoPjn/3ie19+9yYn2XZYeO7TcBXChgQEPL/UfZqVxKv/QKJeRs7+L5OY68ni+XBFYYpO1qwCMov7sRgvlE4uuAbfIPuTkQbsBhH2Woib2xZNxmbAPYQ96awbR9pqcaJ9gdeCyGV7ePZOYRc15t/ls5fZCuK9bx3X0Cv0JS5kT6mlbTjEp0Of9fmQAsobTHnkgh8RPTTSEaGhB1/PgQv4vDwjMwtCKBHCbAGX7nAXsyLeXF8zzDLmMUWfu37lGf58QlN3Y5DcldTZPUP6OBZf6DzFQWa/mgl9+yTJNp8t7LrgM1FaSnM6H5L0JxwB4jmtMvdIoXLMKrVmHmTCeZ3kzkSy/zf7OXysqk5JS47+6tFe8/rFME+81y7+KKNkEZfhsqjptND70SMMjcDSBo0eOS7seNDyN52k8V09MZxOyVhApo+URKnOYFTiafgx/9KhLrAXDAy5XD7YZNMw50KDh0mt7XBkwsk2fvJ/spyFXeCz4h5mce/n7LtKayNddBWLXpTKenwUv8n2sC4AU0T6vtlMxuGEjV/iEqs/mkZO5/NOg01D1+fDqfVzqP2yADRkgnvZ8fvDiX+Ce/jC/dG/AxZ5rQMTEH44iHv+NTuvNn8o6ed4DmvATkvgKGf/l/gk5+jNlxXUBO64bXUuT89HaMx4feRAnNbMeadOlfldy65bNn0malpqzpKB0pAShEjQdxZynUdr48eguDGk0I0RgHJmmWngAcWx4dD+G7UgyiAVuEqllOJD8+jsP8OiWTxFd6JNohlgfxMQvTzZqgh1Y0srAj7KytnqKZGxbnjKZ2lamjHcX0eODT+9Cw6MIEQPrQHDc0QbPaHAINIp+vEngtEcbQbyGAT2G2xAPWfMV8/4hnn7gpJmwa+cn602ZntcwIT/jScR69tuqqfGwG82Ivdb62Ke9J4s2jyLOLRoVgp9W4zC0WiFSM62svWE0BM/GzOswizItjXw9ZWi0rb5sWlmf6mh/2AT4Opobs+TdJ0Nlz6lowUzL5BdL21jQLDWO84ePnCFwLvMvjp4YXV0PL7AURHiyQT82oMfq8Az/dLnJ7z7s8OLjihefOGS0P5zM0mPT9Nip+eBuNTNSKgI3bOlVTrGh+H4+UUCR0vXQxpPKNIxE5pcehTpMT53NibODKzVSaLo+bCVDKNZ65N+j4TAKQWuuGWHck4LAURxqhjx1KeBiH37x400WlgZcUQ1WV5p0r/SJXMl21+cVt57ncPOuXP+0pb82TSqo5nFF2lOzlrWtTUXPuizNxieqhO59/rz3NB5bjvCI9IBID8c+CLQyUSo6B0Za0b5scmpuha9/Z4/F9kV+5NQaHL4b8eJXcFVdpnUWun7M1YHhz++71OIPHpbc9/vz3P6SNb73OVsEsm00PzLTOSuzVGk/2MwjsmYts4SVtbVVJ8+s/fuEoRm12Ov69SgyT0qjtRhgTBNro0nnCMObPSmY64b0e0NcTzEcOEShQDqaRjPGD2I819QXa2NCmILZt3dh/SlXuHihyXf/9iGONq4S9Kf3d9/21C1ua52i3sGfTbbZ7ca8SpujDOyuU66sf+XaYfs0SbvYbRdtbAo2hMEctJcTxpwyPkHT6WYmXVLe9UcC8bzoAhqJRGxdNUw+8aobzx+E7kGi7jKXnU1odOt1veqkbhZNj90KrDut47Gk5XGjhfrdgkpxhBmDTmaxzoxtWXJKnb5f10eRRhlImFRSRmoyqqXTpzb1ENsytLmoTLbeqsUiL+RXtWG7j2zZLDOvz9CFACHFNfn7xKT8SUGdzU8+/2Q5ieTU3JB7FsNMPkP/+2GfjeEl3n3hCo9uneOhDZ/TWw4bP6T4sR+d59GtB5MmpF3TYycaH6PbKNDiKNLqyGpu5P9moZ2UuV50LQD1MoerU89hZ/Nq5MdjBHIYDY+RxgeKwFFGddrXHGzCnGc0PRZ9Yz++4EPXN8CHEb61CXmYUNtTdLyYhgOPXPZYX/Vxtoy9eeg7aCloboa8/jePMs0rJx5IxW8bFdU1iyCe5902ra6iesraKeITVddyLezz6l3SeM1tOB067vKkw0XhwPzhqTkYOG2eczTmJSciwoO3mnrcgHPbPd79Gwuc2fL42Y+0WR249GPBclNz8mNXCH805vcfbpB1RmnT9NCZf7PdjeVfRosjr8mR1+bI5snWOSvl694T2imflXL8dw2pKtJM/p3agJB8FJ2UJ6eUatKZ7wa4aLuwFGiWDvRotUM6c0O6C0O688Pk+4CuZ4Bpw6dNXUqP+bkfxIhHlBXsAPj3X9eFeJhLLTsArMtXy8jGP4s06Ypk9zqHU3XbrqLye7pWvPpmox3MsiqhOX9CmM+ff/k6cXCXSRcONBdy1ZtNI/OH6TcCaC/xngunAaNG2nYX2WQz59BxFzTLJn2vgI/rUWY39dxo4GKntKdmRMl4zm6gslEnbBs2aRyNjdT/UkHeccfOeZMTlCiJ4FIOctQ5vasCJ1IhuQhFtrVty1tUXjC9uKS/qxYUmyBva2OfpqkILJvltKB6Eb69e5ShiifSlI558fGQpcZxnn9ohVvnbuE5B0/xuicc5fN/RHD0gTW+570devEGI8AvD3rMSmVAQxmgcS0AilnNJa81P73e2oMl95OCFOPQlOXjUOJYw1QCE8Bz21UsNWKWA81yg9HfwSbc3tUcbSkWg4iup/Dk+CQxVAKlTfmrA1hfDdBr0Fnt44aKyJN4w5igF7G+6o/HbGXf89eL8tY52Zvl9C7LI8t4eVE7RVQGTNfZPOzT7qhCm0II8JoTaRKHr7izydWBO97AonCE5ktes8rz5ub5j89o8Yx3v5svveMwb372YV74A2Y+/dZbGlwZbJqDGT12SJnKLlPt54CPKeekeYCiAuBIgQhbvvETmQZEZqVrouWxW36bBT/2FACZ1qyZvKpLf+dJIEbRfLKUatJlLRmWG3CkCSstzcJSn4XlPksrPQ4c2uauY31OdTUrCc/2UkvVDH9eDmBz3ad7pVfYn+ZWSM9JxuZwy7hCmGls1DlQzOYt+l3Ed2ftS9n3vGxfJt9naZ9Pw45MWqpeav4kuOx0oQgYEeB4RlUv/R0NIegABu0GzfMOnRq10Y83OL21zT2tgyZvFdVR/ZrVbGUnTk13W75OH2+WELWPRUpNUlKv+zaAo4oyG4E0FK0p6xjNDwEbw0soYj5ydcjxzjBxwFSkgSEyvwXo2JiB+a3M9WyZ/Dy0CdVF4EL+mm1DbMtDLl+uz9a+VFFSn7M/lsupiL+mabb3VFQmn9d8+rKRGaMmfcE/zKcdMfl9mRXCNa+49Sj8yBl++btdvru5yTc/5TyHm3eMQY+UshG46tKsvjV2S3vlfPpa0ieAqWRqM64xwmxq84+OEULgSU3Xj3FCzcATLCgDGjccky4FeMKYuMjkM1TCpEs9AkCabhIeUQq25gLaDACQsSboRxx991Xu/bItnrLUpZifkvttO+zJ55m+42mAMnsNivlrPo+NqsDmqnqr+mnh4/u8OkdV62P+dznAJax8G9ruAl982+Iod6gG3Dl/G3fNGzmmBfB5rxrlf03nKi/51Sb/7kvha777CN/wDWu88KhCa4yDUZkFH8egxLg3xeOqrlbGXmhvVNHeAB0l82g3TvLzlD7znYLzlr1B3iSlivL5s6YtqYNbpcem3lJo3IS3elLTcjVxIJLQ4ZpQxXjSABm+hIZrIrSkIEkKdhiTRGMi4wcxalDuS2ZteJ7m5QdQ734f4tQRxLNeRF25UqMZqh6BbFfmnP49Czidl62r3oOt7qL2qnhxjXf+OODVO9TwyH5mX0Cdk1vbiW/2e8GJrtfEPoBTBr/Ixb7HO69c5Uojzmz+dkk71bzYDdObtfz1dIR6s9Z/LUmr8UZMWd5N6qvDRqPFSuMKf7SApE7DyMRHXx9uIQWc2cqHnc3Oi6ygmcyTiSgxNqZn+7QBG9n2ph6Cpe6iU7/8aaNNQChCv4s22bMAI49nsm206my6isrY8tjqy+efzvuKW4/xlG8MOfdmxZd91zHu37jXmHkJjKbTXpi31DVnyea9XlTUj8c6zXg/Yy0PSp6HHgnU6Z9AInFG2nBSgCN0YtYSc6ARsRjELAURTUexHEQ0XUXXj7m1O6TrTwvNCsEwBt+P8RYUYeCgElVcJzL9am6FfN+/aXGu9zGGqjfqX/5ToRLTxCJQuIx/2UDIotO8/MYYSx4bFfHgbPu2PLa+5IX2OvLfPlVvivIycVU9ddZt49tjMhRnLu/icZbf/md8+88POH7fKr/9LZrfekATqSGh6hOp4UjTI2v2UNsHRPZfTnujypxlL2gWE5xdt31Tgs3l9z6pfTdJRc8j5cuOcBP+7EwAyW5iRthwEq27hgE6jjSNqeHhljFbWQrgCfMhi0GcqdvUESoYDhzCwBp/dkRv/LImHLwDsdRFP3zOcs+aSA8z/Dt7f4kDVqoc9Jbx8CIQI887KwDi2mnZ+svyFK0Vj0/ahYZH1YsrKBsNEjX+5LuKjE+OrCPHyvqmJ68nA564EPLIpuR8L0S2HBZcv1zbo67mw04dlO6FxkfdslV590rLYzfOWm8U7QXqrpUZS/m6qjYwcpxfo4iUGY8GmhDEidMnR7p0vAa3zPXYDPPPV5u2U6e8XmPk+Dcl5TeQvU1ozmMXZKtODG1AQ1FeWz5bfdn0IjS6Kl+aJkZfb0bbwJuHbBstbflu+w1Xh2fpeiuJmaGt3qLNXFHa5Hv+h59psMQ2Jz92ha/6nmP8x286zbMPtnFFQMNtw8iVTcbspIzf2ECMWWkWZ6ll128WLbobyZ+1Msxt1mdRcBqZjsM0HOIoOxolYtqeeXepjw4w2huBo3CTE0YpXAQ+LXeISmzOYw2bocOVvsNGaOzEAYJehFSaxlaIG43HRdCPeOOXtTh/Yshv/EiftrtAdnxvhJcYxFusDkPu6N4xuoO0t+Ubjuz8sc0zG08smsclp8+lZOPbRfUUXRNTP/d5dRkVHUQUr5WPbH2cw82TeLJBlAAQrvTHTv8L28jXlaPmPPI5d/M1bznO7VwE4C/fqHj/v/P410/s0fHW6HrzoyPScYQVMQF62CKn5DVCqgCFMn8TRZvy6Xw3GHzba02Pa+wjKvXJUXw943Q2OaSTOLjSRyuFIzyUiPGkQgpFmERrMSAwgKThGL5rnElrmq6i46oRr/YiE9VFaYHSgssDweltwfqqj98s7NqIXvPKq6we+DR++zsfHvU6pfO9+7nQE3x8LeBltyzjycZE2UC5bKsNE8J2RDaZuOogrgrQTOtIr5Ud+BWVz6ZXgSF18vG44dW7kIzKTgtSsizkQxPOMD3dZnsVtq5gP82w/bafLK8NL6CJeebKLdzePUrbXTQbQ8fdu43+bsrutPwsZW+EH5HHG6mIiZNamwO//Ml0svjFOiLSQ2IdjU5KNJphvD1SCxQ49CLLeE0BFykTwHByXsnBthnvE3OkDhUJXaOGLXlt18rqLkq39fXmPSUUQnyxEOJ3hBCnhRCbQoj3CiG+9Eb3y05ZYKqIpkGKvzijGSQRVjSarWg1F4K26HQ322423yQf/52fXuQ//9I2n/b9ksMPrvHz729yoXeFQbxFpEMmNDvqmBzaftd1Xjorv3usAb3XkvYCaKpB43CVcnyiKF0c4eIKH0+6ifNRxUIQsdyICBzTF6MmbcZjKszLJHqAEapNG7GGViekt+nSWeszd6VPe2P6oOTs5y8V9lOjaLnzbIaS9fDCxJX8HdXjeXlwuQg0qSOH2SjfhypNgboC9s1BNzevrgK/snkmn/f3/uMc/XgTYgN2fHT1LBd691s2q2Xv0NL+uQ/DMORd3yX57l/sceo/Gl539icd/vZCk/PbHgO1lfjySOUXNQUsTIU0LQA7JsPRTv4VURXYsVNnqo8nGvHT3FqWdQ5tPsuf4fh9ysTfkki0PTxc4dByjUPorhfT9Y2W3XIj5mAzJnDUSIMjpTScrSeNSUysSYASUErQb1f7Zvzopx0m6EXora2pa5GOWWlGrIeC9fDi1L33RTgFgmTudpSzHqAxK5VpgOxmLH/iAxiz0h5Eaak6ERgvlIoYWguwdha5doFQxONwtWvnYLCJdVBtXIDeeqKtYQNCIFR9/usHO/zSx8/x8OZpo1qaTuq9Qkevt6lKvuxu6UZHEni8AioqAqVGi32sQ0LVz6iKGgFCCCPUD2ILCJFEJjKmWhYti6Az1pwa0SzCadWmNU9VgnFZO1nwsojswCaIaxMvvF7M8G8FNoFvAT4f+HPgV4QQ31in8PUn22lh9pOp659/6jD/dNmAHAJJ213El00u9h9MgI88CWId0Ys3uDI4nat7+oRhK1rljx81vpj6bY9+z2V96IwjAcgM4FEH9Bg1dY3NRfbBjh3SDAJbgWmLIzwc4Saf3uhk0ZMNPNmg4TRpOJKWC640Ji5N15wajtSVE4eL6TRPbc2brmI5ANdVSKXprA6YWxsg1XS/j/zOFQ49ssH53qWpPna9g2g09yweZc47gP30L30eVRteYclbh6/bQImik8J8P7TlL5/fVq6M9nn1NJWtqdk8FoojfuIFy3zvPw45PXgAP4w40WniyQZ/9Ohprg7PTJRPN6+XB49woX8/D21+PLmeHycavb3OI//ve4l+4W2sD52RA8nmVsjFHmxHMgN0JPVrPQI9JhyXFvDgFLisihhifyLTdercv72g+uYsNfLdqDUjK+dPvIvJZzQRBQc58Wkj27MRCFwZ4AofV5o/R7g0XYeFIGYhiDjUDFkOIrpeTOBoWq6i5ZoIW8a3kgE7YBylJQ0rPudpugvDSpMWgCf85TmaWyHixBOn7vlY6y5cEfDK2zssBcdy9yBpOB0L4FEEbuxUgy5bTxU/LxpfVfy37HrFunNj+PR1pR2YtGSpSJ3Htrga1TclQDY60Fs3TKq1BL1V43Sxt26yBx0mFgW3Af31xLld3r+Bqb/jLfNVd5/FEfDARkDHO8tycALvWjj0vJ6mKrOWux4mJ49Fs5YbTVohhIvWarS5SwV54wk9IrVdPd6xhdgSiR8b22a1jDkWXbcJxZY2S/NWtZvmqVKtK7tep73rRi/TWmd3O38mhDiKEa5//Ab1KUfp87YJ1sKSPvn7o6sP8sBGk8C5wCcdOAUYVdbfe9jnRcdPc6R1O3LCFtwIm3/w8AZPXuqzFOT7M9nOB6+s8ZdvNMtOlz6nryyjtGCotunHPr53kLFdS0JVzttuBgegN4s5y42kdF0YmbUw+zphXVsM6CGEJFahOU3ULkJKYh0aQVySaMnFOM7Yl5EQIgE7zPhzpQYliHVq+qJpe9D2NdoTVs2OPEXKzsPaI3XoMn5ZBDra0ifrnyQboFKlcWVrL/87zyOqhPyqzfsNo5uUVxetqdnnLXLpCamIv714gbOrTf7HRzq86e6Ibusga8Pz/MgfL/CVn7rKF5y6arSbGW9eXeHztb/f4V89Y5tbOtk2GbUnVk5x4tuvsPWsZ/OWL+5BBmCINZzveXT9mFvmEs0OLUCAYNLMzLopFtWb6TIqAjuuB+20zzczTTqCNvza9jy1VghRDDRkHZi6MkBoiVBDpHRQxAiGOGKII0yEoEEs8WKdRGAZAxxRwo/N/lijBChhnJq2Xeh0h5x36/tk7DU8xhYw4/F4oHFL7TrslJ+fRddtVCW72vh5nbx5Hl3Gj2+4/HzDaYdOS8s2UjC98JqXYE5nJFfFBgSdRFCRJhpLKiiNNDkYl212YeFogSNS00bDaXPb3J3c0rmTZywHzHkH8KLECU2VMLobL8jXw1QlX243earudd956d5Q3qwlsXn0ZIPtaI1QDYj0kFANUMRG20MPRwvP5cEj2crSSiifczahuC4DzW6Us23m6xbUq7cKwc7WXSZU59oS3LBTw5wAndI/AgcrC183yi7KVXw6W8bku717lFfevsJTlo5M1HFnN8QVDj/z4fNJiM5xOVd4vPSWBe6av6OkLVPXM1eOcvELFwC4fLiN6yreczFgM5QjNekpc7CUyvj4vmbHmK4Xj63djhknu9s86BH/zJ4m+rKFJxv4sokng+SvYWQN4aD1pBaDKxxcqXGEEbbbruJAQ7Hgw/94/YO84eeGfMfb+qU9+b7XNCgGErL3XAbeVqUX8c+8QFt2cpheLwJG8r9nBS8q+Mo+ry6g/FptA0HyvzV4DT5pZZH/7+lrfOOTBTguAkHXW+GpT1ql4Wg+/+cbXBk8OlHLvH+It70MvuDUkeIuzR1CPPfFNJw5Pvwpk/n+/B8XWB3CIE54dGZOjbU8Jk0hikxb9oJmATtSx6p1HKzO1sebEuSrQSkvtgBTBfx5Mjy4sF4z/pY8AqedaN8F+LKJL1u0XI+ur2l7caL1EdP24pHpYUqxhsDR+DJ1dqpZDuDA0oD2UsiX/rjmX//3iP/2G93SO/y6L1qHh/9h4n7z9z+dVud9ziLzZtsq4td16i/iCTZeXSVnVzS9r+FhI9vCWYQ4kfktkl+axYEDvjSAhOOwqVfptBaMLw+tIB7mnDIKxk5N8yeT47b68SaxDpn3D8FgA6J+faEsBQJ2clK3G42PnZTd17CYnfbSiVQRlTk/TDQ7evE6nmwQ65Bh3EMKo86/HYEnoxHiHTh5ZL0K/a3LXGZBoasQ7TRPvo4qwb+oTVu+On24ofR84EM3uhOTVLXIFy9+fqLWmXVaKpE8eanBB6/0+dwTW1wZbHKsNZdcNTw/kK3SetNr29Eqlx9cYt53OHvLPM+78xx3zUdJm14idCVA9fWYs1naKU+tu2ZUgc2fSFoiOS2POirTE2Wt78KMIUd4ACMnpkr7idacP9Kei9TQ+BsQZoOWniBqFK5wUMII3C1XEDiaFx6NGMSCH3hDm/WlBkdYL+ze+X+5mPmVFTztWlPFVJanSjC2CdFl2lx1BPpZ+1KmNXJT0k3Cq4vW2bJTW/O7SZP1xXkassVACFwd41x6mG+4R/FjH2zzqk+9yvsuK/7F0cn6U62PyT7k3tfGBURniSf+37OjpDO3zvOku1cBaLkKKfykRm25C7XnGhF57Y4iTYTa9Wlldaxqoz25l71aw3bjuNTqEFoiRWLqJyRal0coSbU5TNniULUAnmygUTgiRusgOdAb4EnDlyMdsz50cAQMYuhpidKptocBPSItaLqKhcBhzoOXPO8KSsNPf61L5G6Wblx7r2mZiG9Jb3N3kn0wTB8OVVHdQ6Q0T17+tbVfdZCZ5wX577bftr7s0y5NWvIv0vZyda6EMIPRb5lJuHWFTtBB+S4y6hjTluG2+XMb0DA6eBcGD3GgcUuhN+pzvY9zoHELWinorSXOUROaBSDYTTST3QIfewlilNW3VxFbdkqPVcCmauFKF7fsIpdJE0Liy1YSuhAabofV4UXWh85IrS9wjKfq9WGfh/T9PG35lJkzo2dWxbhsGhNVTDVfbnRDljzZ+qrylLVdl2FbTiKunTfpA0KIv8/8/hmt9c8UZRZCfCbwBcDrrlWHdk75d1Rn01MMjLTceZ52wOXd57dwJfz+w2e5cz7iBYfMgaknp2xZsPH/7WiNg4+4dL8Rvv3oOW7vDlhuRLRcD99pQoXgNd3lawiI7CWPqiOsZvM8VsGPWXl70YagtB4zplLTKhMxQqCFBmVOnVNQJM6ECzeghzmRNj4KjA35IBb87sMO//B3R7h968LIrKX3mhbNtxk54oEXrfATrzqNFJo75/P9Eokz6pAPXz3P05dvY3Lc5zezk/cxnS/9nuYp0sCwzfF83XUF+bLyts14PbBjn1eXUR7Mz3/mvwOOh9QOgWO0nbeiqzQOnOAY8C1PeZA3v6/Dx9ZcfuAPt3neU1f59qcb+bnx8fcj7nwOOJ6lGzH6A3+JeMKzWB2eAwSrB5oEnyf49JMbHGvDkZbxx5CVv1PwQGuNENd+Q1XlIHU3tNdhcEd0U2o0T8taUyBG5oC61tY+B4iINHy4wJhry3EUGK36dLyYzdBhM5RJfBjjRDpEILUmkEYO9iRsR/Bnf3iQf36vCTebRs+67zMPcvufGgfR4Vc3+OXPXQThoP/+j9AfWof3fxjxuV8MwmGgtojUkHee2eKlJ4+P7rL6QK0MsCh+npPpVSBJmXxe9xCyqo/V9HiI0rJLwCNLRS/Xshi2lya/xxFy8wo4vgFC+uuGUYR98126HGwfg1HYzel2VxqniPSQZm9gD0U7CxixG22PtK1rDXpcS9DgZvEVcrNRHbS+6JkkJi2+02Qrusp21KPrrXAsuBVPPsKjm4IrAwdXgpd4sH76gW5yyqDNXHB8u8AywSyLGF8VAFGH4e8Uqc7ntfWjCMUu69ue0yWt9bPqZBRCnAJ+BfhfWuu3XctOzU51T3dtG5eUxukKxdf8+RoP39/l2168zZ3zfZ6+7OHKNg9sPMqfnG7xqtsVS8Fxpt/vmP756v2c3Q741K/v8/Jbt1kKGmhcpAgIZItAtiEOp+dYdt6lJ1226EhFc/NG8qmdnsrtdg26kVTqy6NgLttA+JprdqpOHeuQNDwimNNho0GnzK9kODpCJwJ5jNKCKwNpwiW2Ip77nwVf/6RjvOblj/LqZ2zz0i84zj9fvY9Hty7wlKVbs50jO8+uDB7lQ1ddnn/oCMXAoW2uFc1T2+ljMSBpB09sfbW1byufz2frR10g9ZrQY5RX24B+mGWN67jLE99D1edVr9tgbq3Fyhvhrnl46Ysv0XFjfvpD8D/ftcwvvPoQK3qLFguZmpI2hUTc/Ww+sHmen/togw998iFe9/LzI807V2hWmhFtt2XAxQy4kWp1ZEEPmy+P3Toq3Uuwo46WR3l/b3pNpml+OiWvG4fOWS2P/PqZf0ZlWh7Z/GMgzOR1hIsUDpEa4AqfWIR40kTTChxpImZhtDuATLQWA4JcvOoT9CKUFHS+VfBTJ87ymm86xGtfdImv/DeH2Ayv4EqH6K0/iwgcRMNsa+WLPmN0z++7dJH3XPD5qifmzWGqZM0qwKJo35uXbfN8Msvbq7Q2qnhEncPDx8CYvQ60S2nKtoAWbXjyf5n0qG82dIPNMdgxakKZKBe91UzYzWkaqp5Rq44j6/Ud0W6imuzGR0fdclX5duPL41rTTYl87wHZ7mu0cGhkFLHgH0ZpwUObjxKKmIPeCQJHcXXgsjpwWA8lvVjw0dXVZNEX4LcN2DHcolq4tc2Roo1tFRWf3o3rLavPJpTbQA5b3yxItuBGev43XRBiCfgD4GHgy2sXvC5UtDGxkW2hnB4/Ssd84O9WiO+H7/y1Y2xHkgX/CB13ibvm7+Cu+Yhv/NMGoern6jJtxzri5b++zi9+vMl/+fMlPuXwkAONduKDoUnT6dJw56il3bETx6U7CUW7W5BEqb3hsTut50b78cimTwBUufGVz7fTNtCYSC7uKFKAQI6EbSezSYu12UyloRC7nuZIE771cy7zqttNnZ/9Q5K3/KQ5mLln8TY+5/hxpmkskB5s3MYLj5zg/ZfPUcnDJgTgIuA5LyTXOf2rnsvT16vqLOtDxZqyz6stlN/AZNfAqnXbRprtaI25tQEAF79fsTqAexYVt3Zd/tWdQ+544lX+/es7JkqiBTSLdcSzf7jBv/31o6z+oOJffNZ5nrY0pO0qFvyYW+aGrDTcjMnhrHc8i7xRTXup2WGjT0RHpWUkxDhUbfo3upYHrkqu5fNk6xpFc0miaznCx0uiZAWOGh3yAYm2hyBS0I9hfn4IxwRP+U7Btz11C448iTf83JCf+5UDOP1tOt4SrXf+GdH5LaKzm0Tnt1BrA/Rf//VojXjOwVN845OP8ksf3yx6ChTLSVXXbHlsAETRWlDURhYQmQXAKNuDlxTZ9+Fho6IT37J82d+TL3CgtggcfyyM2gRNrYzWxoTQNCkYNGnC9mo9EGAWYXYvtD1gdgG6bj8fj5oWNzvZ3olWBtCTLs5wyNHWCTwlEmfoinsWTnHL3Cq/cf+AhuPQcDTbkeTO+StJmMNkzI/C0k5UTr05meZN882icpdvpyxfPm03PMNWx40jIUQL+F1MuKiXaq2ng77fULJpWFSdLAtLnvHvBzce5glPXeC2g0M+42jEYpAFJjSfemSZq4O1gjY0Ujg8684t/vfvHeRlL73A8c4QR3QQQowcTgqtizU3Cm/1JgBNbevCtQCTb7QZ4k4pfUdTmh4FAlvRfeZ5al6rJ7k28j0jMSfN2vDYiCGejIm10fRQWrMdOQbw8GNedkvE05ZPEeuIUPX5sjuO8srvD4v7Oe7IxK9nrtxSkscGdJSBBvnNcH6e2k4VbWXTz3yaja9X8YOitm6+E8Sbk1fb+K5NTi4ql6PNS/zd2gCQPHr7Are+bIPDLaNhkfpV+PZP2uT/eflBtB5a6hBI4XDo1BbL71jjY884xGcuXqTpKhwBXT+m7RoHwUVmK1nfHdfGj8d4bF0LsMMWnrWsN49ZKtDyIHUUnonWMqXdgZi4Vqbpkaesj49s6NfA6HXgCM3VgaAJDJQBOiIEm6HDUMGRJjzrMy/zytubaAJW1SpPaa/wJ9/ZJ5JJ5MLPejmNpz9E7wffjtiO0HMeamOI/5wHYeX2UZuvv/tQ/qGM7nDy9+TdF8/XfF1FQIdN46NMri7jAxRc26cq2oXT0jqbH9tGZ7J8INsQbhjTFiFNlJa4wCRlwhGNZqj6xsnecNsOdpSYF8wMEtwIM5e9ADOuh3nNPk1SNkJLCuQpBY6L9gLEIGI9vMiyWDLX3AYoRctd4M7uo3xo1UvU+gQfurrJcw4eoGj+1BeYbIBDEQhRVcesVLVgFPWjoK83aOMnhHCBdwB3Ai/QWl+4IR0ppTrvKL8Jmj71y9Y3iAWvfZrxZfC0ZcHHVn0ONuORPXcgW3zakXMjJ5LZujUKpWM+eDrga774Ap9/S0jTXQGM80lPNsZgx6joDELtTjXprhWVgR1116eyum9G0KPsoCKbrlQ9ndIy0KOsDwk5wjNCuAShjG8PjU58eyiU1qwPHQaxpOPFrDRDDjUPYFS8HSKdmsRoBmrbyChJShWNTBDHHct9z4IekyWnBeQ03VZ+6gFU/J4l3QbQZH/XrXOfV0+TTR4uOxCo4OedFc6ePcOp/wi3i3VecDjidx5yedHxYaKRIVgKPL782Zt4MqfO31tDN+foRessv2OTR16yzJtefI6TnSFKC9peTNN1DNiBcTxs/OVI6mhtpFoDOyFrKNrc/K8braWuhsmegx0307pUSNOgRxGVgR6mpuLnMxnC1k+AMYFkiNLDkU8PgF4sWB86rIeCBR9OtBXPXOnhiC4Np8Pq8Cyc/xjhkRP0onW6/kGUjnEWjuI/4yCDd59FhDHCc1Dv+ivkK8aAx9gHTZHmRBXYWAeMtNVddvhfl6rAjt0cCt44Xn09aQ99eKRUdmJRgGQFHYaqbxBAxx0DHtlNo98apzsuxBH+oA/DK8WTtGzTvtMN/fV2anot/WnsVoDeLSjyiQqqFI3HOEIMe+D4BLTAaUB/c/QcZKxYakSsDz1iD156cpOjrVspP5HbKSMtE6iLTvt2A4LURcfL2rrhpys/BbwE+CZgSQjx3My1f9RaD25Mt/JU9pzqbozG1+9ZvJ17Fs37edvHzvAXj/i844HzPHUJDjVjnrDQ583/0OF973F457es0nYXGKhtvuFd62xvebz4ziE/+BmbHGweG/laAOPNXSKNySLcWPCiKBTuLDQL2JFNm1XjsC7Pvhn4a/a+hbT33/bcdrXOxrjCwxEu6dGJ0IlQrhWOo1knxpOahSBm3l+m5c5jhH+BL5umDIJetE7gt7HLNTaQwAYOFGlbTXS65Hu+fF1tjHxaObBpb6us/E1NNymvLgP8d1IWvuLOo7B1GaTLF/6+Zu6XNvnqwwfoP6vB4WObPO9kyP/5sQa/d2GV//L2dRb9o6yHF3jltwcceuQMF79wgS/8fskLjz5KyzWRMwCargkDnQIdN4KKNtEzhaZl2qfIrL2YvchNAHYU+UWaWhNyoAfT4FJKRaBH/pq1bFK31ODLFq4I6LNBy9VIYTh1qARNV3B14OJLONiMeNJinzlvBY2iF6+biEOtPg1njge3T9PxYhzhEjsSeWiR3sYF/Cii85VPRzzj00b3WAxAzEJlsritbptcWwaeZOuo2+c6sv6+RgjsCvDIv9Q0regUIEvTGzNfNrk6PMNC5zBi2yVudnB6m2YCSmlOwTcuGLBDupNRWEqbukagB1w/bY99J6KPHUpR8qzjvtx17XrGedhgG1x/nE9IjrXn8GXEVgj/55EO//LUaVYap/KNWBquYmp1BK2dCmJ12yu6ZlP3s5Ew/90428DPTj5/zHLtVuDB69eVIio7BU6piHcXneSO6/zSO+Y51LzCQ5sOoYKHNh0CJ+D7njPgVR8b8L8f6vHiE33Whpv8q7sCbp3bZClYZM67C4BYh7jCRwon0ezYAdixV8LkbvnlXp2IzAp83IyaHlXrT/Yei9ZPm0aILV9NEggC2SISIUKZOpSO0WhWmn02wxhPuuMTa634x8sP80kHTgHGPGbBP8zsIGEePCgCJGwgg60eW/tFoEcRyGLTsssL73U1OGyAj737+7w6T2UHFFXrYgm1l+H0B/iVl9/Gtx2RLMkeDzws2Vz3udwP+YEfuMoPvDbga36vyXd++v383cUGx78w5tmHJE9dusTtXQH4xNo4k3SFjyM9JM5Yq+M6gR6jaB6Z8ZVurGcBOvaiJzsrdhOAHWVUCHqMNTVqh+6tAD2KQBDjaNql5S4wiLeQogf0GMQmRO3JzpArA4euFwPGKbUQgl+/b42vuG0FFo6idMipuWM4wh2BWuKeJ+F6H2bYc9j6xb+nvTCHuOt5VMvMZfJqXXCj7ECwCtyoSi/jDenvqvTyLtxAXn3daIc+PKDeILAx8mIm3nGXGMTb/NPmGk8NHJqtBXMh7BunpSoaC8g22qnpxk7L7kYguxagx07K3mjh+bEG1MwSVz2bL33GKkIMIoR0EzDPncjXlYvcMneeD12VvOTkJpf6DiuNAs2owjlYNueKtDiK6qbiepGwZhPkivo1S99uHGmtT93oPtSjOoBzUVrZpsdsID/3RGsir0Lxh4+c4fu+4Dy3zA1puseY8w5wvI0BNjLv3YQL1YzMvOrMpZEfiGskSN4I7Y6yfI8lfjgLZYHgLPCRXks/8/e/kzUq88xd4Y1MaVTGMe6cN0yiuIRcHZ7lXWcVzz8k2Yyu0HGXSAxhWBueZ9E/mlacfJaBirPwrTzYUAR85Ou1AZLkrtmAELDz86K+Vd3jzan18djg1Xk52LYOFr03yxg7cIpga5P/euFPEZ/xWfDceTSKfix4w7th5Y3w2cs9mq7iJSc3eclJkMIdhXDWWo2c/AohJ8COcQ9SXx2zaU2kIMaOorVYwI6ROUVNXlnU3zRtsl83KdAh5bVbAxPKPqOpKCyjALI5UMMCeuTrLMovEASOMRnUjsKX6f4uwpMaT2oGMTy8eY7//uEWX37HgHPDB1gOTiZ1ODy0+SAnO7cAoA7dQfOuOdb+aZv2658Hj56Hu+qM0zqHc0VycBG/r7pe1J6tT/l66oIhN4/8fDPQLnx4ZH8XvdCia3YgxJMNPOA5BzuT2beulAMdo2pr2hIXlYWdAx87BT1mabPOKdpeCsuPNTDiZqbsQiUleE3orTHyTZPR8njxiWU2w6ss+Cs44iK9eIOmM8e0AFTE6P5/9t473rKlrPP+Vq2w48l9Ove9fSOXey9JroI6KoIiYkRABERQRxkcxowRXxkVdZhRR0dlkBlFxYCAGMaEIoooQRDJcPPt2/n0yTuuUPX+sXZYe+1aYZ/Qfbr7/Ppzeu+9VqW9V9VTT/2ep57KIiTSyAVTeWlIKzuL4ChCjBZhrIHr4Lzw3Ufy2fSRpeyNP2eN5mL7Ab7w8CzTzsH8/CaiI2u7R+7XMHgJ7DYmkfWm77kT3npXmqg2YZJA25CedqdIj1h5Nha2rEEvzowMLTo0EEQLuoo1zZcdC6jas6BD1rzzCCRVe6ZHdpi8NEwLfhNxkSQe4mlMi9o0nQny6zMRIslysoiKNJLEVFcB0mNfVhuQNl+a7sF4X8CQBihNQamO+PrRw2j+3yMNXnyLz+2z/UW/6Wj7aCHaJ6jj8Tqie5M/R92X97GTOooSH/0FchbZkXy/1XghyZq3lm2Pe3UkkSGr+wSFmSAakh4wfC5J0sNUZjI9MIjrUbbqlKzagJC2xDLgIwVIoZkrOfzwE9pMu4tIYbHmnWfdCzhQrnGidiKqu6dDu6/4ZuqvfSPixtvQ3AdrZ2D2KOOyMYtIzluzZsnofv4s3bho2Uk5kCSzTfr7VnR6rgtZvZVVuuGa6QEm08UfVJqiAJ2wwXvOPUJXNYf5ipAdg2oy4nlsJ38etnMM4eUQlntZIO/ltu0UpBxV2P129DkWp6YrA/7k0YuE2ufzDrb429Mt3vpAnU3/EuPKT1Kgxu/BuODOsxaZ0uZN/sn0pnakpUnWbWpL/Dv2/gRX/KjDvY3khJeWpmgZ2Quvs637+cxaOVooZkGr6MjwMGBg6Z80Zkd//IiU1/77rSq+8Xz98Wr62y6KfO+dkomXW7ZOMs9m9QHTtaz5tT//Jv+S90IfQn8Q0LEfaDfUPqEOWOme5lUfvMD3/ZPEkvbIqQIKRVe1MBMCWWNqEg+KeNkFCAVjfpMMyFOMk/eK1JW8lsi3L6tTYJrL89JCOhHVv6ahcYnwzb8JjUsAfHjpEd75qODk9GjIEhM5IIWFxOp5eBQjO0bJhxgpgUZrNXztvx/8UxgDkxqupdVnutf/2xq2QHZcybhTRbBFz8MB6dXvAwYvn366wfsC866I/evn6f9JYQ2OFLekgxTgyOiZhNoj0CEPbizxRT9f5oWvPkDd0ZStqQGJYvs+/N7vgN+h/JV30n3D29l4/fvRFx9hXCdN69PxeyadNo18iOdN03/jMMnNLJ3c1L4iciT+XTL6927J6j2GLWhveROwKX38NXk9QqA9CLqUlcVN010a/koU3C4wnNiShytFesDukx67oShvx01uLwv7OPZCO0VsweRUUKXqyCKrHWzwhYd9KtY0S22bRxuCUMPphjAoAiaBmEUqmljsLCtg3rW0MrKum4S+aYIwtbvIQn4foxNbmsxNWmnT0gxf172L/MWpMzy4ef9I6oPlG/iTh23+4ex5Nv1LqH4/jS9mw2ByGRMnLpJERPxaP138L3k9rXzTa7yO7aKIF0uWXMq7v8vuzVvGpKRHWr4ipEeeoUEZ+iNDhV5rjdIhofbphA3edwG+93EN7v3EPF/x01NcaD9Afwx84MKjfNmvm63j+TI1K21RsiRbhxpeS5MBSV0sq62mfFnv9zEZskiwohg+xw1/idd8+Bx/t95G3nECVk6B1+SOuWmW3uzyY++d5f71SM/uLxClsAYeHcOth4kaEmRHn6iI/oZeGIO4G1qP3NdaDeLmKB0ayI9x4iPNuyP+uf8vDZOQH1s6RnevEx1FkSSeR75XJEeGv0++bJvEyyZOfsT7Yn87lRSaUEeBTFuBYKlt87v3VfjZl57h+ANr/MiLKnxqdWlQ3r3tczzl9LfC9EH0+WVaD7RRSsC9j0RE9+gXH2nJ6LW875klt/MMhUXGd1q5k9a1jyS2GLQ0beFiup9chJnK0RE711wBYG5qgbPNVap2m4pVA6ccxfG4nNjOdo6txvaYxCX4ch01u7+tZeegh8p5oD0Egrb0Bwp3N2xxoHwDvurwgYsu0y50wsilbxRZbHEashTfoor02BfKKCvJkqex5iYiJG1SiNKK68D1bnuIL3riBFJykjdZkM3P5zNrbX75fy5w7KscfvMZmmd9b4ODz+qyMONz35ur/PjNU7zyay/w5ccf5GDlZuQIgdB7VQZZElcaTfdiW70G1wbxcBLpTIiXYaojiSuxTWQ7WxX34tYWmGzeSD7feL6tbm/JIU9syx14d/QV7Xp5ga+6wQI0nqe59XNXeeFrjtCYUdTXu3TLR/k/3/cocHNGxWnjCsO1JDmRTEvGvWQ+E9FhkgFp5SZlcTxf8j2xNGnfLbq2L6vTkPe7ZN0fn1t///6Ah39Gcb9t82W/8Vie9roD6BOSTnuWx146B6+H/++rD/GtX7LK0481ibYt9EobkbEKrXskgFCgM8aZUAyEu1axZsnB/aiFBvk7Jo/N5EHW1pbk57yYItlxRwroUFcrwbGdGEhCYpIL/a0t9D4VPiI4bxtNry86skTJisq0RESIHKrM8ZNPjmT2m9ngvscf5Fe+4yLt2gaVps+p24/yaz9wCjpzrP3VWcrTmqBDYqtGGlmRpcuOt9acLytPVhlp901tyiojzTsvy2tvWNb1IKt36FjatA5lmkANaVpr0NkYlFDvKG6v34iyZJRU7sLpuUWw3cX+VgXNdkmPq5Gk2M0274WJKmGFtL0uulSjGzaZtQ6AgGl3DkKFtiQHKzDjKkINN06VhxO1Dnu/U4YADP3oNKMc4iBbMTflid+fVLCbvDnS2mZqwz6TXQxFJt8kCWIiPYavD27exw+97QYOf0mTz/7zPN/UWON7f6DJW75bsAYs0qBdd3nPWcF8qcJTDp5moXxi2Gd7C0usxEI2aZmfdPxbfUVamq38aTIly7tjp5Dn3bGTyJtnrtR8MCnpkZYvjfQoWpbhvghDppwDeKqN29u2Em1XgZKs8s8/0EWKKYKv9NA00VohhaJsRWTHUucR/umcw2NmO9w1dwvm8ZRGJqbJXRJpSUk3ibxOG9vxfCZiJJk2jQxJa9M+to7kAiU+d44vgv74oTO8/8ei398OFN/8nRZf/mOr3PdTo33/wN+t86cHytQdxVMPeUgiudyPmyCxBsTCcHtLrBWxLStCiIjYEP06JLpXjojF6YgWxuHAU0RoPSBbRAoRneXZEb0Oyx/91XTvesIrRasdiu9xHcJoKMgmPUzxPEy/vym4ab8u16oyXzpMN2xSsadROqTpryJEFFfpl94imXLAbpZYcwM0Eks0qNpHwfeZe8mdhF/ylVQvPBDtErAizzyFGvT7ka85IMN0ahoz0siPtPVuFpLpJiUg0tbc1z6RURS7wCQkFW7Tjy5oBWt4qk3VnsVNuh77HfDOIp0ylHoBTKU9WSyPnUJRy2AadpP02EnsVUvhtYaBdVohuk1mxTSEHnH3eyksjtYCKpbqHRE3PVwg9vuGlXCt7vdTvxO9t/qBf03CtiiTHUceeZJ2LU/opnl3pCh5gtGF8z4SyFscJa+lWZ377+FFv3wTN35mmbUDFeqyw9q7bd5EDcfuYAdRvyu1fT717wcIghXOtiTPOPYAc6UaM+6hqMZe/wx1gEZhCQfbcqLtLjAue/reIHkeGv17VixNco1rkqcm+ZrcLnM5idLteHnA3pXfRX/DNO+etM9F6kt73y8n9HG1hKAV6RcSSrIGaGzpAlC26hEpYg1jeYDg+/+hhvWGLlDiFW94mLvmp2j4y7z/Qo2vP3msXynjBEeWt5XxC1FMUY7LWBMRkRzXJs+SLJIlrc6MdPuyuiDyrMQGnXr1NLQ3YPYof/b9o8/ADhT3/dR4LaVOgPqdkP/+6EFe+PRL3D4TcPd8P7aHJtSCVgBKCyp2l4pVHS9k0OSEB4iIj/PomtbhGPmR9PoYIz4YEhdRGWayI/6+KPFxXWO788OYt2Vx0iOP7Ig/v/jztYWL45SRwhqQchVrCiksHFmiE25iVyqIsEnVnh6WKW3ecuJz+etvuAhM8arf7HLUO8sD613e9lCJ137u4uBkon6dUbsVnmqjdEjFmi76w2CWg2nERxHPrWTZRevfBtFxncjqHTiWNu0aKdc0nurwmbVV7pidizqpZYPtjnpyBB5YbqQQO+XoXtAZbm3pH+eZVIhhe4rjTueDrW1xKVLfVrw89rL3x15u204hbplUKtIN+mRH7/sLBIcqPiVLUbWjQL4AU4EDbrXXjxKWnsCLxpDlJvpZEeU1CZMVcFIUEbppZEm/3jTL1j7SMenzTk6Uw7yfXnuA//atJe7ofa40fZYP17h46zRus8XFp85gNRW3f+QChx7dZHqlw2fX5/lUTfIbSnD7XSt89z2PcvtsFIhMa42vhlsTq/YMVXsGEIOgkbZ0kchoXCRJj9SvnJD/2V7S43lhnOxIvs9buKcdGdjPd6Vl25WuPw+m+XtS0iPtGZm2t6hg2G+0Aq+FW50BINA+Ulg0/GUcWcaVFUCw4V9k2jnISvc01huGxb3+5Ta/9JYuvoInLzZJVJZ4hXR5ZiIl0vKOfcmMspL3k0p6nheIqR3xctLasI9iKLog6qG1hv7n9/LQl9zDT7042d+yIZXmlndd5IPvgg8iWfyRGl95IjKuKC1Y86zBFtob6l0OV120VgTaI9QaV7o9MlAx4uER3/oSIz8GPUxrBCqf+IAx74Ak2aG1HoktkkV87JMeMewE6dF/LUB6wDjZMe7VkbwvARWRHIPSLaRSVOyIhOgEDaSwWOmuUrUdKnZEglzqPMKB8o1c8E/z1z9YGpT537+txBvfPs9SZ4mvucEfbGWM1xmoDrZ0sYWLlEW9O6Lco69p99PSmAiLrPTJfGnGy3192YQdiuGRt2iJpxG4ssLnHLgpuhd4hI4LzjydsEEzWI06vQWbfmNgJdRaYakqtFZASHR9AdFY7hEj9tBSOGiS3HmPkMvt7bFd0mOnsNeV5Szshe0sScS8PABCx2W5+ygVaxoLGxU0mCtFykHZquPIEpZ0wC5Hz8HvsKwusVA6QX8M+ZbAFqO8Ocb3SRQRtGnCM+16ntDNIjJyPMQEezL6895Bnpu5acFkXqh8aKnCm95xlFD7fPNfbLCxVuLtL4SyJYHIg6gdNnnF86L0labPyU9ewvUia0z79iqv/vNDTM92+Y+ft8mFtsWRqsTqKdQnpy5xsOLRCtZ5ZNOhGwpumu5yoHw4OoY5TnoMmppYGMfH0kjANYoTH0Vk8iTkx1ZxJbZQ7kVMGtOjfz3rc1o9fQiJ8Ltop4QlbASCaWeRUAdYwuFN957l4U347Lllzp5a5CaWRooKlMeUM8WDGx1O1GBcBmYRDvHrpvdZXlipXy7xOY8AySunyPcxVLkvqxMoMpfmLFaqc4iv/iZ+6V/O8ca3z+PIMl/4kx7aEvzLf3oYvbEcpbu4jHjcPbzsW5IBG4dY+nnF7yDRryhz6WKVw8caSKlxbM1jZyrcs+jxSMPl35cdOiE8cQG+4FCLxUqVAekBIyRHnPwYbJfpn/ySQXz0t8SYf7Uh2RF/zSM++qSHaVvLdUmG7NQckUF69DEZ2TEug0aICcumrOt0wgaOLCGFzcHKIQLlUddV/u7sef7mdI33/yuUHj7EcdZGymoEKzzpQJv3X6jhqy7OqU8gTt4DCALt4fg+2NaWjvEYtj9PbzaRG3mESRpM5ZruFyQ+rhNZvQXCw+Q+k7xH7F76D3++fT+HraO0dJtPrDSYdkMavsWNUx62KNEOJAcrJZr+Kp2wgSsrlKo1fNUh8M4z586g6wu0gw2q9kHwWsNTXZzy4GiuYfN2SBncrrfHTpMeVxo72car4fvuBNwqXd2hJGtYOmTaOUjDX6ZiT1O2pgYsdn9Ct4QT9e2eJ8dDGwELi0REn9fCsV2gHXk+CYfiJEWaOxyJNCakXc/z7tiKYN1nrIsjb3EC488dkrL8Jbcd5U8ePsMNdY8/+OqTifKjtCWrRvjyLtYburzs1xU/+oeH+Yovvsg7/2KRv/jKJb7vBVFchLe8WbAx57L5hTVO3rrG6Yen+bYvWscSPn91qs75M3Wk1HzxnQ2+5saL3FCXlK3aqAKSJD+kBGGhUQjLAR1G4yHV40ON5h372QrKna1uedmubLvS+S8nskiPInmLQsVOEAo8hAp622ijPt5Xuj93sc1b3neYciXgiz9vmY9+tMr8xdagmG95yyJ/9hKLJx7ou0qPEgVxa/Mjjfu5sX4bGHUoGB+TRcmJZBlp4z752eTRl5QdeYTNvndHMezEnBj9/r8yfR/f94HbeOVdLf7pzvci5uqo97YQt56AehVaHQg8lp47x+Lb1/juN/r83PfXOPFdAQ/+3xL/4xdW+ZmXRlu1xOs9FvEIgfNH6yyebfB3L5jmHxyF/FOP2makV//qU46y9lXn+dqTTWbdyjDGh8HDQ2tNqP2o72uBJZxM4iPzG/eJjgSpUZT4GN4bjsN43IbrClslPQp6qhcJYpp1Mk4/7gvQi6URlWUJh6o9Qzds4VoV5OYl9NQiGjhW8/m3jy3idS2OfpEHD4yW+c3fV+Utv1ziGceik4H42L1w8p7e6VybWLaDFB4WDh+8eIqnHDzZy5mld5qIizSYZGxWeUWu55VXpF3XF3YhaGlckc7u9HVnnsByqIkyFXuDpY7DFx0+MthfVbFXuW99lWk35IMXa9w01eYjyyEnaoonL7ZoOCUa7bMsdyR1Z4mF8jy16hy+6hJqn4rtRlZxy44WiirY2jG3JmzH22OnSY9Jt6+kXb9WLIRXGskFlwleixKAaIJbI9BdptwD2MLFU20cWUaISEkQQmILF7zlqNxyneN1n2awSi3sxbYJiLa0jEXV7iPpVYEhTdq1NKQtnicpI03A9+/Fy9TRlesgmvTWkUVIx+8nFzImjxvN8ZrHm+8v8cQDKtpqkihHIvndZy2inxUpr+/7PoCjvObJ0eub3gEve85pAKZXOwQflJySM3Tul3zwDvjHdx3mzn89xwLrBLbkj84c4vx/uMDLH3ueY7WDSGFhi2hLzGicDgXCGijU0Au0Z7tDb7804mPsJ0vZxgLZ47jIOE+Wsd1YHdvFtUB6TLrFKV5eMp2Qw22FTjkymoQBWA6hDpDCYtU7y//4tzq/+4JLBLpLyarzfTGyA0B9JOTvn7HCPYsw4x7Cju0RX+mexpYu084iQI/sgHT9aBJvDtP9pIwv6jmSvJ/mIZZ2bzzlvqw2Ic8aXMQjUyMO38BXzwW8/C3H+LubplBnVrFe9OKoHwPMHka/75/5fzdP0f2cz1J67yK/PXcK+/0zON/zOIR3A7/12of41h+/aaQG1bP0rn/U4eRnlkfu3f2Bs/zV+Wnc74QvO9aiZGnKlsS1Kr2WiwH5EWqfQHsoHSKQhCLasmhhI9Bm4iPzm6ux93nER7R43t/aMoYs8j/vuPP+OqGAl0ca+s8m7W6fNInSRe+7YZOSVcWWLl7Yxi9bWMEGjWCFH/rLg7ztpausdpscrizyXW8aLXHxbINX/6vkBTd7fM5iBW44Aqun8aZn+OTKJgvlgBP1I0hhxcgOyCcnssiI5Lq4ny+r3GQdybyTE6NFcL3I6h08ljZ+3SS4x39MgcTGguYat0xP0w1H9yM2g1X+fbnMc05aPPO4T905xJnmChfaktMNh5qzxsFKmX+5EFlhFssdnnTgAVqBZNoNOVa9ifVwBUWI45aoWLNYnRZ0G1v72iZsVYHcJxeuL8RjBFhuRFJIm1BCqNrU7LmeS7WFJRzcnjdfdGSiBL8d5S3V8bQHCC60L3Fz6WhUXrnO4DSMwYK2qDVpOy62o4REtiJXZJJIa9O+h0cx5G1jyXOnH713qOpz/pfKhH/gI2U5UU68hvSF2Qt+RfM5BzzWPU3D96nYqxwoW7z1QZelfz03SGcHise+9xz/3DzEwfIS/+HwCkoLjtc9arbLXOloRHxoBZY9iPuhdIju7fu1hINl2QidElMjC1knuqQRG/HFdVocj53ETgQvvVpJj6zrRcmnJHpxw3yp+czqI7QCyVMOnkSjaIcbSCy+/BcP88pvOM+B8g0oFCud05CI4n/XNzV44oJHqAVf8HMWb/+e+zhRi4iNaFtiOVkz2eSByYiU/JxGdsTTFCVP0jz90iyTxYmPfSSRZ3TIy9t7lZIpR3HiL5cRv/vFyIsPjqTUl87Q/vuHqP7g11M+cRQWTmJ98k0Ej2zg3H8KfWkNTh7lTb+p0R/+IOKOW2kdvpGqPQ1teOG7WvCZ8RYceWSD3/yzozz0pecJNdw9BzfUAx437/e2G0TeE4H28FVANxQorSlZHo72cWQ5ipWAxeBoW62ixW2OXIrH9xAiHjvCTHzEF9b7p7WkYCtzVg7pYfLymIR46nt5RHO7puEv8ycPa3y1wbc9ZhEhRO/Qixl+6IUud/zIJnV7nln3MCvdMyTH1SeecpTX3XiauqN4+XNX+c13fAn8y18jn/o0bpzymXEP9QJWZ5Exed7PRcd0li6dRXwW0bGL1HP9YhseHskHY1Kgsx9MzZ6LrtXmscNNPnypxR2zD3O4ciu+6nK2CS+4pd6LmKvxVZfHzHb46HKFpY7N2Zbg3Wclx2uKRzYl778omHZLnGnaTDmaz7rnUBp8JThSXed4rcFC5QTCa41bjLaD7ZAeUIz42Gkvj0lxOZXknaxrq4rwbqH/vXonrUhUtIjrBePVRG7UGoXSIRIrChKmfKjOolA4CA5VbukXOPx+oR/1Ja8Vle9WGVeOJxGWWWM6T0lPS2NKZ0JKW4W4LvYa7iyyrLjJ/jGa9njtVn77DzzDgq0ocQVfeeI4IDhUGa33FXcq3vS5mjtjpAfAjZ9e5u8v1Xj7wYPUDvmcvHWdrzoZ8viFRzhaPUDVnkUTBdILlBd5efQC2VnCwRJOFPfG6pEj8dON8mRBWpqdPLnlSnt5XK46dhpbaXOR5205dINlDlY0617ABy4+zN3zM7z0PypWnlrjva9qseY1uXf9Ad74mSoPvr7KFNHpFkoKNubL3OJ2OVS5hb87c4q5i002PAtq0Rip2rPEx1UzWKVs1SO5P6bc5hGTSfLSdM1EiJh0tbRxn9WGrPYkb+3L6nRkkUmm+TopazXM38jnEvLEt3eg2UD908eRT+4g7v4S6G7C/aeo/sTLYOZwlKW9gfuUG+i+5yGCM5uo+1bovuWTlG+don3vJqtnH+b4103RvncFuVjhzQdr/MdXfg36V8djgNz9gbMsfSB6/zc1B/XcCl/6mDZferTNwUoU6LQbBmx4Ft1QonRkvClZiordoe5ExEfc2wMYHFubhpEtKTECw0R8jHp7jJZ71ZIfl4NU30GkndSSGr8jIeOVDgm0RzPY5DEzLksdm9+/f4ln3xDyA98kCOwG//utFT65ssk7T1/iV941z5E/G+8/c4tdvvTocX773vOA4j3nzvC0z38WJWENdOlQ+wghWWo/TM2ZpW4vxNqXRnSYvJGTeYoY+oro5ls1KBYgYq4TWb3FU1r6DzfrB85inMbJkoqs8YSFBtPOEUDgyDKPX7hxsIdLo/nHcxc53azwjGMNTm2WeLTp8PybPRbLJznfvpfXfbTOWtdirQvPv3mGhr/Mnz3icLENDd/ieK23n7AyHQ0q240Wh53GlSM9oLi3x16OlXE1KtBXAv0TApwyhD4i9CJiwnYh8JChQsloQrBFRHYMTjJCIMNeP+0fS6vDaJuWVtFrqQ6lqX5lvVeTUp3GIsfv53mC5I35SZW4tHrSru1jHGbionheRvKKniwevZ6cAyZtT3RNIKhudsdylDoBhx7d5NCjm2zOlPjM6Xm8z19n+aTgcxdXuHnao2xNESgPX3UGXh4CgRBdLBF5f7iyErVdYj6qdqypcvQ1Lc1eIlC3g70qs7MClRYN5G16TUsTdKnZc9iiRNXucHKqFyfsWSX+7LkNytYC//tTNd71vgPIc4oX/vAKCyVJM4C3/GqNjbkK91+K9pz/3F8u8uxvW+Xk1Hy8MjphM9p3jjU09PTujb7mERrErpFy3TQm08Zpco7IIkdNC/R9D4/JYZpf0+bC7LlYYlGSNfRUBevLngIHb41ul+qIr3juiJ4Q/O4f4t+/Svl5T0I/dI7w08tM/cDTEbc+Beff383Sj/w74YUm3rpi5ie+Cb30MEeWQ87mfJtK00e9OeCtX77A6lNW+IrjXQ5VfTY8iw3PohlIfCWQAhypqViKthuyUG7gymq0bZfIO2Dg8THyTUe9BeLHziYX1KMeHaOkRzz//haXHUCKl0cc8eNpB9eyyI7+q5BILLRQOKLMrLtIaWaDm6Yjrw6tLTZnJL/2+lXq9lFe81dlDv3JKkdYYf6HJTfVYc2DB346KtN7MOpf//DDis1vrvM5Bzz68b981WXTv4QrK9ScuZgxcdji0dc8/Tl5PS9tMk3WmE/+xmnvi7Tx+sM2g5aaHlIxq99YmUIy6x5mzTvPnHsU0D2yI7r//guPcKnj8qwTTc63bD664iAFLJZPolE8sF7ingPwB/9e5fu/YJ2yVeds6xx/9alpDh9qc8s0bPgelryArzp0w4ApPUXdXcBWame2uVzOYKY7Xf9OtmMf6eg/o8CD0Ivee63hPcDWEuzKcPIO/cEWmCjIXhD11+rscAHmlKOtLZYTqyxLgeq/N6XNYodNbHFSKc5iv9PqSWtj8jpwHew13DqKeOGkLWJMnh+mcrZSvnkh1t8j/sAzDvJfv+E8f/SgQ+cXhtH6p9a7TH3sIkuX6rztnhpnHr/G5x/qctvMOlOO01NYAgI17BO21JSkRyA9ynYYuar2RZqiuJxMIz6S17dCguxVsuFKY5Lf0eSlWST/wLOSwfG0Go3SAWveCjOu4m8eDfijb5zBkXOseee5ZRo+faLBa755lVumj3KqcYYLLYeVG+vMznc5Nhfwxs+cJfAXuHMuZNO/FFkMkdScud5xyxaNYJmyNYUtXBQhWis0OhbzwzR+07wvktfGfiDGdbM+8jw38pA29hPYl9UG5C2cJl+oCCQcvRPOfgqOPS66GNMF1NvfTHC2QflbPh9OnaP5njMAlG/5XNAK/dlTHHtywD/9X3j6647B1CH4+If47LnHMEWDJ/6U5FtuV7zqX2zCXxk//VAqzY1/c4m/PrPIg09f5alHShytRuNs3ZN4ip6XB5QtmCuFtIOAQ9UWNbuKhRORHTrayjDw+OjF4Ih+rdh2lkRsjkxvj/1tLZcVyW0t/d97IpKpT3oIa3DSjyLkI5fKPOnAGq/7aJ03vsGnZC3y4OZ9HDwyw+ZMiZe8xuMZx1w+ttzkbMsZxC4NDlp867suAPCYG1p8aCng8fOPIIVF2apHhLd02fAuUrVncWWZUAe9eSHsHU0+/IZbW+dmfmGKjfsidW6xXdeBrN5m0FITyVHE5cbMVPWPgjOVc/d8HaWb1Ow5nrhwgCf2vY3QCCQVW/Gnnyzz8s/b5PMPnQTg5qnb+Lln3M9/+0iNT65q3n2uxg01sESZxy+E3D2/iSsr2OUpCDrjR9tuBbtNemzVrfdqE/I7uRVnr6C/MOofl6zUqDGj73UEkfcHgNRDkiO+wLJ6Qzfwon4bNqK8Vu+Elm6jF7A3ToBAccKhfz9NUU5z5Uvz3phkgsggO/aRgzyvizxLr+lzvMy08rPqTV9cvekdx2LXT/D0o/CyXzgzVsLi2QbNd7v8zdpB1p56kYc2a9w567NY0Sht4SuB0lG/idymPap2FKBa2BJXlqPxI+WQ9DA21UBeZG1z2S35kiX/diKOR9GyLjeKtGXS7ahjwWvH65BIAu1xtuny0EabuhNZGNe65/itz0rOtODbntzksbM30wzWmHYlP/neGkdPNKhPeyy1BPedLnPXE5Z5+rF5umGT860NzrZcvvBQnVawzpR7gLI1ha86SMvCV9E2xlD52PZMv7FMJutMYzOLrNxqHfGyJvUi2cc4Jl0wFUgvJCzebEwrv/gLcPx/Qhw8Cnd/MVPPHp2bRcXhA28VPO1HppHPe2lU3Bd8FW97z5/xqeUHmfkpn4vLDj9/vMureHFqE275xBIXL9X4/SdUeNITV7ipDp6CTuzEWVfCpm/R9CWhFhyvt6jadaSOYnpER9RG3hlpQTDjAUkBo7cHjHp57Ht27DAy5pgx0mPS311I+nJGCIklHHwV8NBmmY8u13F7VT+4scoP/flR1h9xeeZ/WeLrTh5hzTvPbCngZ352ltKBgG7FpvZIh42PSC7deYBffPwlmkGHT6yW+fCS5NvvaHOp0+Ro9QA1e45O2MBXHTQKiUWgvQThkbe1pAiSa+fkmnirhIrJILmPPraxpSXLo8NkUTA94HFYgyM1GUkz5RxgtrTOlBPfVzVMc/f8Yd747E2mnOMj5d00dSPH6kt87+MEjzbafNfvH+eLnnKJi22LpbZNxWpgyxJuqQ7tjV7RV3CRfDmDzm3luL+9pBxnYS8RHX3E25Tch6kUuGW0EAi/O0yv5HDLimVHXh6901g0OjpGMehE22KkDTqM+vGAFMkSnGn38oRlloU/rdytTgaJvALEdbDXcGeQZt1NS4shXcpzSE2TZlXOa0N+G2ubHsc/ssK/tA+x9MRVNo/BLdM2s6UQFXP+sYWk7igCFSLc3vHOCBxZGiUN0zw3su7lyZVJ9ljvFXm6V9oxCXaizfH8gce0vcBjZkM2/U18Jdjwl3hkM+AJCxZ3zEq+5MgcEOkoFWuaOw573H9JsTAdULNhqtTGldH9KecA0zOL3DztE2qfKfcASoe9kwZqeGGbTf8SB8o3IGQ81lKeN0be+7QxH7+WNcZNn9M8AbPKYF9WZ2JSK7FJDo8bDXFr5uwHb0UufBQO3sa4rAbxjGfxtH+6BAduHrknPv/LmTn0qxz/5WejP/ZJfvur7oWvzW7pwvkmC+ebvGftKGceu8bCgQ5KC8IQLAuqtqYTQicQ2NKmZCkOV5uUrTpoOUZ69JEZkNTg7TF6P+tUkH3sHGJ9KkZ6FEKGPLely2L5BM88foYHNkq0A8mphse/LpW55651mo+B73uchQgjZm2hrOjcWUJ9RtCYKeOXLBqzZfSUoGrPMls6womy4p7FDRr+Coer80hh0whWEAg6YYNN3+emqVsM7d+KzpyWPs0AmRzjpvW2SS8z6WIk0pubcz3I6i1uaUm+mh5CPE2RdGn5hnnumruFNKXAleUYCzfsCJZweM2TjwJgTV3i+MlNVrqCta5mpWvjyC4HymeZcQ9Rrh+Ithd4o0fOXXZ35e1sK7mSyuteUZz3ItkRh0ohPlQQxfQIg+Hz12r4fcJg4IWknRKtYJ2arDLYFuO1ohgeQg49QgbPI4uEmIQUSUub5uWVlT9NeCdly067D17rSP6maXI3iTSrbZaF1zQPpE24RdoK/+MPQn7whZYxdW3T444Pn+eh9gHUUxpcXOyyULJZKEcxt8o9hdpTIb4S2NLHkR1s6WLjjram6PGlRfLshrfHXpGnewGm32KS36dgwNpp9yCdsIHS0AnbLJQFh6o+86XDlK06694Ffu/+kKNVzWJFsHgi4HwLZt3o76Ypja862NY0ALZwWe2epWbPYkmHmj2Hrzq4VoVF60bMJESWoppGPprGXHJMppEjyXFfhCTNIkX2UQyTWnPz5uEsYlognvnclPsCagvRX7Jsp8zxt/5glN92ufn2U3zjLb/LSy9+O7VNL7O1d3/gLGcvzvDAwVlmFrrYtsYthdSnPTanPLyapuZIpl2buqNwZIAt3NFvpIv3q9QtLimxPK5KXEUBS7eH8eeldMjh6hynm026oeZC2+FoVXHTlOJx8z4V+yiPtB7mlX92mJMnYX6xA4uw9rDFzKxH4AtO3rZOK/ApWzVYP0157ggfvNjk5NQKVRvmSkdpBes4ssTJqRMxksy0Xt1un0ojt02/Qx5BEi8vjfS+RsbANrDNU1pg9AcG8+RKzvt4vmRnSmOzDOy28foQUlhIGfLRf11k9eYNzrQ6fN6ixTk35O75s0w7BylXpqIuHnSGwiUZQK0Idpv02E3vjL0SxyPPrXsvY6sLoLBHVIQpfa9HZohugO24KGkjK7MR2aECWtKn5NSwmmuRp0d1lnQF2iQYs4SiiayIC9m0suPIGr9pyk2irL3QN/cs0qy3eYqj6X6SvDClTSO08kiP9HYdKN/Am94Bv3//Od75qvExJJXmlk8s8XCwwKVbq8wfaFOt+xyoh8y6cLAiaAU2vgopWYqq3cJRZUIZRLESJGbZXgRFt7tMUt5W5ol9MiTCVkkPE3mi1WBPtyMDtFa4UlOyqjT8FTphgz99GG6sK6TQTDsWZUtTtgSWiAg3TwlcWWHdu0DZrqO15kD5BgDWvQvYjotGR7E7hKAdrFM1bmdJIx1M48akf418uZTPJmU4eS9NLmcZrGLYl9UJFJkT04wCpjzJa1myuWjbUvR020VK+KNfCvnPt/w2T/v7Z/Ky/7JAFo4+tM7Rh9Zp1xxWD1ZZmq/QOmTTajgEi23KVsiUYzPthEw5XnSkOBZpHhmTEhf7nh27ANOYTjGuxT1s4p+HyCNWe1UKCyEEUlscrXrMupJACXwVEdIPbjgcqp7mx/9xnltu2gSgWvMpVwJsO6pbWhoVClyrwoOb93Pz3M0Eqs0XHT6Irzo8sLHCYe1hVxZZ9y7Q9FdROmTGPcj4uMzTk/PSxL97EYOiqY40ORJ/n0acG3AdyOptEB5phEVWGshWkLM60yQLJ1Od0TG4b362Qj9bI5jlDZ8+zzs+63LLok/Dr3Hn3CUOVz2mK4vQldFRocm4HpMouFdKKb2WlOG9TmzsFKQcjdPRR/xzf5uKtMGp0OyeZtNvc6x2I3YlsihW+2PJcnvH0kL2eMpjq4uMs7xF7VYF+D47vXUkFzCm+2mLqSKLqnjZRciUPGvkeBkvuvUov/XVbY79v2VjqSc/s4y6V3DhxBQPHJ7l0PEWR09s0gk1sy54ysKRmpKlcWQbW7nYljte0KQxIZJ5s+5dKfk1KWl9Nc8Zk/zGWUe3hwEIxax7hHa4EXlq9CzOLb3O+87ZPHY2YLHiEyjBUtvB14IjVcVK18ZXgkcbgo9cOs3ppssnV9f58ScdHVQz6x4Boi0v/YCmo8c9FyQRRtL0802if2WVk7ye1M/yyI99ZKPIPJanz5qQQ1akLpCKLuKA+Rv4Dx/7Hv5D72Pje1/HL7/lP/I9Lxg/tjaJStOn8tA6Uysdlpt1zs7X6bRtSqV1ph3NXMlithTgWvra8sjYaVzOY2m3tQAe7W/ZpFO2rtKPA2IJByElx2sHaAQrNPyAam8F6yvBr36yxj03dLlpStMJBacqPs0AbpnpcKoJXU9y8VyNtz24yaONGmXrAj/Y/Tjiif8Bx53lcfOzgEaisaVL1Z4h1AHp4yNNr0kz0mfB5J1RFKY1dFqa6xs75OHR/1xEcCbTF2G5iizITG0aTyd6/0Dw8sce5kTtLH/0GZeaHVB3XCr2GrZwcZwyTmmKQUyE0NuaoNnNIKY7oaTutKfI1aw4Xyn0PTryPIn696UNfpt59wjTbogdhBC0GZzW4lSgR4AQeMNAqKnjM01oFxnTeeRkWtl5FqyMvNdBNOntI4voMN3PkqNZ3numhVDaoshkic4iPjS/+Jxz/ML/M5AUPUilOfLIBtMrHR7iANWaj2O3CXVkcZ91LTY8xVypja9KuLKClVS+djI4smmO2I5M3JenQ1yO30Ir0CFlqx7FFABE4OOUTvDFR1bphG1sEXlpTLseq93I0ihFgNLwmTV3cPzmjz7pMADNYBVbupS0OzheXKPZ9C8x5RwANIH26YZNavZsvyFpDcx5nzcO+zCRGWnjuAixmUK47MvqCZBFUBWxFuehiHdJTh2xIOj1//lDsHIKKBWoO8LUepfQkdh+yJqssL7mcrHc5XDXZq0bUnc60alacFm8M6Ly9wm7AXbUyj/pIjs/rUBgyxI1e46qHfUPV1a4e/4Sh6stTjddarYiUIKFsuRcy6ITRrpAqBUP329Ts6NAuj/6pAMI/z9wUV1Adiym3ANYwsEWLhVrmnXvAvVezEhfdWiHG0w7B3Paux2y0kRapv8Sk9VTYGxfB7J6G707uWgp8prMnybM05jovAne1KZkecM0vury82+MToVZ7sInVmw+tVrhkcYyy91HWfPO0Qo3o8WjyTJYFLtp5Usre9K96UlcN3sFdwlbeeZJbw7Tfb8DnY3oFJb2Bg9unBres+yI8IgKiF5CbxiMN78Bo3mNi+CiVsessk0L3Z0Q2vsYR3ICzVKgTRbmLBmd51aZbEOWu7WpHsHj5m/iV/4oX/bWNj1cL6TVdNjcdGgF0AygFQjaoWS1a9ENW3iqHY2tNLfc3UKeJ8hOl7kXytsN7IbXTFLWKoUIw8EfWuEIB1u61J15LBkdhwyauhNSc0IOV31umvb5tjs63Dm3yJcdn2Op/TBd1UQgKMkqypK0w00gOobWlkMi2hZubGtLZmNTrhUhjbN0tu1if9E4GbJ+r6TczXtmWfp2v6w03RjMcjzZDgO6Dd50+A/4b7Xf44G7FwnsYsuJ+lqXUjvAaipaDYc1D8404ULbZsNTveOa8/uTiQzRu9kPr1XiWcrh33ZglMvbfx79LYAAQkiiAORlXFkdyNCKPU3J0tw206FkKUIdHYM860benrfNaO5ZDPiJr17ii4+0eeVdbd57/jz63g8hsajY0wTKox1EurIjS7hWhVD7tMMNbOn2yOn0Vubfy1uzmkjjIuXHy8oivvdl9E6YthhftKS9xvMkXX/ShHgfSaU5WbYpffxBmx96fb1DpRryvg8t8IFHSnxs2eLTq2U+sVLiTLNFV7XQQkTbDfoCYat7rreCy0E87KQb91bKu95RtD/1f9dBEFOP22snesfRxo6pjff3Uh0qM+Rb4U1KjzbcSy5SJyFCspS4gu8FoxP0Tv5dk0jK2qxryXx5903X+8irL4sYG+0b085B3vSOYxz9sexnVN3o0mrYdNo2mx0ZnQQQQjsQdENBJ/Tohk0Cne+CbW7aVSbXrmXSerfJqTixIm0QFmVrCkl04o8lHAQWUkDVhqrt4MoKM+4hHFlC6ZCqPdN7nQUErWCNijU1qCb+Hooe3ZjmPVXUIyRPMc9qQ1I5z8G+rM5AnjdH2lxsymvSt7PKMnnuFW3jOC6dKvHtP/vfefOvtwultwNFfb2L2w5oNR0a7cgKf6kTnZzohW3SjqOdFDvuIbIXSI+d6v+XbSzlyZ1iiMtHKaze6WsSRYjSIdNuDUeKwTbWkqWRQjNXCpkthcy6IXfMdnFkCU+FHKr4rN1+G/Pl41jCYdNfomJPD2qr2wtUrGkq1jQCmeMNVMQ7K2s9nFVOXh5TOpOulTGed0tW7zFs41jaPiax0JrypyHrwSeV4iJuQOPtdGWFP/wfG0jh8PCdZ/iPv3UjUq6y3PW4fcbmSNVH6WgwWdKOrOXbwVZdcndya8u+i/S1Aa3ALtOkRY3pYbyZcn24hcVr9eJ4ZCnDJkEqDK9p6ZNKVpG0aTIgKVdS0u1BQbq3kCS30iwHeWmKkNBJZSZJksVfk3nyJv5RUuVnP/cI9zxFc/cHzhq+C8xcavPIgQXKlRDbUSy7XWZdmHIsmr6i7igqlocXtrDtWYgr1dv1yCqaPisI807fu9aR5RE3ybPp/4ZpRxED6BBbuCgZRlZk1QEJoheLo0+CWMLBUlCxpgmljyWcnsVaYYsS7XCTslUfLMR81SHUQXRqwGjlTE4iJxXsrPzJsWkat/G0yfJMZe/L6u0hbdFURL9NK8tEXE+is2ekqUzzsjPPBcvlzo98hDcd/gN+euMb+Ylvrqbn6Wdt+sxeanFmfo7p2RLL9RazJdjwLda9gAPlECnSg5deUezGiVyXG7s1LjPno6Jrv1EIBAgZnbyDQAhrxOsDorhIjlSEyme21EVpgSU0UoBEYwkoWRpbuJSsGrZ0mXY0M00P3/ZpBqv4Cta655gvH0di4ak2SoeE2u9tNYwb3ScZO0X07DSY9Ke8srPKysB1IKu38A2LKM9Z901eHWkCPc06kWUpzLNKj2LGPciUc4DHzd/EN375BT77sXk+/WAdR9JjCKPBNbCg74Tb1+USltutZzsWwqt9Qtjr6E0qNV2OyA63GpEdMNzC4tai5xD2Ay+lERDx91lsdFb+vLTbSTf5JHl9o8hCyfTe9Dktb9pknVSuk+0Z9+CYpC1pZAdE+8OnVzt02hadls3mpsNSJ9quuOFLljs2XdWNFBnC4rL8SgYfLYKstk0qw/fy98xC/BltlbzKIlBUFNvDlZWxP1u6gz8pouOUNdGxmL7qILGwhI2vOnTCBr7q0u/7QkgsYaNQeKrD5Io0sfdZRMckRiET8ZilexUhWPYRIe83ShLIW5370sivScrLaauw4PBjYfEWxDOfx9e81Ob1n6oXLn16pcPMhRYrS2UurtucasBq12K5YxPqYGxby34g0xj28uI0U/5qxvt4MQghB0cNj9+LCOeKPU3ZqrBYCZgthSyUAuqOYtqNth7a0kUgBkbszXqFUAfUnXmkgJWuz2o30jFcWRn0uVAHtMP+1vA8giJPr8kqI82oFCdbipRjwv742WLQ0izGKfk++bDyFjFpJMikjFaW9TCeZtie73ysyzOPP8xP/eMca11YatvMun4Urd2tRQvHvpfHVqP6bxW77eWxm8fc7mN3EHrRn1bR1pVSPXqv1PD5CJlCa25X+KWNwyKMdbJ+k3KWMt6FuC6CK20PyYmxiLKbJh+LeAflLXiScjxvcWYqP0r3+rdN84rnpcekmV5pc3puDtuJ0i9XAqYcTc2OTmxZ6di41S6+6lAS5dRyhs3YpufHTs0P+/K2GHb6947/7kqBjPZ3O7JMoD181UHpyBptCQeJBRIC5RHqAKWDSMnWmilngVAHAyOKACxho4XueYgUVcdMXh1kvM/6nFaWiehIU+ZTxvC+rDYgS+/NIqWyrMRZ9eR5XabVUzTtsK6Fn38urz3/WZ7+FZ/PjX9zKSdvFHB67mKLR2fnuVSv4pY2ebh3YksnbOPKSm4ZVwzXgpfHbiLt+O/RRLH35r4mkD0vnyjQc1oaGIqaijVFyariyjae08JX0T23R0grQnzVpRNu0g0FU06FurPAserteKqNQBBqfxDA1LIcpLASJ2qlIWvdadJ58/KnXdthXCeyeivBKMhmnCD9IadZJpJeGXlkRXKyTrNAkLge/xsva8o5wI1T0XFZnRCUFpFFMGwTEEZbBfoB7/p/24m+Pym2ekrMdrDv5bF3EbdKBl7vWEUZxZtpXIqu+Z3eM0woqzokXegOEo3nGyBNeJvGX974TiM3TWXsIxumhUsfpudpSlt04tOMl5mmmJueoWmBFS9j3EOkYk3xW+84ShpmL7Wpr0exPFpNh8amy1IH1rrQ8C1agcRTHqHyd45AmHT7xE6Us5vYK+24EjCRVfGYHmHQ+/OxsXBlhZJVw5UVHFmK5KpSOLJEWVaoihpCa7QQKBSWsOmEDTa8JSBS1uVgS4yNqc/TS2loLKNjdjsecaaxOE44jteV1b59jMMke5OkRNZ8mydH0/qNSV/Pm9fz6kv0g4O3IQ4d5ftf8z8N5ZpR2/RYONdgY81lY63Ehh95ebSDaKEb1WaWRzsV52Mfu4jC3pH5+l08hke0tSWSm/3rAhkRz9LBFqXeSVtTVO0KVbsendDWI6XLVp2aPc9CeR5HlumGTTwVkWwb/hIfX3mUQHsDgloge0eUR+1UhAV/gDQSM74WLaKHbwX7enMSW9zSYnpI+crq6P14ecn3aUI2y7qQphCkfU4qB1FZVXuWL75rk4+dc7hvw2HDs3od34+Cl0l7aDnfiSNhdxLbLe96VnQnwV60tGoVxezwO1EfrcxG27C8VswzqMgYSV7PsjoRS2NaSCcJjq0spA317wfCmwBJ+Zi1UDERycm0RSwTeQSYqV1p5IeppuxnNX+hSbgpaDVtvK5F0xNs+NAJo+ClSkcuqhq9s889SQ5fTvm+k9ta9hEhK25KD5awsYWDJeyhQm5FW1QCQpQVlSGQKB328jhMuQcAwbp3gWJyMcugk5S/WUpuUVmeVbeprpQ692V1AiZ9N3kt6xmZ5tOiOnRaW0zP0ESkxZFS9txxnvrMyciv6qZHuCLwuhatIAo03fAt47aWIoh7AuwqKXKl9cCtjoMrMX6SgaC3AdO2FjH4J0fSObJMyariyFIUh5Fo7g90FMDcC9u9tFFQaV91KFlV7pg9iEBw33p8C+1w7VoswHQe4nK1qDFoknrjY7fAOLoO5PQ2WmRSnJNWgSL5kteShEh80WPy0MhirvMmj3EPEVu4fNsdIV7X4p/PCT6xUmG506UVrNOVAVRno60DfcLDsrcn+CYVADvl5bETR9QWKWOfRNk9CAlTB/Fq03iVanR8slMGywG7FN2vzUevXquXqdfnhUX2YjTPgys53iZRjPIITRP7vW9BLIY0C1+SiJ40f7yM5P2k7E+iKGmWbENaHs3P/34r5R5Mr3ZYPLNJuCLYWCtFR9X60Aog0AJfCQLtDRaghTApwX01eHJk4Wpo424jrqSbfg/V8/iIpxGSQPtsepe41HkYpUM2g1WAnoUQXFlG9lSvGfdQr7A0InKScdP/nKVjZZGRSb0qzwNg33q4s5j098xbJOXp2lnlZhEmybJj6dwaB37q6fyvo79doJ4ItU2P+lqHxqaLp8BX0A5kZGSM17IF8mMfexCpc8vkHglm8kMihBh4fEis3ulakddQN2zyyGaHD1y0qDsLOLJM3beZdY9E7+15ylYNSzjcOXcTEouuahKXiUNixWTkS1uLpl1L06tMsjq5Zk3Tr7Pq31sQQtwqhHiDEOKjQohQCPEPu1XXFre0xDHpokST/bCy2OtkG9Im7zwPk/6recJ/64MWy//kcurBGT64BA9slFjutOgEjehIw/7WlkGMhG16e0zKfGaRHvuK6vUDp0wnbNIK1iLloNuIApZ2Nxkbk4PTWoowvqZFctq4MX1OQxoRaSJY4h4eiXL3rYYTwDT5Je+bLITJPFmeGPF+YsqfNT+kETFp1yIcrtzGm95xzFBehJlLbcpNn401l8aGy4YPTR+6oSBQglD5hDpIzZ8Kk5y/nJa+q51I2Q0kiYm8vyLlTZJGqeHf4CQBgcDCFi4VexpPmY7sTOpCJotcEYIyzyMjrZwinhtpeZLlp2BfVqegyDyZh2RfyZrbi8z3cRken4/z2jPaDxr//Z28/X9rnv2u38nJN0S56dPYcGg2bXwFjUDSDYNcDw2NGvkbuZcyjtOubwlXo5fHXvD4y5THxfr/YCtLxjOIyA85SN8nQRypmS8FuK0G5ZULPV26QTvcoBP2yY0hsVGStV7/yiOJkzK8iIE/vfWjMOlDpjRbNBReOTl9F/Bs4N7e365hC6PFRCZkPUCTUDZZcIsguQgqUm+y85nyjrbl2x8zT3irzfoZl4vrNpc6FhdaDu1wAy+MWRaTpMd2sVOkR9Gyt+v5UTT99aqI7zYCj3Kny6xnUwlERGr0/6B3OktvXHQb0XYXIH0MpSlQ8fcm5aifJ1lumkDO+py2+J5UVlzPKDLJpXtPjN9PKsLxa8n3prKTinMWwZKntBebwO1AUdvoItqaxqZDu2PRDKAbSla7vVMA4gpymtI0iWfH5Z4DJsGViP+029iqi/RWv1cyn0lpVwpLwbSziBDRlpX+dpdAx4+1H5eNvuqk7A03ESFZaWBcDpuuD76IoSwT8mT3PoojayFUdEGUVlbaIqhIeXll5WPqf7wSgFZTsXw4edyyGaV2gG6C51mcbkYeHkoP6y66NWUY92P8e0akyH6f3XPIkcemLaypp7XEiAohxMDTo3/CixRR/I4T9Sm6SvLpYIP1mTobeh2gF5Mp0p/jHkbtcINN/1JPPmcRiiaDUP96mufFVgiQNGI8y1Fgz+LPtdYntNbPBz65mxVt45SW+KvpXhx5imrafZP1sYg1owjSLdOOLPN9X3maz65J7pxTHCgHfHrNZc2zeOKBi9jlEq5bjU7IULCjEZv3ajT+vFNirifslYWAilmovdbwGUk7CljqtWDmMCCgNJXIXGTxmDe2shauRcpK8whIY8kF10s06e0hz2si+ZpWRtr7LBIrSYKY6spKn+fpMXr///7xIb71eReRavx7LJxvsjlbptOyaTUcNqZDVrqC+ZLAU2EUuNSuwVb3eefJ/az725HzWXl3ev7Yq/PRlZLBWacPxIOcqoCDzonofeBj2y7tYAPbnqEVrFO1Z3qZhuPDkaVYYXmERB75kOeJkbUINlkJt7BQ3JfVu4Ct6LvbfQYT5q/M8KLXzbDx8Sbf/KQ/4hs+/HzmL6ZvQYRoW0u56XPpQpVDC102/X68pXCweAXQWm/peNpkvv4pSdcEpNwbXhtpSLbNtI6YcJ7pe+n0T28RQo557vS3sCQhhY0rbD5vMdpiqNF4qs26d4EZ9xANf5mqPYOn2sy6RwCoWNNUrOl4C8gnkotcH/lWKdez8mWtxYsbiK6krNY76nKVjS0SHiaywDRBZzHNxNIkrcNZMCnSyTqLtC+5oBq9/7U3Hocbo2i83/Xei7RbFrceCDhUcajZa1jOASxcwAO9wwph0cGfRkJc7iNqt1rPPrKRXDDlLbBUAFY58uYIgyi+zAAxhdVrgZtmeSnqEld0zJNx3TSG0+RDVtn7SEfa4qgI6ZHMn0Y8mBZOaV5E8TKz6k4SIOY8lnD4nbcf5WXPOWMsZWqtw4WpaTptm00fPAWeiuJ49L08RF9hLEpcF0m3HRJ8t2TlVknrvSa7d0I/KnRsYgqSefqf49e1GjnCXiCp2rMAWNKJNwQzuWCy2BUdM8n38XxZBqMsw1NWXfuYHGk6bJGFUdbiZivzcFrdRe6Z4f7Af+EAQOjzc2/+NX78MS9h9pJpW9cQU2sdNrpVOqGgGWgavsV8KcQSTq8VKlrE5pAe/XRpn/fB5TFgppEw/evJ+kfmmcn7XBJZp/v0jxHXWkefsajYU0gRHVLhqTZSWMa8vurijhxRO0lbs8b7JN/XJD+2k+76wBZ7fJZgjKdJs+oWLc80SedZCeOvacI/eT994hYIHj+v+eS/HeBihyjgnfIi99S4orMTp7ZsBZMwu1fyiNp9DGGaaHai7/Ser1+d6nl2EJEboc8IqTeI52FCUUU2mS7plWFsYOKvnw/D+2Q5sXv7+8ILwPQMsuRokvQtUk7aM49fSz5r00IsrZ6ifQo+8+TDxrtzF1vU17tsrLm0uhK/t2NA6ch6uKU4Hnm4HPPA5fZw2CtebVfyZDPTHB//LA36QCxPP45HSRZz808fK6Z0SXkcN+QIw2cYH1NxfasoyZxlYWRfVqfCpKvm6dVp+mqSXE4joLP03d0gsDRIyYm7Wrz9N6ZzU5ebPoEvCXsyuhtG28A0aixgqda6UBDTuPG4v7iN3zPHZZgQe4EMnnRM7OYY6sczKpIuE8Pn2yesJj0dpf/MtR79Uzoc3FM6RKPohA00isPyMLPuEQSSrmqObG0RSFxZYevjpaiOZbqXRYRPUl8KrgM5vRVzT8a9opNjvKz+n4mESLMiJ8uLT+pZynSakp9OrCgd8qe/Ucf1QtY2HJY6NooQiTV6QstOxfEYVL9Nxe5yKqh7RRm+3mDyzOk2cJRg018mrM1GQUxVAO118JqxZ1VkoZmngBUlOEzlFiFN+5/3menJYFJ68yyFWQRYWvkm+Y3hM4m0yetpdeWlH+L9r7Z55CsOjF2XSjN3oUlj06XTjjyepAClI++9UPujsntS5dVk3S+K3ZKb1+oRtUV/r3gQ0Z0sN5mu/8zjyl3/vZCMHGOvQ6SwUmJ0ZJN5BRqWcd2kMKeNuTySNOv6vpyeHFmyD8O9tLkzz2AYz5tHnCXbV4SISb6PlRd4/PFvKH7X/m/8wh9m9+dSO0ApgWWBH0JXSboh9ONuJAmLwbXE37BF8ffKeP2awiTrj0nk4ySYtMwJj3LPe3aDvmIiyWL/ImOHP3JUOEDDX8FzbFh6gClqdIIGoQ5ohxt0VTMhv3PI3rG1bBKm/KY1ajy9ieBIIz32hDw+IIT4UOzvO69kY7awpSXtBzd9zlKy0xRxk1CehA3Pe8j9vMlXs6VZCMlzXt7gm25p8tYHNXfPt7FEORpYfRcsy+aKbgOZxD2t6BaW/W0tZhRVjncyrssk5SoFzRWm3Co4DkwfHMbzALBcIOovWogEYx4bE4EXHW07ci+1URmfiwjd5FhMy6ujS3uQOd57SFuQxF9NebKUYhMxbKovbSJPKuBpeUyWDFPa0TKf8IQV1v5mvGVT613KGz5e1yLUEdkR9j08lE8oAizkcC/0dras7NR+6iKy8nLL0yspv7cqS9Pcp7OQFcC2aBqr96qH5IstLTSR1To6onZ0LATaxxI2vupGxykKC60VluiraUUtgcaGMT6G0sZi/1peuUl5osdv78vqBPJkbFr6tM+m9GCWwf3PpjaY3hcxYMbTGtombZ73XYL6j349Szoka8lhBwqlBGEIoYZAQSuQVOwOrqzQt/IX2aIST6O1GgtyGV2Lb1nYqsX+KsdObW3ZzpyX2YbRPtWP2TG82yfD0revRJ486USIFBZCCCrWNL7qsO5doD6/SMUqM4VLoDy6YRNPtZlzj9JRm1jSwZUVQh0MjhuPI5Lx/a2LaXpQnhEqDUXzxevIKXf3ZPUlrfU9u1HwVrDFbxgXhmk/dP+1yEIpi0BJY67yLAx5nSJLwA2vSyxecAv8/L9rDlUUjtRYwiZQXdRIcbs0sW9n0bwbC+6r2UJ4NWIr/apcj0i40I9iebTWGJwm0G2kZCo62Zu8B0yWRArci5dZpP49wVhfBSjK9udZCop5V5jLTf4l8xe1JmfJ6Ki9b33wHC97zmnWXpcum6TSBL6kM9jSwmBLS7Q9MbFfd6fk+V4keLcjw6/EVpqdqHM7Wz+TW1mS21h697UQKBSe6uCpDooeQdRXJNUw0F6gvcF2Ko1i1TsHwEp3GItGa8WGv2SwagrDX/ze2BfKuGYiF5NIKz+LPN2HGVlW3az0WeRUMn0eIVJENza1yyS/c3Rqy+affuhbeNl/nuFVL8y3r+ouEemhoxO12oGkHQy3HvYXq8VPbbnO+ublPqZ2pzxF4mUUlPdJsiON1Oj5dBBqn0B5tMMN2uHGYLtU38ujHxembNUJlEcnbKJ0SMNf5pMrHlprPrp8bqRPLXceNZ7cErXJ5DFVkIAoLB/69/PG5j5gSx4eeYxz2kPNIiSyHkzeJJzFMGcJ5eTCzSzwlzqP8I1vOsxj7l7FkRqlo2B3vupS0kX34l4GbNfLY7u4Frw3rjS24hViyuN3CGozCKDhr+GVuiyWb4y2tzjlXjDTvm9HyhixS6Sz0WmWoVh+4/v+Z5O3QVErltiP/F8IxQjdYunz0k762VRWEet1PO2w33zRkQZ/QTW7xUqjlMBXfe8OMXBrDZWNkmHE/ud5aGSN0Z326tqOl8fVKo93k1Ap6u2Rtk0pLV4Hgq5q0g2bsYWZwhIOlrRxZRXXqkDoI3R0ooBE9BTtyMtu1j2MRjNXOhptl+31+Vn3cMKaHfX/5e5pABZKx+MNpNjYTvtchOBMyv6cxfe+rDYgOe/l/UZ5xrys+bKo3p03j5vy5uXR6Hvfx52veh/IF6S0bxRONxzI6W4oaAeSViCp2iFajAYtTfP0SAtqqlGgJQiFwOq3EAblbIO82y2P3q1gK16GWeuHPWzkzCI7+t4dSod0wgaXOiHtIHrOvhJU7Q4VWzHlCEpWFVu62NLFVx3m5QJYFTj/aeYa6zz1lifTDjd43EJ5JAbTYuVkT16PYrl7CoDDldtjV03kJbFrRdbRprR54zZLPsTyXAeyeotbWuLvk8xSEStBP23a9UkWQ/E25FkfkunyJhzB//6Uy82PWeNYVdMNJaGOzmeuWIljPqUEJXdH8O3WEYZ56faJkd3Ddlzfs/qYlBAG2M11AGasMlAe3hOxib3/LIJuYvvKoKK0BmSkSQrqNKXIJNBN47GIsN7HOCYlINI+myyLaWmzLINFrIZ5Cuf4vUB7/MiLsskOgFLbp9O2aQYQ9CyIgQ6xdYAiROkQKZzRcWU6JcmEvXgiSx6248q8W/PC5YLpu+d9JxPZQaRoh9rHVx26YYtQe3TDfl9vU7LAEptU7Rmq9gxKhygVIoTs9blIWRZEwdCjE1yG/VykjJuF0gnGdZwiBGfeGDORitsZs/sYR1ErbzL9pDDJ3axFVdpcm3Y9mWc83crPv4eflN9cuMWlThAFLtUhnVDQVaInr31sXIO2Edu6Qt+DanzrS3JbSz+Ww/i3SlunXAe40sRGXC4bTmwRSGMMpDjZMbwWkR2RcbrDpu+z1HbZ9C1awbAfVG1FxVbcUG+yUI5iewTKY5lLhB2f2oHDcOAwVWH1TmepjtQhsfBVB0eWiff/UaIji6BIWwenrX1N+fPGbJrutTcghKgCz+59PAZMCyGe1/v8l1rr7DOtJ8A2PTzyHmSWoE57kMmHZEqXVVbyWh5L3S93XNA90rifD917iGrdZ6PaBaDdY5sHcMrD+Ah999VwFwRHESXzchw1dTnr2UeEvAWViE0SbjUKlNfZiJ5T2DuFolyHUn00n9dClarIbgNsl2yCI81bCrLZ6DRCJCtvjuzY73sFMYnl1pQvmSeLOMkjt4ogzwtkvD0fXjpL0Wms07ZoBtG+8KCvSEufUAUEwsO2xvfiGmVu3niUEhTDNLtNiFwJL4+dLHuvWEZh+J2ytrDErgvEIPidFBYX2xYN3yLo7XeVIuq3N01fQumQqj2DFBbtcIO6vUCgPZQOcWW5pzRDcow1g1VqvSNt+9fM4zH1S8XSFfHswJA2DTlp9mV1AUxKNCTvFyFEknOryVBZ1MJsKne8Dv3+v6Z+exk+UKB5PZTaPo0Nl84Bn04Y6dsNX1JzQhwZIkS08I17cYwFMu15cgzva9JO99ADOZYWF+QqJUB2KpbU5UYq6TF8zvGYHEkk43MIEXnkd0NJydJ8dl2y3IFOL06MJSSWkHQXBb5qc6w2RcWa4mzrEvOlMg1/BU0kt5Pedt2wRZkSZ9uPcmP9tkEr0w16JK4XMebH7yWvZSHLEJmCKyerDwJvTVzrf74JeHinKtrCN0zzrgDzZDppmUXTTWYJzK7HfP3G+m382NNXePCzs1xcHyrVI9HWA29cYYsvQHcS21Gad/LaJPVsJ931iK0sIoSMiDfZ66NOBeoHeltXiMiMgWdHrK8H3lARCLxYgVljOnk9KcCzFOh+GfE0WWRl2v19FEOWHMxSZosugEz9JM9TZFJkzSOa7/mN47z+bdMsP38msxS3G+J1LVpdyVIHGoGFrwSqF8PDV50olgdkT/wyZSGc2vwdUCKuVXm5U/E5toLkYsBEcBQ4aj7UPl7Yphu28FWbdiDZ9CUbvmSpYw/+pLBQOqQbNtnwl6jbCyhCbOHijhAd4+Onas9E8UDGLHVpsjmJrPGct6DLk8/72D5MzyBPXm71vmluLfo8TfO7qZ6oPPHUr+Sbbv5Wvu49v8uvvbXY9u9Kw2flUpnlbrQoXfckzUCy1rXxVWe0NYZjacdPaolZ/XvH0BY5znYfex/JYKSD67H3gfJY9wJWOjYrXYsNDy52or/T6xaPbkhObQrmSyFKRyRGI1jh5umbEELiWhXqzgICOUaIBToygs+X5uiqJkO9yuRZBePy1uR9lTdui8jnrPx7D1rrh7XWIuXv4Z2sa5vamOkBmhTeIsK5iOJdgKUatCOef1IBN0z/lIMn+cHnXEApQScULHUctNaDgHej1creUXS7yJRdq8rvPiJMSjr1lXKlImLDjVzuQhRUZqO+6JTTs69fAL8DYZ/wKDqmTRb9oi50SQ+PeLk5Y1awO+eFXzOWSMHo75v83EfRxVKyjDyyJJkv+X7SBZSpncP073+1RcWa5s9flNhimEB108O9FHDpQpXTTVjpRIRHN4ws9P1tCYPgpXmkR1p8hyIneewVbNcCuB0Cfi/MY5MGyjP0iX7Q21D7bHgSXwmkoBd7QLDSlTy4IXnL/WV+5iORztAKGvyvT5wdW8BFGPbxdrjJpr8cBdoLNib8csnxH78Wf5++YE1XwAvqU/uyOgNZenGazE6miZeVLDe++DLVvdUFUHLuzi7nHc+v85XfW+Lj9/xmodKnVztwRrN0sYIXQjOAhm/1PD0ia/0oiaEHr/1jawd/KUfUDvNuRQbt3YXjGK7WsZIzL+UFoo0TE4HyaPiSdii41LHohGAJaDZtGhsuaytlHrx3ltf81SLf8b9uQAqLCy34rvdexJElBAJHlnt9brTehr/MJzdO0QxWWO2eHbQufXykedQVMfBN4s1lypsjs3dLVu8xbKNFadaBuJCdxIpoYo2T+ZJlZ5WRli8vbT99lEcR8mtvnOPkAZ+VLjy4YXOuJeiGLTrhJtp2ekd99rPKUcvQbih2eceSTppnK7hc9VzLMAmEooukeD9LHrlGxHwrS0J1PiLhPMM2uNp8RIaU6qN92CiQiyxes1zykulM5EbWYnrfKlMMZitxtkdNklRIm6ST5aQtlrLIrK0o4Fv1SBmi0vQ5+OgG5x6scWbVZs0TPYuOwFcBgfIGxEcqYidyjAat3CaKyMy8NFeL3N2r7cwLUGqS1b0jY/tB60Id9c3+scdrHjzagPNt+NBZm/f+y0Ge+8MlvvXVRyjb8CsfXzf0t+FYqFjTg0B6NXuO4mM4/jmNRN6qpTCe3qSf7aMYJl28pMla05w7CRGdVkfe3F7QCzAM+MPXtDh+W/Fxf/DRDTbWSvgKOgG0AkGjt7Ul1D79UzcGNY6dyJHu5ZG8dl2c4rIHF55bxSBIqcG7I/7MBRIhJLZ0KVmaUIveFpZoK4tSgo21EmdP1WlecLDf73H7Ry7wA98k+IVvd5lyNK/4O0XFnsYSDp5qkxwThyu3s1BW1J2FXkwlGB+LI603XOtfj7/P8uDbSn/dKlFy7WELIyFr0jUJxLSFS9ZkmdUpstLlESRpSCdKQuXjVWze/9E57t8AT0XHZXVCD191o4FgxxaLl0uh24l6dmNby7WMvfQ79Bde/VNXeouxsEd2dMJN2sEGH1g+w0q4BG7SpbQnBEt1qEzHgpbmeQNkNirjXhq5kSWMzdZ9IXbn79pEFtGbRgSnLYyKTJxJCyMUk9lpynYa4TKedu1AJbNltU2P2UstWk1nIMdD3ffyCKKAkjqcjHTc68iTWVfKy2MvwuSlk7NYsIRN1Z7BkWVKVhQAD+Cphxo8Yd7nE4+WeehcGc+zuPOJl7jx6xo43ZDf/4d5jtXAEg7m/g6gqVjTrHsXGLccxtNP4g2QBlP5Wd5VOpEnmT5Kuy+rt4ocT8cBsp6Z6X5aPfG0WfXEP5vqTvQPP/Jies9feDz5p4vJy9qmh9eVNINI324G0PQl3VDiqyDVM2MsaKVh+4op7/Ckj2tIll1jSG5NGrsfP5ml9wfgyDIHKy4LpYCyBbXeLu9vurPLlz5pjSMfX+PY/au47YCzN81w39MOAfDhP57lGTf7OH/yduxHPz5yMksci+WTtIL1WPylqBWjr32kGW3S9KO8NXayvu3g+pDTWwkYEHuffLBFrAxJ5Fn3kvVm1ZWlrCffp7kdjeLH/nUFpxsS+IILKy4PbEA7lKieNacTNPBUB+zyuKvzbnp5ZJV7NQQrupYU5TTs5oJISKjO0pEhoeNG/Y+ei7XykFhs+pe4fdZl02+z6p01l2P1Fe40y3uWVXCiBpOutE+IfTfpAoj/1mnkw1aeQZYsj5cfXxBlydg8hTuPsB7mf/XP5rv919e6tJoOyx1Y6Vp0Q0E3lAS96OxRHI8CFtK0mA+pWS6DJ8hel6l7tX1xL7n+a1ImpHnUaYUjS1TtGWq25GgNPueAZK60wBccPsHLPqfJpYsV/usXrfIbT1vghbf5LJ5t0L5k8+wbBGveOd74mbORDjGC4ZiZcQ/FrsHoWM7ynJpE4TTNAVm6XvJ6CvZl9RaQlN391yJyN02GTnJ9UpjIsGjOefB5/wcAr6t5xb/8eeES25dszjQErQA2PGgGgmYgafoy0ytDazWw/ie9PMY8Aojfn2QuTPnN9jIBfrWNmZE1TPR7m55X8hjaJASCslXneN3nQDngcQsBL7jZ44kHWvzQEw/xwld7SKV5xWvb/PHrPF71dRcBOH7/Ki876iC/4SV8oLwAq48aGqmxhMNi+eRYraOvRbAdb46ihHYOrgM5vc0WJYWxScE2udyZyugjz/3SlD5pUUxrY1oZadB8xx0tWrMubklRrfk4Es427V6kfw+NwlcdQkm2Zai/bzn5dzmx094b+9tadh+DgKMJqACN5lTjPBv+EtqywO/gBpGbdaA9qvYM3bBBqOFtD8KlziPks8/Ja2mL5STyFsLJ/HkEZl59+yiG+KIIxp9TnjyOI+0ZZSnlkyjVJi+gZLnxuWZ0nrln8abcGqZXOyw/XOb+FYtzLZt2IFGa3l84CGwHpEzYse9yJSb0q1W27uV2x0/TEQUVNSFpqyYBIZ7qYAmHujMfHWuoPdrBJgLJV544weu/8Qy3z9xKO9zg6Udv4Bmvk9zx4fO8+PsqfO8LQv75R1XkWRQ1Jt4woH/CRBHyYdA4kp4iBX6EAtfyZMW+vN4ZpP2OSTJ5K+Xl6dJZn03Imrc1N73qVgCqNcln39zmwtfPFSgTTty7wvkzdVY98FW0DcFXAl+JiTwy9oOUxrAHF6CpSLQ1IrAiOZnc0pREn/Ta8JZQhLSDDWxR4raZLt0wIs4eWC8hNTzv9Id4wxs9vuzoMS62V3j2DfOcfLVEKs3LXtLlZc85w+tfbtP9P3+MfvhfEzWJ3v/xto7Lb/O95LU8wjn122aUnXX9+sQ2RkDWDxl/iEXZrqRFMH7dJLCTim+aJTDLyyOvMwpun7mVP3j5gzz2jjUsK4ocfa4Vudh1QkWgvKErtFM2W/6ysF3yYze8PPa3tewMtvqbJa2NThmqs9GftKO/8jTRkYgCS9iseeej+04ZjcYWLkqHPLTh8vBmieO1kAOl47E25Sk7putpi2bTGC1ijYq/LyIfesn2rYYFkfUcknIznqd/Py1PP12WrDb1r7wy49fibcmyUA7xN6dNVphx3PTJS9z7iXke2ICVbkR6RNtbguFJLUkkvfcGTb0KSI/9bS1mJOOwCBltDxSWcR7vxwcItM9y9zRe2ObBjVM4soRGUbNnsYXLYvkkdWe+l1lzuunyX/7lLGVrimawyotvO8T/fIvFj/3EOgB3vUZStuIu01neWHmL4X7aSZTnoveShqWcBfK+rDYgSyYnFy95snvSevt5TWVP6u1h6o9J/UAgnva1vPTdj+dJT61w50/dxKE/WS1Uem3TY+3TLhdW3N5R4pHO3Q5GPTyG3hpqMD77i+PsRfH+QvBqg0YPtp72A9QO76nB8eCeavOp1VU2fc3fn2lTdxYo23VuqN/CQing8w7WePyCG8n5IOS+9YDveM9FjtWO0gxWec0TF3nD2+f44d+KTmF57e+1kFUHTj55eDpnKpJr0ay1afJavhfr8HOWUchUfs7Y3i1ZvcewjRYlBafpwU7CGKeRIyZlOE6OZNVTxCKRb7W+dfpW/ufnH+XMqTqfvX8apWHNs2j4kq7q4qtOJITDgMHZ0UXcnMequwyK416KMXK1KspFUeTZZwkFy47IDqfSU8ItVKXOqtWM7nktHjtzkrI1xVK7xb0bD4GKSLiqPcuse4Sbp0MavuRTqxbL3lkGp1CMKVVZirRJuOYJ6qRnQRJ5ZOM+dg95zybN2pCWJ40USyPVshZnaTI6fzHwhAXTqRfjKHUC6g+1eWjZ5tGmpBFYbHgWzUAR6mD09K3U8TnB4qAo8b0T2IsydS+2KU52jHh2iJ4iLcaeWytYZ807Tzds0gravOPhkNtmbkYgKMnodKwD5RsQCCrWFP0++pjZDl99Q4gtHGzporVi1j3Mmmfxf//4EK96wpFE45JkYpqFPpnHBJOelpY3n1Qc1732MRmydM6k/lt0AVS03njeIt45k5JkSSNkdE188ddw2zu/l4v/5zNFGwtE2wpOPzzFchcutmG5K2gE1iBwaR/DoJWTy5nh9pc+YbIHZdVOYg8uQscwQkRH8njo3aFQvZN6kqRVw1/h4c0VOuEmF9oOP/3+aZ59w0HKVp26HR0te8/cUernTzPrHsFTbfTTv5bjdY/Xfb7Gfe+7sIQNQYeSrPKXpyr81i+dY/H1f8PGhzcQpz7SC1BdxKCX1GmKkpxp5SU/Tzpe8+q4PmBvvwiTxS/+mpU2CZOFMK28rLKK1pNlkRy/9tvPXef1n5J88MEKJ6fazJdCLBEpyZ1wE8uei+K19wWvlAzkZ1FhHHevLYo+yZKEUsUEXFr+Iihaxz4mg1OGwAMZgG3RDFYpWTVONR7hxvrJ4TMLA1y7wo1TRwiUB5ZDGWdQzKHKzXzFiRZferRNzZkDHfZIj1jfbq1GBEqpzvg4mES5LaJEJRWitHRpZQuQ+8p2cRQhopPp8kiNZPr453gdaeVl1T8JSd6/H+U9XLmV1/7effz4i6s5eaC24XHxXI1Tc+vMujbdkkAKTd3pECgPW8YCoA7kW38xDII+qT2BgizkzhIbe2nP+F5rz5YR9TdFGCm3ve+kiIjk86027aDLSrfM7/7jHK3gPN/2mGlKVpVO2KBk1ZAM+wtojlVv41ivS/aJEYCvuuEY6eNzOJZ81aGrmtTthURaE1GYJEuKWPLTyO+surKsibHP+7J6QuQZILLuFdWr8+b0IuVktc2kq2sO/dwz+c0PvItv+71n5JQRodQJEI8qzszX6R5qYQnFYtmiFUDJMufRMf05GZcjkt1yn6qT8uqI89dD32tHIBDIHukRIgWI3glZ0fGzgo8uV9jwYbkD4vUeL/E2+b/PsqhubtKdmcf92PsQj/tCFCECgdIhx6q3Q3MZHvckpp2Dg3p/4PFHgSO4Lz/JQmsN5m8YzgsxrHRP46kWhyu3Y9ZvMXxOXk+S2kXG6aTIkSnXgazeooZSlN3Kszok00zCaCcn4zzWLZknq2xz3fOl43zDTW28rsXHVuCBjRKtIIrULpDRtpbqLIMtKttRAK+0VexybWu50t+zKC53O1UAbjUiIgApLNa656jZMtrC4l+MPD96JwSVZK13dOFIo6Fnfaw785GgDgNGx4uITmlRaduqTMptUYY7LgcmWQBPuui9PBBC3CqEeIMQ4qNCiFAI8Q9Xuk3FYCIh8tLmITkZJ591WtlbtVDntS26d6x6Gz//+4YjmJOplabVtDmzanOmCe1A0vQt2kGIp1oMPaH2MHZSJu2WArwX5XsmQS+IB8QbZEEy5R4gUIJ3ni7xS++ep7Hh8o73zfGcNwt+9RPn+f8+1KDpr8bKylJYk55LacYcjaK3ZXZkjJjGnzbcM5HLmnGZHEfyetbYnESXunzYu7I67/fO8gBJy99HUZIiz5Mnb3EWz1OE+OpdO/EkxPw0vzD35pTyx3Ho0Q3OPlqnseGy6sFKV7DSsQexHEZbM96WQUDLeADTHTmV5SpfHO7hbQeAce3U98AZyGc99PKwpcvh6hyegrf84zz3/VTvlJbf6vDSb/N4zYMWP/eRdcRNjwGngjz7GRxZwha9EzZrCzB3YqxOEFBfhMWbwXYNsZY0JatGK4h5BqaiiB5tchwoApNuvXfk8V7BFj088rwh8ogL0wRsshAWcd0xTfxpbLbJchHPkzc5aB47ewvV+jL/fu8UmzdtEuoan3+oyUK5ZxEMY+7Quuf9EG4zPkdRl+iiXh6Xyxp3zVj9dhH9hYbtRiRH/7MaPntFiC1cqvYMnbDBJ1cf5FTDZdp5hMfOWcyXjhsKHhWYAgE6BK81PEY5DKJTWoQFlZlEviwUFcRZ3lP9upL3TUp97L7gSk7SdwHPBt4PuDlp9wiKkg+mCXMrE2+ROpJyN6udaRN4WtlRkLs8lNoBwQXBxoESncUApaPAeN1Q4KsuofaxJrEHxL034p4fO+nVURQmubsviyNkyQ6lwDIQXbHfruFLnnUi2jr1NV91gf/0i8epr3t8+E8UnmvxonmLhfNnAHj926Z7W1tMfTzq+77qoggpyRoaFcnpBEqy2vMMMZVT1GuLRDrTvaL6UZpMEOMf92V1Amn6bNrCp0h+E7L0Z5O+W8RrJNmmyRf94unPofLnPwur+WkBKk0fpx0SBBJfRSe2LHdtTigP1xp64cW3pSRbFo0rcz+MPAeS19LTX5PYSx4fBnkhYv/6iJ5RJKuFEFi4hNpnuQMvetoKb1aL/NBXL/HW79HUNj0e/pko38sQwGlO33Ijf/u1f4646QSceCJp46SrWoTKp2rPEmiPbtjEtl1ARDqCcKjZc9w8lTQ2mlCEeEyOxzRS3JRvG+Pzysrqy4ZteHikMdVZKEJg9MtJWonT6jF1ojy2OmnxTCM7zBaVlz+5yebHbZbXHVoBLLVt2sFmJGyDxD7yy3Ec4U7gSlri9qIVMI7dbJ/sBcrTajjpuFUo18FyBhN5K1jHV1081aIdSBypWerY/MUpSah9Q8E9xrnbgPZGtD2md5TtAF6L8YVucmykjYs8y17eIjlLIYuXkVb+FcGfa61PaK2fD3zySjcmHXlWZRj9/U2TbPz5FSEj8uRwWt6kjDcp4HkWydF6+keGZ8H1QkqdgI11l00fOqHAU9ERtYMg1HEIOWJdykVakNMsbEXO7CXZuZfaUgQZz2e4eBIgJL7q8unVh/nARZdACb7zsYq1rs2rX36Rl39/g6lXSVwvZOF8c1DGK563wWs/cq4XCNesxziyRElW8VWHDW+JeP/3VRdfdUmXtWn6Udq9IgpwkhBJyoAiY3pP4CqR1X0k5W0RmZf1e2fJ3yySI00fMLXBhDS5PHxf/bEX5JQxiupGF69r0epKmkFESgc9+Ry39o+0IkcWjQS8TMTxuC6xJ709BPFgtNGVXgBpHXm+9cmpdrjBOx7q8ncfmWHKgd9/2SNs+pLPe63kiT8lsb571LZ//IE1XvK/7mHz8AnM8lVDzIOkGaz2PKyHxMamf4muajKZ3CuqUyV1org8No39IobKfeygh0f8etaPnyXMs1jnNGIi70HnMeVphEp6HbNuwFOfs8oDZyocKHeRAjqhR0W1cJU9tOrFX2F7CmERb4+dtuIVtRRmxfHYtyzmI+4VBBFpVqoPXJl91UEKi07Y4HzLphFYdEOJrwShhkcaD3PT1C29yWDYVwUyIk8G21UEeM3oGkQeJalkX9pYI/E5SVrkWY3iedLqjddlIEWu0F5DvZWoaJcdWYpu2mImeT/5vkj5eWVnpc1KY2pLlpUZFsuLPHxHiZOfWc5oA8hQobHY8GHNg7oTjak8V+dI+TJb41NxJTw99pGOjKDiwwVQCCLazqLR+ErQCeGH/+Qwrquov6tJbbNPZpj78X0/pWi8ZZkZ91BUXUqfcWSZujPuXZK9ACsy1kZLG+aL589Ka6ovWVZG/fuy2oCkLps3J/YxqU6d1Mcn1ZWTn/NI7GQbTXkF+twpvuoXHsNf/ECxR1Rp+qws1Zme7eJPe2OE9nAL2jDoqMCKSI+MrYlaa0TKz3HdeXn0kdTjr4TnRxYR3TuNRWs9kKWC6IQ1X0GnbfHXP6j40/I0pU5crx7/Hnag+C/f2OFNv/EALN4Se+Z9otuiZFUH26dKVo1O2KBs1QEIddAjRIqsU0lcy9N/TWvpLIJykjVxCq6DGB47ELS0j6I/clYnyOokWSRIVh1Fyk7LY/4+d84d4dFLTRobLvett3GkzbTbP5q2HlnOrR38aUeavAUC4UpuaymKvdaencBWdS6lwO8gnTJCCLo9i/Om71N3BE+cXozKljb3tx7lnadrNP3zvPi2JlPOAWr2LCCIApTKgZt2oD1stza8XpvvVbgVAtKUtsjnNIVokvr2kY40cmA7ZRS9V+R+Mm28fVtp6ziq9gz/8HPwsudkp2vXXOZrXfxAsOFpuuXo2MNoS0uAI0uxMTyMFi+2I6fyiI8rKQd3IwD11UD0xH7vfkC8PhRR/CRXVqjaimM1eMLjVnnoVI3WM6v852d0eNN3RfkDW/Ljb2xzvDZDqH181eWFrz7A975gjbUDqzzxP3V5zZOTJ7IM9RNLOLFrkfdH/97wdStjxEQQJi2IceIkKYezrJLJcvaRj7R5Lo2AziJA0vTaeL4sQ2GaYWGSZznJIg/ErU/k2FN/E24p5unhdEPCTUGr4bAx77HpC9qBpGINZfEgvkM/ZofokyDDrQ9aaxDDz8n7w2vDxXQ2dma+2tO4wttdhp4dIpJQeiif+2FZtFCUZJUpp8XRGxrc9/iDHH9gleaUy9f+RMC7fmjY/v/7x4dY6jxMwxf4SvCf/vsxXvadl5h61VmetADftujD/I29uiMCxOqzYl4L260NApculE4MWpk9ptPGYZqOnByTaYZHgzFwrNw4rvG+WgA7qN1sRVgmH36asEybcLPumdImEZ/okxZFk0IQwZUVfu3LfKSl+fuPTbPatShZCikstO2QivhxtdtBnsK8G+XuY/cQX2RICZVplOOCH22P8lQbIQQ3T93GrZVjUTohodvg1tIhnnOyzZEq/Oi/TPOaD7dpBetRmsFEFfVnWzgM4niMCNMsZI3lNGLCJLjzkCwrQ4nbjfPCo2dwQAjxodjfdxZo+B6EaXKcNG/aZLqdtpiuF/EqScKkrBepc4hu2SZ0JG4ppOxoHBnF/miHgqYvCfToVoI+2THq+izGCQKTfN/trS1FA0zvtnxP+547Ne/tBEzKe+x3iZZN4eB44oi46KBRLFaqXGzDT99T4R3fOM3bXhjle/TZC3zhz0ne/NYj3DF7ExV7mpo9hyVs/ui1a1z4+jmW7p7mB59QilWatvjM01u2Ol5M6U1jMGtOSMqGrLb10u3L6gSSMivreafJYNMCK0lYFZ2Tk2mTfaUIgZ5F3BjSV+f4gr96JkXRrdgEtsTzLNbbkrUubHjWCDHZD2A5OKI2sf1wZAtLoj0jx9Lu68DjuCJbXXrkWKIf972eA+UN/jphA0XInXNtLl2o8hc/vsxbfmuaN7zRwxLwyS84yhf+nORN7zhOoD3qzjyHKrO0A8mv/+AZlp47y+3T8Kz/7/d48CVvg9VHo7oG/as3RtwaGo0XtnvX0wji5D3T57R0GD6bCOlJkeeQsEuyeo9hlzw8TBa7vMmxiFW4CGuW92BNAr9/3WStSHqYRNdm3SPMzl/i7Kk6nRB8Fe25FVonzpEmopW2Grg09WtMaAncqvVuJwLgFU2/F708LtcE2Nvzr90KnmrhhW00ik7nHACWVWfNO8eMewjhtaN2VWfBa3HIOswt00t8pO7y8NkK3Se1qDITBSTt9+nWWnTUrVPuHT/bR5ZFJg1paSYZh8k8eXl3YvGdi0ta63suR0W7i+14YRQhwNKeWVp5yeeat6AqktY0xyTvmaGkYG2xgqiBlBor0bVCLQhVEFlyBvJouI/YFOiuEJJbHa8lFJHbO7G1c6dgmmukpBs2BzFcdO8o2jNNj9NNh9WuxTfe0uBHPgiHK20+8TrJoe+2OfGXy7zoW2sACCTtYIOSVWOudBSlQ/7qpX05PB9vQOxV9Fyla7H7JiW3yOKz0JdPvKaVkzauTAvayyKf47hKZXXewiYNafqwST9Ny5dXR7Lsom1KvhaYu489Djid05YIyhIIK5LVUmo8FZ2qpQixcHot6ZMd0evAqyNlfBTx1Itvaym8xeValO2XE0pF6yUhSfYbRUgnaBBoj1BrAiVo+JJ/u1ThYytdVlo1/tvXnuclb1vkrltXOf9zcPLVcNe/nOU7vncBznyc0tE7WfXPULLqPH7hGO1ggz9+kcD9s3fxUEPy4KdDbm6twdwJLDFcHi91HmbWPYwjy5Ss/GPvx+Vimo5rGl9Z19LyZa2f08q8/rBDhEfWRJxn4Yvnz5o4TQLfVFZeOTtxPbp246KHUlGQsnYgWSy7w+CQe23hbsJOEgw75Q69F0mP3UbP6qgrNZrBKoHyUDqkZNWwhMN86Rg/82/neXDV4tef1qDiTg/zlqYAeMrBGk9YaLDUOcucextjY6U6F6swS0nOIhfzlKs8AZtFZGQpTLF8gj3JHO8dFFkEFUmTRWrFX5PvTfXkLazy6syCqbyonl97a43//Pzm2F2pNO2ay+x8l3IloNzzaPZURFybaxm1/mkEWvTsT1lK7lYV4ElO6NpJ7Ma2lr2GlO+ohUDpkFD7hDpAILCly+2zc9w559ION/iynz/I3IkuZ9/dQn6jzdJqyE++aY2afWxQzpSzQL8PRkpz/liLyI74eEnKxTRjzCQwLU6TZefVkxzDGcTkvqzOQVF9l4x0RQmMnUQaKZJHfo3e+9Xjv80rT780s6Z2zaFbcSKio2vhdS0gQPa3rMSOmA21RmlwpB56ewirF6tjvE2m7SyDMoUk2kaxA9521wL2wEkuoYrkcjeEVmDhSE3dUTz7hpCvO1lCa81/emmVS59X4fxb17nvaYfontvgla9vcO4l/4vzDzk86W/mOTx7+6DMKedA9ObrX8LNz7jATZ/9aI+Mg7j8WyyfHOQxE18mfXkSctPkEWIaT0XH84Tj/jqR1VsgPLIWLWmMbx6KslzJdmTVH09TlDkvek3z78sP8e733sDtd61wsQ3LHZtbpsugY52mL/x2Q1DsliK8W4TDJOVeT6SHkPglB1u6hDqgZs/R30Oq0VTtGUR7k+++26JiT+MGCizz+CpbdU7Ubse8KB1UmLiX1s/j5IPpvSmPCfF60hjoIkTKFbEiXgMoQm4UKYOUcvI8LUzXTQqyKW0ekWL6PHy/5i0B6daY+cU2CzP+gPBQOjrhJVACxegpLUPFejg2o+okqUfQxpXE5L2iRMh2vOMu1/G0k5a3l6ygMa8bgaRk1QiUN9jGUrVnsYXLr3/qLO/+bIXfeuUjBEow//yAo9Vbe8rvwqC4VrBG1Z4l3Qg02lcVIdGBi5LRoHlpMjxLFprGQXzsJsssIhfSdCpT3fuYDGnzYfJePE38/k7NiSZdoGjZWe1Olju8p//yDzl/XwUqpKJbtlk9WKVbtlFKDP58AzHdj90B0fHilih24koyrkfx+B3XIS4X6THw8rCIbzcSQlKxp7BlFynaKC2YL80xpUp8x780uPedc/zsL13A12dZfJnPXXM26FnayqHyi1/FkdmjXJSrHEyrd+oQ4p7RrVb9ExD73kByhCArSkiMy/1xmNbUSfmdljd5fdL19/WDLRAeeRNuHkwLne3kSWPWsiyURVnx9LRnmi7Tj7S4tFjFP7DBpm/R8Jcpl29k4OGh+6djJIvfQwpfEVwuxflyll+k/t2CXR6Wr1UUnC4MsC0H2huE5Qo2Fq1wg5pyoTJDu32BGfcguPGCshZ+RKe/dBvDALqWC3ZpOKm3N6Ayw6gwNbHTeQvePJLTJLyLCOQUsuQ6iCa9dZhIB0gnFdIm1rQyswiUtHv96/FFk6kdaWWZiBDzAjKe7sdfnE52hHYUv2OxDGULKvawW4WawbYGaXCt7W9piV4vw2K/qLfHTpEe14OXB0S/gV2CcHistyVsfDqUrTqeahMoj7ba4M//dYZ77lrnsbM3A2JATkTu1T4lWQN0CtkBo30f+n1q07uEJW0cWcZXHer2PPnjKA1pZOFWx2+a4ShNxhiwL6tzkCT7J9FbJ9Wj07BVXT6vDekEin/vJUpv/2r45vSSuxWblYM1LEfj2iFSRv0sirUkUboLwh0jNpQGS0QeHkqHCCEweXLEodGQ2OaST35s15BwFaI/L1wG4kOj8MI2tnQHBgdHltFaM1+q0wrWCbTHCh2av25RfXbI5yzOUQktAseBlVNsTtWi/JcuIo7exUHmBqXnEXyB9nhw4xTTrqJk1QmUx2L5xkivtpwYuT3ecjPBMekYzVvnZuVLM0xm4DqQ1Tt8LG2RdPGHkcUGp+Uh5X3RRdYkDzWNuRN0w+hzY8Nh1YuU5GbgMaf9SLReTYTGTiFPUd6t2B87jd1+dioA240EZ/9VBRB6ANi+D5aITlvpNqC1yuHqbVGw0d7pLDhlxhUlGBG0lh3F65CS6Hi2aNEpus2IdLHdSNm3MgLtjqCIclPk+iTyY+8oFUKIKvDs3sdjwLQQ4nm9z3+ptW5dmZalIf77FbHqmqy5WcRD/FqWop5WV16avLqLljMKJQWdaQfbjsb5dIxEDDV0Q0mgvJ5rc7/kYQwPgRg98lDK8RhNRYiNScmPpIfIdnClCeW9ACGjIw6lphWs0fLW8VUUwPYjlyrcUPf4+EqFt3+sAh8O+JEXj5ujtVa4sjIIpmcL11CRCVFf6h9XC1CSVSCK52EJO3ZKy2ie4fs8EjOeJ42sMKU13TO9LzI+rwyuHlld5HfbCUvtlbb6jur6uulz33o5M0fgRPKpPu0NriklaIfRFvIw9rNF5Eb0XopIjkvBiAyfBFs6jet60vnT9PydIkJE5PEW6C5e0GLdW+OhjRLtUFK3Ff90PuSOWYf3nIf7frvGgtfkt19wAThA1wapFczfSOCdBQXh476Qln8RV1YHp26NG9RGx4gtXG6ZvhEh5MCzQxGy4p+loqdxZYVQ+7HTtWB0fE3a8bLG5qTEaNG19fWFbcTwyLPoZuXJY5SzHrwpXVEhPklb0+rQtIJ1fvV/z1G5R3HXTcvcOg1Hqz5TTgVLMarIqhQvj+1gq4rq9WK52yp26zkly1UBhAamPAyG6YNOFFQACCt1BAppudDZiEatVng6ivURnQtuIhTEKJnhdwYnvyCDHmkCxQRt2mcTi7xVItNUTuKeEFeyDx8E3pq41v98E/DwZW2NEWnEbxqyCJGkEmBa3KTJ1DyShJw0WQu1tO9UbEL3XYtqzcexh2U4kkHwUktopBg9unC0ZdF9rdUwjkcWdsOjL83r40p4412t0ApLlNj0L9ENW5xt2pxquKx5gnvXQekKH793Gv/fNK/8gXVq9nEGpHGvD1rS6XkCWQgEnmrjygqjQQ5NpASY+vZy99HEcYfxtCaSIymLk+SjaZzG76WNo3g5eaRlSpp9WZ2BogsjE8mVhGkOTrs+yXydZWQ0pckrt5d+9TR/8P81+YcPZfcNv2QheyyG6yqkFZfXw/fJk1WiOB70tqtAka0tu4aiBMC1opebvsekJEisDFuUWPbOstR2uH/D4WMrsLRa4dSD0/wFcPyfV1joNDn8o7LnHRch1AFBuIEl7F6cDk3JqrHhLVF35gm1jdXaiHTxqYOkjYs4meGrDh9fOcdjZmdxZBlHJgm7SfTovPTJdMn0RdbHWdeSSa6orL5s2AbhYZo8k/fS8sRhEs5ZDy350LfTkYqkTX7PaP92qe1TrSlun4abpkKm3RDXmo4WqkWwVSV4pxXX7bhBm65dK14eO4W0Z6yC6J5dBilRlouUduTl4ZRRAqQGAg9Ptakop3ecbHSNwMOV9nBLyuB3ylA+nEr0h47KAPIXxNslM9PGZhb5maZcXVlorR9mLzQkE1kLmORnk1JrSp+8biIn0uowtSOLRDPVlUWWJZG3SIui/ge+RGmBKzWhBodoa0vJ0kgBeTRGZAHMdpO+LNiOfMzKOwk5fhXL6FD7CCE431IsVgJmSwGO1JycKvM5B8rce9MSP/m+udT8kmj7quqRYq4so1H4qpuwIprGxvi1hdLxjLRJohHMYyf5PgnTODeVnZXfRHpewYVlAntfVhdtWp5eXLTsojp5Wn1579NI73H9uajeK0ONdiJZbds6CjBdCahYIEXydK290/cGmGSh3097LS46txj7QyAIdBdLCD6zVuJYLeRQRWCfaPORox4vvq3BPz2rzjtfBceqRPGXtIcjy8ggQNtO5HG3fo5OfYp2uEHVnsFXHTb9S0yVD1Cxoi2EijAWn8M8VhxZ5nMO3ARE211MLc76NsWupRmQiqLIOvz6xQ6e0pLHUqeRE0UmyixmK42dTt5LU7BNREo+NuYqzJc2OFyFxXLAXMlGEDudRdrRonYf28flUKh3ywKbh9AjlO6g6waOgyUEsh+EK/CiuF6OhNp8RHoIGW1HiSrqldPra3YJ8ziIXxMp6fIWv3njYxKGu+jkYJAP16JSsOMoQkD0MQlR0f+czGeyRKYRJ2ltzeo7RTAsY6nzCOHLa1hv6I6lsnxFuCloNmy8mo8b606W0DhSj+3lzkRR2XS5jqWdxMvjKiYrtg0hEUJStqY4WFljsXILvurQDZvcMr2GFDVum6ny7T/Y5EK7H1TU0Ee1inw5escYCoRhO8pIBsbHkMmaniT88gjJ+D0TmZE1ztPupZEgaaRHAvuyekJMpocWy2OS50XubadN/bLMeW+4qcSb3nGMlz3nTGructNH+Bq3pKIjaS2N40RbzkY8PDLk8xX17tgKrlUv7ElIj17QUi1AIKnZc9w01eBxC1N0wxatoI0lyjgSvvBwkzOvqXGqGcXdKvkKbIW2I8+MQHuE9Slcq0LZquGpDg5l6s5CT5ZHfTNUPiF+Ya8Ne2wbC8Z0o2WY0mWtbYugiF5dUH+/FvtdAjtAeGRZY5OKbxbLnGb1y3qF8TKLdJo8kiPLAhkxiS974SVONaATQqijeAtVawq0NwwSmdzXvV1cLYrpTnt57DZ2Y/ER/37Jvfc9Dw6tFZ5qs955mJJVI1Q+961HC64bpwSL9pEor1uNXsMget/fmuJWCXUQHX1oxxXsWP8exBowKadFrTumz4Mvl3M/mXZrBONoXfuYHMlFFIz/nkUJChMmJTdMVmFTG00yP23hN7z34h9b4OhD68YW2IGi1A4IgmiMhjry7oi2skTWw+Ee7qiPJt2mx7/WFQxEvVvbWK5V5RsGv43SIYHyCHSIF7bphk2Wuxu883Sd/3jHFL9x7yV+7y2LHH1ojT/8oi5e16LyYJcvePEmr3nykdTf2VPtXiDTNJgIhryxYMqbVm4emZFXRhZhWoRA2cfWkKYrT6o/J+9n1VekTUXbkpW3l69+gD/73RfzOxlkB0Cl6SOVRimwHTUIWmqJSOceyObYlkOlxeDI2kGtE8bx2NJJLWPblrcxF1yrcrco6dGLOae1j9IhnmrTDCTdsEXD7/DIZok/fcTm6cdu4bX/doF3v2WWG+5d4WlPs7jziat87ENVXvjM87zy7ujIWUeWCJUP0sGVFTzVZsO7yKx7BPw2OJUE0THs3wrV8/zIIhby+koWobEVkmPS/PuyuY8dGFUG6/HIPVO6JNIm2LR08bqSi7giD9aUJqutIpFScEM95FgNjtcCSpbCkj3Gr6/8hDvs3XG5CQKT8l702m5gt+q53IsUp4wWAomFEHKwR7DhN3h4U3HDlMfN0w4/+WGXpeBcRHBA5C2kAmitRWSHkNDewCK2jUWH4DWjV78dXQsDCLrRtZFtLElkKdVFFKassd1PZ5INRcZrYsKIVqU7/3dNYBIFtqh8LoIilgZTujQk+0eyP6bJ6OjexnzGeYdAuxYFLfVVpED3H3//aNq0gHXJeB7mpu9RZTVN1u2EDLyaAvb1FxO9oKXNYBWl4YGNC3TCBu86U+PrTzZQOuQf7ytz28cuUtv0OPGXy/zyN53hK162ycM/Y4qhInq/Q3/vdxbpF722w002/UspaSYhk8fLNiNJdpuI77yyTN8r5bvuy+oCKOJlkSQOkvnz5HceyTUJiugPGfmEZLOTLyP9koXTDZESbHtIeIwGKx2/ZkL82NrsdGY5Nn4a1y4vIJUa/l1LKELk9AKWQuShcb4V6a1/e9qi4Uv+8EGH77qzjdIhf/7Xh7jh3hUAbvuHC/zCF1i89CsvcL4N1n0fik51QRPqAF91aIcb+KqDLV3a4Qba6RsKo1N9PNVGo/FUpD9veBe52HkQje5tY0nTk4uuO03Edp7unHZ9krVuAZl5HcjpLXh4mH7AuHJa5EualOOkB0ey7Ekm5ax2ZC3kkoRKepsdqTlY1ti98dv0V3FKR5ChaW/XNnE5tnPsdB274eWx1zxDJoWQEHjRYkortG3TCTeZLx3HDRQnZmWPlJB83Y1rrHsBi6W+t5A3XFSU6oQSLGmP/h5CDgmS/ikuduzUANtl0P/DoOeJlDW+TGPdNLbiylbewjc5zieUHddJcKXtI8ujI2/BklSe0xTsONJkd/J5m6zYpnrTSOmk9wexa8P8L3vuRd7/YUMRRKe0aEdgO72gwL3i4sfSxhFXgpOKb+7RtFvcvzwxtuvlkZb2WrQ2xr6n0iECQTeU/OO5CudaULPBlVX+8tQK8g8tbvkJyQ8+oY4UFiVR5okHQL4jHr9FD2WzsFBEXiNKhLgjlsMkiaGpWNMMT8w0jbkiRKRp3BTJn5TDW60/rfh9WT0Ok5xNm3vT5k6T7I2/6pT3Wfcn9SqZpOz++yFe9wU+P/Ar2fO971ooKXrrfoG0NGEY3bNiXhxxMiPp3bFdbMnbY6dxrcX3yJoTZd+AF52KFiofKaAVSN7671Vef2GB+rSPdXeL13/yEnf+q0K/wuWXvsTDEg4lq8a33N6h9MY/gsedQN4eCdd+UGlfd3BlpCP7qhN7tgKB6MVdYhB/adY9Mmha/ASu4ekseevTOEx6dFa+NN04a82bpl/nNe36kNVb+IbJRUryXvJ6mhXOlD9+L6ucNKU4bfLu30vrBHmT+2iHs4TDLdNdPr0mWOtGAyrUPY8O0RvMO7Uwv5oX+Hsdu2GVLOL63juWVvcUXtfvBTCV9mA7VNOX0dHHKjoJAqt3fK1W0G1Enh2DyQHSmVxh/ssVbmnCchLisd+uvHGeVe4+JkOyH6QpyGmyLrkoo+D7uExOWjKSZEdyAk+T53kKv4n8iF5PNciE8DUqFIQ6IjhUrGqlxZilL3cv+F5QForIs92OHbKT6XYTPVkbKI8zTY93n63S9MFX8EWHfVyrwu9+uMYDj1vku++O5vi+N94wuF2s/4le/C4dIpGUrfrIST/D9P13yd+gCKlhem/Km6ZDxe8VsTLmKc77MntyxOdhmOw3zNJP4++zyA5y7mcZBIu2Jy1t78+p8Jen8gM+W4FCKh0FmFbDtviqv6XFDJNhOT7e0rz0TDJ+S2THbhHc15K3R86xtkqHhNqnq7p85FKF3/23Go0NF6UEX/6EDQ6Uy7z99+YA+MnPXyfUAY4sYQkHiYVa6UAnit8ldUR4BNpDIAlUF1u62NIlTZYqQsO9YZpRD9AsI03a9UnHfVZZ8TRJ0mMfcWw1vHvvNetBpFnh4tjKQ0lTlpPIm6DTlO8iZWke2izxiTMlljqCdiAHxxQiegtRGQtgupewXaE5ybaWvLq2ovjuBWU5D8k2Chkp2E458rJwyoS9SdfR1vB3Cj2wXD7dvsgXHG5y59xNICWivRmVUZmOyrDsiPyI/75aQeANJ3M/57SgQVyPkYYn/iB7/JJybaQihuM8a+ymvTcVKXfn75pCkoBILlrSrAZp5G9SkS4y8SbbMwnJsVWSJnr96XsWU9oHUmmcbkinbRNq8MNx0iPUAQKBRmWSHVfc+peHSeTlVuX41Qbdd5f2AVjtWtw2E3C4Cs0A/u2Sw3/++5C1lRL/9SVnCbSHF7ajQHVC9pRhM4LBPR2zCI6Sj4qQDW9pkC5bB8lTpuPlJ/NkjW9TujRSJa2NBXS3fVmdgry5NM94GEeSpM4yDibrSM4TkyA5J8B4HzHlgRd/9G8J7OznWGoHWIFCKRH9hQKrx5O0A4lARkeDowZbEbNrvgbk2LUki1ONBFEf8cJoW8m5Fjz1lja3P2aNwJd84JESz/qFIyyca3DHT0o2PItN38eWJaSwqHzwn3G/6gmIZ3w9AG3VRCCoWNMoHWJJh1CNBihVhNGWFa0ItMeZ5gNkyd4h6Q355KXpetp6uSjxbUqfZbi/QrJ6j2GLLcojE9LY4nHFNL/M5GSbvJZl+UgiKdSLkB0mCB4/L5FSc6YJSx17qNxIwy6htIef1yGudIe5GsiFreIKxO7oQwkQIiLJsGxwq6yJBqHj8unNU9y3Hu0rFFozsk0FAW4NSlOxvtHr050GtNcQXjtK51RG7w+QJVhF4s90z5QnDlN98bGaptTlyY99TI40K27WZAnpxELaoiePyCiiFKd5imD4nKW0R9csYVP+AbMVUSrdC4Yn6ITgqXGroRBijOjYEwpzfG+3SfndrpfHdkmPvTBnFPG20QpL2DywscFiJeCuOcXFdnQY1seWBZ5n8TVPXeVJB9porag78/T7qByoTeP9WPdiePQ+xe4IuqpJqH0kkhn3UDLnSFrze1PazC+Z8T4u59PGehGZsI+toajHTLIvTEJuicR7Ux3JtFu1POfdH22nfMrjefPvZAX1jeB2AzptG69rjXh5OFKjCTPJ6EmwLdl+uWXetUR6JNGT3VF8O5u/P1PnpinN151scXGpjO0oHvzsLKV2wOxLQ551okPVVtzctXFDEP/w//D++X6AQay8kjXsZ65VoSSrQwM10bPvBJtc6jxCI1zDFi4nareTPWa22++yxmESJvIii6TOkgnXN7axos6bJPMWSEUfcNpCycRkF13MFWlfGqJ6SlaNas3nvgsup5tR8LPBtpZ+bAVpb83V+UqwY7sltPeil8flCICarMNrRfE5/A4yjI439MI2zWANQo9Z5yDr3gVWOjZfe+MxDnNouIUFImJEh9EfGixnGIND9/bZV2ZpST/RqPhYSROUg0anXMuy/iQXqGlkCYzWP4nlMpF232qYgSz5VVQmJ2W4SLm3VST7SVafzPs+ps9R+b/41PTApVagCHyJr6AVwIYXkR5+TKkOtZ+rUBdWuLfTv7IIjiLK7+UONr0VQuVyoe95KW181WXNs7GE5q0Puqx54Ep40qLmnhu6/L/fnuYza2VKVg0pLDb8izzSiJTpqG/0FeaovylUz2potq6VZG2wLWaIPDlcRD7GPxc1ApnkepbuVlSvS17el9VbRxHZkiQVsubVov2qqLU5rR2mvIn7tXnOvuw3Mku0A4XTDXEbAa2GTadt0+1Gzz7URFt+J2qhHHm9qnGtkB7G9ZEYnM5yphltUXrVXy9y/kyNwJfcftcKJ79wg/BXAtY8m5qj8OejE7PE3Y+n9KrvoXHX4/FVl8j7J8QSDoH2eobpKKh0XBYLIZkvHafeyPGMNqLIWDJ9Lpovq59nrXUnxHUgp7fRorRJz8TqJlHUbSeePr5oS5aRpkBnIa8NWZ4ogr893eLC70eW+PkStMMWnmoRSJ3+sE3X8z7vRUyqQO8l4Xy5FO7kc9RqeNJK0EHpcCCMO5YGv8Om32a+HEC3EaW3SxHp0W0MCZPB6T8x8kBYkdeHXaJqz6Y1yHAtOZ7yFpd5ilCa8pWsM542TzjntWsf2UiSFXmKcD9PPL0pX9GJO34/SznPItXS+kjagi1671pV0lBq+7SaDo22RTOIyI5mAA3fohsKlA4IlJcZsHTXsdUo/btFCO/GdsgrASHBclA65GjV46PLVRbL0Wk9t0zDyx+7yHffPc+xZ7b4hZ+a4lUf8Hlg40H+/ozPK191YHAUeH87UxTfP6QbNjMqTfbxLP3C9D6tzP5rXD/KWWzm1pNHcO97elweTGpJztOBi/atSfTovDLM6P7GO3jn27p89osPZ6arbnqUOgHdtkWnbdPYcGkG0A0lDb9/msce6o97Sde9WtAnPXpkdD/oc6gDbp/R/PVpyaGjLQJfcvzkJr/+DMGbvmKWR77iAG/6Lsl3/vFR/urRSzzineXLfn6RRrCMJWxcWSbUAa1gjVawxoZ3cWiUBvr9RiCjWHqyArPHKNafsjwrkunSyIus8V3U0FN0jO6hMXIFsQUNLk9pTnp5xO8Ru56mQKd1mv5r0YVSGoo++CziRvOVN8zyhO/ocHTB42AlwBbWMFq07UZKlZTmLS5jVe1dRuyyKKm77eWh1fDvcsBEdiRghQqBpGTVONU4zzoNPrpcYa6khkFvW6s94qMXsNTvRF4dgQftDbqqia/6jLRmxANkWHm8YcmGUly5SfMOMXkHpFkDi4zZnAVAP5r0bvxdEzDJ1aJW4vjCKe05bUcZLtqOPGthXv4IlrBZfv6MMfXspTZrKyVaDYdmABs+g6CVXSVpBYKwF+MhGcBUMDy2dtdieEyiOF9OL4+tbG25nLI3D0IOTq4KdJfjtQPcOddmOYpvR9mCS51TNIIV3vhlU/zsa1fp/ELIz324zp98n+a3fmkjUppj3ncSicSibNUH1ehUeUnGtSin+b0pT1HDThrhaEKW10jW/GEqal9WF0eWt01a+p2ufzv3TWnS85Re+WK+4vku7/hPD2WWWGoH1Da61DY9GhsOXjciqDf9KI5H0RNargmvjiSuSXJFEKpo3vVVhy864vO0I5qlC5G3ZrXm87HlJqebD/Pu77B45W/4LL59jT/4eIXvf+Usf/ezkee0LUu0w020VpStKTQa16rgyBKB9mgEy9C4BN1NgMj4SGgIVlrUC6OIzrRdZM0NeevpnDbslqzeY9hCi0xeHGmTe9ZEm/TYgPzOZbI4JjtalgUlTaFPWyT00423tSRr/Nd7DnHLdCR4m4FCiOhoo8EiVdrmbS27TWzkKZdXQlBe7jovN8mRVk/GdSksXFnm9soRbOnyeQdbHK7cFsX7KNcjcsMpRzE8yvXovbAIraj/lJSN4/uAjrxAmivQ3jB4geSNieSriSRJlpNmseynNVkcs4jSeN609u6z1MVgIpqS95PX488sLa2pL6SVmbZISqs/fq/InJC12Iqud8IGC29dN6aoNH3ctYDGpstGw2bNi5ToDR/agSBQoueFtQVFf7syZydk5XZPTLlaPPa2AhVZhev2AiWrxrQb8kWH29xQhxlX4amQWfcwrixz9/xJfvEP4eZZxdJzZ1kon8ASdrSlMDGPR4uqqC94qs1kci8qYfR9GiFtGqsa85goYiksqBSn5itS1z4iJJ9ZUn+Ofy5CQqTN22n15s3zyXaZ8uSRZWn9KZavfoC53/sufuRF6dsOIdrWUt30qK91EJuaxmbkldcJo+2HGjV2jPigNX19PAdx0qQYCqa/HDro1S6L+4itkYSQCCGYdY9Qsac4UA54/hNbHD+5ydxUQCOQHKocgW6Dz1m8gR/+rS5HDnRRz3bBrVGxp7Gli0DiyDKurFC1Z3BkGYHADkI6QYOgNkPHjvpHN2zR9FdpBxsDQ0evNbH3eWMsD0XWu1nXJpGvWYbI6xcF3A/yMIlLjUkRTxO68TymtKY8JuIlni9N0S7KzA3z37t+P79/f5WaDX5FsOFZHCiHkbtr7wzn6Og7BcoG5aXUsYfQP2Vmq2knyb+duvPKudIo0gZpY0sZkRNhQE1WqVVuAx1GRMdIsFKiz060P9wS9vjv5Vajya9cN/yOcZIvjWDIsuwnx1LyNS1/3vhOu2ZqWyztXvSE2jMwPeM0krmIxSBr0WbqV2np8ybePFIrrZ542aNlveJ5GyllRag0PdaWy1RrPm4ppGlrZsPIXTpSpkfboom8sjR6vNfulOK51XKUyreo7JSMnaS+vQjVI4QtGwvJocoJpHiUrzjR4OGNEo4ckhcb3hIlq8aPPLGO9aTesbSDh5/ep0uinPN7j4/TVrBGyapF8n1Qbhq5YCojayGapeyajE9Z9SZlf0b9+7I6gbjMzNND87w74s8oax5N04uz2pj2Oa+ufnpT3x1e1+/5c571V1/OyZySIPLy8F2LctPH60YBTNtBOBJvCaJTWrK8PHYFkxLL++MhF335F+LjiipPXuzy4aWAl39ugw8v2ZQsjSNL4DpcbD/IXAle+7l1yk+towhxZLkXrHSoPwskJdkLXupUqMk5bOESEK3LSlYVjaJs1UnzBtr0lylb9djpLsnxZyImTGvY5Pg3jSnTmI1jEmK7YJrroG9u8xvmKbdpD9Q0seY91GQHKLp4Sptckm1JtsnUoYYT/PmWw4c/OzVgmiE6RslTLQLtRcqyZccCmO5NF59C2CnFdafZ6DiDvpfcposg6CCDYLxPhAGPNB9krP/5nciLo7U2iPGhLIkqVaO+Jqzo2NpMoRUbCzpMXEtLn8dCZ43xrHGfVmaaLNlHMRT5jZPIko/xNGmf02Rn3rPPI7CLpO9fH++TSmZ/p6mVDnodGhsunbY9OLFF6UimKx2ObWfZVWxXPibz76aXh6m+qwHxuaJ3tLcrXA5XbsKVLrfPaurOwsC1edpdpOGvAJHXRidsxAsj3u/iwUvTt6j2x8d4X67as71AekVI4LhynacPpZHd8e9hupalE5kW3fsojiK/eVq+PMNh8n2aHC5Sl6nurPtpn0fzqVNLfO/LV3LqjyCVptz0cboBgS9RSuArCHQekXeZUXRr4U7PKVejHE5DTy8VRIFFBYK6Pc9TDlaZdkK+9GiX22Y6tIJ1AkuwWD7JA+vRFsV2sMGGdxHrw/9AJI2HfcNTbVrBGs1glWawitIhBF3K2gEd0j+2Nu6llxyPU86BiGghito0jizSIp4mabgxjeWkXE2O2zR9Oe3evh4NW47h0X9NIyuyFi1FGOI0a2EaE5ZWb9b1SRZW44u3JyyUuevWaP9XJ4QNz2LN8/HCNkqHQ3do2x1a7Eequ0rJjzxsR5hfTYTFTkDI4faT0INug/tbj7LU7p3I0g9UChHhEcS8hEp1lI4C5Qmte254ceXXJEgBv51shOG9iaxIpjGx1SYF2SQnsha6SeY7mV4wEvPmGo4ovTtII4W3ovwWVdqTC68ipIapX+b1x9Eyfu0PSxn1RJbDctOn1YyshqGOYnh4SqC0QGvVU5uGCs7g9WqVVde7st1vrwpGLgshmXUPM+0uUrVnEQg2/IsAbPqaDX+Jbtjk635H8qnVhwi0x0hfNv6uk461NOXWpD8liY9Jx6/pWtY4S5sTsurdl9XjyJOBebIxz3CY9T7vXlq/M8nk5GeTvmHqT9F16+lP5Zm//NsUheuF2L4iCGRMVkfERxqKblUR4gosBq/W+eMyQCCRIjpSfhArSwhum6ly87TDjLuAJWxWuqcJdcCFts2jjYssdy/yine6iMd/IZ2wiRe2e3I6OlUrMkRHv3vJqkU6d3MFLUSkX4/18/F+4am8U1yyxpcpXR5JktS30+pIEuBF1tmJ9lwHcnqLMTzir3FkLZTykLRaJBdKaZaRIh0sq1NN2tYovSVtlpqSTzxa5mIb1jyLdiAJtBexh4Oa9Gjg0r3u5TFpMNArWf9ewU60uVSnZYUstW0et3AwIjvCYKiYl3oB8fpCxGtht5tUuiF0Gj3LoKYfuHQYjTqhFDk9dzxhka6spikrJK6ljck8hX5Sy8w+O709ZMlFE9EEk8lHk6KbZVVMI7xMZFccWdfGZfp/+cZs5cT1QmobXTrtiPDwlSDUQw+PQHsEqptZxo5hUuIgzVJ4ub089iqy5tk+yZwgPTQaSzhIJKEO6IYtWsE6N9aPEyqfujOP15WsdW1s4fa8QHoQEqGjvi2RtMPNwS2VsAYGA3LaBFN/ziIbs2RpEX0sb9G6j51FGsmQ5z1hyjvJ8yliGEwS0/H3aXN8so+l1ZMg5pwy3/XwtxRo9xBWoBC+RoWCTghNX9LwLZSOtrPsKRSRmzvp7XG1Ec9ZiHl59I0O0VZShS1dbBEFHl3uaBr+Ml96TFCyNCeCKQ7Md/m7pWUgCnraX4uVrBqOLGOLErZ0udR5hKYV0KyVIw8+OzquVqMiL74xw2DUb93edhYxZvDTjMrlsS+VcS+eJq2sNNmcNf6SnyfRua9NbDXgQsr1LEU3r6w0YZ5lLTSVNanVo0gbkws7TTdscf5MtAjtC1xbRgqT0gEaTdh73YtM15YU7J1Iey0J5u1A2hB0QCt0ZYp/u/Qwf/1ok8ctTFEKJUibTml4Tjhhz7ujXAe7HHl4VKcjAqMX2wOImOrW2pAAGTDXRdjhPKSNK9PYTRvXaXny6o3VuW81zEAaEZ1Mk2chNl3PW6T138f/0oiU5PtknrTyixAzxSd2pxsS+oIgkHS7El9BqCFQgm4IihCz++oVRPIElO3k3wlcjTLdcPKOFBZ2b0tJJ9zk7Q+5vPHTbT65epYX/dg8rWCdv3255PELNUAzFhBxIEMEFWuKfj/snzrQR6j9Xp/KsvRDej8u2teLyNgsMsVUnml8ZtSzL6tzsBPz46T1TEKsmCzMJmNkXj2jfUc3i21nicPphthB5OXRCaEZCLph+u90RTw39rEj6Mvj5GdXRrE2umGLV79rgW99xzQfXgp43utvQf3tO/lfTyhx93wXS9hMeWIwd/fJj+ikljIHKzdTo4orK1E/ERYahafabPgX0U7kHTr06Egz/CX1j6ROnaYHxdPEy4uXW1SPTh9nZhIzrahrX05vsUV5Dy4tXTx91gIsD2mMVtJKmNbJTB3EVEcSw3IXSid42wsVv/jll5hyoe6EOFKjdDjw8hjsAU97+LvZIa5Wyxxc3W0vir51USsutO/nximbbzh5mLpv90iKSDHGKdNVrSitW436TC8ejEBEZIfdd90XvRNdpiHsKdleq2CDTIrupOM6ni9tfGYp6FmEyqReIdcr0giKcdJ2XD7mLZ6yiAdT+iSpYmprHmGRpkgk2zRez+v+wMP67uy43FagIiXalwS+xFO9Iw/DSJkOUoJNiysxmRe1CO57eRRD//dUChGG2L1YXIqQi+0V7jnQ5dvvcHnXmTJ3Pn8TV1bohJuUrXpMEc4j6Ojt+x6iJGv0A+O1gjXSF41FCMY0JMdGsuxJ8ufVvy+XiyOPTNpK3qz7GV4WqfXmGTYmMZSY6xInP5dfeovk6I8Vl6NuN8TphjQ2oqPEm0F0QmI/WOlowNKriOzY9/Iwor+1RQoLR5apWNNApBe/74LNl9/Z4g1fd56Gb/G0Lz6P95znckYs48oK7XAD/E4UqLSxBIArK0h6W2UQYNk4skzdnh9cc2WFA+UbeyEJFCvd/5+9946TJDnrvL8RacpX++mxO7NWK2llkFh5BDKIFyeQQEh4wZ040HEHh+DgODjgQHeHO3FweOEOBMJ7I2EEQiAk5N1K63fH9kz7Lpsm4v0jsqqysjOzsrp7zO7MM5+eqowMl5URTzzxe554ntMJhUdyj0niXvL7pLkySUGfJRellckDRx5D8+Ey0gFJbVkvdT/ah/jLy2Pk077INMQrrY1JmhdNyarxxw/X6QXGc7sxifaRwiLU/nhYw7iA/Fg91pKWfrnCGj5ehew4KQXSRgqbhrNoQsrC8NhJzZf0VJe3nd6gJXusq7WhBQdeJ9IwRuM08AzI4RurEYJIIHer7B7LKZvQMBjL56telLZfISJNG5hsv8jc06NblyNe+LU+JwtT3gI5SZubp7lNbuwmlU0DtdMEguTCnSZMJPlzlqYxnl9wqHwLv/SiZfLI9hWWr1DKWHl0AugG0Akk/VBGoPWobUEENF5pyuOHN6w89k4qGP3pkX+WE7UTSAH9sM3ffaTJjz9nCUeWKMkajizhyJIBw4a/Y54GLkkja6aqPRO7zpsTRamIgF2E0ubulGvBDV6dQpNA4gFN2vwULZ+mJc7i85PyFenPpLV8tP7PWUt81y//KitfPDehHUNWoCh1fXpdm50dh7YPfSUzw9IOWzxQcDrWWJyHxvnf1eTHj1U+nEFSWAysOwbhapUOeeERC0vARt/i5/9hlh995gJKB1TtOg1nkZJVY7MqYes83s+/ldL6RZyz9+CrHp1gE1/1YWhBYvZtoQ4IlFFW+6qPQLJQuomsqC2DsaBRseONES/v78TC206yzsibb3kKrLy6p6TLxauvMdpDj4qgwUnGOY1mMG8jlBSiJwnwWdrFNOE9rb0sMs/QDbb503+dYdODzb5F25fDOrRWKB2M+fO4Fk18btBVopjzvIXSCdwguh5oHSMHpVveCs861KVuLzBfOm7Sq7PGp4cf81EQetDdNBYdcasOaYPXTjQen4PRXLCcsRy2dHedcR9RUS1VUQuRLC1/3gb8BqVTlqCcpUVOK5tlUZP2TkjJW4S/5/HaPB6eBYgky5u///Wh8zntgOMZM2mvb5zhtT1zPrwfSvqhiI4lXmYhcpKQuhdh+KCtPB5vpNTIl0ecwgArEotO1AWWdDh8rDWMzmJJBxWd85bCGgrNGkVfdXb568iaj8ZqL09bmKxjPI+K/iXrnU5xk0dJxVCeguiAhe/rltKA3OS9SWXTaJKl0EFb6MTrzal7+wKNf/PpLP/RRqFa7UBRbvuEXYHXtyJwWuzy32E9Vofh9cJ7i5BWDMZOHLAy4WZNeNlPW+jjSM36apkV/wxCSCxh46seW94KAok+ez/O5zwVvXiS7tFb6as2VXsWR5bw1MhPh6/6dIIt+qpNL9wxFiIYwMVT3RQZYDRHBXJoOTJI06WaAVUSeQs+fOz7JIV/nPL2tBPm4nVCe9iB5wmbWS8H4kJo9nW8vizkK5leBNRIE5rTBIMi2udR+hv+pUcQyMi8ThDqKFyW8lCEo/PfMe3REPS4FtCvPEH7IHx23LDySKf4s4cBVhCMC9+WaywzKk3Od4yZHZ0N6G4ZHx4qAkT8HgMnpUOqzZvwtAMnp/1WZDGSpT1KZ8TD4zIZd8cpKahNQqzjQnTafdg9F2N13jgXPoGSv38W703j5Vka4iIgRnKTlLw/qb9ZeZP9mjRGRuXu28h/r1JpqjvGcengb8c35tKdQBp/C1phLDvG67oix1ouJ1BxEJZ8cXosahczjgk5ssSse5htr83nnwrpBFv0wjbdYJtAedHZ79H7FwhKsoqcIFINIv7Ywo2u1djdccqej51gMzoOM157elnIn+vpPR0vF+fXWQqtlP7f4NUJSgNzi1hWZPHIrPW0yAYr2V7eul50w5Rc/7P4tuDjX/T7iOMnCtQ5otq2R3XHo7Xjsu2bIy2+GvU7GYk8yaOztfXXCB2EzPtY5MMZJKJ/SSpZNW6bKfOpzTLPfd4KD+9Y7Pir7PibKEKaziHqzjw8fBYx06QTbAFQs401kbHm6KNRaBRCCIQQVO1ZKlZzeMylE2zhyFJKH9J432h8b3oX6A0dV8fllKz9ZRofyNuH5slMSXC6CF0mXn2NUf4B54mUBiIkF8YsQCSpNYDdLzxJaRrgoqBHXnqRQbF7UfreZ+xwz80+/3DepmZrAiXoBpKK5WE8CwtsUcpnYkJevo39wH/ItV7n9U6DcLOD31UFKMdl27vA4apxtAS2AUH8ngFEQs8AaQOgxI85V+q1TChktzoCPsYoT+OTtllO3suiSQJ0cr5OEuyy+naDsikLSErjvWna2+R7IpYO6XUP6oxT2tqQbD+ed9ICntZWvP/Jfps8Uup4LI1UqrR8LkUWHr2uTTvw6QQmPG0ngKrtYZMf4nYiXQ3wVqlxYD2Ndz+e+bmUk4V/pVLUPnJoPbFQnufZy2tIUWHTO0/JqvGb93f5+AasrJX4pmcay4/POHICjU4BPKKxqkMQBjRThNFGbOD4NE0mylfSGIE8TeYa0DTXaZvlJN9P+8yazzcom7LkzmnXtzQ+XQTASss3aS3OAsEn9S3Zz/G+POkn7uJvwlrBOg2VegGVtkev69IOYNMzMnfdCWNgh8gAqLN/n2seCLkeKVqbBBIhNAIMV9Zh5PhZ8/TFDrasYAnNJzZsFkrwn/9ec+GsRb3R5Re+6G4e3rF4CgJbuHiqiyPLqCiS4SCaoR/5ZJJIdoINbOniUI6OHMLu8UwifXxszblHE/nz9rRp8k8SOMyjvLmWlu/6pT0AHnnoURYDnySEp72kNKY5CcHOE4Sz+plGWQx+PP2nP17n/LbFM5ZDQi3Y9KzIcZJPxe5Tsmws4VBy5xHSNkcNVHB5QY5rjfIE6qRAPm35xxpNEr6lNBFYpMQLjbndjLtsfMJYLtbgzGFvG7TCrzZQBJQ6ndF4GliBCAmI2FEVMw8UIVLDKCztJIEpOYeLCktpeSbN9YJC3+NlPFwWygMVBukk8hTZMMUpDSTJA8vISE9urPLaKdK/3fWWHY2fkjNO5bZPbcejVzeAR6cvafuKfmjG2bgPD2HOEkfPYgTly7TZm3aNuNy88vHEi2H8eRLn8bU0QrDWGkfarPX6gOCfV+Bfzksevn+GTsvhP39ohi/8/Ius9s6zXAl4/uGbxprwVQ8hJDYWvurjyFJk/pw1rrME4LT8k0CLtDzxtDQhPm8s7wF0fjyNlwOhLAApa0OVtjamybNZ8nKRTVNW24O0QZ6ssZmsP+17sh44/YMf4qVvfTa/wVZO33ZTpeWz2arR7VlAOGbhMTjOIsTANiD6FwM7pvXBdMXBkIPgs0Vk68cCaQXCQmKiqIgIzFI6pB+2URrqtuKhnRKW0LzlXoeH39OkudFlp+LwHb9bpvQfJXfOdThS7fAlt8wMLeMagQNSsOqfo2TV6IbbSCxqztyQR1vCZmzcBx7adjLGRNbcnXQvSWn8IV6OlLQ8oCOZPyvL42C8TKB9HGmZJORlLcB5Qi+xe1mCefx6NxMdbyNeX/J7Vv+TG8C0Nk36Nz2phW0rznfhUg8u9SRn2i73bZVZ69n4KsBXPfphB2XbOUcEriJd7mMtj3cq+txFzAyVMpYbgUdZVph1DlGxGtjCZcdfxVNdlFvm3mCD93TavHtlxYSfHfSh3DTHWQaMy+8yHLNRnlD5xulp6DM+ttOYapL2ihDnbVzj9aYJckVR7htkaNLCB+M8cBKYkPcONNljJU0DmQRG8gDsPDAkb4M23tf/9JSk/5rdZMec4XXaNp22gxeFp42fD4+DHXHQYzj/0njBQfHXtLIHFbFlr0dYirR3rVMUpSWZZgkbO4quorVmrWfziY0KSsPtc4rbn7jJTbds84RnrfPc5YBNz0SK2PDO8yWvXeVVX3MJhUKj8VUPX/s4YgQ++6pPoAcRgMbnSTfcic6XT5KLkveS13mgYFp6cr5lyWrTgKM3aJySvDRLTs66nlRvWhtFKS3/JNm6yHqTnu/Ef30q3s/+2jQdBKC64xEEEhWKiEeboywG7Ei37ihCe3ZGneWwtCiPvkETKKZwiNZdR5ZxpNlP+Vrw8A78zTlBr2vTvMVn7XCdUtfngbuWeNExzbEaHKoE7HirfMW/F3zxd5VQf/nHKEvSDY3/jvnScar2LBKLvmrTCtYZjFk94Ht+j5Xu/bSDjdT+jV9nzZs8WbjI71B0DbhBSdrnkZY0ymPee9EQZ6FkRZnsXtouxviOVm/mWP0SpxrwtntLLM73aThwW1OyWJYESqAtI/BYwkZKm6F1h5AGbrrcAuK1cKzlerfymMZfSRiY4yiDYy5aYUljKfRo6ywVW3FH89YhOBIioGrOG2LZxmdHGBhwbVBnEEUT8Do4pTq4VbRlpYzyLG1THqOdRiCbJLBPaivK81geC1eEJmlpi+Yd3E9ufCaBHGkbp/i9NLB7EuiSlidtnI6Pn1sat/GTv3OJ//hl6eFlwfjxcPohVl+hQuMQT0Vgh9KMNEsxsMO0cJWsO5JCdXw+3LDymJ60MrbSg8eK1ujB2e1Ae/RVEFlvmk1Ww9Gw0OeLTga8+OgJ7tl8kD9+uMKPvavKL/58C42mH7ZxZQVbuqPw9BHA4lhljP8OQSfYxJHlKLpbwJa3wuHK7YwDhEmQkpR7pFzH0ybNsUnzd9qxfoNX76a0NbDoGjpJVt4r7VUGL1JnVn6NeNpncPGHP8wvfdnb+Te/87LCvbUDhfA1SolhhBZLgMCKLDtioLRIBz+uaFjxpP++omVuzB1DkZVHnASSklVFCoulcpdeaLayXt/C8ySiBJ1Gic/8glW+7NZFVnuP8MHVMm94Z5mffdMq/VCw4b6IBSwOVY4BYAuXvjIKEolllInAev8MjiwTaA/HKfHIusuzD82SLavmgRtFlXhpcycrfxEF16Q81wev3scTpi2MaRsmEvfi5SH9RSTzJbWFycW3iICfhqxnCdJp5ZP9MUzz5Sf7vPBIm+ed6vP+v1niUkfgSAiUGDq9M7VosyF9rNENhDqbDsqRX3IDEwYGpAg8811INIq6o6lYVXztDyOoWL5nrEKkRKPRpZqx8nDKI9DELpn8gYnk4kd2n+v9M4NG6YWtDK//e6EignEWb8jTGN2w8phM0wjFWQt1GjCclz+r7klASdENVN5mbXLdTWeJz//x/KWuuuPh9EOUEkip8aKpIIURrnaDHbFnVymavIMOIzsNJes/aCuPx+uaMHiPESghtMYRDg1nkbvmNE9b6HL3Uo+nzCuev6z5slt8nrnks+Ov4UjNP36syZu/sMOnNgNm3GUs4RBoD60VY+HpLZv4+Kzas2g0oQ5wZInDldtIBxqJXafJJVlawyygMC3PHkCNGzQFTfN7xWXd5LpYBKguer/AZqhQnWllctZyaXPsa48jTy7zq//PyciXTnYU1c4S4JgzurvBDkYOL4tafBQGQq5E6Nn98tnHmrVdLunhuzUhao1/xIrV5FitysuOe/x/xxUvfUKPJzxpg2c+Z4UXvXKdL7zJY7N/Hlu4/MK7m/zay5b42HqFQ5V5bOGy6Z0nUJ45thr6lLSLKys4sjz06zHvHjGgd9jCV32e3ZgzcvnpD426t7MykrfHKCnXFt23xssO8k4CnfPk5hvWH7BnHx4w/gInMbci2tt4vuR1mqBdlGEXbatI+fHnlFg8Y/FmAF55y728834b7zkWvTDgXMeh5oSoeFjDrDPDl5smocUHdd7vekKlLwfYkUwTEqSkLzxs4dJ0lhBCDo+3bPQlbV9iS83TyzOIchNfhDi9rnFYWp0dvpOW5VEHCAMc3wcRUrJqBNrUXbKq7HagR8p3yNYyJvlA8l7e92S9adeD6q+TMbZnmgZISF4f1OKYp3XO0kKn9WlSG2ma6SQJ2lkRliPySxahI5ERry5bIIWO/iySx1iG1h37PfaXxiOmBUuuNb77WDg/ngZQxddmFYA2nuYty6bhLlK2ezTdPvPlFtuexbZnsdlXlCohP/DPM2w8WOJrfq8B/xjQbgYcfXATO1D84G90OFG9NdZY5EsJi1D7WMLGlWU0GhGGESCSxn/TQJAshUzWfBKJfHmUZdlRZM4Nsl7j4+CapOQaGU+bpmweTbnm5pYp2qcEWQ7iC14DCN5z8WGKbkeUFHiuhZQaSxhQ2vDoBNgxAKcznJVmgSB7PtpShK41Xv1YoZiVhxASC2cYBdOWLrc0+xyudtnoWxytuqz3JWs9ON9xeOpClZf/SpkTf7HGZ3y0zK0fE/zkUYvmuk/gWPzuzwS0y6DsCrLfoaMDLOHQdA7hqx62dJnb7rPeqJrjLiUb8CjV5o1FtVs1VtZWErTLk43JSMva1ybrSWsn6zpZVwZdB+PyAKO0xK/TrCnin1kmcnkvLW2jlNaPIpuzvHx5gyeb0VsCejWH1nmXzeUtarax8hjLHwYjDdJjna7XYy0H6bOjSDvCpqRssKsmlJb2mGcG7AUO24BT5t0rjxh0ubXKkOVW54wwHZjjLXVVhlod2usmokvgUavOgg7BAaVDrF7HWIekjv/d86Ov2pRkjclzJA3oICNfVrlBkrj2N1PXBOVpA/PAhWms5YpupnQifdp24/Unx1oa79+9LlnpbHtIvmvhVyxqpYByJcCVhqcPfHiMNIaDsZcBdlyNMLJFaK8RW7L47rXCj68QKR2idIjWCksILKHph4I/eKjKp9bg0QebPOuzL3GiBl/5ilZ0JKWCQvH6d13k554z7qzclwElaRzl1e150ApP9ygpiS9CAu1RsZpR62bM7/hrNJxF0sHDJACRnHNxKsoPsuZkWhsp+W/w6gmUJU+mKQPS8uSlZ8nIk8CqaZWARWiSbDAddWsOTklhO2YuSWG0/3KwIY6DHhMsOzLBkLiPpqKUtNTNyzeNJcnjlc8mj2IWyGsclo7ckGut0VoTah9fCZQWlCyFJSQ7Pvzs383zM/eEnHhgjbNfsMDdT17j5a9XPG+5BJSwhM2fPLrCS47ZtP0NpGWco2oU+F3Wg3MsV27Fnz+CFYWo3fYuseUFnJg9goFegNDjdO8hTtTuiHc69pkyH1urUF8aT8sdc/sFGXNkrOuEV+/xCeMvMsnMpviRx+rKo+TLS2OaWW2nbbDSFvwsIGSSMDBK90oW1bqZjHVHUbL0GBO+pumG89J8uhxAVRGT8YG20etg9TqUtAuDiD+9bQgDLvUih7gypqHcOo/V2R4efcEpmzLlKEyt7Rp0GtAoLKRJi1JGlI1QmzOO01hKxQXzpHCe9n28vRs0DWUBC3n3igqj8feYdT+tnrT3msfXs9Lj/U+CH+ltf/Epl1PfIym/wSKNAkeiQ3BLIW4pxJEjC4+Bhd7IZ0cM7Eg7zpKkg+Cf07bxeOXDSVJq9LcfSttURBZ2SZLCwpaa9b7NhS50Wg7ziz2evgDf8pRFA3ZoBX4PrRX3/1HDWG2oYPhXCiV4ber2AhoNQhpgW9q0gnX6YZsL3fvY8i4CwvBoEddPJXko7J4Tg7Ss63gd0/DxJE1T/gZlA/5xmnZzM6l8mnycB6hcDspu69m24Bdf9qf86i9rLn3JbG4tfsnCLYVIqZFxUJrBcYcR2DE85iImgx/pPY6XKQLKXwbaL9B9LVKS3wpZANgZrcXmmODo2QwQYr7b0lj9nO0IOo9a1Lf69Ms2i8sd3nh3necvHyJQ/Qgk6fOWDxnrZlu60agxMoJvCZrukuG90qFiN7FliUd2NC1f8rbTG1zsPQpA6LhUrLzQ9Slj361m5Jl2nKXx9ywg9fqmPVh4ZC14g88sjUIaap1lVQGTX/6kBTbvfhriXERLnXZtPkMNs6tdHnxwiaNHOhyryQhxDIxmCA0qzOnvZaYbzkv3RntZbPazyCTBjvi5QK2gF0BtHuW4yG5r+JtdlBscqpyEXgsCc/YQ24VSfdQnt2osPCzbOC51KwjMGLWEEwM8klqgdNDQFoP8JO7naa7i+dPy5s3BKIdI37TeoCxK47fJe7D7HU5LWe8sT8Av2k5Wn/K0laP6G84C3/9MwWtfcSYjH5Qqhj87jrEIqdgaR47ODu8COmB0vRfLjqstjE5j0VG0/IAu57GWrN8trU0p0/NPCg0fe66B/w2BxJYurqhQtkKes7zJplfnlad22PQsXnKshtIhCMeUt1081ca/pWSuB1HatEIJkEGAp7qE2qciayjbRiKZ0wtDXrzpXUARsuWtGEuQzDmcBnzE7yWBwXi+IvM9T5barfwZ3r3Bq1MobX2N0yRFXZYSIm3NzlqHi4BjaZQFoBUFvNLL64fv4y3fvMbfv12wxGZuDX50nKVR0rhDYNreZdWRBnKkKR73FXZ2rzz88Wy5MYnynjv3yH86nxpYZEghqDshs6WAo1WPGbfEypO73PWFfS60Bd/zzHVavsNS+SROqOlLRS9o8WknQyr3Pwh3Po9+2DFRtcIeM+4yaGj569SdeTSKTrDFk+bmsGWJp8/aXPLPArDWPx1Z3+3uXeYcdKukz8O8+VRExpoku6fT9cCrD8CLZhY4MAnEKKLZSy7Uk+qatNHKGwB5IMzkQWYJw4jrDQ9HwrYP277FbBDgSnPWbCoTrscyXU5mfiUXissJduxV6ysktFZNxJ/qLAAvP75srDcAKjMQlMcE+kD7WJaN2FodaRmH6LIYWWoISTqjhN1zIWvOpwliYw+TKD8JlIyXu0HTUZZgHKcsfpd2Lw3IzqsvjbLS91ImeT+rjskbOi0FSglsW1G2NDUHJBpHDjQ/e4jIMq0wvF8noZMituwVhNgrzz1I0KPob7nfZ1QKrFgkNWHRCzbxVR9f9ajYTSp2E4HAFi6vvNnDlU00ipJVM2G/taKve5RklYrV4B9eH6t/oCBRPtJycIUFVMBrG56uVQSMCNb7Z5gvmQgCc+6ReGfZPVfjlAQh9gqQTJvnBhWnPJ6cdq+IzDxpTU3Lm0d58nKRdTwPBEmMYbv4ZssvWVTrAZaAqq2pO2oXkBE/qpJ1bGUsf7J8od9nn3Q9Hm0p+gypgHQEPAuJQIMOCbQJKbvtCdZ6NsfrHk1nBiEENWeT//Eyj7mSItTG4b9G0wrW6YY7LDmncN0K3/G0kJ6qUwbKVh1HluiHBkCrWE0I29DeIChJHFmiF7aoSYcV71GWK7cCmqXyqQzgbNI4Kir/TpMen5NFgcjrg/YiGSSuiwrCRetOLtTx73mIdjw9rR9JwX/SwJkEdsTr0SxXbuMV/8Xj0JEOcy4crmiaTkjJ0ox5aI/T1dbwJemgjrXkUV49lwMs2Atdbj8rWXVHIWNTgbFBWnUWavNGA9g1R1qGIEbPHFHBLo0cKAUedq+L8PujuhuHIPAQ7Y1EB5JjPyngZG2K4/NPJNKmpbwNcfQ5MH886L/HDU3ig8l8ae8ymX+ahXMSQHy5KA/INvd/7LfSreyUNNFZpNRUbZgvGUHaFu4wPN0oc4Z1R561x6Tz3XsBOw6CR+3HD0mRvPtZ4w7quEpRAGTwLANfW1LSC1sE2qMbbvOuCza/eX+Xtm/4phQ2FauJa1WGYIcUFghJSZRTfxtFCELiCAeUwlc92sEGgeOACtjSEU8HXKuS0sn4nM2yrIjLS1kyTxqvT5v/abJSUa3+DV6dTUmePIk35gFcyTqLaXR3t5uloEyTvYv0Jyk/ZNctnvwCXv2fHX608RbWXjWT2+tuzWV+scusC3UnpGKn84f4BnTsu8gPVTsedjyFrsQRxoOka22fUZRS5vnQMS0CpUO6wQ4tX3Cm7fIXp12+6x/mWetvo7XGEoKlsk3JqlGzGygd0nSWqNsLzLqH6YUmfPjAN1MvbA2dSFdFDYlFK1jjQniWTsmibNX5+PoOs6UjBMqjHSiGIMwuhUhRJU0yLYunF603mTcJfmfRZeLV1xjtoUeTNjR5zLYII05bSLME+GnQszykuUha3j1jOve5J3xuPtJjoQwVW1GxFa40YY5s4ZrwoVfzrPVBRRYpUudeBfWrCXrsF+jYa1QWAMumS5dNvckjapU1uzPOMGTkp6O1GuV3jSAvIyTcdiOHo9Hc7G6ZvF7H/LXXzVEXrUy6VlCqE+jBkZks4DJPYI7APL+HHgtpGwc+4nVltTOJkmDLDcqntAUuD8yYBsiYRGnvPu1+WrsHpQ3JGneCit3cVbpbc+g0S5SrAeWqcVhashSzpQBbupEfJl2MN2QdeUlSPATqfnjopPUkeb0fE+y93CtKcXDjIECOA6ayVadiK+5e6lFz5gh1wLZ/EVu6aK2QGN8BQ+VGrP8KRU910ejh+XCEBV4Hp9+nRhUdaXBt6RJIjSKMjrAMKLnBTJs7IpEvC8CI15MHmsTrSZZP5rtBxSltLSsCTkyiacCItHanlYWT5fPKTrA+sRzKr/ssyo0A6x1p4T0Nea6FX7IoVwKOVKFimY1tESejaVYgRY687H6WtNvXFr+6Xmggdx6vedy9pPjxF61z8wOnmdnY5IEtF0eW0VoNQ81ueStoFI4sDy1/Au3RCbYoWbVIuSHo0jWWHPYcdWeBbf8inupSshSXug8TKI+T9VORH7y0sZ2mLGT3fR3iqS7okHTZaBL/LUI3LD0GtEcIJktbEL+Xt3lKUnJDU1RgLmLZMSk9DQFLsxpJa2t8MC6WT/LMRRPSEIi8BdvY0jVOIVPjNF9h2g9jvtaY+kH250o8W14bYUCFCrNynmO1kzSdJQNiWC6D8+CqVDXARxiwo7ag1IDOJmiFLtXM+Nq5SDDwYq2VYcgq0lgGnqmr0jRp/RY2cVPS6TegAgk7FxFeN1p8sphzUfAx7V5sDgouDxJ9DaLR+6M8ATO5sCZ526R3lcf/s8DwrAU8TpO0hXHNdV6+7H7V7Fmq3z7+rgdhDqu1gGrNp+GAE4U8dGUl4SxyUN0UVh3T+vmYlPdKOU8+KEqCF1cD3Njj0RpL2IQ6oB92+MwjcxyrSULlDy1/Qm38cHSCTQLlGeACYfwkCQlhgNSMwn4HfVNx0De8ud+C0MNRZqz2whZb3grdYBtP9SIAJW2OJil7XrWDDe7duj8j34SN6H4Ajhu8OoOS8m2RjUjae8rSKE9SLqTdT669RcDwLFBsQGn1ZY8fccuzcZbK/PT/WcvM02m6OLMKx9Y4EpYq/pgvsV0ARuI4S5p1Rz5Qkni2a8kH02ONUudtEqjNKJOxRlnCoRtIznVcXnS0zFrPgqd/FsweZb4coFF0ww6dYIuyXUcKi1AHsPIpSqJMP2wjhcVi+SRKh3SCTehuYQuXslVHILGEQ8WawVc91vs25zuCS70d1vtnCdwS+TJN/BnT831s/QJ/9MiFCPTIKhunopYkRWTueDOPfz59QD2axOzSrgdpSSF2PxqDaRaRSYt+0f6PM/VX3XKElxxrs+1ZdAM50qBfowMglR4rVh77pUka1mloUp+LtBE5G7VDjSNL4NaMT45SHVSADJUBK3rbNHoh7KwYEMM3ZtH4PQgD7PbWMAILsHvcCQmNZbxKNTKfnqTZSQMvdfQtOnNuxR2ejucpTllA6eDeDYR6esp6t/GNVBKkznv3aQBwFi/NEs7TeP00AnMemJ5W53j+n3n+kbHr0DbzQylByVXUHKjZiprtDs2bh5Q2z5OAxqQ8k44OTss3p7XyKELTHnUpcrTlSoAb+/UZEh1lQUhQamix4VoVNr3zWMKmr9oALJSOAxgzaOngyJLRKA6s3aKjKaiBjwGBsm16YdtYe1qRHya7jLKN/46F0gkqVoOaPYcUFoHyyB7Tafxwd3rNnuWOmVuzHjjxmSZc5222b/DkvdH0wFV2Hcl3nqcwzCq3FxAmrc5J95N8eryf5e9/A0c/8gCem+7To1+xKVdCFkpwrBYgU5rdBXKw2/ojHQgRUf6B5cfjyHrpmgRn0vZYaS90fO80OIYikBHADLNuwAdX21RsxVrvNJQaPHH2Vqr2LCVZwpLmWOps6Qgtf43t+Tla4Sah9g2PvfQAXgSMXOAigfYItU872MBXPfqqjUDy9AXJsZrktuatLDnH6IftRL/z5I90RdFtMzW+8OShKd7RFEDGDRqjPUoHeS9VJ/7SysQpD1jIYzhJzUe8bNbgKzpQJjG67E2ZRrPRt7jQgZWuQzcI8VUPhTICDuwWei+3z4gk7edoy7T93OtzXS7nnwcJcgzoIMAOgFKD+zuned0/rfOb959nOK4G0VasyPt/ZTYGMBiq2rOjELRxSyLbNYBEuTnqS+CB18aVZePvY7yz0WceADli3AJh+tPbZhjJopAmPtlevO4J+W5oDSdQET4XBzuy7mdtqCZdZ2szxvOktZlXd1beIuB0/J7g3/1cMEypb/WxAzNHXQk1G2ZLIVJYuNJEMirsl6No9Ja9+M44KJ6VB8jsh641C8AkTQJDknzActAYPxsfXgv4yY/VaLqHqNsL/M3ZRzndvg9bONTtBUqyOrQEMpslMWrPGlkISSRlq274t102FnuWbcaY5eKrHlV7llD7hNofAi7plM6bFQPz6LT86UJ3so7xz7TNaUFB+wavnpIOas0ssI4WKlPEGm8SwJIHZifa1CGsb/GWNz6SWpPvWjRn+xypmtCjTVdGRw6TtcuJ1h15fjzSHyPDuuNa53vXAu36XSet0ZPTB/xxo2/x+w+VeeNfLXHzA6dZedkfws4K77v0EK6ssFg+yZx7lJI0fjkaziJSWGgdU9rNHkURmihcskKgPHzVRwoLSzhU7RlC7eOpDlV7hnawSUe3d/v3ylXs62G/R7csKlaTbriNtrIc914h4O064NN77FFSoE1bQPM2PFnmbUntRZFJMQ1jz2PekzSQ8XzZoIyv+jy4XcKSsNaz2fYsfNU33ttVYqOdponL+ztIuhbqOyiHd1mbh8v5+x00CQndLU7Wj/OzL1zgK04txG+Ofx9oBi3XfNYXkUgDbgwADqc8YjjlJkG5YkAP2zXWH+31qL4J8yQMUo5iJeapHR27CbyRVnN4v6j5XbLe5LNPEqpukKEkb84SNtMA6sG9tHJF38N+3lPehmrSRiBtnGUJ8JpnLi0PU6TSNNe7lCsBhyuwWA4pWWrcJwOk++XIM3GedKSlKH+6WvzraoIyV4LShLMYKKIIafnr9MOAbiD4tqf42MIh0B5Pmfc5Ubsjypm0MIr+hATLoa86wzrNcUMd+VyaGb5boTVohSNLhNrnQvchfNXDlRUmj/0RKUK2vYvJB6UXto0pd651VLz/MD6P0kCQpIXYDZpM0/LGND6dBQxP4vWD62nazuLnae88WxG4Oz2jz91tvA+ep/8b/0S/PH6U0HMtdo5UmF/sUrVNFC2lw1z/HUMnl7HN11gEl8S9fdPlkIP3U/c1SUXGoBj7M97iTPjZAYXa55EdzaZnsePD//zCFcQTnsmT3/mtfMhr88ylk4k6zVizIpDaEjaurBq/Sk5leERx4Cy6E2yitaJiN6n7NjV7jqo9S8tf5w8e6tJXHQNe5zyDIoys/M1zhNpnrX96rD+OLNMNduiHnXEwZKrfK42mkb2vD9pHWNqkYFxEY5hcHJNlJoEYe+1fckHPQ+GKag7jzzIaTFJYvPLmY2x452j5LVxpYQnbeGUPd8yGcMBghZyOiSXzXm4ELSvEX+RgrTDl5Z9UV9Ewg1drMTgo646Y0zqBBCvEV31e/ushP/byCzx57hZ2jblKEy3EaLEvNUaRWLyOOeIibei3+NedLZ5bbxhLEdEx7XW3I0enyTEfE0isJIJNIi+jqAaWDbu0LWnzKU2Qjt+fYHVwUKEuH5eU3HyJxCewSxDNAguyFklBfh1p5ZPXWfnj4y+tz1l1Jfl3EsQZb8+VZd7025K3nYazbbjvHWW++ImXeM6hPlJoAiXwVY9Qm03uLoqDENMcITmo44Jp+eJ8dNJ1Gl8tytcn5Zt2fbiSlHzmwfuIp0d9Vzpko99nredy95Ji0/MJ9P3Ml44zVzrK7jmQNmc0JVkdfpfCQqNHPNt2Df+0XUJLYiHwVZ+yVTEhEaNyuyl9fkoks+5hALrhDhWrMXZ/27+ELVyq9kxuPbspOR+TczNDprrBqxOUp6BLW/+KKPay8kwCo4rI7Fn50srl3c97vtg926X0X78VznyE36qHvLsf8M8rLh/9PsWll83y8mesYAnohbDjW/RDcKVCCIuBVUdWdJU8646JYWgfD747DjJM+AFRHMCIv5NxJ/gMFQ9aqwj80IQ64P7tEqdb8HA98qUAAQAASURBVOW3ejy849J0L3HctbhjZh7Rb/M7Sz/Fq/7gCYiXfgnJ8Vqxm4gwxLXL5tpqGt9M0iFUPr4yR8wD5WH/+R+x84UvoWI36bDJybqPJSpoRscV00hijVkgWcLhUPkWAFrBOnV7AUWI0saidNO7gCsrNJzFzF+s+P44by+bkvcaGxuXg/bxhHmagmm0fEmBOalxTKalpRepP44YppXJQqezruNpo8E0cKD0j+cVR6s3s2wdZtazYfui2YAOaBBhYz+mP/u1XrgWrDyK0LW4wBzEefTBu7ddqM5Fps0SQh+E5KGdM8wu9Hhy47gBJ8YLQ7TIG4rmwsAiw60aMKM2D5bNc5dPme9x648BKLK7Y6RvHJPC7gAUMefP8XsmOkymBicL6EhS2rwuMtdvkKG033wSz8vS1qWBJ2n5i7SXR9OUm/QsacJ2kgQ1e46GozhWg5/6yjN83RPqPGluiW4guW+rzCc3LHpBizErjyS/nXSUZdLRliy6VqzSroU+7Icmra/JdVjaIG20U2Kzf54/e6TKxzdcGu4idUdRsmq4soIry/z5o2d56wPnCbRHO9jIOUoySlPaANnDuRTxf4WKNIGasqxQs+foh23jmym1rry5a67jgMmjrQt0w22aziFz/DH7B0u5LiL73ODN01PWO8xbO5PXRTc/RcrnvcM8cGPa6zSwOyLXgIPeb7+TYGaJP37E5b0Plbn9v0l++kvP8upbO2z78K8XJP94QXKpa5uwook6B8BHnnXHgdBjnT9eCcrgv7tBDRX7G/+ndDh8z0qH9MM2D2z1+bV3zfChsy5PW1hksRzQcASOLFG1Z1nV67zqz+6i96LP5kL33sh6QjNwDmoUiyYqy4B81aPtb+DIMjXHWHRoFHzpV1GyapStOvOlE9wx22e1t8Vq79GMh54sg9VtY8UtsfjzRyts9M9RsRrUnfnU/OlgRxFZ74bsPKA9WHgkLRuKor8wvjim5Z+EXqWV3wsDm8Scs9DrSe1G90Ofl990xGw+u9u7hdfB0QO/ZyCn8ADPT08LnuzVwuJKWnnk9eNq0EECMJYJN6tRCLcK/R0jdANvfE+TmdkosopTNuMp9Mz3IPqMC6T9FvRaUK6P/H4gjOUHMABJKNWHbZg6kpSlGcq6Fua4jGUbJwipliNZWvj8DUJq2rWqPb4mKAFGTV0Wdi+iaaB0Hng8qT/JckVB6CLlkn3Mzt/y15BC87LjitnSTUgNW/4qbzvjcnpb8pITkjvn2ig9b6JsjTUT4+mTQtBmUZaF334A7BtWHukU/62T/YpfCwmWzVrvNGfaIWt9h15ogIqF0k2s9U/jqx7/tHKR3/0WCSi+5PcVlnBGIWd30WjMaa1QKd74JTKyBBEgJGWrFu8g6XOBjPTdm9k7Zm7FV33ec/FhnrawGKt/EnCRtekuCHRcS2PgmqG09TVrLSy6FmeVK5o/nmcaLXKRtvPqS8oF5hiA/YbX8+D2Q6x1a/y7u1vcNR9gizKf2gx4+98eZv5im+3PCFg7GnJTw8fC+NsRWKmgRtynx0BJlGfVMdLcM86Pr0Xl22OKdv/mA7Bq8HvHrTkG14PvA2uIT2y0+PhGie1NF7cUonTAsw4t8N6La9SWtvmVT/Z5//daPPSkl/GXz+tRsWYQQtJXHRxZJlQ9HDku+3bD7aEFXMNZpCQNjxx81uw5wFiHLpRuYr4UBReYKMNO3qt+9e3zBNrjLx/d4QVH1iMrkEl70Kx2SOQrOJevA169hyfMepFFBNhkuTyUOVlf3gJb1GJjkhYxqz/j6UaTk1VOm41fdxt622bjGRO01OxhNh2P0I0cliX9euyXrrZmcNJZ9L3cG9DVXmymteooct5d2ihhfL9seRcJ3TI9GfKetbNIS/Om59bMeLLdkSXFAAAZUjRe7bLJE3jGeiM+Z8aEbBFZelTG80zU+uRoFe0SbJ4zoEsqiJJWLtn2DRR6/5RuzTD6FKTz7SzemAQs8jZaaW0V2SRlCfVJmlarHM8/3r+znXt5tGV809SdBaQGhMVb7g+590cEH37PIe7dMiaouxyTpQnAcUuOtHmf4iNid3en5N1XMprVNOlF7++lH/upM83aI368NHIg2tfG6u3hnRJNBz58psQXvwFe/q19HFnm3Ssr/OrrTblPvfAwjixFAIKOzKDTx7FGY0lnmDfWibFc2WlkpGWByOPC8d+du8SnLS5TsqrspknzOS3/DX49PWVpabOUedPIvXm8dhJgluTx07aXVndG3sCL5JE0YEQjsXjb6XP85aNVyo7m9pnR5vTX7itx13vOcfShLR59sBmFD3eiVsePrAysO9LAjt290ynfpwR9HgvWHldbho5oYN2R/N0H4EYS7DAePAzY0fLXAXj3BcH8Uo/Nj7q8/lUtvvFLN3ninOa9F9u8/3tN+cWzO1TtWWbcZSTGYlNi7QI7wPjSKFk1ynYdT3WHvYLBnm80RxxZjhyap42novNkvO3X/32XFx+TzLpHUnJMMyaLyFLXJ+3Rh0cSlc2yiCBxb5J2N107sZuSAnsRLXER9DoNIdudFiofpEEkQx2gI2djAwq0j12dNRp5FUSbT6DS5D0rp6k7iqfMSWPhoeJOHg+QprH4uFJWHgdBV8PS43ItEjEBPFB9uuE29211uNSzuX/L5Svv7FO2lsx4GsSaH1prpCC9lgP1pViaNu8oGn+6VDMout+LwA7Gy8fLDdPGtS/j9xPlSnVzdKtcN+BLZaboD1EgT9SmENcFEr13ShNWs4TeIgtzmqVEXv60e0nQZFL+LMoaf5PL/tTHznHPFvyXp7c4UbudOfcQ8yXJUuWC4d1hyNvOn+O9/1XgAjMLfV51S5e50ikjnA+A6TyLjGkE3qR1x9UCOvZj5VEk737XiLzfe1K9Wb6y0q6FOdoSougEWzy8o/jkpuBYDX76s7v8yk19Pvp9ipf/j2Vu/8jIKeg/fqvCiFFmnhg5IH08CmL+lmKp6HDYV4WiF+zEfGzsevjE9/jcypv7gs85fpwN7zzvvRjysuPHUvqSBm4nZTYy2kmhG7w6hbKAjeT9aeoqUneWkjEL2JpGpk6jrOfTRv4QVUAZi1Wt+Np/6PKJDy/y81/3CM9oLPL8wyWet6z58FqPUhTl6E0flZz79RLzGHnmy1+2wlPmFVK4KWO5OGmthsde4t93UZZT6syKrzErt2uUdMbvOA6GGMs4L+xyoaN490qZez8+zxOfus4vv7LPT37M5fQbFV/z20vY/+LRcHu4Xshv/8qMsZqOrJ1n3GWy9oC2cJlzjxCfKzoCsZUOKcsKIYqN/jkWyyd39Xa6ubR77/x/P9PlXRda/PEjG/zIszvU7fmcOrNoj2DddcKr93GkJQ/sSFt8k/nzQIciL22PLzZXYxG/n/ZsxtxOCisyX9XRRjQcHjfQboVeaFDFUIQ4sWgZO7LPX5x2+e5Pqxtt+JWggzJRPqg293u0Ba4s6LHXzUWRDYw0ZwjDyDmSI8vMl3uULE3J0jx3eRYAOwhB+qAVyrZjptPaWFQICW7NCM5eB9wqvvZxet0RqOaU8R0bR5YQrVVoHDJWI4UEm6z7CaG30oTOJmxdgMYhwnI1ph2fZp4m+ccNtHpvlOTTk/KSkq/Ie4jnSVsfknkm1ZFF02iUx/n3Zxzp8dxlwVL5KMDQj8Ghsmt8KghwjJkHW1/e4E9e2aNmnTLHDQd+HuJTWgXFQhMmj1LoqL698JW0MkleOO2xlqK0H769l+OPB015x1gg8uNhEagenaDF/dt1PnrO5U/vm+E3Zz16XYvFxvYQ7AhsyRf8L7CEbWSCSB7I3liOazKHoY4HQFp0lEZiYUkHhTKRt4Z1pM2jtO9Zc96kbfTbKF0m1EHMGW8yb1rZ+L00KgiC3KCI8pR1efkH38kpP0lZmAZmJSnN+qKo4jJnbxA56DWhmgVYNgrFF92+zWeevMAT5xZgY5Xm3HHwOjzrUI21/jmUbvF5J1xOX1R88pmH+aV//whHaxUkFRQDHx4KtBw2Ocm6Y8x5cOJ6UN9+gBRT0VWQvx8jlBVyewzoGDgp1ToKC9vlTNvhg+dt5j7YovfXIa89OotfsjjKFst/tAHAw3cu8DWv3cAWLj3bw9EBGo2txFiY8G64jRQWJVlDYXyDlKwastui4xjHqBpl9nndbazqLNu+R91pJSK07HUfOipXtWe4Z6PLA/fO0n3mReq6mji2npxTaXJZ3vy+wZ/3AHhkoLa77se/F1kwcxDh1HJZzDhZZ1abyT5lLdjjdXeDbXzVi8yOYmUsBywHgXFCo3TIev8sGsXh0iGQknec2+KZi4JKq80uLdW0EVumoYNgptNaeTzWQY/Lbfo31OiFhDrAU12ksKg7FovlOrc0HSq6BF4X37ZwhNlwSa83dOxFd9v8Bm6V4Tgs1dn0LhAoj8X++O+z3j/NA1slnt9sRu0XBRSzhJtEeSGheWgo0OzyezAVkJGT9zoTDKajNJB5QFmbobzNSp4VyKTNXXLhzWs3rf28zd2gTJoAkOyvYLVn89JjNw3zDDapjiwTaA9bONw1H7Dx6kX+6MvqCL8/jHLU1+boiy1dLLUHy4w83l6E719uXrRfK4+DoL2sfWl9nHZdGJRXCixprDcxkXt6XRvPs+jca1Hf7OGXLFRbMP/tgv/5rDqWcIZOSEtWFYGIxpIBksf8AMTmXqh9bCwzvrQidMtYYiCKxaO6DB805/ug3rQ5F2/XpM26FZ67bNELdyINYrLOIsBJst54XxJ0g1fnUB4wnAUiZF3nUda6nQdaZF1PkAF2pSXyb50zfsaOPim6a577Qlfw+icuR8Dflrn30EcoKUXtjjsoWw2ec/69PPJjd/Ljx8/jyhnA+NbZ9tpIoXGlRdmqo7UmzTfp4BjFeDQQA3IMLDs0GsasPEQ6j550fbnocsryV5GS4MeY1U303iQWofZR2ubMIw2WtzcBWDrXGisrvtnh7z/LpqVLtII1ylGkKoGgpbepY5yFtoI1bFEa+uiAyNHzxmn0pdPoW56IJWy2/UvUnXn0/R9B3/MIh7/ki4ehayc91TRzVCD5nBNdnrvsseP7LJZLifUj/plXd1beCX25Dnj1PsLSQjrSmyW4ppWJX08SbtPqSqZlMeMsJCyvn+NpnWCTit2kJurgzo3qHITktGUUZmh+6Nm3YjdwZRVaOzBzmF//QMDvvGIGNs6ONzFwIHm1zwBehyhzKu13g1HkPWplAAwNJasahStUhKpK2W4YYCM0AnFfKix7Fmk5ZqHqbhsNs5BQajKcL91tcMrMuMuIrRUgdlzK73HYPUZpfgvc2QIPkTaXs4DDmJDm1gwAs3HGCPRuld0MeNIiEBfi96lhuUFkAxB5iH8e7xxcJ0HqSe9rEsiSBZDHN3Rp5bKADuMbJ9RBDOww5Qfa8x1/dXh84FD5Zv741Qq0EXqxXHwR8qHVSwDcfegE42YejIThooB1msVH8vuA9mNd9liw8jiI9e6ALRNtWaJiVTlUgcCXzC/2WMec9xZK47sWP/L0S1hiDiks2oHx6D8QTC1hE2gPX/UiDeDusWljRRahAUiJEJJOsEVV1PBFOHY81tA0gGT+vJ4vHYtKaUYa7bSNdxoQOUlbeEODWJzSZOfk93je5L0smbqIrJsnJxdZm/Moo48bp2H2KGr+eMR7B3L1FoHyeP2TjsLqQ7B4M8ydgNBHLB7GWzyO760w6x7hb175Pl5z7/NZDw0/DrXPjr/Dmz9pNO3/4a7xiHNaq2hIyzFHpslQqEnQI5P2apl3gzJpYMGRTMu658gyR6o+h450cL2QwJbYwfg7+d5nbbMVmve82mtzW61pblgOFavJlrdCN9yi7iyM+TPqBtu4soJTX6TbaCCFROkQgeSRnU3ufMoLsI6eikW5mjRvi8iu42DEE2dvAQSrvUfohcaKxAB0RYDGtHqL9uP6oH0CHvGFMin4JikPtc5CoNIW2DzmnaadSOvrtBspPRrk1vgg8kWIbbl4qhPlMfULBE0xA4FH2FzkBW8K+eHXbCPWtxk6nbTLRhseeBD0QF8DVh55+R7vVh4HsZgVdeQ32OB0txG2izsABrQP7U0GJs4oqNpNYzVEiPQ6BuyQtgETBr9VGAyPr4jO5m7fME6ZNb3Ogn0Ydi5CfRFEXlSBSULSgDI2vH7PHG/pbMLssYy6iraTSLvGtBPXFk0LQJPIk7bpSX7PKrvffJMAkSL3BvdNfY4s44zVPa4VH493ryEMCC3JGe8cn9ws8+4Viwcu1XjJLT53zq4yY0VacWmDUEPgMpcHwm5AY5LwPA0vOgg+eBBWHkVAj2uNEn12ZYWaM8fTF1Z49Alwui2o1nzWa2VCJfnsr+hwqWsBZ5DCouEsjvwrYTZwtnQJtR8TVAGMZUeoA1xtj/x62VW0VlStBgiJg824qf2keQC752kagDGqS6P55wunuXXG43DlNtLne+qPFasnre0UusGrMyhrjctT3mUpAbI2QpM2ZMn0omv+HmjuOAAy0b+aL6G8CK1LMHuU4XiyHIKlkyjlsSSX+FXrh/jqXz7MltrEU13+6rTkI+vwng/cxMz72+iXODxwwuOJcwqNQGkQQkTOqDGydYwGAIixwpLsOs4y5Avm/mW1wL7OKBmOduxe4jc270SiCRFCUrKqnKjv8Jm39vn1z1qm/lCXhfNtXM845W9/TY23n5HcMdOnYilum7EJpDYgc9Dnkv8olnAINUPLDzAWn91gmzYbVKwmjjAgt6/aaBSnGovIRz6EOvUMtrxzQyeogyeafp6k7WdNPYqQ//Iel8+9aYcXHWsx5x5lN7+dViFVgK4DXr3PJ5wG5Z+0WcrLX4RZJ198ngYxWUfadRqQsrsfGk032EbpMEKuY/V1N81HuM13fPEKT56P/dyNQ6zLnfEoLpeboV5rDDuvP0X7uh+QYhB1ZdroKwdB8ecLI8e2Qd8AFW51+BeUK8bMur+DDI3Wmdq88ZcxiAYEEWCmDHiWBDuah7lo75iwWr1tqMzmgB2QPi+ztHtJ7XtEpboBO/wevbCViGxUBHmeBs2+QSOKv4887WyWJjBro5RHkxbXSe+5CF+Ot5MlpGdtwtJBcV/1I2/sxgf8pd4jUW7Nd/39PL/8XS5/+ffLdNoOlgAprFHEJGkTd3aJZY/+4tFAkiFPk2lp19cyFQV0ryQVtayZBMYoBaFPSVa5pTnH5xzvc3tTU296LC53mV/s8f6zDrOlgLJVZ849aoRjWWbHXwMENXuOkqxSt43ZtK/6DMaeEHJ0dMUxEbU8EYz6h3FaaqIQDOZFmgwzSdidDHYuV33++UIVT/V23Uu/TuP/B7ghvi4pDnLkra9x3pXHK/Pk3SLfkxTnt9NQ1vqT0pYKjD+7wDOR3hARJKdY75+hG27D9kW+4kdmEF/7dcN6f/nnmlz4n4qTb1tldtVE1ChZxudCqAOUNhE9Rt+D4d/AAabWOqotsihAZzrQzH/ca5AnTqJrzFIlHpkljYQQCCSWcKjYDT7/phavevElgic7dJou3ZpDt+bQeofkztmAJ8z2ePriPEtiAbvX5Wz3AUJLslA6wWL5Jo5V78AWLhv9c2gUXthl4CtE6ZBA94cWRHOlo1QfvAexcBPb3kV81UuEIM+bQ3n74Ww+/tzDmjf9+RIXOl0DBgb9lHJp18k2blCc9mnhAfmLZBbCnFwosxbueJ154EZW2b0K4Dl90CEB4VCr48iScQSJNIPSdrnUe4RZ9zCOtKFUp+73eOWxKAyS3ATb5XT7PuZKCyaqxQBNlvaVAz/y6EpZeeynH0X6lJX3ctG0C1/8XWsFIhj65FDCnFm0QwXSAX/HgCID66DAeDen3Mxuw3ahvshOsMGcexRnZ4OguYAt3KFmI1Yw+py0+UxjuIM8sfT6ogFmdi5S7vVRtSJnHtPaTwrv4rG1QbzilK7VTU+D8d93ElAyCVDI2mhl3U+OnXh63jMk+0/O92Td5roXttAoKlZz6MNjqXwKQp9OsMFthwPWvgl+7OkP4EqL8x3B+U6HdWuDim1Rs+eoOjOIMBwBjINxKdVoTofR0bNpnJruhQ7CeelB+fK4lo9GZp3Hj6eHASWrxq0zXUpWj4oN924Z3x5fc3sfXwnKVoNN7wJL5VN4qjt0qCeFhRTGh1egvKFjO+MzJrLqiSz0OqJPVTZGYLBWkR+n+fH+7ZoPaXM2mTc5V0Z1CAQn68c5Uu3yLysXeeGRm2L1jv0wifrS5l3eZvgGr06npDVHMh12v7ciMmta3iLKwsE1KXnz5PlJ+Xe3q1EIrzvyQ1adNTKMZY/839z/XtStz0TpgFn3MO3ZLdz/9Dq80MyvtZ7FV33DNm+7t8IX3tmnams+udnivZdKfGQd5kuKmxs9jlQtbOmStCgQ0REXiYXERoiBv51x3x7Gl0dCMVT0WMu1CHI8hslY2SjQBvSo2VU+62iP8gs2+GN3ho17zXv6qq9eR6JZKp/gUu80y5VT9MM2K5sOlngYW7gm7GzYpRt2WCqfQCCNXyXpRk6jbQLlsd5vcapxK3L9DJx4Mju2z6yuo8vLjCJrZe1pRz0fUZE8Ru5/za1VXn7yPD/wgQr/425BY1f5PJ6wFyD6+uDV+4jSAntnymn3shbpZPk87QWk15E3SKYBRQQoRShGYUJ91UejkFgEwsPV0gjOfjcKhRSRWzNIXaXJBXWBT62Xef5hB3QvtuGVIw/CeWEQ90NXw2v0QZ/9TqNJoMflRrT3i/LrSBAOQZdqtPxVGs4iwrJNJJZBeWmb4yLR+W9DGWNYSNi+SKM6C7IMtXmjpU7PnNUxxudPEaYrRv2NvK3LfmcYGmxyO/E+JQQrwXXBmPdOSf43CfwYlMniqWkgQho4McnCI62NInWnURYgkwWY7H7eQPeHWvhQ+yZihQ7Bcnj9awIeuXOBH/nm81SsEmW7zi3NkNXeFr/zQA1PwVPm2zxveZvZ0hFsGR1pSGyax76n+fp4rJ4JvxYBjWnWtbz0qA4bi7o9z7HaKnfN+TQch2csdrm1uYwjS2z7lyKHdhfphx1C7WEJh4azSDfcph+2jUXdoHqtQFgRzzaKjao9AzpEEeKpLmWrBgxM6yfNDRJpeXObXelB5Ih3BHZMAzIWpBu8OoWm4ZN5QEYWZa2hyTU7bQ3PyptWd15f0vpq0jQa4cccr3c2zaftYvdaUGkibns2a72HWCjdhEDiqx41e5Z+0MaRZX7063v0/02J/3j3Dtu+xXLF53hN8L7VEj//54fw+5InPn2Nr31Kl7vmW5Qs45RyYEEghERqCy3MeB6AHqaXA6BDjq7zjrU8loGNa9B5aRqNjvcZ0ENrKFt1TtR3CFSfC3dt8fFmhZfc2eULT7Y4VLkVpUNsYbHlrXD/VshK16FkKU426mx5a5xpuZxqWENeG2rfWJkIQagCBJJDlXl2vFXsZhMi5QhhxJ9VCNbl+O3MPDGWp/BTzzuKp7r0tE+ZEunzF7LnbLLuDLpOePUeLTySTDL+mbw/TX1p9ebVl7c4JOtIu95D3yyHEs7wOu5gTKlw5PTIMRptX/WMpUfQZ83p8XOf8Lljpsarbl6O4pFHGnu/B5aNbiwZ5trZMDHKBxqhg6T9CqzTWnnsl/YKelyJzURRB6VT3BetNZpWCXSXwHHoWwF2zXgf1ygqOhIWLJvtYI0ffV/AD376kcgXTGT54ZShVGdH72BLnwpl1sKLLNgnjCZFicgfzaQ5kQU6Ju8lKB7ydiDc5GqgpgEerx4JIZ4E/BTwXGATeDPwA1rrMK/claeim5esjVKRutM2W8nxkgc8xCmtH0n+XhTQyKp7RAbsMPUaL+2af1o5wxvefIzP+g74P0+7yELpFN1wm3ee3+JpC11+6/467/8pl9ZsiQ88P2T1rg7PPnSaU435yLmYaUogELZrgA4YgddjXUoIzwdxRvxyWXmk0UEB1ZeDBs8Q78MkHyvxayFBSxxhMe8e4bnLPk+aW6Fim/e82nuE33vI4RWn+lTt2aF/LwMma8pW3YQ2jJk9W8JGo+iE2wjE0FGuEmDjYFsu8fE9ivYyaa7FAY3ioEQldn59VD7eTh4omjcXrx167PDqOGUpBrNAjyKydhGgIq3dPKClqKJzvH6JBbWFUR3V2dF8jaJiARwq32KydLeolxfwVZ+L3XXee7HG//rNDv2wy+HqUc62z/GTH6vx/GX45T9c5kn/et6Uew+86UvmefFTt3nZ8R6HKmaMGr8eFkqEWBHQIZAIjNP4ZOja7Me5DD49roYy8iDpMgMoAoHAAgEaSdWe4YlzIScbW9xz1Od43aNqz/Ku82f48X+Y47tfbPHMpWWeXW/DfNVYSLd7uI0TLJZNqFnr4oOcfv2fcvz3v40df5Wz7R0soTlam6NsNbB3VkAq9MwyPPx+OPkM8Dp0rZAKTfLl1zzAMD+PiQJqyJV51tFpQGaOIvIapCvJp/cYlraIgBn/hOyNziREKjmYshhzWvsHQfl9jjPIUXgjcwpx27vEjK4SuJI/eXiV37+nwuuf0eb5h28yUSzAmP5Hjkv7luLBzYdYrpSYrR4xWnGvw0AjdMXR5GvFymOavlwpjelBvYuselQAoYftwY7r8/5L2xyu+rzrQpWvf0KT0+17eWi7xGrP5gWHFSDMEZLAM0BZ5Bek0e+CboPVZmHmBFveCiWrhr29AY1D0bnZiZ1kMuPOm+tZ3ycJU/HvMV5wlRZ7IcQc8DfAJ4AvAm4FfhzjC+l7rkqnClGW1jft3iRK2/RMstooAnRkpadttNI02lmbvzQg3OTRUXkRRQ34xW+yuP3fbfB9zzQhRwPtUbGafM7xBmv9M/zFO5e4fWOF5kYP9YjgF1+wzNnPvMiLj27zhNltKnYTV1aGvhqEFfFtKSHM4ReThOe8e1diLuz1uOKVBsCLpMd/6zxrj6F1DjhasWgfBss2YREJ+fRFRTtQwApzpaO4ssxgjAkEX/NXayzN+vzv5wzC1xuBvR+2We/3uK1pAI9AebjCTfRb0/Y3mHGXEx2bBHoM8pBIm4bSgNLkXMuqO0UOu8Grkz3bQ5lJSoFkvrz7yT5MA5Yk60m7Pw0QIkBYYEXAYH0pVk0IF++ns7BM1ffp/+gv8CcvfzULZajas/TDLTb6ZzleO8H/fq7Lg9sPceLe9bHal35/k999+DCXXnaRzzwSclPdo+kGODJARlsfoSRSWmgSkVzi1nhi3NXqRMu8x7LVx15p8HscYIhzIeSYVc7gu2RgnWNhCYe5Uo1nHfIItU8vbOEpwQuf1OKhHZdNb4XPqUvCP/gD5E1LdP/wY1Tf9F/4tn85zxef9Hn2L/8Fh19aQyBpOoe4KLf5xEaZYzVJP2xz3u1Ss6tIb4XZU3fDhXugNs9Hul2esejGFN5pMmseODlpTheZP1kGAnuhq8OrrzSf3qOFx6QXmcyXtTCnDYC0OrPqnSZfVn+z7hVF59LviTA0sZuFha01774o+OZntnhuvQGtVajN8x/et8XH/1hz4iVrHJsLuK0J86Uy3cDjFnGRWfdQpCX0RtEQD3KjfaWtPK4U6HE5adrff68bGdtFl2rG10DQ5wmzAcuVW1ksn+UPHjrLplfnq2+fxxHO+G9il8yxqK0LUSQg1xwlsVzu2XyAo9U65e0NaB7eIxpfZM4WZb5FGHo871XXJH4jUAFeqbXeBv5aCNEEvl8I8SNR2jVEab9ZVtqA8ng1KZ959U7qUx5IMQk4ydNAp5URidyaHX+VpnNomOdX//DYqA6teO0rz4/1+3ZWhldSaZY+uc3bnUXC567SVwFPnlun4Sxgy5LRRQ3m5jShDgdC9pU67rJfXx5XCvTIMiU/SEA+jYaCfA9CCW6ZbU/xD+ervO/RJr/2eVXjADECBNb7Z3nDawQW8FW/6JOMuDJfOsZcSdML25StOpr47zMaezPuaFxGHUx8T4IQ8TxxTR+xtEn8NG/eZc3vrL5cdXqM8WrY23o6DXgRz1NEBi7SZta9/W3AfO3TnZ+jaTXQtuDsPyv+421vp/XKz0MgOVo9wav/bYvmxlpUokyD/q567nz/Bd6plul99iVefLTE7TN9qraiZIXG2kTArrCoY3N2kJZxrKWopce1ILum0UFF+JqCTFSchFVzDNBIpqd9jx87EhhLe0vb9MM2Z9oWf/PBGfqfEPzdDwloryOf9UT0Hc+m8sIv4LWvOGvq+Bmwvvvfj0XZuq15Gzc3fB5pPcyhyjyOhJozZ/ze/f0fI+64jY1SyKfXjyWOh0+Sf/JkrmS+PLAkbZ+dohQsBJhcE3RF+fQ+R3pSmMwDOwZpgnSmmKaxS9KkduLX026Q0gZQFiUH13h5jQYZafkQ4HX44Wcvs1A2Z7nD2ix/c2mNhRKcfNkOc42ASx3BWg8cqanYilAHaCFGXv/lBMF5L3QQxzGuNF3N/hxk25OAEK+D6LepyBqOLLNcPgVeh6Z7iKcudPn6JxzFaW8Z8CyNSnXW7A66sWSOlEjJExs3MdPHjCMVxBYqzfjcS9ssxjWKsHuOxMtkCcbJOZYl0Cf5RWJuDzaRB/03mT4XeFuCCb8Vw7A/s0gFl5+mAZGSaWl8r8jimfKOMvuTtfCngR+TKK/u5DozuhdosxGNgx276hASJfP7YPuK/rbFA5cczrZtVnvGOaVG7RKix7stxz/3Q0k+khQ+r4Uz5/tpc5Jl4ySgIn6/iJVkMnJXvIwwbg6XK7N87R0+b3rJGp7q8NYHtukEW+z4q8yVjvKtb/b4sd8Kecr8EbrBNvdv3z/ogKkGOXRmOrIKNcdfNRpf+/l9jNUVdazAdxLpafM1a8xmyVJp8lgyyw1ePU55wFCWLFukzvhn1r00sLmIjJyVZ5r0pHyRXTaMxn/TOQTCoh92uOVP/iNiec74yaGGePOvMffV5sjgJ595OLf3J+9Z45HTNR7clgRK4KvR72B49Ygn5IVM3UXXIoBxLdAUAIgQcgxcyls3ReyfFNboM+ZvxbUq3L3U5TtftsZ/+9ZVNvQGv3pRsHnqFGfbDyACn1/8/UV+8Dc6PHFO82jrEd55/pGxdqSwOFxZonbmQQ6Xb6Uka0hh0XnBC9lYnKMftobRf3bz2yyAMpk+LTicJlcl99HTgp/JKh//fHofUVrSNHJpn1nl0tKyGH4Rhp71stM2aVkDMrkYpD1HFoo2yuOrfuTxN0or1RFo7rDn6JddHCF56bGbeOmhLmtqlbJVx1NdvLDLeh/mS2ALl0B5OCk9vWboalh5TJPvoGivAvt+BP0h07Co2E1zVMUpI1B0A8k7zz/CC4+cjIWrguEYFBLcKgtWc3SrtWoc7jYXsRQmPK1bjZzkZm1yk2l518n0NACyaH1pG9drArG+E/i7eILW+lEhRCe696dXpVeZlKbRTb7bNMH3Sml5k7w52acsKtpW+rohUseoNtZ0A6fRCP7f7x8j1D79sM0v3NMh1PDR7xvNaWUJRAncUsiOD91ARkJQVrcnaAOz7hfVIh4kTaP5uxz8eJrnLeqrI6Eh3PU9DSRJUEBIJ9jifEdQsiz+09/CyUPQC1vUnXk2++f5sW8s829/MuCzjpRxZJmGs5aoJWXchgGOdBjooNb6Z1gonUjJO60mMZmeLJ9Xf5oSalpB/arRNcqr9wLkFs2ftoZmrb/xtKSsWxS0LtLP6ddtK1RYdnl4XbbqoEN6n/Eiat0+VGzs1/1bfkb3KD3hYThyhJ/7pGShBH/+ht1zt1+x2d5yaQdtWr6k5uzm0caCw4pd6yHPSO19lgXetaYgLEJ7tfLYpwWiQOwCOJKWNck0kbHODEIZ94IWj7Zc7t+yOFbTvOL75pj7NI/nLV/iWG2JtfAi3/j1JX7n/92GQDDr9lH6TKI9ScmqIU7dPUxrBxtorZmzlujIKh9cXeHpi0vGirMQJedBUqZN2wNn7WP3QteE/JxGV5RP7wPwmKQJnLQhykpLCtZFtIvJfHnARpyyNgDJz/EFQaNoBxtDL/9J2u1kJpqytXlKUb3b/kUEkgX7MLRWqSHAarI8uwgIQu1jCRuCtmGiccd3WUx1L0Ln1ThKchCgx5WiywF2FLmn1dAJqe1W6cmQrr9CoDxuqte4Y7bOnzxyhpefHJjhJ2iwgLWjc639DgDWxrkhIOJZ4I4VSgP5sgBKJuRJ1plXPpmWBTCmFDtYWhRCvC92/Qta61+IXc9hnColaSO6dw1Q1gYmD7AoUn5wL4tf5tWfBmqk5UkCFEUAlqJ1j/JYwk7ci8pau5dDISRn2qvcveRwqWfzR3ctUWl7lLoBO7NlFpe7LNcVsy6ULB1pngZaq1ifpgEt9nus5SCcl6bRfvh20XXmIGjQVprz0gEl30fe7xwrG2ofjaJkSR5tubzqyT0+76YZAL7xHS06v+LQnXe4tTmyvFuu3EoW+DgIiYwKQEsgwLId5kvHEnkzO5dxnccHkmlpdeXN5TwF1ITuHRw9Dnh1kqbdlORtiqaRv9Pqzcuftp4nx05O22FgfNgt3pyex0466wWEZaJk1KP0i/dTAvzDt/JLn1zh4R1492kbgberxrUjdeZn+ljC8GhLmHqHlgFagzCfqSFqh/xjn8daHk80aV3KXIPMWEk71pIkHTu2AjlgR+y3D7SH0hXKNrxrRXDry7b48edrZt1b+cq/WMP5RUENjzPt+1n+ubfhvuE/cFvz1nhtxMfGev8MJatGqAIUIZvhKp7q8sS5OqWeB9Uq6Tw3by86CdwYlE+bz3kyWLL+rPYz6PLw6muKT+8D8BhQHmqVlRanooBGPH9aepF2stqd1Ldx8EMgE2BH0XaF0ciHHkJKPrLW5olzPea1MH4W+i3jewGwpDQeqwdHD3TsL4v2eq55PyDDlY7YciXrv9r1htHiLepIYVGz5+iHbWzpcqFzjpefvD29XL8FdtmEzfI6422GgfGM7laNs7yxeZME92CycBPPk7YRTaPkApHHkK8oKr2qtf70CXkm7QKuMcrS9BV9jDxtb9r9ZB15G6z4/SxhIavPkzZdRbXYeb+DQGJxx8xt3DYT8uD2Q3zX6y7xgVXJ++6tM4PPkeNtbqrD8ZpPxTbhyQemtqb6HHB6cE9K46NpP+Dq5eCF0zqg2yvocRC8Nqvu+DPsB3SRBiRW4Q6WcChZPssVn64j6YU7/NTHNPe8+xDqSYLbn77BXGkUBSg5xjzVHSpFvLADVhXbKdNXHUqiTDfYphZFfknpYM51nvxVhD1l5Slaxw1ePR1lAQVFgI+sTU0RZUXW+p0lD08ro+fVDVgOLJ7aQ32aQPsEyqMsJOrv/hHn+Z/Gl92ygGtV+I37+rz7m1zu+5c57vig8be0fqiKfUpz801tajaULIUUGhmB3gNgI7m5LkzTgB0HqVw8aJ4/jZXHAfuWEoPfUKT78cgjrc3xUa0V/bCNrzQlS1G2JMeqULMVvbDF1/2txPlFn+25Mpee0eTE1n3wba9nFyvQig1/haazBMJipdun7nQoWy4b/YC5kk03CJlzm2jHRaTOX6ZMKyL7pu1Fs6gI+HjF6Jri0wcQlnZwDelMF4r/6FmgRNGFYZJQX7RPeahY0YXItO2pnvHgPjCX3rkI9UUawuXuQzVcbcOsY4AQ2zWOJtvrBugIog3vJMuOXV3YA/BxpZzPFanvah9t2a8AftBWIV4H17LBKWNHTLbhmCNvGuPvJe54aRgGtr1uIrc4ZbQQiMA3Y2xouj8O5I3P32GnEp2ZUrgZf0CKzcE0Df8or9ZXTV7dAGZT0mdIR6mvIcrT+BbZ0OQBxvH7eZumZNtZ3/PWl3jbkzZoad+zyguIjqPoKFQhwL1b93PHzG2Eyme5Msv//qjH+bM15pe6LNZDDpXhUFlTdxSzroMU9tDKg/jxlkGklknaweT1wMrjcmsRD9K5aNF15KCfJ+9oy16eb5A/Uj4oFForXFmhH4ZsejbbnuSehzSfOFdi4VSPesPjJ17comLFHOAmxprx7K9RqGFIW1/16IdtSk4tClmbNr7zwMp4epJ/Z80Vk9YJtihZtYTlU5IvJJ8lD7yMpdzg1TmUJttOAhfSZNO87/tRNk7KM41SJJmeVSZRZ2cDqnMoQiSSHX+VitUkWDyB/fJX8DfrW7y0dIxWsM4rTnX5pT+7mSd/8BzthsvGoSpby1VuObRJ04FbmgopwJEG6EhzTIqWQysPiFmHjR1vESZNMtr8Xy9WHtOAHROsPADSjrUkKdWJbPRbD8COQHsEykNpwWI54P5tm7Md2Ghb/Ie/WqL/V5paWbN2pM7v/puHYSuy1AlNNEQjMwsQFjPuMgLBlrfCzY1lHFlCown1IyyVT8VChhelrHmSl3/E67f9S1Tt2SnbTNZTIPfV4dVXlE/vUcJJQ6jyNjKTfsgkYJKsIw8ByxPUBbv7ltWnNO1lVr4iZNpt+WtmoluO0byXm2bTaZdwZYVL/lm+8V3n+aMzq+zQ5v7tBwirTaOhTzKLaelKMeAsJrhfZ3UHma9IPUWc2u2nP1n3stqNb3SiMLOys43YucS8asDWecTOJWMSHSdhGcuOfsvEtu9sIrwuq4GxHqLfiqL/jBXKuc6bRxMo9MFrkydw706bxE+uGn0Sc65wSEKIE0AtuncNUR5YO0kQnlRnGjCWl3dAWRuyeJ/S0uNpaXUk141knfGNYhYwE10LycicWXPHzMjcteEscPo3Koi/8nn0wSbtAMoWzJZCKrbCli62dEeawrRjFGmfyftp96alg3JeejnOqR8En82rO/ldpaTlWd4kHa5F1xLjIM9XPUqWKR8owfkO1Bse1ZqP17d4y30ul3oP46veoNFBRdH/5jNU/vC+I0tGq6hDhE6Thwb17GXejj1g4rtg27+Ir5IRLrJAw0nA5jVDjxFenbfuQvF1s2j9Re8VLT+NUrCosiSWb+3hIdjRVx3KVh3XqpjNX6XJS7rnuOez/g/lX/19lncUi6d3AKjteFTaPuVKwNxCn1kXGk5I3TGOJgcb7cGxivimO2/jt+sYRjKYwLV0HHtaKnJM5QBJpGw/86xstFbDPxi9s8HnwJ/GpmdTtkBpCHzJuUfr9Grm3s2fWOVVv3SKd+oqF7r3sxWug1sba0ci8VSXbX8LT3XohS2UDlk+vw46xAs7KHJ8daU8abG5tlvmCXXAh1a7xhLw8UlXlE/vcXYW1QRmCbNxig+GLC1kMi1Zd9qCnPw+SfDOEuLTFqS8PLH+6ZC50tHRrVIDXWmwE2wQaA/QzLjLfP8zd/j9T7q860KXli/phtsHF5VlWudvB1VXkTIH1d5+gZWDBE0OqkyaAz2ljOVPGEDQA7fKRimEMEgwYG0As8YhcywqokUxb6yGKk0DwGXSNGDDbsBxEKmiF7agu2msllLnedq8z5pvo7+BZ/WD/itAfwl8jhCiEUt7NdAF/qFIBVeGimj2ssplaeDi5YqAzGllsvLH7yfbL6qFHJRJ5k/RGqa2T8Rv00H0133JKq99xVkWLrQpt83GdmfHzKGKpWi6IY4sDzezZjyJ9A10GskcoVlmACRxOqhjIQdBV1vTmQcs592P54uvCzriuzFS2rznXiio2VBzNVJqPvuuNp9zokvdmceWLmowDkxFY3UYSw9jBRrqgL7qxMZKnOft6mDse5HNZRL0G93/+/OPcrhyO2VrXOjf3V6Rje3uzfoNXr0XmvR+r5QCYFpw6yDBsAiMOP7UIU8tySoVWUNeehi6W4BAHH0Cd/7EU3lR77V87g8dYjkCPACqO8ZCetbVHKoQ8Wiz0VaEsY1zNK70aGxpPRi7euzYxBilgKKPW9or2JEJvI+PkWS0lmH2HF4dzz8eIhZ8BVJAr2tTrQd0a8Y64uwXLPAln7nK4apP3ZlnZn3d7MXGFIACW7gsV05gC5dOYICP9WNHCVFU7CYyphRJp73Mgeh5gj7oEFqX+K73XuIFR45HloDTtDVpP7w7//XApw/IwiP+42ZpBpILbtpinsXcswCKePuTBOVkH9PqmLafWUKJjuWUEJuMgfJwrQq/84BxauZqm4o1w7F5nw+vWRyuBiZU3UEfH7kceeO0V4Z4tUCPgwY6JgE7aRrWLKuO+G8ppDl+YrtQroM1MmvTlQZzYg5UYGLKDyjwYPtCFJklMKCH5Zq6bHdsPGZ0mME86QSbrPfPTMgflenvQGfDnIcNQ8qyAvXFRHuT5nK8/bhGc48WJgdLPwf0gT8QQrxUCPENwPcD//ug44Xvj+J8OM3yIS9/Wp5JG5tkXUVA43j+PJoGLE/jwzlWI1qRPw7N9c/dc24sdWPZHBlzSyGOhbHuENYuoWtUTexoRJqVR5aG8LFs5XElQI88q7wkuJEVgaXI9cCHlt/FFi6WdIwfgGjsbHqwtuVw8XyVV98aMlcy4LNA0g/bKR0XeKpHO9gEwJVlLGHjyDI6czxm8cAi4IfJ1w42UdG/bmjY1WcduSm1f+NzJWsOZss91wA9Rnj1gOK8qqhVRrxMUbChqAVJ2tpRtO48sDzte7ocPXAQaiwBojp7Lag06f7QzwMQVuq8uXwnt/7txTGwA0x0FrcU0nCgbGlK1u7+GyAj+huCG9m8a2QVEnvGpO+LxzLwkeT1SXn0IOpMUBrQMbw34beMBarFkSUqtkVJKixB5JwWOi2b+laPUi/gh7/oAoerEGpB3Zplfa5hwiDHHJYH2uNc5yE+tXmeUAc0nEWq9iwVqxEBM7HxOElZv3kWTn8w9xkGZS9072PLWwHLZtO/iKrP8yPPPhwDV7Lkpqx98zRy1lWjK8qn9zkzk4yrKBod38RMqnNQR1r9ScY8WXgtdp0FoCQXlZyNmNcxG73BuXAU9HdwZJmSrPEVtx0FBMqS/OslY3L3rEMBdWcBW0Z+Fq62piyNrsbRmoMEPQ7anHovgn8RoAOGZ8cBEJIQNabtFVob6wlnPCqQth2z6Jab5kiLinx2DE33sjWHrWCNXtjGV336qk3ZbpiwcJ0NaF3CU10zlrfO46kuZzv3Qb9lNJkbZ2DnIpbvjSxRxtpLAy8maf53M219mf5NIq31BvASwMKEy/oB4E3A900sfEUpj+fG72cJoWlAdZYwm9yUpdWXBT6LlL+sfmS1WYAX7+pj1B+RJkjsbvtd/238jpKC2fk+81VF0zHHGoQQWMJJCGixvk8CMoR8fFl5HHRdk+rO4+tZoEdRMGQAeghJqAO0VjjS+G3phRBqsB1Fa8dFa0XNnqdsGYVVxWqQHMPG+m0HW7pGuxi1Yxzepo2f+IYzGxAZaKRBcLH3IKEOAEE72KQb7rDjr9IJNvHCbnQePFlXmrIoKWOlt521Sb/Bq9Moi6cV2aBkvbN4WlYdadd5vDSPivQvq19pAIoYS++rDmamDOZgSC9sG+vU+iKVN34HoLFCxW/8+nxqi9vzFZozHr4CW+roGNru5xrXSutRWoqVx65nyjuWeIPSKcZn4/xuYOUxWEOLOpEd5FOEdIMQpQVlCxZKoJQgCCS+a5QRUmjunO2xVLbRQjBfOm4UzDF+ZwuXc22XozWHTrCF0kahWLVnxxWLUf7BkwBw6QHYWTEWSO11o/BrHKIdbLDpnY8skzR/efo0+F30B/6Gle6D9MIWv/tAjQ+u9dnwV7CEEwtxP0mmyQMT43Jb/py+Hvj0AURpyQIc0phpHgKcVmYS083T/sX7U6R/WfknodbpfezboMPW0FR027vEjLs41sa9W/ezVKny9AXNSw/N0RF9E/858MejsxwETeO0ba8O7PI8PefVWaS9on3Kync1BPlpwI40UgpEBHyV6nSDdRynTMk9jK99Htl5iG4gafmSO2YtFkongGgRmY2OUklpIgAhjKmcb47CpIXgxOtQ9xTdiuKNH1zn6QuaLz6+SLXnG+YtJG57ffgbu/0Wx6pz0FlFtiNNdXUWz5YEqj90yBd7eEZzJg/0nDTvrx5prT8BvPhq92MyJX/nItrDJKXx1yJa3SLl0vqUd52licxKK7o2ZJU1FGifH/61Fj/64TqbP6Lo1hzWj9W5pbbJQglmXEXFVlgxh2IDjdOuNuKO7pI8Ks4X4g7wrhVnpdNGbJm2/qJUFGBO+w2FHA9VC9kgSLw+aZsylg3KH7PkOdUIaDg2dy/5fPIjinbgEWiPslXnNx84x1fcdiRWmRmTAknTORRvJPG566FMV1F4YdcA0MCfPHKG9b7k9qbPrTN9QNAPoWa7rHRgpXOaO2ePUrbqbHkruLJCqALq7kJGW3lzNG8+74W3XH66dnl1UoGXxqOzfsu8MiTuJb9PA45M6neWEjKrH3l17R53pW4PVAvZWDb3V+6ldPgJY/Xpv/9jxJOexNv++xFe+MNHeNK/nh/e81yLncUyJ2ZbNBx2WXcI5K4NtQlPq1DaHJEYODFFKAYOTPUQ5BgPXbsr0lYW3z5ofng56IB9dQzr3LU3MO/f/I4jQEkkgKSBBcju96WGn0qHKB0ihcaWmjB63SePdag3PO54ccA/v7nOQvkiaz2bhfIJHth+kFv/5UPojRbyuU+Bm57BYDw++9ApAHphK+K3mlAH+KqPI0tDfyHxcdwNt9mqhxz2FOde+wscfeu38bp3XuRYXXCo0qVswVqvzeFqm4+sW/zjhQ3+01NuQ2uPfzi/wbGaZNYNmAsaxs/e3OKu32r39TRz7NrizwO6knz6AGZeFtI/Ldix13t5AEly4cjrX/x+2sKfzJffp5KsEeiRI7Cmu8TgjLiv+hD6vPE9TUqyiiPLdKWPI8tYyJFW/mrSfnxvHHR707Ybz3clj67k5UtLyzMXjC+c5SbdcIe2v8lG/yz4Pe7dOsuv3VvFV4K+SgvnFY3lUt1YWnQ2TLJTToAdMU2LVtDdprK9xfc/8yh3zXfZVOvmOIzljp5BKVOnCqCzOYooFB2bcWQp48xh1vwjJW+6MKRhzHnVQf49Pqiohm8SkDv4LLJIpmnvsu4n28i6l6epyNYMZvch2W4embo0mn++cJ7//PfzfOSPZgHYni+zeKjLwmKPQxUT5tCRAw2gStSRQWnWGoOjLXlWHnuhScda0miauXDQ9eXVsdd+xcGMAf+K894ssCNZPgI6NBopzFGmi12bf1qBX/2BMk945wW+7geP4SvNlrfC278jWV9yTMfG6y4n0nEyYz9QHh9eW8VTXUDT8iV//52KH/ofs/zIh+s8suMQauirPicbdTb6Fju+OTpbtWeZLx1nxl1O6Ueyb8n+7Y1u8Oo0SuN7eVrYSe8grUxxre6ojSzZN1lnskwar07eT5ZL62fid6nOQj/mqHH5jhHA0N0i1D4f+s4HeWfo8urfVWNgB8DWYoXFQ10q1ZCKHTmwVCICOgS7o34k5IzBZjpxvGVg7TG6J3bz6f3y7cf0+M6hNOfRsfEQt+6Ip8FusCO1+sgiIlACR0LThZV1l098eJH7/rti6VyLf/tfD7PasxGXHuLb3zAHno/uBehg3IfHgMpWnUB7xroIsKUbAzvG8/fCFm/8YJ1HrBZHf/vbuX/nIfyfCHn4hxR/9X/KvPuCoBfC2TZ8xmHFI5dc/umCQycQLFd8nn+4x63NppHZ544nfp80uWe8r/nX+XzgcvHqa432YOGRZ7kxoCSilIZKT4M6TWozrb691h3v7/6Qsbq9EKtxNGFt6dILO/zKyxb40OqjPHFuwfj2sKOjCZdL2zcturwXNHqvVh4HTQcJcuw3X9q9PBR9kH9w5j/oYTsujizxr5c0f7C9xUM7Fb70Zo9nLN6cVgHDsdtvmSS3CnYpkSc2tnU42nSVm6BDTtaP47S3oLc6OqISH4uDvsY3a5XZ2FjPEpLS0oow6msTob62KPkbJ/ltlgCaJ+zGKU+IzVsLsq7zBOw00COvriywpUgbu4EigeR5h4/w14dWce41ETecfkhjyedIxYQ37IeSTiCpOX3sYBvLcUbHBdL4+OBzEG42fm+sayl8v0iI2r3w2KJlsnh7kfJ75f3T8t94G2nrZvI9JPOmvYcBhb4JWanNGfHNvkXZ0pyqC773Z7tUrAavf9U63/tV1Umdjf4381HpEGssTHhyTJprKSxun7FxZQXQfMVtR3n5766x1rvIx9Yr3NxUHFKLBJUaK92H8VSZj62HzJVOE2pYKJ/jRO2O+MORPTfSKMlLkmk3aDIVkVPzFISTfvci7yLtHZL4nlf3JAVm1v28dhMkLIjLNnH/SG4Vq7XOp/3zN6P/8S/4teVnj7lsbzdc1o7UWXK69PuSjX5Iw7Go2Q4Vu0855gcti3TEv4WQQ8sPkLFfZ3eoVNPPa9A671qijDVEINEohtYdsd8tDnaMnILr6N2oKI8YAhG+FlgCznegWvM5dKTD/34rzPslPve7Nd/7O0f54abPsdU1xBd8OVbGnNGo6EhLSNWeiQEdu+evIsSRJT7vRIfF8iIKxW3OEt/9q5f4i0er/O1HHZ55qMerbgk53fL56zNlel2bP7xX8S+z5hTAHTPwtXfMgHBIl9WSbU+6d4MvJ2mfR1rymHHeDz8ts94LilV0UEwaENNuuCYvZgIxNEs1G1bN+9Yf5tOXFsHvjrJOEor3QgfFJPcjwF6Joy37pYMAOtLuTxPCN4yONXkKpyeZbx7hSXMP0A+rfP5NXU7Wb2e4sVIqFnnFnOcW/bY5iiJtqKWdcxW879JDPG3hCI4SUV5pQJL2Oo7tjoewTW4QFGDbURQW6DXnKO+K/pIFbmYBHbt+mF3li5wNvL4pexOfnpZFkzR0eW3Hr/P6kVVf1mKf7Fu8njTgpchYye6DLRx+8NOP8lrOoKSgNVtmzm7RDWGzD2VL0vYtqrbCkV2jZUrdA2QAFfEN+IAGx17i9w+SLgefPUi+vB8LkyTIFE9L+y2LgB5geKAMEJapUwqb5arPzU2PzzhSoWRVqdoz3Pl9Hf7wb5d5y+se4uGd+MZqN0ggkHSCTarKwbMDXFlOe8DhN1/1mHePmLQwMCEVgUOVZT596RKH/DqIADsIOVy5mbJ1gbqzgCUcAuUNo8KMg5/TAJ27+zSp7A1enUaTZOE8peC0Cr04ZQEcecAHKXmnkZnzFIqT0jLuWY6JQAeIz3o5v4TgtT85cqwe2hKnH7K96VKuBGzWuzT70HBsjoY+JUshsHYBFoMjLCKWrLWKHtscbdE6tiGPH22JH1e8QfkUP1Y4jEglhqAHMAQz0sCOwffhEZjIn50UFk6UvWorbqpLnjqvmbttnfMdn9m5I3zTG87zLxfbvPHuw7z5+SaziiL0WMIe1o4O6ak2D25vcGtznpa/HlnHRfcB0Og/+g3E57wcVa5wrr3Bsw6V6Idt+mGb+3YCNr0ST18MsJ66w5ffaqERHKl6fPbxHoer8PSFHkuVkE4At7iHId6HqI1smrSXnY5XXA+8eg8SyiSN2aSykL3QFll887SURdKyEOmscrpAnni/8geqip1Viy80T5htcKF7HyNteQyLSnr23y/tR6AsQvth+kXau1yo+cDkuWgfJll1FAE78urRCiybXmOGD/g9fu6TKzhSsNKVnKzfZvKE5jhJj77xNj1YBBDmuIm0jeMkp0JyLPuqx7mOg6MtPKkMcKGUATm8jik/sOpI9tGKzrWrAEp1wtnDuNa481SinmSDHaO+jH+Pz/E8TdIN2k3J3zmNn03DL7PqTrtOS49vrpJpWVRE+E3jy3HKes5BHdMI7fDGt3Q4+V8E3cMuva5NJwBPER0hEPRDw5tD7Q+Ftl0+mKbhW8mw5PFIL5OoKMiaVyYrrYhlWt79rHb2wnuLthcHMvK+D/7iR15S2hFC0vKNczyBRc2eG1pzfvNdJd7zBovbmrfy0mM3merRsbDh0djXCvwuZbsBTnkMjAh1QKB9AC71HsFXfTzVM0dfVZsL3fsh6DFDnRnRpGI1WK7cAmGAri+AU8FSsGAdoiRr2MKlbNUigV6z2nskRQaZ+KMXyPv4F5gvD+UBAEUBqbS6kmtrkndmWWFMoml49ySLkWkAdQ2hjwkEIIjz8Z/4bYtnvdHwRscLKXV9Al+ilKAdQC80Dit9JcaOrwz8LYkI5RBCDPl32gYwfqQlk+KRty63P7nHOg1/i4HcOgpPmwZ2mPtymG/o6DRS8oUaJJpzHYmvYK4U8NJjJ3jSP72X9r//X7y85vM9v/ErCCSvu/MoihBf9bC6LfB7DKzuEJJAeRyuSsp2w7gkiMhXPXOssN+C48u0HYUtHBbKJU63fN76gESjuaUJT1/wuLnR5/NPtrGkzflOhy1Pc7ga8rLjXZpuSMNZ5JbS0eGe72zn3pEMkTqf0gDKPLohOw9ojxYeeZuXIlrcLEZbRBAdlIu3tVdLkbT05LOkoe1Zz5u1oIzIVz0T2i70x44YlK06H1tv0VjoUBv4S4BxK4+DZJQHoZHLq+NyH21JM2HeTz0HlTcrz15CO0obpE2ZEk+ZP8Izql2C8iI3Ny6w5V1kxpqHoAdeh3K5aUAIrSAMCC2J1TgEKqBNhwp25GFam3C1tXl81ePmhocnAtww6o+U4KVECEpqTiuzkSM/F1Vt4oVtKqFljs6YTLHCWSBjcr4XATcKCBzXNU2rrZ1GoC4qnBYBh4uWT/YvD8iedsM1SXsy+n6sejvndla4/V0rrMzOUW96dEqKTgDbnkXTCcfPrSaBjrwxmzSDTpafdOTlWqKi1iN7qTeP0vhrfP2ZZGY+SEuutYpIc2t4osAiVD79UPJoy+V9lyxedcsF7pi5FYC6Pc9g7HiqSzfYRqOo2MZfF+ih4zucClIPQtiaORlqnx1/FUs4lK063/u+Er6/yQ8/t897Llrc0uzzxNnbTESsMBha1w18fg0Ef2E57PirVLUJjVu1Z4aPuli+KfbgRcDHIoqp9Hl5g1fvhZL8L2s9zNPcZoHVk2TXSQDFNH1Iaz+ZNglUT9zrbYNbxbcETswiajasslQ2fhYqbZ9K22e7XaHTctjZcWiXfZQWBMq0KcRup6UiR/+btP4YHHkpZOVxOXn1Y/FYC4zvD1IsPUgJDZyMYDUAq5QAgUZiYQnohpK2Dx9fF7zlr4/Al57mpZ/9BVj/XwjKwX3yIly4h/biYc531tjsW9w2U2bWmUMT0gm2qIU2ZadO2aojVx81QQDsEr7q8dH185xs2CyoJr/SuItf+4UqP/PlD/JjH6jzhAWLr7y9RcU6hCUdfNWjYofYwkWjcaRGabPfO9vucLzWNLy/u02/0aSE4Fj1jl3PHn/uywNgXB+8+gBmShqDjG/4pxFu8+5nMca08lna42nQ5CSSNkk4Ti4eKYtFf4eSrBpNi5ARUm3q/YZ3bPHBNZd7NjaN88c4xbV9g88raTo3yZLhatZZVCu433J71V5CuuPANEAhzYrCsqHfwunsgFtl27vIu1ds1vrbBuxorxvwQQWwc9EIwd1NrLUzBthorVLz5SiWt298EbB5jioVnjJ/iwmDDKbs4H7Wswtp+uR1ho5LJRYlqxaBHZMENCbkuUH7oywQKU5xa7T9aGWLWGIk2522njgwNilfvL5JeSdtCMZ/w4u9hwh/MkAqzdZZF69v0Q1how87vqAVWAQ6JNBeLJxcWtMTgI8iaXuhIs5LD8LKY1DmIISnIpYceY6fk85J0+qMf09zYqrVWHqgPWOxoWGtZ3GpZwCvXtjGUz3W+2dpBevD5vqqHYEdNbrhDiCQwsJTPbrhNsPIMRGt988y6x7Bli5CSH76+Yv8t7t3WLQP85xlzVwpNA6igyj0t9cxf1pBbZ6znftY7T3K35x9lNf+ic0HV89gy7jvpl0/cvRZdOOclXaDDo7i/C4L7J3EXzNk0NTyRd9nGh/OksOLWhAVlwN6YQtqxoJpGEUlaqPz3T/NH/zXEa90+iGlbsD2psv2ZolNz/DpTiDRjPNn48Q0bk0w+p606IiHqZ1IaRZ61ytNsnhOsfSIU2q4bq0YAB8D2vYkgRL4CrY3S7Cu+di6zQV13oTpdqv4D22iH34YX/X4yFqFO2ZrlKwap9v3EiqfXtji3t4KD+2cYdu/hDd/eKic/uTmOW6faXCx69F2FF97xxL/9zVnedLczXzlnX1qNrjSwo4iubiyaiJvCknLX+f3HqzyzytVvuntLj/69SV+4Z4QKcyRcxMed9KedC/z9gYNaB+zsAjqn7yfVn6StiBP65tmUZJHWZYpRSiNsae1rUll9oEX2xAClk1f94bX/+eFgvevGK2RL8LoyIB9eZnm5Ub0LvfRlmT+aU2cD6LeSUDHXjcaYEyUSzW8chlVn6dFizd9VPLkOc0tjdsNMFaZNXndqgErBrG/a/PG03ll1sSuj8bZI/0zJs3rwNrDsPIpZGfbgCWdTTNOs8zwB6aaShlhu9I094M+cvOCKZtL8TmU/JtEOvbt8vx7fFAczEijPAu15H1S7sfzZIHN0wIp0/DiSWDGoO084V+k/CX7Mt4nYx1l6OiDm6yuVFnvC3Z8aAcQKGj7Ei/smihcyYgraSRjedI8/ScjtuznWMt+aC+gx6DcXvltEYuOadaXLPAjWVfSQifeH2njqx6rvZCWb3G4GvCcQ/CXp0v81Me2+ZeVi3zba+Arf88BBK6scKh8CyVZGx5p0Ziz4q4sU7EaKNRY1Kyl0gnwjKa6F+7QDbc5Wj0BXoeFruSw14DNcxBGgEdkeULzMG3p0fYlS3KJZx+q8vV3t/jOPzrMJzbOD6MMFKMilnZp83B3/hu8er80iVdPC4JMaiMrb5qMnpY/CaBNku+LyACaQHuUrOownyUcRHuDwe9i/fi3sPrSkRVTc6NHfatHuCPotB16oWCzD+t9i21PjlnjaW0OnY1aK8ZXBsCHGZGxMsnjiAMqejR9L06nr1VK8vIpQY/BXyxTej1AqAO2PYtPbko2PDi01OPFX7jKhy9afOc/zvKpzR1e+4qz/OU3fBXiOf8fdWeBz72pQcVqYguXlm+x7V9irnSUm+qHOdU4hisrkZNoQ3d94hM0qLHtWfzrpR3+8cJ5jtYcemGL5y43+Lo7S8y4y1jCNvYnwkIg2fIu8KE1l7U+fMFNPq97Rpv575R8/PsVf/rINo/0HiXQ3vgzps6JIvN5b3Q98Om9eJ2MPrME4Un5stKLaCUn5ctbEIpqB/Pqy6L44p+BftslRp6mTboxcTXkqz7VkmKtL+irNthlsN1sxllE6L3WaT9WE9OUuRxC97RAx6BMkb6CEYKd8hC93uyf5+2ne3zZLT2abogiZDW4wKrYBLtEiBq3DHKrZpzEwA6Ak55rgI7o6AtaGQG61yr+PqQ0oIrfQzeW2NE7ph0r74RckolnbTjTyjCWx2hZDv7v8UmTwORJguw0lhPx8pN4YvI6ayGPAxh5lhnxvFl9yqpv0jNrznfMhvX8y+cpt322HnFZOVdl04O2b6K1tHyLQHt4qmM2sjACKqbxw1Q0zGFeXcl7l2O9mAZ0yOOp04Iceb6Qsv6y6kkTuNOsPwBsFy/sYgnNQzsu771oc7oFSxXY9uGXX2/W9z9+TXNX9yQWbX8jJgSa+WGAtNEY7oQ7dKRPpdun3oeK1cTRlgGoLdsAHYMN1dxxA2yX6tDdxJYuh6tNOtJnvb/BD/6/w3zGMza4a/740EdBNsU31GkCd3IupwGru+feDV69V5rWQiIrf5YFRrKdeN68duLXaW0nx0daH7LA5+y2beHENr5RPbWFYbmWv86xP1vj1T85ep65lQ7VHY9Oy6bbsdj0oOVbbPTtoaVWEqwY+PcYB0AGwIYagiPpmzkxmXcfNNgx7OQ1NCcm8fMpQI/YjZQ0k38wLkLts9a3qdhw35kKH/nQAveeLXOkGXLbYsD//QbjUP/lf/1nEAZYD32Q8ulP4cgyThByvmPuC8TQp1LDWRxr7hNPfiLvXl/hSXN1Pm2hxHLFx1c9lA4JtY8tSsPxsdY/zY6/Si/coWLNULcVzz2k+cSGifC1/sOKjVc3edWSRdMd+Fgqomza9SOkpE1P1wOf3qPT0gElmWyeWVsewy0CLExi7JME1+T3POQ8q59597SJsBL6KfcnIetgCZt7PzXL+1cFH1/foS+8cUF5WOSAHZgWpb0eQSli/lyk7b0CHwdpzTEpzyRhPJmW1wZEAILAFS5Kh9xU97i5OcPdSyXkxjkW9SyLYj7aZHVh5rD5ExbKksZRqVZR5B/Nev8Ml6qa7sLy0Mv5mGZzcFxlcOZUKwOcOGUjVEt7BKQ4ZbBcfNWn5sxBqcFY6LhdlNSaX3vo7+OL4r91Fviwl3cwSRuXtiHK47FFNZSTyk/m4bpw3t1Ce80xgMeRP1nH9UIOP7xFr2vTCwVKQyeQtHxJyxd4YZd+2EYLUQxoSFpyZN2L349bhuxlLThILeK01hZ5YERW3ftxlprWZlLrmAZ2xO8rBb5xHLrtGT7XC+ETqxZHq4p3v9WAHD/w6100ih1/lb5qjzmSXiyfjFkKJeUVM+befqbNP69ss+kanxzW1kVzNNGyaVdcVqug50+YIu11LlgbBmxuHCJQHk1niaqocbh6lMCWPPuQOT8eV66kUxpYmCfrJdNv0MFRkncPPovw3SweOk36tJQGhqUBIsnxND4HemErpuXOa2N3n6Ww+PjzjvLr3z5SurheiNMPUMpYeWx4sBZZeWz0pXEwnWHlYXqYv2kbWndotXttSR5D3wvQPS1dTdBjWnm7MOiRIm/ELfOifAKBLV3qtuJ8x6R6fYvTH25QseAjP2HGxY/+VoD9xS/mfetnWDt6mJ1jJ41FXXebzzp6jIXSCQQCK1S4soJGGdka4PQH6QaSbd/i0dYWADfV56k784Ta532Xevzug9ucbl1Ea0XDWeT3HoRAefTCFr4WvOBInyfMBrz4mHnXr/70FlRmmXOPxAC9aWXk/e5frx/ag9PSNGZM7HqSJUZcAC4KPKTdy2Ku8TJZ7R2ENUmUJz75Ag+Cbfo1E3LWnMlKo93PM+se4ZdffS//eL6OI2N1J48U7Kpqn0xusNG9mlS0D5err0UF5jyaJtzspDQhozPaCl2qEWifklXjpsY2derQ3zT3W6sgJPbCTXSVB1Y0xjsbnFarlCw4rBdZldv83v0ObV+yUHZ59qFL3DFzE1Znc+SzYwByWGVwI9CjvT5arCtNaB4Cu4y2LHzVx91apWc3qIpZZCrYkRznk+ZdkpICkvGwfj04VzoYivO9ZFoeZfHmtHqS9/JA7/hnsr68OrLSiuQZPYNG0/JXd2ltdvdhNwhzS+M2fvUP4bWvOAsYk+lLO018X9AONO0AdnyLzb6iZPWxZAspLLPRjPOsAQ9LG8PJ9EnXybIHQXk8dhL/jfPA/VqU7DWyzDSUZWqdFKhllOb3qFXnOFFv4SuPk3X4cNXlp/9unm7N5vTnLVCzL7LlrVCz5xBCIoUVOSktMyYzCAkIAu0Ran8oK/zpQxbhT2re8s2SN7+4iRVUIfDQlQY1r8uHNuF0+wKvOXoYQg9LaC6qFWbVkcivgUZYNiUc/upbH6HhLrJ7Lqf+GNFnch5mAaRJWS/FuuMGr86hPNl3soIsvZ6stGnbSaOs/haRldPyiihlBBr4qsdDOys80Zk1c6S+lKgn+1nn3CO89yV/y9f9851jd0vdAN3GAB5tj0tuSNmyqNk2Jctjxh2Y3huHxGDG7WSLqN2k485LB+M+63uSDjoC45WQ6S/n3E5ba9LaUwos44DWFiVONdd46ryDr7rcfrzLpgfv/cUKjX6PRz5nkdXeWR4RNk03ZNsPqGoPxy1T8TpDP3fdcIfyB94NZZetO+9kdqvDzve/Fa9rMfsTX8r//QaHjz/vZv7g9ffjSgtHlljt9XjKvM1bPiV563sO83tf7tEP28y68FenJS89LrhrTjNbOo6tBHgdPv/H27zqkBM5nM7ys5SnNJoGyMwfz9cLr96HhUfe4le0jiyQQie+FwFCsrQTeYMi2U687ASN4gCMCDyzMfQ6ZlNolyltb1LSbkbZ7Oe5Z6PMbc0+C+UAW0THWQZ+POLnvR9rdJDC614sNybVVSRfFk1j1ZFWV571hwoQ7Q0c36cROCyVTxlfG72WuW+7UDKau0dbW0Y74nWgOsc7ztX4lrcv8AcrHf7wYYfnL/d4w1OP8NqTDW5rHjN+BuqLxs+HlKPjU7ZrrDjKTVg4ZbxTzxyG+hIdK+Sif5qN/jncnU3QimZQMmPVdDz2mTdvD0KrdIOKURZ4MInHJTf/k8oMKI23p2nnksLvJEuPpKYzS6s4ThoV+U8QSKSxRsqsO+u3GtX9lB8Y8WDrbMjF8zW2faLz4ZK1vs1az6YftvDCrik30PYlj7bkaf7yTKGvJkBdlGcWsczIKzep/Szeute/5LPlOJqu2A1ubno4UnOorHnuUzd58Reu0usYU/lZaxGn38eONlDtYIOxsRV7f//tfav80Ae2CLVPoH2+9s4+s/9Zsn6pwne85xJ/c/ESbUfxF4+eI3Rcnr9wiJce8/n+T7RZsbb580dd/ut7Kmx65/n4+hr9sEOoA1rBGg/vKBxZjvnvyAI7skCNeFrefL2hPSxOaWtjkv/kAb9JyuOVyXbz3vMkkDlNaTn4ntb/LPDZlB1YT/TDDlveCv2wTcNZ5HhthnW7i6rP5/Q/be8hCD/wIN/5K/2xUvMXzbGW7U2XzbUyZzuCs21Y6Tqs9+yhlYcBXUZWHmYDqKPW4pYck461TKA8q+2DpMu1eT1o+TtOef7uJuQd+MsAOF7zeGo0fPqeZPWOBvd9+jIn37bKx9YrnGpIjlRnmSvVKFt1trwV9Ic/BP0WnupSsRr0fu+D/MOXvo9euMP2bIP6lz+Nxpu/hd97sMrKF8/htgJe8yOn+J73VvnYesh3vm2JCx3Fm14g+e6XrvHin6zykTWPP7vf5pd/rkk3CDnTDrEvPQKX7ufjvVXecR52SoKOTDsVYJ4qHezIkq+n2YNfn7THsLSQzfTi9/ZisRH/PkkbOanMNEBJEYAkZbFXgTH3d4z5kypXsfotEz6rNp/Rx3R6+oLmL08bpG+hvE1d1qOm1cg5mZTjEZuuFVSuiPZvEmAzrQXHXtDsaX6vSUDHNOWKpsVpEHKwswlCIrQygEQQHXdyysYBqZDcMXMMe+M81BdZ6T7AC48oXnvHEbY8nxnRNH46LnwSLBfHreJUmsZCBIbhb3Gk8etRsk0EmEEbTtmcUyTElRVm3cNgeTBzhHikofR5V8SioAiKLWIpNwTs6ShNAzvpNyxiZZEmmBdNm1RvWp/TtM95Gy8RjZxRPqPFMd8v9h7CVwHHqrcn6kr/vX7mE+f56PeN5uzC+RZnFueZne8x5/pYAhxp4UiNJcCqtqjYzeHGd9StFMsNyYinJO/HQ5PH78XTp6GDELCn4dXJPibXgf0C4gctfCeP+cFwoyIQBlDwJb4S2FLzqlt6nGm7WC+4xF+drvFlt7TBAeOiXBh+iSDUPpaCj26d5ilzJ0FYrPcFP/eCw/iqj0bzgiPHCfRZ3l9t8QUnjb+Qf720w5Zn8wcPXeLWZp93nCvxwCWHP3kE/u7+Emt/5/IdfYtHH2zSaTv83bdsY4sST5htIBC0/DXK1sDadFpeUDRvhpXHDV6doKLybPK6yGZmWo1v3rqb1tYk2bqoDD8+Vur2vPGZEPo0dIUN4dEJNrnQ2aAbSJ4yf0tGe+P0ey/6Av7q63bzgtlLHc7MzLFpl3BLIY7sUbagajssVztYthP1SIGWIEZ+ISZZe+i4xV5WiNo0y46xMimUZ2VXlCblLcq/r+QeI7lPyLLqGJA0lnIahdIhlhB0Q8mmBxULpNR80Ysuct+G5CMcZe1f2tz82avYcpvbZ+YoW3Vm3SPwGYe5oC6wKE6y5V2k+QVP4oU/8vl89V9s8Guf2+SBp95Ja+M8X31HwNkO3KtmecHTNpECfumjFVo7Lv/5T5d52hO3+NcPH+LUe1f54aUFeHfA8XObfNnP30Jl1cPtB/zO/2pxDHjj3ZqaM8fZ9gNU7dmMHyQp32QpnYrI2fl0PfDqfQAekC6Axr9nMdQ04TKPgWZpF7KYaprgmgU8pGkMk8J0CqAjLHPfreJpDydidFaUNkRB/R4d6eNa1ZgmPE6jNu7b0nx8A45ULVr+GpVyEwsbrASiGheOryTt90jJ5QA9BmUGlFd2r+cLkzTN8ZWs9EltaGXABxh9+r2RY1AVDKOq9BozxuFRqQ5uleWtKnQ3of1JZspN6D0c67MHfWUAuUEfwmAIbCCkcYzXjyyWhAFBnMDHseqsqgumXBSma7ffjiLgR5aGKwu03D8zv74oycPSePPge1q5tDrIKZfMH8+T3AhlgRRZ2oq0dgb30zSQWXWPvsfPZc+XDqf0LX39etUtHu+NLZmNrT6zp9usLlap1bcJtcaxTFi6ktQ0XY+G42FbNUieBc8zb47ngVG+oubRSZrWMrAoD96r+fS0/j72cm8aygI7BhSzxpHCwhI2dUehdMBCOaDmKBbKPV567BQXug+hKFHXxvJuvX+W+dJxQJtwtKLPUiUcRsP6secYmcDp90Erdpw2NVvx2id4WKKEpzrcVJ/nw2urvOOcy/tXS/RC+PmXOmz7m/zZh+p8z3esc7TmM/vCLt/+5Rbv/4qQFx65aQhyLJZvij9s4uGz5ms6gJHzI06R93qmSZYWWVrdva5/eWtnkY3TtIrLYmv1AFBwrYqJEldxwLLRloUT9AhVgNJGFmbjtLFELdVJ3/CZ69eU1/gr5na11dzosXChxRp1NktlbEdRsz0WesbRdMVSaAQiJssMjqeMgyB5PsomPXCCl8fTsvIWrWevdC0oS9PWmrx9QsraMQA7lA4QSCqWomH8j7JUMcqHm04ovv9Z9/O3Z+v4SnBL06EdbPCeizu8uFkBy6Vqz+CrHu84F/CCZz0Nemf4rueErPbWmStVWChbvP9SQKjhy5+/SdmCS134hqd2eUst4EN/u8QHPtzAVj7f+X87rHR93vz2Es/5H5JXVs9z52yP//HaMr/7oOR1dx6Jei84Ubtj1xOly8+T9tRp5ZP1Xt+0T8BjLwvjgIow8yxhOE/TV2RQZPU7b7MWLxerT1i4ojK6P9CKA2xfBMtmTWxQsmwOlW/Jbe+5y6f440fO8cFVGyjzlPlHWSzfhEUElMRDhg4sPaYRfC837RcQOYh60haUgwI64PKAHVln38OYA6/4EaoBRUJ4edX4FaA6a6w44ve9zu62VEa9QRQBoHHIWCfpCBgp1WlbAau9C6z1bBbLlnGE6pTJEj7SKQ/YyKtj/Pp6OGu4P8qyqDiIDUwe2DG4nwVyJctngR9pdaeBG1lt561LeihsL5SM88dQByke0nf3e6l8kp2ZVRpbI7Pp+ZUOD64uUW96lA93KVtQtgQVW7LtWcyXepSsKiJumZcFFBgF4Yj/ZVl5jJWJ1XGQIPg0PLgo4Dxt+wd9bxKlgR3x9TZ6Nlu4OLLCjOshhMASJWbdw1idbY65J0DY0F6Fct347thZAbuMsGxcITns3GSOwc4coR66Ix7tVqlakrsPzeGFXYQQzMvj0N/hjlmXO2ZB6ZCGs4hA8CMfrvFFn9bihUdODrv7wF19Vnur7Phrka+avDmfJQMVkemKKa1u8OokZa1tWUAUsftp4FRWmbg2OM86JJ1HZsvPafy7qCy/+57EMnJLVFYgcGUVJMyXesYvzdkHwHUQdzwvv+/H7uLU95zn4R/aPeaa6z36FYftiotbClmr+ayVNduexUI5jFSVGiKAQ4gRMD4APbTQEV9kCIKAzJZ4kpZ7g7S8OVGUf15Lcv/loDTQI/XIy8ASxxxdFUJQsRWL5RBHWrhSY0vN0xa6zJeW+KJTXUIlqNqzeKrDUrlFt1KiH7YpywY7/irPO6yo2vOs9S9yuOoiRQWBcUZ+rAbfepfHWs/GkZpPXwqZcW1edARe+JWXUBo++7iHIyv8h5+YZ/7z+vzbO8vYG+fRn3qA05/3Qi50NrjQvZ/lyq2IzDkbvy6icCrCt/OByOuBVx+AhUf8M4tpF0WnJwEOgzwiJU+WdjiZlta/5HUaw8/SjKYMSKdi0qqzoBUnAhtKTRRhzEt7sn3Nb95/ng/fM8Ndd27xaMvmWM2n6XapWLXIsc2g2AEzu4MCKopQESsPOJg+XQmgI6t80bRkvUX8gcQ3TF5ndN1eH6/HkmbcpAnyybpUEAEotqlH2sY/iN8D26XmB3Rtl090Jafb93JCz42Avam1QHstc4OmoyygYXBdxNoizVKkCIgxCYhIW5wn1Z3sJxn5irQBYiJ/GdWhUHhhl5/46Q2+96tGES9KvQDR1Xh9i52epOEoOgF0A0k/FMOQdVbcXda0/Dtp2QHp5Qd89aCch15Oa7si5Q/qXhHKAjsGv310X4QhUthUrAa+MMCXJW0DmAUe9FsMrOJ69QYlUYP2w8bqUyljoSftyFJPm6ODYWDA7e4mluVCuY7lOCYcbXcLVMCc3WBH9nnPpQ7zpQs8aW6J73q6GVuKkM3+eeZLx3jHfxc8uN3FEnGH6dng3+Q8WZQEO5L13aDpKE/2JJFWRJtbBIRIuzcJIJm0lmcBIFmg2G652pVGgVhz5tBasfOEJxrwLuiPLEsTfQq1j/q5N/PN79ni2/mqXTlK3YDadp9Ow6XTtum0HToNjx1forSHFs74rxodZYkfadGkW3lkHmsZ/I5JsDrPafXjjYqsB1nrTHyvkOffY1A90kSksvocqfpUbPP7l6SibEls6Q7HlhQW921dxJaaitXAEg4CQcNZROmAQHs4kqFFnxQWFbtJzfHpBjtse5ojNZ+SLCGE5IVHurznYolf/0CN916Cf/fEDt/3jT4Xuza90OfjgeZZi7O83X0L6ulfxcVeC41OzIas/Wga0Lj76bPv3aAB7RPwSFI20r+b0hj7pEV3EhAB2YMhb8DkMem0Mok+RD4VtGWYofD7xteC7UJ3exjK856NBznVmMeR5eHEi7f/mtuWeduDa3zkE7Ns3rJFw6lwUz0EYTE0q73WUbgiQvI0oAdcvoWg6G95uY6w7OXs+pj5dbD7frzOMOX+pHoH/kFm5o2Pj1LdjOGZw8zoBi86GvAP5+HE8UVTv+WQPp9gJJzlzfVJWqvdG+Dr4azh3imPp2YBHElKA0fi3/OBhPF8g++TgIy8fhXdjGVtGtLrlkgUComFp7o4soQYjtl4v+HB7Yf4oa8tYzwzjFN9s8fqSgW3FDJT6dININQCXw2icARYyeOMWWe4k1YekD2Hs9aDPN66VwDjIMGLtLquJNAxiZ9n1akCUAFlp06ofRxZRhEaKw6vY+57HQMSu1XKO1sGPA69iFfaoF3jKwnMkcTetgFAACzX5PF7OJ1NkyZt05/uNo3qLM9dbtALW/zt2U0Wy5K75ue4Z+NhSpamarexpcsdM7ey5V2MNmhFZLHJm9Dd+YuAHDd4dTol5c00JV3epiYtf7KuPEVDVv3JdopQ1iYtL+94/wPtIRBY0bTzRYgtXcpWnZa/TtWepR92KL3rbxFPe4aRSapzY/X9y8o5fvHtX5jZslSaxnqPdrOEN2sT+JJuCC3foh+K4ZQ0tUXHWQZOS7UGkW3lkXvUZcCj00AP2J88fy3vB7Jk4L2AHll1x/Ib8MA4Ll0oC3wV0HRDuoGk6YaUrAYSC0c4xul/ZYbbZw5RpgT9Fq5dNrKs6qG0US6XrDoVqxmFFjfhZUPtY0uXO+cM8KZ0iDlG0+D5h3co3d3ifMfi/3ysiu8Lvv3T2vzsx+GZSxJ18zOwjt+F+vlfwf26zyVUPjLyQZLOE2B8nk/aW0+SofPo+uDVl2EnmSVgZi2QkzY8aXWnARFFBeIiqHVaX7VBmHf1BQNsWA4C49QMp4wWwkzI6ixd0WfTu8CTZ08hhYUt3UTdgi1vhVe8tc0j72jQ61ooZfrUDbZNFmmPImlcTTooBjvtOe6DZuxFrTquJNgxeM74X7x8LviR0tesvsTrS15HQj07Fw340YvGXxjgdNu4VoW3fspltfdoBHaMVc5ofqZpivKEI5GSLwlS3kCvJ9M02ta9agWyNHXxz8H7T1sL9tJekXLx9or9DgMnppO87t/WvJXATue99a0+clVx8XyVra5kx4deCL4SKB2iiQlzWdFW8iK2DO7LlLJXaj1I8oqDqiuvzr3ei1M8WkwRfp6sM74x0coAGv0dLK+H4/uUApCtdQNeDHwtCQn9FnrznAE7hET7PQOEDACRzqYBlFXEcwPPfIbBCKgOPFN+wJP9HrVewELpBLNuyMmGT9We4XzH4Wc+UeXDa6t0gk1AMOMuJ8C7JCWF66KKqkH+G7R3yrKqKGrNVgRUmKSEKEqT+pRMyx4bvuqRNs5s4WIJx8gTloMjywhtjh42nEXs7TXKnTbhZ36ukYPdJOgseD4dvvhN+c/leiH1zR79bQuvb9ELoRcaYFprNYzWMv5Uycgsu/nHKIJL7NOcN4+6F/GQNCA6L1rXYzEy4yS+vF/wOoU/j8AOm5JlIq/U7RkWyzMcq1VZKB2ias8Oo7hg2dDdotz3R875vQ74XRxhxl/VnjVHB4WIrDycoaWHJWwkgzQHKSyksLGFy91Lgmcf6tLtWDxpQVGyFPduCr7zLcf50Q9fYosWH/vVHovnVqJQ5XmycZ68nMUX9gJ2XD90gBYeaahzmlVFUYQqzYpjkmVHsr20gVAEBcvQPtux8JtDlNEar0sr2LqAqM0PfRyUrTq+6hGiKFk1c6wlYZ7XdJf4rS/d5nXNNl/1lC5n2hZHq/6QeQrLGaGg1yqqO6CiptBFLT3i9cbpoE2mBzQJjCkKaqSl5yHWWWmT7k8COqY52jP4TQe+QoQcc5SqdMhnnwyZLxtHfOlzdEBZWiwy0pPzPL18mtBxgwZU1IqCnHx7bS/Zdp51SdG64/3Lq8toCm3hZN5Pr8N8lmRciE7Xaluvs+FnvV13qtsevttlfb5Oa9tls9yjF0I7kHQCqDs9SrKW7ocjvqEeCL5xK49BnsH3gS+QNFPhadeFOP+cpux+j61MU/809/5/9t48zpKkrPf+RuRyzqlzqrq6unqdnn1hZkBAdkUREdxQLy6IqFwG0Suo9+J6r1d9XV9RXy963RfQC4KgF7gggopeFFyAAdm3YfaZnumtqqurazlrZsT7R2SekycrIjPPqaqeHqae/lSfzFieiMyMeOKJXzzxxDQ+TFxyNj+GaWWACiHH/SipyGxjyftWSuqj0zopM5USqXKdAlhaQZwpI13mTvOl4IdWBnyePcT5/kkevRDiiSZvufs0Bxvws08QzAaX8cY7l3jR9bC1H255cOwLSNn4quTu23uyuozKZKdrca/Kim+WXOO0TZ8uA0eKwmxjtvlNjwc3MSpJPS4/NIoL/TM0/f0E6QR17hAs3Y1fn4PWInj+Vll/8Dr+4M8GHGWFlUMzLJzN+S5LqLnWZ26lQ3sz4Pymx+q+mLW+x1w4vt185L8jfReJhQcaLXRS6lbrDvs2BT2S4a4tGlnK68RFeS6F+cCk5U9j6eEkQWrZIYVnto96wfD7Da06Bl0gkdHS33oggFagJCifwKuZWZeODaCRfGMhxJjvcYHES+oqhKDmNVkfLPPvSw2+7tqIUGq+7y+O84THnuevf+hOfFHjNz8FP/l1CnHF43HPdyehatYbVXg/EmT1NDNGy3XRCy0KdymlRddFA7ENsChCyapSkmdM0VFsPaECE9ZaBK1QSQO60D/DQPVQOjZnjqvNDHgCYNDsGX+e6w/3+YcHPQ7WNc0gxhM+sY5Gq4Q2YbBTjkJ3I20ZbcfRXpVVwnz6srqU+erYbbAjX0ZZ/KTgjO19uZ5L+qZt9drg+dzNGp1oje+87hgyinKJ830PxvtqFTngSrO3olidqqwCut6nzcrGpdzmZX8VMKuofFf+qiuM6Qpitt62ZzDHio6U7+p1ed1XH+LX3tjbEhP2zYw17gj6PTMmdCPoxTLx5dEm0pYJ8ViRDouNSSxCtjMObMdJ9E5bfxSVY6Myue3iVUVuazWyukgdO/fbI0uNQXdonaGjvvkbdMeADpRpY8Lzx8EppUb8Bl0DnHTWzHVq1RH3R2UACEndM85Q79+4wGYEX7Rgjr71hM+Lrj/KVrlbtMiT/tr6WpmuNKm1wB4Vy5uqumqZFUcR2eRjNrxInk9K4zyG1sqkWwG26goCSd2bNbI86pnwk581YF/UN07ZN5ZzJx4K8ALe/ZIZXvN173KCHQBBIqv7PWn8LvVhpeehdETWQiNP6cLjyOIjAUT0yDpwFJa18si8h6yMfbhZbthoOzJ/GlDbGm7mTing4cuQQNYIZSNxLl0fyc/0b9Adr/9wMSH1rSSGPH0RJlYfNWqySd2fJZB1ZMbaI5A1fBEOHd0easTMBop/Pi1obwR8+RHFoca1HPjop/n5yzep//yPEXlpH3MBhdYXkLmuYkCQxk+z2PSFSVNYeNiEpMMiotJg6+JvA1FccTtljlkwQYv6I0djMPKf0G8n+wkz5NeAcOi/uRUcMGh0v81ccDA5ESBFiNM9XIZiDaGEpa5goWaU59QDsSdzaK6UoC8BhNdGkyC1k1p6FJWZp6oI+DRWHa5wW1hVsGOS+zKek/IDtqxqNuagMc/HV+4jUj7Hm4kzPD/cmndMWaraJ6usVomx1One2j0qomnekW3C4wpzyXwbT9fKZB4Eca0OFlHqTd84H8um34hWaHhzmRNYTFz+3v58tnvN/toxNmfXaa6PW3q0LvRYXZxBeiZtrM2Wlk4kiVSfSPXxZcNu5ZGGZUGPvJWHLX2atqq1R5k83onVwqL8ReVPowBXBTnKnskmM1NF2OVvROWAHiERfqJOpVtYEqADKcw3S508p1YbKkLHEfQHhl8YIMK6ic84KddR35zyE9TphMYRbida54Z9l3H9Pk0gbY4ch7kd11nK62lVLb+KZfeerLZRFVCjDDx2hU+Sz0XTjNlF7cCE9dQmc+GhYajSMed7JwlknX3h4bH8da/F2JaQxavQtSZ91aF26LrkUACSfje+6Lh26wpX/Yy0ntSSklSaXiSJBpLNyPjxWOtLDtTt6bVWQwfXBuDQQ18eZY6vRzp+QnlrveELsYRtqbhl7Hgo6GLMN6qOB4nPOQHjljBa4WkFqjtyFj20oPTHTy9MrJh1UMMAKAJ0bHjGvVHaoI4MGmih8KTZzi0QCK1BePTiTT55zmdzILlrTfAfr+/x3x9/Hwu1yxB/82aIYni8OTkrnQPaqWxxsEo/d1mH2emRIqt3aEuLC5ywmezkJ0VVeLnyVkG5XAp3GaAyilfEaC8xX0qdj6XKS28jOX1FsrUcQ/24je/Pc4EN9onDgFmNFELmkGqz93t5w2M2iDlQ8+mrzdHpLi6TtuGjXgLCcFraKdAjT1WsOqblMS3YUSXfNNtSisyzy8qzhc/MM2juYyM6x1WzDebDo6wNjDO8mmxaMuf76JAp1fpokdCe1uRvj9zkmtwXAQ0uMKCIdx70LpL9k5RlwmJttlqFclxjbfkHaEerzPj7Snjnw13KvLHWa65vHTYbmwP8SBENJLE2gEc3ho3II9ID4/hMNEf9K7+vexoQI+v41BZXRK743TSRnsb02UbTgtNl6bJgx7AcBzgV9+3vPJyBWguhIvSF0xDFiCA04dIfWm7oODk9K4rN3/DZkvjsMyoF9RbMzBPIuvFxgGSgusz489jBChtV6XeTgJ5leffITlXHtSIdt6olRpEubYuvQvk6FenhZitLujUg1WV9GXKgfjkn23eyz1vY4vx87JSTzhqiNkvt7P1w5EYTnp6GFDbJvqfm9Y1SsEMojYoF3Y7Pah/WB4JeLBNgI1lkTMrOWm2kJ7BoRDIt9kiPrE2dl+ZPbBEJID/2PDYZ6/LvAeNpXdsid1v33w3+kyyK2uqRP7lSMeIX90egNAAG7FBBSDdaR4Y+9X7CL5wh9iRK9Y3VhopHC9qpb6Xht0hOXNOZslRMX/RpRxeYDTSzgQICap7iMItw8vPwuEfDZY+BM7eDihCtg7aHY/L+XDXfHsGOAR4u4V2EXrsUa5fVSFFeW/n533ycq9ytjUcgEkEoRv4MUlIRst/NOVIar3uQKOLZlRhzvfX9/NhjN3nh6y5HPmqVozOK022f2aCP74VG2Hn+VsVsp2gSAVQl7aQCrQrKvZO0HcX5CwTsSBVrIS2Da79NsHqG/c0F8E0bNsd2ZRT00j5ftHLv6uu2vjvK/0jYa7g7VLRCkKU80JAPS+9d8t1lOWHjUxbnqtPo3hcBCLPi0o03CWRtuIIihY/ZV50qnPl3YBtL3KCHafv2YbO+2ae9GbDR8eg2YroxRAoilRybiCreQ5r65wCcvjxI7rNWIS7Q4+FKRc8xqRPpqmmtYEcubbpKKDH7vXXGRweA6ptWJSS0Fs31+lmzOJIukKRgR7c3sgBJrDtQGp1uFczEiXrNyF8h8fs9kD6tYIG+6rA+WKYVLCCs7RqKQUxbu8/HFVtxjNLaaE9W26nqyqtLJ51mgpMHSCZZgCgDaIrrIzVIJcCDbrQ+BO0APAFdetTJOkDP8WvMmd9aaxRWm82kH6Wt/eQr4JsfLHg2A06f67Todnz6ygDT7UgS68hYbOQsN4ZbXBI5m7XyGP8qiXWAMyzTx6qcwJW9LgM9doIeivFjGtDDxSc95SqxnhsDq5NtiRII/RnWB8sQzlFXIcr3GcQdNIpA1EHFZttLdrtLatEcR1vLDRr0ozU0muOtPpESBFKzMfD4QP88jztyJXV/1lj8L15jwJgRg+S3CniZps+nK5pnV6VHhqyeduOuI9wmCPNxtoHY9bGL0KwisMNWR1v5RelGccPTV9L4fnu0p7Yxb1E2x+uSCncpjLOl8f3j6Z/J9+mVGuFtfe783H5OtaETSfqqY1YxBUboW7cTXKI0jRC9GGZ70/jqyMZXCdvusbMuvl7F03psvNIVCzWurGfvh+HSN2UBbb0J6bqGyJ/OAtk27KhM8ls0obTlqbqStUfFlH/f2XcLbvmblU/5dPnwrFzOD+RFfGxhVUC0rWBb3WuNtU8pPHpx21KvfH2wxG/9Pdy4loM/ae97zbU+66cC1lZrrPZhrW/8ePRic1pLrAZsOW0FGPPULzPX+TS2axufhzMVWXUUnWw1CRWdjJCWla1L1hTaCw34EBgAQmS+pZCJhUfqf2P+GCxcYXSE5Gj60bPo8eeJYoiS7S39gbmOIohidLfH8MhbgKiP0JpI9VnqnqcdXci+EIrbtav/FulPNrAzf79n3TEdlb1bsINRZXlsPKqSa+HBxccFcqfR3nChcMafRxET6T4aRSs4QCdes0y2MuN+Cm744bjuPZbWUPTHf8yfPO/vuPDCWVYObT1CHIyT6daFHhtrAZubfuJvSdBX8dCiA9jiz2Pk42Pcl8f4KS7jJ7YM843588i8w532xZSl7Ja7Kn8PJWVlcnb7iUtO5y0gpQ9egNn8r4zlT7pAnJIfooIQXwl8EXKud5I11tkcnKcXbw5P6hkCJlkwJt1mGPfH/1QEgw4COVzQrnnQ8BWREnxuNWR9cA55/qTh4wWJVdKw8uyObls0BjxyaUoLj6ofqEg45j+0bUC25cFxbUO9qqQronFQRBGz3L2P+fAoofANChjUt6J+jjLqXsuRzvB/+70nefuPSC48cYZH37TMiU3BZiRZ628SSrNvMZQNo3QNhf7DAJWbBsXdLWuP7ZpDbwfsmBTcyN+n235cdfSSI9vaq4W8tKVuwvONYt5eMQG1FixcgfYD+qqDVhHFqzyuiXNVpclGdkXqkXBe+M6QDYAos6LI5ylaBc6GFYFWrrLK0pSVXcZHb9nmMs7LBsSUkeZXn3KQwVt6vOqTa9zxi6O+1LrQI+jFbKyFbEZturFRpNuJH4/sKQXACNywUeUTWyzWHWXWHrvh5Hq7PKfZvlIVlJb+1nFkUv9HaX4vPR6+NXzPoj5nJmLdNVPWviMQNNAoRGtx5JRUK2OKn93G5HsQYcCNYd2S9uh74HuImSbq0DVm22xvIxlPPbrxBlfNXpOcImCzzoDx/gPu/lsWZ7su7597srqMSsACwC1bJ6GyxYXdJgFRD33nh6ldfhO62WCgerT8A2a731g9s9eZOs8etsQxTNP79d/mTT+5DsD//IbXcOVrv51bvmfrs4b9mMZmn41OjX7fY20QsRlJerGk7hl4YkxfSq6HfjzQBVYeCkitRLxx/x9j/jxy3/1i+Fe6VElkgYnkbXr5rfpyKxCd5s38Gn9ZPRQxzdp+wDjLrXlNvCgiUn024/O0o43Ev9YFZvwAX4b4skawds4cOpHy9MMR2JFuoUnnM4lFSeR73LFynsuaMnkCSSuICaTm6UeuhI0l0G5HupkHxd038/PXIn1rkrl1NtUXvqzegS0tLiCiiGwWGvnrKnld6HjZqnG+zOK0CoWMIqRf41DtStMB/ExniLrj5naVKK8wwDdeeYgve9NJfvyFp7mzs0jrpoh/bawxUIIvXlxmPjxq9kDmzdvih0gQ7vQWGBvZHPxNSzu9hcUVvhNgB2wd5KQxlWZj2Z43joyX/wLKgx3CD42S3lyAfUfh/hUDfkR9WD1Jf+EQn1k5y8HGwKzI+Aeo1mddCpZtpSofPw04skfjZJuUFJFNppZNeFw8qtSjCDwBrONKES+wt7lJ61lUZ0M/cesSK7+2tb9KpZlZ79Ht1MzKYS1iMxJsDjwi3SNSfULZMDXbIseSslKQI87KyxzYUbS1ZcjOYjJdVf6WKddVQd/t0jRgR/55bVYzWhmFWiv7ekEeZNLKfA/pj6w8vATYWDNbVtS+Q8j2Gvgh62xSV+aoRA852geulNET1s+Pns8PEY0Gupec/tMfjOLCADF/ELRC9tqm3HP3o47dyImNO2j64dhRmuV9fdJVPxeYaJMVX/jK8s6QTUa5LDby8S49tWxyU1RWPt42XhctOObrYJG9cUQkNb4fwo1fSqQj/GRFXBHTVx0a3pyjbFedt44NtVd8Ly9+/mfp/NY/8AP3vgC+x5ItocbGgFonor0RsNrvcaHvsTGQzAYxMuMINW/xkcpGLYwfD41Aa5G8ImmOLh3WbLSdJb0uBD2slEljA7sfDlQ0BiUnoyTw0VhUcuAsxjGtHsnlLN/sN0ER6wGR7hPriPO9k8yFh+irDifbyxysz1KnxYfOxhxu+ARSU/MUnvDxhI9GEe07aLYNpvX0QmNhFCXyedBN5lwR+CEDT+AJjxvnD1GPBf1Q8tnzp3j7vS0ef0BznX8e/d7/C0cOIJSCxatdL4lqAEYV8MKlQ+/J6B0APPIvcxLQI5uvLI9NoE6i+LrqVlRfE96N1vFkgFDKWFioLmifnu4S6wEzXgMGXWLfNwqOkBM8z+j6fadO8UvvOMyVLDOz3qPeMM+2MfA435PMBRGxGiBTxSu7F6xsT2BV2qk9dbvFe1IhX+Xcc3jowI5JvpeQxnojHfiyCrmNv5AwM28AkuS9j5qJQtRbsP84/cDHlyEQI73QKOU143xvY7DCxkAyUCFNv1MgMVxC1qWslK3qu+WD3o3J1Rcc5eWxDWwoetc2MDkNt010bCC2a7XORjYFuqg+NuDbBlQIPnP+LjqR5EkHr7Lkzz5T/vnswMhvPO0ot2DfJ97YHLCyafaHd+OIbgydWLDWl9S8DjXdND5HxtqwZTzbAmowptxZaRrnpZPSxep7k/rqcAHGUo6O18558TcgSGrCbOGV/RVyZOY85O2bbStBg0h10PUQpWN8QiLdp9FLVopXT474+CGiVoN6A/wQ3d0AL0QcWIT6HHRWTf16G8Z3wf7jICTdeJNOfA65uMibPneGutfku65b4ANn7uVLDl+VVjz3O/aCcMuB/DWWNGWApT3/nqzOU9G4mE9TNbwszeS66Nb7qosQlri10/jJli4xM0+sB3jCR2ycQwy6vPuCZLW/wS1X7UOHDcb9LZXRKE30Z3/OG77/LABLr5vn4FtXnbmCfkzQj+l2fNYHgqWO5siMz4F6Fz8VvUl7Tn8FYgh65K08jIyArAPTYd7EysMNeoC771WkvOy/VPtddg6QgB0KhdLj24mESN0IxMNrIbxxMDonn4X2ERgfK0rHw5OAunGfTuRz11qbgeowHxpXAZfPewgE+2vHht8mUn2otdA14wzdXz1DNH+Yjl6nE68xE+wDoB7OEusBAoncXIV6jXN6hffdJ/n1t1/B4uEOP/r8Dg8Oljiy2kb0B4jDNkelwyfGPhd29cfSFz1x3keCrN7FU1ps8a6PUIY8FSHURYiwq+HY+LmfYcafT4SUINJ9/LBJN94wcd7s0GR1Jd6kFSwkaLWtrCLSvO1enyvfvczqYoNB6FFvdFkbwP5axP6aSkyiMfvAJjFh3k2aFMTYTUDFRruxhcUVvl2ww/VNs+8rH1YG1HTWRmmFNFuwBl0TJH26vmCpc4KmP0PNa9Lcf9wAJAmo5quQSPd48oH9uXZtLRB3W3cBIjbKp9tDpienahMSQ1UtL8oAjey1YCuvqiBXUf2KaCuP1f4pGr7iX043eMzCJnUvv3+2zNrDXsbTf0Xyb/99a9/zBgqvp1BKmJNahsceeuyv9Yn1wAAeRZTdypKXA7b+X3TCSxlNkv5ijDHbtepIKQt2ZPeEg3m3qcI8BD+S3zhKAJI0PhqljTBARFAfgSi+WaWWwhuu9EbKfOdGpwNdswVFdzYRYWDA5NaMsQ6VPqK5YMqcmUeFdURjFjHoGWen4QxaGLW/7jUJvQYSj+decTuzwSy3rZ6kFYC9/9rAjKJ+7opzgSj5MqrqOXtUTNO8xyr6d5ZsOrMtbjt1SnlpAwoCCA/6m9TCGaNjaMXpehe51uBN/7afWxbOIIK6sZyaiEz97v6LzWHIWz7ze7yc73LmCHox/iCm2wlYXws41+izOTDbWhreVh013aoytNjIWXkIvFz6EdCRpneDHum7clEOCH84WXm4ZPbQ54bxbaWIh87wU5+Jo1+dAB4aIYSxQJbSyOPUcg4FdKmHLfqqjVKGX1/F9GJJzVP4UiBjwVrfo68EM/4+al7TlNNbh3CGgerSjTeMpYjqE7YaEK3Sji6w1PFoBivUPI0ONFJ4NHsK/Zl/p35kkfrcEb768jr3P/ssX3VZl3efqPPlRwV6oPC+6jlJuy5ahJ+mj1XRq6saFXzh03Y33mIf+GzxsPWFT2rdkeVZBSRxhRWh4Ha+qXAyyqomkDUe2DydmKUaJ2YH/aM0vNmEixrmLK7r6N1893V9fvMvJT/6ixusHzU+Ox6zHxbrEb4I8USQoM16qyB5ODmry6KzD3U9iuJ2extL1bTZumQdOdVaI+//rjqn+w5Tq6D63Kit9Daob6xT83w+dz4x5Yz76EPXcs5vgx+yLzzMs45dUQB2lPXFIhCyCo0LdFPa7vz7wqIiOeyaxLhklW2yUya7q8jofJk20GRS3uNx8+ERrpm9no+fE6wNzubS2fJWIc07P27fwiiVRjcEUmqUNicArA9EcgpAn4HqMnyujCVg6gRvPI5RX3dt5RtO4uXWsDK61MaMIquOScEOz89YdqTORsORHMxvdxluV8nE+yPn0FopdNQ3Js2DrilHmpVoiYcUHkLIZA+5sbwczB82PpXiPmJm1vDqt8ffu1+HcIaOF3Pv+t2s9k+zKbqmDkHdnCaQtIn0WM9W0KIbr7NQj3jX/XkfNdP26XxcFXldDJTuyeqqlAeNqoDEWaqyKCAyf1newhGXzzsJ5dqFSLclAGHTfL9aC/2xWznUuIavv+IQ8WcUHxfabKuFpF9X0StGdP0PLXLLfd/Ii95wjMaPfTvr312+zbzf89hYC1kbwPpAstrzhu1r6Hw00eWHv8NwY+Vh4ux1zTs+HX+CSXTgh9lktUhmJ/I5BTlibSzXY535S+4VcRKWpolQGLApdRw9LC/qQ9QjkPXheCoFBFLjS40nNJ1I0olNnlPtM8aqIR6gasYCKfRGjm6FkHTjDfpxh9tWa7z1njoPbIR8eqWB0jHrg2U+2lmDuSbqA58Cz6cXt/mu6/tcv+8Q1+/r8p//bhH/Jd+XtGtX/6vazm3XZYtPaZoS6w52R1ZfarRNCw/bSmzV1VmbQlv0EW1lVqlbtlG5Vj9s9SkGR2IdsVhv0qWHRtHwGwxEnByulR6DOFmdn3roSl74zhWgxrHLN7j5aJ9H7RtwZCYmkE2kMIrVUOGyPvZDYO0xrdXGxbb2yJe9U3FVwI6i+LK0qbPS/D50FY07zE23sFg9mWMGiNTTf9pO1s8ia/N8YiXg0QubNOrzCCSzwSLF/dPVP2wAh6uvu5DuLG3N90gwvds+la3w2sgGbBTxs+Vx1aEovOqkzDUu2GT9ONU9zf7wWEF5trHBHv7cN2xy8K1rlvpC7EvCmlmpijX0lfmNtGCgYKB6xkqw0MojKSv1z+Sy7CiiScaB3R4ztrMKOakszgLCWeAjdYqX+u4gc3xh1kFeLEFEyQpiPylGDZ9DR33EoDsCkr30+GOPbrxOL940x1tiTrMKgroBputzo3F70IX5a5KjEvsQzhANzhFIzT1rPW7a34L5y2BjibnaLNn2KJHsD4+mD8tPPt7WJ6Fa/xx7cQX5i/QnW94Rjz1ZXURZwAHLb/66Co88sF0+2dnKa9K4PBWXLzDAn7j2asTGClIrLhw+yOMPXD3KX6oXZtui4S+e9yL++ab/wVM+/TJe+m2rzLJRyCHoxwx6kiiSdGMDTq8NPLpxl4ZX25Jea222r6Slp1YeWiMEyXYXSLe1ZC1CSNJnrTzMG8pbelSgS9GBadX6ZHx2GLAjATZ0PASABIklh87KlMT6Q8vhPMgT/vAY4SENugR1Ywmn0XgiNuciK8FSz2ep69NLAY9NzaHGGjXZpDM4TyDrNGSTGX8f923ciyeg5mnWBorNQZ1DDXj7fQHfcEXEyfYGB+shx5o9WAvY/OcHmf3yZeaOXGtOa9lc4fEHrubN31ykK5MJr67/2vv0JDLDRo8MWT0F4FEEDBRNamxgiI1cH9KmjFcdDMj9FoEdRTxHcaFsEIaNYd5I980ZzluUhioDyKgRP+54j2+5us2ffn6Gm+Y1Dd8IRGMyq42vBdEreujt0zRAxHZAD7i4wMelBHa4+NnMFbOABxilO5wZee6H0RaW1PIj6pPuHR+eZy8TZT/xAbMYHOMHbva59ex9PPWQadO9eHPozCkpnGIg0gVguPqDTYbYKK9Y71E5la0SuuR3EThh+45FfIvItVJRxFfn0ub5FNftsQtwsn2CK1vXOdKWTRBH4c967Bqfeqs91dpCnWggUUowUKN6KE1yCoCxAPC90dHi2ZW+MQU47e+pbJWMy4RsXH5rSxoPbmBgEnLJrN0yqZ5GRuctO9KtLPnTcFLfHcPtK0larUCmRw9GBvhI8yXPKaQ0cjfqG+1JKwNgNOZYGyzRS3adSqEJZN8A0GBkb3MB/Brrg2VWN+/gTDvg5v37mQE68RpHZ67isqbHZ1bu4zELLaKZWXpqk5oSBCKwfLOiPg5uRdomU4vkQNm9K98eucn1TosmP9m0VSZPk+ieOMKqLFa48rjGeGH6QnMhmfxLnvPMk3x0uccXL16ZyD9XXbLPuJUe9cSYjyydg9wWkzxJpfEHIzmitKAdJRYAkTmtxZSoEkBjHKAY8+WBwratZfjkqY8JhBP0ME9l20aTDbc898Xc1rKdCfHQ0i712REN/Wykf6NxMB4CG+n7T0/y8YQ5USV1MeBlx0gAafL5MgQFGo9Q+nRo85nzId14VKUZ3yNWEV29QSdap80qpyLB51frfPZ8k7vW4duujrii1edMR/Lt1wzwZcirPunzg4/u8cBmzEqvztwVj2L2d5+EetufE3zLzebbbq4Y3TxoMNk8tQgccfWDqvz35PMUgIdtUpMnm8CrIjTzH7AKal0EuNjKca1Q5PMU1XO8zrEe0Is3kb5n8Zruzrf1WvPjj1vkz25f5oZ9MBfGzIUxoTczPvksUvgerijdxQI+dhvsmJR3NlzlrmcXYd3sdUVhxvCh4z3s31slSno4M1rVjDPbWvpts6e2s2p4H7sZhUKgefTC7JDNbJA9jaVswpz2vSoodVUk2x1+KZrKXTpUBBikZFudLZvo59tCFaDCVR9bvA1wKAK78/f2Npaatr74hoN44mguf77uZeOCCWuUjJpSavo9jzgmseqAgRJ0IkkrMP4dhorzGNiR39+tk2OoLcCFS9a7gNIymmbsuFTAjuz2lLyDUpEJy/tCSp/ZC0fp+22Gvj3y701pdBQh6tKAHJsrUG8Z+VprEWvNUidACmgFMU1/gApDZHPByOXOKgy6zAZ1ZmevZ61/N7dfWOG6fTGBrOOfexCCOo+aP0qsI/qqQyhnCGR+IWX44Gxtr7Z+XtQ/q8rS6Sbge7LaRq5Fw5TKxku77uiWZy5+rgUJG/+iMm3x+XILQAsd88qbAmgcLKirqw7jdPhPfoDFsM6rOWONT0kqjRcpwn5MNJD0epK1fsz6QNKOJPO1ccuL4dYVrRlZb4ziNNrqvHTM70cq3zOgh3mSceAjT1NZgewUbXc+kbO2yzooNWCGuVeMnJam70kRI7TIHOkOoQShk/clMm03racyPANZZ163WJc9pPDoqzaxhnvWwRMwH5oxeSO6gCdA4FH3ZpkLGizUOjzxYIfXfb7OO+/3+ZrjgoWaZjGaQbUW+G+PX6UT9VnpSb708AJ12YA4Qj7jy80jI+HQ9dmXiFtnhuJ2XTRPzl5XmWsWAyOPBFm9jV6UHUzLV8W2UhlCbVvRy14XAR1Fca5BIJ8m+5dNNz5A9FUHgKY/jxx6l65CW3kBxHrAh5YEUkDD07QCczJMIOt4MgAdb1Xe8vu7dwI0mEbQ7QTYslv+Pcr2gU8LhOxUWtvkIQU7spRdkQxnRltU8vFRste8Pgdzh8yKyvLdENTZpG3SNeZgc4X1/jIanRw5W2V1KHtdpoSUrc5UWc3ao+pUZVJTJPvy8dtdPbDJYlf5Njk+7fc3eZe69/Int53h1bctFdQtPzHMAnhby3/ZzYetJUaJe/9exyOKJP2ex2YEmwOGxx72YsFAdYn1aCtadr+rVenIKo3pfTYuH7ado7ur0m6AHdPI6PzWlaxFR9bKIw33ApQnUb6PDmoQNoklDPQAJRj35TEmn3PKdW9jBBorYxWiNDywGbLU9TnZDlmoHTc6QWoF0l6Ffcdg9hB91UVpwXLXp+XNsz9qQHuFeG6RUDY41z3Bg5vn6Ks2bv3EtfgD7n7r0m2GD+fgkfaFKuPCHk1GNpljA5pdoLUNeCgDorNUBCgXlWtrd1UBcnO93L2fn/jQGV764dg44y+d/NmA8QwNutz9Nb/L2v68b5ut5A0U9c0BG2sBq+fqrPZhfQCrfZ+BikitC0xpqY+lTG20Gk7cxy0UtlJ+u8D02weSd7LTC4OpfM3/bYeyMtgLxsCOrP+O9FrpmEj3GagOnbhNL96gG2+wGfVZ7Sl6sbEKiVWUnJAitr6HBEyqeU0QklawkGyB8RkoeGCpxqlVn/s34RlHNZEy/S6QNWb8ec71znKhv8HJTTjXM9ucvuxIi2+88hC0Flnrn6W13uYT5ySfPOdzpnMKTt1myl68KlsR3LqYayEHS7ir/9tAxzJee7SNLS1FA18V1CmfPktFA7FLaa7SiFx8i8KLwBMjCC/0z9BXHY40rt8S7yb7M2utubAa4h3uJWdEh8NzwZWOEcI3KV0C76G28tgpvxxVLD52CmCZJn67Cv/MvFkhLKPmgknnJQBFaq2hIrN9xeYLZMxJX/L+UvNtrZAiQDf3ETWa5vhEMWMGIUHOQqloxSd/nc+Tp8lWatzxulCx2KM82VYDysimwO7EwOlSwIvqWFVxt8cdql/NVx67iytbx3M8XXmLxiOjdEiH6bIfKWqdCD9StDd8ogWzN3y1D3Uf9g885gYxDb/DQHXxPPfwm5rsbqHUgiO7XSW/tSWbblq6GONIFf5VfJVkAY6x63GwQwtBlIBNqYm0FB6R6iOFRwhIPxwp+3EfhER4vvHjIaXZ0pL44tBRBKptTg0YdNkYeJzuCOqex8G6AjQ88EmYPWSA6X4blu8mPnQNS90TvPdkC0/CM472CWb2IQ5cRaT7eCLgcONaDjfA3S9ck8yyRSb7xNPOy0aTyIE9WW2nIv24DIQqk082IKyKrp5NV6SPTzK2u+o8uj4Yz/LTX9xn/vSDQP50Fle+grmFViydViw9dZa58wW+7oBaJyLoRbTXQzZaIUubHQ7UFGt947x0sT4CO8aKSHx2mDjzTyTheSsPISBr2ZFubTF8xi09zBPan6uSlcekMvtizBMS+ZsFNVIgaXifAEexjtiMFJ3IY2NgxthAajYGkkBqDtQjap7Zht309huLd6mMnjtIxj8/RMbK2N/EEWLpHoLFy5nx91H3epx+oEm9ETM332MzWubK1vWsDZYI5QyhrHOkcQW3rZ7k/WfqvO/fDjHTGrD5xFP04k002jikljM8+9ghnr2wAX0B8/VkvIgSq+oqC4GThGf7aX6uWyRL8uHF+tMjQVZPuaWlygvOp3MNpEUftwjEsAERtnpVmbQVDSLuwUKh6ERrzNeOJohtFbTNBdSY+9Br8ENPOseJzcAcf4TZvyaFh9SghTYKlg5HR+ZBouAyDhQ8XLe3ZGk3n2Faq45JJxLZVdjZRVg9aZTxKnVrr5rf1CO15yeCNfP9s99aq8QRnz8CPlLfHX4IzQOcuHAnN+ybQwhJXbY407mLhjfHvtC+cp0wprjtDh+2IH4SYV1U9h5tn4qABJfMdgEV+W9pAyxSssVlQZUiMMLGr6x+cN3ctY7y8lTWdg295D1uc+nWhR69hs9ms87GWojvKzwRUffgfOjR8n3mwh6+WBvuRx6WoEfHGI7XJ71N4vK+PPJppgU9qo4ZO7GVbzt5U5mXt27Jgh3ZbS1agfAZqK4xbVYjwMOsKvaQwkd5MV6wH5n6QPJCoA+E5kuk21+0Qnc7yXuIwe9yRq7yutsb1D2oezAXCDh7J3ptDaEidN+Ux4U1PC/kyIGr+f6bjXPTQNaMTA/q1OQMqW4xUN3kHtx9CEd4Hthw3adpqwKLe7Qz5JI1Nj25aBy18SwaKydZbKhSZtHYbBs/MmmjHlw4zfyh6+DovIXnpBNFoDHP/W97Idf+uM2ib5zCfszcShctBSt+k9Zsg1ONTQ42JDPtgFbQozaGbWvGtyJmtrUkfjxSXx4jMGMrUDHmvyMn820Wfi4QpDI9VPOAoQwWYxYtBuyIhsfQgnnu9YHZErgZyaFj0XYkWR8Ymaq04LLmHJ1ozQAkRAb0SHXjuG90XJVY4KkI6i18PN55X48/eeMicSBZ90PmD3Q50rgCNpaZa+w3FtHRBkG/zbVzCxxsnOVbXrZBKGeYr12JRBLpAT4e+tZ3op/5Dah6A7/eQn/mXxGLh+DIjemDZ15CXt+p8i1dgIltjuqaL4uC+EcuTbkUX1UQ5T+aTXC7lFdb3jwa7WoA5NLlw4oU5iKFGHpqk0j36cabKB3T8OeQeHgib46XJRfPrZODt959htd+PmSgjHd/gUTpeGgGbVBib7R6ZaOL6QDURg8HoGWntrBUyScTnxq1mZGlxcbyKL5oApHZmzhU9NOVYdd31sqsJm6ugIqIiI25tlZ04w0ONZqc653Ab68jkHzwzAxL3XULI5uiUrSamBfurrhsfFm6XBptBszd+PvCpLJV4SJAIS8XXbyLVhGKQJWqYTYZno23hbvqYsubjy8G6198s9tpdGNzwNxKF38lZm01pNvxudD2WBvASg/WBpKlTkBfGSuP7AqXlbITd1d8EV2M7S0uc+jtyFFb3mz4JLyFZKB69FWbXtympzaT999jM+rzseUGHz4b8tnz5gjCroxRQWis8JLjYY1PpMRqLlWqwViO9LtsRopAwoMbgtMdeM7xmvk2ShlLkCiCbtJueht4IiBUcgR4eQGEzVGVEfTiTYontlAqL60Ao032unSksol1gRK/J6tLyAUQg/27usKLV27LqcpEyNVWygDtHO/Nc9DfhI0lMyHddwT8WuLc0VUfl+zf+mzdX/k9XnDHvxY9yBg11/vMrnRprvdZXalxftPj3nU43/M51/WJdXZOMF4vGxCR+vMwtVPJ/eho2qyfiqqU5ZeWbH4yQO/DgIZ+O5L3MXIEq1kfDFjqmFNUznZ8znQkS13Jg5vwnrtC3n23zz88GLAZnedUO+bE5gk2o/PEOiLSA5RvtnjHKLTnjXzZeSF0Nzid7Pw+cGqDhbOb/MzTV4d+QVCRkefJ9vC6P0svNm1XCg/Z70LUpxOtwcYy4vprkPd/nLvX7gch6f/tx9F33oUbmLTpUUUAtgt0dulWMF520cKNg3ZJVl9qtM1jabNkQ5jKaDtoVz6+qHyXAmCrZ3GdAllHIJCecVA6frxg2XO7kXpFzFr/LO+808cPFOsDzebA43xvwP5am1A2IHHYo3RM4NXMxDdr5QFMdYShi7azPWWntrbsBu2GZYcrnx+OVgtTp3r5k1WqUFAfgVxaGcuP1IfHviPmPuqPjksUEnxj5aF0TKT61LUi0j3mk2MNo7CPDzzvqstcD8VWpaWojxchyvk+WAXoyMftIdTbp6oruK7JfzbOlt5Vlu3aFV+Fd1FZ+TAXiFPGY6tCf/uFu/ipV1/JDQVO8ebOdxnUPJZqs6wEDZQSnJE9Gp5iNvBo+op2BJ64AEDNa1qV5yHlHWhqVWzRl7fUGDo6zskbV/gktNPgdpFVhy0s+16UGj9iNr33fAbRJgPVY6C6bAwESgvW+pJe7HOm43H7BVgfhFw5e5Kljs+N88fM9pZwZgQ6pcfKxhGiXkMrhWgtgpS89e6A05uCs6eaLB5um73mi1eYbUv9NtHV1+I/+DljGdKYox2tEuk+LQ5kvv2onQkEc8HBzMPa2nKebJPnqkpnGYCdTVM+8dwjF9kmKZPooLZx1JZ+En3cBVy7eBfVtwhEFsanWKoH+TXTv5x1KhvvM/FRD87eiZwJ+MSTnwR/VJI1Q7MXDBD5YGM/Z1tN9jfXONeD2maAFJqDjQhPlNdp5NB065bHkUXI1pNaUhBEXKr68japbKuE0jGrPZ/VvsdyV3Kua45zP9czO1XaGwEP3tni7k7E1xw/xfvPhHz9FR0i1U9AYTN/Ms6dQWqPoD4HKqLnm+/y5393hEPLaxw8ucG5I00e2AxpBSc53DhKfX2Fu70217SugX6bXrzJAxshPSV40kEP+uYYcl0X0GvDZY8B4IZ+GxDUfuIVmadx9QVXn7HpVpP0O1f+/O+e7gw7cixtNsylLNo+bpGQzca7+JchzrYP78pbpkyM4mViFDNSUvKNapIJ3yjsXPcEL33FHBce1+SaR62y1oelrs9AC9pRxP7aGk0/JJYRnjTnWIfpYJEqYqmp86WyneVSBD12EuwoSp/sJxw6F/VCtOchooHd2WgRH88fASdpua1F6K6Z+/aq8fUh/dGRiZsrBghJUPW+alOvz9GPzw17vS9Sc3pXv8vfT6JAueJdE9D8tT2d6dmXQNu+ZCn/jWzfzyaXt1OObZKVL6uMXO0sX2aVAb4sD9jbnY3X+Ht65S31QrAjpQOnN+nXPFZqLaTUKCW4X3YAjScCPAFeq49gzfiPkA0Ll+RZbMfN2kDuMrnvWgm8mEcb5mlaH0rZeBfokyZJ3mOsBgyUphdLTrUDzvd8lrpw6wM+J0/M0u9JXv5Akx9/xqpRnlVv3OlprWVkK4AXIlpzUJ9DzczxHH0fb/8/x+m3fL7ucRv46fdcuIJuvEldhHD8sQitaMfrzPj7AJGcxGJb3a4KPuTJ1sfz+pGLT5Fe5+pH+XLHc+/Jahe59OOUXCDtpHnyZdrSudpIUfkunjb5n8nr+ebXl5ayXLp/PmzrM+l7P8brHvV/mb/rJbz9eycfz2Yv9Dhy3wVOs4+7fAVXbeAJSSCDxH9EnDyNHFomDGugFSLxs5ff7qJRGF8e5RPOL1Tgw7yzZMOPTjf+qKF8iPUApX02Bh7dGNYGcO9ywMZayOpKjZlPdbn55CkAfuMnZnjKj0Us1Mx7Sp1/G2emG/gyxBMBgd+kGw9oD1ZZ7Xd4yXObfOinzfud+VrNzfs7BBLOdE5xPva4rrVAR22iPcUDG8tcMauYDxfoxhugm9BZo948Du0TwFXmwULbdkPzxMVhVQBKLPd5XmVzzizvYnqkyOopelYR8iRy10Uoc5GgdfHMUl5g23hn62HjlU9HQXw+LKsQ26hsIjdO8+ERHvU9Xfo9j1pNsRnBHRcEd6/53L8RcrodcKYT0Y03hshmRGyUsOxe5uxxfDYP/hebdsLb807RNGCHUpODHZB4pk58aHghkdQMVM+Y3lV5J1kP180F851TK4+ob8ytG/Oj/Yu9DZCSeGaOTqsJB6+BWgvVnKc2UPTiNhf0Ggu14xQrD6LgL5sm22/zfTjf3l2T7qp9pFxZ2KMsFSmJ+e9VNrHKp7Xxzn87W/p8GyoiVzn5eJey7+Jj+8uX4VLqzf0fv/VASd1HdODUJvtPbbK2GrK2WmNlucG9F4yZ7n0bAee6PpHu0487iUPqkZwe7vueVnbulszfLjAyyZaXsm0t2Tql4Wn9MsBH9jSAgRIMlOB8z+cz5+GTK4KTJ2aJb9fM3NZlYz3kiQcPwKDDmK+Q1LouBZ7nDkFjnkG9wdvvOU3N08wvtzl07xqrPWj6xqneQPVYG5xlbXDWAC9CGrAjqV8o62xE50bm1abyuGWebTKZjXP1jfQ3H5cH9/J9Nt9HiuTH5BPNRx65AIH0Oq+Tlo2RNv3VlacK73x8Po1LR8jnLWsXLr3elc6V3oSLy27iGV/boH3za+m91AYel9P8cofL7jzP6Qeb3HVvi9Nt2Bh4rPbMCR/Z8m1+Ocxv1lfFVvmV39pio+HpL5mtjs5tLQ8llW1lHMpjY8mSt2JMt/8MlGYjMmDH5sBYdWyshZy6vUl4W5+DJzfG8t3yqD6+DIfOw9NjbY187zJQXdYH52hHq5xq9/i5f5vneHP0vk+eaHG8eR1zwSF6seBTK3U+sbyB1ooZXeMGmhyduYqGP8dB/yinvfPQXSM8ey9c8QTcfYPcvWtuadNbqvCy8bTxz4e5xotHHm1zS4sLWbJ9YNeEpuhj2PIUKQMuYKToQxeh0nneNoTO1jjJpXUp0aO0gazz6089xhsPnOR/vW8/p5oDWnN9ZucGXNWS9OKQw40BvVhxrLlO3ZtNPAaDn26RGKvGDll57JSVRn5Fcrep6rMXAR3T8MwqyX4dGvvoxGv0Bx00iprXpJEcBzukohXWOIJu8m1bi0RSE9dDNnsPsuDtN+U05gza3F7F8+v04w4Pds9yph1ww7xmf+0YB+OjrKq0zHIAbhSXT58NT+NSxdc28c0rzWWKvA3NzqW8BPcGXjpU9G7KwNmivPlvY/vWeV5l396lCBcBclXCXTI3Xz9Xme73FMoGl/+05MQvl8uCsB+zb7lDpxnSDTykDJFSc6rRYTaE1b6XeJ6PhwrtxA7q8tYdF9O6b7vWGZPky4dl77XCqDKjk6hG+9v9BPAY0IsjNgc+p9oB92/APed87v6XfRw6sTY0a7+3eYC6DkD1DZ/Uqi4/ZsUR+CHBYMDjF9u8674mG/N1rv7sMm/5u2PMhSd52c2HkcLjUP2arc8j0hVuaOkZunGHutekXF/J6yGufgjuvpgnF6jo4pX9tYEoGS57stpBRfKwir6apbz+6rouiysrazv3Zbq865mwpLXwb8xxzTt/mKM/8ypeeMUneQmTnJg4otkLPeZPbLLWDDnVgSMzcKButsDZKJXXZXI73e5S5MDUnTfvzDpbgRJ5f7G2HJbkEcIzVh5aGGuX5NMqHdOJJJ1IsNoz21juOVXn5IkWh09cYOHsuCV0c73PweAy1tR5AAaqi0aPAUgR/cT6Y8Dhhs8TLxvwW39/gMs5B8DV/7TEG75G8K1Xz9DwFd90pUfNm6MTr1ELmnidVbxPfQT94DL6iTdy5PDVrB6ZZX5pmWIqmuNl02TjbHNnWx/Nh9nSFsWV6xWPBFm9zZlnFeXMpdRWSZcNLwM7Ji3LNklz8cmmLXtmlzKSD7MrEC+49iCPf8x59v/lGg/+S5P1tYB7NwT3b8BKss9tqePTi9tEumc6errKv1u0G0Jzt3hOwtuWzmbRYeNZhGynW1D8kPXBMuuDZVb7a3TjdXrxJquDsyNzuPRXpicD+CMrHZlY7MR9Y9URR7SjVVZ7p1npdTmrzsD8MTbryerjzDysn2VfeJiGr7huX8QHzhiFn7g/9N0xTlVRaltYlbaeF7q2vkUujUuJ/8IXyDtDRd80H26bsNjAaJsy7lJC85OiPA9XHV1pJnkeV3m2+mXDXOXl+Wl+6UlHufynq8nboBczs96js+GzulJjY804pF7tQSeSiXO0tIRUec7xtoGhZc5M0zST0nbGkZ1yWGrjmaVURms18mGVOp5TkQEjMvliHRGpPhsDj3YkuW/D4441wZ2f28+1n14agh0AV912Du0HiY+BkLN6yQDX0mdDdo3FXjgztPTo+JpjM0d51mUd/vfPPAjAY249yYd+WvH8t66zNljCrMombSzqwfB0guTbB/kVaRtYnL3Pyl8X2JAHom2y2tXXXP2gCBiZEKh7RJNLjyzTL12TmyIQo0xeVy0rz6vs3qXrutpXlbboGqcSvsKj8fP/mft++qP8xsLrefqvTCfLwl7M5krA5qbP5gCkGB0gIIQwv4ghCJEHI1JHpaMnK5aFVR2YFqbbbcelk8p0nZHPGSsPKYzvw/Sdaa1oR5KNgce5Hqz2BSdPtDh059oWsGNI5+7FEwGR6vPek8I4/ybmjgsaRUwn7iXbZCLuuKD5siM9vv6p5/nh15jtiFJp3vNfFS95l6IXS/Z1FFJ4NP39dOI1OHgdzNQRj7uOwV99GKRvFpcv+yIKQTcouM5Tma6R513Wt7LpXXPMPdqhXlI0MBYpxNl7l4Ka52cLdykIrry2SZUNZbPFFT2brQ5VQJVRfTzh82tPPczv/O86V912jrs+Os/SmQb3bsBSV7Da81jq+qwPNP24Q6T7RhBmT23JCr9LxZ+HjbajIO9E3pRSBTo7sbDxrlJe6hk68ejfVx3OdTUfONPkE+cadOMNPnmuT3923qSZO8LQZNrzjVMvGDn2aswbIKO5ACpCCo++ijnX9fnkOZ+ONEcWE9T553PLdGf30VcdlIbDjWv5ymN141261ip6IY7r9N7VX/OKty1/mZJUVL69X448fe/s3yODXIOgS5HOxuflYvbXpaTm8xWVb2tX+TJsdXK1qaJ4Wz2rPLehX3rSMTq3uBzujSjsxzTX+iyc3UR0NGurIcsrNTYjuNCXtCOz5WIr2KHtsid7f7GoqmXbNHxdcryKjI6j3HaWBOxI/RhpBXEfIQSKmM2BZLnrs9qD8+dq6Av2av30h0+jgxq9QPLTtzbAC4g8YbYFxivJEbINOvE6d68t8bHlZW7afxWHG9dy2xOPDPnMvmGD3/gkPLB5J8M25IcWUEkn1h3jYcVARj7MRTZ9xAVc2PpdVYDRznNPVuep6P3brquQC5C2pSvjbxvvbWmKyBVfVL4tzib/iyaIQNDgqrf9EAu/8m1812teV1JPOx04vUljc8DqSp31AfRiQSD1GNhRhVLLA+f2l4x8m+zUlhL9cydop8DrjFxOAQ8hpAE+hIcnjT+rzchYdyydadDZ8JlZ7zvZflT6NAeSU+0ev/WaAwyUZq2vWO173LMmCaSgF0ec3Ay49WzIX91X4wce3eLm/UfpNEeHTDRe2+ZXPtLiTGgOhfDe+EZam/3kSFuFuPHphC/6Wlg/y2L9yuyDTfISCsKq8JmkLJc+XqW97o6svtRo2iM4SuLzwmtSpNjFzwVSuBTkKgCKq0HkeWcV8kkGgTKFIYtcG/4Sj7pnJqjXfGqJ9kZAuydZTRDQgTICWOk4UZYT6wCXKfBOb0nZLSraE7gb4Mik1hxVeKZxgBIgEHQiyXseEPzTSY/71gMONiI+fPYsxH16MhqCGQy6RiGWvllVTMGq+hx9D7oypjXwmQ8X8ITmfM/nvScv8F9v3eD2C3fxjKNXUPea+DLk8uYNAMwGi+wLD0/wsmx907V6lE9jU5rHXkxBXlf5ab49lLoa2SY4tjSTTG6qvP8sn6oDehXALF9GGSBiq4sNpHZNAm1g+NY6DVSXxmurOR4WSuNFRiZIT7OxFrIZmZXDXmwAD2PanG5zyIAdyiHvdtqib9IVwknBl6oyvExG5/NrZQCIFOjQW60/AlknkHUavqLmKeoe+IGitdrdUvwv/3mbVz75KBpNLYIX3WAUbz+K2Rce5q33xLT1Jkvde1kfLHOmE/Cz7zzMc1/X5W9PnOAdP37/cCUR4MQvK/7tdIvpJo629LZ+amujWV2l2gTN3lds8sEGPO5RNXJN2vNpqoSXjZtl/F3gmW2VGEvaMv55mhSUsdXBVZdMO/VrcOh6/uylLy6tqYvqm6YPd2IDeCg9DnYIMbLyMN4pRtYfW2tazdqjCPSw+/zYgX63E/p1Vf5xNHyHo19BM4iZDzWzAdQbEUFNMQi3nnQD8No/ifniT34c/e53cGQm5KdevsS+cJ59oc+1cz1e+Y8L3Lce8MEzLW6/UOP2NSODn/dj8M77Vvgv/6PLL7y+M+QX/3bEoftOof/5HcjvfgnsOwrhDOJJzwEEHLoODt+Qq4ULYCxauMvnr0Lb+b6uufEjm6bQmlwDdxFqW5VPCXo7lq+qsLbld8WXoe9VBLWLipT6fL01m9F5NmdDwn7M+RPGFPpcD+ZDzeHGAF9qPBmY1X0dVz/1Yzt0qVqKZKmK8J4W6CjiOWbCZ/4kHn3VZrXvsbpS5z3vO8JHlkMON2o8brFF/9BVdKI17tarDA5dDYvG0Sjzx8wfmO866NKOVlkfLNOvhcwGB/iiA4dY7cNf3+/xgX8+wm2rddI2JIcTp2HlmKzt2iaK2XQuIV8mXKutCpYNEErvzt8XHhWBR/nv5gJyp1XO8+Vl77PXRW2qCvhlk6muiYENDKoyedzKa9zJZDHFgaTTDFFSIKWmNdcftrkscD2qggXsKLMqs/3mr7dLVf0aTQpuVAGji2TxUPbm+CijZEskgaxxoB6zWI+4rAkzzcEQhMrSG+8wCw0yWUR4xpHEybMf4sea517R4b99qI0nfA7Wr+LxBySvecEDHH77ef7yvwg2BjGPP3ANr/oLzR2PPQTA3/14vpy8/mHrA0WT3irCKg9W2OLLysr3j6K+ZuexJ6urUtXvCuUyqixvvtwq5ZQBELY8eSoDQmz1c6UrAYXWz/DBn5peX5XKnKwVSoiUGG49FEJawY6xGjhOZLH5SMgDGVnQQyf/doR2G9ioUra5GdsKJJDM+Jr5WszxJhzc32fxcIdew+5ecjB/GHH9VYinfykHBnW+hhVmP/xhDsaz3DB3NT/37HP8yB9exhMPbvLMYxt89WWab/mfcPS+Nd7xo5qDjQFXtq7nt/4y4PqfNfV4Ddcw+PB9QzALv8ZYWxc28KVsrlo0B82mKZLRrnKK0uf5lvEZ0SNBTk+pDdkUShdwYFOubb9VqGziln/D+cZUZeJXRYGfZBKQVd6LkPURLXfv5+V/H7L6FbPc8czDXPuEVR57RZenHNQcbkTM+Iq61xh6KR4v+iLs5buUaBIBvl2go6jsLKV7yOMBUvjcu+7T3gy44jPn+NBPK/7X5yVNfz8fPHOWf3xQcfXstfztiWU+s3Y/9DbQYbKnW0iYPYSe2YdA0o4G3L/xIH3VpSabfOWxNnfctp+Fs5tcv6+XqUC+HU8DPI4DcO68LsFf1MazfcImmG39ZAdWMx4RVKY8VgER8nGTtp9JqGjAz5ddJp9dvFzhrvblmoSO6lSTM+iXh1QhJQVaCpRMwJJYUPcgSA9iIUbpqHD1z0k7tXc7f7pJEdBii6sig6vI6qpAh60uVhAkgn6bUM4QyAZzYczBRkR7MyD2t767z/y84kc/aI5AzJ90pv2Ag42rePR+ePnfzSAQ1LwmV7SuZfM/ZrekaA7ULueHX7zM835T8Nq3Hc+/CEZtL68rYbl2AXhFVKbwTjrJLOsTe1SNqsjZsvc5zartpCBJWp/8WOzik28nk5Tnmija0pVPDPW9H+Zfn/Z6XvvqQcXyt1KvERDWDAjdjY1VdSqfJ3YsDVPJ9vJyLO/iUtLPs7I8I5OzJ7Z40ieQDQ7UzFGzB+uwsNhh33LHyvL7vnWZtSOXweJVxG9/B73X/SPiqV9N73dej37fO7lmrs5jn7XMK/7rIr6o8bTDbb7pyuOc+qYFAG7Ydy20z7MvWOQ/3rDBa577NwSP/lOCJ17B9nTMoj6Rv68OGLvJteiYj9+TzVnaxrG0ZQhTVTTYdl91MM4LwHwjKKpDXiAXlTlt48kqNnkaD1fE5sxn4I4LGik1N950nic/ZYmnXdHn0fs1C/WY460++0KBJ3xibQS6sj2elOMK207SQy1UJwU4dhLocJU9ZrrXNyeraEUoG1zWjPmup63xa799gX7o8R3XbSAQPOPoFXzL1ZchtOZkW3LdPrP/W+mYTdqs7WvSq5mjDTvxBe5Zr/HJcw0EglvP3svlraP8t69dwosU7zuVOr4rmvRVnQC7+EyKGlcR4jaUO9+ns6seEGuxK39fWFR11bZopSIv9/LgQMkqW+nqRlFYkcwuU4qrUpH8LwICNX3VRfyBe59xlnxzpiFeoKk3YvxA0VfQjVOu3mg1Ly9X8vL7oZK9LkAkf52lSQCOaYAOW12yjkvT30GXUElawQJHGgd5ysE5fuZLL7B++chZ6FU/M3rPP/ToTWtVRRwjNTz/mpj7754DzKk9AsGbv3me2554JHOag+b51xzleVcdy3OhbMFjK7naZpW+VVZe1f6SB2bKdaI9WW0j10Ql+z7LJjNp+knewzTps5QFQKqsIE9SXhXArSR/1IONJdAx+tbPsHjY55bvC8qzWijyJf4gpt8zC4p1TxvAo+IpFtl0+RNEsuGlfBid4OI8pSWl7R4XvhNUpG8DqRWewPjw8ESAxCOQNQ7PwHVzPb78yICXflGHR//k+Pv5jb8YXc/48yA8vO9+KbWf/mG059E/sYl4/JMIZYNfelIPL1IsrvU5ElwB7fP83Rd/kMH31dH3/rsBr//qjVyu9w95brz5046Hss0TpwERigDsornodqnaWLNbsvpSo20cS1tFoc4PzJMqzEX8iwb9bJqqK93ZfHn+NiW7aOUzj46nYe46/PJHz/D371/kr77vAX73Iy2uOdTnUB3ma3B0JmI2UBxsDKh5Gl+GCCSBDAhkPdnCUEI77bw05XWpnQ5TJviLViwnSVNWllbQWWW+Ps/XHAYGXU7oDW76b5qX/dhB3vjb55gNDpi0wuNlNx0zg7Y0AMetZ9e5arbHNTP72dBrzAYHuXF+mV/8aJMrWqd4zMI8NdnkKYc2eddvt5Dswz0ZLFult1HVVZexhy4ouzr4tzXvpSc4L13Krq5VUUxt6VwTGxcAYivbFm9Lk+WZl7kUhLmerUpYPt5VTr6sUfjP/vv5Ap4jinzJoOYxqHn4viKsxYS1mKYPA2VOagFl/HhoBcIfKob26jrk+Hbke5l1Rz4+S1WAD1tZLiqSxVXKFxKj1vRH7zEBR2pCQn2Onugz4yv+w1ee5S29Y0ilee7+M/z8246jUWxdWU2+v+dDHBHKBs0Hu9y9fgqlBdfNXQfAB3/GB65LK5OtJLY2NMbbeZ8Nz/IrIpvuYtNpbGmrkg1Q35PTk5FLFrvud7LMKryL0mwn7yRxtr6YTWfa4anv/i3e/eY+t9z7XD74y+d42id/GL75VEkd7dRpBnSaId4gYjMyYZES6Mw2xlIAAhJZYvfpkQ13HTvrsvCw8bRX4CKBIGUyXSmztJ6OUTo2W/EBJAhtnue6fTHdeJ371kPqHnzuy45y07+ab/gj7wt57U9/nDO/8XHe4Pe55fQL4eC1gHlPs7/3U0R6gK9j5sKDDL5Cw6Hr+MeTJ3hWGCOuuJY3hJ/jtls+wo0/fBviK54JrUX8b/wKbnnJNUa2W/Udm3y2ka1f5eWvjar2eZseXUXX35PLWdoJb5aZP7CvLrju81QVObaFu9JURchtdcuXZRPEZcpJlre7zh/+ozoHPrHOf35PfagQH6jDoYbZd3ywMaDpS3xRI5A1pPAIZB0fz3gVBjv4kDPL3XHazl5AlxnypDyLUOV8OUV1r5KmrKyU4j5srsC5eyGo8yBL/NvpFu958372LXe4f+MCamjmmPQfraDeohdvcnRmwL6wzkDEvO9kh1gPOFi/in9/32H+4NMNpPDYiM4xFxzMbG2yTR7T8ElR4yqKcJW2n69DUbyr/42XtbcvvIhs361sslQ0oOfbku0bFsnCsvJtk0AbuQBj2+BeZYDPKjNlz7d1TDr9K9Xk09pCnfX5OkoK/EDh+wopDb+6B1JoNDFCCHZsv3YZla3EldEkVh5VVv1s8tm1dSWfx8VnaOmReN3fXDH+kPptYjVgf20f/+kmxT/86GmaCwO+6jLFd77gVOZEFUc7kpLZ4AB//HOn2BfWE7BDW/7K25Bbsc3G59tpmQKbz5PyzCvzYOeTrVeRHmOr/9b2uyerXTSJXJ2GdxkVfbtpQTAXf9tzuuR+FSAlm878vvvNxtru/c/9R2o1wfdMCXYA6GTrYb0REUjoK4EvNXFFC4/K5eyEvJ92AW8naNLxIwOgpye2+DI0ALK/n5rXJJANOrHkxnnFj3/zGZ77KjNned2XCp75oa/g7k8bXVd/4uNmgRBIZZHWilDWqZ29n3d9h7HuedaB/fTf+Dcw6CKuewK3/ssGn/z/ls3BAEt3w+FHgRewVZ6VzfXK9CgbH1uaqn28mm68HRnySJDT27DwSMnVGLaLMrkGgDLe+ftpB41JG2JWkLsmG+TSjuJOXj0PwJWdNQ7u79P0oRUo5sOYuTBmLggJvUbCUeLLkEDWMixzFhdamW0tF8vU7WKbWk9jyeGKqzJo2LbEFFGcxAd1OP8AjZkZnnM85nH/7SR3XKhx4/wV9OJNBHJ4Io8Oaix172U+PMrlLZ8/u73Hh09d4Mp5yVMPdYhUn3/7yUU8edDuv2WMbH3C1Vds4VjCbWHZ+zJQw1Y3V3y+bnso9WQ0idLqklv562y7sPHIx7nCyupTBJzk62ADUGzp8+Xa5LOL5yjvr378FPduVGuLkS+Jfckg9NANA3ioZP9hKEc+PAC0HpkvA1tld1ULjmktPapad0wjK8vSF8UVgCraUo6QGKADGC7IpvmS01xmZuZh5hDomI14ld//jgf55j85DtdpLmsewi7TkutkfL1mNmvJMVkbKtYPyiaDtn7oArpd+bL3+WvXM7jGjT2ajLLjWZGsrDLxd6Wxtce8nurKk28Hjn6w5bqKDp7n56pvGaDnbn9f8pYvQV3/ZPiWMwU8ikl5gl7DJ6zFeElVlBZ4iTPSdOthFSuPPGmtnU5Ny6jUp8elYtHhzJOZoyTvT2SA63rQQgqPxy6cZ6XXpeYp7lmr8eifD3jKby9w/M7zfMnvP4on3XoP4hlfa0BsKWHWnEIYaA99+wcQx2+C8w+w9rN/yV0fbXDwiojLvmEV0dvglvYrwAvNaYhAcTvOUtV5ZVl/KOJviy+StVVkxTRz7y9cmgLwKBqoXVQmbG1pXXl2YhJka0RFwIQtPF8XG598vPv5f+oFp3jdrbMcPtLmSAMCDzwBc2HMjK/N1pWMt2CBxBMTfL6d3tLyUNFOKtO2sO0CHXmK+tCYY8ZvUo80B/Qq0WyfBzbvJdawWN83BDwEgkP1a/iJW0/yU18M33L1Bq/722v5ox8xqxee8M055sNTWKooPNn7fHu0CWFb/y7rf0UCvUjxmUQemLRai8w++UubhBAvAF4AfAlwBHiJ1vq1F7cWZQBAle9gmwy5ypgkzlav9D6fdxLgJlt20QTSVYYdHHnpP50h/u3q/b89GzKoeWhPMNMcEIZmS0sQaOoe1DxFzduGQlIk03db3rv8Z5SlrxpXIJe3AB2qIC4lKREqgqgL7VVj+SF9VBizUIOr/2GJW35fIUgdjNraxfbb0Pi1q93b2noReJfnX8bD1UfydaMgPM93ax32ZLW1JIr116pAR9X3mh9zy8CFokldkU7u4lEl3EZV9JtRfV74i01qP/Qd3HJLDEwPdgDEvkTUjIVH3YNQ7jyw59ruMgmHbdFD5fMjTsDorLPtxApdIPBFyIw/jy/bxDpifaC4eb/i5g+f4safkyzf+BgW2130x/8Voghx7XUwewgQ4AWIG76Uu5/7Kq75o2cz+/1P4xNPfD+3/NMPmnKCugE6rKeuQDnYUTQfzPatSebGZWlcunsVvtV1i4eTrN4OTQF42ISe7cUWDZCuVZE0fTavSzGvori76lWkWGTTlPEsEjpVACBDf/i5k/zVh+Y5ftUaRxswF8Khujl+Nk2vUcNVfU8aZz+xjvCQIyEy3MOc0KXgyGi7VOUZJlWmbeGTAh2TTCaiPvU20N1gcOAyDvdPEsoW9653COQqAslssDhM/gtPnGHGn6evOlzzqFVCuYC93VelKghzlmxKe5V+X3XiXCTEXfkelvRtwFXAO4HvvThFumRakazDEuea+JSVaZs8ldUhX04ZqFL2LHl+tomla+XEHXf+9T5zRI76j1OUnADSawTETclMq8dMc0C9ETEbaOZrMOMrpNAI5NSrfsBklnxFR8tWte6oAnZMI5NtcRWBji32s9k4Kc3m3exWl6gPzRZzfp033hlx/c/CM48aHx4j2vk2VExF/F3giav8KvzTMBuoUaTc53k9bOVzli6SrC4bM6tOqorSbqf8KryrjttZXtm8RVSmp2wtd/0HX8m73yjZeP722+HmbMj6fJ35hR7NZkQoQQpzfHgVgCIFMrKWHENn1IJtghwO2o7sv9g0tLbL1MXzyVp9pAt7nWidxywovvu1V9J5qs+vPuouDooj6KuOIVr70B//FPrEfcbS5tD1Q3bX/Pl3wPwxxMIq19zwUZjZn68Ek7XdIjlYpU/uRD/Fcp+vR56K5uePTNqm01KX8LQpyUWTrkkQ4iLhabuvgkZPojDkedqeqehZx3lc6J/hn26vs3Cww437NXMhzAbQDBR+giwba44AjR56Nh7SoJuwzSihD2dLjmn2BE4StxtARzbMdqpCZy1ximTM7iQem9E6y906vVhyvLmQZgYSL9RoQtngTd/QYCu5AMAylNhFZe1as7Vv59Nkf6uCGC5QMV/n8bj44aNfv0BrrYQQLS4a4OGaaNkmKFUmcUWTK9tkLBvnKtdW50nIVX8XT9czVAeAlrv3M3e+bBvZiFJHpZEvqTciwtCcztJsRcwFZjtLKDWtQOHLGqnn+h2jizkG5E9XcVFVmWwJGwM7qgId2esIA3yk4+PAWHr05xb4lqu79OKIc70TzAUHM1tFd7YNVcub5TEJYFIEGE4CPGbTV+lnxbz2ZHWeJtF9i3TdsjHWpfdWHaMnmeSV0S5Otk5+ho/8Q8hXfnPMj/237cm8fujRng1pz4bsa2zS9M2hAXNhbIBpByhdZK2xfUsOxrbOTOywNA9ePxRUNhYpBcKkSbftBLJOw5/l7rU23/VVy6z34b0nm3zTlTGNMECfPkV8cg3Z7SO++CvH+c1fZn5n9vOMz/34Niuf1Yny11lyhcPWPm0D0svkgksWVOlb1QDKh5Gsnpqm3NLimhTlyfVh8oN5VdTXxqesnjvXILY+bxVgpriTbEQXWF05zpc9bpW5EA7UNL7UeELjCdhfixHCT5zaqTGhO7TusAmUKnuvLxWaVBjvklJdqLhXtfDIhwtpVhS1MiZ13TWa9f348SY37V/nw2eb9OJ2gmivEchaAnhUb0PF7bxquy6aOOYntpMoWjZ5oXNxRaDjOD8NDxvTO207j+7ilJy7d33fsomRLb1twmabbNnabpUJlI3K0tvAFRdVmeiNqBOv87nz5/jtKY447DV8eg2femCcq4VhTN3THKjDwbpiLoxp+MaBW3ryVqVnmGS7SlUrjGmtO4pOcNmB8G0DHel99ph2IY3TusYcAkHdaxHIiNPtDh88s8xzr7jMXrdRraaMKwIYbXlt/ctVnm2CWwS4uOpSlWxjwnj+PVm9XaoKSLgmTGVjcRXQJFtW1W9ZVG7VPC7amuYp3xTxA3e9qGLd3JTK69mDfRYOdjhQg6ZvLDwavkISJjXYvTY9Ee8qOu1DRZPWI5HLsR4Q6wilI7rxBiA5VNfMBYLPnIf3nurwh485BuEM3hOg//efptZeMfJ8/SzUWrD/8rLCqj4E5UBHNt0k5NKLi+aUNh7Z+Enzj3N6uMjq7dCUFh42BTY7AFZ9cVUmWrb7fOOzTcZsjchVt/xKTpXJY1VwxFW+4B9P3s9nVlocOrrJbGAsO3ypCaRGCoYWHp4IUMlRTumvL0OIBiPBIjKrVy66VIQhbO90gEnituuQdAInegBWCw8hjYVHIpQbagHdaLDJOu1I8Oa711jtwZ/97REWD3X4P9+9RsObo0obcrddm7DOp7GlKyJXeUX9qqjvuYDMPdo+FQHDZQBBFSA5m84GhOXjbMBZVbKBJzbeVYGPIoBtPP9Hl87z6pdPDnZ0mgH9mk9QU/i+JqzFNGZiDtZHFnyB1HhC4IkAT1QowwZ0bNdJaZU0ZWBHVd4ThDuBDhgHO6oAIlKYv6xsTizufBkiYkk72iBSPl9yGKaThWnY8AkseVyAYT6uSK5XqUeWdxWws8pkdDv598hOVXXNMqoyCSvTE6rynaY+k5RV/Z1Er/ljvGd/8Y6AHUoKus2A1cUZLpvdYN9MTN2DuqdpeIoZv3zMmsQhadZSo8j5aRonymTLpaLbT1OPBIzW6OSIXkGsIrTWBmgS8OCmSfrMo5oLMz77mAd5BrXep/PK19JdVvzVn8bMNCXffuIlsO+oq4JUa182ANlFk7TdSQDHMsrrO657cuGPXJoS8LBN+qtMeop4FVFZ4ytrZDaF2MWvSFgX8XY9Z573qDH+wa2znLy/xZOfskTopUf5CAKpqCVWHinFOhoqxSJx8MOgaxcw23W0uZu0k9YcRfFV0e+qVh3bsfBI944D1Oeg1iL0Amqyxs37u3xqpc6pDjzv2ae55VEb+OKKHFN3G9p6X4RE53m54opWgmx1qdKH8/XMlmNTzt3A4i4ed7UohPj3zP0fa63/eNdK21VyDXrZuDzlJ0c7MZnKpnOtSJTxs00Ai3gX5XX1JTvfpx4+yqs56+DtJi0F/YaPH5h+Lz1N3TPOSmcDjURT8zS+CB1bKHaYqmw7mdb8ucyKcFqgI1+XSYGOlHw/8XMSGdA5paiPCusIIehEknM9n/c8KFntn+Rjd7TY9+Z1ZIbfr78p4mD9SiZtQ/Z0NuDD1h/S8KK4fNl53q57m4I8DdDjljV7stpGZWM0JXEufkX5y3TTi0HbLW88/z//6gVO/uDfc+J3f5TL/+bctjgPQrMFsdGKaM31CSQ0E2B6Loy3lL1dym510Xq6E1+AS8MvR0rTzjOkD344BHW01mgUnvCJkuc734czG5K/f99Bfq8WM3Nnl6P33QDcwOefcYQbvnKFP3nq2/GecA2EMwWF2UAAl247KU3an6cBBst4Tw9gXorHyO40bcNpaZZcgyuWcBvwUCSwXV+hrFFMiiaXod6ue9fE0HY9Ugw2onPML5jTN6SAtb5Z+QukpuFpap5BNgcKPBEPT2QZns4S9RNWD6PTV3bKP0dR/DRARz7NpFYdRZRaeKjInAzQmAMvZKA6AHQiSTeGhgffcnWHpj9LIOtpZorBAUqus/2trH0XTUaLwAqX0lwm4F19f1qhvyO0rLV+kitSCLEPcC0dDElrfduO1mrb5GpD2TjY+g0npSLQO19WPq6MisCVovranjU/0bRPRhWK7/2WycGOXt2YRke+pFHrD3141D1o+MY8uhUoGr7CkwbEHvrveCgs9Ipk5qQOTavy3RK8w0BHGhcGiDAwSrBItoCmk4twxlhM4rEv9FG6z9s3fD716f3WCdSFfsTB+mRtyA322cgFRJC7LwMmXfVI48rq7qpbUZ1t9d1VehjLatv4mqUycCufz6VPF43DNplcZczNpykrq2r+fN1K6rV8D8euCPjQW76Ly39he2AHQL/m0WmGhLWYmeaA+cSXXsNLAGvhlYIS23I8beU3Xt7EvkAu1rxgO+X4IQQNemoTrTVSeMTaHNawGfVpRyGB1BxvCjyhuOdsjytuXxlmX11s8Kh/Pg3/DP5bXgKnb4PGPiq1IcDdF2xUNV0+jyu9a0wo6k9VdKwy3f2RS9twWpp9iS4BnKeqwjXPyzaQln30skaWT+O6tuVz1TVfv/E8KvEAP1Bd/vP7Iu6/e57FQx3akSDWmlibbSxSGFOugRLUEj04VYjNsaRi5BdiWI1LeD/fTgIdrjQ7AXTk7wvi8gq6kAWDkVYGxZYJUNXbYF2epxPFfGKlye0X4MxKyC+erPGrz1xhPhxyZWu7syk/6b1LEYHytl6mxFQFAMvkgGt1ywV8jKfRGuKHbq/h84FXV0h3CY0sLtlpm/S4JmdlE6NJJj0uOVplQlWFZxmYY3s2+8Tyhd+9jM1lcBn1ax69RoAOTPlhLabZjGj6MOMbULvmKbxEQU5Bj4tCLvlWxbpjkjQVwp1HyG4H7NhyOosEv272djcXYGPZgB9BHYI6Qg8QQtLwZznbWaXuwe8+/yR3fHWNxyx0uLx5Od/xPeucvnIfR2c205pnKlvchuzhZX2qanuvkt5VjyoT4ypluJ9jT1ZXKdalO6a/adsq0lddPG3yz1VuVcDCJbcn0Ytd9yWATmR8IdHb4GPPfTvxO5/Hbf9p+zpuup2l1/CZb7YJfE3TN9tZPGGOD9da7lpLsQEppRYfVUDqlKo6lbZWrkSv3Q55PnghfdWhG22wEV1gLligF28S6R4bAw9PgBSaq2djznbMHOiZvyZ58sEON+2/iv7P/ib/8we/k/9wVYe7vvF3uOZnb0Jc9hiK9dc8ADwNUFeVivLkZeakc2gXryI5YKeHWFZfNNqG09Ii8KIMUcoPtLZJXBWEy8Unz8uVpgqV5SviPX7/rP/eZ365zeriDLE/T3cuoN6ImWkN6NZjlIZeLBh4gl4smQtjlBZI4Y39gWBoObAd64PdpJ3eulKUZho/Hfk0UwId+XDRWoTuGjq1wCEBQ5I942hFrx5ybk1z91qDZx1r89cfOUK343HhXI0rvmF/jnMVwWfrK66JYpV+lVe4bMLUFe4CWVxkA3IuTcGrtX4N8JqHuh7lVPbd8uny4RSE2eLLQBAbv6rlldXBljY/UcjG296NjQR/+tqQz6+2J3ZYmp7O4vuKFAf1BNQ9CCXUMqdv+SI0K1t4JfWpSDsxBrisOqZJVxXosIbprXFVwRApwfeMdYdWUGuhwwYsXI5AQn8TtBouJLSjC9yw7zL+3ydLfBFw0/yonfz5n0TUvRAImLQNFYMjNuDDJV9dPIvKtlGRfLXV1SY38vE2wOehpUtfVtvebRWAoOz7uXRn15ifxk075lYFNqqAa8U6wGtrvz52v+/Gv+APXubx8s9+5wT13UpKCgY1D6+h8X1FM1HV0oVHpcXQwno3KL+lZfxUFtt7zAdVlPm75etpGhISvBDteQgV0403OLUZ4LXOs9YX1Dwx9GNofHh4fPf1bV7285sciefR//5hBp/8O2rPvoFn3vImPvK5LlGkufZdX035/HGsIhOkraJPVNVjXX2+rJ9U6T9F9Xtk0w5sabF9oEkRs7IJUlm4rYG4lPlJJ1RFDXgyXj/8/Sus9gV1r0eYdOTf+YcF1lZrbDbbybYWCcSAMX32hYcvUu/Qie8OdLIXORqdZ72TDu22SxfDosMVvpNWHbl0hWbXycxGbyxbqqQQqSm1kASyzvHmPla6m/zSB/axekdIHEiuvPMcgoNb619I2TZe1p8mRbbLFKiSFRknvyp1sl2b+0sI1rtEyQVETDrgTTK5sk3iytJl61Q0OLvui0A923W+vKL6a5r+PA1/hUmo0wwY1Dyk0ijA9xV+oPCEOYo2kEaRDqRGCDl0Qr1jlJX5RRYZ27XuKAM7dgLoyMdPavmRlCl8CXGfXtxmoLp4wsf3a4RxBL6PQNKLIzrxGrPBYppz+Fv3mtjbVHkbst9n+eTjpgEFbVSUrqiMovrYwI/i+uzJahfldVUoBzPy+Vw8y4AFVz1c5ZaV6ZoIlqWbrE4v/NVZ1FIHMRMg6kZmbtzercCvmJQUKCkIazFhLTZHhnsGpDZW1qNWnAIQUx0VmyFbnt08/eUhp/x44Pmk1iMKs2V/qSu5/UKNGV/TCmKuaJlFQ6WN09K71mo8+aCAYA7x6MfgnTrHrf/lDm77VGfEdziWTjKXnCStTc+16b1Vwb0sT5tMcOWr0peK6reVHgmyepvH0kLVl+nO76KiD+6eDJXzK0PdqqJqRRM8e/rnXTV+3J0i5lfWYH6hx9JqwKH6gIGCmqc5UI+oew18EZpTWUQNT/hG0MZRwqAA7BgWW7JKt5N0sYAOV9x2rDoK4gqBjnyYlMaiI3HERNQ32488H/Yfh6CO0gbQWu17NFsRUmle9K1necG1Y5WhWrtygX3ZPC4qKqMqau1SsorSVlkVmkRBujRJCHEzcDOQOmV5khBiA1jSWr9vF0umGDBIqSxN2b1tIlcGYGTz2NqerT5FoIVtElYG1OTj3RO4m+avAR4s4DVOqfI8qHnUEgVaDk/bMml6ShJrgUDiST8BPHL1nWbbSBnZxgKXDJx23JgW6LB5TLMBGVXBEImRxX5o5HBjHk9oYuHRidc5vX6OQw2Pum4xK2a5rGmEr9giz1xUvQ3Z+00ZVQEbXUCjrU+48toADpv8yPOz8d6T1dushSWsqn7r4lUERmwXEMnXzVVXW7itfRWRpvYTrxgPinq8qfbrXP/vkjt+cXr9dpDsG5dSIz1N16hnSKGZ8RWBFEOAQ6N3HJhwbV/JlmMFVS4lh6VF5BpDpCRSfSLVZ6U3oOaFBBJObAg+eS7g6Ud95kPNYw+0uXZOcHnrEDWvCX0DcsVn2szOmW/3rS8TzP7UN5VVhOrzu3x4tg1POoetomeX1Tkv2ycBPrK8Htk0pYVH2WSsKD4/eBYJc5tCW4aE2RpS1QZVFWTJ8iya2BWHCQS/8V0nuHs94O/vCoaCNlImrdYKBENnSJrU9FmZSfQWhgnwIaURhrZtL7tFF8MhqSv+YvnqKFLSZeb7euFwfyK1FtRaRI0mnvC50D/DQPWYD49wRWuNb74q4Md/4gTXzV2Xe4Bqbag6yGCjIuDS1nfKeBel3U66URrNw+q88G8Hfi5z/4PJ3/uAZ+5esdNOlGz3rrw2MCOf3wZY2CZZrrKrTPrKgBNXvaq/g9e+7Ti3fPMDJfUw1Gv4DGqJw1IZIaUxk07FwyARIZ4wivPQCbWLdlN26wLZZktXZt1RZhFnK6cq0JFPq5Q7XgrwPfOXWNUR9wlEHSVDTq/3uX8j5BPnPJ59fI3Z+gFkIYCR0vRtaCtVBRZs5AIai8DESUCQouewPbO9P+/JahtVmeCQSTPt+6sKRlTh4aKi8b9M/863R9uY4tITkjjP50VvOEb/LW/gZS+/BfEHfaah2JdEgYcA+j3P4KXC+FqSQuOJYLjt5JKwwqiyEOiS2ReTXGUKsyCohUDpmFgPqHmaw40BSsM/n/Y5e6rJ628P+M/PWeFwY5Fm7AN1OHkbur2OuOwmghsPccMLz3HzK69DfNk34G5Lw4KzlcPePm3hRXy2PLQlTVVd3lVWkY4/SXt0p32YyeqpaYqzkFxCsMoHzg+cVZRrW/lF9y7wwqYg5K+L4vOdoqgOReWNwgSS6+dj3vCv80SRpK9gtQ89JejFAnM4U5yAHBk+KuNpPk9CVrP82Em6WGCHVuVgh1Lb28KS3GulRgp7nqc5PzhXbhKmFHqQmFkKaZzjAVor+qrDfHiExfoVBLLOTfuP8+zLjnDd3LVM24bK+51r8llVubalcd0XTXTTe9tE18Xn4U1a65/XWgvL3zN3t+QyAMA2gNomYC5+eZ5lSrFNuc3nzU8mbWOFi38Z5Sdp5O7L5fktv19NxmkpGIQewjNH0aYUSugnLGrSXCh2eDsLuOXnJJYb06TJgcRWq7hSGZpPYwEzsmmcYIgeWdqlMj05wl0gubw1y6m2x6fPwxvvqBMlpwIU03bbUJH+kOVXRQa6lOqiCWqR3HXVy1aWC2R5eCvLF09Wl72nSUGJquVUmaSV8ajSJvN5y8b6/OTSNQlNfzNtTXjIL76Bv/qtiD89986CupXTIPRQyULjTII/D5QgSP0t2RyL7kKbT8sZbp1BMrLuKHj/l5L/Piiuj5Dgh0NLZ4CaR3IqpeApB+HU3U3CT/X5rb87wEB1jazvrsGxRyOufTI05hDPei7B996SgB0wVRvaWjlLniLdxJY/z6OKXLWVbwurMid2he3RlIc/u4CBojBwC8GyQT5toEWosEsBzzasskZua1i2iUA+Xb5D5NOQSTP+DGt9xfqJgG7bZ30AmwNzVOn5nk837icpzXFNw5XAVImTchz4SK+lJWy3aKfADhuYURZXtoUln892b7l2WnUUKelZk2ulRscGtxbBDwm0x4X+GTrxOkvde4n1AF+EeCJgu20o8xAlPGyCvkwRqjoBzobnJ7GTTl5tA8aIxxBb2uG/LwwqklVZcsnw7PepAorlv2++jblkcxnvqkrGpG00W78q78qE/cFP1Bz8RhT5crilRUqNigVKCfxgq+zypUZgfHjoKsrJtAptmXzOyq4iGVtxpbAU6IBqQMeYPM2AHdk0tvg0PIpNXXTmT0VEqsd96xtcNzfgvqWQd7+6yfPf3CHSttXhnW1DboCiivBxKbrT8izSuVxyPBtfbWK+J6vzZJO7+bCq8taVf9LyyxZK8umqyFuX/pFPX6TD5NNa2tzyKt2O4u+e/tWWOlWj1OcSmG0tqYPpdAKuc94N7KeqbA/8mBg8eSgtN4qoSI/Ppkn0Y4FMTp6UBFKzMfD4m8/XuPxR6xw+sc6V717m5S8WdEMPfeJzcOEU+o5bDYAdNiFosO02VKpD2ORsVcrr9jYe0/RZF69sWJksGadHgpyeYjacbzCTTJiyQjCNyzaIqoN6Pr2rEWfrlx8cysrMlkdJOpeSUTS5NOmPN4/w1c9dIooksTZCth1J1geSTiTNthZAowwimvrvKAMyUtBjtwGPqjSpP46yuCpgR9X7qmBHNtyqyGfCEh8ekR5wOn6Q9549xetvr3Ohf5pf/0SdXpw/4nD6NjR+b1NSixSbInIptjrzly0rH26rE2x9xjxfVz32qJxsg3/RJCyf1qZQ22i73yMr+21lFfWDIpmcz2+T865+4VbS77vxQEFZI4oCifbE0G+HlBqlBLE221mM41I93LY4XmWL5UIZVXXcnA2v4sy0iK/jd4vstAEdNjnqvM+lzacpsvpQyoAecWSU60EXoj6eDDjYiPn0+YBoIDlwapN9b1rnuT/StVh67GQbculCeT0mnzef3pVu0n5bpe+48rpk/R6VU5kcKxvrbONpkT7u0nvzVGWCWAX4sIUX5bPpN65n2EricU/m2d9S42mHp2uHKrMVORpI/EBR94ys9kRBuUkd8741pnFgWpX3jlAWAHb9bYd3FUq2GaaLuBIPX4YE0jjzvvpwj7MfHx0IX+tGPPdPPTZ/733oez7HfT/2AXO8+IhhvgBLWJlMLAJBBFvbsI2q6kt5WV2mS1XVtYp07z2CqS08slQ2yNqEsU3ZtQniMoGabzj5j72Tjco1EZs0r85caT54doW7lgKWzzS4cCGkHUGswZdmFXCgesR6gNbGq/+Y9YbzUTJpdhMBrqqYTwto7AbYUWDlYd3C4lpBzN5n/6REBIkPj6BOpPrctx7yrvt93vvpWf76vhZ33tfkfafWOd25g+22oXLlOE1TlX+R0M4LfdtA4EK08/XJ883LhHxaPfw/1mJX/r4wyPb+bKBxWRvKh0+iQBTJynwdppnwVWmrZWltk1c3jw/8jOTc8/cVplJSDFcKAcLauBwKpFGi29HIbFkKL7dlMVvsDsnuaXwcZcMmdY5XdvJKPk3RdhcrmJGLt6XL/sV9A3rEfbTW3L4a8unzMNMacPKaeQCO3rfGi9617nignWhDRWQDU8r6pK3/lJFLLrvy55/bJTPs+fdktY2q6LiTAglF+VzyP/tra0NF7cQ1+cuGF4EitsWYsnIt4Tom/j9/zQN3SP565nX8wVvmLHnLSSSyJKzF+L5iPcE9pTAnJTrlcxHPChYfeauOSie/TLtwWFgROf6X5VcFDJkUKEnSCyQajScDPOGT+I7lM3e1uPqzy6wuGtCj0ww4+vfnaf1/P0Tnf32Az308ov/6d6A//b6UYb4A20M6wl3k0kOr5qmq/7ryVJlPV4mr9sy7JasvNdqBw6WLlNQqinTZ4D7JAD2Jsl+l4diUcRe5Bo38JG5UvtIxr/0Bycq3NlBK0N4IONXpc1nTpOzFkqZPIhiSVTQhENI3StyQZSKktOIhO4rWRTthvVEUXqS477RVh6vMfH0yA0Tda3Ht3Dm+67ouP/KxBf7tvyuiI4pffPAof/qS+zjSyGaevA3ZKRuf/3WRq0/m+4AN4AC2CGuX8C2TE5Mo8Hu0laoqy7B10mT7LePronxbyfN0ARg2mV+krFQB16pOKrLvIxtm/p9fKD4CUSqNjDXeQBFllOTUTNoT0FcCTxhl2pPBzvvwSKmqDK1qIVLVusMlG6Ww27gWbXepekqLC2BRCkgcl0Z9iCPqXotWsMRN++BcPeZ7f2SJpx+5kovRhqqnz6ctmiDawMtJFf6y/LYw1xixR+VUVa90ARU2naCIl218LatDkc5QVf92hRWVPQHvqM/rv+c0++aNDG304q1pSkhJQZxsRZSeQikxPD68lsjtUQ12vo3nt8e4yyiQTy6Z7nIyPSysZPHUNpfYibnFkK95Jq01vgyRyuNgPWJuvsc9Ny8yqHl8x0/2eIG4F267hz+buzN5pAFv/JHzvPgfVlKG+QJcBTvCq7bzScFlF1UBuKdta7a+vSefU9pBm6kqg64tjwslLkKRbSsPRWhzPh+5uHyebLwtXz5tfvC35d+qAHki4Hf+d523faeH7sHqSo1e3zgvjRKTutRvxxbE1yWstmuWVpWqrPp9IYAdRY700nTZvyhGd3tGwe5uwKDDfO0onjArvquLDR7/0g7ve8WAm+az59BO14bsZFMoytp6duJbNjC4BociBapI6Gafr3yw2tsXPgnl20LZty2Tv1myKeMuBb2M0rpVmVjmlYUiyrbZoud38Rr1uz94tqT30oYlzYi8SOFFiiiSdDveuONSz4AeDV/R8FXyJBWG4CK5BpPLYtf+b1s5FXiXgh0pZU+ySi0V098yS7o0TT4+b/UxBDoYyuPhWKki6G9y4/x+Ag8+de8Mr78z5ANn7uUd9z3Iqz55ik5ss/LYuTa0NX8VZdsl9/Ogoo1fvg9WAV+y6cqAEhcQY2hPVtuoaCy2jd35NK5vWCQ7bd81H++qh4t/1Q9h012qtKkifkBQ55ZTL+CbT7wMgH998p/wx2+ttvUwpXRLi/YEYaiQUtP0oembY2l9OV4X1xGyLqoi37dYeow5KbW8j53w35H3/Wf7y8btBGXrm/CUwkPpyFiwI5kLY77uugFepLjxI6f5vdce4O+CY6x81dN50fu+GICvel6dW9Z/EPGs57kKonIbMpVxhLvSTcLbdu8CNyYdV1w0HXjySJDTO2DhkVLZS7Up0EVIswuZcg3K+bJcHz0fl1X0qyJ6NqUiqxRhubfTbLAIwA9820k+cx5Wuya9n+qDwttqHpcVQpJqCu/FposJduyWv44yoMNFSqGjPqK3Ae1V/H1HqHmKJz5mlaVrBK94zIBQ1nOZpm9DI3K1Vxu5FOGqeaqkL+pbLt57tH2qChy4Bv2qeVzXNt75SV9V5bksrUsJyYM3eT75caR8dG75C5z8QI+r6VjjpdJIpQl7Ef2Oj2oIVCwIAj1cNZzxVXLUIWb/svDGy57Wf8d2AeGi8BLrjh2laWTyljiZufcM0OGHw3Gz4c/xXddFzAVt/uLX6/zR2XRlWPHJP1rhqYdmk/udb0PVFWjtiK96bVusKap/locN+MyHV33ePRqnqgsVRflcesE0eoJLh7aN22VguYvKAL0sjyo6RqbOh64D4Nt/xOPs5wXvP7PEJOu4UmkGNY/IT/x3NKKhn6Wap/GEcIIcVX1sCNw8hmm2xFv61nblbb4MW51kRnbuhuV4bkE2Pe5XqRhzRK3gWDPiZd97nlf9n2M8+v0n+cv/IvhLAB7Hn/7lMuIJN8LMfhtzJm9Dabo0rEqfKtNpp+2XLppkblo1/pFJUx5LW0UJtikLRYLPxdMWbhvMbTzL4qoo/C4eRYO9S1nJlzuKu+WGYzz7shilBE3fHF1Y8zSpB//hlhZ0NT8esEW47BiVKeU7BXa46l9U/nbADtve8C3XOejS5scjDU/LG3QTZ7Rw8sQsDX+O3WhDduWlilKa76uuflFVwS3ikeXjAlsKytECtUt/X9hUZRWhSv6yONfgnE1rAyfyinVVQK5oHLCVWTaJzOcbp7f+P2ecpaRbWtI94VLqoYVH6vnfH25vCZNTmjInb2VpGrldZXuKS3ZOaTmy5VSWMrJZeeTD82UX+utwyOu8lUicHOWujNNvIQRftNDle/57h6f/iuR7ft+AHp3YNbbuXBty88vnmVbHcdXHNiGoQkV9sUCu7MlqC00KOtu+0aTPn9clJl0UcfHMp3O1pzI9x1ZWmc6QTSeY+R//ncu+tsFTfvX1BWVtpThZWdSBoN6IqAdGPreCmFYQJ6fo5Uscr1u6IFkGgAgkQgjza3m3AlEZRNk2ZU90zP6llA2z+feYhGx+87SCOBoCPVJ4aOLhMcAAj3/KGXovbdB7aYPf/t8hANED64j5I/kC0ofKP6QjXZn+kaYpas87AWIUhZX1r7LyJ6zfI0RO78ApLfmJkmsgn3Q1oGxgrjJwlzXaacPS8rPx+QElX3b5ykra2QcKlBYMlBGBWiegh1ZIzeiklqp0qfj0mGZVMU9VVy2rOCfN8quyd9x1wkBewY5i6PbR/S6oiNj3eWAzZD6Ea65Z4zvf4rHaP8VutCE7VVGysr/TAop5pLxo4lsFoc7n2aNq5BosXSsTZd87mz+fLy/vXUCeLc41scuOJTYqAkhcdbddl1F+gghVjpAd1EaGkyoWSKGpJ646PKFpBrFRbYVfid9EZAOJXbIw74yuyHKjShqbwmyjPLiRT1fmnHSYzgGEJCe0pH9Ded/bgKiPQFCTTQ41NM+76jJeeuNhfv2fFgB41T/tt9d5B9vQOI8iYKKMLxXSVZ1EZ9Nny7f1Z9u72IlJwCOBXIt+WRlYBUyYFKwqKn/S9pzyqTLBLGsXRXOEIj1l63glZraCE2XkRYoo8AhDA3gGEg7UjYiqZbYj7ob/DjdV+B6TWNm5tqtk5W7RtpbsiY+Tgh6u+sURqGgM5AnlDIHUKA2h1Bxpan7n2Ru86bnzvPpzZs6z9rJv5K9vfhOc/Ez2AbMF5itgSVcVUJ6mPbvKtvHPp7UBya480/TZLwwSQrxACPF/hBCnhBBaCHFL1bzb3NJiQ47LEOn8h80qBFlhWhW1y4a5Go2NV1mDKgqzKQS2iWNRHcfrsj5Y5vc+MMdMc8BaX9GJxVCf06jJkd+HapvLpOBKVbDD9jxFYIcjXWWwo4rz0qL7MDCntcQRSsfcvD/iiw/U2CfnWXrCKWreQibTzrQhO2WVKVf/LMpnKz9LRRNpG08XH1vZW3lqzClGezQJ5RXpKm2hSHam90VysKge2fRFZWbT2cYGG19XWLZ+RbxsfLKAR7F8U55AxiZNNJBITycrHaacMDl6b8cclrrkvNWRZ4UtLfm0k8rzPHiRNZEeCxdGrkrpqGuBTK4Sl25niaIE9GgjtILOGkifFZZ51/0NznZO8o53HOSGj13gF17fYakzAFoJ891pQ+54F6hSVIarjkVl5dPZnqto8m0rd2vYnqwuIpvunL/Ops3HuXTqKrpukZ48DYDm4l2k97t4u/Ln+eTizt7Be37+PF/y9ZMBE0oaeZ1a4tU9A3o0PLXFf0dVKjqhJavHC2EsPdLfMUrlbh5gKLPQqyKv80DHWAVzp7RsZ+t80QJnEpeeVBbIOhrFvrBHL474+wd8PvBXC/zz2+Dqz54C4Df/UvLO+yTf/c/PgOaCnfd22tBE4Fw+vS1vlbaYr0ten3bp19P2Q3fqh5Gs/jbgKuCdwPdOknFaGyW2DojZl1ul0dgmNFngo4hPlS/jUgSKBnRXXW3htkEozzf7l027VWGfDRb53qesG2d3MWwMvDGEWelo6z6/nXImNAlNsp2kLG43wI6CdNsCO2yO9KwO8zL8pA9+SBBrDtavwpchyvc5WL+KhjfHTrchtxKa7Ut54WpTsm1Kk60u2TSugaUKuZRrl+K3R8Xkmpi4wAnXpAZHmuy97VdY/iZVJrI8bYpzUbsrAmdck8p8vnw/MfT97yh2iucNFLVOhIg1SomhhQdA0zf7wkPpEchaciRtiQyv6nejaEtgkeVbUXn5MItSLaREVNliWWTxUURFgEZZnFKJTM5MHqSEegtP+HRj+Nv3H+CGj53hrscc5MjMMZ548Mr0yXIV2bk2NJ4ur+i6JotVydZPi3SpIllhk79Z+V9dod6jlGztoAo4kZWjeZr2/efbsautTsMjT1V4F/Uv21iQSXPoOr7yx5o0fu9HS+o7TlJplGfk0kxzQNOH2UAxF8Z4QuAJfysYkaEycGMEaMjSo2oNGJJ7PzYZXpXy21WyMrjMUaktjcylK6KqlicY0EOj0FqjiTnX86l7IJTm6s8uD5PvF/t50XWHETd8Cew76iqYqdtQYVhZv5uUp6survtJ+7hLVnxB0Au01k8AfmTSjFNaeBQJr6JGlcZXBUXyeVxoWP6aXHpbvcvq55qEuhpgtm5lqJy97EGiIA8UdGOzpUXp2GxpGWOTcSY0VoUpBeNu08UEO7a7jWWnnJcqhR50EVEfBl16MuYDZ9b50sOCGX8+SbTTbahKWJbfJMLVpRgX9XdREm8rt6xPc0nuDby0yDU4u8AJnUuTHyzTe1uYLZ9rclfUblyDs6seRe0t33dsv/k8tufZyjuKipU9P1JIpfEjRR9j4eGJ5JhDTydOSz3G9nHvpL+Ooq2AVYGOKaw7UtCj1KdH1qIje1StlIBizICm0Hojn8YSNyxDIYIG1FrmD6h5TQ7W+1xxzRpnLp/lV3/wNLCYybh7bWgr/yrps2FAaf+x9XebDHf1aZc+lb8vVqz3ZHWeXGNdlsrGVFv6In5FdbDxLdJ7i+rjGhNc6cvqVaSrsCWN7kUTTwvTI2nDWkxjJmYuMH6WpGCLBV6R49FJLLCzVh3D32z+ndLhqxw760pXaOmRCc9Tlbpn+SVfLFJ9OvEa57rmnR9qQLc52qL0Pb8fo9/9dsRXPAtmDxcw314bsvOq2q9s+avo75P2k0nKr04PF1mt9fQdZEqnpWU0qRCvys/WKIoUhrTcMiGbH8iLwrN8XXUr6mB2pf/OtTv51IokiiRSGJRZacjv8dYlKDFQrtzuBk3in6MqlYEdRXG7DXbYjlHM7h/v9qHdhbjPZgh/+vk1fvsXWzznl2bYiM6lNUt+d6INFaXJkqsdF/EoK6NsIHGVWaZ0b+Wr2WpQ84V6hNb2aBJlOY2zTXaqTsTyvCahIjk7LR9X23W1tWzZtjHDpHvHdza566sOOWsQ9Mxe8PS4Q5k4KQ3l6JhDkf6raq48KRXJf5u1x6SnrrhWBmFo8eH6A+wO8rLX2XsXoFHkuNTWsdPnjPrQbzMrZvmKY12arYgn/cdNnnb4GDU5w/hq7u60ofH7fPrsfbY/pX/akq8I0MjXydZvbaBJkW7lKmec9mS1i8oeINsOigGlrfkmLassnW38LsrjSj/NR7M9u30s0v/6Ts5/rMe/3PA7QwfEVUh5gtiXhKEikJrAM770PGGOSXX5WHIBHGVWHMWki2VwFSDaZbnh+pN+cXzKs7TqFeccef9ROap5mpYf85j9A778OWcBqP+Yx5Ne9UZe9x/u4rXzr4ble8rLGVLVNmStbC5+0jbsmnvm5XCVOlaNmy79bsnqS42mdFpqu4ZicMGWtkhprqJspHzzSoCrLjZlwVa3PG9K0hXVNx+WV3BM3Mt+8Qj3rgn6PY+mDw1f0fCNQEiFqNJxMZKcNzd7OFt8VAE7Kvjw2DWwwxqeuZYCwgDCGQaqS6xAfKXPX//UWdrRha3PZmqb+7XFgb0NTTvJrLLiUhRepNS7lLV8nYv64yUoNR8WZPsORd/cJWerfJP8xMw1sSsrt0y5L+oXVcFCFz/XeDROm9F5rn3P2cI0IjmeVkrzF0jw5MigQQhp37c9rMqUK2e2PFptBUCqjAtVnJRmKT/2FAAiQ5Jy5MDUKqcLwI5h2nxc5j6zrUV3e+j1ZdhcgXP3gx/ykaWAIw3NiV9WdOI1fv+zp1gfpGD07rWhYl6uNu6KqyIrJ+ljNsDG1p+rjB97ZKeqbSufflLKf8MiXbqoXRSBIfn2n4/P1qOsbxTVr7i9ve4rPs7S/SEHjwT86Q9U942kpCAO5NCHh9IjkeQ6SaUKpXp6djtLdovLjpBLJufBiiKgI7vVxfPNn+2EljzfbB22MzYl3zWQdWaDRfbXalwzFxBpwb++/xD33LxI91Ux/tEWL/7Ak7ml/Qr0yqkixgX3k8qsfLoy8KKo/efD8/3SVpZL7k4j84vSPnJoii0teSHqmvyUTcJsHy2PbLvSFfHKhxU12rLVmHElPdYD6zFV7jqU1W9U7v/5lTafOtfnNVGb0INaoihnzeo84YOujl5fNJrEumM7ZnCu+4sFduQhS5eSLqVJ2+1Be5X52nG+6aplnn/tBreebfB1l1/jeMjttaFqvFyTzzKwsmhiaQMvXPXIl+8CPtzv4mHkXOkhpjzQkVLZCywCLVxKsqstVlF0y+pVVp/8r00BmYS37bkMGbAydD3A0LLDixR95aOUIM4o0WBkuieCcuW5TK6mQEb2r8zHUlVnpUVkU9hdTvYseUY7MrMAsSyWuZUcl2bS+gFDa7t2x/wCeiFGzO2HqM+XH53hE+d6fPWvS37hIz0++dl5vv+mdHzfvTY0TmVpinhX7VvZdEVAqKtuRVScZk9WVyHXeFemQxcByXkqAh9sfIrGdBtfWxnpdfI76ELQKOGVl+dF9R/F33LqBcTv+Rc67z8FDxQUkSOpNCLW+L7CE0ZOB0LT8NUYMGEDKcp9crjjt25n0eNyHar7PMrKdJsfDhtJC3iR1ld4DL+bJ5NxI7ONXkqIpwQ5huX7pBZ3gV8jiD0gQPoem4PzSCH4zuec5e9/zxxL+4brv5W/+liLt37mL/Bf8n0FhUzfhqbrhy791tV/iubPZfNpW3kumkS/N7RLsnpRCPHvmfs/1lr/cTaBEGIf4HLKMiSt9W3brcw2T2kpeqmTTqhslP/IkyjPRXWyNcryxjYOdpRNDicZhGAuOMQN8/dydM4oZlJoYg21ZL+3FH4idLOb6DBCSzISQLtp0bEbJ79U8duxU2BHURlQbtkxCSkFShg/Ht01js4fZ6l7guccz+4/3Nk2tJVs7XtSwV0W7+pDZUBMtj5FZVUV8Hu0lYomRUXhrrAi0ME2oaIgvIx3to5lE7KyMvL8XBPAKnmLSSqNPzAyQylh/vRItASZLS27RlUBkGz6onCbQp2nqquW6f5trRBSmjcqgYgELBbJ6qK2gBwV5ThAfwC+B/3kCPd6CK0ZRKMB4QxsrtBqLXLt3BK/+ycLPPa5K7z7JUWTseGDsjNtyDIh3JLGxtumBJeV5SJbmZOAK9PoY3tkqEgvLUqbpSpjchWqwjtfjzJ9Pq9v6ATsKKubTdcvexYBh65HPvkc4VJnMsAjNtZ4qW+mUBofHkH+6Oyx0qT1fnvbWRKy6Z5ZMLjKdpb0OiUbcCIk2fdqTh+z92UhPPClOUpW+qCioQyfmuI+aD/ZYighnCHyBP1Bh07c43hTItG8/vLDHPiTU7zt+ft4x423Ih7znexOGypKMw3IUIVXPk1VYBGK9fmqQM5FoWWt9ZNK0jwfeHUFXtt+iCkAjzJEyobu5vPlqSoYUlXg5tO7JlRF4IwlXMcZQWLj60LKs7zsderGG3x02WO1D49dMFtaQhkOFWONQul4ZPGRFzg2AfRQbmeZ1pJjErCjCrm2orjK2ynyPXM0rZSw/zgfOXuCxy8ewRf51eGda0NbqaivlKHc05SfV3Sq9GubvHDn1ZqHjXOlh4bKvlt6XzbxqsK7bMLjUjjyEzrX96w6+bKF28agomcsqt+onv/1hW7rDjBOS4XS+IOYeCCIBpJ+ImI8MeI7lONV5JsN1J1Ebrk8/bu2ERaZSm+H8ltetEIkK4g61USUGmklSoGa0qJRSmPVkVrc9QfQH6DjCNHdgMWrWFPnGSj4hR86y1ccPZ5WjN1uQwxBvNyEcAuvSQAGV5xNPuevq/QzG+9isGNPVpeRSy/N3xfJR9uYm81fpqNXrVueqo7vo3uN2gIWFJdZVbcB1s8S/cPH+Pr7X8xxVgvy5bgk2w/BOJYGhv47TCmTt98tgEhuO4vTusMl08tkvcuqo+j42eR7pKTRuH1BxgghkV4wmgMJCYMJF1mzcxQhDegBBkSJ+/j+LFJ4zPgNzrQjPnEuwI8UT3ul5Ps//3bEtc+GoM5kc8lp9WMbT5eu6irfpXeX1auoDilNAo646pKJfQhltdb6NcBrLkZZO2DhUTSZKmtEFKSx8a3SQMtAFpcCXoG31QSsDKl3lTNexnP+IOTQ0VmecE2berKfUKOMIEKNC6MU8d0uyroTtJ395mV+O8oU9CrWHUPemXZRFj8N5VF03zc+PKTPhcEyb7wr5Np9y8lxtOAGBqZtQ0WghI1cypSt7ZcpYvk6lfWxPC+bvKg6GO3ROOW/t00Bzk+8bGSb7JQBHLZysvVw5c8rFK6Jmyu+imLhAtNsz5hNly+/nKTSeJHCjxRKGX6pAV7NM8p0qcPSqtv88tYcVX1uTDpu7NSR6KmyPOapH4RU4Cl0HI1AChiBFZMAPFJC6BvH0WDk8MI+qIeIcAYac6zo83x8WfG1l0v2hUeTic3FakO2fpgv0xVX1Idc9zYwsAhMKeqPNpleNtbs0ThVnWCVkW0Mr6JPVxlbiyZyLrJNDLMcpSVtel1GLn3J0GvnX83R4yHHn7BagdeI/GhcrtQ9Y2Rms9bIgh9pvNN56RDU2CF/HXkqA6W3bFkZf57h3CLhkzpnzd6b42J1msEcoy488CTSaxj+KjJblSYZT6QPfjiyvvNDBkFAPzrPQHXZGHT52xNN3vuR/XzvC5b4ngM97njFMtfdeBdieBxtvg1svw0V66hQrZ+59Fkbv2nIVcdJ+vIjl7YJeNiAgqoKKWxtSFUmeWX1mGTCZrsvUnqKOplL2NvrpoiJ1WCI9B6/an3o0T9IfsEIII3OOHvLvdtLAfSoQjttfTLtVpYsbde6I/0meT6+B1GE8GegPse+gc8rn+zRCo5QbXJfrQ2N0yT9pQq5AJSisouAjqL6TFbXS9H788ODbBMrcE9iyF1PWo5rMleUx5WmLN5WrmssmrSOgm68gaaavPAHamgqnQIeKUmhtxx3uOPk8vGRjctT3roj+zsN2FFmUm2bCMTRCAgKFNpP3lO3b/xw9Evef7odpjVjtrAojWjNoLs9kBJRb0FzgXhukTk0q70lWsFCDnzarTaUz18GOLr4uACUsvST1MmWL19fF9gzTnuyOk9VJiw7OUGpqmNX5ZOSS/dP0xbpCzawzcbbVVcB8QCiLimA+rSvaOGHk+tzUmmE0qjY8Dd+80YWHnna7uksKWhS2bqjKmW3s4yBHaa8vDUHjOYW6W+aTmu9JQ8khyZkgB7fD9Fa4oUz0Fkz4EfZlhsvhMYcA9VDhPvpxZv4UqJ0n168yUqvy2fPN7hxHt6xWuMZR9us1ptc/+ffBAtXZJlR3JYrtKEt12UAiGtsKJtf2qiIty3NJPq/rZzyOcHDRVYLIW4GbgbqSdCThBAbwJLW+n1FeXfQwqPsI9sEZD7OpmTYeLvubY2mrC6TpMndjymDrgZpL+fWMyf4qbcdQdwTIa4TLB4SLC70qHtmD2GsEwEjjYdnlTgrNchrvoicX4+Hki7GVpZJLDt2kvL7OsMgOX9JjRzkgbHumKlDYx7mj4GQzBoGJQVM1oYMlQm0MkFYFF4EBNp4VC2rSBbkaSeVv0cCuSZqZUBuPn8+jw0EqTKJKqtf0cTSldd2X9b+i4AUW5rx9C/7ttWSumVKzJhJAwwiQZwokVvMRssA6yo+jqpSGdhRhaoeVSgL0uV5pPVKHdmlycIZs288jND1cAR8RPHWrS4p0DE/i/BDCGfQGyswd4T48oNmK+GFU1BvsRmd5y13R7z6bw7ztpvO8/qvXUyY7G4bGuc1Cf+ydEX1tvUXF2hikxe2/qUtefbkdDWyybmdendl8t6mS1cBJsr0iip6x9b24t7iUjQBNeHqbW/kz15wAgDfF/zfr3+xhU85KSnGgOlYM1xstFHZtpx068rw3nI6y9SntFTZajgEO9ItK5q89QZJ6GhOYUCOFOAYAiHDez18lvQTaK3pxut4IkAKD79WI5Bz0G8bmZ0HP6QPM/N04nUGgyX6cYf9tWOsD5b54JkZGr7i2rke57o+d69J3vGGeR59+0l+4NRh/vZVdeLF5NCGpMbJgxe8rPI2VE62eWU+f173rqrbumTnpICkrYxJgJKHJX078HOZ+x9M/t4HPLMo4zYBD6guQF0CsqxR5Kmswbomf7aVibIOU9KYhit1ZYPG1rgvOXwV//gyxa99/DRv+bsjHDrapulD3YdICSIlaEcQygR9FQYAMT4gLPuapQSdCLvUiWn+iNrtUJVjYm1UBQApAju2YxmS9/yfDXeVnZCYn0e3N81JKy4Kg1EZSiNa+9DLZ8H3EJ5x7tQVAx7YPM3+Wo1PnNE869gVlAupPFUB6KoAD670VcAPW1iRUlMkaKsq+PYaxXv7wgtI534nobwyPskErwpwkQcobL9pujxvW30mvbfVozxdVcuOlNJVQ8BY7XlGkY61YKAMeJ2ulgkyMhsMbp1x7Dm1BV8VEMMld13WHVUd4qVpbHmz11IaRThflueP6peYTAuAIDRWH+2ukctRZhysh8Znkh9CfQ48H7H/GButOvetnqDmaa5rXUlfRJxeX+PBzRl+9D+c5erZHrA44rNLbWhrmiry1wY6uOpmAw/z/dE20S6SF2UAp6388ZA9WW0jmw5aVSe2pcmCGa4Jl0vvrTJprFKfMtpaj+rbPbaCNfLbXswtUczSi/8H7/rzwTbrNqKBSmX1IKltAlQk8kmkQII2C5FFAMjE21mm3QoODI+YzYAdtm0q6X0KeORBDpPO+AvUWg/fQ5pfa4XCxKXjVyBr4EEcR4RhAxlFsHy3cQ4dzpjM4QyxjohUn268gdIR64Nl3nuyybvuCAhrMa98Wp9/Xwr52CmfA6c2OPH1B3jlfziD/LdPw/HDRP/08eSElmnapQvwm5Rs/dTVnyaZB0/S3138qvKyc3+4yGqt9c8DPz9N3m0AHtv5AGWAxKQgSD6uKsJWVB9bWfnVjioTxeJG9IkHagQ1hYoFS5uSy2ZUckKLyTdQPePYDRJEdpqJDDsLfrh4TOqAD7YHdkwEfuS+g9I5ZV2N2XTptbWtdUvjU17dvlGyazWTp7thwI56DT3oD798J5L04j5PP5JuaYGda0NlIEkZQFilj+Trks+TF/yu/udStiYFI/eonIomZEVtwcXDNqEqK7usDi6Qo+qks6hsG9+q/Mb7zTe9fJUDE9RgyCV3zlv2tBal40SZTk5tSY8CTI31bGBHkbxzbV+xOSytInPzZdlWJYuc4uVPDEiV8fQvvfdCA3CIBPzwAsbaTr8NvQ3DJ+ojZuRwiwqhHD8GPIrQUR8RdcFrofYf40L7LlZ7Ne5eD+gt3s/hhs9tq3WefVmf6/cJFutXU9zu03vYThuyt+mqgEOeryvNNH3URTZQ05a/7Bn2aJyKxraysTpLVQAM1/g6ab2q6u+T6BjTju8ZXkrxuQ97PHfzBbz3hdNxU1IkR4nroQ9OpVOLvAFSeAidnqyVyupESCegBzAEAMZrqhDYty9u2c4yLdiRl6cZsGMMxLBsWxkCG8m90nECZqjhtdIRvdgA9b1Y0IlMGbGWeEIzF0bMhcZXkicClI6YEU2YO2LkulLGX4dWeKKGJ3wCWacXb3Lr2YgPnvXYWAs5c9sM/3UguWIh4u7b55n/2h7f+vjzPHWmid5ow+334T//eVve4nTkAgWLqAyktPFy6fhF91Xrs9PpHhm0AxYeRYO6S3GwCeWyAdhVdlVkrArAUYWnq+75NGWN3QjPP/zqiO9e63DiE7Nc/7TzrA2UESq1mF4sqXkxfrIiGEiLh2KbACxbGZx01XCaow0vFbDDaeWRvsdMfBrmB4iZpkGnB130uWUAxOKBERDie2briu8NlXWROmMK6ohQweI11L0aX7TQylacnWxDW/PawIcyxdd2X6UPlKHmRUK9qKx8vvGwh8tew4eWqkxmbOCCjUc+bRmYUTQBspVbNLGyUVGbdk0sJ6Hx+v/1H+zn+f9xQHO9PxkXh220USTjoRJtfHokK4WptUcKekC5TK8qC6tuGyzbhw1bt6y4LEFSKw7PHzmr88KMku6hiIlUn1h3IO6QrowqHTET7htuawHAb8HqKuKKK9DtNePQNAxgo22K3HcEWoucU8uo7glqssaJzYCPnYOnHtJ04h5ff8VV+HhoIVjpPcBC7bLcQ+58G9pK0+g7RfyqlFsFnHClyffRanXfk9V5coET+fsygLps7C0r06VbFFERcOIC4qrWsYgc78wLeMY/PZe/vuov+PV7v4OfeOHk05ns1kMpSLaSGytrpAbikYzOyGCxDT9MO3YkuePklSzY4QI6slYeSsfEekCk+ygdsTGQDBIr84EK6MWCtYFHLxYoLQik4deLBVe0+vhSI0WHUEJfdZjxGsSNFt7GCghJr+ZTk024cIqNWod71iR//0CTWMPlTfjAiRbXf+oMB77avP8/edH9zIWahdpxTvce4MjXfvtOvKwK15PmzVIRqOfSl219uCroWCQTXOmKhfEjQVZPu5mM8Y+Z/7Bp2CRCOUtVPpJtEmhLX9RAJ0HUs+mqtAzbZG7rezvXW+PAmy/wn75jiXvv3IfSsDYQLHf9xDLApDfmYw6ww+X0qGiv4HaOGJwGjNhlsKPQf4eUY38irCNm5hBBaACLXDxhALUWzB5C99qI49dAvWbKTUGRKB6ZVWtljsvaf9w4VgpnQEjW9AVLZXa+DW3NY5uckgmvKtnK+odr0uxStl33VQCZSer9SKeqQJJLbtvS5qnsmxXs7En+AAEAAElEQVTJ3XxamywuUgJc7atoHLLxsYXZn//Nf3aQux5z0JLfTjpZOVRKIIVxQp01MtOosdW00Z5pwRaLiGGmKcCO1LrjYoAdWQsOLwE5PAMA49eNXKy1iD1JT/Rpx+u0o1Xa0SqdaI1evEk33uBC/wz3rJ9hbbDESu9BonoDGnPmr7nA+qNu4sSsh7jsZsTVj4ONNuJRT2Ltxpvpzu1HBSGzwSIz/j4AZgPFg2fq/OefO8Sf39HCFwEkJyi0gqztzm62oTxNqxuVlZ0Pdy1IlfVPl0KdL6MKzz2ajLJtowhImJZ3Nn9RW83r1GXtNa9z7GR7sI83+u67ObcU0fyZN/FbfxnsSEmxFkPgIx6zhBg/1STv+6KMsn48KlHeQq90K0uSLQd2pPUfWXOYsSfWAwaqS1916KsOG4OYpY7PmXbA/Rshd63VuHc95L6NkFNtj5NtyemO4LPnJe89Jbn9guBDSzXuXa+x1ld04w3O93q89u5z/NHnlmD2MCfEeWoP3s6da3fy+rOaf3ywzkrP48Z5ONTAbNuvxfiRYuXXFP/24QMcqNcATbj8APtrx6q/r/IXOkVcWbirP06qA1cpu6wueZ1tT2fO05SzXtfHza/82RSFbNoq4ESWh2twtSnvRYBIkVI/ST1djcrV0Lbyvmb2evTLQz6yDJtrAQ+2je+OgRJsDOTQyV1qYjYx5ZXm7dI022EeSrAjQ0JKs8e71jL7vGstcw9mK0prn7HsSP12RN1k28oMYuFoskLpb/EBons9c6b4zH7Wg5izjQEf983g+38fvJ/dbkN23kX9oYqgnWTlJ1+PoslyWV/O8srWw/xpzCrDbvx94ZBt9S4/OXGl3+5AXUWuVwW70rSuNNl2XqUP2ZT9KmOKoX/5pRq9evkqokqQDSUFvq/whHGEp/QI9EgVUCfoMS1lTaRdAEle3pY5w7P57LCBHelvatURJCBH8quDGj3VphOv0Y026MYbtKMLtKMLdOI1enGb871NPrUS8OBmwOm2x0D16ERrrHCB1ZpZYZ3TTZQWvPT9mxDOsHTDlfzAv6/zvlMDzvVOonRMKOvUvCaBrPPYA23+x7OXkUpz2y8ovvIPe6TfPpQNLmYb2kpVwN70umgCnE9ftZwynSsP+BTx1Lm7PVldnVzjZ55skxosv1Xyu9Lnx/+iscHGuwwsy19XmZzZ34n40q/n0Y+b4YGP13lb49V80S9MpusqKYh9iR8olDa75IDEusFYOaSgx9B6gnHLiTy5wp1UtgXRRkVbWRgHO7JWHcaaIyJWEQPVY6B6dOM+a33BStfnZDvgxGaQAByCu9YE967Dveuw2jO/Hz1RY7UrONeDzQHcu+7zV/fO8P7TM8wGgicf7NCP4VtvWeKBjZD/Kw/w0z8wy2vfu58PLZm5zeHGgONNxYE6PP4Jy9z4c+bFX/0PSzzv1Yc5/E8fBa3wXvOGsTfrvp6+DRXH2fTtbHg+PpuvKuDg6rs20DqfJx+eDauuZ+2WrL7UaIdmwkVocdFHcjUmW9o8r2y6KsBFkeKfB2ry6W2NenwitpXy/PK8R/V+1TM6fPJdC0ilubAacrZjlOOapwmkHjoQGgM9dsIPRxUgpAxMmBSg2EGwQytVCnYIKRGeb4ANvw7NBWgtmt99R2DfEcT8YZM46kJrEdGahX1H2GyExDc9FdVK0oYzIAWiNYNYPISYa4FSiDAwAIqOOdNZ5a/urfObH2nxgjc2+eW/PkSsI3a3Dbn6RdU+lM9XRVjmFWJXHttkN6tEu5TmqvXdIzuVKcJFbSu9d737Kop5Nn8RiOJqN2UKfLb9lCkrRfmzZJvcjsv/Wjcq4GlIKk0USAN4BIpAQuBttfDQOtk7nYAeY0p0qswW0aRjQBHQUUXJTqkK2JFadyQnpsQSenGbftyhF7fpxht04jar/QEPbAhObvp8ftXnPQ82+fcln0+c83lwM+Q9DzaI9YBYR9S8Jl1PQ9TnyuY13P6ZBb7892K+/n9exV1vbvHhJUkgfbRWdOJ1PrJ0krrfYqF2kMua1/L9P2p8gVz57mVu+eYH+MPPneLit6Eq5BorbOlc+kmWTxHZdDKbDlWk401S3h65aRIgOE3v+i5F+bOytWyyVEXWVyHXnCBbXtl4Y7sWPOktT+f9/7QJwN/cun+iWg1qHnEg8X0jp4fhQ7AjNfUfOfkcP+1km3r4Dp4smLVAMff2LSzp9pWB6tKNFWt9j3Ndn6Wuz/mex+k2nNiA+zfgng34zOmA+1Y9PvFAjVs/usDJ+1ucPWW2pHzwzhmuaEV0Y2j4im6sONzw+cFHL9CeC/md/1LjDT8kaWwOCD/VZ38I82FMIDWv/2iTIw3NVx7VfO+NA676GfMBrn3PWV7y6i9Df+azvOHlSzDoJE+0W22oCo3P2bbmrQI6FPF28bCV5Uo7iZ71yKQd8OEB5Q2haBJlE9rZa5dQdg3KrntXvbNka9RFZRWlzfPLpx/dL9av5LHPPcnJUzO0NwK6cY9uLBKTOoZCKtYTfq7snu9pvf0X0UMMdpSRkHK0X9wPjSKemFuvs0lXbzA7s0hd+oj2CjqKEIMu3aPX0os3ONdZY6W7yvX7ZtjXl1BvES9ejh/F0F4FIRFeCHOHoD5HX/eZDwO+9WrFsZk+r/zcfq5/7xmeGR3ivT84SI7V2p02NM4TR1gRVVH6i8ou42OrZ1a5tvVXC++kT+xREeUnLTZAN9/+qijINr6usot4lrUFF9naiw00K6uTq3+5gL1R/U58/QEu/5tzhaVJpVFS4AUa31eEiX/NQBoAW5AcM54UKfHRwqzHmSDFcPviJH47XGGTyt2io2Rd4S6wI6gTa7Oa2FftZFWxw1pfstYPONfziZTgQl9yrgurfTjbhZOnZhhc2+YVX2T8H3kioBEJojCEmSYAb37p3fzkdzZ43m8KPn5O8EOPHrAoD7ManWc+OMRTDrUQSLN/HM1/uOooz31rf6j4h15q3XFx29Aoj4ts/cMFDufLzPeRbHg+zFb3skm3C4i0PPuerK5ALj3V9T2q6LOusd+lRxTRpHqEqy5F/Ivan02nH88rrnkKX/qV7+eeOwa863tiXvY31WsZ+ZJ+6BHWYgNMS/CEZqC26h6B1AZAyMjqqrR1K4sulsM2yspmi3WH4Wrz2zGy9kjB40j16cWwMZCs9nxW+x4rPSODz/VgqSu4sBqytlojGkjamz69NY+gZxZcV1Sdb3j8Br/xtHlOd5ZY7vrcvL8LwOKgDqv38Le/fpxbviPmhb+j+X/+7Bi/esuDPGvG4z6/z/7aPv7n1yyxOZAcajSZ8Q/x84+J4I9Ogx+i3vs+Xvec+wF4bf1V3NL9MQgarrfLdtvQ6LqIbPp3ypNcWJbKwIqyfp4vp0hPt6Wp0HcfIbJ6m4BHEahQpfFUobJJWNX7orgi5cXWWfINvmq5+TJHaV/55JAfeM+Absd8km4Mm5GkE0lm/MSxkIpQOkYme5ArKb1FoEeRkJ30GNpLCOwQ6UCQOslLlfAM8LE5OM/tqyF9tcKj5rtcfvnjEWduh7hPvTegriT15jH+7fQFBqrD4w7M01NtdP8sC6ubxldH3Dd16xmHeZs1o0TfeWGTLz/apNcxjq2+9unLwEHsbWqn2pBLqJUpP3kBWdZvbAI/5Zfn40pbBGrY6jeJavFIJ9dgWjQQlgETtkmOS2YWxed52+rrItukMt/+8+QqMx9WBraM4srADjBKNJgjaaWnCSTUPWh4WaU0MT0Wqbm0QOvkHADhMXGbt8lMm4ytCnbYtrOUUeq7I+OkVKGIdJ9I94hUn4HqcL7nsdTxWe76nO4I+jFsRgbsOH0+4IF7Z+n3PP7+QyHv+4TmXS+uE8g6Wvjcu3Y3nUjyRfuvZH/tGN/0G6t8w5UH+cYrBZ4w2wi7nZNEwQF8zH1fddAoarJpjlB8iNuQvT9WKcfF18ajqL9lwRPXr62cqvXYIze5JjM2Khtvi3TeMr3CxauqHuEaV/L1rQLauMZ5l969Ne8Nb/omVp7zdl72bauWNG7SCTAd1mLmghHYIQXDBUepR/48/EwV9DadlxZXrIIvpWxy7NYd2ftxB6Ux7chjY+CxEUnWB5LVngE7Tq5LVpYarCzXGSybsoJezIH1DnMrXRqbA+545mHe8oF51von+VEe4HmPfjJnOpu85rYmV7RiXnjdIVr+LK/9S4n+v3/N17ziObB6AdYj/nbjMN99vUfda6B0h+XuJq1ggyMrXfBD9Pvfj3z6UwEDeHzdC4Pc+9iNNuTimy9zUn29ilws6seTyAtX/J5cTmmbgEdVFKlKg3ApwFUaYRWypS1C17O/rnS2SVnR4OFuuF/7/85RPx4jJZyZ63NFyyCpvVgS6xgpYjyZ+1xZACOv4LpOJ6lC05zKUhRX5jDPFTeFVQfw/7P33nGSJMWh/zerqqvdzOysd7fnOcMdx+EPgTA6QCCDBAiJd7gDHsIJIyEBEno8CSQeCIQTIIQRh0cc6PghEEaA8N4JOMx5u35nZ8e1q6r8/VFd3dnZmVnVPbPH3U7Hfma7KyszMiorMjIiOjKS3nGH2R5y5RdHWSrTjJfwhM/Gcsxnbq6kx8Xe/CMQHjKOoHkNYmoLH7i1w923NLmwupF/v3mZ++1osrmyh5+UbuG8Ukhrw0aq7CLOcvl15ljszHPmhlnacYOv/cUKMEvFnyKfv1fPQ8M41e+jzk2bw8FmMOtKt8D+DC5wP6+E2+XewNsPmH5N0A0hmxOjCD5XndUYSzBMq60Pk8Lt6s/Vf56h2q9z5dHrgHJO3VSJlt3tLJVqRMWHSnd7YtDdoihE6tzoOzq83i+Ho/56OEyARU7aZGvRyD/bMbRqdEfmaO4eMZvINnHSIU4iItlmJRIcagTcslzi+kX4xYESC/NlkkSwMB9Suq7Dtv3HWHjoFN5mydaPzXNxYwv//dSYdhBzytRJzLVuZSVepBZs4NGn1UhI8LrHP+5vXM326hn0jn1EEkr1uFv4VfJQMVx6HZezRDrq6m1sz2y7djmC8mEiq03gWhPznGAmKILHVM/G+0Xfl+7U0PsYVd8w8WiReylctuND/N5TfQ5Up2neucwpnz1c6CmWZ8JURlcjSh5d+SzoJOAJ2XN2ZKA6pzNnR3okbf++yMkU0JdNFnDlVLLk7ug1tUV3SJnm7pBR76jZRuTRiNMfVVciWOikDo+5Q1X23jzF9L4Ge25cIIiG6Zma6XDW2fN8+r0zfC66B//6yr3cuFjm4t0RX9obcM2xY5z1d/9C9eLTWPzNiyn/4/sIf/+e7D15G8+4+tuIcx7J1HKbjRvOwFtZSLeb+zcg992KuNfdkY0FLp1/etrZ9DaK6b2r46H8ezbIw2uq6yrXdWhdh7fZEnlg57n1IqvXaEsLjOaFyiBPQJoW6aJMXMQzZlroYZih8vAXFe52p8vLnn6Au23p8KxPbWJlqcTRVtwLq2vFgkBI4iRCeolC5hpvUQG7s2MUB4cLXNEbozg7bHR6SmRHd/94VKmm2ajjBWjDTLiValJiW7XN7JkHuOJ6uGTLDqhvSof20HUkW07myz84wmxYphUfoxr4bPe2cc3iTcyW05BzT/jpcbQkzLf2IYTHCz67lX/9nZgtlZO7BN12PGR3bhTxfNugyL083EXkgY6ryDNMwAyjODRsbVdn7JhpMZXnGWsm0JV1l8FmqpNn8Ok4UvjMzVWe8bY2//JM97KZ+IJWtZRmnw8S6gHdo/vS5KVSJqlzoxvlMRJbm+SeWmaSk0VlbgbjRHmoSnj3s/erInHvKN5W7LHY8dm3Aj/bF3L99zaw87p5yo2Ize2YRr3EvV8U8+zzYgIR8mene5zymsO0/ngjJS9kuXOUqdKmboi4gO4vlp7nA5Id1TOzB+vT5gcMv99fDQ+Z65sckja8484XU33THLTNV/WevoaNSs96BpPxYtNTbYLBpPPqRpIOtjU5T5dw6dw6btMz2eaASydx0W8ek8e/eTMfe9jv8YnPfICnf/aRxjomiAOPWj2iVk63HvoCOlLgSdlNvJjSmkV6+MLN43nOjiGwReKNggLz8bP9+/3ojjTCo5+UtRULmrGgGadR5a22x9JiSPVwm5OunTf396yQDz1wDiE8/nVqgZ/834RWLLjXto0stA8RJXD27BZ++uUKd9mwl+nKV4mmQ1p3upBd3/wSTNXSdckPUkd1WIN9PwPh8YVHf4+HfPsUxKn3GnrK481D+TqmTX936fZ5urCt3LYGCO26KEz0Z1izpKUwuKgWfRl5L85kBBXxSI/yck24DQwYRww+Y16/NgXLXPabJ53Etsrp7NmVbo840oLDTZ9GlJ7UIknSo2mzqA41smOtEh8VxbOa6A5b3bVwdgDCV35lLFXolMvcunwj1xw7zE/nmtyyvMRC+xBNL4a4zbbqbn48B3J2J7JUTp0klSk8fKq1mE/8meSNzy1z0fZUMJ8+cxolT7ASHUv3hi8dxsNjQ7id6dIW3vKIo93jEPOE2Nrz0GCZSUAWwWeiVa2fB0UdKTbcpj4GcSXy+PydOJAnU/MMmgyHzkMmfi7C52j1TMbiqAqKax2wGXam+6Z5YVLg4YUX7GShkx/C3Al9OmWfSjWmHkrqpTQBddlPAKEoqMoWF/rXY0NeUlJTHRVGPc1LPabW4CRRTwlIZEwnkd1fEz3m2+kviadeeZjpYy3CdjeisRrwwgt2UvVnCLyQl94tTUZY8ip4+EyXthDLDmU/zcuB8LrbVCAb2zRJdI9I7VOF256Hhtub5odrDbDNlSKg49QNU70/F826fDDDRFbrYJKV+eNoxqEbRS5Dzta/7X6ecejCoZalPDbWCYNO3CoISs96Fo87YxfBvc7gxt/cUghr4glWZsr9KLyuHzvlsX4OvVg7cUJ1JgAIUXw+CnU8R4l8BkaJ7lCvBxJjkwycQBMlqbOjEaXbCpuNgGbDp9zoGEnYd8oM7zn/EJvaJTx8nnhWmrfjtOkzqFJlW/U0qr6k7NeZmvH48CsavOc3f07p9+5H2a8h7vcI2LQBju2FqS0sdg4jgxJsPwu2ns7Fn7l/ekDAkKwZx8hXYVTd1XXfdO26Z7q29Wl7Vn1+58110/rihvUgp1fp8LAtnHmDa2pXtC+XkajWKTraattB3J2kSW/CJSYlSgcTQ+o02Z49/b7Y9KhUI+pd4dvoJi/1RYlAhAQi7AtK03FWtzW4FOiieTuKOjsSzbmjzyxPUcC7kR7NeIkbF0M+cl2Fd15Z4b9uqdKIj7ESzcPUVgQeJQ++czDdL0jUTk9x6TR46/230Q59Hv+amI3hztS50VhiB9vSbSpRC6a2Atn7KbGtcnpvL7nyFMPjdhx5aLCuqz+XgDcpvrpB7GrrUqxMbV0KVt5CNIE+uOSpWc71wWR8qeU2A8iGR6cpj2fUuqMo/+qcMOG0PZcNpPbXp+lNX9rIz+6109k6KnnIacHUdJvZME2EV/UTyn6fTvXXuKHeTY6LUX4FHHVLiwq26A71OFp9O4v+56VHd2en0PR/YUx/UYy7hoTny56jI4PL3tLOCEQg2FE9k8uu2N3bsgKSmdJWPKPqkjo7WvFyRpz5GYcfmtuSh9z19X71NjY6baA6JU1zv8haol6bPidyeXXg4qeiMrcI/jxDbjXv0aB/NxZ6uKOkrcm6Is4Zlz5vvl648ELecckthSheng6R04KwHFMP0jxLwICjI7tOP/PnfWGndV7euqxMzb8H5mNohxwdkr4zXYnuICaWaY6STtKXw50kje7oJNBuuR36nzz7A8iDB2B+LxvCbWxLNvIK78Pw8Q+A59FJWtxz2xbipMO5n38WAJe8fiOcdFcW2oeY7xzk4O6tML2NSHaYLm1B4EFYh/I04qz7pt+N9mRRJ8LqeKgY3rWGIuvDqPh0u3gip8dweLiMlCLGkKndOGBSJtR7LiXGRVPWHnxPMVxLFdpJ09C365lHceiQ/nIVJtTrEdsqMF2ShL1Q6KBLTxd/JghHie4YxTlRtJ0NRklSau3WEqptch9m12Lw10ZJgi/gB7+YQb65w+Uf3cKO6llsKp8ESEKvwmwIn745TAVvqcqS14RS+oviuz5Y5+Lds6xEx6C5BO0V8Dwi2TaESpuMRLVc5w2bwB2fhwbBtkDYDFLTvTzcNm+0is9kzKrPVaTPtL6E3kK91n8nJozzbosaaaa6pv5N+EyGo74429YR/ZlcTj8TvePD+/9wgTt/d5/1fjv0adZL1KY61KY6vYSlZV/iCbuTQ4VeRn/TdpQ8yEteaqozRIDD2aGDto3FdJRupoC34lTRbjp+7H3BH8U88hlHuXHpGpUgbDyUkKC+50CE3Qg7uL3y0CBufR7k9VfEMWy6X0TpNa07NqVZd5QMz+eJrB4F8nRltc64Bkyes63oWpGn53ShVOl/9cqIlWM59Nnmoc6Dhr4Q0F7G+9N38ndPrlAElmbLA3mWSt5ovCWEhyD9K1o/BaWTPOe07kxOC4ecHepJLPoxtFl0R+r4UCJYSCNXMqdHJxEkiaDd8pGemVee/rnf5Sl/exbfLm8EBNQ2UtsQcesH9gMQehXKR/ZT8ioQlLl04VmET/1DOHozzTg9FnxDuB1aaR69ofEwyq1R+HyVPOSEcdcCk06TB0UcNEXb5etxx0tW395gDIeHiyFNC6QORRdcHb9tgdeVIZfyreM01e2X9X5FiiNoLRF6lTTqI2rD4sHU+O0Kn74zxKSc6TRjuCe5cekGEik4awZOn0nYWonYWo2YLglCv5pGDujbWdTPcWHcLTFFnRhjJCl1OjtM5Z7on86S/ZUqdJIW822fhV+mzqtT77PAj45cpxAguOvmiIfu7pDx31Swmez9VP1pKn6dwCvT3LQdNuwgCsvpu1AWIO2hGBS00lDPZvBln+Px0HC5Sci67pscGXkGr20hUJVhEw0u/Lqx0q8/UaJdoI65DkUcXjquovWLyF21rv5+Tf3Y+nO/rOVonoXOIa5ZuAY5wGOjvGTTHJNsDHex+IQpa6tWNaBRD5ma7lAup/k7pksJZT91vLp7VJdjOSw3XSdnmZwaiUG25kV26KA7O9TPoagOc26TbFtLJxG0kzQh4EoEK0vm+psOrnDK1J3M9AxgjTnWPgCghMyvDQ8NK8pZ2ep5aJiO0RVUu65kK1Nlgk0+2PrWZUpxZ8pEVo8KJn7IynXHUhFHtO2eCqY1WtVBTG31tVtqdUS6PZj0h7xWvAK1jdBa5EjrZn4ydx2fuukWJAnEHVg56nieYuuK/MFXuOXKOi9+dyu3bhR4dMoBtXpEvd6PqM4g6fapRnV4Sv6O1NkhhrazFEtYmhHskN0wLJOVrYNWZ0d3u03P2dHL2xH1ojti5ZkyB0gsodNJnR1JIig3Ilxwn22n9r5vuGgjOx+9GTIHxtbTgfS9H/Dm0yjo+ia2R1PMhjupRBLCGmKIj3UeQrmfpyOodUcpLwL6PDDpyy5wyV3b/MrqF9XBTeMy2jOvBzm9iqSlLsUahpm0iDPCpmToZQbhOoTD1lav47rXvfYD8KdAJgReCMuH0qNIu3kikm7YmB2HDQaf0xewoyrZXYft1Q5bqxG1oEQ1mCb0avgigLjbT57TQ9/2MtCtI8ndWoBJ0TZdF3F2DOBV3ntWp1dXXxwCOp7k5mMdvnuowub9qXe59I4mLz6wg888JekJ3Ifs3oPNiDvWPkgnaRL61e6+cUEgQtKjvTrpFqMiPDTEv7pzUOft8XhouLyIcTrKImFz0pjqjbs4qfdszzcBO5jko82wUst1njTVwVHfxNcmfDqNOj36dxvdZhpuWJyjFXu85SdTXLRzP089eytCeJZtECaw973YOcz0+5tDLTJoVwNa1YCg1KAWSGZCqAbZdhZXj10FGpFu35CanB8FCspYYXNmZN/1aA3d2aEeQauf1ALKL5p9iGU/lHppMXQ9BHk85OGlWw2RyrtdGx4y1xtFg8trV8TAtCnWprVCHyObDFCvTfTYaLbRNYHVQ55eXLSdTW/Wr01tiuogNr1FvR5s54sSvl8iIcYLKnzw5z43Lfv84rIqH3/cAd7xwJ1dmWHi96K2Qwqn3rfFs56Sf5JWp5xG4m2cWWamBPUSPYd0LAWBxuN9Z0earF44aNHvGesWPRzAFt1hcXZk9wYdHR0kcS9nh74TPE76xmmSCGROupUbf3NLuj51HRzBH/8xJhnqixLbj7WR+76NqM9CfVParjwNnQai3UgTlhbgodXpgKPxUL9N1p+NFhM+k55vw43h/ih9mdqMK0vWB4zp8HAxkGlhNgnJoi9QLTNNBhfDFbk2QVpHdidu7xipOEI0F9KID787dJ0mvhfgk0YQLHQOUg82ps4J59gM07ESeZw8BbtqqbOjHoRUgilCr0bghYgsWdJqt7OMAqPm3TheW2NskR2OvkpemVOmKzxiT5vdL4T3f3ErO74xz8secYirj3U4a0OW0b//HtLFQdKIFqgFGxBCUAs2UA1mVOSGPB1DBDDIqzr/FuH/vPk16hwy3TMpO6MoGy4BX+SZi+Lulsjh3UwT0MHkYNDLTWAzlmxtXQ4OvW8drwmnDY/+3fQMkoSERrTAoWaJ+ZZP1PH44Jdm+ddPC0690zxv/03JdGmzoa1Ki3tt6CR2Z8fydMjyTJnqbHrM4UwJNpUlM6XYmt2/5+jo/lJo3M6S57RW1wPTd0NeJGHYepIS4HByZJ8mZ0d2nX0XHnS37gi87vOlR/P6Ig0f97z+mLzpI6nzI5ExFX+aXxUP2fEWgVGUapfhWaRNHm2meaneM+HW2+fVsdHWLZnIagfYHBE2fjbxik0XMK3Dpu82yDOybHNS481epEJqGHeSJreu7OWqhTr7D1QptWI6b4i59A23cGjXFO9//T42zi/DxpOgVNVwFphbK00q99kFv8h/wka9hNgAs5uabC6DuoPDFxKv+wyekL17nlDltX0rS3qseAGnx1BDRU7rjg4tugMYcnYkMkKN7Ihkm1h2uqdjid6Wwlbs9aI8ss+w200QJAg/zUOlwrt+/zN89/XH+MVPGjz5uechv+ojHvC7DL2PTiN9jvm9MLsLhIfYuAtmdgwmty7p244sPGSso9azgUkPGUU+F9GB9fmnl+kyOCvLm7emsqJOjOK69MDddSKrx8zhoTOSLqBNI2dzfqjtXH2qbU339H5UhUdV5F1Ma8Hb9ZTSWkqdHUGYTtikW9ZaguYCLB9hxtuY5ndwMugwHT+Zu465ZsDZGzqcNNWmHoSUvAq+KOEJX0napoCqtI6aYR+OT3SHCmNsebFuZdHLsgSmyp/MlHyZ9LYdbSxt5/xNG3j4niVe9DuH2P0nMd84UOLL+2oqAb1vnvAJREjFnyKRMe24Qa2TnsrSPz99DB4aqlfEKB2Nh9x9q+X6n20u2owOFVzKlE3xKrpwmRaUCeSDTUYWUSrU+3kLrUuW63+2tnqftrVE/W5WNI629tJJmpwx0+RL+wRnbon4nV+b48H3P8jd97T49sEVImnOPu8yDi+/bh+LnSP800/38Yynm6MSEk/QqgasTIdMzXSYnulQD6DkSfTt0P1oDq9/8hb0wqQL8/oYiUyt0XMDBHrDa0u2bcXvHvmdffeUa8XZkSnlQqS/iPqiRMmTBEJSD2CmBCedusgN56QOqDf+JGKmtJXZcAeV7ASWXwEPDZcVkcVqG5uDwbVGFHV26P3YaDWBSoNtPClYR62nt5lAcSjilMhzOOTxZ94a6lrjbW1N+rhOs+jrZSTQXuYHhw5xqFHi4l2SY18N2Hv6LFc/aDtXX7CN+bPqvPpH0Ni8vbtVvChI/s/39iKv+RZ7LzqPB3z/D3NbJJ5gYVOV2lR6HG1m25tC8H2ROkDSTwbktQ0y54Z1e4t+2EAeKP2pJ3tl15mzI01K2ulGH/edHZ1Edh0eHo3I6zk/0sMQJCU/fTbPk72cJp1w0N64/P6/xd2fWuHXf7PGkff+ksbHfjLwxL3n8YI0amN6G8QRcnmelenp9FSW7CjxgXY5PJQ7F2zrQ1EHQYZD/TThKaLj5+lKeXMsz6529Z1XPsp4nLgwRoSH7aXZBG9Rj1peXdXTNaqXbtS+M4HVvRYSXwK12f4vZ51mKpjjblb5sNZTFMuiXrCfFL607ya+vr/ChZsTTp5qU/Ylvlci8FKnRyBCkLHZAZAdU9tDX0zxXRUUie4YI2+HFYq4HjPHR+bskEnqkOo0qVdmKNdOpR4cZEulzVXHQn77ZHO/mcFR8ipIEuqljXx67zEesntrPg1WZ2CeMmJzDBTpxwameVJEebd5n/X2eUqPyUvtos3Wp9pvCrfHvYG3PzAZUCZjzAQm40pf2F1Khq1fF51FjD5T3T6vzIY78EWJ2XAn/+uMG/jhkZAH7mxQ9hPmWwGnz1SULWg6XSYnZtrX3bas8Nw/TOVFnTYmaNRLNOsl/GlJpRoxXUqPo81CodOtHPZnG9rbrSvDri2Kajv9c9QEpfo99c8WzZE5PQaOou2fHJCFgKdH80o2lmN2133aJ69w63SbX27bwYGftGjedTk9/SolfmiEbgseGqyj4jLxv+m7qy8d8hTaomB6Zp1uE70uPLZn1/G7x3siq12g6rSjtsnAtV7rUFSvsM2JvO9auywKGokMq9xj63Z8EZDImJufOsfHvxnw8HsfpeJDO4GH72lQ9rdD3XwCk3leCl5xaotHvvQsNh1c4QwO5TxjGom3tKHM7vpSz9lhUjHVqLxUjned1Nk/IXpyeyiXh0GeDjiziySS1rcYdtunp18pR85mzo4k++wQywhJ3HN0rEQerdhLj6SVgiihF+nhizR6pRRIgiBharrDkU1V5hbbbDqYOp8+8Noqj3vVozntpP+i9a29VJ/7WwzxiPDA7zpKqjNpRPzmkyn9ywfhfz+RsXhoqNymixSxO+08NEyHDZepjQ2fSQcexR7OG6Oifart7LAeZPUYYQEqqEqvLrhHNa5Mgt+EO884tHkLTXVM9aVyJ/VOJyQsRkdZiueJPfqKXjcpE+UpqM5CdQYpBMgYScKBxrUsdA4O9NxJWuhZ+s/bmHDaNEyXYmbCmECU0yNovbC7NUaht5Bg1OoWcSqoSrZJ4V6No6QgHmN0hytvh+HgZxlHqaMjjlLHR6cJK/ME7RZTpc2cMt1hdz2iXtqo9GzmFYFH1Z/hEXv2pFmnB6k1fDcZfDaBY6pTVOKMIzDz2pnuqXTpypn+3dXWdT+rY6s7jlI4AfOYjrqi2Qwel3HlMo5GKcvjXVWBSP/6slJyzmwVX/Qz0l+45WRmwx3Y56B6PahUfedgjTdfXuX3X2/mwXbo06oGLE+XmZrpMDXd7h1zWOpu2+jtl1a6zBTmASU6W5JNsttUpp/k4mhXKLrDBPoWFj/sOzn8sOvs8Hu5rPQ/SYIQHoEnqQUJM6WE7dWEM2fg/O0Rd7v3QRauK/GYD6lHwetw2/DQII5xHBU2R4DLyWCSbzaZaXLq5Dl4ioIJZ57MKKLnTWAYXGuaja+LGksufddUPorhV6RfxaiXCSLqIA5dTzi3Hz+K8ITPI09dplaPaCewtQp/cv4G7rzxNLw41beJO8iffRUO/LKPW8bpUbcyRh2/r3Q8rvj1K3jr5fZk0ioszZapTkW901l8kcrlbK1QIdvSom5nSakxOTTMZUPOjzw5rJ00qEbbye6/9Htmnww7OyIZsxIJVqLU2ZFFdjRiQaNb3orTE7NiSS+CpVKNqE11qG/qcHDPDDecs5nl6ZDN+5d52N9O4d3rLpTO2pRuO+pS0f/U9Gi/BBt2UnrOs9O8HQP1rQ9vqFfUyZenU5ucFOPoRjZZbXJC5M0tm85kum+zbU39uOhcv7BKh4fLmFNfgv5CVSXDdF/HrztWdCYxKR0uJVxnCn1SyO63fhK56dJmljpzfP7W/f2q2daWsMZjP7HEcryQHr21dBixdISN5V3sX1kgkm0imZ5D7gmfZrykOEIkmyt7+N1TQs7Y0GImrFP264R+lZJXSQXsuCeguOoWjcZYLT6Xs8PWb95WFhh2yXuiH0IZR2nkTYbf88APSWRMyatQ9kx8119EzPw5Hg8N866pvassTyCa6uh964uSSbFXP3Xc+jx3jYd+z+bYMLW1LVL9a8kk838+mMbT5qwytTPhcLXT64+y6KpzQ/3LMxBhmE9ETyHcEG7nCXea4kgzYGdtMx5p9MF1i9ewv3E1c61bSehHIQzSpvK84JIzd3HV/AIf/9NhmqLAo9mN7khmPWr1DtVaTMVPHR6BpxwDKM1jN5i/Qw47MYo4KvK2ArrAFuUhvK5TIxjcwjKUw8PXjj7sJ8uLZYc4iYiTdCtRNUiol2Jmw5hN5TQKBqDUjtn6sXllO+ivjofsuPV6rjk0yENmurN6pjXCpjPpuE196/hd17axHlU5Hh7riaw2gU1XNem4Krhkb15/eTI7jz4Xbkd/wu/KkDJsPQO5/yai97yPRMbsqO3iXY8+xHIEv7G7ydfPeitifh//ftNBPnnjPr55+FbEmXdHXn01tBahvZziDEJYOKg4QuAB23Zx/SMfyLMfu+SgNYXl6ZBmPaRSjalUI0LPzFNZ1EP6N3hTCG8ooiMPejk/TNF3Lv27l/ein6wUuo4PKfvyVXYGnB1ZZEfvM/FoJ/1tLSuRx3IkWI5guUPvqPBsS8vMbJvypnhga8uu64/B9Da8aXVbpzYOcf+0wz6sgodyoYhObdKNR3EG5PVhq1/kOVxrjn4/z6Y2tbfhHb6zHuT0Kk5pgWEDahQowkQu5tQXf32xUD/1fmx0D/YXyQ4C0UtQOV3awq7aUWjMQ3mKduARlrcjSbj8URVYnksjCpL0SKewvcJZ07tpJm2OtPZS9srUgg20khWy8LZIdjjcvAGBT9VPj2rKhGmat6NLk56ETj26alSHxWqcHWuxJcaAx/jro2sri7ox3jOcKJD1IRPwQ2LfY6F1kO8fKjFTohva3qtIxg+teIWynx2ZtXoeMgszlxA09edSdG0KuU6rqdylkNsEahEBnncvb0EYZTGawDDYFsVR5PQoi6aLR/V5oi/mJmXEhFtvZ+67k7SIZYfQS09VesDOGSA9tvRoay++gKq/gcALWYnmacUrHGy0OXf2dCvdX9x7M+99jjlJcat7KsvydJnaVIdKLaIWyG6Eh+we+TcsJzIFuPeLYZa/w6YI27ar6HUs21lyozv0XxQ9xbkxcBJLYHR2qNEceoRH5vCPkjRpHtDd3pIAPseOhWy/eYGL/yGNqPtV89CwDqGDS/aPQ3eeIpvR7tJ71LZFdTLXM+S1t+lcE3CDbW1zGTDZfdt6bmqT1dffkw55/G2bJzjqdCFzUoTpFm+x61T8C5rsbd7EbLiVbd52XnmvGsiEB1z9fL55cC/z7ZCzN0SUfckPFg5SPf9c/OYB7nT9TTBTTxNgLs91j8CW0FriQHKQv3uyHoFrhma9RKNeYqa6QrUWGw2zTF5njg41f8coJ7SkEXxiuH5RHVo/mSX713UsZ86OTP6qzo4oEZrTIy1biTyaMXSSdBtRM0qdHctRN8qlK5+DUkJYTojwqS6nzupL35rQ+cAn8bfU0qj2/gP1v67MQW1TdzvTGvDQ4MA57tnq5eF2rRej9jUq2NYLm41hkxWmuiaYyOdVOjwysA1kHuO4GFPHbaqrluUtwOo9l0HYZxgvO1e6W1ZPQu6y8RT+7NsHeN1FVcKoBV56ikssO/hZ5uGonSqNURuaC1Sqs2wMt3H94hHgCGfMbMcTPreuXM0tSyEzoc+mcr9P41ndpmv9l0BTfRcUrZtXb5zojgH0Ob9O6tEdqnPD87pu+O5eSv2oxW6dZrzEdw+G3HNri6nSJuUUnUEeqnSPnu1Sxmp5aLCt2k7HqeNFK9dxF3VAFK1jazOKkWwCk7AehfZhbLdHz/HtD4os7DqYFljbd1N9W1+merpRmedIUctsfQlCrwpUhu4LBFfNx/z0aJ27bV4hlg3uvHGKvcsdPnNzlXNnVVyDNG2tRMBwstLEE8SBR6fs0yn7zIQdwjCN7ij5/V8IXaA6PgQeJJ1hGd97FIsMdW0/dMnugeMOGXR6DDg+tKSlFmdHpoQPfkbEsk0rFgOh1a2k3+/KUokNy02ONNWjKX91PGQuL+oIMPOQHYrKQNNcHEcQFpHrNj3KNh7mdzaR1aOAy8Gh3jfpySbdWGjfTZCnc7vu2UDB6Q2aF63ZLZTu/RCav/VG6p96PpEfE3TrBvh871DIZ340w0V3XgDgEXtafPbmKv995RT/38OryO98E+ndgrjP/cEPkT/6b+R1t7D10Y8H9uXQlcrrdtlHlCEsx3hCEnqD2w3139h85TjabHtKL2+Hkr9D1dlNJ7gUTkatn86inMyiRnckMkplbiZ/iVGPnk0dHamsbSvOj+UodXaojo7l7vdW26Pd8mk2AtqtfmSH1x2UCzZ5BBdfgNh+inKKjsZD09u1slXyUC4uEw/n2ZRFaHG1M9mdLsdN3jzLu86rp9dV5XGx9WW9yOo13NKSwajeOpf3SsdjUlxc9W39uqDfziM7HaVbFtZA+LzmPmkCy9j3aHePKTzWPsCy1yapz6YJe8JaP2NxEBL6NXbXaxxpBjTiBRIZ86PDNeZaPr5IE71le/2yU1kkCUM5OXTl1hSGbFKSpeY0WC2sVU4PFQYcH3KwLPsMAgj89C8sQeAjggDhp3+9XyazXye7bQMv5FDT4+v7K9SDjUqnx5eH3GVFnCAmni9iZKrlNgeMrY4uMKWlng3UeroB4TIoTIrzqH2vd7DxmX7PxS9539VrlwNO5W3buy9qeKllefw8TI9AcNH2k1low+u/O817rk4dGEdaAedvig340nbH2gd59TPMvx4mXW9GVPIp1RPCckxYjnv5O8p+0jve0KRM9LayIPrbWYrkT8ru67LcVAZD0R1DR9JqzuGBU1nUaA91S4vwjM6OWHZoJ43u3worUYcjTZ9DjYC5ZsB822eh47PQ9ljqpEq150taT6vy/LvsyAhSieP2wkPu+WAClzJdBJcue4v243LwmOSsaQ1yjddaOcPXKxRZu4vyjq2uzdixrc15/drAwTdBOf3rQtmr4+Fxxv/3LBAeQRTDytFUntzyY567O2bLthW+/89lvnD5RgJP0ozhAXdeguoM4vxzSfbPw7H9ELfpfOXniC2zPOGPDlIEOqFPq1qiVu/08ndkTmnV0eGLNLqjv60l/VEzi+6wbWcpdPSsnnw6bzsLoJ7M0ts2iO5cjnqRHGpUR1txfmSOjYU2zLfhaPdzbsVjbj5kYb7M0kLIylKJleWAZqPv9Ljy13ZR+/sP0/7Yd7v5O24jHsrVwV19jaK/Fykz2ao2h0zemqTLYFNfprI8R4yJnomczmCNIjxU0Ac5z3Nta6/Wz8pNxtOok6soXg3aK6nzAnpbXCLZ5lj7ABvDXYR+lf+4scGDdh1iW/UMvCiiM72RTtJERvO04mVCv4ov2nz2ZugkCxxpely4JQ3fy6JJsgiPXujcWm0hGXr8MfCa2owa3THuVpZs24onEIHCtplirl77faU8TbIXIPC4cHOTHx3Rf/29DXloCPKUGhttugfchcvV1kaHLjBtnmYbfrWurijbBHKeZ7x/fz2cFz4+uAykIsYTmJVq1z2bYSa1+/p10T7zjD3JSnSMSjCNRz+bfb9tei0QPP8uW3izOMzXr6ny9p83+LMLTjIoqrL7v+TL+zoEkb33xBcknuiGAMfUw/RXw0yZHqirPIae/E4g0kTLidKZvo1R/eyRmpgTl65m3dBlaBbtochUVflOt7Gk+8g7SYtO0qQVQydJIzoW2j5Lkcdyx6MZC2KZ/qo434KDTWi3fKam28p7+NXwkLm/rMzkGDeVm/rTn0ttZzN2JXb8owrAPIPa9swmmkbrfyKrTZC3/o7S3rZ2unSL4wEOB4tMONi6kc3lPWlkbRYJ3VpC7r0asec8qM7SfONHuOwJ9+N/X3Iq3vuWefsvKrz1Aj/dJn7gKqhtQpR8mu/8PHKlQ2cxIX7OEwlfv1CIwlY1oF0NmC53Uud0L79Sej+W5og8T6BsTzFHdwxuTfT65UKNCul2ZJPN+qlY3Z5gMLrDtG2wk0Cnu2Uwc3pEsu/8yLaxNCNYiWChm7djpeWxtBCytBiyMB8SddJ+44agtthm41wDgCBIKF90Es0v3WB5z2sBRQz6UXRRG86iNmmeE7ioHVvEoVOkL5u+rLfRdfBiY7UeZPUqHB6mQXf9YoByz/Rpw+0yfmyG3yhGnY0+DcIan7jxVu67PWJTeTee8AlEyJbKKXj4NOIFzpxpce2xMqG3D0/4lEWdz9+6xAN2+mws7yJK2sy3G8y14EgT9kxB2UuIJUiZCjWBh+wl09NoyYRgkUSgpjpFjjZca1iLrSyQKtyBn0ZxqAtCdlJAVl6qpH8DxymWIGlw+swUu+pL6fYjke3Jvw15qAemfsZRgoo4PkYRgq45ndemiMdfp9dloJjarQOJvGrIc1ZguK8bby5ntcvgLAomPrDhLPI8UA1mFKXSbqAGIuS552/lj89d5uM39BVX03M95VF7c58k29ZSKSdpwrdustIwO50FQdI9jjZTplVnR39fuAey0yUlcUfpjbO1JXs605Y/9VqN7tCPoe2WpSHV/ZwdvWR5SZtO0mCp47HUSY9AXIp85po++xuCaxbgwFz6S+LKcsDygRJT803KF8K/XFzLHWvtYVlrHhqsW9TJ4Vo/0Mpt/bscGqO0zXNi2PowtbHRkSdDJrB2kGdUgfuduww7F26Vx4salo6+hMeWyilE//hWvN+7F+KUu6aypDyFOONeqXxZ2E/57jtIvvsz3vUbG+EtO3jbDW1ab/sw5cc/BHZfAO0Von1LtA+0WD4a8Oj7/Dln/8F+nRArdMo+napPpdqgXB6WjX6XXK/32Y/06Dsv7ONhOop2cEgKOqJVBzPQO5VFpqeypJF0kRJZJwccHZ3u0bOtOHWANGPRc3YsdtLIjsWWIIo8mo2AZiNg7lCF2euXmZlrUm5EvW0sAIsbynz9TwXi4D2onH4SdBrdLS23IQ8Vul+kL5Nus1oHTpFxMF0XbVfUCZTBap/nxIVVODyKeMQcXt/C3jG1vkuhGJWBi3oT08+EhN89JXVaLHYOM1Xa3Nt6AjBT2sY9t6Y0HWsfpOSVqfoz3GPrfqZLpyHw+PgNc3QSj5PqCQcbXk/AJlIgiXuKpCf81KOb7R9UHRWyu81lLU5WGbd+XplLIc8DfSsLDDo71FMCwm7ypPJUL0lSM15iOZrDwycgJEliOp0mQniEXpXvHypx8e6o6/C4bXnIjNvlNLSVFZl7pvs2xbrIwqQr+3k02JRf3cFTZIHq15Fyfew1XDtQF1fXO9HbuOrrbfJeiEluj9LeZmRJpYbLyBz87ouAWrCBS87cgIuXz/g/Hte+wi6z4sBDeoK45AEJYRhT8vr5O0yQJr8bzNshhAcy7st3Xd5n39VPU/6moqA7N7LrTMn2tbwdapQHgwn0sjwd7aRBJ2my0M4iOnyWOx43Lnn8fB72H6hy/S83cMZPD7F5+Ribu6TMb6ny7j9sUfF35hB9/HnIjdPVl0mpzQPTM4Bbz3HhyHPqjNJP0XnvhomsXi2sxmDMW5+L6N+rMZwGcXhRRPiCZ0LUhIPXwJZTuz9UdX942n424pFnp9r03iuhtcQzz92NbJwHW08HP6D56nciOzGVM6dZ+FaTTz/nEC/4SnGKWtWAMEy3HWbHhWf8GWuiNNvqkjo7/AFdv/eEWnRH+rQ2p0jORBiK7EixZSezZE6PweO+JVnejp6jo/sXKREf7SQ9iWVBcXasLJdIEsHSQsi+m+psv3mBrXvNp9x87B1TcNOPoDJD9IUfUzr5vK7D47bioaL3TY6FcZwBozoaXQ4MfQ7r7Yro0kX6zgP3M60XWT2Gw8NkkGXl+iKqX6vtTXhtL0RnlqLGqcubVtQ50xXYXWEnhMdUabPllI+0/oZwW+/O7tqduHHpGp7/p5uIgypHdk6x+y5LnLOngS+g5KWZ/FuxwBMdJOmex37CVFJFM7bEVbucH6rCvNrcHXnbWfLauLay2I6azcATg86OIEzzpFRn6XiSZnyMTpTmUukkLT5ybZUv/bLGqx96mKWOx3LHZ2e9yWnTu3nQrqVuYsPbnoeGcdnumeaNTZib+iziyBjlWWyftjLTtVo2qqPItnhNYBiKOLXyZPC4hpOrja2eqxzLPVPbov3Y6b3y6HW85qllfvsfPT71wnx5GZW8Xh6PIOhHeITe8FGGg3R0leXuySwePsStYXnpcnbo9Qrk7xjI3aEr10YHh563o5+kNPu1MTuFpRk3WO6kzo4jrYBbl32uWYCfX1/n6A9DTr5qjvOT5V53r3j/CiUPakHITGkXJwoP2XEWpVnFb+rL1S67j1anSD820NcQnRYb/gmsDeSteXnru0kvd9XTcbrW6hGcMVm0WETq7ChP2dvuOq/3Vdz9IcjvfJb33Pe7nH5WhV9e8QT+903f4K+/dhH8kSn3khnShKUBlVpEWI57PzZCauj5mc9XyK6jQ+ILSeCl281VfVzdstIr0xKV9h3ayiktNie1KVm06Ed697YPKnk7stwdWa6ORNI7maXdc3qkW1lWIjjSSh0eiy2R5upYDFlaKCFuTjj/F4ORjIsbylxWfzvTr/0T5L5fEH/0c3inbEfc5wJKT3gU1DZqD3Ab8dBIMApOl65tqmvSxU3txtFb88bQtK6Mo9+vTxjD4eEyrrJP/ZcHGw7bIm17gfq1yaBzGUt5bfS+Bssb8QIVf4rBMGiVofUxSPFMlSpsvqRDrZxw8JszbN3WYKYEG8KETveoKE8k1EiFZiDCNAJBdgW6KTndOOAKic4LxyvadwHaRt3KIgLN2VGegqktdGSHRrzASnSMHx6uMFNKKPse37ihzGPuusKu2hYe84ESlWrEm3/nAAJB1Z9m+P3edjxkdmzk81B+O7VPk5C00ZCnGJme2Wb05inHNpqLCuMU33rwRK8OTPLUxfOmd2Z7p3q9vPpo9Ux1TWU2HtOfUQebkaj3MUzXeRtPZ8df7ivk7Mi2skQlj3boE5QSymFC6EEpy5Us+z7cNAGeZCCyo5urCSQDTgvXcbSm6I+Bx3ckKNUy//e+66ew9Law6Hk7ur82dpXvTOluxw0W2mlEx3zb56Yln2/t87jul7Ns+cYCpy4u9rrbe9oGrviHiHqgJr47cXhouL1NWJnK1TY6ba65aqpjApPsLtLeRIuJzuHxnMhqHfLWR/3axaOma5MeYcKZZ+SZ1g4VXIacgW7hpclGp7Z0o3HVerbPbtMdW7jbfepUa/Drh3/AU/75Iku/duiEPp3Qpxp28JToDpU/s+NnSz1HRxbhkTqldSdHOgpi8OQWQ530UXPydmTfe86OvqOj5+xIOgwmiZbEUhDLfoRHJLM8Hl7X8QFHu86OY400X8f8XIXFm0vsuOEYM0ebA+T8/P47+dAX30CwuwTNBS6/8AvUp3x+6z9PTd/bhp38ynhoqNy1boyiV+bJRbVvFy02ma23z5PTenub/uXSo11rpxnWg6wuaOWqYFJSsuu8ETO9FKF915lPWuqifXcZYbYyGwOaJ3DJq2jCzDRR9EVLsLm8h13TCd/6+g5q9U43sqPPYIkU3dppBujAC/t4TA4B/eSWPDAlv9NB/aXQdFKAC4pue3HSaOCd7laWgQSkQQiVKTqyQzNeIkraHGr4vOLdO/i/r93MkVYatnjJmTsp+3XOP+cYl9xzmd21s/jV85DK37oCrcIwDw3WMynk+rOYaDD1abqXt9DodOn963PW9YwmwWwbo6IL2HoGk6FVxOiytTHVMb07vb5e19RvXn82GOUZbbSZPgWvunfe1ooUoq6zo1MOKFfjXhK8TEk2QbqdRfT2gmffiaNheWmK8DDlYEoM8jrvV8TsU3d8mPJ6DGxlUf5JSZS0accNmnGSRnd0fBbaPtcvwlVXbmLTtxapL7YHUH7udVPUg1lOZB4y43Ip9yo9uj6lj4WOWx+/Is9gujaNh06Hvv6Amb4J2CHPKLLpKDY8tnXbxpd5uE3GlY0m23yz4K70tx67dSpNXzj57mzaGfON/17mfQ/+kYVuN7SqAZ1y6pgOSsmQcZdtYcki8/oO6sHoDmBgK4vu7Og/kdASllJcLmeyVg5uHczkbpY7KTuCNkrSCI+O9tmMU2fHcpQ6O1aWS2m+jsMV9lw1N+TsuPqCbXyt9naO7BNU//5FMLWFez+wzMV/HCAufDC/eh5yGfV5enXe2lBEt3TJcJvcd+lMJlxFy1T8rjq2tWJ9wioiPPJenK1tEcMl737RhVVlwlEWEZPRhbaNRW9ju07h5ffcwcvvCQcaN/OtAzWuXUgjO5Yjj6pyDEAm0HwRDCu4pqSl2ZYW/RdCR5jzmoAJ71pFd3RPY8Hz0l8o1dDqbt6OTnSMSLZJZMwv5ivsvG6eD/5rjbK3h4edlAACX5R4zX122Sgp9JhrzUPm9sV4yCzU1b5MQjePBp3OInVN9Uz41HJ9kdNpdS2G/WeT3DGySQshZoAXAo8AzgYawDeBF0sprzp+PZsUB2n4brq2gQtHXplp/ozap94m7xlsBrJaV7/ut/vXK3by5M/M4f9Ly0qd9ASdsk+rkoZJV6oRJY+e0yN1YktjLo/sF0NflNJTZZK2WXbDsAwvcpyhft+WoBS0Y2i1UwKUv4HoDhnTSZq0kwatpMXRVtBzdly7ILj+QJnaNU2mjw2O35s+kq2fJz4P9fFmZS56dZx6WZ6BkDc/i4BJbruMA9O9QWafyOrcng2f+rpadK3M60PH5apvm3OuTxfd3TrlaUdf7rZ7PvqnXJokyCu/xuO++HHmnvEwXnJJ8UTHjakSotzdehgkPad0P49e+pk5PTwhe6cneqIf3eFydgycuDUKDDiWQT+GNpO9maMjG6NYisG8Hd3rRAqWI8F8K93KMtfN2bG0EDJ3qMLGfctUlztDZPzX/1mhFDyTez2z20epwqmfeAGZEybr91fKQ86+irS10W56LpNObHKquGg09Tes15r7dNFbRAYUtVnuOLJ6tTBGhEcGRRQE00Kut9E9YHpdk4JhYhSTEagv3iaFyka/afLp9Vxlek+pkNxRvRO/c8pWViKYa8FC2ydKBFOlBKHs21vTk1SKKsl57cetn5f/Q51pgZ8mKA2Cwbwd2QkslRnambIdL/PmK0u84T+2cvZzOpS89LizgSPA+kQon3dMHhrG7eqz6KJrW3hMtJiEcxHctnLbQnVCwMnA04HPAn8APAPYCXxbCLHn+HefjatrPF28rJblGZ02I0+fM5LhOVGUtrx6rvk8Gk4PjyhyL41JV1OWvqBSzRLh9feC9+ppXQq81NEhgvSXwyy6I8/ZYcvpkefY1qM4RgUtuiMhJpIt2kmDZtxgvuWz3PFpRB6HmoJrFwU3XL2BnTcuDKC56axN1ILZ7tWJz0P2+qa1x+VcUZ/fVm8c+nQahKXcdN8Gd1jZfRvL6jznl1pHf0d5Ml3H48JdpO2oekSRdi6dwlImfPBLiAseRPisJ+Mbf3w0QxR4tKolwnJMUErwuwEbaiRe5pj2u46Osi/x1RO0dHIUWWpydmT5mXrJtG2R2rqz2TJuvZNauv8gdaqnf/Q+AZpx39kx34bFhRIrSyVWlgOYk2y5dTA56bXnb+VBr/Yo+/W0/15ES/bdxA93QB4yts2uXfVdzp4RnVtWemzzfTUydS1oO7FgDU5p0RfmPCPSJtBdDGxTum04bIuzjUabl07vR8c5qmKQlnnC577bI768L2BTWVD209C57N4QuBwOavSHrgibfhlcDYyzdUW5L40RHcrYeiLN16H/wphtZQlrxL7HUvsQ7WSFL946xTffN8UXX9cm9HYgnO/mxOKhQTpUI6DoQmNyxpjmaN68ttHn6qcIffZ7d5C9htcDZ0gpG1mBEOKrwE3AU4G/Pf4k6LxnMy5tzrMiC65eR/+u86mJLhsu2/y0zScXv+bBYF9339PiSkftxBNpIrzQp1KNqFZiqt0jabNw6H6YdHqtJsDrZf2Xhu0sMOzsUD/178bHcTg3spO+9PqmbS1qM5kQJW1a8QrNuMFcM2Cp47HQ8TjU9Lh+Ea795SynfevQQLt//ugMVT/L6m/ipxOTh8z9mBRbHUdWz/UcrnE0zWnXGJuudbDJCDdMZLUORfjLNmimNd7W1ra2q/dMhpxNL3DVMdGo0qL2q89fWx+2fgQEFeJ2m6LmS6NeolUJqFVjKtV+NHXGm1nujtCTBF56kEDJc0d3pJR4VmdHLozgfJYydTX3T2xJt+RkqnOaxyNdZ1qxx3w7dXQcacHiYursWFossXgo5KSbjxK2+8lexZ+U+PLFAV68FUQp6zG7q1LBCcVDhcDWbpy1waRfq3hNz2nDM+59tzC+g8jqVcEqIjwgfxKo9XQowjB5RmIm/IsYUa5FpkibUQwD9/c46fCBX5TpJLClEhF0kyhl3uRAhH2BuJaRHmsFRbez2Gg3te9GdaTbV8LBBHoDifMkQngcafpMlRI++bqQslfHmCgqJUL5fuLw0GCfNiUoU5rz6DXNT5uAlsqfDWyKukqrrX+T7Mjr7/YHUsplVYHuls0BNwLbzK3WAooYdXkKgemeSQmxGV46DrV9EUVGb5t9dxkCNuMyz0DQ+0rr/sVdd7HjLz0e/lr7EhkHXi9/Rz2AWpA6PCBToiUemWzPHCF9hwdISKLh6A6Xs0M/eWuUXEtDj1xgy2P3fnYaSxbZMdcMWGj7zLd9jrV99q/AvgWfYwfCAYX6dR+Gqj+Tdch64qHh/ouW2+SxCra5bMKdt765dLi8Zx9V8b/9wW0rq4u8N9M7U9uo9YvwqknvyNOLiuoOJlDbmvjJhqOgYdpeYd+DPsVlV+zmrZdP5dACnbJPeyqgUk1PaMmc0SUvk8npZ+DJXsLSwJN4GI6ipb+VRXd29HN2iIF6a6XDS9l/Z6btB+kBCIIjzb6zY2G+zMKxkMa+gK23Lg7k7VjcUOZ1vx6n2yp91dmxDnho5DoZTa76rnUsr48i66Cprmn+j0vDiQ+rPJY2T1HWDT2T8my6zsOnl+k49LI8/KZ+MLQpOtFsk1/QSpb5wNXHOGkD7KzRzbAskEQkUjlmaxQh6UpYtxqFuEi/RcqgH91h2soS+P3tK6qDA3qODkqV1BFCmktlZy3hZ0c9bl6+me3VLd1EeOuDh+z9unC4wGSEmBSyoor/KHS45MIwbZI7ridaCLEVOBN46/HrZRyjUuedvHZZucmxldfGdq2XjXNfp800b3VlbBh3LDtc9Fqf876R8BlDD5A6O1rVErWpiKnpNhUf6qVMeR50cmS/GIquAp06tsVwslKTs8MU1WFKXqqDnvcpK7P9spizTsTdJNFLnSWONAPmWqnD41BTcLABNy8LDh+oUVOSlP78/jvZVO46dobgxOYh83O4nC02GmzfTTJTn78Y2pn6KyonTDTaYSKrbaDLW5tO4FqDbfyA4bpIX6Z2Np43lRU1tFy05dEjYXmO5qvfzdEr/pBLH3Wro88+LGyqUqt3qNU7lEqyF9GhOjtK3ciOoCure9tZ9OgOzdmhR3Vkjo7s+1qAEAIhs/wecsDxAdl6IzjS9Ll1OXV2HDqansYyd6hCeDhi4/wyM3ODSUrv9YIOsz/4HzirA5tOZt3w0FDdPEdGXh+2+y6boyh+1zswrW02cI/vHVlWjwKrPJbWJHRtDOYygPIYoOhLti0Coxhprgk7LvRx/uDQIb65v8zuKUkjUj21AimT9Jc/mXO+uOn4Qli742tHxTHOVhclS5Twu1Edas4OSPc1+mHq7AhrJEFAnDTxhE/oV3n4HslbroQblpq87f7rh4fs/a4Wj01g5wlSHUfeAmPr07WAit5lLNdGkTDAFiHE95Trt0sp376G+P8RWAI+vIY4DWAzaGzXo94zGXk2YyoPr8nwzerbDMaifejGp15n2Ah8zf/s5cq/STjP0gNAqxLQmCrRqJeYrS5TCiT1IE1YminR2a+ImQKdhkgHvSg+T/iQdJN6mpJOZ+Xqp8nRYcrnMTAEBseHDZIEvGHckoRO0qQVL3GoUWLfSoljbY99K3DtIhw9WmbuUJUjByqcdPhor3lYjgF9i+aJz0Pmfl10F6HD1c41praxGaXPIuWGZ5nIagvkGUYmA8mlM+fpKrY1uIhxmkd7Hi16P0WMQltfgp935nnlrz0Z/zn2hNIqtCoBndBnSonuUHN3hB6Eft/hoW9nGezdsrXF6eywzB2X8xmQ6lbwroMjdXyIgdNgIF1jlqN0K8u+Btx6oMLc4SrHDoTsun6e6lKHcjNChy++d5qnHf4xh2/6Jed+6QWsFx4abldEVqttTHhNkGcLuOjW67i+m65NtDjg+Mrq2w2sIocH5DNp3oJuYlwVr6ntKIan2o/p01TPRosL8hg3jexYaB/izd+f6iqCce/XP0hDnQMv7AvZUbaNmH4JLPIr4CigK92uOkPFWtuBvB3KKSx+2L+GXt4OwjqtZJkoWulFwXjCx/d8fvvkBZ77mp185YwbecDOUziReagYmBTzPKGqt7P1qePLq+Pqu6jAvk2F8GEp5T1tN4UQG0iT2TlBSvkLQ9tnAU8AHiOlPLIqKguB/o6KGD0ux4gJn6mNjQZXua2eXmab06Z56sJlqiN4w09SZ0ceNKZKtKolqlMRtXqH0Eu3sqT5O2Q/fwey96thlr9DZL8cSjm8nWWAXK3M5ewo4ujOi+7o9ROBDAbWlSiJaUSL7F8JONAI2LvisX8FrjpY4qbrZhA3J0zPNznn8DxB1Me1ZVsDKCkdnfg8ZG5vK7e11+kxjZmpfp4TxAWu9q61z9T2NoETQFbrzgyTQZe3/uk4ijhL8nCNek+HUfrPM267sHyED+5r8bm/KONTzNkBsDwT0in71OqNgfwdWYRHqSu7Q0+i5lryRQn1BBYPX3Fm9E9p6T+F6G1n6ZeZkudr0JPh9DJeS0ubzFEupNfLDVX2ExbaPoebPrcsw6H5Egf31Wjv8zjt2sPG01gyWNhUZcvDTuNTv30VZ73/XfhPeFpGFCckDznbFaXZVC/PGZRnB4yi99voHUVmrF9YpcNDBdvLtSkGaHVHMbhMZS6DSl+cTXTYJouLmW34Bustdo7wyHdNcf4FIb9+Wpsrj6Z3Nlfo7iWUeATdYwoVr/IoSerUeroSbWo7TsZ+vZ9x7qmQJICf0pI5PbrHzuIHSCHoJC1ancO04mViGeEJn5JXBtIw64W2z/aLGpy/KQudPjF5yC3E1Ps2XHkOiCIC34Z7FGeNbc7bFLbB9pJf6fFZjwXeUaDewCAIIR4J/BPpMYdXHA/ChrtX34ltwEyGSwZF29juuQxC3bAqopiYaFLxCczPY5vvw7T+6GUJN5yzmX//myP82ePs1CSeoBP61OrpcbSbyzATpspzJs/TsGi6xxsmvRBpX5TSI8ejjvkIcdvJK64ti6YnNDimeyLflLBUVbzjqF/HC4hkh2bc5kCjyjXHfK5agL37atz8y2lO+5lZqb724m189Q9DijkrbPfueDxk7sNUrss72zMVwWu6Z5PPpv5N7fV6Jjrc8mUiq01g0hOGyBgDh2n9LTonbNc634xisOr4XDqWjlubG4eu5bIdH+KLv/8UTnmpx19+4DKefcOTClGwMh1S39RhaqZNKZCEXhbNkf5l0R2BJyl76eksgZ6sVNnKkm1HVJ0bAxEeei45U54kVZarufpkAlpUie78yGjwhaDkSWIJK5HHkSbcejTg4L46jcMB2/YvOJ0dt5wxy7eefi3xF5c49Ywy/gPvPjjmSo9uuIPwUC5+G+48h4XN7tVp0eWkKtvzbIg8GeGyPdQyO/yKZfVtBqt0eJgYzKakmKCI4aiWuYwqEz4To+QZe3lGQhF6+/CCb+7jl1dt4JWP2s89tm4n9KocbF7HW6+spPu+g4SpUoLvpYqwJ7oOACxOA5NibHJyqPVteKyPNoIzJMe5MaR4m2ZVdiRXqUJSn+VY+wALzWUgXYgSCUvdow9nyx1mSoIOLd72s5BrX+FTZYXPne/xuDNOTB4abdFxLQr6XC0CeeOVR7vLgWFbHDB8/9WDlPKdwDtHaSOE+DXSsOi3SSlfc1wIG+wRu2Gig0kZKYpbL9PvFTEEXbRl91x0uQzAPN4ZpvmyK04CJJHcxp6XHqbiw9UvH5RfUeB183cEbKk3mZmKmC71ozsyZ0cg5MB2Fl+E+CLo/nIoupEUBZwdtu0tRUCVvZ6HTJLuyYfesLKt5/pQ+klkzHwr4KYlnx8c8PnpD7Zy9nf2c+do8HjDDB7+Wo9HnxZT/H2fODyU71jQ69gU7CKOF1edvLnsGi/bO7Dds9H3q4Pbr6xW36/KG0XWbQx19DXU9mnD5zK48vSFUfQCUx/5xu/VD3s9P/xWgyd98CQu/d0a1DbCzt/l3bt3cbS11+mYXp4OWZkps3mmSViOlVwd3a0sPQc11IKEsp/+eZmMhq7TQ93KMuzsUE9tMc4BzxuUxZn8lVpZkoCf4stfiT08ERNLQSQFR1pw+GCNo/vKzM6tsOngirXtpW9NeJC8kb3P+08+99E0Wqb5ji9QefmFnIg8VAzG1b9tz5c3XnntszLb8xW1YVxl6w/GTFpaxHByeat0YZ/H1CoOEw2S4pNglAmoL+4mw83NSP940VbkRZJAnNprt61yOi+7R8zBxnUstNNjaT18fK/UzeGh/bLnUnZ1ZVlXnMcBm9OkyHYWde/hQIJS3fFhuOcFNKNFrppv82/X1bn2hikecdcl9tQTvnXQ43/eUeGuT2/yOye3Wex4PGR3m5d8bAcS2Yv6SOHE4qHBti5njosmtW5WT1es8553HMeMTvOoMmMYxx0luZIQ4jzgk8BngOfdNr3mGYkuBVmtk2cI5sn3rMxWbuvH9Dwu+tS566LfhlsHQSBKvOKeOwHBp990M//2vP5ztqoBjakQvyqp1CLqAVQCqPjZthU5EOWR/gl8EVDyKgReCFG7n7DU5uxYjaNDhUSmWn1Xxqaj2Y3gGMGxvRJ53LQE1/1ylvO+sdda743/VmJDuI31zkPDcl/vx7Q+2JRmfQxMeG24TOudqZ1JLpiex9aXeawmslqHUY3JvHIdXLgpcG3qV61n0puK4M3TMUwguNOnnsmdZAJhvV+8+3xE1Gbj1dfy2g+dyp//Lz1PUAor0yFyWlCrdyiHSS93h+74qAUJgQczYUwtkPgioBfZkUV3DCQtFZaojjGY3aBrC+HjCYjlYN6N7GhaEyx24OC+Glv2L7LxgNnZseuvPF72qQ8RfnMr0VyTHY/eyqWXPSqloayednNi8dDotJlwuMZDxWWayyYdOM/WcMkBLPdWB3cUWb0aWEXS0iLGj8nQ0dsXEfomhnMZjC7myWNc9Z6JBhN+E80p+ELfw5zW9fA52vJIJPgiPX3Eo5u9P4mNgnAoe7/JMbJaZ0cRKKCMm0KqjaAq98Ijlh3mWj7f+Pw2zvn+fr7wnyFbnxFx9M0eC6dWgSYv+9Q2dp28yOt/PaHkVbIedQo4UXhosH4eDS6a9H5NZUVwjApFF1DXO1h7AX88QQixjVR5XgLeBNxbSTa2IKX82XHquUAd3QjKM0xtdV0rpMnAGsXAFIZ2Om4dh0qvSakoAoPPN1Pqbrsj3crSrJdoVQJqUx2CIEkdHv7giSxB1+mR/mKY7gcPvJBAhHgSiNtmeZ3n7NB/ESwKA1F1SToqHmmUiRcAWsTHwHAkCCGIEsFyBNxqHsdz/q/HSy5UUyZMeMj83dWnrhOptOjtXX26aC1Sbhvjon3fseC2ldUmfdikJ+v1i+I19TEqba6ytaBvhLalqrk8KMPBOern3g1YMFbplH1qUx0q1WjgRJYsuXSlK7tLnqTak9VhfzuLEt2RUjtIrxiamwzosr3PPF1cz9Xk0XV6+Miu/BUyo8N8oEE7gcpCh623LuElkka9xMnPjZlvCjpviPmnXe/hYxdJDv1+ha+/9hbOPq/K3a64H1Q3WIg6gXjICCb93IbPNT/1+zbcNjk+Cv15zo5R8a0/WMMcHhmYFB31XlHjynQ/j/F0yDME18Kwcykmpvv9eyfVN3CoeRRBQOCFqde4iHDUBaQpDHqtt6wUcWB061ojOzLF25aQNYko+3W2Vxc5736HefglHlOlFm//5gzve19ExfcRbKV9twaPu7zKbDidNWa98tD4gtTVp81AWc1z5tFVpK90R+sdxBN9Z+Ck7vf/1u59GXjQ8es6z4gsWje7bzM6bXVtxqnLgCrqdMFSbsKd10bvy23YdUKfRr1EMutRq7eoT0V4XXRZsrtAyfZf9iVlH3wREIgQ3ytBp22PyCvq7MiuTdtQZJLKX2t0nQdJjPQS8Lz+LPOD1BHTPfq7P1xpHx0pONbwqCy3B26/9fIpVqJjbKmcZByz/riuZx5y0Wii1eYkUa9N9/S+9LK88ckb+9EE70RWm8C0BhZdQ4+XQTOu/lQEp63+KM+i102/i/PPp5M0jS1alYCl2Qp1L42SUJOUZttYKj7UAkk1kN0tLf3ojl5PSqLS9FN3eijbWHRnxyjQ07MjkB74aWJ+KRKklAjh4eEjSYhlhCQhkYKke7JGo+mz8eAyXlfHri53OPQqePfTv8a+8Gam//YSLn3dFvACHrc8x6fu/H7utst1FtnID8AdkYdGB5cubsJt0muL2B+m57bVL6Lru+vcgWT1qmAVDg/9pWZlZmVxEFyMvJaMqfahM1ne5DTVsT2zDi760+ul6Ahf2ddkd91nNkyT2aXbWVxkaQqxaw+4C2z3iwjqUUKtTaey5Jw+I0nwhGRrPeEfP7iNb/11wCP2DFSm6s/w/z1uwkNmWl305OEZVxEr2p/tnu7AsU2C46HsHR+QUn6JXwnBJiNIBxsP2gx+F06TIWsyCF0GlYtOm6GVt+YUMSxdfaafqhLQLvskvkelGjE102a6JHuKdPonKSlbWtLtLX43wqOcRnckhq0sNlBlbG/bn2e+b4JEKjJY+fS8NEWUJ9O8HoGqqLfTzyDsrQe+V6LsJRw+UGPnjf1fU5/0loRaMEst2MDwu53wkLlv0xi5xsXmqLA5PWxOFr0vnQ69jd6fTpep/I4Jt62sthlDJl1GXxdXCzaj02VU5vVta5eHe1xDtQuHr6f9wc9Sft5TgPbQ7aXZMnHdIwgSPF/2nBxZdEfoZ0eIpzK7GiSD0R3d7SxA7xhY4dKPVxNRnclkFUccgd+PEFe3ssQiQuARd+sHQlKtxGw6OJik9I3b3ov4rb9k129p4ze7m9/e++IxiT2BeKiwE8ZVJ8/OKDJ/i4yZSzZk13nlExjHFYlZkTG9eNNgu4S47WVldUdVdEx9mHCbFvS8yaTXUcfFhTsta8UrxDLdPziQiVn/1c4EusIcawnwxoXVbIvRoztMjg1THo/MGdL9xbKTNLlhscxXP7WFc76/n2d+bV/WgdoZEx6yjUeG13ZfV3KLKri2MbWNpUsJz6PFQVvXhjsefycO5PG1ZJDX1DYuo8e02NsMOpvx6FJuTHO5CLiUFRPu4spXonyXXv97GKahxZWu4qyC3432yLazlLxymmOo0xyW1bboDpOzA9y5kGygytneddIrl1GURnckUUpf3E7zjCRpro9WvGxEe8UNARMeGgeXjlf9tDk+hPbdpoPZaNTHV6fXNN6rfMaJrM4B/d2b1kHbu7HxtYvfXTq6i/90HC5eGhXydDEzfrl0GGJplU2NeuooqFQjPG8Qd7a9peRB2ZfUS3F6cICauyM7PryXy6NPRy+PRxbdsRbbx03b1ZME6DvNve42l6DrmCl5klYsONAo0WoPm3Jf+w+fKx/wRiY8VAR/ER3ftrbkjeMozgaXLLbRost/k82TY48cJ1l9e4MxHB6mRdXFaKaF0eUIcSk1ah2XUuXqw4TbZJzlGZw6qMzmwp1+l5qQTGTUL8tOLTEdH9hrYFKao76gXEtuW82JAaZtLCp9UYRsN1PaASklix3BaT87DMCDdmZjNuGhPh6bIDXRpj+nbZEqSqOOwzSWusJmG+citIyyWEygONicea7xVvnKxae2Oefqw7bI2+jUcZuUAZW/TDypw3D9c2c7PeXZjxK8OKHd8llaDHvOjpIHnSRtqyYtDbpHaJe8CiKO+86OJHJvZbE5O1YLidT+uvijOHV6JN2/SHF4yDSE+lAzzVuiwuJrEkZ3Mqw/HjIrpyj3TXVd652JTr3cNBY6HhO4xthE92qNlfUIeXqCzWhBKyuyNtp0F7UvtcxltLrquJ7JVkfFZatr+p7KLpkkzDTNYzB1rJU6O3xJqSTpdH2+nlBzeUimslMSu06EDLxu3iYll8vxBVVv1/R6QZr02sPvOj+C7rqS0rZ/BW66bgOtymDA/rse/jTO+I0s58eEhwa/m+hW7+X1a1ujbOtE0WcyrTsoZUX0/3F0+vUBY0Z46GB78aZ2Ji+UisOlnJgm7Kh1iizSNiVMr+OaRCYa+p8bwu08aFfI0VZAlLSJZJtEdoWT8AZDl3tdGBwOmYDMlGiTY+F4u91s0R2mI2iHfqGU0Gwjl45Ba4nAC9lZizjw8I0APO6MXQC84xd7edvP97IczTPhoexTV7xNgk43LnR8rrp5wtJmuOjPbaqj922iRX3G9E+S7jU8Hn8nBpgUG1OdojhchpcO+uJeZG4Vve96QbZ7Ln4rojDB9urpvOWd6T7x6nKH6nIH73DC3KEKna4oi2Xq6Ihl3+mRHkWb5u5IT2ZpYnRwDJCW2L/reZtMbdX8HTbZq0V39B3PqdNDxl2nR6cJzSVoLbEh3E4tSFhaGMzvcfUF23AbzhMecuO2rQnZpz4WRXBnbVxrmK2Oeq9IX3qZGLozkdUmKKKn6HXBPW9s5TbDScdd1PjNcJn0Gx1nkXs2sMmAlG/FznMoP+nhJP/1WS67fOtQ69nDDaJOX4f2Rco3mVis+Gl0RzVIuttZUmdBlqw07Uk5iUU5ncUZ3TFO/o4MHHmd0mNo/a7TI835F3pVpkoJZ83GNBs+5ebgqS6XP/QTVP7mzwDB0nP/nqXn/j3M3Zh1ZiPCUHZi8pC9jfq8trlqwq3qrHl0542lbf0zratFxs2tzx8vWX17gzEjPEZ5EtvLML3cPHApQa76ap8uRrfhNikBtmu1T7tQKXkVpktbuHW5xMGGoBWv0IgX6CSt1KwTfl942pTjcbexjOIEyYvuKBTloTlCoP8rYwbtDjQWqLZi7rKpwwPudpRDj5nlB4evBwQHG/Ctv0p4zmPVEMb1zUPDONQyfZ6q/Y+jOLkU86LlJqFvWiBMdBcx5CcwuLC5+Mv2noryiNqfTUlw8ajenw13Xv+6smHCkdeX+1m3VE4BwEskm/cvs3n/Eq2DPvvmUyU5VEKms+iOsi/xvRK+V0JI2ZWzhu0siSbD1e82x8gQ+ZY6umPD+CchinuRHkRx6vSI29BegZV5vKU5HrBzK/c+Z5Gf3z89iWXTiz2+/reZA2TCQ8Wf1VS3qLMkb/3IymwyQF+fXLSa2rm+T2A0sI39qAadqczEi/r8suFTDdEi8yxvHdHrqrjz5pXN1pDp6SLbzqRz1Rzyi/9hqAPVm1tA6oDOwBNpHo+SB9UgoewnBMLvOjLMR9GuORTV19UfMpG9bS2pI71M6FeZCeucM9vkT3/7EHPbar2m//KxjXzy+QeRn/wwAEtHSnz0rZLLtr5P62Qd89BQu1HWqzz6bFBkfuetQUX7moAOY85ml7cqu29Srm0LtsuJMsoLtU1OUz2dWYoyuQuKGLB9ugQeF25Z4UAj4GgrYiWapxEv9LIwD4DqeEg0ZTm7v9rESauIBrFGd5jK9V8fe78yNmFlnu3VM3ju+YJTTlvgTU8vcd3i1TztnKVe9fddvVdpvL55aJCWonjzPMMmB0OeE8f2qc99/TlMdNvHXDL51TAfTIZjdq3XcclyUx3Te86bB653rPNQ0f5NuPOUn1EM3z7EcnArx+zhBtNzDZqNfghxdiQt0D25RaS/xuH3t7KAWY73yBtBnieG9qOCKuN1p4jqgInahJ2IZ58X8/zfO0D8jDJPulNTGZcJD7n7s+FzGQ+utccmS9UyfWxUQ8HUp0luuxxQpv4HcU5ktQvyDB/XfZuBlr2DPNlehO/0tcNUB8NnHqj1bWuTqX5WR6FLeIQPuBPJEfOxtNXlDkncb1/qJiz1PTWPh+xtZVGPorVTv8a5O2xg2ebYOzWml1hVUPbh7NkmD31uk5t/azPXnr+Vo629nH/PkPf83rXIb3yK7S+6Zw914y9fzYSHTM9VlB5Xny65bcNhu5+3FhV9fmmoO1xjPcjpNXJf2owvdaD1hdLEKKZyGJ5kMIyvKNhevq4QSQbpKaLA6G1NfQ/DQtvna/tr3LgYshLNDyrYo/7Ct5rEo3n4x4nuGKWbdif9VfHYPmrBLA8/KWHPSz0e/87T+NLeKaIgZdd0Ik14aLB+Hj0mHOqnTqupjf4sowh21xiY+jGVTyAfXIadWq7LaBV0/ijab1EeVJUh9buL99TvNkM0z5gzlenyoE+PRPL3P9zH0x59YKh1dblDWI4p+ZmDI9sbLvGFRE18Z4zsGCBdKy+y5WVc0B3ONsd2lkPK6zp14oip0ibuurlBfSri6f96andcbIrthIfylWrX2NgU86K06QaMjje7pz6vvu6YxizPEJrA6kEfS9u71Hkpj5+KrNe2vmz0jTq/bXiK4B+UDRLJf+y+K0/7yMOMWFrVgKCU9JwbanLp7EStwJNdJ8YgbbbtLE4YZztLYtHd1TJTMxn3/iDdQnnmhpi7nH+UzfuXeNETajz9Ls/k4t+v8J5f/yGtj/+w3zie8FA+3nHosfWZN662eqPSZJL14+A5cWEVx9LawGT0jaLEqOCaYEUU+3HbufotOulNfaafnaTJc79xlDiGqz9zEuWzJI+6aL5bw2cojM6xZ7uQ82Et9xka0Y8Q3ZGPDJKIUIQ8YKfPObPLPO6MG3nNU8sEwNs/tpnQq2J/D+uDh4qDSejpuEZxXJjwu8bcNv9teE3tBvsowkbrF2zKsq3MpETrCotpEdUNTJvBabpvMixN7V1GWx7dJhpMdU3PkX5KJFe/PJVdy9Mh9cX+8YcLm6pMs9w72jALm86S4vV+gdMTTQNDW1ls+Tmycv3kLpP81vN32L73+vD6npok6eeM8rx+0mw1j5QfIKVkoe2zvBRQWeiwPB3y0Hc1eN8lt7CjeifL+K5vHhp2xpicCXmGQZ581mVs3vPbjJsihorep7veRFYXAf0du3RlF1/mOdh00NfWPDqK0GMDHbdpPhXBL6GxwJW/+W78APzL/oCrL9jGSXdfonrZylCvmcMjc3Zkzg/PQrYqs3U9vB/dMQK4cja52lh0dCnTUx3Tz/4Jj51E0Ig8DhwpM3u4AcDJV83xkqdfygcui7ls9h0AXLr4HKht1LEq39cDD60l2OSgaTzyZPkotoup/6I6txnWg6xeg2NpbWUZ2F6iaeHPa6vXdzG8DVz96YqUqU0RZcBES4r7VT/ay9Mfc5jrr97AwnyZj/7fgzzv4jlOmYrZXIko+7Xe8VjOyA0VRk1EOkoUSE50hxy1bxd4AvV0mtCrMhNKjjQD9rw0LfvdP+uw3nkonwb1/jjCXoJzThfFkde/Cb86fqMo4BNIYRyHmMtYylO2XQqIqS/1u65MuZQGF648WnSFrdhc9PB420dnedNHQj70nllu/q3NACSeYHFLhc0bOvgCkixZKbLn/Mgy6gsERse0KcpDz5dkA1PboqDn74DUsaHIcaGeEuYH6Z8X0IwXOdQMmJ+rMDPXoL7YZvcnj/CSS6rcunIVEx5y0WvDVURJV+WxbX7rMtu1/hR5Jy46dDyjK9cTUMHFoybQ37Nt/PN0H73OavQNUxsXLXkOOvN8PfCEf+CyqX/iyh+tsP+WDr9d6/C1B36KD87/O41LawO157bV8TxJO4FO0nd6hF7fSV3yzM+lns4yHN0hh2XuWv6ImOXuMN1C9hwdWYRHJ4FW7LG/ITh8YHAMSu9ocumTOjzxX3cAcNn0W/rPsE55yN3vqGCT5fp9/bveZ5HnNbXT16WMjom+bIIxIzxMjKeCyVAxvRhTWxfkebxMCoswfNqgqLGbV89EQ1r2kgt3wRVZeQ3YhCduJpGi9ythImN8URoWoqb8Ha4Ij1GEsNp+NaF5RaI71F8Uofurohj8dTFJhXqn284XsO+RmwhKCeudh/r3XXjyBK2LljyjJK9c5NxXn8elfAv0Z5fcPvcG3n7A5cwD82KYpwQUMejGhTzlxLSGjGJcusptTrV+PxV/iopfB2DH7nSveCf0qdU7eAKWOzBdSvdRQ7onPNsXnjo8vGGHtOrYsIUw62WmtUCvNwr0aPKABAJNFcj6y7a1lCo0G4scaEyRJMNz+qWPr/HI193Ko0/bZehsffPQ8LPbxsM2d/W6RRRaF52uvmx4ijzPcMuJrNahyFpaRMfIc4CZ8I8CpnXZ5ghT6+vzqwh9pvXfrFdsf/+LuPT9g/ein38a2Uk47dwm+5VyvyRZWgipVCPCSkIs0zwenkhPaMmcHXpEhxrFkbuN5XiBTNLcT0E4dKu/lQUkSS+6Y98KHDsQstuA7mkffzjv+tfPcMULDnVL1i8P5eut44AJZ54tgeGzKJjwmu4Xe7/rRVavwSktUvnDcG9cj5PJy2djHtN9vd1qJrZr0ur1dbrzDdL7bg840PBY7vi0k4ZWzXJE7UD3FqV33Dwetu0ya7KH3IIjLCHCGvhh+rzdvhIpmGsF/OSGGrNfWKTxE4+51i0FO1s/PJTfv4uuIkbyKP3nLVTjLCzrQBqvGahKg2kRRruvO9N0HLpsH8VZN0odmyGgfjfRYHIGuvo1OdtM/QzW+YcHzQHgRwnRAcGxhsd8O/31MJGip0CnSnW2NVHB65LFpq0so4K+hcVZV5rrZFF2XtfRITwIQg43b+LaY2UW2uB5kqg0vCa995UVrWTCQ/Z1Q1fubfhd9LvANe9dz2mTHbqOZ8M/ATesxZro4h1T3Qz/KO/MxHs63+e1N+FYLQw/c+n3f53oxgVedefSQHk4H7GyXCLqeLSTNLIDuolLhXRubUl7KvhOijpFCuTmsLYRHpIE2dOLY2IZkciYRuTx1f0lrtlfJojseP3/9b9YWojh5h8y4aGiUMQZXARnkbpFdXYd8taqCWSwRjk8TMZcUSeHSWHQJ5aOT/deFfXYjWqwunDbvHemMlsPCa/+0X5+eGOF3dubnD4TsL0WEcsIT3bw/dKwYLQda5j7OKuM4DDgWlXuDiXKQ0xPQXkKwhpUZ+iFUscQeJKpUszMbAu53OHkq+Z4xn/WufxRAwSxXnnIjBNDG5PzsajBUKQvk9e6CLgMFF35liDXhyd6fHAZYep9kwGa19aEx1SeV19vq9PkWjtsilLReeJyNOr99r9f/I4me/4zDRcOooStexc5fKDG9qklmjHUEkEnEb0TWgRemrA0arsdHQOk5GwtdMht49bCvFwe0JXD/mA7JaqD6gyLXotfHoEv7Svxs30hSwslQtrU/tzjrffbhSThQW/tsOvkJSY8ZOMhEy1FFNw8Pcp03zVOebhtddU2tnvDqCay2gQuBxiGe6Z6ebyq6xIm3cJleOm06P3a8JroytOhXPqIg7a4w4Env46ff8fj5O8/lpc/sTVQc+PBZY7trNNsBFRrMY1YMhv2t7Z4QuKL4T4lCQIficyn6DaMAJHdf6mzo0MnaXKk6fGzo1WuPupx4w9m2HZz/8SaxSdM8dEzfkD8vWuYe8JDWHnxGwH4/AP/i4dcdzcmPFQE8p7Phcs0TqPSqbcdVT8vWH+dyOo1PJbW5MEqMjHGrVOkXUZbVmccJrcZhS4hUISuFO6yKeHF9ztG6MHVxwIaUYwQ6QkBCbFdoJqiMEbxGq8m+mNcsIV2Qxq6N7ubY2KFVgCNZJl20iCRMBvGnL817p3S8uqHHNYQr2ceMim7pnJb37Y+8hRfva1NMS+q/Nvwqm1HEfbrFfIMSJfzTV3Ei+CxlbsMMBON46y0Jr4b1Xg28dhwm2a8xJ7/PDJQVltoM3e4wmIni+hI22YntPTyd9wWMHZEiOx/JppD3Ovm7vBDYtlhvu3z45sq3PrVOlP/0+C0P1jmjffdBKSnHHz52SEf+p30esJDrjZ62yK4bcq/SeaajAz9nq2/ov2q9daBlrzm4JofRdqqnyYoYjCa2hQpz8NbRJcwPf8YstLz2HjPOg98/wW8/EnVoduzhxssL5QIyzGekD1xV/Ig8AZPbbGBdMlWp7NDFKiTdTKa/JYk3dwdkkbksRzBynKJ2kKL2kKaWPvoH83wsQcfg3YHf0sV/9kf4SNvSvu5+P3nDdJo6WVd8NBIUGQ9G0e3Vcv159Fl7iiO84m+rMMqIjx0D16RX0tcRot6z+TZsuHIM4Rsk6eI4lWkTVGhMUynwOOBu6o85n2bOOnUBTyRsH+lxGy4RDWYwRcBxnwdrnDlcSI5XG0s21lGju7IC7EW6VaV33vHNl7/R7dSDRJOnd7ETMnnlqTBg3e1+NYlsyy2fE6fPsn2IKw3HrI/r1Dqu7zRtmfPm9NFDIPVCGIzrZL14YleW3AZhzp/5BmapnZF+h/FOFJ52Ua7y0DNM7pt/ZiVqKc8YYmy1jJsp3uofZHyY6Y8x7I/HkJ4IOPRFFpV3ppk8Vr9otiTz2Jgy6QIAtT8HbHvEUVtVqISh75R4dSrjvDYN8IjTt6J1/u9ZMJDeTzk7gPlU2r3XGNpekaTzFfx2+qanEkmGoq+Wyay2gkm4yYD03o9is5iapPXXu1PL9fpGxVs/Zjuq5BjdAuf8PGP4CuR2YzxEomXSKLI6yaWThOYZjyZOanTnrJkoALwkVKSBX8YIz2scriIgWrAZYvkThLw+vey/B1ppIfgSCtgsQ0rSyU271/m2O9O8+E/ajL1zS8Bd0WcfDLJ9fvY9OhTOOlLN9NqSsSv/Xbvqdc9Dw3VNd0vqnfbxsalf7v6KDLni/STb2esF1k9hsNDZWjXANuMKNP9Igxhaz/OJMqbADYGz+vXNQmHn1eSECVtHnVRi0ef1uSfflphqhSnodC9Zt391JnMMynPrhweKp4isJZbX4pA4Ke/JgYh7eQYu751lJfcsJXPv7ZONmanzVzFjupZjm0s6vX64qH8Nup10Wc3LVouvCYabPdddNv6sZVNYBhMBswobdHa6u9LLSui0JnoMSn7NoPQhMvVzmWs2a5dxrTkQx/azpHWLTztT2q9I/8STxB1PJbbgk4i6SSCBEGUCCRahv21zuBvw2fawmKsp+T4UEkNS6ksVpNHA4eaEbculzj5qjSHyeXPh9++Ik/hn/DQMD51DHSnhc15YaPRNM62uqZ6Ltps7UwG+jrQktccTOurbS0suhbb2hWtr9bJ06HyYBTnTJ4N4dAXZAydJnf/fx/nAZf/Oa/48RGufcWgzCu1YqKOR6cjaAeSTgLNeHBXn5QJKDp3eg1p7JrfKxNC2/o3AI7x8jyIC/5IqUISpTI4iSCOkB5EsqWcziLwheT6JZg7XOGM5QWqH5vnuR+Dy674XbLxE+fuR1zwIB7yOxMeKm4fmMpGsR1M41akX5XePPvB1k9RGtcfrDJpqf6SYDxGV1+kvthn912Lvgou5cWEW6+j0mOi04RTHw8X7v74POVRe/nKvpiHnbTMcz69kXqQZo+WJHgi3UOIb8ien5fE1ATjbGHJO4p21OgOUzK9wE/3itc28T9HqhzdVuOka+dRx3JH9awCxK5PHrK3ceHSFW/XmOj3VeVep8/03SQr8uh2K+eJPD5/JwbYFssM9PK896WXq/dc/K3jd60JRXmk6NpS1Hiz8W0fDjVv5Nlf38e/XTuo7HbC9Lrd8nvRHVECrVh0k5jGXcVZk9Vr4fwwJb9zOjekJX9HtzyKB2VzdiSt8OgkLfatlLj8i1s5sqPOM94W8bL3aom1JzxkqWuiMW9+mu6b+nCtRTouDP2ZxngUIZi3bqQwkdUmMPGWzUBaa2PFNjfHwWEry5tvtmfN0xcG+7ks+H/Ivbfy7mc8BfnTr3O0PUxVECV4nsTz+s6OzOGRRX0ASJkgpUz/lD7075Ik53nWEJIkdXZoefrSCI+ERKbH0e47XOaMLxzk5t/azEuXL+dBn7gMdfzEBQ9eYxpPHB6y43Q9j023Xw3Ynt3Udx6MJzfWg5xeZdLSjBFdC6t6T13MdSYuIkRcXrw8PC4PX1FPmK6MmCZIHu7+M9zjFR7//oKEf6fKymNKnDzVYKnjsbGsJF/KwtoUJTRFo3xmIXFKRmf3Y9wGURw2J0j2PUtYGnSjO0TEqz65ldasz9s/prPlhIeGP4so7kUXClO77L4u3E2Ligln3hi7wFRP9v5fD6F340PeYuwyjmzXKk4bfle/+r08Gmx1bWuLi3YbDeocsuGBx71vO3v+8wjfAWbpG/pRycPzJGE57pV5It3S0knS6L3UYV0alN0yKf6LnyuaYy1OzIJUTmddRHGqYANZ4uhmvMhNSwG7vn+U+mKbf3lmwFsvn7UR7rhevzxkp9GFsyiYxlUtG3WcbX2ouGzt5dDVRFbbYJT1cNT6eXXH0Zvy7tvmlcseKIJXv99nqEf9b48jb/4B3z92Z57CWfTDoPvQqqa6pO/3tx9CKqdjmV4HIqMs3dIiEUgpkHgIgSO6Y4T3Z9q2YgLb1hbSk788MjokrdjnQMNj/y11TqHBnv88wgue9HQ+/hmbWTfhoWL6kc2+cPU/7ny29TcqPhUvI7ddL7J6nFABzAu9Xkf91NthaINSrhpULjrGYQqXkZeneLiULtvE1PGnfuJj7QM8886beOwb4YZzNnP2mQvUAslC26eTNIllJ90jHWvh0WB2egx0O0I0R17dcaM7bNBz/yn1/JAoaTE10+GsHx7gjx9zhMVOlph0wkPD+F00j6I8u4wL15jk3TfRN+pikH2uAyl83KCoEy6rY1NCis43GHz3RZxrRe9nuIvyg0qDy6A10d2HTVuaRuxx4LHr5CWmy7KXtNQTadJS6O+1HpDRnsFh/asAPeojint/stlKT5YBCCq0kwaHGn3DAWA5mrcgnvBQcTmn43QpurYxcDk7XAq5aU1yXatjNq6hMYFBGNexVcTpl7d257U33bP1a3N82Z7PxIO257K0lzHs/zkb3vAcvvJ/n2iok8KBPdPEZa/nlK74KNF4ovcn1X+GKA8zjMHv48h8JX+fQFDyKgReSCxhqeNxpAlLi2Gvuvf1Dh8ovRYOXWuhecJDw/SbdOZR5KWtzAV565vet82mdtHmGv/1CWNuadE/ixhwJg+W+sJ0Y87GbDCIx9SvCa/ru06rWmZ6JrXMxYC6EpP+xTLin38W8/Eb5phreWy9d5PTptKaqVjrvpY46v/ipjsksl8M87a3jOP8GBfGOZ4WIIkoeRXOO3OxV/RbL5/ufpvw0CC9eU4RHVxj4Lqn4i6ywJloNtV3LXq6Um14dglJIo7L34kDujGlzwcbuOaJSRGA4XdlM1yL0mq6l2dkme67FBa9vdqmfy8hof7eZWPLVjWgVu+ws5oq0iVP4iEJPIkvQCKJZQfjtsRRYBx57DqOduikLEVWJwm0O8hOM113umvL1iocOmcGgIe9xmNr5VQXwZay9clDxRVhXRaqdJtkpq1flWaTHHWV6/27oMAaNJHVOVBk/dXr62uiCiYDSL+v9uuq53KC2b6rZXk8pOPQn8sx99orHPjzT9B682V84s/sz9uqlti0pUlQSih5ab16AGFXZe4kaZSH7EaGSJn0vqe99q/zHSBrCKYojyQCmW51D0SIL0SP/tlNLdqhz7UXb+Mxb38LAJft+JAF+YSHhnGY6NRxm2yN1YLJbjHRZ/o00am+G3UMTGvuMCnrQU6v4mcm3UDRB9X0Ak0vy6XsqOW6IWtTmnTlvsj3rJ3p2fTnU59Zn2QQyQ7DMPjiAxHykgt3cesyfOkWj4vPaXDuxoiZMKbqJ+npLJAKuszpYXNcjPKL4SqjPnKjO1xgqSujCNorlBrL/OWFPrf+zmYAdt1zmfXKQ/YFwAYu4WwSdrrgFpjHWgd9fqvjbJr3rkXPNvYm0N/nBOxg4jHIf7+uxVa/b/ueXdvamXC76CpKs432In3b70WJYVN4FzYeXCFJRN/ZIdLPsp86PUT3n1FRPp7RHUVksautpzjb2ytU/RlOn+7w4Psf5Bf32MGG0IV/wkN2uooYFqZnttWx4S6inJvKXcqzCUzr6wTyIY9f9Dp5/OcqG3XNLGrMFgF97hSkpb3C4DgYcJSn2P7+F5Mc0nMJDULQiXv5OyB1TFf8dOthBokcflbV0ZFep+2dR9Rqbfs4xhhLW96nJOnR4gmfmTBmdx127VnkurtsZeNPlnj8m1P9+eTT1HPF1hkPWXVdFYdrvdCv1fXI5Hgw4ShCq2qDqDZJEchba3UbZaJDw6pyeJgMGdcCnoFJmdGNzyITcRTDyTUJTUZZVtdFk5nh0mSjCWmUhg132v55d9nOR649wClTbapBKkzLvsT3qkRJm3JQg9YSA0dUAcakdcqWE1Ek6gOKK96j/MpYdItLIoHur45d5XpD/SQuf+J+vCeFTJdmSY8DM9F4YvOQ2bFgAle/eaA7h2xtioxR0ed3ganesFItgSSeKNj54DLIsvsmeW1zgNmcW64+UO7b3u0odJpoNhleeUqSy1jr3+s5ng0QRAnLjQDf6+AJScnr//lCpEfSDjzGan5bcOTyWGtQ5XUcUQ1nOGX6KGfOlPjytOTy58MDLz/CVLCJCQ/pbWz1iyqbtrGygc35bKPLRJuORy/X19a8sRgsnchqHUZZm4vWh0HDydTWNH+K6EyrgSI6lVbuBYp+6iv1Mhx9Pqz8w4vgUXutvZdaMQkeSSLwRbqdpdRzemSOA8WZITwGkpTKhCyPxyDVmX7PgGPkuIA3uCVSdHNDCTzKfszOWsRpm+GmLRGbvr/C0z/3u1wW7UzleNzpRheuMx4y1jfzkJl+ta6rf9uapMvTUfTfUXRmve04tkL/7nqQ1WsU4ZFdq0xTxIDKrk0GrN6XDfR2LqXHVMc0UU1lpnb688rulae106ErbPF58K4GU6WEWpCwsRyzsZxuasnO2raCIwJDJknvzwnjnNwybu4OW92on6OklaxQDzZy6/IijWiBdtKgES8o+TwyONF5qIhjJO/5VEMiTxG29edS6tVyF75xjRQT7iJGwwRSsPHxoKwaLFfb6nX1hdyl5Nj41cQXKq68BXcUo1HFaaNTfbbh8fJFwGs/FGODwweqlLxUkfZ6f6kyKkidHsL0TLq8TcaQwwPoRpDBoGxz0cZTdZR36fFFQLlrd1RqEY99I1T9GSY8pNJg5yFznzb5WETW6TqXLqtN90202RR8U18m3Dqc+Mry8QebQVeEZ4saVbZ2a722jkNzt42anN9YLy0TCC77yGYr9g2HGwSlhCBIunmW+vcyeQ0gpeL0yIFetIcWBeKEtXJWKw6ZbF0JPJgugedLjuyo84BXechvfw4W9kNjHub3wsGrWV88VFR/tenbpnXE9dy2siLrUYbbJYdN4NKx8+qvX1ijCA9dQbB96u1tSoqtrnrPhFunyWWIusDEeHkOnBQ8fCLZJhChgVa1jWQlmufn8yW2VzvUSwklLyD0quk+PS9MHScDwrIrYNVtLjnKcqYMjx31oScrdcGouTsAUa/D1tNpeB2qXh3ay5wylS5iJa9CJ2lSDWZsrTkRech8z8xDw9c6vSbDxAb6WJrGwjSXTfS6cOvGkKudhlvePvcG3n5BV0ZM7wGtDob7LvltKnfRoNLhosuGy0any0jU1yNX/RSvJOHxH9rKTuaG7i5uKLNle4N6kGY3V0WcEGmknyf8VDEtkhTadq2Wr0ZxVnN5DJQnkIj0D/pOjy4drXiFnx0tc7QF//m0hLK3W2k84aF8J4Dp+U3PkIfLhtOG11RfrVeE/lHAgGciqx3gWkdN66PaRm/r0rdNeoitvYuOPDp1XDYdy0WbSCMSWktQznK4mfrrwrH9vOJaO/8e2Vln00x/W2IW5aHKal+k8jqNmrgd8mqSDPwsrTtlEgmdBO5+wVFe/tQpPLEBsXBBerMyk45lVdWf1wEP9cBFj6mOjncUnT9vPtvoN+n0NrrzaHCNUY4dsE5k9Rq4Hm1GlulTbaO/EJfilOEwLd62xV5XSkZZ4PWFxtZOx5m28YaOsDIxkuBnR48yU4op+0lPCGdJicp+avwPHWcVtQev1e8Op8TIUR8uGCW6w1U3LEFlhijweeNPlkiIScIKgVfG90oIBGWvji8Cbl25Sov0OLF5yIzPVCbJHwuboq3TYaPXJqBHGRN93PNoctE6geKg84arTG+Xd99UnkFef3nKgYrbppTkLOJG/rQZhfp3SSwjdn5i2NkBcNN5m9mxvcFy1N8Hnm1t8fBJozy6sluVfeoWxLU6WtYEesJSZ105UFd4Xhpa7gU04gWWOh5ff0PYdeJnMOEhO622vk1rlLCUu+hx0eiqn9dPnlKtyu0TXzk+vpBnHLp0B/2eSd924XLNjTwa9fv6/DFdq+X6PNHmpfDAD0HGmOdKf77IH36Ha19hl21LGyq0Wz5R1DdzYknvNK0091J/27S+DVEID6HvZzneYHJqa5HYiYxJiPGFpBULZsup06PiT1FOAihV0j/hQX0zhDXkD77QjfTodaR3bPi8I/JQHrjqF11b8trqdYvYw3n9FrElbH1MZHYGY0R42JSOooNZxFPlwmfzXhWZWC68LtpsxuAgzk7SxBM+naRF6FeVM7MH6//jj/eyrQpnzghmyzGJFHQSKHkRUkoqpen+kbSZoNOTlw6Qojgysk9HREehqA+lj0Ih0y7Hhqt9EhHgE0t46DvafOHpVUKvOlClFa+wu3Yn+lt8TlweKl5fpcllCIyq4GZl+qerfxMdrrq2MdKfwVxvPXiiVwd5CkQGeXXyrk3Gmj5H1U+9Td7CbzMwTXKgyPPa7g9+j2XEL+Zv6uZVGpRFANffeQtT0+1edEfJk1T9pJvDI1WUswiPARmeQaJdr4Xzoyd/5QhbW1RniN/HoZwAFicdDjdL/NWrF/HFFtw8MeEhd19q2zzI61+vo3839Wtqq4+/PtY2fHlOnxQmsnoUcOkNo46jba116RxFabLRZqPVVG7gr9ZS6mhtLkB1FgLdwZrWW3zOK5n604sRd7s3YN9y2Cn7tFt+L2lp3O3SFxB6krKfbnVJKRC9z56zmsF7LtC3nw+18TyIC8hlV8RfkoAASUIiY2LpcbTl8939Hu+c/jEfqr2bS1ZeCkF5EMfSYcTdH5Lm9BjZOQB3KB5ywii6uV6u9j+q/VFEz86j2eY8KUpLVtcO60FWj3ksbZ7y7Lqvvii1zMZw6ncdh/5dbVN0UhYxok3PPKwElLzKQCjz4B6/Pu7772hz5VHYt1ICsh/YBAKf0K92w9ZEKuzidj+qI1NOLdtOBqDAL3uFIj5s4HRi5GxlUZ0yfshSPM+zz4Pp76zwvUPXZ9QBcKR1Mz88fJiEhE7S6t47cXnITpOtzKawq23UPxs+/TlNz+1qn1d3NfVOfEG8tlDEUDJ9N13b2toWa5NCos9BG7+OSosJh63MprAN9/PcbxziNU8t8/InDTs7APwo6Z3QMhtCNUh6CUtB9OT/wHYW/Q8GZXp27XxUw/2xjq3V8nf0jq1V6PL62ykv/9aGrDMFyYSHVjcGpucqatjaHBBF1oFR8WbvyLVuuvqdwCDkjZHu/Bt37bM5v0bBN8o6ol/nPYPBeC1Pp04OP0xlTxxp9VP4wl9cyv0/dQ8uvdTu7AAQZajVO5RKspdrKfToOjr68rrfQ+rsyANp+GeqUxhUfd4W4TGkZ6f4p0oJV359C7eceyrtlkR+/H30eUgib/guyWc/A1ErdSiNNEfvgDxk/BwFt83RW1Rn1eevrb4Nh6mt7mwpgscEEz16zC0t+iKYlZm+q5OkyOLuWlxtzhYbjSa86p9ab1RlwPxcAo/Qq6J6iRc7h7vOD0lCzN22bOfBu1Jh3epmxq0GaYI4KZNeyFqaYRmGtrVkoP5SaD0NZY0cHwXycbidH4a941EMjXmmFpbYWNrOU/+6wZ021LoV0nEJRMhdNk/zyRv3MZwk6sTkIXOf6rULv4o7r+5qwEVvXju9jusZ5VDN43Fe+Inl3bY58FxGT1Gnm1pf5fs8x5XJyM3Db6OjaL8mfCYZIGklKyRd+fJnd1nhF/fYAcDP779zCFOplSriJS/9pbCfsJSes8MTQRrhoedaWmWC0iGHyahgkt9qpJ+6FggPITyeeL9jnD5T6laY8JCNh+x922gwORZGpcOGr6iMdelnpr5sCrmZyoms1iFv7rh0miJlej9FdQe9jskRaaprw2nTnfR+NN1JeGnOCb/U1X8lHLyaVrJMLDsQtTh/U4M7X5hub158wpSx9yjwqFQjPF/2TmIpedlf6uxIjw/vJ5bOtrAIkbmrj+PJWKPmY/L6224yulqxYO+Kx8Y9LUoeXPIPGxAX3ROVh0RlCu+B96f1+rdZ1osTkIeG2o+6LuXR5QKTE6aIzuui6/jLu+Mlq29vMMaMzl6Uy3jMc4ToTKtPENdCrl6bjLs8ZT/PK6iDSTHQ+3GMQ9xhWnQTMMURi+3DfG3/Ia5d8Ll1WfCL+QpHWwGtWFDyKgx4jHUngUwGt7V0v8soUvZhW55D/xXPRK3B8eF0hLicILYylcZKmCZW8gJYOswjd25kQ7gNgCuPXkszXmJDuB1flPjiXo+yV1cp076fSDxkUipteE10mNqNI8BtwtqE21Su92t7N3pZ3kI3ATvYDBcwv09T3VEMQB2nSclxLfomuqXh2tSuKH/m82rZq/e2ID7xXadyzvf3A3Du1/bx0/vsYvZF/aWyutzpLeZlX1ISaXh06rQudf8ChJT9XylNUR6Jdq3WywNT7qaiTmtnne74CI84CNi73OGUqQ4zXbk84SFTn7Y2apneXh8n03qUh1fHM+rY67jUvnTc+v0izz2BYTDNG3Ws83gxbw7Y+KaIk06tr9ax9TeK83MEY7TnDBDEMqKzZQ9lUUmLDl7D635So/OG9AfD8kcaLD+pzkWvNJsytXqHig/1AOql9EjazNkxkLBUOVFL4PXyeazG6WGN8lCdHV5OdId2Yk1GYyeBhbbPTUtw7tnH8EWI/NOnwo5z054//zFYPAg7zoGwxs1fiKGuHiV+gvOQU0ab6pp086KORxfY8Kh2RB7OcXXfid6sw5hbWkwvaRRFw6ZI5Al10wSwGam2vvW+9EXdpXAUUfTgcPNG5tv70m0YIibyBd8/dCNv+NkhXvvjmCtuCPjpUZhvp2F2WYhdou9HzMKKdeU4jnrXMo6GnQsuxwcUcnyY28l+exfuImUZvtosK1N1GrUqn9p/lGwcZ8KYx388NRaq/jRvuO8ulUJObB4y0ZS1czkG1LlpWgxGEeBFFiQVr+m5MjpWYwRp/UtIYnFc/k5MMC3kKrgcd665osMoDjATXTbDNa+vImBeg1aieRY7R3jDT/ax2DnMV/bdyKWPupWzv7Kfe7yivzSe/+29zP9DX4bFgUdYjgm7Vcp+QtmX+N3TWfwsuiPbiqg6OAbIcFwfz4SmKnie9tcd2zhCIvnWwQrzbZ9AlJjwkH6dt3aYnI06Pl2m5/VvGou8dcdGk+2+yzjJrvPeMxNZbQTT+q2XuZ7PtJ4W1X9stJje4WoMv1H0j+EyecP34NafsBwdpRkv8fxvziF//g28G3/Epc/ZwMprEy572zIAYTum/t5lvvVXg/IyDjwq1RjPk6mzI0i3s/hCEnh9p0d2JG2fGsW5sAYJSweikl1RHaZ7PZmcJpDGD0hkjCTNPRJJwU+unuYBOyRbW2XEm97d7TSB2Wk+e/570usNOznzM3+G+Z2eiDxURGcdpb5Kx1rKHlWu5tkZrnXGBupz5bzH4ySrb2+wRsfSZtcqQ9getohioDtMRmVGg6FkBL0vFZ9ufLoEwyA857PTVC9bAfYRP6PMdb+c5a73LLOtAoeaaZvzZiXTIeyqdQBoxR4Vv03ZqzGU8MikNGeRHlHcPbrKIDD7R7+YH9+R4HTA6TFKslITfrWeck9UqrAyT6V+Kl67yW+dvItsrPfUz+LfHt3GDCc+D9nnl9qvq65Km61MB/VZXPVNCnAeTld7vcz0Lm9/wvP2CS4nW54R6Gqf3bPxugt/kUVX70cv02k08akLt15HUgtmAYjlXp77h03A558+UkEIQTNa4vvop22l0KoG1OqpMzaL7Cj7Cb4I8fDxRIAvSpA0hx0duhwvcmTtao6jdUHm4Ah8CILep/BSJ3sjWuCJd5oh9LNcJhMeMstgU796OxudtjKVtlEcGLZnUeWAq08XjXp5Hh9MYDQY1aDS64+rf5vwuuqb1vM8nab4s/33Q77ETden+drOu2uNt/zdLp7y2vsD8I8b30/UFiRf2g48xIpjYVOFJBHUQ0nFh9DvijovjchLT2pJHRLpgbRBz9mhbmcRiIHoj1WDuj09++55w/pzL3F0kOY1KVWIZUQso56TZqHt88qHH+Ium06jlaxQfu4zAAnCQ9zzofzmVfeluP5qur7j8lB+mzwd1wT6emHCo9ZV77t0WlP/NpvBtX7aaDpx9GchxAzwQuARwNlAA/gm8GIp5VV57dfolJZRBjVPITAZizYaXEakqY6trY0WG01u+Mv7z7P1oREfvmaKzZUOf3L3W7nr5i2EfpWbl66jGvi88ocVagFU/BL33d6mk4j+cVNeCU+SRnIo0Rx66LPRKbHGjg9r3XGSlWbgCahV0+0sm0/Gw6cVwBOedJhHvDTiqWduBZkQ6Nmme3Di85CdXls/owjtvPo24WpSwl0LgA42OWES0GanimR9ZJNeO7A5ydQy0zvW2+QZTDYDTXdk6TTYjLq8Mp3/bP3q34d594UX7IIrBus+4m3TnMFBrj1/KzNnd7jsDw7zyH8+ibO/sp/5LTXusvMQO2ppDo/sdBZP+PheN7pDxsNODdPWlSL5OIo4PoZyJEm3o9oTisMjc3b46TbDsAZTW2jER5TTxlxG7nrmIbVMr+viO51+HYo6FVxtXX2a6uXJCr3cNA6DNSey2gZF1sk8sBk1pvZF1nubrgNueorcK2pcCh78pj2wYwtzb/4e1U0tZLPNZe8tQXUW+YO7wUydn23fCR+G2Rd5HDoa8oH7dbjX27dz3jf2ArCwqcpJ25d6jo0031I/sqPkSaVHs1w1lUuZDB1fu6YwsN2l6+wIQpLuySyRbNFOGhxt+TRjwWkzGwAoN5pcNvN6/vB5HpXXv5hExgRlc34TrcPu54nEQ2q/LnrHcQa49P2i42UamyJ95dk9ap+jPdsdSFafDDwdeBfwUqAG/CXwbSHEBVLKm12Nx3B4mAxBl8Fjm1C2+i7QJ06Rfm2TxEWj3pf+vLZ6KVy4+TRA8Kw7z1P26+mvfd02u+un0I5X+MZ/T3P23ec4c6bD0ZbPxnLMdGlLmuyu04ZOM43iAHr5OhJNOTZtTVmt48Pm9Bg1WekQPYN9iqkalGvQXuHyWxe54i8Ff/O2FU6f2Qp+wKt+tI9n3Bk2hrtYjzxkptPtDBgGV3+m53eNs14/7xmKKnMuIa5/z1P+J5BCkbGy1TEZSzb+sRm4o1ybFAjd+FJpy8Ot0+sy6lS8g7SE5ZirL9jGy55+gFd+YhtXzQsefP+DfGv/JvacvciemYTpUroP3BMgSE/n8nAcR6vKbd3hUQRkwsjRHjbZrDs7whKEJUQphKCCrE5z7YGjXLglxs5LEx4axKvToj6rTc7Z+rXVdb0L03XeWlSUBlO9iTweH1yGkEsnKYLLpR/lOxyGr9fyPefraeK3HgdINr/u3DTvRKnSo0NceH9YnuM8L+CF72pwF1Hiyd9NOFb1uOCeB4m/AcvTIc2ZEkGQdLexpHjVhKXZCS26UyNNWur1E5caojvWxOlhOohALetuYcELkEIQJ20i2SZK2rSTNo2oxC/m4eLdiwT/7z18+G9WePKnz0Hc536Az5Env5atr3w4nHRX1iMP2eupcjBvntme01VX163zdPZRbBkXHh1f0TG6w8H1wBlSykZWIIT4KnAT8FTgb12Nx9zS4hrUogxS5L7JwLQtuqO+4KKGl42BbTSl9yUJi50jCOFx6/KNnDy1m8ALCUQIPrzxaTdz3+2n8s0DNzDXCthcScPVqsEMNI+m+7+BwfC3iEK/CML4jg+93aiRHK4tLp6XKtdZbpJOk2R6C4/dusJjP7yF/c1rqfozgOQld90OQg0pX388ZL62Kew2sM3VIotg3rjYnBNFcbkWDsvYydtn9ufbF5h4BAZ5z1ZHxWH7bnpn+ndXX676ecaXae665oYJh432fv0vPL3avXcSb/mfo7zjv3wgoXNxwPYd85y1gd5WFk+kSe88/PRT+CDbg7LatIWl+6lG6om8KDvN6WHNt+QJwBAqDX3Zrm5pCUuIWh1qs1CeYr69n7ttSaMSl6N56t3tPxMeMoGZh4bvmejXn80mJ/NocV3nrYu28dBpHBEmsvo4wDi6ymrfwa/gHcYRcu91iN0e8n++i7jnr6eRZ2GauD75xEe5y2OeQPKx93PNrZfw/H8+0Gt6bEuVk05ZpD4VdSPv6B5Lm52olZ3ckurpdOV26uQo5siQXdldzPGhjV/ethah5O7wS8SyTSwj2nGDVrxCI/L48VyZS85cYWvlDK563iN50v85DX78FdiQniq25b1/gT1F4zrhISMU0YNtzggVh0mHN+HPK8+jxdR3EfxF8WVV7xiyWkq5bCibE0LcCGwzNBmAMd2URRZb0wJZVDFwga40CKXMpAyp9UzX6uS3KSmu/vU6ovu/x/bqGVT9abZXZwm8sHcKQCBC7rv9VADus30P523skEhB6FcRsZK4VP8FUMnYLJOkH7bsSkLqvCfzT3XJ6mVlpnp596CfgCksIWZne577dtzgGrnIU754mG3VM7IH7T7n+uYhM915As70mX030WnCoQp0Vz392jX+Jlx5jh61zhgK9wRwL4R5Bqve3mY0mt6t5qgy4iziqNPnxyj84ZIVJlDnTZ//3/nGeS56pcdVd9vOjt3LXLAJNldiNpcj6qVkKDxaIBiI5oC+LNeOpTWdiqX+mckcIzokg4EzdLsyOQgQlTKUp9JthvVNtOJlHv7XPk967AF+440zlrGa8JC9/iAPDfatfjc5F9T2ep+usbatIXnODR2/6T24+prA6OBy3KlgGue8tb/oXDS1H/XeqFAElwS/hLjz/WHDTsS556Wyye/+PhvW8f7gySA8vN9/HF9/fsAz3pZGQ89vqVK6u2D7rhW2ViSzIVSDVIX1BARC9iI+QJHX3e/9sn4ODxuMFeWhtsm+62V+P0mp7G5laScrtJJlmnHqYK/48MpLK1z6qFt5zkd38z/3eRPizr9GNr6Djpt1ykNj9TsOnaO0cc33UWRq0ecbdxzuOCCE2AqcCfwsr+4qIzx00BUUk9HkMmpM/ZgUAfUTQx21rs3Q0nEVeSa9vQv6fScy7jk7dCXmhsXr2F3fxVJnjpJXgaW5wXBok2JrU3R1B0Phe12abFtdjH2NMYE8AZVyuj2nuQTCQ4gp3vqzGnfdFnO4eSPbKqcrDSY8VEwB1WnOm4MuenVnjMv5YjNIbO1MdLscPWb8RU7XnECe8WZzuNkMIlOZzZA1GZDqu9b5y6Zg2Yw/F30uOnSe1cvNuLdVTudbf3ULj/nbQ2yuwMlTEeXuXvD0hC260R2i/yuhSYZrf9LljPYUx7YBhBqB13NIy2EntfrLYfa95+jw+9tZqrNpdEdtFoCVCP7yOYc5f1OD1/wPwE7D+E54qCgPFetXh1FksY5Pr2+6NsnjImWm++Z6E1ltA5ueYQJ9fF3vNs/RpfMvOeWrAb3/bP4W7adbt9MEv2RAnyC/83nEXS/iX54ZIJ8VEh8OOeWUhTRRaTdvhyfS42g9kW1BTHMupcfPCu1IWjG4nWUVW1f6yU8N88e0paUXedd1eAifWLaJkhaNaJFjbUkr9gk8yS/mQT4r5JX3n2dnbSfHvtVEXvd9xFn3tVCzznloLD12rWjQ+3Phz+s7w2HS8Yv2Z4bjJKu3CCG+p1y/XUr59jXu4x+BJeDDeRXHmM15i58LTAZN9mdSZEyT0oRPZUDThNYVM51xTApTESXOZdT1220Itxv7/PC1e/nEDXUON28lEGEqdDNBqB5BO4Be+aUwL3eGrY61XJqPuM3aqJ8u0Ot0IzsISwg/4OC2jbDtTNobtlAWFc6cge/8nRg6IswM65OH7H26BKWr3EaTjn9cHFm5yWAZhe61XkDXA5gMQ9MYuuaFqY6O3zT3MFyj1dXLbX3l1TfR6VIEbIaZHecHr9nHpY+6BYCPfmFbT4H2hKTsp3vBve4xtJ7w+wqu6zhaSB0ZUdyXubrsVeW3/tdtL1WZbEpg3Wuj4FVzdmRbWSpVmNmGrG/kWPsglz7qVn50uMZF20J21s5QjgSf8NDoPOTCr+PUoQhOVz3TemSSyS48eeuIej2R06OBi28x3LOtnXnGnNp2FOPINC/1+6bvLnymclc7AbvOM/bR/Ns3IC64N5c+Id3OL/65Ta3e6R0XXvHB99LPip/Ka6+7rSWWoB4Za9rK4onhU7rECP8Kge5QyRKV+qVezo5O0uJgQ3DLcsiVRyu8+illliN4y2/4bK+eytKzXs3/969xup7YO1I+1yEPjdSHqT9Tvy4nid7W1a+JPlt/prXHVOai6zaFw1LKeyp/Q84OIcQGIcQ5eX8m5EKIZwFPAP63lPJIHjGrSFqql5muXQqSjXFNE0rHN8qE00FVWNRP2wTJw1tEWTH3ecpUm9OmoeQF6XYWKbueXW9YUdZPZ8mOo9UdFEXycujl+j3DEbKDnxYniCnqI/s1sVZB7DwHoibbkpA/+94R5l6d8NoPxTzrznu44mEtXnxJzGVXZA0nPFSEh4br5glhl/C34TbVteHUr4sIXVu/wzRIye3yfO/bH+S9W5tx41JoVAPV1Z9tIdeVJ1sbyTA/m+pKQ329H9tz2xSEfr+RbPO5v0jY+hKP06ahMneIgw3YUeuL2ZIneolKe+HRWXSHnpw00b6bHMNFTrjSj/s2rQPGtlpUR+b8KFXolEp8Z//NtBLBZVfs4W0/38fz/6jDP3xIjbqb8NCoPDRYz4XPJPtMc9gGee/fNFa2cTTR7sJpfqcTWW2CvPlhq2+7NtUH8/zJrk00mL7n0afrPkV0KlcdF13D7coX7eGxz5Zc+LJUd/2PT21jw1ybbVubVHxoJ7DZh3qQHkfri2FeHtjS0ov28LryvBjvuvN+yEFd3hadnW1XD0ISAVHSIJYdYtmhkzQ50Aj44eGAfQ14y+V16nOHQWwlER4zb30x4XtexXvu8mUujX/dTMOEhwq0A/s4FVhfrWBbw0axEfLq2OyHPHugW/NXK6sfC7yjQL0BAoUQjwT+ifRI2ivMTQZhzHgtlZFdhppNsVHxYKjjMsqy+zaF3oTDVGZT2Ex9mp7DpZCpdUx9pmX33X4q99l2KpvLe9JknaboCedRhAb6i+TlGCXqYzWQKdhhNxyxucDhUpOfNI/y1a/uAODP/1dqKHz6qT5X/tou3vGLvd3GEx7q37PzkJ02G5gMAZ1eFx6VHts70r/b7qk4iywoab9JIo7L34kDtjHOq6e/h1EMLr2e/qe3zzN48/oc1XBw4dLvp3gDEfKqD67wmydFfPOmEq1WXxb3RazsKclZGLTVyTGwlaWLYCBXkjuqo9fxwJ8W2VHkKNpKuRfZIaa2wOwulqOjVIOEmVKMRPLMc3dy2RW72VY5TRuXvDGd8FAKJsdHHj7bc5oMDvV73prlGhPbeuDqT8Xhlt0TWa2Dbc3Pq2/iK1v9PH4roteY6DLNvSL60Ci6hd6/zc6QiIc/lr954yInT8Fn/n0zleVO724s+6ez+KJ/mlaawLSfx6O/daWPvyfHyRwi7r/C4Mq5JLzeNpZO0qSTNGnHDVaiYxxoRFyzELDQgZdc2KT2pS9y2c5/47Lw1bzX/38gPC45+jwAVp7/9ybkvTHrX094aBhnnpx1jVkRnau4A6K4fMju541rPvyq5LSU8p1SSpH3p7YRQvwa6RaWt0kpX1P0GceI8NCZ3XWdZ8iZvuuQtwhneE2Ki2tC6YabbbLa6pkmvf35ItnGFwGCNDP0sfYBPOFT9tOs02mWf7WJ14+6kFmyUoujAux5OTLQIz/yoj70U1qKJDDt9dVtm/2aWCkjdp9P24dv3HyEz9xS4l6/tp+X/UkjzVtCmsj1Gy9sItnIhIfyvqtgW3z0fvPwuHC45vgoz5Bd63jzZMoERgfXAr0ap5Op7qjXJlxFlAm1rs4/pvrqtameWqbyc7/+VGkz7/ie4Ob/mWbnecs0N6/QigWBED1RKGXSa+4JH5LmkJPDGN1h2oZiAjUPhw5Dyast8jk7gjYs9ROUlqegNkvDj/ne/g5//e49nPP9/cy+aD+vv++O7qi45I4OEx7q181zBrgcIy7lWcddZIyKjm3eGGRlOi0TGA103stb61x6j+l+0bZ5a24RA9BVx6VXFQGlXXs5PZ42cyjv/RmUKpy5ZReXvrPKeT/dy9y2GoutGksNH7+Wbu8oKept5uwo+wmpZOsnJjU5MPKdGQV4X5X9apmeqLSbrDSSbTpJk1a8QjNuMNcMuGmpzJEm3Pz3CS8i5LKPPpL7XXwTZ775PqksByhPc+nSc7E7VSY8ZMbpglH6c+nPeh2TTVHUjnbhtV2fWPq0EOI84JPAZ4DnjdJ2zC0t6nfds1TUoLJ5oUY1hkwKg47H1mce4+v9u5jGRm96L5adXtjz876xj/mFEs+/xxKnzbQIvSpAuq2lVEmTeibd3B2qcFQz/9sUZtuxhrbEpEW2u+TlClHxZ32Epd52FlGZgvm9hI15HrnlVB5ZX4DKFPP+VM/hA3SdHxMesvHQMA5X/TwoIvSLvIc8Om0Lmut57O9KSu7gv/DdVjCq8Wi7Nhlmtro2g003korSqYPNANPb6nO/CL7Bem/7+V6+9VcJCxsrzBxtcKbX4CpvO7V6h80VOKkOM4mgk4DvSyRJ72ha4sgY2TEQ3VFkG0omq53yV7qdHV7XUa7kUSKowPQ2ktoMy52jrLSPcd/tm/jSXzaJZJU4iZBIpEzwRaYiTHgoH99qjX9bexde15ia2uc9T3Zfat9HxzeR1TYooguY6o8KpjnjMqpseoStXG/jMvCKPINJT+tCewX8EHyfqx76Ju705nvxlL/cCBzlPI4CsOngCq1fBMzNVgl2LtMpSzoJxDKl00N2jxFXKBSqg0MMJTFd1ZzWnR0up4cfEMuIKGkTJxFHWy1uWqpw01LASgQP2d3mnv/kc8ZMiy8d3MddP/lHiGON9KjeDKobHMRMeKiYzqvT5rpW8ZuePe+Z9PZ6X7ZncdkypvJ8Xr6jyGohxDZSR8cS8Cbg3kp01oKU0nlSyyojPPJeZFFjSn2R+kuyMa8Nl17mYlrXZFrt5Bi8V/b6hv3f3iMhkStsCHeyHB3tlUdJmyAIEUylAl73DENXiMq+outyVIA78kOV/K5TXPS2eVtfsl8SwwpsPpVGKPjBoaPcb/cFcOQGmpt38o6fL/DMO28i8EIWO4cJvDDd1jPhoRHpcy0kJrDR5Bpr3RDQ+3A5dPLGxOX4mMDqoIjB4mqnt3EZvTZezDOwitCVR4/+aTIAR8EtuOTMgF//10U2VxoEIuSJL5pl662L/NLbRFg+xENPhq2ViE4iCL3010RflCBqMeCYtkZ3SLMcdeVSGuUULRWyLYVhCRGWIKzRrlS4+ugNvO/qKoeW67zzwRsQnRZQg7BKJ2nSjJeYLm1hwkPj4DY9l4teWx0X7qLjotYzjf04jpZR6kzADaMaifr9InJBX1vzDEHXmm7Ca+oj+14UTHOxC1Nbe/jOeveDYPOpPO8d85wzuxFPBDzzD+YB2HnjAjdUNzMzG8B0h3YCna6YLXcTl2Z4eqexDJzWUtDZoerlquNinKPCgxCJpBO3iGSLTtLk6mNl/uMmn+99eTsbd7Z45vTleI+6hLnoAGd6ZRp//S4a/+/ZVKnCwWvSaL0NOw3IJzxkx21yKIw6D236re3ZXfbzOI4RG4yi39+h4M7ASd3v/63d+zLwIFfjNYrwsBkvRQd53IU7z6gztbfhdYGNiU3PbjMO+7g2lXf37vuiRCdp9o4zlEjCoNqP8ojbDPxCGEWQ/UG+o2Kc42j1YwzVujbcvT5EH9/2szgQ7eUHe0O+fbDEydPXs2fDTv7jxgWefu4MJa/C33x/L/ffEXH6zFFOn55hwkPFeKhYv/rC5FrYighlUx3bwuTqJ48em9BPIZ4kwisILgMtu28zBIvgMxmDtjrjLrhFjMVRjL8MZ9bO1EYyU9rKeRu39urPzB2gutzBjxLmzqyyf8syO2o+szLuye50O0snldv6r3lD0R2OLSoquBwgWX1XNJ7npQ7oWgUR1tITAGqzNONF/uPGKl/63DZ23HiMa+9xHafPnEYnaVJGUvLKlLyyZXzSMcnGasJDZh4yf9dpcjkddHr0uqZ7JnCVmwwXFw15fQ3CRFYXAZNxkgd5dfLmrfq96Dsyre+mOelev4dxjkKTgD0XAoK712ZTY7+1BGzt1ZiebzI/V2F5U4fZGOj6e32RntLSx9Td0iLULS19x8fA87ocGUWdHNl2HENkRyteppWsECVtFjsd9q3UufaqWUqtmHbLQ2wNkVd+DXHO2TRf/z6qL3sa+DPs/aPXsO3iGfzzT0L82m+rRBUgaB3z0FjOjTywOWtsz2nT+206sTTcH9cBMgx3BFktpfwSq/DcjOHwGOie4cE3ea3ATWNR5hvF+FXrj8rEpgVe/bT1qTKujmtwnA42r+NQQzITxpT9AC9JM/37skQsO/h+CPFCGh4dt7th0lF+UtIMXM6PUR0fpjLTHvEs83+WHK+1xMG24M1f3sjUdJvKeW2Sco2HnrRELDvQOMYlZ65w1tQpyjnrEx4qykP5C5ZLMNqeyzani7S1OXBcdJkcJuOM9wRScL1Lm2GTx0uueZXXTufdUQ0oE502Q9DUX54xquIw33/uN/ZSXU7lXakVs7BcZX8Dokyc4hN4YerwkC2GtrJkkBfdAfk5lfJAz70U+IipGkxtQW7aQyNaoJ00iOI2Z20IOOUXR6gud/i7J1d41QevY0f1TCSSWEZ0kiZVf9oxNhMeGqbBdV/tR52bep+jOl9c/ZjWMZ0OV9uizz6B0cCl0xTVT1THl2kN1e/hKCsK4+hSJrrGxZfikVd9A27aBzNTvMy7C2p+u1Ir5tBcnfn2ErOhZFamp7R4op/ItNe7uqVlYDtLQWfHqKDl7pBC0IqWWegc4hfzZeaaJfY3QhoRbNu5TPUbHo+8pMXfLj2Ql56/iY2JgD/+bRAeB5vXsfM5ZyMf8NsI9CN0JzzkxmNqM0o7Gy15Dg6bLm/67qJ5FChiP60PGPOUFnBPApOCYmpvu3Yxtr5gu3Ca2uXVzerb2uhKklquKxGmiSRoJw1e9+M0WWcnEUgpUyU0aZPILHKj6+hIop7TQ0aRpjRLuwNknBNZrLhkv50NwhKiXkdMbUBs3EVrz7kcDlYo+wm/e48FqpetcOkLZ3nqo/bRjJf4gw+EUN3AWRvO0JT5CQ8V4aHB++PAKONq6t/U1iTgTSAZfka9HxNukPL4ZP2/I+xfLAYmI0Yty3vfNl7T35nNGFLb6/PRZmDlLeYuvlD70ZUNE1+p14JhenVDFL737W29lvXFNksLJdoJhJ6k5EkCL0wdHnjD+Tug/z0vugPyZbbr9BYVspwdtUq6z3vDLm5ZvoZvHVzgw9dKnvhvW3jYngrvuizg9f+Wyt+/+PIsAIeaNyAQ3S2GGUx4aDU8VPx5XM9RdPzVa5OcNdUfVf6ZDITB64msdkERA0sF2zwxrbl5ePP4y1THZCTm4dDb5s2BInQBK/Pc9Bff7MnTz/z3joHbU8daNBv933JLXpqsNIvuMKm41uTMa+nsANQEpfgl2kmDVrLCLUsBn7sl4EM/qvGhL27hUx+Y5X33h4//nxt48nc/TcWH4Iuf4rLya2DzqXz6nHeyrXIa4gGPHKHzCQ8VA5cNoH+36fP696xunuy2yWvTcxeF/Pd5vGT17Q1WkcPD9DA6M5vq2crzDCWb4WfDkV276NVx61D0hdmMUZVJ+3UCL+SpZ69Q9iWdRNCMk1QY++CJIE0UF62Yu4piu7MCzPu8i57IYsKVl1gvax8EaRbt6W1Q38xy62Z8r8ShBpwyFfPNZ5X5k3MX+bfnCf7jxjqfvkTpM47S01yACQ+pfarlpkXJpuTmPVNROlS8RXAUGR+X0u7q9/YnPO8YYOIRdUxtPGS6LvrO1Lqm967jNCkNLqPYNVdsypKpvhnaSYPQq9KMl3jmH8xzdrf8ka8TBJ7kX77aYlsFakFC2ZdYjycseqS4TWYXieqwJT/1RBrdUQqhPEVLtLl+ocznbw34+fV1Gr/wkFIyXdoMwGVX9JvOhjvTaJUeTHhoVB5y17fJd/WebS1xKdguRb0IvTp+E816HWGpP4HVgTrWrjljmiM2wyxvbS/Sj+na1LfKHzoNeXqaqe8+7qha5+QX3glO3slT/nQn57EXgD95e4drFsp87G8DpqbbTJckFX8wqqOTiO7JLWmiaSkTJBIpDLy7WmeHy7EtvN6JLO24wYFGmZuPlLj1J1NsOLxCdakDpQrJN39CPNfk4qd+mM6l6Yksrdf+M79x45/0xiORsSavew/AhIfUOjpuG81F9Wdbed445tktLh15Nfr5BFaZw0N/sSYPXt4LHNWYcvVlqmvqx/bdBa7FXKfJztBL0RGOto7wvqun+L1TWtRLqVBMpKDq+0SyRUIVz+++mswjnB1TmzkGosgdjQHmLSrjbHVR6+jfIVWsw1JKY22Wbx64gVbicb/tm7hoe0rvg3e1iGWJ+32kQy2YBVFS2odMeKg4D9nLXTTk0Wi67zJ0RgWTwjwertuj5/j2BzYFooiBlIcX3PxoMojGNSSLGqKj8IQZVyNe5MdH5njLs0os/tEUGz60OHD/0aftAgSbyzfysRtS+dVJoOSlimvghXhBOJjDQybgdSM8sq1+nkxX3rzkz0USSdsgi/DwQ6hvYqF9iJ/Nh3zp01s564cHSP5ohtCvGhoKwu5R4RMeckGeE0F/BlN/+ne9jt5H0fEoSmNeXbXM5tRw45/I6nHAZIzlGa4ug7OIoboa0Pu26XB5upa7j8XOYW5YXOAfnxby9x84h5c+vjZQ4x6LB7nnxh1UXi555QdL+AJiCc0YGpFHK/Ao+2lUXiwBYmIihPBIZJw6DvBJSB0IQvgUmj+j5PDImpCe3BglbeZa8It5j6u/t5Fzfrif+S1VHvCnbZLyRrxHPZZQeNzrKQvQaXLp49owuwuUQxACEZqIYsJDeX2sFmzrxjj9mN7XqHZNUfxmWA+yepU5PGwTSX1heQarychyKVk2o8xkoOo02vq0QdEJZXuGPrzgm3u58eY6m7Z2aDY2ceHuNlcdK3PXzQ0SmR6dtdBp43tpluayF/QFpOeBDBAyQQZ+6vSIfGh30ogPGySy+DG0rnt5R9QmEpkkiDiC9grvuybkB/+9jc+9eI6Z0jZi2aHiT1mINAk1tXzCQ8XbqP0VHSMXrqJOF1e/tvb6ezE974kvgI8/2Iwm232XkWQqN71Hvc2oc8Vl2Jl4xeVAy3v+PjzrDxaAAA/Jhg8tcvUF27jTjw8O4GrGS/zNp7axZfsKK6d06CSip7h2kiblsNZXgCNSZ4efOjxEkPR7TwQkalLoEZwfA+VyuE73OHAgDZ0WHrHskMiQ6bkGABv/bYFn/Bvc+Js+//3MMvnzdsJDw30UocOGw1Zm66eI80GtW6SerV9bWd57nsjrtYE8g3RUB12ejmW6Z7o2gWvdNhmpRfEO1r36Ya/nH176OH72oz3ciQO89PE1tr7E48A/SLxMBh6eh80n8y+vq7E1XmQ5gtkkdXi0YsFy5FH2E2oB+N2jLBMZEycdhCcQMi3zKSGR9BOYpklNrfMo09Ndjg8t2i899luSyJgoEcy3odxIt7Mf3Vbniq9O8aOX7SMKPN7/wY1p5PRI82vCQ+a6tu/j4M5ra7IrbM9hWpNGcWQcb4fUiQGrcHi4BtLEpEWYPqunL/qmCWQzaF0Tw8SMtn5sNLsmjE0IwCvuGfI7nyxzeGeVTVuaLG9v005gvhUwE8YEnsQXdM/h7kBQS7eIxMppLF6ASOPxkJ6Sz0N1eoyTkDTvaFtbuZ7bY2YbB5KDnDZVZeX7+3neH8L1d27y5b8vm/FNeMjQxs5Dg32OuqDpgrHoAmKi1YV7FBrV78UWxTvKeeG3D3AZOi4ezu6bDJ6sjmmh1ctcc9BFR9ZW5adxDWHb8wwbwLecMctJ187z2DfCvbdFbK2U6CRbePpjDgPw7K/v5dC/BOxebHP9Q7dyyzktZsIYX8SIoIHoZjkvl6cHlWDZdXoAQniK7JaDkXp5Tg8X6E7p7LqrhHvCZ7kDx7bU2HnjQn90biwyxjaY8JD7WU396M+jQxGZamun17XdH/ee/p7cvDKR1SZwrY0m3nfx+6j9quv+OIapC59alrf+U7BO//6dPvBI3rZlC09862H+9XGfJz68QvSxBSrPOJOn/PNFALyuchd+cmnMdtLIvP0H6kyf1MAXqYPDE34vcelsOaLkgSQmliCkBwlIIUlE3K3r40m/187p9HA+wvDpLGrOkE4iqAfQ3hJw/Z23sOu6eV7/1wt8/MFT/PG522EoIemEh/p18+qo9116t3pto6fIs5l0bJvOrfeVZwup11l9lWaX/eGmfb3I6lU4PEwvczUTQn/xNma2GVwq6DSZ+jBd62CioQiD9uGvvruXA0sef3xek40HBXvrs7z76QuUvAqShPf8Eu6yWbC10sETEk+kIXWxjPDDGkTt9JfCgPQXwTgNlxaeh7RFaRjLu88ySo4PUx0Vlw5eQCRjjrbh2vO38pg/PMKjZw4Du831JzxUoL0J8hYB1/jZDAvXmOp1ioxB3qLlWnwmcHwhzzgz3Xe1MRlxLoPVZZzZeEuvb6LFxG+u+oN1Nh5c4UGv9jh7doWtlTMAukezwlV328437hdw6WtvBWDqygbfuRtsqwYkUtAJI2bCJaSUSF9SCacYOqVFeBB7CC+AJEKqzuzu14GcHCZZbQJrklPZxd1GCI/NFdh+5gr7Ds9wZMcU209a4UtPqAy3LQQTHjK3c0ERR4TJseF61rz+s7Yuh0mRe0XqTiAfXHNaX1+LGkCj9Gtb70c1il1rvn6vqK4xDDc96rUc2etz1xdvhkedzGX3/igLj/oDZo+tUEoijr3sY7Qq92f/A2d5d/srPIU799ru/VGdmdkWST2hnaRR1KrzoBqkufN8kVCmSSJKJCLGF6X0xEQRIIUk++eLgLGcHobID4GH6Dphyn4LgB27l6md3eFBT0zYwXbutvko0T++lfCFz2VwXCc8tHq9Oe9Z9Hqm9aCIzmy6VqGIQ6WozVHUFlm/sMotLWA3oFzepryX78JbBFfRfvKESB6NbpzNeIm9r0yAhH8mYJYGs4cb/OFLZ/nca6ssd47yo0MeNy0HPPrUhGqQJlHqJC0C0cL3y2mUh/Cg0+x3FUfIASeFtq3FuW1lDMdHnrMjitPtNQv72b3jHH7vlBuZf/gRfvSyhB8Bv3H5HFPB5uF2PZjwkJvOPHx5eF14igjJUXHarlV8pvdQDHdyBzgvHEAI8TbgQcBJQAf4CfByKeXnb0MqyHfu6fVchqCpvnqt9mHD5+q/SF39vs1oVHHn47r8vVsN9MCrPrjC5nKMulx2yj7XXj/NT6cWudMGr7t7MILSck+RDctTfdkpvDS3h9c9Xjz2Uqo8Lz19C9JtLhGAcgrXqNDbItPdLpNEEDWZqm3ivI1zHDl3iYOnwSvu6VHx60pDlwE74aGiPGSmZxzQx0Hv3zR2al3XuJmMCR13Xl+28R+EiaweFfKMKde9ojpR3ppeBI+LNpueVaRvpZ/FA3zxE6ne+z+PXQZend5+0bu4tP1iOHIDN/ykynufeAXBmdsgGszpsev6eW46eQPRnkWYjQi9NNIjkT6J5P9v78vj7Diqc7/q7rvN3Fk0I41Wy5JlGwO2Y4MBE4wxe0LCzsNAADsEB8gLeZDkZXNCAiRAAlnZwhKwgQeEPZCwE5Y4GJvFBuPgFVuyrG1mpNEsd+3uen/07bl9655TVX1nJEua+vTT73ZXnTrnVNWpqnNquqsxHMSoFiJ4AohkjJLfQiCiZNPDK0Aiho/kcNMUnvDhwfJsj+Xq039ITA+7rgQxdo0Cd26oY36uhJ0jIfZd+V787FNN/AzAlVfcA6zfaS/P2RBBo9tEsdGX8/M5OTa+cDad4pHH97aBntfJMlevBAN+llY32E1OT0pD8dINQi4t6+TYODY6+TaydYt97wD63qHDfVTXfHYbvv62Kjz4+H93teB5EnunS5hr+ZhrBphrtdGMFtGIFtAWUfJJwaDY/d8520P4QfdgunIpOdcj++42oP8Eos6htv30YZY2jJJNmaMHcPpIGxd29jfuefIGZrPD2VA/dA6/jl+2vErHybKtL1XeVE4NPFRZpoAkb8BywqIC4B0Ang3gxQBmAHxJCHHx8VXDZE8UrQnUQm+SoQsAbWx6EAcuyzvriHAOFfryN1XOWn7SI8XDXrAA8e02vn9fET+f97AY+lhq+2hGIVpRHa24hnbcBCqjQKmabFz7nTncL3Y+S5jM5yIIeudvzxvs6Y6e1xQFEMeQYQuoz6PSjHD6SAsXrg9x3Xen8JrrF5BvDnM21KXJb0O0LGojSeXPtQnVdpLIU51vda3TrQ3Ze10dTHQnBY7TXG1qbypYMpVPYRtgcnS6fCqdsx+bMWb2maLPfKEv9croalwZXQ34ARZe/wl4nsD1/7SE9v8cSM7w6GDiDz0ML7RQ/FETB+6vYilMzvKYbwHzbYG5po+5lo/Fto8wFghjgWYkEMoIoWwlr5bLEJFsL/9PDzaNERnqaEaMCAICgVdEteDjFyZr2DEWY3JDA3/80W3wrnlml3j9jr62odrL2RCljzq/cjrm2ZDI8lH55ml3qu7qZoftnEr51if1fHxMMOATHlSn2hgPZRRq56rOhEkWtfBzu4GccVLydbJMtEneZZtPwzVIHoFulgO88C1hhlbiNx+8Eb/y7kWc+2tLiGUy4S62PRQ8iUC00IrrKBTWYfmRuHaj9xC8YgzZaie9mH5CNgwBeLQjrHviA9A72epmh7qREiaPaIuohYnSNrz4rDYm/24B26v7AOwkGDobsrEh2snlHG4otJR8XZqJZ7aszWTKLRZZWWq+rr5J0snyrqGU8orsvRDiywDuAfAiAN87Tlow6ZRtqfSDLLw2Mij7tQliKd10eVkaajyYnIOufjEivPDfjiL9rsmBN8eYQA23/WgThoanMV4KUBASgSdR8JpoxwWkn6otFIex/Fe+dAlIX3eRmS9wpV9vCdF5NVHYP+VBna8Uy+5GdGMe42NbMFGaxuMecwhP2RYSbTLIRoKzIbp8No2jpeY93fyrc9q5oIFyqLk827WN609FbzdXE+DWYi7wsSlPQef7UL6KjT+g6rSSvtX5MMm9f8XLgZf95XLqi/5mrCd/5B9/Hz8svRWXPa0CrxzgHTseB3Sexjj818nvhn2LuPeOSRwYr6C8pQ5PAEUfKPsCXujBF3J5ig285ABReBJAGyIWgAcIeJCIOxsdHQ2E4SBTY+2TA1E94aPglTFWrONh62PUD3rYceZR/Hi2ghf+VRXFcybZ9ulPczbE8+X8bht/V+WR8repu04nXd+ockwyqHgiR9ueRHP1SjDghgfXASbjMS3kKh8bOZQRmQwxpVEXf52cwYzpXz6zEZEMk08WKocPefDx/rccxJ9eP4ZaCAwXPIx6EdqxQCibiGQb7biBQvpaixcAcj55TLnz2VpRRPJIdAggjjqOM7pOcxamzxyyB5wqEynzpRa02kCrhoJXwnTtPnzkjRP4zAe48zucDeWb7PNMzBxvboMhq7vOqeZ04RYcnc429RlkUTpxIaWMhBBzAKjvyK0iuOAy255cH1IBmk0gqcrkdFDLUkETNXa5wI3iYaq7LU33+oaD96FyTf9yecYt07hr/TqUC4fhiwJKfoySH8MXLaQH00lIFAoliOzXWzqb1ogzh9l5AstPT3vMpjUF7gyPMEyeHIlaQNhCuV7H9qqHpRCYb3EH4WXbwtlQP1+dXIqGo1PTs/rp+oLiq5vrOT4cuPYy9aMp7+TC8ZurU6g2qwsy02swdCo/Ko8ry/kDlA4UOH9nsMDyyvprk3PsikNAUOqlCYq44vsX4+ZX3YIdz3kKfngVPUfuuG0WdxanUK6EKEy2UfSw/KWWWughkgJx5wstw4WoExTJ5CkM6SW/nQ0OCQEpRefTtcDypgf1hRZ1Ds9ALP/z4IsAnvDx8A01HG5W8V83DGPnE2bxsasXcWX0f8h2oeFsiJdBXUuClls31Hqoc2NevUz9Q8Uf1LowiA5rF6v4hEc2fZBAR9fRXL5Jli6fMyKqzOAG7YsCfBEQ5ZP7dcUpnDlRw61HgAs8HwUhsaHSRiSTL7aEXgu+V4DnBYmT7AWdjY+Oo+wFEEGmFupGB3uIqeVTH1QZKi19taXdgFiYRqEQYPRIA7VwDpEMMVJYT9a/H86GzJMiJV/XbrqFjZPDtROlJ1dPXR/p5Ko6d2kkxEm1Ey2EEEhOShsD8FIAZwF4+TGUCHOAldLkCQBN/E28dbQ6GkoXKqBV86ly6rhSxw2v80PWVZC82t+LYiuCf1eIn1dHMVKYx1TFx3AhxlCQ/IUweU87+VdIP1kbh8kZG+mTHdJLDqFenpMFkJ1qbTY7+ubsePkcDxmGEK0aUBzCRHUbfv3sPXjoRPqZw7yBsbOhQW3IXldVPxM/nUxKRnZ+ttVHpTe3v5urOXA2ZOrzvP4QFcTl8XOooNFka7og0+SfEfoUh5P/jByx80HYvOtHaLzjMwAuZzltv+Mw7hpfj5FHHsJwANSWH7b2AMQoeMkT1oHnwRdx5ymPCAIeYhFByHQu9+zduOy8nW6IZL/U0nnCwxcFBF4RcRThoeua+JVLZnB2vY3vAsDcPqBVA6bO7G8bZ0MEjUkfSo5U0nT+NCXXJsagyur8X0o+509zOtjjZJurB8WAZ3hQsF10uWAsvTYFazYdSw0YU1BKlTEZgLr4qzroAs0E8+1pfPFz63HLvUO4d0FgX62AfbUi6qGHULbQjhtoRkuQfucvcn6Q/Bde93f5L4TMYaPaszyIszo4OqpcFmGU6DKyESVvGG/7WIRyMIKfzC51CEw24myoXwezDfXTUe2hazNuIlV5CZh5me65xYxqh1NiAr4cSaQ8A+CNAC6XUt547MRRgdNKedjm2eSrtHmdIVu+nA1SdmZ2wMaKU/jnT42TudvunsPizwIcXPQw30reCW/HEpEMk/fBO/9jxMvndsDrzONeZg5Xz2AC+Hk3O/9mNzuW8zppYQSEUXKWR6sGALhxuoSSNwR+rDkbOhY2xM/xqv7qxkL2epC+4NY9dW2h1kmdXahpJ/18fZzmai5go+zNZgONWo91QVxWFmUH6bVtf+p8GMo+dEF3Fib/AJD3/xxf/tcWvv3BCP983sdZDUuNEMP3N7CwUAAARDHQjoGlEKiFHhbbyTlM9dBDu3OeRyRl99wOGUEiRixDxIiQfrmFRfaz4FLZ+Oj8X37GQ/gIRBEFr4SCJ3H30aTeV9z5ZKC6HtE3/osQ4GxIz0el18kwbdBQtBxfXX9Q/rTKI73m/Hkqn9NRxWqsiyc3VnHDQ2ckHFQD4XbEdIbK5dk4bWl5dXBzg12nOwVTYAhsqpyJa39vD3bfOYr/vquC2+aAephMuLEMk6c8Ov+XneXM4aXsZocnaOd5kM0P471cfl9cNurA4jSGC+tw7R0F/NZ1h/C+V/l41//sI9pDhbMhmr9OpywfatHhyug2cXTyqTxuwuX0seHF1yGOxTH5D2C9EOIHmf+/2aOhEGNCiHNM/xXFvwLgEQB+GcBnAXxcCHGZRYOsAKYgxaZsHmfFVhcqPU/QmKXT2btuA1JHr+dR9odxzWe34prPbsNZr+udW0+74zAO3F/FgXrymHTyWmLiKKeH3yVzeGeTw/N6NzpE8pQHCd28rTvjQy0nPHjtFiZLwFf27sWTfn8Jf3XTftBzk7Oh/vSV2xBNr9NfN5fnqauOt2mdUukF6L7tL+fmahXcJhOU9PSasp2s7Wb52ARBtptaNv6SyjuPPel8EvuxJM59HK745vnYv7eF6z4S482lj+K9j/83vOxdUR+HqfsWcHimglgCkQQaYbLp0YiAZuShGYnlDY9YJgeZSqSbHcnmx7L26iaGCm4uV2slOk+NAIhkiJIfo7ZUwIcXxvDX86cBC4fw4Rfvw/yr3gza16Q2NJwN5Z+PU3669YqLD9R2VPOoe45O1TXLP48/rcJUt2MzV59oWIXP0qZQBw6QLwijDIYLgtQ8VbaN0VI8qd23lB+nmwrOwLkyAmeOnol3vuxevPsVAX5w+SjOGV/sfCormQQDxJCQkL4PUSgnxfwW+h6T80Tn/A31/W9JnMthOs9DsynScy9781rt5LUWjOOSTS3MtYrY/Fcefum0JcSIcMPB+/CeV3bNbu+ucXz9bVWijZwN5bGh3l+V1saxt9HLRJOnDdUyprLHZfKckVJepMn/XwDeZ8FnWVkp5REAP+jcflkIsQXAGwBcOrCWRpgCIV2+zQYY12ccP7VfTQGVDS01P6h5HI9sOueM6QJLiasv3IIDH70DL7x2O07/ygyCMMbcnUXsfRCwbThAtRCh5Cd/FZQyhkz/WugBXlDsfKK2cwB1HHZfU0w3qj1pfupOBUXfeWpE+MlGuSyUUPJjvPeGEVSPNnHnG2Ls/vCdOL16lqHOKpwNrdSG6HSOBxckmNqagirbNqCgdKac/eOCk3SuNgU2HDhfhvItuHImGSpvW53UX5u1O6//wNuYuPTpuOLTR3Htc3ej2SjgiQ9t4rG334xLP/OrePQ/SDzoOwcAJK8fzs2WsL8OlP3kA4eRBNB52iOMBZpxsunhFyJEEmhGAmU/2fQQEJ3XW5JXUaSMAaGehaQq52H59XMA6uZI8vRI8iTgQruNfbUS4ljgg+8fR2O4gC+MAdd98/xE2bCJ+HMfx4cuv6+Hx5XR1YZ2pdrQ2ZCZX956q7xsYgzbepnKmep63OfnExKr9IQHtRBnr7kOVB0dWydqJUGbSR8132R8nJ42fLo4Z7yC5/wD8Ponz+DciToONwJM1wMstNudT2R1/kJYqCSfOAyKWP7rIND962DfUx2y+2vzGVoO5JMfBD9PAM1F+FGM8ycn8OpzG3jS1hqGAqAdN/r2Xd71Zwc6V86G+vXMZ0O95alFxxRYqPQqLbdpQunHLUY6eRz/bjkpk++FH4v/Jkgp3y+lFKb/BjY3ATjDKGxg2NiIDY0pSLQJjrM2ZAo+dXx00NlUNt8mSKPS+XbYVDkL33xleTll9Egd9x8JcPuch8W233lKL338OZk7pYy7T+ilT3n0XBOvtQD6+ZuCJ5Y/WS7KpeTQv0IZS+ERnDXWxGPPrsH/JR8/u2Qz/v6W9B15Gwc1hbOhldsQJ5Nap9Q0Uzvo+NvM8Vk5FI8sfapLrz5urjZqmSPPZs0dJH8QcHa00rFJ+Q8mnyPJExdfhBf9zRie9oGdKD1mJ+L9s5Bf+iS+++iv9VB6MzF23zOKRgS0IuI8/s4mR/LER++TehISUspkAztbF8GEUek8ns1Pn+oTPsI4eWW9FdfRiuqohx7uPBrg9Kkm1j2yhXbFR7Qg8MGNFwHVIaAx3/eHyyu+cZ6mbZwN9ZYx1YNrL5t5Vjd363TLyqbmYds+5PSxw7Gaq080DLDhwTkHWUO1cUyo8nmCOwnaSFRHw6SDLuDk0rLOVx4+WX1688aKG/GM07fh/MnTEUtguhHg/qUi7l0oYa5VRzNaWn4XHIVy9x3wlFv63rbnZf5KqHGcBzmYVOWTRfYVmlYNWDiESlxANZjAWFGgWphAyRvGaLH3UcM3XZkGDM6GVmpDqwOVt7oRYVvXbB614OTdSOnSH8PHpI8pOofiPRrJ5w6PE1ajXtwGH6DvOyogzNJyzo7Jvk2OBKeT6pRQY4tKpzYCum1y8ZuSeW/D/Yu47ZZJ3L0gMNc5y6OXe8dRFn5m00N5NZGD7pPhQO8cHXT+fFksQBQLQFBelhWIEtaVYpw7EeK8HTVsOW0RN3xvSs/b2RBBt5o2lOVp4q9bE6m1T+Wv403BdvOG7l83V2ulZK5tAhvVX1mt9Z/ycWx56/RW+eYNONMyFvXd9GAUf+/VEE9+NhDHCHfPo33HDKKf7cWr3rN8Qil2/s8MDu4dwmwzuY/i9ElqoN2xq1iKzmstSVoYC+UsD7k8lyefqhXmTY/svV9AO26gHTeWX1dPzgcRGC8CIwVgdLyJ6kgLYeDh3Z/fBLFpO1BdDzEx1sPu2ifeomkfZ0PdMjb1zbshRM3FXJunv7bzZ8o7O3fb6j5Y356M83ReDLDhYbvgcqACHZsy3G4b5SxkjYXaSTPBZmePc4RsnKUsXe/k86E7DuK3PrYN3zsk8I19Hm6aCfDD6WHceriFxfZhNKKFxGlOHWUAy4/Jpa+0BEHi9Kb3uve/qY0P6mA8tZyKIEic7CBINl9qc0BjHpVgFOOlTQjjFu5bugPrSjEe8+ZE78NTQzg8NaRpIw7OhnrpTAuYbkMnTVPlUc4zx9u0oKhzhlqGA7dZcuJDCPFYIcTnhBAvFUJcJoR4LoB/A3AxgDcfO8mc00HZlpquC0ht+sHkMOgCLpOdq/pwY4WaG0xj0daJUuUk95fvSlK9WGL7j2dxZLaEQ3UftTD5ZGH6SouU8fLhd8ubHenGdPrFreUn9USSHvjJ3JpuZJuQ0gfKUyMAIDwEXhFFr4LNQ21srwLrN9bw1EsPMfVU6+9s6FjZEK0XJUM3Z3Lrom3AwK1zWZ1tgqiTCw/cXA3Y21fWF1HX6JW2O9WvtuutTgdqs42DLrgz1bd7X/vDt+Hap/4MB66PcN9XW5i/bgaPvPkHmFtfAZDM0aNHGpg+UsRSmGx0JBsb6JztIZY3P7KbHt2zPOLO5kSESLaTTZDOIaYQPrpPcBDn63nJkx2RbCNG1PNfwEPgSawrhVhXAqqVCEPVEMIHzn3YNBA2IH/wNWD9OJ7/27ZzJNfGzobsN2dMMm02OGzWRxPvtKxt7MH5+Wsbq/xZWhs6LpDUOUNqB1LX1GCzodOBc5hMgbOtU6HyEdgxEuJ5l86iHAD/+v5RXL+1jCc99hB8EWCi3MC24Q5t5umO9MA7aXrnm/sUbbqBYfwrosWAiWMAfufTizEEvOWD+76wuwpPAK988Bbseuce/HC6gVc9tGRg6GzIjifnsOt00KXp5FKTKac3lZ5HnlIXiRNy55jAfUg+Ev0mABsATAO4GcAlUsrrj48K2fbTbUghk6eW0QWD2TQuULNxJHRBpE62LR9OZy7oNCGhj2SIejSP117enXeHF1o42izhUD05CC+hlpmT/RNn2fcLyVMeUQt9jzwvb3YgsSDEADobF+onx7O/6eYJN49HLQRiDL4ooOQ3MBQAW6sSO0aAGBE88u8fzobo9NWxod750dS+qp6qDqpulByuHqY86trQtm6utoBNEMONizxQ7eV4gwq+TP4Qp7Oa1r0vnbcBz375IRTKDXziHRKbtxXxscMXYxz15dKVxRbmDpfRmGphOOg85eElmx1hLBB6An4MpJ+s9USyIeL5nbP0ZGceB5AeOCogIITsXsNDX78Kr7sOyCQve/hpyY8xHMQo+0DBA8qVEFOba/iFqQiIQiz8yw8gPImRd/4JXnzB+zH37Vmsf9vlcDak5q/Mhvh0G6hl80AnR409dGtlnrioS3KSzNUrwgoOLdUZv22ncWV0HU/R2RpkHl05GZRBU3l5BkmXbq7p41k7F/FnLx7CNsxh/nAZt+2o4Bc3LiaUQkBCQqSvtKSHIgH9DrHu1H+KhjrcNJvH8fE8IAx7y5YT5ztGhFZURyOq49x1JTxkXZL9hC3b8YQtzoaOhQ3Z6UPJ5mh17WpahHSTra7ddf184kNKeS+A5x1/yZRtmgJU6jdbVg3qqICWk8kFuDDQ6AI1rk6cLXK8OR66wK5L99IvHUXhfQ0AwPSWKv7mL2fx1peVsLhQxHRjCQfrAbYMt+D5nfM70Dm8FBGE8OCpr7R4HpJTTTuHlgLJCh1n53W/97OzceYVQd2rjEBnnZDwhA9PSLTj5PHpb94boB0fxFXnbCbaUG0TkjHzS7Whs6F+WbZ66daRLI26hkBJy+bpZHF6p9C1/cmDB2autg2MqOBNBWWzXHqe9ZrzRTgaE98sPcXLRme9TxQeWsLoVRfh2kfdiCc8o4zX4QUAgIPPWof/uKKMlz17P4YWWtg7XcWeqRrGN3Vfd1FfbQGAgicQS4lYdl9ridAGAHjwOxolf9gTnSNhki+vJJ+bFal/7nkARO9Tfh1aGUvIzqsx7VgsP3UStj1MbV7Cf99VwQUXj+Gxj9kI7xfPBSAQ/PpVWP/rzoZoXVdmQ/00tj5oHj/aVo+UzlYnm75du1jBhge1eKp5XJksqIGl6zS101diSDa0aj1NQWLeQK2Xz+deK/E5dF/zmL1oBG969AFcsH4U9XAeHpLDjopBOflMrfHdbw9ApHm6I+7f9AB6Ny90mx3pdZyhDUPIYgGi3YAHH624jptnhvCt/R52jS1mmDgbOhY2ZM43yc+CCkR0Mkz03KLF6dvbtxIn5mFIJw5MAVj2nnJIKHo1nQr+OBmUHjoHhJLFjWWqrqouumBTzePqTdVL4v89bRJ4WpJ2w6F7UfITukbdx1IILLaTg+/KfudJDykhRfpXwri7Ya08Ai08D9LrPNUBAIgBL/M1gHQDJHXMU589e35T+kqj7LyW6CP5lfGysz1VibB5SOKJWxu4YPIM0H2r1ptrP2dDNE+9DenTqDqo/Kh6q9cqKF0p3rry5vZxc7UOtu1i8mlsedv6U5w80zXFhwrQuHRuPHP60ekf+6MFADcCAP7z8w1chmvwko9swY+f/Hgstod7SrRaPmphiJFCch/L5EstvgB8IdGWAiXZPdOj5CebFZ7w+z5RKzJ+uAcfAh4kJHwRLOfF6J4Dkv4HACEEws5XYlqxwFwTaMXA+EQDj9oS4fmXLKH67s9j99ea2PmQBaItTHA2ROtnm65Ct55wsKWneOeZQ21iKKrU2pirV+mztLYBF9eZtgssoDcGNfDlBpjNYDGBG7CcI2dCQveWj9bRjiUKHhCIEqqFNkr+aZhvTWMpbEGIBZSR/JUu8DufMsw+5QF0/wIYE09+UBsf6qYHkO9Q02WZGR61BrBxHEda+1D0KtheXcJduyfxFw2B9z2Orz8PZ0NmUM63ylM38ZoCl0EXAirPdnGw6VuHfqjtpmvHPEFmeq+WU68F+nmZgmmd7diA42EbxJvSVRndgO9RUztwuLkXAFC9o45Dp5UxO9pAM0oecF4uLePlTQ9AJJsefgDIYjKHe8mSLAJ0DqIWQOcviX1zdhglnwEHkus4BrzC8qGlwvO6n7uNWkAUAI1FlEsjWF8Gbp9r4klbN8MXnBvgbMherm26KoPbuMi2l25OVnXLtgO18cPpx+VxmyDcpofDyjBIO9r4TllQ/g6VtxKdUl62AdxKg7yE/oofPzbxPYsFYHQYYsMOYGgCDzt4B1BLDsgfXmjBm4uxOF9EfWMTk2XAz4hLPkebvM7SjDx4IgZCDwVPouxHiOJ2Zw7vbh5nm1HAgxACviggFgUEXnH5dcEY0fIXF9MNECCRH3gS48UIm4YCFDzg/iDEKyeXgCMh5DlTuP1v78HS7/wU5133JOv2GKQN+fwsTl0bMqerOpn8Y84vV/kP0j42frWt/37qYxU2PHQdrzotXINT+WqAS/0Cgxm5KUDldLGBLR1tqJsqZ5HUt83VMdcqY8tQG1OVw/BFIem8QucrJ61aZ99DfV0lIrihf+ODewJEpdfBE8kiE3TMKmqhVBjC7sVZfH+6gvc+4wg2lHcQBZ0N5ZdP8dRNnJRzqqu3bZvkmUwppz4PJCDXxruGqw81iELmN4VtcEkhb2CqjkFOR2q8coGfKtc26FTLUrqZZEiU/Sqa5QY23reAn/5sC87eeqDn8egeDp2ND+EXAT9MNiXiIPkPIDmXKXkiI71PN0MSBjFQCCHTzezs/FwsQPhB90temb8+1osC8819CGWEp2zb2dns0NVVrbezoX5ZVNnBbEhfD6osx9e0mWHioeqedwMFbq4eCJyfk9f3UfN18mx0stVFV9bkE60kKO5cnXspwUMi/t4PIYbKmFv/RIzP1HH6bbO4e3QKxQctYLyYbHJ4IjnHIzkySaIdi+RB59BD7MeohR48EaHgxfBRQCyj5ac31E/VCgj4XgEFLzmjruCVICAgpUy+zCJbma++RB0aiT2LRdy7ANQj4I8viIH6EOTNP0D9K3fhl775BIgzH2XZHs6G8sG0CaHbWDDJM/nlK4Ft2xvW4TUyV6/Chkd2oJgCKFNwSBkaRZfN54JZHSgana4rMQTTBKKvZ/p+4P5aAV+9X+DRUz4eNVXDeEkkjzn7RSDsHHwnM092eALLB915cf/5HhTybnakj06nKBYgCkWgMp4cyFddjzg8jJ0jG/Hg8apGsLMhPVZmQ/rJmqJX702LXTb44PhybWXT1qvRhmsFNsErFQDaBr0cbPmZAkFVRyqAE0yajUNH8ab0MQXb/UHfUDCG93yohd95PnDWjw9hz8MCLG3zISGRPuWxfOidkIhkCN8PIArl7hdVhJfM5+lGx/L5TMSZTVEIIRYhwzB5wsPzgKFyws/PnA8SFIFSFShVcah+L3YvFLFpSEJWbDctbNrBtpwOzob0smxA9YuqEzX36nSgghWKr8NgsFmfbYI8ne1mbWmlsPEfbMoNEqiaAnmFZ+cJ6PYdh3HPV4HPvOILeNnHn4RiK8LY/TUshd2PGQLJxgcg4Mn0yy0CCGK0pb98qOlwIULBawFIyvkioU2+6iLgCdl5YqOJsh/Bgw9fBJCQaEZLaMX1nnM82jEwXS+gHnq4cVpgti5w/qREtTAJLByAePjFGLrsGdDPA86GjpkNLfuvVP3zxgwp1Hk3T3sMEqc43xkYaMOD6qA8wQtFpws2TcGOzkg52WpZqpwujzNSk1xVxyyPbv0jGSaH2nXOvyh6FTxovIG3/tt2TD5mGg8e9xHFbUBUEidXdt7NDkPl9ZS486lYv/vIM/W6S/red7HzMmPYeSpk+S+HxGZJSt95fBrFAsTQeOJYj23GfPsQavW7IOBhsnwa0RZq+zkbspNrZ0P5Fy1uYlXzKN7ZxcC08Ko65u132H0taM2DCqi4AFdNUx0rKpDjbMA0JlW+FD2nIxVkIpOmCxJ1Qb1NAE3x7q/XaGED6lceQeWaGu65awNmH9SCRKtzgF2ibyxDSMSIZBuR8OH7BXjBELzSCNBaSjY8osShRtQ5oCMoAsWh3rSoBRTKycF3xQJEaSihSzdO/CDZEB8aR000MV2/F7fNlfGGT2/C2ecexnseHwGioKmzs6EHwoZ6ZZl4UnrYllfn4UHla+DmagXUGLENdrjgRfU3VD/DJl8nj4Mt7/Q6q6+tn6BCl9Yp064n819QApYOA8MTKD7ydHzv6lsxvC7EQ//Cw61/EWPLPUdx6GgR7Ylw+ZWWSCZvEUay287Jkx4SS8LDSCFCLfRQ6hxEXfJlct5HLJa/ypWi5MeoBA2gCISyhVZURyibnU2VZKMkksBcM8CexSLuPJpsdrT/IcJ3XjqMV4/fAbm4ALHzF5R2djZ0zG2I9H0pP5jz9SnoyuvouXpS/Dj/2gJrYK4eYMODamQ1zzRYuI7U8bcZYBx9eq/yV8vZDlpuAJqMV2pou22TnvIMACUvOWjpppkKNt00h8c9L8R0I9ktBtB5l7sFGYWdr6xkP0vYefoi3eAIM4eXqq++BD5Q7nwiNt3w8ET3PfFGs/tXxDQvLesln8VN//IYyTZq4RzuPlrCl/cW8Lvnz6AaTCDwikg+1+Vs6FjbkF26DpR+Oh6cDK7NTPcOg0Ftb87O1LRsHhXs6JyAbADJBcJCoVHvqbKqc6eOTapOqj46XqqOHK2qI1X/Lj72zDG8YVsDd/yHh1roIRDFZMMDHpLP0srlzxFKmX41pfM/8FEojiYfaUmf9PACICihFs5BQqJUSA/fq8ATPjC+CV59EWguoucsJ+EB5SriQhHt1vzy6zUPuWAGBz9aQv2x8ygWy5p2cjZk5qXqyNGqOuptqB9cO1J6UO2SzVN14GSra5mpPRzMWMm6Z+NbmPwmGPK5Mnn04WgHLWuLDq/MAdCobgAANL91NwBg81NH8NobvoDX/t+nY+GtMebnSgBCeCJxk9MvtaRox0AtFPBEcpjpQtvDSCHGcCFGxY8RxhKRFGhGAoebSThVLUSdp0YS/7seAhvKSxguxCh43vITICGSg62PNH0stAWWOvvYd162Eds+fRhy12Hsff0Pcdo/jQHrdyQb3kL1n0Hcm9rI2ZBZho4nFzNwuphiFFudOF5Zmqweq902Jz8GfKUlG9ypaRyNztHJ0ym6ADULU1oeo9BNDnkWfWpCSO+7vNLPXWXxgjM34vkfjDDTOILb5soo+1WgUQPqc5DtRneTIvCT97ezjz6nznVnk0NkXl2Rcdw93C79K6LwgFYtKRe2IING5tOznbLpRkjnqRIZd06qFh78xhCqhUlsHDqCH3xoHPteP4Nzxn3Mt6YxVtyotIGzoWNhQ4NNdJRDq2t7Sg8VuvI2i6wqTsJbAzvRK4caEKr9QOVL4p7ip2t/dcHl9DEFqJyDwOVRMrPIE5hTPKmgjy7rwcOfXLgOW4encdmWRZT9KQjhJU90xCEi2YaE7DmpH0BnU0Qsb34EXjH5rHdYx3y9hXroYb7lAziKyXKI0aKELwoo+UOoVibhBZ2DTzvzNuIQEF7yNCCSd8R9ITFclDhw+hh8b87QTs6GenH8bKg/36a9dDyz7U7NyVxf63Q0zMNurtbAFHRy6yKFbP9RtsbZoY3N29aB8udMZWz4g6HX+D9BCb3jRqD8ulfjyj8NIf/nBsQ//jn+/tHrcfmLa7jkrCWMFWOEsUCj85WKdONjqQ00IvQ8kVHwgOFC8uWt4SDJn20AB+rJl1ViCXgiwHAATJUB3wOGgmTujaRAwZMoeBKeSD51e6TpY6YRYKEja36uBG8mRhh4EBMT+MbnGrjiFbdDXHYGcPAOYNM5hrZ0NtRPC4Ze50Nn8219XI7GVnYeWdy6ku0/nTw1e23M1QNueJgCQWpxNRkRx1M1Qm5R5nhkwRl0XpgMXMeXczhU3r35HhIneCgYx7nr6vCjGGjMA3EM4QddyuxhdUDPqyvLnIWXPBYtY4igc86HXwQqo+jZKGk3ANHZdvZE8gRI4Cf0izWgVu+qGviQ7RbQPgwRh6iOTOGM0Z344z/ei63DQ5CQqBYmoJ/UnA31ylldG+otqwsIdPqa0rngiGovrg1X2rYOCbjghAqiqECN4kcFPdk0ind2LHP8VRpqwTbpago8ddAFcqbyvbIi2cZrrj+C8yeSewkJyOSThqFsoR3X0Y6TLwEk732nZaPOO+AhqoUYkQT2LxUx3Qhw93wBs01gtJA43luHi6gWYmwbbmPHSAvNaAmxiBChDc/zUS2OA7N7gChEYXwLCl4Jw0ER5040UPRK+Ms/mEM1mDC0ia5dnA31Y/VsSF9PW8dU13fUGqGTMWhA40DD5P9QabrAiaIzpenSbWAqQwV0pvpk83TrvylIVuoVlAAUITZvhT+1Eb/4RgF/YgjFB9Xgi+4XvhtRstHRjoGlMPmfbGJ0ORc9YDhINjh+fqiImYNDqP3cR1TwIMYSmvHJJjxPYmrzEs5bH6PsB9hfSzZPhgNgy1AbG4faiCSw0PYxFAiMF4GdmxsoPibC6x8zB1TOwUs+8kOIcx+cMJ3cYdF+zoZ681bRhqz52iBPG3KbIyq9bUywdrGCQ0upDs82tMnIbDvYNlBSHQRTMGcbwFG6qXLyBI5ZPjoDRV/+YngYV38/xJseMbz817v0Ky3p0xUIisnmRfYAUqFcCy/ZzAgbCW2hnDzZMbQOkFH36Y70XfHO0yOiXEroZQwZNDtPfUTLNGKokvCpjOOQfxTt2iwu3XwWAIFQtog6Ohs63jaUb2LNljOV4eSrOqjXOieaphWRrsxah65NVVtQy6S/XF+aAk5bUHbA8bSpj0pL2ZZaR0oPSp7JPvuDUQmJpaUAB8oh/udICWeNTWMoqGCx3cDexSKmG8PLjzC3lY9oLYVA2Qe2DgNDQYySH2NfTeDTn0sezY49gbGtLQwNt/GgHUt43OYAu0YLCOMW9iwexWLbx2Q5xMaKxEj6xEdtDkND61ALj+LGQ0NoRp6y2aG2jbOhB9qGBiurrq0cf46v2meUHqb+V1LdXL0C2ARVuj6nxh41Zji5eYI6nSybfBtZKwzkZu7FnS/5Is765AswNlPDYruM7+0pYmlLC5Nl4P4l4J7ZAIdnKmjUA9QWA7RaPjxPwvMk4lhANgG/IjGxoQEAGB1vorYUYNdPD/WJm900jH0Xj+CCDUcx1wJu2OcjbHvYMN7Gg8cLGC+FKHgSGyttVAIfC60A37xpHKPjTezcPw25uBv+C1+WMGst2dezB86G+suovHLYEMvT1A7UvW05nX42cYod1sJcvYIND11gk4LrUJNhcnx0DkVeAzbpwA1Y0wBU020CTZsJSaLoDWFHtYmbZpZwyeZ18MKhZONDFgEZdF9LSZ/wSD9jmD61ITpPc0SdV1P8ACiUUSsXcNvcEYyGs9hQHoHvB6gOdRziqNU9zNTvvPLSXOyeF4Ioua41ID0PYrIKjE5BNu7FF/dUcdU5Sd0CUSDq6WzoeNsQL8umPU2LGKWzirSszWSepXcYDNnFVdcnahkdvVrGtFBSYy5PeS7Isg0UdbIoh8QEqg5AWj8Bgfm5Ev5r9wjGLz6CWJZR9GLsXhzGHUeBvUd9zM+VUFssoNXqbkbXFguoHGgh9gT8M4BXPvEIfnn7Zjxpq4f5Z+zHba9Pnrzb9TQPD10nEXgS9yz42DW6gEgK/GimgkN14Bc3ApuGBDA8ASzOJBvYAEbLUzjcPIJv/LyA/3WGRTV76pXW19nQ8bCh/msTD9OmTh45tn3mcGyxkoDRtD7b+E4rWXfzBNnqveoj2PLMliUwNI6JbU3Ib34dsfdM7PrpNG4d3YKwPYegEGPvvSPwd0eY3L+IyYUWzSODI5eP4l1PaWJdrYEfvaCNf7oq8ZMn/tBD0QPEQgt3fGcYt+4SiCLglu9vQKkeInzEAs4ea+JgvYCNlTaGCzEiKbB1WGLucAnt2wA8KMLSB29A9WFPSupUHNbqwsPZUD6e2bK2snUbGGr7q+VsfGkb2SY4PxpYta+0pOnqIsoZAtXRumCKM5ZBYNvxlG7qvW2QnCew53YABcK4ibsXgCdujdCOGygVysmGhBege7hdsfuUR/Ysj85hR8lnbgUQthAHAWrhHA7UZvDqt29HdUeIqc1LeOIZTfzK9vuxobQBqK6HWJzpvN7SeYIEADCfeV1GAEGQ3EchIHyU/CFcsqmmaU9TG6T5zob49Pw2xJfPs/FAlaMCitVYbHqvhcSaeNdwcJgCOhVqACnQPw4pelMwZqMbpwcVgNk4ILYBn6lug6RhOe25vzmP8Zk6oqkhXD9Vxp6lBmIJ7D9Uxt57R1C5r4mxmTo2Hl0ktW0VffzjnzcxVtwID8mGyB9csBFP/dUWLjj3CP7sYVsAJF/zakZLCLx1uGnmIP75ixswVA2x7pIjuGjDMNCoJ5sdwxNAcQieEHjOzgDP3hGDHq9cEO5s6HjbUP91Hh04fmo/UaBk6PjxvNxcnQeqT8QFbFDyV1O+jp+NPM7HsZWtq7POt8tCI79Vw6Gfl3H5dc/Gtt1zAICHfncfDt43gqAtsPPAtIWuCf7vB5o4e6yIwuEm5G234iePuxkfjP4E4uhB1P/qWpR/8zLUHvYghI89gkZUw1//uIJzb9gHALh9fBMuP/cgSp7EaDHCfMtHMxIIPImrn7cfl2wCxMw4hp/9EKJOOjgbOuY2xOqqo1utNtbJM8UXeh3Wyly9wq+0AP2DjHJ+qI5QnZMsLwomx0nlrwv+bIwlKyOblnfy4fjrBiSla8Lv6NEiZhohltpH4BenEJRHew6oSzc7Ig9oxUsI4xZiGaEZLy0fhuoJHxISIkreL9+zWMRZPzkE/ASIXlHCeFFCIsa8WMKPDszinPEmNlW2J7zbjc6nbzP6ex4wVIaoVIBCGYvhLAQ8PHh8F1MPZ0N2OBY2ZKoP1xe6IEXlSfWFru8FQ2cTPDn0g7I3U19w44ej1dHpxgJFS+VxZU366vTL0lPlqfai+Otljs8kZxtNHKrh8BeH8N3TphAGHiYPLGLXfYe0jsXD3+jh1eduhto+Hnx87TcqAMrLeb4IMBSMAQBuPFTEWd86iFbRx5FHAJEMUWjMI33tsYY6ppem0Y4Fdo2eAbtxx7WJSp9No9rH2VA/TDJ1cz0nVzfvUkjz8q5L2XKUXg48TP2k2o+uTSlb4+ht1nmVr8qb8mlApFE+WzZd1Z3TleJtS6/SSkDGOHI4wra753qoNt63QJSl8ZS3enhR6SDk0RBiYQ4yjhHdlmxkXOu/CeecV8GFv1YBwhDDs4cQfvE7GH34mbhv/wUYRvJaStw5IHXjUBvtWGCx7WPvUgE3zSYHnT5jpA34AcQTnpWpX7Y+zob6ded0pXivwIbY8jbQrRs6Pbi20EHni69drOCVlhS2nUB1GuVEUWUoWqoMFXSZBiZ1z6XrBrZpoFI6Urx5R7QcjOAVF+7FwXoB9y40sWPkEEaLUwi8zqstXgDp+2jHTbSiGmrhUcw1Y+xZLGLvUhlFD6gWYvgi2a9YbHtYaAPf+aMYF7zBww27S7jq/CU8Yct2AEArruO2uRBf3Rvg5efswYbyOowgebRODA9DNlrJ52o9L3ntZXgCKJQhpUTJH1bqpZt8nQ31ph07G8oXZFA6U4seV97UN1wapVuXdi3sRA8Oqo9Vp4ZyPrgyuqCNsiuOPmubugCQ61tOTpa3yivPPTc3UODaOEl/1XtC/M1/jWPkI4uYOFTDxKFaPwsCL35HjCdt3Yb+utFtM9+ehoCHVz+/sZz2+NdLLLWBfbX9OGN0O7BwCPAC1KM5HGn6OH/ydCSfBlf7ISvD2dADbUO995Q8igcn26SX2qdpGsdTnft5+W6uVpG1d5MPoVv/s/ncug0iXxf02ehhkpXSc4GpzofLK4fTl6EZmcKj/ngdfvHJbbxxTwl3vzE2F+tgz9kT+M8/OYrW+z+FVsGD8AWiww3ER5v4xN9HeObLfNxyXQEX/fYIgpdfBUCgFs6h0Ajx5A89YnmTpfkbFXh3Stw4DWwZKmDTUBthLDDbSM5u+o1zYmA+THxpto7Ohh4wG2LnSCpGUHVQ/WVd21PtrdPXVn8ea2GuXuGGhy7Qyf7qDNG2U01BGcfDdiDbGBhl0JzhUQNUdQ64AUPDg4eFto9/vyvA88+JMVpswBOzKHoVeF6ASDbQatXRjBYxXQ+wr1bAobqPW48At987jNpSAa2mh0suOoy/ePgGvOXmadz5hhiF1/h4zXmbIc+LITCJA/U78Zabh9GKgX3XlPDcP2jijMr2ZFPl6L7kKQ+g+8WWOE4OL/UCYGQKtfrPsb48xrShCmdDx9OG+uXp6kbpmk3P6kb1TzbfRhalv+2C5ZBA1/+6gMhUllvs1XKcPXKLukmOiZ5K58aLqh8nc9B2SmQ+amoHPv1c4Flfjpaf9jDhg5/dAkHOD3Q9JGL8zvOT98y3/ImHN16UHGq6e/FeHG4EKPkSDdFGOSgCYQue7wNod16RUcemKZi3yXM2tJo2xEO1Ea4NVVl528RUZ5MNOeihs2XbwDMFFRza+E15gtyV3pv8MK5OIGgp/ipPoi2DEqLpOm7+5c/iD19SwnM3vQiTB8yHge7dNY4/+eQ78PF3hmg1YzzhWSVs++hvY/aKt+PL/xrhEY+pYt37/g8u7bw6Ln/6Hdzzhz/AFQ/7bWx71PPx8V+fRbUwiXbcwL/c1kS5MovhAPj5QgHrShECTyKSyYbHRGkb5PR/QZx1kdIWFJwNHXcbsp6bqbpky+viHp1OaRpXJy7P+dEA4JlJVFDOh8kJsQ2wKBmcTErWaizoXHnTwMzypIw4e63WK5vHDYLk9+ZZD3fcOoGFtof5lo/DzUUcaR3CdGMf9tdmsXuhgdvmyrj1SBk/mPZx/SHgzvuGcOSmIjZ//jDC+wX+4BeK8EWAqQrwzk8O432PSx6hFh1z+KubhjH3NzH23z+M+i9WcNGGOhqinbwP3vm6i1xa6mxyiO6mhx9gMTyMd9xawdfvP8C0I9dOzoZ6eR47G+KDjuyEyrWxyielo/hyky4XpGTLZel66UUsj8n/tQGqT20CFjWfK0Olq4u3aZwAvO2q4Jyh7C+lhyrTRp6pnXrH5e++gT6jQ8U7PlmBsGqTrCSBP722gbNe5+Hl5yzBFwVISNx5tIzNw20MBVV4wgeq6xFXqgCSz+Ca+NrB2dDxsSGKrzrfcnqqyLN+6frXtE4rHN1crcBkv3mDR6qcLkDT5XF2R40n9Z6yf8qeuHQKJlrbsdzFkRsXccuPaohn6vj0O8sG+Qne/ZP3Ye/uFhr1GHEMbHvXc4BCBdXJNq6cuQIP/c5rEnmdryHe9bs/wne+XMdXFv8WH930Q6z31qNcr6MRLeIbt4xg82iE0c53APbVCthfKyCSydn/mLkHe//sBrTff01HOjdunQ3R/FSsvg2Z5el4m2TY+O2cz57eU/6/GWthnl7BGR7cosjlmaDrMNOgoK6hSV/JzpdqgDonh3LSKEPkdOifnP7ogg343fNr+MdbgFiWUPaTQ0RbsUAtBOZbwFwLmG0Ch6bLmDlUQX1/gLNvOwgAaIwWMFJYDwC46pwtUCfUv/3JPiy8NXnU7z3PmMVIYT3K/k6IdhOQMRCHkI0m0GglmxzlEsT4OFAeRXO4ikBKvOa8GmIZYSk8gsArouRRr7dw9XY2RP+ung318+T04/hyunEy1fS8E/IgtuDQRXaxVNMB3jGR4Mty/HVjWr2mdElpbAK0bFpWV0ofU6DJ1ZuqP9cmvbpcuvl01N9+Hz72at5+3/vpSRQ9yvFW9enHmaNn4uoLuzI/ePsMzl0XoeIPYSgYQ+AVsRjO4VD9MO5dKOGyLaexepjr5mzogbAhWqYapHA6mW2oN49ai048p/XkBudH2Kyhtms+hTzrPkXL6UeNkTx8BqXj0vQ0Gz/w27jynfM4+L8/hNE/ezuu+fircOULjoLC3PoKrvqP9+O/D7Z7MzbsAgBU3351n4zZl70F//2NEABQedVlEBt3AoUyWoGHl354COOTDawrAhsqwHAA3D3vY88icHDBxwcvGQJaPk77+8dDttrA4T3JVxGrG5R6ORt6IG2I1ludk1XYxDXUWsG1N7XWUnTUurx2MeArLTaBko6Oohmkoyl5WTpTB9sYISUzqzOUPJ3OnCxVb3Vi68rzRQHv+Ok0vvTdSWw/Yx4jI8lk3Gx5nVdWfCzOFzE/V0RwOML4TA2nZR7bu+CRBwFsZvW58Z4y5p5RxubPH0Y1mICABwGButdGpTIOLMx0n+iI4+S1lqCI+rpJHG3uW+Zz21wJF08VOpsdthMP1T7Ohvr5r8yGeJ1M4BYSahGl5Kk0tui2sZAS3hr4XvjgoGyWCrbUdGqhVGm48aALRqmFmbrWyeHGdJaW0p3TxxQIcnpxPCkkNE/dtg0/ff1+3PLn/e+Lv/tTo8xmh043Xo97FoAtQwFiGSXzbtRGGLdwsFbAoXqwfGi1ua2dDZ1INpSfN0VnK8OGH7XO9pdzc7UNVmN9zCvHFNxRtGrfq/6FSQ4XgOrK6OhM/kWWPkNTHMbMb74dX/pYiHPOq2Dsv/8F13zqmXjyv23C1n+f7Sn9rA+8G+p3Wx7+6Koiv1fWLdf5KFdiNOoxxNTpia8sfBTn57C4MIk4FmhtbWJDOUbgSexZ9LF/LsAl20PIO3+0zC3+4Z3wn7MTqK5H/5jTtblN+qBwNsTroaaZ+gzgbCifjjrfO5uv9/HXylw94IYHFSSajFClT/NtDZWTZztwqLIpjW1ArjPIQWAz4Prx478ExiaW0PpujN1jQ/A6jw9JT8API0zOH8XmVkSW3feJCvA4nve/PnMdauFRRC8uY8gfAYSPSLZR9quJtsUhiDiErNWBEBDVEWBkCgWvjIo/ipHiely3fy/mmj6u+lod731yjIo/iv6B72xIT2eLwWyoXydqYs7bJ1Q5tW11dTVtqCQ6roXDlQYHF4hQC7FQyuQJntSyFL2Jp6qLLnBNfyl74uSYeHMBLxf0q7zVutAyf+/8zWh/uonrDyZP2cUQuHCyhIo/ouGdZ7MgwZ9eOISv7l3CnsUYY8UGCn4J987VMdcq4PJdm5iypj51NnQi2BBNp8NgNmRHZ79J4uZqFbo11eQHDJLP2aRuTTatw3ZBVD+NTR1twelg9sH+/cPJExi33ZKcr3TDxv+HXwNw8eOqiEJgYT5azlPxw+sXcV4P71RmIueyO/4vcOQ+IGxBjm+GgAe060B1Pb70ynn82qcKuGM6wNljIaqFCP8z62G0GuKqszYA00eBDWci+sSHEe5fxPcf+xFc/O0XAWPqHyidDT3QNmT2i1V9OZ259YPiaeoHXSxgbq+1MFevwldaAN5wdIZpa+ScIQwa6NkGYqZJIaXJY1xcIEjl8zSFV/oYfn8LpUaIypLyuJ0Gv/Q2Dy/YNW6kSz91iCgEOl99KftV3Hrkbjx0dFNyeGkwn5zh0fns4U9m78dfXD2Gz7/Hx6WbTwcg8awdqsOqa1NnQ7QMqtzKbahfhs0COsgiQ03gXACl0+XUn4xXB6bADJo0NchReXG8qbJcwKmmqfzUOUPHmwt2OXun+KljJXutyrXhSdVHouCVcOnm7QwvjodJZi+GgjFcuH4aX907jF2jRzFcWIf5lo9mlDyl1y+H4i/gbCjLg9JF5Xd8bMgOK7OhfjqqbfLq5NCFjQ8E2AdZpvJCuab4cAFTHnmqXI5GN8ZMvHV09j7iL1w0jB//oP+w0u99W3/m0gv+rILyX/wewzdzvy55dTCWbfjCAxrzwMgUhr/+LfzWJY/En35sK87fdABbh5Onscc2NiC/9Clc+8y7cWX0p/Bf+DL4AC7+o7x1VfOcDdkhvw0l0Pnfqn9PzZU2fcP53yvxydcuBtjw4AIqaqHnAkNb/pSsFJQDxDk8NkEhlc7BFBzqgkwqyOb0pJ28f3nievzo/D0AgHYs8J5X8t04vaWKDfsW8Yy/E3jqaRVGt6yszLWfnKxU9qsIZQunVUcQBUX4xaHuF1qiFnD0AB42PIFzX17Hf+zZi8u2DKPglVH0Kgr/bH2dDdFpx8eGesvlXTRsbCgPHdWv9GIt5NrYiR4cpoVVF0TpAsb0niqXJxjigl2KhqKjdOPGiY5/qiNXF10dKHobWXn52OrQRTMS2LMIzLXmUfKHsa3awlLo4UD9TpT8KtYV1XOb1Ppy8pwN9eefmjbUW05dj3SbH71t6ObqvDCt/bp1WpdvCthsfRZbUEGfjs6W3yBl+3Hhd38bF3zh44DnQbZCfOjy+4xlXviGYZRe9RwL7l29fFFI7kc2Aq0liPPPx6QIMTLTwJ3zAqNFic1TDdy3v4KvPuISvODq+9H6h3eg+NKnA+VRYGgcdu1IyXc2ZKZbic463xZKXl45aqxjqzsXU5nXgLUyV6/g0FLq3sZJoeiyHUMZjO7aFJBKgkYXBFIOGxf02ehOBaeUnhz66+XBx0Ubdi6nPPqzgESMq79/APveFOP2SzdhdHcNn35bG9VgHMC4hj83YfXKvXnmftxxtIQX7fIAmZ7d4UOGIUQcAoUybt8j8JKzZjEcbDPU3dlQP93xtSFaji1vXTvxNtSlTfN0E7naL6u5mJ7qUBc6XVDKBX15AieVn7rI5gnUKNrsOOLmANVmTDqb5OvKqeOcoud05qALvrl+6uUbyjY+cPsQIgnUQg/tuIHhwMORpo+KP4ax4kbQfcXp72xordlQPy91vVPzdDbk0I/smqf2h25N5gIobj21WS9VWt26Tumh46lec2u5jrfO7+F4qyDy/ALEs14CQEJA4MpIAjLGnuf8Pf7z841lsisPvBDYcEbi7wovw0/166i69dLJL38Ordtn8HvDr8RQvYF6zUcjDDFSAA7cP4yHPGUa3742xlM+uBWY2J7hR7WHs6EH3Ia0NpBNB/rrbypP25C+Xbh75zdnscJXWijHQF0Y1U7SBTw6o1PT1cCIWmy5QNAmMKPKZGkpI9fJtJFtKqvSZ1MF3vSILcBn05TsuRmD8wWAGBHOHh/GQ9YVgcYiELaSjPQpj/IoPnf/YdS+XcXcJdTBeDr5zoZ4vXR8Vt+GzP00uA3RbW1ahGj5J+Lnrk4cUIurmq72AbWwqnxMQQ3laKiyVflZWs6Boe4pZ0HVW13wKZ6cI8LpSelIzUXqXKKTQ+Xlue+WD+MWfvyRYTzhN5ZQ8mOEsoWFtsR/3FHEE7YexhimCP24OjobWos21H+v9i+lHzfnuLm6HytZt7Ogxphp/aXk6mSb7N+km6qnqouarpbl/DsdbxW6Ns7IET62f/b3cSWli/BBQ6dfhnfYhHjURSj9yhnY+byDuOch6zE8vIRGlHxNcec3p7Htsv34xt4W5OF50J8o5+zG2dAJY0NkGRN/SxvSIo8sDZc1MFev8JUWrpMpOp0DZTJaalHXDS7KieH0pMANTjXdNDGl95RTmELXnioN5xiZdLGRzUHia3v3YbIU4qL1k8DiASAOIYIAMowggiLk2EacFd+DT/5dGb6YQDtuoOCVNHVRdaN0VvVzNnQy25BdP+VdsB36oQsI03QoNNTY0i1+al+pYySP7atBHyfHRj9zAKbnYXJ6VCdLF4BmZVB1MAWcFE02TZ2nAE/4qD6+ja3DQD30MFJoYboe4DE7WvBF0VA/G72cDfH0qn4UTnwb0tfZzc8rh7rWqWsiF/yY/G7O1zH5Bdk8bn1PaTjbVPlT1yofTl7ee5Pt2voVXLub8nTykt/2+z6AYOso6k/bCAAIp3w0Wx6WwhhHjxYx9DttiE3bceX0SwC/CNSPApUxhSelq7OhtWJDveVApAnY9c/ahmcmUWGzMKZ01GKrc1iQyeOcquw9NQAA2qDVa05/yilRZVLGZXLusr9ZHShj1pVXeam6mHjkw9t/uh8Xrm9jW7WNeFmkl0zMAGTYgjh4Jx7ajlENJvFLryuiHs0zuqr6OhviZVA8s78njw31T+A63dR0JU8m7xoei/+nBkwLH9BvO7pAkBvHKR3Xblkblwo9Nf45B0yly/Km8ky2bQIXUKoyuWBTnVeo8jqZFH+Orpc+8Ir43xcvYK4FTDcK8ISPbdUWCh7whd1FTDfuzeio4+lsqP93bdhQLw23zur6ujfZzdUqKNsy2XAevpQMW1D0Jr/IdmxQdIP6EzqfTf2v+nwcL4o3pafOj+nH0Ve8CcHjzwO2b8ZQMIah3/cQLQjMHS6j7AMbJ1q46fqNeNS127G72MI1E/8CzB8w8Hc2pJdng5PHhvR8dXrkY70W5ukBNjxM0A08bgHWGS3nMNgMEtVodbzVcrZQHTCVr+ogcIMnzyDQOR2qk6JzKM31/OXtNYwWpjBROg1f27sPteow5NQuQHjJKy0A4AVAcQhfv38PXvjiwzjamsNMYzejn239OB2dDZ1sNtSls3HYTQGQgxl52tDUplTwZgpQueCUCqBs9OLGKRVIU2PFxIsL9Ew6UuncOM3e2zqq2TRdQJvQePDxuM3bsKki0YwEItmGJ4DxIvCcnQITpa0AgIX2DFMPjj+Vp7vX0TsbonFi2BCtK1feIT+oNrRdQ49V+w8yfm15rsZartKuJMjVjS2VN+WT2GwEdH2d0ZecD7H1LIizL0T73f+Md50r8PRfOYRW00c7BooeMLmxgbe+aC+2fe47eN5vCUB4kD+/UdEhq4ezoXx8KNqTx4Z603TzOwc3b6dYwYaH6pSkv9lO0RmGrtNVuixfatDrOpQzMk5PrryqT5pnCpp17ZG9thkEJp4qzSDo5Xfm6Jkoo4SfHj6AnSNNvOLrbYi5/UBQhCgNQXgeltZN4H99cwh//t5NmG0A3ztYxfry6Yw+VN8CzoZOXRsy06i6c3IFBCS86Nj8PzVALcAcuIWaC+Z0CzvFm9o4o/iaQDl51Lxhy9tk79wYy+NY2ASZWVo1cKV4cjp026ARLeL+JYF7Fnz8ZDZAIHz86ulVjBc3wRcBAImRwqSGV8rP2dBataF84Ovj5moKedbUrJ+irot5AkxTvkmnQTa8TL5InnbIU+88vLL10vmQ1Ngy6ZGUFZf8KqLqBOR/fhnBQ7fi5y/6KL74nQ04eO8QbrxtBCMF4CsvLaD1mH/Fh6/Yj6UjARrv+jzEGY/K8FH7ytmQnUyO9uSyoS4tNe9z9Gq6XtaxmqtPNAx4hgfQ24Emw5TobXRbR4sLBqlJgMNKnTpdgE0NIsrRU+klc02V0dGaeJv0yfYLw0dGgOfh7PFR+KKAv71sBmiUgLFNkKVhCCkxXJvDe3+liW9eUMMTtwaZrwHo2j6V5Wyolx8l7yS3oZ4+4K5NujnYI08QqN6bF8f8vKmxxgVheWSr9mUThOpo1fGXx7mwka3ypdqAolN58XUt+cN45NRhPHjdEApeFbfPzUCIGRS9SucziVldBgnoTXTOhux1OTFtqJ+XTsaJ59Se/FDXyGxanrI6DLLm6vwNG510eXl9KRt6nR9jkqvza8Ck9fuTMSJ4woe4+GKgUMbOd27GE2am8fJzlrChvAFfvz/Gfx7cjydd/0w03/9FlH7jl4BN5yi6mHxjCs6GeJxcNtS9Nsnh7lVeaxer+JWW7D0X1JkCMF2nUYESpYdt4MfR6YzHZiBTvFVepnYYBNQkRTlzqjy9DlIICHioBpMAJCreMFACZlv7cNP9R3DH0QC/NX4E68pVfPLWKp5yWlrSdgJyNkTLPXVsqJcmuwCbJmWiLeXa+F74ysHZdjZPF7BRMAVz2XGRzZNKel65Wf6qrVHjlptTTLzz0Kt1Un8pJ5MLHNXgUeVNyVX1TtI9eHjU1I7ltPMmKjjS3IcjzX1YV9rSJlnaMQAAGTBJREFUecpDlZHVk5sHnQ3Z8c5Df2LakLkOlAyC3s3VBnDrtbpeUjClc/6NamO2fPPS2OiUzbPxYWx8r+y1Tk+TXHUe0snj9faiGPADYP3OhGpsCm/YISD23ITwq9/Ek26bxSd+8wp8vjSMrZ+TePgryoy+XLs4GzrVbaifnw04eTz5WpirB3ylJduRJgMxNaJNJ3KGlL3nZFMBFrXgc4PA5AyovypUvbOODOes5J2gOD3VgUbpom9/ge73xyUkFqM5QPgIRBEPGm/glQ/ZiOamHXjqG9bhY786CQ9+T+leUI6ls6FT3YZoPVXnnLo26evAgwsKdXmc3XLldfkUH278m+RRY5Iaj1S9dHWy4WvioQal2fI6R0h1Fim5Oocoy4OiS/h5wsdocQojxfWYbd6Hfuj60tkQXXYt2BAHnZ24udoOpjXalMfx05WnfBsq7VhipbJs/MlB5eTtC0sfzy9082QEb2YPhJRAcQj++adj6G1/hCvP2ojDZ1yD9d98OhBwX9Pi6u5saGXlTwIbyiVXt1Hj5ucBnvDgFrz0V12E1XRqEacWS1PnmxZYXT7lRHDXahn1Pvtr4mdyhDjDNenGBai6YF3tB6oOWQgICNy3OAdfHMGu0dMxVtyID9y+D884vY1/+9tJ/GzublQLEYaCMZZHfz3VOjgbOpVtqF83ypHndOxQrIGd6NUFZ0PZPKC/D/NCFzirstQ8EziduPmC4q/aKldf1e659qL04+YckyxOX5WnSXdVK4GiVwEATJXP0Mg29YOzoV75a8eG7HSm9HVzNQ1qfc1Ct+aq5dV8E+9seSj5Jn+IojHJsi2v6majF1cPTq4NnVqGo+fGs6YthAf5058A+AnEY54Csekc1F77ZlRe/URcefQVkD/4LmS1AnHRVtA2oPa5syH6/hS2Iav10eS701gLc/UKX2kB6A7gOowLZnQGwS3+FC9ToKUzAG7A6/hyk4ealmcwqmWkBQ03eXB14/qLmgCzkHjw+C4AQCuuI5YRGhEw3QgxVgyW86hyvXK4yZnSydnQqWZDvFyVn1rOIR84pyYLzlapPLUPqD7haHVlsrrmKaPmczyy6ZwjSfEz1Vflm+Wv05Wbj0y6UvpSeYPQULxVesDZEKWfsyGHvNCNJ92arNLY8uRoddD5OjbruM6OOd/ItkwK23GSh6cNb10+ny4ue2YiszYHxCHCBoB79wGbz+nkZXmY5gFnQ2vRhszpWX1NNrS2MMArLTZO0qCLYBowpdem4Ew3mFQ9VMM1GY7N4En5cDTcwLKZhLJtocrR1ZOTwTmjlAxz//1k9gCueu4MZhvA3/14CLfN7dOUU+vnbKiXdm3aEA29DQkcm++Fn1rvL5psWKWj+lKlz7NwcrTHuo2pMUyNE1PAqJbj7lV5tmNZnaOyyI5Pbq5Rxyc35rhxzumoc+6cDXXznQ3ZONNurtZBHU+mOplsLZvH+TIUPTWW1XvKb7LRRx03Jt7pPUdL8eHkq/ORqRw3v+l4q2nceOwvJ7/+RVwz9I9YOhLgp6/bDfntrxC02bbgZFLyOTgbovmcnDZkho0NJThWc/WJhgFfadENBlOeLlDM8uccDls+ajmO1uT42dDr9Mi2h+ok6ZwJG6eKo1X1tHG0oNxzuibXzcjDea/38NrzN6IV1VHyNxL6ZfXmJiRnQ3reKX1Kc+rYkD1sF1+HBNQCp3OYKAdBx1cH2yBQXYxt9ODy1XT1OjtGuPJZmJwb1fZN7a0Gk1S9dbJTGrUOnCPDpevaiSvrbMjZkMojj2Pv0I+86z9XRgUVSObxb/L6MWp5FZT/YLOGDyovD51OL7VNqPx0jOnKq2W7NLLextOvCDD53v+DzfU5oLpeo6dtvzgbsuN5atiQPQbxuU9NDPhKi26gcQPFFABmy+o6iOJJDTBODhe4ZX/Va06WTSDGtQfltKn8uXSVB6XDIJOUafABMWL81nWH8JStAe6cF/Dgo+xXoe8v1VEz6etsiNb31LAhfR8abEjihPy+94kH1SbVPBD5VBlTeZVGF6RygVgKm0BVlc3R5dUre6/jpWsPEDSmtoSSr9OPCz45vmq67WZDnjo7G3I2xMDN1Qx066SujEqr2jLlZ+gCUMpvoWg4UHZK6azS2tRXLUfxp2hs+FNzAFfGlk5XNoOojVsf/06c+VSJA3eVMRkUgRHqj4Umf8nZkD1OMRuy1tXG5+6yWQtz9Sqc4QHwQaDuPk3jBslK9chjsFQZW/1VnjpayvHh9OUmC5uBphsQVBlVB74eraiOtzwSGC9uxXN2RKAnPq4OJkfV2dBasCEegy6aDjxMwRFlC7oAh+pfNdgz8UxpTIFUlrcu6Mw6QhRPXRlOjlovnX46J4zTR+XPpZvmTJPOqo46ek6esyFnQ3lsyMEMXeCly6d4qHajsyWunFom73proqf8A07PPHrY+Fg2baHSqfpRcqhxYkDtMB76sScCWx6CB1/N6cuB6meOzpa3s6GTzoasbMCBwoAbHpwzQi1+3IBS86l7nQFQxs4NCIqXzlBMhqfmZw1aEvmUzEHkc5MSpQcnxzRA9HUv+1UUvFKHlWeQpdPL2dBatSGzPjo6uSZOk14ZdP2rpts4NFlQdkvlm4IsbpzbBmpZWpMjoebZ6GoT6Kl6cgEwBS7PFMiqOnBtmJ1HVHkm3VIaE72zIWdDOri5Oj/s2tW8ZprWUU62LT1lC3lkqn6WSY9BYAo0bfwa0xxhq0MHIxuB8iggPHi562lL72zolLahY4K1MVcPcGgp0L8QUgOHMpIU1AKqpuvKq3J0Mkxpabo6+Dj9TM4apbfqgHF8TfrqHBCujK6NdVB16l77otBDGSOCRIxIthHJtqJvVm8oec6GaJpT34Z4epXO1vl3SGDTv2rwRY1Dym7Usjr5g4Dqd+5eV5ZzLtI8tX3yzjG6uYprT1tQ9c6zcaDqnC0vlHImZ87ZkLMhtbytDTnQyGvX1BjjfAPTOE3v88jmxqLJR8neU3S2PgTH24ZOF2Sb2kEdn6a65tgAKFR608MmICOgXU/+9/FX28rZ0Jq3ISvk8b3XBlbwSou6gJp26KjFUS1jCkAH1U9d0HW7cJQjQt1n62IyJrXuaiDIyaHqoNOJ2kTIO4i4+lP9LTpXAgIehDA5sVyas6G1bkO0zv18hQS8OGbyHXr7WQ1KqEAme6/mcTYpoOdBlVfvOfqs/VE6c7zUsafasK4ueUGNO3V85XEgOd42sm0cGbUtVf0oR8zZkLMhtUweG4Kbq0lwtsKtz6Y1Ubeuq74SpYvJ3+LoqHK6fF39dDpwsjjY6jAIb06OLRhZwuv97eNv69s6G+JxituQkb++zFqZqwd8wgPoXyS5xdoE1dmRmf8g0qh0G/4i858qQ9VHd59N000knE6qPqpzozoTedqTcthUR8XWyVGv+yc/0TEjDz48eARvTp6zIWdDXBlbG3LoB9XmJnulFvFsOhXw6GTn7as85Ux1oZwUFVSQTdVJl5Z1tMwBoJ1M1aGzHTMcL5s5h+pTZ0POhrL6qeBsyMEeXB9Sdsz5ErbBj015XR/qAtO896oc0zxgU0fKPzCNJ66+tnOfjkbn2yo0fgEQHhCUgKBI0NjIBpwNUVgjNqSV43znFAM84aEuzrY7d8ikcfRcOV351dhZs9mxU/Wj5KrluOssna5M9t627tn+ofiY9NHl6/Tm5FBpzoZ4uc6GTGkn4ve9TxykbTxIG1HBi9pf2TQuGDLpo5YzBVV5yqk62silaFV+2TZQHYssjS4tKxdMuk076HTjylGOsc6BdTbkbEi9t7GhXri5mgK1vnJroe1azJWzpc/SmHwoE2x9uWyejT9n69tRaXn8PqrdbORm9TX5fpwcWx2dDfXnORvqIn98sxbm6gE2PLiOVBtYZ1iq4ZiMVM1XQcmijN+kG6cPmHSqHpzcbD10kwhn7Fl6Xd1MOlIybScmSm+qfhyoujkb6i/nbIgtLdfG57MGB9U2JmeAulfLqvfUwss5GFRZVWeKXoU6/kx2oAZzJh1MvLLjU+VH6cfxpcaU1FxTsjh+WTodvQ7Ohmh6Gx1MvNaKDbm5mobJz9D5LDb+iVqWGk9cfprO+Tc6eVno/KI0X/VR1LGr83Moubr5KQ9WysPWJ8xTXuXjbMjZUN7yhhJrZK4e8JUWdSHmjCdLQ11zZdRrFZTRczTZex29WkYdvNk0SlfKgcuWp3jrdLChUWWk5SinSc23cQptQNWP4kdNAuqvsyFnQzp+p/6EvHJwi2X6K9E/9lQ6NY3rF1P/U3ZjQ88hO/6yaSao9c+Wo5wiWx05Xbh6cHMX18ZU+9n0hTqnDjIncvydDTkbyu9QO6Sw8QPy8rLhTa3tar5JD52vpUKVwQWjlAwTX9txZtuWurnANBdSPNSxm0cPG5/N2ZC+rLMhBxoreKVF/eVouHI6YzUZNCxpOP42/Ki6qWlSoad4miYnjt4UWNsG6Vy+zqHKMylx5XX942zI2ZAOOhtaG5/PWh2oY8xEC4LOZixlaaixrdKYeHDI0+/UOKN4UXrb8M6WTctRPLg2oK65/sqm6fqHureZM3T1djbkbGhQG3JzNQ/T2szRp9fQlNfxpMYZBTWP81lMvhvnB1DjSs0z+RGcv8XJUcvr0nSg/DXqWufnUvOhrm+4tnA21L2m8pwNdaGbi9fGXD3AEx5UR8nMf5WWWkwpqDTZTqU6QqXheJryTI4YVafstY0TlKWn6qXTgeOvG2BUvXT1sOGb1dXGqVTbiRrMzoacDenk62zIgQYV5KSgxl16z/UJZztpOdVBUBdcgLcL02KscwZUh0Bnc7YLuWmOoHiq41fnZHB0Nu3FOZZqf2TpOL1NzqWzoX59nQ316s3ZkIM9VDun1ntk0iibtQG3but8H26ccfMBV5YbN9RYtPFLVL6UnlzgaDuGs/xXQqu2s818w/UN1ybOhrrXzoZ4WjC0awsr+Cwt0L/gUsEb0G+81L1qrDYOjprGDSSKl2mQ6dJUh4ByEEyD3aRLlreubXXlKPlZqI6uiSfHh5JtO5k6G3I2xMkmeMq1cbjS6oIKhky2YFosKZs12QAVWOlkZum44FCl59Ky+ul4UXxMdVL5mOpp0lEnS6Wj6kU5vSYHUucoU/nOhnheFJ+1ZEPdWzdXc6D8HvU6S6vmcf6QjZ+i83Fs13AONjrqeHPlVT6mOlG8OXqqbB7flfJfqV9b3qY2oOicDXXpnQ3Z21CXfC3M1Sv4LC3Qv2hmf1VwC6TKg+vUrMPC8aUWbJs8E1RHRGfYEryuHD/O0cnythnsVHkuLcvbxmlTednKMTmWzoa6vLP5zobMsh36kbV/rk84Z0jN09kYRUPd56HLylCD1CxsnB51LqEcvrz2yumhy9fl6RwotayaTtVLB7VtqTYUyn9KD1WfLH8qz9mQnu5UsyEHO3Djh7MHnZ2o/g5FazOmdGN6pbAJ0Cg75NohW4bya0x1z+sTqbzVcjqfLivLZj7kxryuLbL5zoacDeXxx9cOVuEJD5s0gHewTHkpT6oD1UWbctRMu2Kc0XGOA+dgZOVTPDgdOKeEKmMzsKg25QaVyXmh6mzalEjzqLIrDXCdDdHl144NCayNnejBwS3S2fbk0lW7o2gocOVsynNOmIkPJTO95saMrm4UH10ddPQ2Dqc61pHJ07Vjlm4Q/dR5xeTUORsy6+hsiIKbq03g1mKqL8HQZml0vorNtQqdz6KDznfRyeL46MpwNLb+ji7PxDtLY9OmJj/OlJenjZwN9fLRlVlLNsRTroW5eoVPeAB0B1CLtc6xkkSaylMNsnQDU5emS7ctQzkmlC4mg6MmAl076Rw7rv2kkq/qqXN61UFogkqbdZBM/aveOxsyy8nyWes25JCAWxApe0vTVRqOlutLk3PCOVycQ2Gqgxp8QXOtBmWqLFV3LoCjylHXJoeEmoNUWnWciQytbozq5FC6c7ycDfVeOxvKb0MOPLgAUO1H1e44qH3M8dSlc3ah88VM9Ny6n8dmuPpwbUjpY+JrMw/azpU20NXf1Dam+jsbMtOudRtauxhgw4MaUGo6hawB65wRjh+38FL6cQbNDS6dvlTaai38pgCbm8x0GwQqvc3mgcorb11M7apOYM6GnA1Rcig+in4y+V74sfh/LCGEeI0QQgohPnVMBWntlbNh3XhQx4LqQNmMKc7x4sZZ9pqzSSg0NjZvuqccMBuHReVlqpfKW9cvVLopINYF9zZwNuRsaKU2BDdXk8hj47oglAPVj9SazY1t3frO+XQqKF0H9W0of1HVJS9M/t9K+NrkU75qem9qU9U2VDgb6pfnbMjI+xjN1ccCQoh/FkLcJoRYFEIcEUJ8RwjxJJuyAz7hYTKeQYIdlY8NP2ox1zktgxgo5zCohmfL2yZgVOUOOvipySXrcOaFqQ108tV7Z0POhrL3tjZ08kEIMQXgdQCmj59UXZBK2fegAXHWDkxBlU4GxSNrv6Zg1IY3l7aaNsYFz2o7ZZ2wvGPJNIaofGqOoeY20xztbIhOczZ0KuCBmat7NFB+s+mrtYbbytUFyZxPk0eHQQNwVQ8q6KOu1XI6/qZ5yJZGBzUY17V3Ht7OhvRlKT2cDZ1EqAB4B4BnA3gxgBkAXxJCXGwqOMAZHroGzdJkf4H+BTm7YOt2pKiFX+W9GkEph7w6w5DP6c05Myt1Lmx46PopL51tPZwNORsazIZOwu+FvxnAfwA47YERzwU3VJ4J2b6gHBYu2DTx49LV/lfTqIWesn1Vjnpt6zSYdFd5qbRUHbI0qm55oPKi2snkDNk4d+q9syFnQ1nZmRQ3VytYyfprKmvjU6g6mHiagjQ139a/sZFNQa0D5U8I6NuM01nlkZeGouWgziu2sOGtK+dsyNkQjZNlrpZSXpG9F0J8GcA9AF4E4Hu6sgM+4UF1ZPZaEmlCyVMNWu1g6l7lq8rW0enKUHmUs0HV28SLagsTTEa8kkmP04OayHSwGYxZWrUdnA31ytLxcjZkH0CcWBBCPALA8wH80QMgPUda1jZVGnUMZX91fE06UQGmms7J5IJEqozObqgAUZ1TsnScjpTs7D1VT2rOU3nZ3FNplDwVnC55ZaVpzoZoXmvdhk4OPLBzNTDYepon8MzSDOrbpDw4WasXgOn5m/J09aTmHd0Yt52nuP5S03Xzks7PsfHZnA2ZsZZt6NSDlDICMAegaKJdha+0ZA2XWrxVJwQETXqd7TBuQc4Gu5ScbBCcJ6jTBc4qDcWbksm1i0kv0+BQ68ilC/S3a1ZXVV7ewabrY6pfKDgb6s9zNkTnCQgpT5rTpIUQAsmjd38jpbw/uT3mUpHfEdHZOmcnnEzOnjj5FH+TPBvequ2YnEjOUVTvubrpHBJKJsWDm8vApFPydfRUOtV+zoa6eTa8nA2RpdxcTYBbH7NpJp+J4smNPTVPt6ab+K80PZtvkqvjofOxbPw2Xdvb0OfxCany6hjl0ig+1PxE8XY25GwoB/eTaK4GludrH8AYgJcCOAvAy03lVrDhwQ0Ck7HoDEwXDGV/1bJZWvU6K083CVBOAFUPdbCrxmaaNFT9VJ0pfU1OJFcPlTd3T+mQvTeVV9snm25jC86GemU6G+pNNy0+Jyx+HcAmAG97YMRTQRHlNKlBC3WtC/qyeXkWTdVOVZ042MrSjXnOxrnxy/FW8230Nuk7qCxKf66sLZwN9fPWyXA2dJLiOM3VNmtY3nWOWmNN6282TfURTGs15S9x4PwLEwaloXwpTg9bqGVVf4TyPTn/caXybfk4G3I2pJN/0uNyAB/rXC8BuFxKeaOpkJDSfgETQkwD2D2Qeg4ODg4rx/kAfnKMeJcBNDL375VSvje9EUKMAdhsYiKlvK1DezuA35FSfqJT/lsAZqSUz1tVrbv6ufnZwcHhgcTpUsoNANJ3q9cfIzkn3Vzt5mcHB4cHGMvzcxbHcK7WztMd2dZzdabMOgC7kOj8awCeC+BpUspv6Xjk2vBwcHBwWKsQQrwcwPtMdFJKIYT4awBPAPDkTNa/A5gFcAWAhc67hw4ODg4Oqwg3Vzs4ODic+MgzV2t4fANAQUp5qVaW2/BwcHBwWF0IIT4H4JkaksdKKa87Tuo4ODg4OBBwc7WDg4PDyQshxNsAvEBKuU1L5zY8HBwcHFYXQohz0f+I4D8AOArgzwHcJKU8erz1cnBwcHDows3VDg4ODicnOgeYXgcgllI+Vke7wq+0ODg4ODiokFL+VE0TQswheS/8W8ddIQcHBweHPri52sHBweHEhxDisQB+D8BnAOwBMInktcOLATzdVN5teDg4ODg4ODg4ODg4ODg4OJyIuA9ACOBNADYAmAZwM4BLpJTXmwq7V1ocHBwcHBwcHBwcHBwcHBxOOXgPtAIODg4ODg4ODg4ODg4ODg4Oqw234eHg4ODg4ODg4ODg4ODg4HDKwW14ODg4ODg4ODg4ODg4ODg4nHJwGx4ODg4ODg4ODg4ODg4ODg6nHNyGh4ODg4ODg4ODg4ODg4ODwykHt+Hh4ODg4ODg4ODg4ODg4OBwysFteDg4ODg4ODg4ODg4ODg4OJxycBseDg4ODg4ODg4ODg4ODg4OpxzchoeDg4ODg4ODg4ODg4ODg8Mph/8Pagnw+S0g6VoAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "labelsize=15\n", + "\n", + "my_cmap = mcm.get_cmap('Spectral_r')\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 3, sharey=True, figsize=[21, 9])\n", + "\n", + "\n", + "# Plotting SMB for year1\n", + "\n", + "vmin = -4\n", + "vmax = 4\n", + "\n", + "last_panel0 = ax[0].imshow(smb1, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title0 = 'SMB (m/yr w.e), ' + year1 \n", + "\n", + "ax[0].set_title(title0, fontsize=labelsize)\n", + "\n", + "\n", + "fig.subplots_adjust(right=0.8)\n", + "\n", + "pos = ax[0].get_position()\n", + "cax = fig.add_axes([0.32, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "\n", + "cbar = fig.colorbar(last_panel0,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "\n", + "cbar.ax.get_yaxis().labelpad = 15\n", + "\n", + "\n", + "\n", + "# Plotting SMB for year2\n", + "\n", + "last_panel1 = ax[1].imshow(smb2, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title1 = 'SMB (m/yr w.e), ' + year2 \n", + "\n", + "ax[1].set_title(title1, fontsize=labelsize)\n", + "pos = ax[1].get_position()\n", + "cax = fig.add_axes([0.56, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "cbar = fig.colorbar(last_panel1,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "cbar.ax.get_yaxis().labelpad = 15\n", + "\n", + "\n", + "\n", + "# Plotting the difference in SMB\n", + "\n", + "\n", + "vmin = -3\n", + "vmax = 3\n", + "\n", + "last_panel2 = ax[2].imshow(diff_smb, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title2 = 'Difference (m/yr w.e), ' + year2 + '-' + year1 \n", + "\n", + "ax[2].set_title(title2, fontsize=labelsize)\n", + "\n", + "\n", + "pos = ax[2].get_position()\n", + "cax = fig.add_axes([0.8, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "cbar = fig.colorbar(last_panel2,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "\n", + "\n", + "for i in range(len(ax)):\n", + " ax[i].invert_yaxis()\n", + " ax[i].set_xlabel('')\n", + " ax[i].set_ylabel('')\n", + " ax[i].set_xticklabels('')\n", + " ax[i].set_yticklabels('')\n", + " ax[i].set_xticks([])\n", + " ax[i].set_yticks([])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e003a818-7fe2-49e3-b973-fa90e169acfd", + "metadata": {}, + "source": [ + "### Ice Thickness" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fe0a05e5-13e7-450f-a8f1-6f7be9a90c12", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "labelsize=15\n", + "my_cmap = mcm.get_cmap('Spectral_r')\n", + "\n", + "\n", + "fig, ax = plt.subplots(1, 3, sharey=True, figsize=[21, 9])\n", + "\n", + "# Plotting thickness for year1\n", + "\n", + "vmin = 0\n", + "vmax = 3500\n", + "\n", + "last_panel0 = ax[0].imshow(thk1, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title0 = 'Ice thickness (m), ' + year1 \n", + "ax[0].set_title(title0, fontsize=labelsize)\n", + "\n", + "fig.subplots_adjust(right=0.8)\n", + "\n", + "pos = ax[0].get_position()\n", + "cax = fig.add_axes([0.32, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "\n", + "cbar = fig.colorbar(last_panel0,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "\n", + "cbar.ax.get_yaxis().labelpad = 15\n", + "\n", + "\n", + "\n", + "# Plotting thickness for year2\n", + "\n", + "last_panel1 = ax[1].imshow(thk2, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title1 = 'Ice thickness (m), ' + year2 \n", + "\n", + "ax[1].set_title(title1, fontsize=labelsize)\n", + "\n", + "pos = ax[1].get_position()\n", + "cax = fig.add_axes([0.56, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "cbar = fig.colorbar(last_panel1,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "cbar.ax.get_yaxis().labelpad = 15\n", + "\n", + "\n", + "\n", + "# Plotting the difference in ice thickness\n", + "\n", + "vmin = -300\n", + "vmax = 300\n", + "\n", + "last_panel2 = ax[2].imshow(diff_thk, vmin=vmin, vmax=vmax,\n", + " cmap=my_cmap)\n", + "\n", + "title2 = 'Difference (m), ' + year2 + '-' + year1 \n", + "\n", + "ax[2].set_title(title2, fontsize=labelsize)\n", + "pos = ax[2].get_position()\n", + "cax = fig.add_axes([0.8, pos.y0, 0.015, pos.y1 - pos.y0])\n", + "cbar = fig.colorbar(last_panel2,cax=cax)\n", + "cbar.ax.tick_params(labelsize=labelsize)\n", + "\n", + "\n", + "for i in range(len(ax)):\n", + " ax[i].invert_yaxis()\n", + " ax[i].set_xlabel('')\n", + " ax[i].set_ylabel('')\n", + " ax[i].set_xticklabels('')\n", + " ax[i].set_yticklabels('')\n", + " ax[i].set_xticks([])\n", + " ax[i].set_yticks([])\n" + ] + }, + { + "cell_type": "markdown", + "id": "38529a7f-2e31-4853-83b2-02218e85290e", + "metadata": {}, + "source": [ + "## Looking at time series\n", + "Each output history file contains a few scalars that give information about the state of the ice sheet. Here we are going to look at:\n", + "- the grounded ice area\n", + "- the ice mass\n", + "- the ice mass above flotation" + ] + }, + { + "cell_type": "markdown", + "id": "e8014b04-4075-4eb0-b01c-157383637c7b", + "metadata": {}, + "source": [ + "
\n", + " Combining all time for each scalar in a single time series file

\n", + "\n", + "While we could create an array using python for each scalar by looping through every single file, it is convenient to extract them in their own file using nco. \n", + "To do this we will use the command \"ncrcat\"\n", + " \n", + "On derecho: \n", + "```\n", + "module load nco\n", + "```\n", + " \n", + "**For mass**\n", + "```\n", + "ncrcat -v imass T_GrIS_SSP585_2015_2100.cism.h.*.nc mass.nc\n", + "```\n", + "\n", + "**For grounded ice area**\n", + "```\n", + "ncrcat -v iareag T_GrIS_SSP585_2015_2100.cism.h.*.nc area_ground.nc\n", + "```\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b3bfbfe2-c8f1-4bf0-88ed-a967843ad2db", + "metadata": {}, + "source": [ + "Now we can look at the time series evolution" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8935ff8e-37ef-4238-b8cd-35841f0565d2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Loading the variables\n", + "file_mass = path_to_file + \"mass.nc\"\n", + "file_areag = path_to_file + \"area_ground.nc\"\n", + "\n", + "ncfile = Dataset(file_mass,'r')\n", + "mass = ncfile.variables[\"imass\"][:]\n", + "time_mass = ncfile.variables[\"time\"][:]\n", + "ncfile.close()\n", + "\n", + "ncfile = Dataset(file_areag,'r')\n", + "area = ncfile.variables[\"iareag\"][:]\n", + "time_area = ncfile.variables[\"time\"][:]\n", + "ncfile.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf483b28-980c-4311-bb32-2eacb1b4eea2", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Ice area evolution (m2)')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "labelsize=15\n", + "\n", + "timemin = 2015\n", + "timemax = 2101\n", + "\n", + "color = 'black'\n", + "line = '-'\n", + "\n", + "plt.figure(figsize=(12,5))\n", + "\n", + "# Plotting the Ice mass evolution time series\n", + "\n", + "plt.subplot(121)\n", + "plt.plot(time_mass, mass, line, ms=3, mfc=color, color=color)\n", + "plt.xlim([timemin, timemax])\n", + "plt.xlabel('Time (yr)', multialignment='center',fontsize=labelsize)\n", + "plt.title('Ice mass evolution (kg)',fontsize=labelsize)\n", + "\n", + "\n", + "# Plotting the Ice area evolution time series\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(time_area, area, line, ms=3, mfc=color, color=color)\n", + "plt.xlim([timemin, timemax])\n", + "plt.xlabel('Time (yr)', multialignment='center',fontsize=labelsize)\n", + "plt.title('Ice area evolution (m2)',fontsize=labelsize)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff2ee88c-52ec-4dc2-b9df-5cfd71984df4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CMIP6 2019.10", + "language": "python", + "name": "cmip6-201910" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/cism/cism_exercise_3.ipynb b/notebooks_copy/challenge/cism/cism_exercise_3.ipynb new file mode 100644 index 000000000..5c6e688ee --- /dev/null +++ b/notebooks_copy/challenge/cism/cism_exercise_3.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "69f9f86e-e033-441c-99d0-3e5bd35214b2", + "metadata": {}, + "source": [ + "# Computing ice sheet related sea level change from a CISM simulation" + ] + }, + { + "cell_type": "markdown", + "id": "b2cc6d1d-7ae7-4c31-ac57-862997b422dd", + "metadata": {}, + "source": [ + "Global sea level varies as a function of primarily 3 component changes:\n", + "- Ocean thermal expension\n", + "- Glacier\n", + "- Ice sheets\n", + "\n", + "**Note 1:** Here we are talking about **global** and not local changes. Local impact, on different time scales, needs to include the effect of tides and winds among other parameters.\\\n", + "**Note 2:** In our case, we will look at the sea level changes from the Greenland ice sheet only, as that is the only actively evolving ice sheet in our simulation.\\\n", + "**Note 3:** Articles have been published (e.g. [Goelzer et al. (2020)](https://tc.copernicus.org/articles/14/833/2020/)) arguing that the computation of sea level change from stand alone ice sheet model output needs to correct for other factors (e.g. bedrock elevation changes, density correction due to the small difference between ice and fresh water density). In this exercise, we will only be accounting for the changes in total ice volume above floatation. The other correcting factors account for about 10% of the total sea level change magnitude (in general). " + ] + }, + { + "cell_type": "markdown", + "id": "2f6f9e38-9ac8-4a9d-b632-6ec56bf11cfa", + "metadata": {}, + "source": [ + "
\n", + "Step 1: Understanding ice sheet sea level contribution

\n", + "\n", + "\n", + "Only looking at the change of mass of an ice sheet is not enough to compute sea level contribution. In fact, any ice already in the ocean (including the bits still attached to the ice sheet) is already displacing sea water, hence contributing to sea level. What we need to compute is the change of mass that is not displacing sea water, or in other words, the change of mass above floatation.

\n", + " \n", + " \n", + "Step 2: Knowing the conversion
\n", + "\n", + "Because ice, fresh water, and ocean water have different densities, it takes about **360 Gt** of ice to raise the mean global sea level (MGSL) by **1 mm**. \n", + "A good description on how this number of 360 Gt was obtained can be found here:\\\n", + "[Calculating glacier ice volumes and sea level equivalents](https://www.antarcticglaciers.org/wp-content/plugins/antarcticglaciers-pdf/download.php?p=7657#:~:text=In%20the%20same%20way%2C%201,sea%20levels%20by%201%20mm.)\n", + " \n", + "
\n", + "Step 3: Compute the contribution of the GrIS to sea level change from this experiment.

\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "bae11c14-9fe4-4f5a-95b7-ab47b05edf8f", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + " \n", + "**(1) Compute the change in mass above flotation**\n", + "\n", + "Luckily, this scalar is part of the CISM history output variables and is called **imass_above_flotation**\n", + " \n", + "**(2) Create a time series for mass above flotation**\n", + " \n", + "You can follow similar steps as done previously for ice mass.\n", + "\n", + "**(3) Load the time series data and use the conversion number above to finalize your computation**\n", + "\n", + "**(4) Plot your results**\n", + " \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "5005a00b-2a07-4cef-86b3-4e264539b6e7", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "\n", + "Create a time series for imass_above_flotation scalars in a single file:\n", + "\n", + "```\n", + "ncrcat -v imass_above_flotation T_GrIS_SSP585_2015_2100.cism.h.*.nc mass_above_flotation.nc \n", + "```\n", + "\n", + "Copy and execute the text bellow in a new jupyter window\n", + "\n", + "```\n", + "# Defining the conversion constant\n", + "mm_equiv = 360.e12 # converting Gt to kg \n", + "\n", + "# Reading in the mass above flotation data\n", + "User = 'username'\n", + "path_to_file = '/glade/derecho/scratch/' + User + '/archive/T_GrIS_SSP585_2015_2100/glc/hist/'\n", + "file_mass_above_flotation = path_to_file + \"mass_above_flotation.nc\"\n", + "ncfile = Dataset(file_mass_above_flotation,'r')\n", + "mass_af = ncfile.variables[\"imass_above_flotation\"][:]\n", + "time_maf = ncfile.variables[\"time\"][:]\n", + "ncfile.close()\n", + "\n", + "\n", + "# Computing the overal sea level contribution from the GrIS during this experiment\n", + "slc = -(mass_af[:] - mass_af[0])/mm_equiv\n", + "print('The GrIS contribution of global mean sea level is ',slc[-1],'mm')\n", + "\n", + "\n", + "# Plotting the time series of see level change\n", + "sizefontx = 13\n", + "sizefonty = 13\n", + "sizefonttitle = 13\n", + "color = 'black'\n", + "line = '-'\n", + "\n", + "\n", + "timemin = 2015\n", + "timemax = 2101\n", + "\n", + "plt.figure(figsize=(7,5))\n", + "\n", + "plt.plot(time_maf, slc, line, ms=3, mfc=color, color=color)\n", + "plt.xlim([timemin, timemax])\n", + "plt.xlabel('Time (yr)', multialignment='center',fontsize=sizefontx)\n", + "plt.ylabel('Sea level change (mm)', multialignment='center',fontsize=sizefonty)\n", + "plt.title('GrIS contribution to sea level change',fontsize=sizefonttitle)\n", + "\n", + "\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7e76b55a-c994-4475-ad9b-09ae72e9663d", + "metadata": {}, + "source": [ + "
\n", + "Food for thought

\n", + " \n", + "\n", + "(1) Based on the sea level change figure, the Greenland ice sheet has been accumulating more ice than it has lost leading to a sea level sink. What do you think about these results?\n", + "\n", + "(2) Do you have any suspicions about the forcing used in this experiment? If so, what are they?\n", + " \n", + "(3) What else could you be suspicious about?\n", + "\n", + "(4) What would you do to validate these results?\n", + "\n", + "\n", + "Note:
these questions are quite challenging. Contact Gunter Leguy (gunterl@ucar.edu) for further discussion.\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1f57aa63-503b-418d-a955-04fbd8c1bedc", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for some answers
\n", + " \n", + "\n", + "Question 1\n", + "The current observations show a positive Greenland ice sheet (GrIS) contribution to sea level change of about 1 mm per year over the past decade. For this reason, it is surprising to see it contribute negatively in this set of experiments especially under a strong warming scenario and these results are highly suspicious. \n", + "\n", + "Question 2\n", + "Looking at the SMB forcing, the values are negative along the coasts and positive in the interior of the ice sheet (especially in the south). Looking at the CISM thickness difference plot between the end and beginning of the simulation, the ice thickness evolution correlates closely with the changes in SMB. It is difficult (without further analyzes) to draw any conclusions about the forcing. One can always suspect whether we set the experiment properly. Also, this experiment has not yet been analyzed and one can suspect that the 2 deg version of the CESM model could lead to unexpected climate simulations. \n", + " \n", + "Question 3\n", + "In this exercise, we have not talked about how the GrIS was initialized and then used in CESM. Initialization is one of the greatest uncertainties in ice sheet modeling. Here, the model was spun-up using the SMB and air temperature of the RACMO2.3 regional climate model. This way we could invert for the basal physics using observed ice thickness and bed topography. However, there are some biases between RACMO results and the CESM simulated SMB and air temperature which will impact the ice sheet evolution. Ideally, a simulation would correct for these biases which is not the case here. \n", + " \n", + "Question 4\n", + "At least a couple of steps should be considered to validate these results:\n", + "1. Look at the biases between the observed fields used to initialize CISM and compare them to those in CESM at the beginning of the simulation.\n", + "2. Compare the forcing of the FV2 (2 deg model) output to the FV1 (1 deg model) output and look for potential biases due to the CESM model resolution. \n", + "\n", + "
\n", + "
\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CMIP6 2019.10", + "language": "python", + "name": "cmip6-201910" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/clm_ctsm/clm_ctsm.ipynb b/notebooks_copy/challenge/clm_ctsm/clm_ctsm.ipynb new file mode 100644 index 000000000..fd254f236 --- /dev/null +++ b/notebooks_copy/challenge/clm_ctsm/clm_ctsm.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Land\n", + "\n", + "The land component of CESM is the Community Land Model (CLM), also refered to as the Community Terrestrial Systems Model (CTSM).\n", + "\n", + "It can be useful for people interested in land science to run simulations with only an active land components and atmospheric forcing. In this exercise, you will learn how to run one of these land-only simulations.\n", + "\n", + "This exercise was created by Peter Lawrence, Erik Kluzek and Alice DuVivier." + ] + }, + { + "cell_type": "markdown", + "id": "ea051c12-642e-4194-8291-2c1924d2e1e8", + "metadata": {}, + "source": [ + "## Learning Goals" + ] + }, + { + "cell_type": "markdown", + "id": "346cbd7b-3b8e-41f0-b120-b369ab20f6cc", + "metadata": {}, + "source": [ + "- Student will learn what a I compset is, the types of forcing available to run one, and how to run one.\n", + "- Student will learn how to run a case with satellite phenology and one with prognostic crops and compare the two experiments.\n", + "- Learn what inputs CLM needs and what they look like.\n" + ] + }, + { + "cell_type": "markdown", + "id": "47c3af32-d018-441e-bcb0-c74e86ef4272", + "metadata": {}, + "source": [ + "## Exercise Details" + ] + }, + { + "cell_type": "markdown", + "id": "df216c41-3ec4-43e0-b7bd-be2b1e4294bd", + "metadata": {}, + "source": [ + "- This exercise uses the same code base as the rest of the tutorial. \n", + "- You will be using the I2000Clm50Sp and IHistClm50BgcCrop compsets at the f09_g17_gl4 resolution.\n", + "- You will run a control simulation and two experimental simulations. \n", + "- You will modify netcdf input files. \n", + "- You will use simple, command line netcdf tools to evaluate how the experiments differ from the control simulation." + ] + }, + { + "cell_type": "markdown", + "id": "fd2ff959-6ec0-4534-8713-f5dfdb13b955", + "metadata": {}, + "source": [ + "## Useful CLM references" + ] + }, + { + "cell_type": "markdown", + "id": "baa2afd1-4289-4baf-8bdf-83de7592d76b", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM CLM/CTSM User's Guide](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/index.html)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "523878ee-2bfa-4363-a8ec-5ff151b15d57", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "\n", + "[CESM CLM/CTSM Discussion Forum](https://bb.cgd.ucar.edu/cesm/forums/ctsm-clm-mosart-rtm.134/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "cb8a0e10-a920-41dd-8bc4-5c03395d231e", + "metadata": {}, + "source": [ + "## What is an I case?" + ] + }, + { + "cell_type": "markdown", + "id": "69988cae-fd26-4fda-8fee-6ab52e79f42d", + "metadata": {}, + "source": [ + "The I compset has active clm with a data atmosphere. The sea ice, ocean, atmosphere, and wave models are not active. There are two types of options for I compsets:\n", + "- SP: Satellite Phenology\n", + "- BGC: Biogeochemistry \n", + "\n", + "We will use the Satellite Phenology option for our control case." + ] + }, + { + "cell_type": "markdown", + "id": "68ca54e2-d8ad-41bc-be8f-31a85eec6e65", + "metadata": {}, + "source": [ + "![icase](../../../images/challenge/i2000.png)\n", + "\n", + "*

Figure: I2000 compset definition.

*" + ] + }, + { + "cell_type": "markdown", + "id": "c93817fd-8031-4917-bf45-eb0f442578f9", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[I Compset definition](https://www2.cesm.ucar.edu/models/cesm2/config/compsets.html)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "da3d9215-30c1-4152-abc0-ccb1e709cde3", + "metadata": {}, + "source": [ + "## Post processing and viewing your output\n", + "\n", + "You will use [ncview](https://ncar.github.io/CESM-Tutorial/notebooks/tools/netcdf_tools.html#ncview) and [NCO operator](https://ncar.github.io/CESM-Tutorial/notebooks/tools/netcdf_tools.html#netcdf-operators-nco) tools to evaluate how the experiments differ from the control simulation.\n", + "\n", + "These tools will need to be loaded into your environment using the NCAR HPC [modules](https://ncar.github.io/CESM-Tutorial/notebooks/tools/unix/modules.html)." + ] + }, + { + "cell_type": "markdown", + "id": "828e538d-3558-4747-9b61-3b29da04e294", + "metadata": {}, + "source": [ + "1) You can create an annual average of the first year's data for each simulationg using the `ncra` (netCDF averager) command from the netCDF operators package \\([NCO](https://nco.sourceforge.net/)\\). \n", + "```\n", + "ncra $OUTPUT_DIR/*.clm.*nc $CASENAME.cln.h.0001.nc\n", + "```\n", + "\n", + "2) Create a file that contains differences between each of the experiments and the control simulation\n", + "```\n", + "ncdiff i.day5.b.clm2.XXX.nc /glade/derecho/scratch/$user/archive/i.day5.a/lnd/hist/i.day5.a.clm2.XXX.nc i_diff.nc\n", + "```\n", + "\n", + "3) Examine variables within each annual mean and the difference files using `ncview`\n", + "```\n", + "ncview i_diff.nc\n", + "```\n", + "\n", + "4) You can also look at other monthly-mean outputs or component log files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0a882d4-0c0e-4d2e-b59f-df2a648fa93d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CMIP6 2019.10", + "language": "python", + "name": "cmip6-201910" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/clm_ctsm/clm_exercise_1.ipynb b/notebooks_copy/challenge/clm_ctsm/clm_exercise_1.ipynb new file mode 100644 index 000000000..666c7ff50 --- /dev/null +++ b/notebooks_copy/challenge/clm_ctsm/clm_exercise_1.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 1: Control case" + ] + }, + { + "cell_type": "markdown", + "id": "6457c1d2-0530-435d-ae27-d0f1eeabe583", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Run a control case

\n", + " \n", + "Create a case called **i.day5.a** using the compset `I2000Clm50Sp` at `f09_g17_gl4` resolution. \n", + " \n", + "Set the run length to **5 days**. \n", + "\n", + "Build and run the model. Since this is a control case, we want to build it \"out of the box\" without any modifications. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Create a new case i.day5.a with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", + "./create_newcase --case ~/cases/i.day5.a --compset I2000Clm50Sp --res f09_g17_gl4 --run-unsupported\n", + "```\n", + "
\n", + "\n", + "Case setup:\n", + "``` \n", + "cd ~/cases/i.day5.a \n", + "./case.setup\n", + "```\n", + "
\n", + "\n", + "Change the clm namelist using user_nl_clm by adding the following lines:\n", + "``` \n", + "hist_nhtfrq = -24\n", + "hist_mfilt = 6\n", + "```\n", + "
\n", + " \n", + "Check the namelist by running:\n", + "``` \n", + "./preview_namelists\n", + "```\n", + "
\n", + "\n", + "If needed, change job queue, account number, or wallclock time. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013,JOB_WALLCLOCK_TIME=0:15:00\n", + "```\n", + "
\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "
\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "i.day5.a. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/i.day5.a/lnd/hist\n", + "\n", + "ls \n", + "```\n", + "
\n", + "\n", + "(2) Look at the output using ncview\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17cf7e19-1211-45f2-97cd-fe2badc69dac", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CMIP6 2019.10", + "language": "python", + "name": "cmip6-201910" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/clm_ctsm/clm_exercise_2.ipynb b/notebooks_copy/challenge/clm_ctsm/clm_exercise_2.ipynb new file mode 100644 index 000000000..d5651c9b9 --- /dev/null +++ b/notebooks_copy/challenge/clm_ctsm/clm_exercise_2.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 2: Use the BGC model" + ] + }, + { + "cell_type": "markdown", + "id": "0037b73f-f174-48e7-8e4f-0744d7d23fe0", + "metadata": {}, + "source": [ + "We can use a different I compset: IHistClm50BgcCrop. This experiment is a 20th century transient run using GSWP3v1 and the biogeochemistry model including crops." + ] + }, + { + "cell_type": "markdown", + "id": "bdd131c8-d1ec-4568-81dd-701f8bdbe6cb", + "metadata": {}, + "source": [ + "![icase](../../../images/challenge/ihist.png)\n", + "\n", + "*

Figure: IHIST compset definition.

*" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Run an experimental case with prognostic BGC

\n", + " \n", + "Create a case called **i.day5.b** using the compset `IHistClm50BgcCrop` at `f09_g17_gl4` resolution. \n", + " \n", + "Set the run length to **5 days**. \n", + "\n", + "Build and run the model.\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "\n", + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Create a new case i.day5.b :\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", + "./create_newcase --case ~/cases/i.day5.b --compset IHistClm50BgcCrop --res f09_g17_gl4 --run-unsupported\n", + "```\n", + "
\n", + "\n", + "Case setup:\n", + "``` \n", + "cd ~/cases/i.day5.b\n", + "./case.setup\n", + "```\n", + "
\n", + "\n", + "Note differences between this case and the control case:\n", + "``` \n", + "diff env_run.xml ../i.day5.a/env_run.xml\n", + "```\n", + "
\n", + "\n", + "Change the clm namelist using user_nl_clm by adding the following lines:\n", + "``` \n", + "hist_nhtfrq = -24\n", + "hist_mfilt = 6\n", + "```\n", + "
\n", + " \n", + "Check the namelist by running:\n", + "``` \n", + "./preview_namelists\n", + "```\n", + "
\n", + "\n", + "If needed, change job queue, account number, or wallclock time. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013,JOB_WALLCLOCK_TIME=0:15:00\n", + "```\n", + "
\n", + "\n", + "Build case:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "
\n", + " \n", + "Compare the namelists from the two experiments:\n", + "```\n", + "diff CaseDocs/lnd_in ../i.day5.a/CaseDocs/lnd_in\n", + "```\n", + "
\n", + " \n", + "Submit case:\n", + "```\n", + "./case.submit\n", + "```\n", + "
\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "i.day5.b. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/i.day5.b/lnd/hist\n", + "\n", + "ls \n", + "```\n", + "
\n", + "\n", + " \n", + "(2) Compare to control run:\n", + "```\n", + "ncdiff -v TLAI i.day5.b.clm2.XXX.nc /glade/derecho/scratch/$USER/archive/i.day5.a/lnd/hist/i.day5.a.clm2.XXX.nc i_diff.nc\n", + "\n", + "ncview i_diff.nc\n", + "```\n", + "\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "d69c456a-fdc6-4625-bbcc-ed32ab6ae8e8", + "metadata": { + "tags": [] + }, + "source": [ + "## Test your understanding" + ] + }, + { + "cell_type": "markdown", + "id": "b3f28ecf-02b8-4cc0-bdc4-c36c8fc9e7aa", + "metadata": {}, + "source": [ + "- What changes do you see from the control case with the prognostic BGC?\n", + "- ... OTHERS?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b5a8cee-0ca9-4076-a731-e6ec200b70d4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CMIP6 2019.10", + "language": "python", + "name": "cmip6-201910" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/clm_ctsm/clm_exercise_3.ipynb b/notebooks_copy/challenge/clm_ctsm/clm_exercise_3.ipynb new file mode 100644 index 000000000..0b1e84c14 --- /dev/null +++ b/notebooks_copy/challenge/clm_ctsm/clm_exercise_3.ipynb @@ -0,0 +1,197 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 3: Modify input data" + ] + }, + { + "cell_type": "markdown", + "id": "0037b73f-f174-48e7-8e4f-0744d7d23fe0", + "metadata": {}, + "source": [ + "We can modify the input to CLM by changing one of the plant functional type properties. We will then compare these results with the control experiment.\n", + "\n", + "Note that you will need to change a netcdf file for this exercise. Because netcdf are in binary format you will need a type of script or interperter to read the file and write it out again. (e.g. ferret, IDL, NCL, NCO, Perl, Python, Matlab, Yorick). Below in the solution we will show how to do this using NCO.\n", + "\n", + "NOTE: For any tasks other than setting up, building, submitting cases you should probably do these tasks on the Data Visualization Cluster - casper, and not on the derecho login nodes." + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Run an experimental case

\n", + " \n", + "Create a case called **i.day5.a_pft** using the compset `I2000Clm50Sp` at `f09_g17_gl4` resolution. \n", + "\n", + "Look at variable “rholvis” in the forcing file using ncview or ncdump –v rholvis. This is the visible leaf reflectance for every pft. Modify the rholvis parameter to .\n", + "`/glade/campaign/cesm/cesmdata/cseg/inputdata/lnd/clm2/paramdata/clm5_params.c171117.nc`\n", + " \n", + "Set the run length to **5 days**. \n", + "\n", + "Build and run the model.\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "\n", + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Create a clone from the control experiment i.day5.a_pft :\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", + "./create_clone --case ~/cases/i.day5.a_pft --clone ~/cases/i.day5.a\n", + "```\n", + "
\n", + "\n", + "Modify the rholvis parameter in the physiology file:\n", + "``` \n", + "cd /glade/derecho/scratch/$USER\n", + "cp /glade/campaign/cesm/cesmdata/cseg/inputdata/lnd/clm2/paramdata/clm5_params.c171117.nc .\n", + "chmod u+w clm5_params.c171117.nc\n", + "cp clm5_params.c171117.nc clm5_params.c171117.new.nc\n", + "ncap2 -A -v -s 'rholvis(4)=0.4' clm5_params.c171117.nc clm5_params.c171117.new.nc\n", + "```\n", + "
\n", + "\n", + "Check the new rholvis parameter to be sure the modification worked:\n", + "``` \n", + "ncdump -v rholvis clm5_params.c171117.new.nc\n", + "# and compare it to the original file\n", + "ncdiff clm5_params.c171117.nc clm5_params.c171117.new.nc ncdiff.nc\n", + "ncdump -v rholvis ncdiff.nc\n", + "```\n", + "
\n", + " \n", + "Case setup:\n", + "``` \n", + "cd ~/cases/i.day5.a_pft\n", + "./case.setup\n", + "```\n", + "
\n", + "\n", + "Change the clm namelist using user_nl_clm to point at the modified file. Add the following line:\n", + "``` \n", + "paramfile = '/glade/derecho/scratch/$USER/clm5_params.c171117.new.nc' \n", + "```\n", + "
\n", + " \n", + "Check the namelist by running:\n", + "``` \n", + "./preview_namelists\n", + "```\n", + "
\n", + "\n", + "If needed, change job queue, account number, or wallclock time. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013,JOB_WALLCLOCK_TIME=0:15:00\n", + "```\n", + "
\n", + "\n", + "Build case:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "
\n", + " \n", + "Compare the namelists from the two experiments:\n", + "```\n", + "diff CaseDocs/lnd_in ../i.day5.a/CaseDocs/lnd_in\n", + "```\n", + "
\n", + " \n", + "Submit case:\n", + "```\n", + "./case.submit\n", + "```\n", + "
\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "i.day5.a. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/i.day5.a_pft/lnd/hist\n", + "\n", + "ls \n", + "```\n", + "
\n", + "\n", + "(2) Compare to control run:\n", + "```\n", + "ncdiff i.day5.a_pft.clm2.XXX.nc /glade/derecho/scratch/$USER/archive/i.day5.a/lnd/hist/i.day5.a.clm2.XXX.nc i_diff.nc\n", + "\n", + "ncview i_diff.nc\n", + "```\n", + "\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b3ffd3cc-676e-4e7c-9ff4-cd65d4745397", + "metadata": { + "tags": [] + }, + "source": [ + "## Test your understanding" + ] + }, + { + "cell_type": "markdown", + "id": "03ac3664-5360-45d7-a3ad-0797a839a1d3", + "metadata": {}, + "source": [ + "- How did rholvis change (increase/decrease)? Given this, what do you expect the model response to be?\n", + "- What changes do you see from the control case with the modified rholvis parameter?\n", + "- ... OTHERS? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27f332b2-5799-43a8-9060-50315ebdf6dc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CMIP6 2019.10", + "language": "python", + "name": "cmip6-201910" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/paleo/exercise_1.ipynb b/notebooks_copy/challenge/paleo/exercise_1.ipynb new file mode 100644 index 000000000..cca93f3b8 --- /dev/null +++ b/notebooks_copy/challenge/paleo/exercise_1.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 1: Preindustrial control case\n" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Run a preindustrial control simulation

\n", + " \n", + "Create, configure, build and run a fully coupled preindustrial case called ``b.e21.B1850.f19_g17.piControl.001`` following [CESM naming conventions](https://www.cesm.ucar.edu/models/cesm2/naming-conventions). \n", + "\n", + "Run for 1 year. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**What is the compset for fully coupled preindustrial?**\n", + "\n", + "- ``B1850`` \n", + "\n", + "**What is the resolution for B1850?**\n", + "\n", + "- Use resolution f19_g17 for fast throughput \n", + "\n", + "**Which XML variable should you change to tell the model to run for one year?**\n", + "\n", + "- Use ``STOP_OPTION`` and ``STOP_N`` \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " \n", + "**# Set environment variables** \n", + "\n", + "Set environment variables with the commands:\n", + " \n", + "**For tcsh users** \n", + " \n", + "```\n", + "set CASENAME=b.e21.B1850.f19_g17.piControl.001\n", + "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET=B1850\n", + "set RESOLUTION=f19_g17\n", + "set PROJECT=UESM0013\n", + "```\n", + "\n", + "Note: You should use the project number given for this tutorial.\n", + "\n", + "**For bash users** \n", + " \n", + "```\n", + "export CASENAME=b.e21.B1850.f19_g17.piControl.001\n", + "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "export COMPSET=B1850\n", + "export RESOLUTION=f19_g17\n", + "export PROJECT=UESM0013\n", + "```\n", + "\n", + "Note: You should use the project number given for this tutorial.\n", + "\n", + "**# Make a case directory**\n", + "\n", + "If needed create a directory `cases` into your home directory:\n", + " \n", + "```\n", + "mkdir /glade/u/home/$USER/cases/\n", + "```\n", + " \n", + "\n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET --project $PROJECT\n", + "```\n", + "\n", + "**# Change the job queue**\n", + "\n", + "If needed, change ``job queue``.
\n", + "For instance, to run in the queue ``main``.\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=main\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "Invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "\n", + "**# Set run length**\n", + "\n", + "```\n", + "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", + "```\n", + "\n", + "**# Build and submit**\n", + "\n", + "```\n", + "qcmd -A $PROJECT -- ./case.build\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "**# Check on your run**\n", + "\n", + "\n", + "After submitting the job, use ``qstat -u $USER`` to check the status of your job. \n", + "It may take ~16 minutes to finish the one-year simulation. \n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the history files into the archive directory. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is one-year, there should be 12 monthly files (``h0``) for each model component. \n", + "\n", + "\n", + "Success! Now let's look back into the past... \n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "472131c7-88f9-4863-a2bc-d7364333542d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/paleo/exercise_2.ipynb b/notebooks_copy/challenge/paleo/exercise_2.ipynb new file mode 100644 index 000000000..94549c8a6 --- /dev/null +++ b/notebooks_copy/challenge/paleo/exercise_2.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 2: mid-Holocene case \n", + "\n", + "The Holocene Epoch started ~11,700 before present (11.7 ka BP) and is the current period of geologic time. \n", + "\n", + "Although humans were already well established before the Holocene, this period of time is also referred to as the Anthropocene Epoch because its primary characteristic is the global changes caused by human activity.\n", + "\n", + "The Holocene is an interglacial period, marked by receding ice sheets and rising greenhouse gases that were accompanied by changes in the Earth's orbit around the Sun. \n", + "\n", + "Today, we will use CESM to investigate influence of Holocene orbital forcing on climate. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Run a mid-Holocene simulation with orbital forcing

\n", + " \n", + "Create, configure, build and run a fully coupled mid-Holocene (~6 ka BP) case called ``b.e21.B1850.f19_g17.midHolocene.001`` following [CESM naming conventions](https://www.cesm.ucar.edu/models/cesm2/naming-conventions). \n", + "\n", + "Run for 1 year. \n", + "\n", + "Compare and visualize differences between preindustrial and mid-Holocene runs using NCO and Ncview. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "
\n", + "\n", + "**What is the compset for fully coupled mid-Holocene run?**\n", + "\n", + "- Use ``B1850`` and modify preindustrial orbital configuration (no mid-Holocene compset available) \n", + "\n", + "**What is the resolution for B1850?**\n", + "\n", + "- Use resolution ``f19_g17`` for fast throughput \n", + "\n", + "**What was the orbital configuration 6 ka BP?**\n", + "\n", + "- According to Table 1 of [Otto-Bliesner et al., (2017)](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://gmd.copernicus.org/articles/10/3979/2017/gmd-10-3979-2017.pdf), Eccentricity = 0.018682, Obliquity (degrees) = 24.105, Perihelion = 0.87 (for simplicity, we don't consider the other forcings here, i.e., CO2) \n", + "\n", + "**How to modify orbital configuration in CESM world?**\n", + "\n", + "- Edit ``user_nl_cpl`` \n", + "- orb_mode = 'fixed_parameters' \n", + "- orb_eccen = 0.018682 \n", + "- orb_obliq = 24.105 \n", + "- orb_mvelp = 0.87 \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " \n", + "**# Set environment variables** \n", + "\n", + "Set environment variables with the commands:\n", + " \n", + "**For tcsh users** \n", + " \n", + "```\n", + "set CASENAME=b.e21.B1850.f19_g17.midHolocene.001\n", + "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "set COMPSET=B1850\n", + "set RESOLUTION=f19_g17\n", + "set PROJECT=UESM0013\n", + "```\n", + "\n", + "You should use the project number given for this tutorial.\n", + "\n", + "**For bash users** \n", + " \n", + "```\n", + "export CASENAME=b.e21.B1850.f19_g17.midHolocene.001\n", + "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", + "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", + "export COMPSET=B1850\n", + "export RESOLUTION=f19_g17\n", + "export PROJECT=UESM0013\n", + "```\n", + "\n", + "You should use the project number given for this tutorial.\n", + "\n", + "**# Make a case directory**\n", + "\n", + "If needed create a directory `cases` into your home directory:\n", + " \n", + "```\n", + "mkdir /glade/u/home/$USER/cases/\n", + "```\n", + " \n", + "\n", + "**# Create a new case**\n", + "\n", + "Create a new case with the command ``create_newcase``:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET --project $PROJECT\n", + "```\n", + "\n", + "**# Change the job queue**\n", + "\n", + "If needed, change ``job queue``.
\n", + "For instance, to run in the queue ``main``.\n", + "``` \n", + "cd $CASEDIR\n", + "./xmlchange JOB_QUEUE=main\n", + "```\n", + "This step can be redone at anytime in the process. \n", + "\n", + "**# Setup**\n", + "\n", + "Invoke ``case.setup`` with the command:\n", + "``` \n", + "cd $CASEDIR\n", + "./case.setup \n", + "``` \n", + "\n", + "You build the namelists with the command:\n", + "```\n", + "./preview_namelists\n", + "```\n", + "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", + "```\n", + "$CASEDIR/CaseDocs/atm_in\n", + "```\n", + "\n", + "\n", + "**# Set run length**\n", + "\n", + "```\n", + "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", + "```\n", + "\n", + "\n", + "**# Add the following to user_nl_cpl**\n", + "\n", + "```\n", + "orb_mode = 'fixed_parameters' \n", + " orb_eccen = 0.018682\n", + " orb_obliq = 24.105\n", + " orb_mvelp = 0.87\n", + "```\n", + "\n", + "\n", + "**# Build and submit**\n", + "\n", + "```\n", + "qcmd -A $PROJECT -- ./case.build\n", + "./case.submit\n", + "```\n", + "------------\n", + "\n", + "\n", + "**# Validate your simulation setup**\n", + "\n", + "\n", + "(1) If you want to check the log file, ``cpl.log.xxx``, in the Run Directory (when model is still running) or in your Storage Directory (when the simulation and archiving have finished). \n", + "```\n", + "less /glade/derecho/scratch/$USER/$CASENAME/run/cpl.log.* \n", + "less /glade/derecho/scratch/$USER/archive/$CASENAME/logs/cpl.log.*.gz\n", + "```\n", + "(2) Type ``/orb_params`` to search, you should see the following\n", + "```\n", + " (shr_orb_params) Calculate characteristics of the orbit:\n", + " (shr_orb_params) Calculate orbit for year: -4050\n", + " (shr_orb_params) ------ Computed Orbital Parameters ------\n", + " (shr_orb_params) Eccentricity = 1.868182E-02\n", + " (shr_orb_params) Obliquity (deg) = 2.410538E+01\n", + " (shr_orb_params) Obliquity (rad) = 4.207183E-01\n", + " (shr_orb_params) Long of perh(deg) = 8.696128E-01\n", + " (shr_orb_params) Long of perh(rad) = 3.156770E+00\n", + " (shr_orb_params) Long at v.e.(rad) = -5.751115E-04\n", + "```\n", + "\n", + "**# Check your solution**\n", + "\n", + "When the run is completed, look at the history files into the archive directory. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", + "ls \n", + "```\n", + "\n", + "As your run is one-year, there should be 12 monthly files (``h0``) for each model component. \n", + "\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here to visualize results \n", + "
\n", + "\n", + "**# Use Ncview to visualize solar insolation**\n", + "\n", + "Earth's orbital configuration influences incoming solar insolation.\n", + "Take a look at the ``SOLIN`` CAM variable for August in the pre-industrial and mid-Holocene runs.\n", + "``` \n", + "module load ncview\n", + "cd /glade/derecho/scratch/$USER/archive\n", + "ncview b.e21.B1850.f19_g17.piControl.001/atm/hist/b.e21.B1850.f19_g17.piControl.001.cam.h0.0001-08.nc b.e21.B1850.f19_g17.midHolocene.001/atm/hist/b.e21.B1850.f19_g17.midHolocene.001.cam.h0.0001-08.nc\n", + "```\n", + "\n", + "Using the right arrow button in the Ncview window, you can toggle between pre-industrial and mid-Holocene August ``SOLIN`` and other variables. \n", + "\n", + "\n", + "A few side notes on comparing pre-industrial and mid-Holocene runs: \n", + "- Changes in Earth's orbit alter the length of months or seasons over time, this is referred to as the 'paleo calendar effect' \n", + "- This means that the modern fixed-length definition of months do not apply when the Earth traversed different portions of its orbit \n", + "- Tools exist to adjust monthly CESM output to account for the 'paleo calendar effect' \n", + "- See [Bartlein & Shafer et al. (2019)](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://gmd.copernicus.org/articles/12/3889/2019/gmd-12-3889-2019.pdf) for more information \n", + "- For simplicity, we assume in this exercise that the definition of months is the same for the pre-industrial and mid-Holocene \n", + "\n", + "Now, let's take a look at the differences between the two cases more clearly using NCO. \n", + "\n", + "``` \n", + "module load nco\n", + "cd /glade/derecho/scratch/$USER/archive\n", + "ncdiff b.e21.B1850.f19_g17.piControl.001/atm/hist/b.e21.B1850.f19_g17.midHolocene.001.cam.h0.0001-08.nc b.e21.B1850.f19_g17.piControl.001/atm/hist/b.e21.B1850.f19_g17.piControl.001.cam.h0.0001-08.nc diff_MH-PI_0001-08.nc \n", + "ncview diff_MH-PI_0001-08.nc \n", + "```\n", + "\n", + "Note: Running ncdiff this way will place ``diff_MH-PI_0001-08.nc`` in your archive directory. You may use ``mv`` to move ``diff_MH-PI_0001-08.nc`` to another directory. \n", + "\n", + "**# Questions for reflection:**\n", + "- Which orbital parameters are different at the middle Holocene (6 ka BP)? \n", + "- How does the orbital parameter impact the top-of-atmosphere shortwave radiation (solar insolation) during summertime in the Northern Hemisphere? \n", + "- Do the results look correct? You can compare your results with Figure 3b of [Otto-Bliesner et al., (2017)](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://gmd.copernicus.org/articles/10/3979/2017/gmd-10-3979-2017.pdf) \n", + "- What other aspects of climate are different between the mid-Holocene and pre-industrial runs?", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "472131c7-88f9-4863-a2bc-d7364333542d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/paleo/paleo.ipynb b/notebooks_copy/challenge/paleo/paleo.ipynb new file mode 100644 index 000000000..ec64c4fa4 --- /dev/null +++ b/notebooks_copy/challenge/paleo/paleo.ipynb @@ -0,0 +1,88 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Paleo \n", + "Paleoclimatology is the study of ancient climate variability and change, before the availability of instrumental records. \n", + "\n", + "Paleoclimatology relies on a combination of physical, biological, and chemical proxies of past environmental and climate change, such as glacial ice, tree rings, sediments, corals, and cave mineral deposits. \n", + "\n", + "CESM is widely used for paleoclimate studies. \n", + "\n", + "CESM simulations of past climates are a tool to better understand and interpret proxy reconstructions and to evaluate CESM skill in simulating out-of-sample climate states. \n" + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "## Learning Goals" + ] + }, + { + "cell_type": "markdown", + "id": "346cbd7b-3b8e-41f0-b120-b369ab20f6cc", + "metadata": {}, + "source": [ + "- Student will learn how to modify Earth's orbital configuration in CESM for a simple paleoclimate experiment. \n", + "- Student will learn how to validate that the orbital modification is implemented properly. \n", + "- Student will learn how to quickly compare differences in paleo and preindustrial CESM runs using NCO and Ncview. \n" + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "## Exercise Details" + ] + }, + { + "cell_type": "markdown", + "id": "346cbd7b-3b8e-41f0-b120-b369ab20f6cc", + "metadata": {}, + "source": [ + "- This exercise uses the same code base as the rest of the tutorial. \n", + "- You will be using the B1850 compset at the f19_g17 resolution. \n", + "- You will run a preindustrial control simulation and a simple mid-Holocene simulation. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/pop/pop.ipynb b/notebooks_copy/challenge/pop/pop.ipynb new file mode 100644 index 000000000..65afedd26 --- /dev/null +++ b/notebooks_copy/challenge/pop/pop.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Ocean\n", + "\n", + "The ocean component of CESM is the Parallel Ocean Program (POP). \n", + "\n", + "It can be useful for people interested in ocean science to run simulations with only active sea ice and ocean components and atmospheric forcing. In this exercise, you will learn how to run one of these ice-ocean simulations.\n", + "\n", + "This exercise was created by Gustavo Marques." + ] + }, + { + "cell_type": "markdown", + "id": "45a57a9d-99e1-48c2-a365-b09f3aa40ec0", + "metadata": {}, + "source": [ + "## Learning Goals" + ] + }, + { + "cell_type": "markdown", + "id": "a39c7159-f7ee-4515-920f-68a8d345e392", + "metadata": {}, + "source": [ + "- Student will learn what a G compset is, the types of forcing available to run one, and how to run one.\n", + "- Student will learn how to make a namelist modification that turns off the overflow parameterization and compare results with a control experiment.\n", + "- Student will learn how to make a source code modification that changes zonal wind stress and compare results with a control experiment.\n", + "- Student will learn what a G1850ECO compset is and compare it to the G compset." + ] + }, + { + "cell_type": "markdown", + "id": "6bcc23d6-04c4-49b2-a809-15badc7b5ff9", + "metadata": {}, + "source": [ + "## Exercise Details" + ] + }, + { + "cell_type": "markdown", + "id": "59f7b9fd-7a3d-4b54-b874-61ddc264b102", + "metadata": {}, + "source": [ + "- This exercise uses the same code base as the rest of the tutorial. \n", + "- You will be using the G compset at the T62_g37 resolution.\n", + "- You will run a control simulation and three experimental simulations. Each simulation will be run for one year. \n", + "- You will then use 'ncview' \\([http://meteora.ucsd.edu/~pierce/ncview_home_page.html](http://meteora.ucsd.edu/~pierce/ncview_home_page.html)\\) to evaluate how the experiments differ from the control simulation." + ] + }, + { + "cell_type": "markdown", + "id": "f1ed4850-1e61-4b03-b036-69ecaa06f23f", + "metadata": {}, + "source": [ + "## Useful POP references" + ] + }, + { + "cell_type": "markdown", + "id": "27190b16-2c11-40a1-94fc-09fe0fbb1a57", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM POP User's Guide](https://www.cesm.ucar.edu/models/pop)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9f4fecc3-e03e-4d35-aecb-7daa16a9acb0", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "\n", + "[CESM POP Discussion Forum](https://bb.cgd.ucar.edu/cesm/forums/pop.136/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c082b63d-a408-4b01-8fe8-c446d25a1c91", + "metadata": {}, + "source": [ + "## What is a G case?" + ] + }, + { + "cell_type": "markdown", + "id": "9ad378a2-89e1-4afe-ad88-e0c0759b9864", + "metadata": {}, + "source": [ + "The G compset has active and coupled ocean and sea-ice components. The G compset requires boundary forcing from the atmosphere. The G compset is forced with atmospheric data that does not change interactively as the ocean and sea-ice evolve in time. The land and land ice are not active during a G compset experiment and the runoff is specified. " + ] + }, + { + "cell_type": "markdown", + "id": "3ce9e152-c915-4e18-8199-040a26cf68c5", + "metadata": {}, + "source": [ + "![gcase](../../../images/challenge/gcase.png)\n", + "\n", + "*

Figure: G compset definition.

*" + ] + }, + { + "cell_type": "markdown", + "id": "346ef398-2703-4990-9387-d9006e75c5e6", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Component Set Definitions](https://www2.cesm.ucar.edu/models/cesm2/config/compsets.html)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "cecd306b-bc35-48e2-8b47-fec1362616cc", + "metadata": {}, + "source": [ + "## G Compset forcing data" + ] + }, + { + "cell_type": "markdown", + "id": "b6e0b74a-4578-40b3-8af1-920e6bacffc4", + "metadata": {}, + "source": [ + "There are two types of temporal forcing for G compsets:\n", + "- Normal Year Forcing (NYF) is 12 months of atmospheric data (like a climatology) that repeats every year. NYF is the default forcing.\n", + "- Interannual varying forcing (GIAF) is forcing that varies by year over the time period (1948-2017). \n", + "\n", + "There are two datasets that can be used for G compsets:\n", + "- JRA55-do atmospheric data \\([Tsujino et al. 2018](https://doi.org/10.1016/j.ocemod.2018.07.002)\\)\n", + "- Coordinated Ocean-ice Reference Experiments (CORE) version 2 atmospheric data \\([Large and Yeager 2009](http://doi.org/10.1007/s00382-008-0441-3)\\).\n", + "\n", + "In these exercises we will use the CORE NYF." + ] + }, + { + "cell_type": "markdown", + "id": "e77543f2-6f2a-4d29-8919-827a2d7f96e6", + "metadata": {}, + "source": [ + "## Post processing and viewing your output" + ] + }, + { + "cell_type": "markdown", + "id": "221e2616-682c-44e5-835d-0fce3603555d", + "metadata": {}, + "source": [ + "1) You can create an annual average of the first year's data for each simulationg using the `ncra` (netCDF averager) command from the netCDF operators package \\([NCO](https://nco.sourceforge.net/)\\). \n", + "```\n", + "ncra $OUTPUT_DIR/*.pop.h.0001*nc $CASENAME.pop.h.0001.nc\n", + "```\n", + "\n", + "2) Create a file that contains differences between each of the experiments and the control simulation\n", + "```\n", + "ncdiff $CASENAME.pop.h.0001.nc $CONTROLCASE.pop.h.0001.nc $CASENAME_diff.nc\n", + "```\n", + "\n", + "3) Examine variables within each annual mean and the difference files using `ncview`\n", + "```\n", + "ncview $CASENAME_diff.nc\n", + "```\n", + "\n", + "4) You can also look at other monthly-mean outputs or component log files." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/pop/pop_exercise_1.ipynb b/notebooks_copy/challenge/pop/pop_exercise_1.ipynb new file mode 100644 index 000000000..56334d731 --- /dev/null +++ b/notebooks_copy/challenge/pop/pop_exercise_1.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 1: Control case" + ] + }, + { + "cell_type": "markdown", + "id": "0bdbbd2b-8255-44f3-8c8c-da725d26f845", + "metadata": {}, + "source": [ + "**NOTE:** Building the control case for the POP challenge exercises is idential to building the control case in the CICE challenge exercises. If you have already completed the CICE challenge exercises you can skip this step." + ] + }, + { + "cell_type": "markdown", + "id": "6457c1d2-0530-435d-ae27-d0f1eeabe583", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Run a control case

\n", + " \n", + "Create a case called **g_control** using the compset ``G`` at ``T62_g37`` resolution. \n", + " \n", + "Set the run length to **1 year**. \n", + "\n", + "Build and run the model. Since this is a control case, we want to build it \"out of the box\" without any modifications. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + " \n", + "**How do I compile?**\n", + "\n", + "You can compile with the command:\n", + " \n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "\n", + "**How do I control the output?**\n", + "\n", + "Check the following links:\n", + "\n", + "* https://www2.cesm.ucar.edu/models/cesm1.2/pop2/doc/faq/#output_tavg_add1\n", + "* https://www2.cesm.ucar.edu/models/cesm1.2/pop2/doc/faq/#output_tavg_add2\n", + "\n", + "**How do I check my solution?**\n", + "\n", + "When your run is completed, go to the archive directory. \n", + "\n", + "(1) Check that your archive directory contains files *pop.h.*, *pop.h.nday1*, etc\n", + "\n", + "\n", + "(2) Compare the contents of the ``h`` and ``h.nday1`` files using ``ncdump``.\n", + "\n", + "```\n", + "ncdump -h gpop.pop.h.0001-01-01-00000.nc\n", + "ncdump -h gpop.pop.h.nday1.0001-01-01-00000.nc\n", + "```\n", + "\n", + "(3) Look at the sizes of the files. \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Create a new case g_control with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case /glade/work/$USER/cases/g_control --compset G --res T62_g37 \n", + "```\n", + "
\n", + "\n", + "Case setup:\n", + "``` \n", + "cd ~/cases/g_control \n", + "./case.setup\n", + "```\n", + "
\n", + "\n", + "Change the run length:\n", + "``` \n", + "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", + "```\n", + "
\n", + "\n", + "If needed, change job queue \n", + "and account number. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "
\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "
\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "g_control. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/g_control/ocn/hist\n", + "\n", + "ls \n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dabace0e-c3f2-4c88-b77d-4b28828c0944", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/pop/pop_exercise_2.ipynb b/notebooks_copy/challenge/pop/pop_exercise_2.ipynb new file mode 100644 index 000000000..2acba9fec --- /dev/null +++ b/notebooks_copy/challenge/pop/pop_exercise_2.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 2: Turn off parameterization" + ] + }, + { + "cell_type": "markdown", + "id": "33cdee65-f03f-4c72-adfe-b5ce02416d12", + "metadata": {}, + "source": [ + "Oceanic overflows are dense currents originating in semienclosed basins or continental shelves. They contribute to the formation of abyssal waters and play a crucial role in large-scale ocean circulation. When these dense currents flow down the continental slope, they undergo intense mixing with the surrounding (ambient) ocean waters, causing significant changes in their density and transport (see figure below). However, these mixing processes occur on scales that are smaller than what ocean climate models can accurately capture, leading to poor simulations of deep waters and deep western boundary currents. To improve the representation of overflows some ocean climate models rely on overflow paramterizations, such as the one developed for the POP model (check [this](https://echorock.cgd.ucar.edu/staff/gokhan/OFP_Tech_Note.pdf) report for additional information). \n", + "\n", + "![overflows](../../../images/challenge/overflows.png)\n", + "\n", + "\n", + "*

Figure: Physical processes acting in overflows (from [Legg et al., 2009](https://doi-org.cuucar.idm.oclc.org/10.1175/2008BAMS2667.1))

*" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Turn off overflow parameterization

\n", + " \n", + "Create a case called **g_overflows** by cloning the control experiment case. \n", + " \n", + "Verify that the run length is set to **1 year**. \n", + "\n", + "In user_nl_pop make the following modifications:``overflows_on = .false.`` and ``overflows_interactive = .false.``\n", + "\n", + "Build and run the model for one year. \n", + "\n", + "Compare the simulations using ncview/ncdiff, etc.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + " \n", + "**How do I compile and run?**\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "\n", + "You can run with the command:\n", + "```\n", + "./case.submit\n", + "```\n", + " \n", + "**How do I check the lenght of the run?**\n", + "\n", + "Use ```xmlquery``` to search for the variables that control the run length\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Clone a new case g_overflows from your control experiment with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_clone --case /glade/work/$USER/cases/g_overflows --clone /glade/work/$USER/cases/g_control\n", + "```\n", + "\n", + "Case setup:\n", + "``` \n", + "cd /glade/work/$USER/cases/g_overflows\n", + "./case.setup\n", + "```\n", + "\n", + "Verify that the run length is 1 year:\n", + "``` \n", + "./xmlquery STOP_N\n", + "./xmlquery STOP_OPTION\n", + "```\n", + " \n", + "Edit the file user_nl_pop and add the lines:\n", + "```\n", + " overflows_on = .false.\n", + " overflows_interactive = .false.\n", + "```\n", + "\n", + "If needed, change job queue \n", + "and account number. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "g_overflows. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/g_overflows/ocn/hist\n", + "ls \n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f19ab341-b76b-462b-9bc9-49d4793ed409", + "metadata": {}, + "source": [ + "## Test your understanding" + ] + }, + { + "cell_type": "markdown", + "id": "31d67bb4-3e04-459e-a6ac-866ee9224776", + "metadata": {}, + "source": [ + "- What variables do you expect to change when you turn off the overflow parameterization?\n", + "- What variables show a difference between this experiment and the control difference? How different are they?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/pop/pop_exercise_3.ipynb b/notebooks_copy/challenge/pop/pop_exercise_3.ipynb new file mode 100644 index 000000000..b71259f2d --- /dev/null +++ b/notebooks_copy/challenge/pop/pop_exercise_3.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 3: Modify wind stress" + ] + }, + { + "cell_type": "markdown", + "id": "33cdee65-f03f-4c72-adfe-b5ce02416d12", + "metadata": {}, + "source": [ + "Wind stress plays a critical role in driving ocean currents and is a key factor in shaping the overall patterns of large-scale ocean circulation and, consequentialy, the climate. Further details on how wind stress affects the ocean circulation are discussed in [this](https://doi-org.cuucar.idm.oclc.org/10.1006/rwos.2001.0110) manuscript." + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Increase zonal wind stress

\n", + " \n", + "Create a case called **g_windstress** by cloning the control experiment case. \n", + " \n", + "Verify that the run length is set to **1 year**. \n", + "\n", + "Modify the subroutine rotate_wind_stress in forcing_coupled.F90 to increase the first (x) component of the wind stress by 25%.\n", + "\n", + "Build and run the model for one year. \n", + "\n", + "Compare the simulations using ncview/ncdiff, etc.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + " \n", + "**How do I compile and run?**\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "\n", + "You can run with the command:\n", + "```\n", + "./case.submit\n", + "```\n", + " \n", + "**How do I check the lenght of the run?**\n", + "\n", + "Use ```xmlquery``` to search for the variables that control the run length\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Clone a new case g_windstress from your control experiment with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_clone --case /glade/work/$USER/cases/g_windstress --clone /glade/work/$USER/cases/g_control\n", + "```\n", + "\n", + "Case setup:\n", + "``` \n", + "cd /glade/work/$USER/cases/g_windstress\n", + "./case.setup\n", + "```\n", + "\n", + "Verify that the run length is 1 year:\n", + "``` \n", + "./xmlquery STOP_N\n", + "./xmlquery STOP_OPTION\n", + "```\n", + " \n", + "Copy the forcing_coupled.F90 file from the control case to the ocean SourceMods.\n", + "``` \n", + "cp /glade/work/$USER/code/my_cesm_code/components/pop/source/forcing_coupled.F90 /glade/work/$USER/cases/g_windstress/SourceMods/src.pop\n", + "``` \n", + " \n", + "Edit the file forcing_coupled.F90 in the rotate_wind_stress routine after ```SMFT(:,:,1,:)``` is defined:\n", + " \n", + "```\n", + " SMFT(:,:,1,:) = SMFT(:,:,1,:) * 1.25\n", + "```\n", + "\n", + "If needed, change job queue \n", + "and account number. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "g_windstress. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$user/archive/g_windstress/ocn/hist\n", + "ls \n", + "```\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "286e2e7f-ccea-4c5e-acc5-5f9867341102", + "metadata": {}, + "source": [ + "## Test your understanding" + ] + }, + { + "cell_type": "markdown", + "id": "63f2688d-9857-4a49-93bf-2b3117ec0d13", + "metadata": {}, + "source": [ + "- What are the impacts of increased zonal wind stress? \n", + "- Where do you thinkt he impacts would be largest in the ocean?\n", + "- How do you think the changes would compare if you increased meridional wind stress?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/challenge/pop/pop_exercise_4.ipynb b/notebooks_copy/challenge/pop/pop_exercise_4.ipynb new file mode 100644 index 000000000..cd2bc2a6f --- /dev/null +++ b/notebooks_copy/challenge/pop/pop_exercise_4.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# 4: Turn on the ecosystem" + ] + }, + { + "cell_type": "markdown", + "id": "72423b27-32ee-492a-a023-ffd418e2d6ea", + "metadata": {}, + "source": [ + "You can also explore setting up a similar case but using the ``G1850ECO`` component set. Note how this differs from the previous ``G`` component set we used in Exercise 1. " + ] + }, + { + "cell_type": "markdown", + "id": "8f13d092-c9d8-4e47-93b2-caf3cb8335d6", + "metadata": {}, + "source": [ + "![gcase](../../../images/challenge/gecocase.png)\n", + "\n", + "*

Figure: G1850ECO compset definition.

*" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "
\n", + "Exercise: Run a control case

\n", + " \n", + "Create a case called **g_eco1850** using the compset ``G1850ECO`` at ``T62_g37`` resolution. \n", + " \n", + "Set the run length to **1 year**. \n", + "\n", + "Build and run the model. Since this is a control case, we want to build it \"out of the box\" without any modifications. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + " \n", + "**How do I compile and run?**\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "\n", + "You can run with the command:\n", + "```\n", + "./case.submit\n", + "```\n", + " \n", + "**How do I check the lenght of the run?**\n", + "\n", + "Use ```xmlquery``` to search for the variables that control the run length\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f639e182-f48a-431c-a594-9c34323417eb", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + " \n", + "Create a new case G1850ECO with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case /glade/work/$USER/cases/G1850ECO --compset G1850ECO --res T62_g37\n", + "```\n", + "\n", + "Case setup:\n", + "``` \n", + "cd /glade/work/$USER/cases/G1850ECO \n", + "./case.setup\n", + "```\n", + " \n", + "Change the run length:\n", + "``` \n", + "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", + "```\n", + "\n", + "If needed, change job queue \n", + "and account number. \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "G1850ECO. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/G1850ECO/ocn/hist\n", + "ls \n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dce7f4af-243c-47fd-b4d6-c37832aa80fd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/additional/additional.ipynb b/notebooks_copy/diagnostics/additional/additional.ipynb new file mode 100644 index 000000000..6c1317db0 --- /dev/null +++ b/notebooks_copy/diagnostics/additional/additional.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Additional Topics" + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "This section provides other information about how to use CESM output including:\n", + "- The difference between timeseries and history files\n", + "- The Climate Variabilty and Diagnostics Package (CVDP) (**In progress**)\n", + "- Links to different analysis tools and resources used by CESM developers and users" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/additional/adf.ipynb b/notebooks_copy/diagnostics/additional/adf.ipynb new file mode 100644 index 000000000..8056d9f6d --- /dev/null +++ b/notebooks_copy/diagnostics/additional/adf.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# ADF" + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "## Learning Goals\n", + "\n", + "- Enter learning goals here." + ] + }, + { + "cell_type": "markdown", + "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "## Subsection 1\n", + "\n", + "Info here" + ] + }, + { + "cell_type": "markdown", + "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "## Subsection 2\n", + "\n", + "Info here\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/additional/analysis_tools.ipynb b/notebooks_copy/diagnostics/additional/analysis_tools.ipynb new file mode 100644 index 000000000..0e551fc31 --- /dev/null +++ b/notebooks_copy/diagnostics/additional/analysis_tools.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# CESM analysis tools" + ] + }, + { + "cell_type": "markdown", + "id": "55b5588e-6e74-4bd7-a278-877611c4e87b", + "metadata": {}, + "source": [ + "We have provided some information about tools the CESM users and developers use for analysis of model simulations below. This list is not comprehensive and is intended to provide you with information to start your searches." + ] + }, + { + "cell_type": "markdown", + "id": "6a4f8751-c312-49b5-a578-604b7f39099a", + "metadata": {}, + "source": [ + "## Analysis Software" + ] + }, + { + "cell_type": "markdown", + "id": "d31bdb0d-5afe-4b38-b304-34c3179ac6dc", + "metadata": {}, + "source": [ + "Many data analysis and visualization software packages are freely available for use on CISL-managed resources. These packages include some developed and supported by NCAR and CISL. Some of these resources are open source while others require licences.\n", + "\n", + "Some of these packages include:\n", + "- Numerous python packages\n", + "- Interactive Data Language (IDL) \n", + "- MATLAB\n", + "- NCAR Command Language (NCL)" + ] + }, + { + "cell_type": "markdown", + "id": "5bd4569e-601e-47d9-ad24-ae2da7087b7e", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Data Analysis Website](https://arc.ucar.edu/knowledge_base/70550011)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "cc1d09f3-dc55-46b2-912e-deee2147e45d", + "metadata": {}, + "source": [ + "## Python" + ] + }, + { + "cell_type": "markdown", + "id": "0275672f-9536-4bb6-bfff-4a2eb9bc3630", + "metadata": {}, + "source": [ + "Python is an open source, general-purpose programming language. \n", + "\n", + "Python is known for:\n", + "- having a wide range of applications and packages available. There is a huge user base and rougly ~1 gazillion online tutorials. \n", + "- active development in packages related to the geosciences.\n", + "\n", + "Python is becoming the dominant language for CESM developers and users, so most of the active development of tools for the CESM project at large are done in this language. We provide more detailed information below about some of the tools available for python users on NCAR computing assets." + ] + }, + { + "cell_type": "markdown", + "id": "8ce79e09-e9e2-4bf0-ad12-4aede3b2d072", + "metadata": {}, + "source": [ + "### Jupyter Hub" + ] + }, + { + "cell_type": "markdown", + "id": "f82c8f41-42a1-40ce-86bc-5138a9940d08", + "metadata": {}, + "source": [ + "The JupyterHub deployment that CISL manages allows \"push-button\" access to NCAR's supercomputing resource cluster of nodes used for data analysis and visualization, machine learning, and deep learning.\n", + "\n", + "JupyterHub gives users the ability to create, save, and share Jupyter Notebooks through the JupyterLab interface and to run interactive, web-based analysis, visualization and compute jobs on derecho and casper.\n", + "\n", + "Information about getting started with JupyterHub on NCAR computing resources, environments, and documentation is avaiable at the website below." + ] + }, + { + "cell_type": "markdown", + "id": "4b1cc783-53e5-45db-8954-385863b1a778", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Jupyter Hub Website](https://arc.ucar.edu/knowledge_base/70549913)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "71c323bc-a8e3-406d-a08b-92030f436863", + "metadata": {}, + "source": [ + "### Earth System Data Science initiative (ESDS)" + ] + }, + { + "cell_type": "markdown", + "id": "2d21da94-244c-42c0-970f-8daec7bacf61", + "metadata": {}, + "source": [ + "ESDS is an NCAR initiative that seeks to foster a collaborative, open, inclusive community for Earth Science data analysis. ESDS promotes deeper collaboration centered on analytics, improving our capacity to deliver impactful, actionable, reproducible science and serve the university community by transforming how geoscientists synthesize and extract information from large, diverse data sets.\n", + "\n", + "More information, including FAQs and a blog with examples can be found at the website below. " + ] + }, + { + "cell_type": "markdown", + "id": "a6813683-9206-492d-b477-2aee1abe4f17", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[ESDS Website](https://ncar.github.io/esds/about/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f4055e68-100a-4ae7-8ee3-47cefcd43d73", + "metadata": {}, + "source": [ + "### Project Pythia" + ] + }, + { + "cell_type": "markdown", + "id": "f25bcd2d-a7d5-46fe-b5fb-05c14eb6a19b", + "metadata": {}, + "source": [ + "If you are new to Python and its application to the geosciences, then starting with Project Pythia is a good first step. Project Pythia is the education working group for Pangeo and is an educational resource for the entire geoscience community. Together these initiatives are helping geoscientists make sense of huge volumes of numerical scientific data using tools that facilitate open, reproducible science, and building an inclusive community of practice around these goals." + ] + }, + { + "cell_type": "markdown", + "id": "552bc788-8a39-4ce9-b7ab-8d8f283614e7", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Project Pythia Website](https://projectpythia.org/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "efe8a781-b9e6-4ad2-a590-75ceab387fb4", + "metadata": {}, + "source": [ + "### GeoCAT" + ] + }, + { + "cell_type": "markdown", + "id": "4583d068-c9c4-44ed-8ca3-67352c4fd414", + "metadata": {}, + "source": [ + "The Geoscience Community Analysis Toolkit (GeoCAT) is a software engineering effort at NCAR. GeoCAT aims to create scalable data analysis and visualization tools for Earth System Science data to serve the geosciences community in the scientific Python ecosystem. GeoCAT tools are built upon the cornerstone technologies in the Pangeo stack such as Xarray, Dask, and Jupyter Notebooks. In addition, some of the functionalities in the GeoCAT stack are inspired/reimplemented from NCL." + ] + }, + { + "cell_type": "markdown", + "id": "207a8b16-9f0b-447f-b027-1a47ba747d52", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[GeoCAT Website](https://geocat.ucar.edu/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "56d3130e-8f95-4a5e-a797-0b33d538141a", + "metadata": {}, + "source": [ + "### MetPy" + ] + }, + { + "cell_type": "markdown", + "id": "76b9dc6d-74d0-4444-96d4-e60db58f8257", + "metadata": {}, + "source": [ + "MetPy is a collection of tools in Python for reading, visualizing, and performing calculations with weather data. The website below has information about getting started as well as examples and a reference guide." + ] + }, + { + "cell_type": "markdown", + "id": "53f2c96c-f36a-4080-99b9-e7e2fb1d899d", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[MetPy Website](https://unidata.github.io/MetPy/latest/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "7787ed12-bce7-4e49-ac3f-2229de327823", + "metadata": {}, + "source": [ + "## NCAR Command Language (NCL)" + ] + }, + { + "cell_type": "markdown", + "id": "c83303a5-24e5-4758-aeed-0cf3672c665e", + "metadata": {}, + "source": [ + "NCL is an open source tool developed at NCAR that is free to download and use. It can be run at the command line in interactive mode or as a batch mode. While once a widely used language for CESM developers and users, NCL is now in a maintenence stage and is no longer in development. Much of the active development is now being done with python tools.\n", + "\n", + "NCL is known for:\n", + "- easy input/output use with netCDF, Grib, Grib2, shapefiles, ascii, and binary files. \n", + "- good graphics that are very flexible.\n", + "- functions tailored to the geosciences community.\n", + "- a central website with 1000+ examples. There are also mini language and processing manuals." + ] + }, + { + "cell_type": "markdown", + "id": "7b185d5f-dcdb-4275-99b4-67bf6e5dcc2b", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[NCL Website](https://www.ncl.ucar.edu/get_started.shtml)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "7e2bcaf7-f5d0-4d20-b2ed-840841a02972", + "metadata": {}, + "source": [ + "## Panopoly" + ] + }, + { + "cell_type": "markdown", + "id": "3f774a9f-c4c0-4411-97df-99afe765259f", + "metadata": {}, + "source": [ + "Panopoly is a graphic user interface (GUI) application that allows the user to quickly view data in a number of file formats. Panopoly is similar to ncview, but it's more powerful. Panopoly works with files in netCDF, HDF, or GRIB format (among others). It also allows the user to perform simple calculations, apply masks, and quickly create spatial or line plots.\n", + "\n", + "The Panopoly website provies more documentation, including How-To's and demonstration videos." + ] + }, + { + "cell_type": "markdown", + "id": "483efa17-9787-4bb4-8348-2d2ebadf1dbd", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Panopoly Website](http://www.giss.nasa.gov/tools/panoply/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "124c346f-9a8a-4589-b74a-e04194a3e473", + "metadata": {}, + "source": [ + "## Image Magick" + ] + }, + { + "cell_type": "markdown", + "id": "38488672-40e0-4383-b00c-e3128cdc5304", + "metadata": {}, + "source": [ + "ImageMagick is a free suite of software that that can be used to display, manipulate, or compare images. It works with a wide range of file types (ps, pdf, png, gif, jpg, etc.). It can also be used to create movies. You can also alter an image at the command line. There are many options available when converting images, and more information can be found at the website below." + ] + }, + { + "cell_type": "markdown", + "id": "23b653c8-95d6-4a28-a2cc-4d6656c3ceec", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Image Magick Website](https://imagemagick.org/index.php)\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/additional/cvdp.ipynb b/notebooks_copy/diagnostics/additional/cvdp.ipynb new file mode 100644 index 000000000..13e608e76 --- /dev/null +++ b/notebooks_copy/diagnostics/additional/cvdp.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# CVDP" + ] + }, + { + "cell_type": "markdown", + "id": "8f46aef7-947e-498e-90b1-a4ed6b077f6d", + "metadata": {}, + "source": [ + "The climate variability and diagnostics package (CVDP) was developed by NCAR scientists to document the major modes of climate variability in models and observations.\n", + "\n", + "More info here:\n", + "https://www.cesm.ucar.edu/projects/cvdp\n", + "\n", + "If you use CVDP results in oral or written form, please cite the following paper:\n", + "https://doi.org/10.1002/2014EO490002\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f5d5800f-8a0c-4cdf-930b-48be1cb40796", + "metadata": {}, + "source": [ + "**Additional documentation in progress.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "def586e4-6553-48b9-b05c-2308dad9181c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/additional/large_ensembles.ipynb b/notebooks_copy/diagnostics/additional/large_ensembles.ipynb new file mode 100644 index 000000000..6b12beb9b --- /dev/null +++ b/notebooks_copy/diagnostics/additional/large_ensembles.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Large Ensembles" + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "## Learning Goals\n", + "\n", + "- Enter learning goals here." + ] + }, + { + "cell_type": "markdown", + "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "## Subsection 1\n", + "\n", + "Info here" + ] + }, + { + "cell_type": "markdown", + "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "## Subsection 2\n", + "\n", + "Info here\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/additional/postprocessing.ipynb b/notebooks_copy/diagnostics/additional/postprocessing.ipynb new file mode 100644 index 000000000..ff5b3e1b1 --- /dev/null +++ b/notebooks_copy/diagnostics/additional/postprocessing.ipynb @@ -0,0 +1,93 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Postprocessing data" + ] + }, + { + "cell_type": "markdown", + "id": "5bd5c142-f778-4570-8edc-cc760139f30e", + "metadata": {}, + "source": [ + "A wide range of tools exist for postprocessing and analyzing data with techniques and methods exist. One of the first things you have to decide is how to store your files.\n", + "\n", + "In the diagnostics notebooks we have have provided examples of how to use both history files and timeseries files, described below." + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "## History vs. Timeseries files\n", + "\n", + "When you run the CESM model the default output is history files, or files for a single timestep that include all variables for a given component and time frequency. However, most CESM community experiment data will be provided as timeseries files, or files that are a single variable over many timesteps. It is important you understand how to use both types of files, and for you to know that for some tasks (e.g. debugging) you should be using history files instead of timeseries files. However, it is much more efficient to store timeseries files because the overall size is smaller once the files have been processed into timeseries format.\n", + "\n", + "CESM does not currently have a supported tool for the community to create timeseries files. We can recommend you investigate using [NCO tools](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#netcdf-operators-nco) in coordination with scripts. We hope to have a better way to create timeseries files by community members soon.\n" + ] + }, + { + "cell_type": "markdown", + "id": "784300ed-3d93-4365-8776-adcce4d4eb1f", + "metadata": {}, + "source": [ + "Until then, we'll give you a sheepish grin." + ] + }, + { + "cell_type": "markdown", + "id": "74d0734c-6b19-4c82-96cc-670dcbbea861", + "metadata": {}, + "source": [ + "![sheep](../../../images/diagnostics/file_types/sheepish1.png)\n", + "\n", + "*

Figure: Baaaa.

*" + ] + }, + { + "cell_type": "markdown", + "id": "1775714f-10bd-4e6f-aa07-1c5b2325584a", + "metadata": {}, + "source": [ + "![sheep](../../../images/diagnostics/file_types/sheepish2.png)\n", + "\n", + "*

Figure: Baaaa.

*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/additional/uxarray.ipynb b/notebooks_copy/diagnostics/additional/uxarray.ipynb new file mode 100644 index 000000000..d98462609 --- /dev/null +++ b/notebooks_copy/diagnostics/additional/uxarray.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# uxarray" + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "## Learning Goals\n", + "\n", + "- Enter learning goals here." + ] + }, + { + "cell_type": "markdown", + "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "## Subsection 1\n", + "\n", + "Info here" + ] + }, + { + "cell_type": "markdown", + "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "## Subsection 2\n", + "\n", + "Info here\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/cam/advanced_cam.ipynb b/notebooks_copy/diagnostics/cam/advanced_cam.ipynb new file mode 100644 index 000000000..9838c6ed7 --- /dev/null +++ b/notebooks_copy/diagnostics/cam/advanced_cam.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced Plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023b`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023b`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_______________\n", + "This activity was developed primarily by Cecile Hannay and Jesse Nusbaumer." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "## Exercise 1: CAM-SE output analysis\n", + "\n", + "Examples of simple analysis and plotting that can be done with CAM-SE output on the native cubed-sphere grid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import xarray as xr\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def make_map(data, lon, lat,):\n", + " \"\"\"This function plots data on a Mollweide projection map.\n", + "\n", + " The data is transformed to the projection using Cartopy's `transform_points` method.\n", + "\n", + " The plot is made by triangulation of the points, producing output very similar to `pcolormesh`,\n", + " but with triangles instead of rectangles used to make the image.\n", + " \"\"\"\n", + " dataproj = ccrs.PlateCarree() # assumes data is lat/lon\n", + " plotproj = ccrs.Mollweide() # output projection \n", + " # set up figure / axes object, set to be global, add coastlines\n", + " fig, ax = plt.subplots(figsize=(6,3), subplot_kw={'projection':plotproj})\n", + " ax.set_global()\n", + " ax.coastlines(linewidth=0.2)\n", + " # this figures out the transformation between (lon,lat) and the specified projection\n", + " tcoords = plotproj.transform_points(dataproj, lon.values, lat.values) # working with the projection\n", + " xi=tcoords[:,0] != np.inf # there can be bad points set to infinity, but we'll ignore them\n", + " assert xi.shape[0] == tcoords.shape[0], f\"Something wrong with shapes should be the same: {xi.shape = }, {tcoords.shape = }\"\n", + " tc=tcoords[xi,:]\n", + " datai=data.values[xi] # convert to numpy array, then subset\n", + " # Use tripcolor --> triangluates the data to make the plot\n", + " # rasterized=True reduces the file size (necessary for high-resolution for reasonable file size)\n", + " # keep output as \"img\" to make specifying colorbar easy\n", + " img = ax.tripcolor(tc[:,0],tc[:,1], datai, shading='gouraud', rasterized=True)\n", + " cbar = fig.colorbar(img, ax=ax, shrink=0.4)\n", + " return fig, ax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Input data\n", + "\n", + "In the following cell, specify the data source.\n", + "\n", + "`location_of_hfiles` is a path object that points to the directory where data files should be.\n", + "`search_pattern` specifies what pattern to look for inside that directory.\n", + "\n", + "**SIMPLIFICATION** If you want to just provide a path to a file, simply specify it by commenting (with `#`) the lines above \"# WE need lat and lon\", and replace with:\n", + "```\n", + "fil = \"/path/to/your/data/file.nc\"\n", + "ds = xr.open_dataset(fil)\n", + "```\n", + "\n", + "## Parameters\n", + "Specify the name of the variable to be analyzed with `variable_name`.\n", + "\n", + "To change the units of the variable, specify `scale_factor` and provide the new units string as `units`. Otherwise, just set `scale_factor` and `units`:\n", + "\n", + "```\n", + "scale_factor = 1\n", + "units = ds[\"variable_name\"].attrs[\"units\"]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "location_of_hfiles = Path(\"/glade/campaign/cesm/tutorial/tutorial_2023_archive/cam-se/\")\n", + "search_pattern = \"f.cam6_3_112.FMTHIST_v0c.ne30.non-ogw-ubcT-effgw0.7_taubgnd2.5.001.cam.h3.2003-01-01-00000.nc\"\n", + "\n", + "fils = sorted(location_of_hfiles.glob(search_pattern))\n", + "if len(fils) == 1:\n", + " ds = xr.open_dataset(fils[0])\n", + "else:\n", + " print(f\"Just so you konw, there are {len(fils)} files about to be loaded.\")\n", + " ds = xr.open_mfdataset(fils)\n", + "\n", + "# We need lat and lon:\n", + "lat = ds['lat']\n", + "lon = ds['lon']\n", + "\n", + "# Choose what variables to plot,\n", + "# in this example we are going to combine the\n", + "# convective and stratiform precipitation into\n", + "# a single, total precipitation variable\n", + "convective_precip_name = \"PRECC\"\n", + "stratiform_precip_name = \"PRECL\"\n", + "\n", + "# If needed, select scale factor and new units:\n", + "scale_factor = 86400. * 1000. # m/s -> mm/day\n", + "units = \"mm/day\"\n", + "\n", + "cp_data = scale_factor * ds[convective_precip_name]\n", + "st_data = scale_factor * ds[stratiform_precip_name]\n", + "cp_data.attrs['units'] = units\n", + "st_data.attrs['units'] = units\n", + "\n", + "# Sum the two precip variables to get total precip\n", + "data = cp_data + st_data\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# temporal averaging\n", + "# simplest case, just average over time:\n", + "data_avg = data.mean(dim='time')\n", + "data_avg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Global average\n", + "#\n", + "data_global_average = data_avg.weighted(ds['area']).mean()\n", + "print(f\"The area-weighted average of the time-mean data is: {data_global_average.item()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Regional average using a (logical) rectangle\n", + "#\n", + "west_lon = 110.0\n", + "east_lon = 200.0\n", + "south_lat = -30.0\n", + "north_lat = 30.0\n", + "\n", + "# To reduce to the region, we need to know which indices of ncol dimension are inside the boundary\n", + "\n", + "region_inds = np.argwhere(((lat > south_lat)&(lat < north_lat)&(lon>west_lon)&(lon \n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/advanced_plot_1.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks_copy/diagnostics/cam/basics_cam.ipynb b/notebooks_copy/diagnostics/cam/basics_cam.ipynb new file mode 100644 index 000000000..8b0dd52e5 --- /dev/null +++ b/notebooks_copy/diagnostics/cam/basics_cam.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "33070195-2d5f-4ee3-b6cb-a658209b1f4d", + "metadata": {}, + "source": [ + "# Basic Plotting\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f2a265a-5258-47df-9da2-86afb69cf660", + "metadata": {}, + "source": [ + "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023b`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023b`." + ] + }, + { + "cell_type": "markdown", + "id": "4ef5d835-37b7-4a4a-84c1-8053093f1500", + "metadata": {}, + "source": [ + "_______________\n", + "This activity was developed primarily by Cecile Hannay and Jesse Nusbaumer." + ] + }, + { + "cell_type": "markdown", + "id": "b35face4-1542-4ab0-8f04-8220a00b0086", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "For the atmospheric data, we will look at common variables in the atmospheric diagnostics. This notebook covers 3 basic plotting examples:\n", + "\n", + "Exercise 1: Global lat/lon of surface temperature\n", + "\n", + "Exercise 2: Zonal mean of short wave cloud forcing\n", + "\n", + "Exercise 3: Temperature zonal mean with vertical levels\n", + "\n", + "Some of the plotting in these examples are based on the AMWG Diagnostics Framework (ADF) and some are natively from the `xarray` functionality. `xarray` will be used for the data I/O, analysis, and some plotting, `matplotlib` and `cartopy` will aid in plotting, and `numpy` for calculations" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "72e6f251-c6ff-4478-adab-783ac1795e34", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import cftime\n", + "import matplotlib as mpl\n", + "import matplotlib.path as mpath\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "from matplotlib.gridspec import GridSpec\n", + "from matplotlib.lines import Line2D\n", + "from mpl_toolkits.axes_grid1 import make_axes_locatable" + ] + }, + { + "cell_type": "markdown", + "id": "3d56ba02-c652-4a97-a466-d3633e53aeb1", + "metadata": {}, + "source": [ + "The first step is to grab an atmosphere (CAM) history file from your CESM model run" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "20d4eec8-5332-44a5-bd5c-910048c5d95f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b.day2.1/atm/hist/b.day2.1.cam.h0.0003-07.nc'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set your username here:\n", + "username = \"PUT_USER_NAME_HERE\"\n", + "\n", + "# Here we point to the archive directory from your b.day2.1 simulation\n", + "monthly_output_path = f\"/glade/derecho/scratch/{username}/archive/b.day2.1/atm/hist\"\n", + "\n", + "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", + "# this provided simulation data instead:\n", + "#monthly_output_path = \"/glade/campaign/cesm/tutorial/tutorial_2023_archive/b.day2.1/atm/hist\"\n", + "\n", + "# Name of history file to plot\n", + "file_name = \"b.day2.1.cam.h0.0003-07.nc\"\n", + "\n", + "files = os.path.join(monthly_output_path, file_name)\n", + "files" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fa978aa8-bf89-4971-a59b-ae2fafb9e0a5", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b.day2.1/atm/hist/b.day2.1.cam.h0.0003-07.nc'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n", + "\u001b[0;31mKeyError\u001b[0m: [, ('/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b.day2.1/atm/hist/b.day2.1.cam.h0.0003-07.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'da467520-dd8b-4048-a691-3445d0f0f93e']", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m ds\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/api.py:566\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 554\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m 555\u001b[0m decode_cf,\n\u001b[1;32m 556\u001b[0m open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 562\u001b[0m decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m 563\u001b[0m )\n\u001b[1;32m 565\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 566\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 567\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 568\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 569\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 570\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 571\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 572\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m 573\u001b[0m backend_ds,\n\u001b[1;32m 574\u001b[0m filename_or_obj,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 585\u001b[0m )\n\u001b[1;32m 586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:590\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m( \u001b[38;5;66;03m# type: ignore[override] # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 571\u001b[0m filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 587\u001b[0m autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 588\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m 589\u001b[0m filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 590\u001b[0m store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 592\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 594\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 595\u001b[0m \u001b[43m \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 596\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 597\u001b[0m \u001b[43m \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 598\u001b[0m \u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 599\u001b[0m \u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 600\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 602\u001b[0m store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m 603\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:391\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 385\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m 386\u001b[0m clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m 387\u001b[0m )\n\u001b[1;32m 388\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m 389\u001b[0m netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m 390\u001b[0m )\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:338\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 338\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m 340\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:400\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 400\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:394\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m 395\u001b[0m ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/contextlib.py:135\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 198\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m file\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 215\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 216\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 219\u001b[0m \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2464\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2027\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b.day2.1/atm/hist/b.day2.1.cam.h0.0003-07.nc'" + ] + } + ], + "source": [ + "ds = xr.open_dataset(files)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "39d93758-aedb-42ab-810c-257a37dae487", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 1: Make a lat-lon plot of TS\n", + "\n", + "To highlight plotting the variables from the CESM atmosphere (CAM) file, the first example will plot a simple global lat/lon plot of surface temperature `TS`" + ] + }, + { + "cell_type": "markdown", + "id": "1e6b83ad", + "metadata": {}, + "source": [ + "### Grab data from first time stamp\n", + "\n", + "NOTE: This dataset has only one time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "feb6d347-cbcb-407c-bb4d-6d89c69fcf32", + "metadata": {}, + "outputs": [], + "source": [ + "ts_0 = ds.TS.sel({\"time\": ds.TS.time.values[0]}).squeeze()\n", + "ts_0" + ] + }, + { + "cell_type": "markdown", + "id": "cf1a965c", + "metadata": {}, + "source": [ + "The next step is to set up the map. Since we are plotting a global lat/lon, we will use the \"Plate Carree\" projection. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b183aba-6eae-48ad-9545-f3c918fb99ff", + "metadata": {}, + "outputs": [], + "source": [ + "# define the colormap\n", + "cmap = mpl.colormaps[\"jet\"]\n", + "\n", + "# set up the figure with a Plate Carree projection\n", + "fig = plt.figure(figsize=(15, 10))\n", + "\n", + "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + "\n", + "# Plot the first timeslice of TS\n", + "img = ax.pcolormesh(ds.lon, ds.lat, ts_0, cmap=cmap, transform=ccrs.PlateCarree())\n", + "\n", + "plt.title(\"Surface Temperature\", fontsize=20)\n", + "\n", + "# Set up colorbar\n", + "plt.colorbar(img, orientation=\"vertical\", fraction=0.0242, pad=0.01)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ccd1f942-a16d-490b-98e6-5f57e92a1cce", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/basics_plot_1.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1bedaea9-73cf-4011-b928-e8c2de752737", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "The colorbar limits are set automatically by `pcolormesh`. How could you change the arguments for the `pcolormesh` function to set the plotting limits?" + ] + }, + { + "cell_type": "markdown", + "id": "e18f7699-44a7-46f9-9503-1d97fb9418f7", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "Choose a maximum temperature of 300K and minimum temperature of 225K.\n", + " \n", + "```python\n", + "img = ax.pcolormesh(ds.lon, ds.lat, ts_0, vmax=300, vmin=225, cmap=cmap, transform=ccrs.PlateCarree())\n", + "\n", + "```\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "20dc7be8", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "How could we change the central longitude?" + ] + }, + { + "cell_type": "markdown", + "id": "26a14d27-a027-4774-87bf-63712f1f88b6", + "metadata": {}, + "source": [ + "\n", + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "The default central longitude is 0. Try setting it to 180. Then try other values from 0-360.\n", + "\n", + "```python\n", + "ax = fig.add_subplot(1,1,1, projection=ccrs.PlateCarree(central_longitude=180))\n", + "```\n", + " \n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "c632aed7", + "metadata": {}, + "source": [ + "A second quick example is for `xarray`'s built-in plotting which uses the `matplotlib` and `cartopy` in the background. `xarray` makes creating a basic plot fairly simple." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f48694b-1eae-4b88-87b3-9aec97ed7ffe", + "metadata": {}, + "outputs": [], + "source": [ + "# Xarray native plotting\n", + "\n", + "# Set up figure and axis\n", + "fig, ax = plt.subplots(1, figsize=(20, 10))\n", + "\n", + "# Plot the data straight from the xarray dataset\n", + "ts_0.plot.contourf(cmap=\"jet\", levels=np.arange(220, 321, 5))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c47469f3-3b57-41c9-9bb2-f56a295bf027", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/basics_plot_2.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8d12294c-691b-491f-bf88-be4c267a5c73", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "42b295a0-4675-4d4e-814f-5aac53b90973", + "metadata": {}, + "source": [ + "## Exercise 2: Zonal plot of SWCF\n", + "\n", + "The second example will plot the short wave cloud forcing (`SWCF`) zonally." + ] + }, + { + "cell_type": "markdown", + "id": "765089ca", + "metadata": {}, + "source": [ + "Grab the variable data and mean over the the lon value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "801cf760-f576-4cea-9dd6-0dadfc1788a1", + "metadata": {}, + "outputs": [], + "source": [ + "ds_swcf = ds.SWCF\n", + "\n", + "# Get all the dataset dimensions\n", + "d = ds_swcf.dims\n", + "\n", + "# Grab all dimensions to mean the lon values from\n", + "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", + "\n", + "# Make new dataset of zonal mean values\n", + "ds_swcf_zonal = ds_swcf.mean(dim=davgovr)\n", + "\n", + "# print some values of new zonally-averaged SWCF variable\n", + "ds_swcf_zonal" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b92a7bdd-b2dc-4a2c-a6e9-9b75e361492a", + "metadata": {}, + "outputs": [], + "source": [ + "# Create figure and axis\n", + "fig, ax = plt.subplots(1, figsize=(12, 7))\n", + "\n", + "# Set Main title for subplots:\n", + "plt.title(\"Short Wave Cloud Forcing\", fontsize=20)\n", + "\n", + "# Plot value on y-axis and latitude on the x-axis\n", + "ax.plot(ds_swcf_zonal.lat, ds_swcf_zonal, c=\"goldenrod\")\n", + "\n", + "ax.set_xlim(\n", + " [max([ds_swcf_zonal.lat.min(), -90.0]), min([ds_swcf_zonal.lat.max(), 90.0])]\n", + ")\n", + "\n", + "ax.set_xlabel(\"Latitude\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "47ccd4c3-3c40-4b61-9ac1-3df14249eed1", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/basics_plot_3.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "a4cd24f4", + "metadata": {}, + "source": [ + "**Question**\n", + "\n", + "What code could you add to set a legend for the plot line?" + ] + }, + { + "cell_type": "markdown", + "id": "afcc9ee3-0312-4440-b720-a547382a960a", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "enter the following lines after the `ax.set_xlabel` command and before the `plt.show()` command. \n", + " \n", + "```python\n", + "label = ds_swcf.time.values[0].strftime() # -> '0001-02-01 00:00:00'\n", + "line = Line2D([0], [0], label=label,\n", + " color=\"blue\")\n", + " \n", + "fig.legend(handles=[line],bbox_to_anchor=(-0.15, 0.15, 1.05, .102),loc=\"right\",\n", + " borderaxespad=0.0,fontsize=16,frameon=False)\n", + "```\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "dbe1163c-0ec0-4f9e-94df-4163078c8803", + "metadata": {}, + "source": [ + "**Question**\n", + "\n", + "What else could you label the line legend, if anything?\n" + ] + }, + { + "cell_type": "markdown", + "id": "a5cc83e2-3385-472f-864a-88307c6195c2", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "Try setting the label value to something else like your simulation run name, the season or month, etc.\n", + " \n", + "```\n", + "label = 'Season '\n", + "```\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "d7018dc2-c95b-4643-b078-d5033a6f236e", + "metadata": {}, + "source": [ + "**Question**\n", + "\n", + "How can you change the color on the plot and the legend?" + ] + }, + { + "cell_type": "markdown", + "id": "3bfc85e8-831b-4ead-a5e9-aa25ce2194ff", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "To change colors, try a different named color (e.g. \"red\" or \"goldenrod\"). You should make sure both the plot and the legend match or your plot won't make sense.\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "55a42e67-2609-4f07-8485-bc8f5035e14a", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for a solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/basics_plot_4.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "4d50620e-407d-44ef-bce5-e76c076bcbcd", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "49402bdd-648e-4021-af0e-9280067b7d76", + "metadata": {}, + "source": [ + "## Exercise 3: Plot of zonal T\n", + "\n", + "This example will plot the 3D zonal mean of temperature `T` with the pressure as the y-variable and latitude as the x-variable. We are showing you four different ways to make the same plot since each is valuable for plots with pressure on the y-axis and how to format those to get more information from the data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "619d661b-84c9-4a5f-8366-36d8023f082e", + "metadata": {}, + "outputs": [], + "source": [ + "# Remove all dimensions of length one\n", + "ds_t = ds.T.squeeze()\n", + "ds_t" + ] + }, + { + "cell_type": "markdown", + "id": "3eee4a65-9094-41e6-b905-b6623d62bde4", + "metadata": {}, + "source": [ + "### Plot 1: Natively via xarray\n", + "\n", + "This plot uses the xarray native grids for the plot\n", + "\n", + " - y-axis is increasing with height\n", + " - y-axis is not in log pressure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d7c3b5c-96ec-4da6-af39-7c4460827f71", + "metadata": {}, + "outputs": [], + "source": [ + "# Average over all dimensions except `lev` and `lat`.\n", + "d = ds_t.dims\n", + "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", + "\n", + "DS = ds_t.mean(dim=davgovr)\n", + "DS.transpose(\"lat\", \"lev\", ...)\n", + "fig, ax = plt.subplots(1, figsize=(20, 10))\n", + "DS.plot.contourf(ax=ax, y=\"lev\", cmap=\"Spectral_r\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5324e5a3-c6b4-4213-bf11-6184338a63e2", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for a solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/basics_plot_5.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9b8a57a1-0e26-4740-8182-bf4da973c911", + "metadata": {}, + "source": [ + "### Plot 2: Quick ADF style\n", + "\n", + "This plot uses the quick style of the AMWG Diagnostics Framework (ADF) packages.\n", + "\n", + " - y-axis is increasing with height\n", + " - y-axis is not in log pressure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2440adfe-60ab-4feb-832c-496423057404", + "metadata": {}, + "outputs": [], + "source": [ + "# Average over all dimensions except `lev` and `lat`.\n", + "d = ds_t.dims\n", + "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", + "\n", + "DS = ds_t.mean(dim=davgovr)\n", + "\n", + "lev = DS[\"lev\"]\n", + "lat = DS[\"lat\"]\n", + "mlev, mlat = np.meshgrid(lev, lat)\n", + "\n", + "# Generate zonal plot:\n", + "fig, ax = plt.subplots(1, figsize=(15, 7))\n", + "\n", + "# Create zonal plot with vertical levels\n", + "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n", + "\n", + "# Format axis and ticks\n", + "ax.set_xlabel(\"Latitude\")\n", + "fig.colorbar(img, ax=ax, location=\"right\")" + ] + }, + { + "cell_type": "markdown", + "id": "bd0ac9e7-4a8e-498a-be2b-3a7ddd8cca25", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for a solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/basics_plot_6.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "98f31b9b-db5d-4e2a-81f9-df146980dec0", + "metadata": {}, + "source": [ + "**Questions**\n", + "\n", + "- What differences do you see between the ADF quick plot and the xarray native plot? \n", + "- Why might you want to use one or the other?\n", + "- Where, vertically, do high pressures occur and what does this mean for where the ground would be located on this plot?" + ] + }, + { + "cell_type": "markdown", + "id": "96e8b966-f242-4e43-bb51-9deaa77a6bc6", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "Notice that the colorbar and axes labels are different between the two plots. Also note that the y-axis is going from lower to higher pressure values, but in the real atmosphere the highest pressures are near the surface (so the plot is inverted relative to the actual height of the layers above the Earth's surface). \n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e866346a-2d31-4df9-955c-b7a2ae00b888", + "metadata": {}, + "source": [ + "### Plot 3: ADF style with reversed y-axis\n", + "\n", + " - y-axis is decreasing with height\n", + " - y-axis is not in log pressure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c08d64d9-40d9-4064-9b06-2b8489d916a0", + "metadata": {}, + "outputs": [], + "source": [ + "# Average over all dimensions except `lev` and `lat`.\n", + "d = ds_t.dims\n", + "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", + "\n", + "DS = ds_t.mean(dim=davgovr)\n", + "\n", + "# print(DS.lev.min(),DS.lev.max())\n", + "\n", + "lev = DS[\"lev\"]\n", + "lat = DS[\"lat\"]\n", + "mlev, mlat = np.meshgrid(lev, lat)\n", + "\n", + "# Generate zonal plot:\n", + "fig, ax = plt.subplots(1, figsize=(15, 7))\n", + "\n", + "# Create zonal plot with vertical levels\n", + "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n", + "\n", + "# Format axis and ticks\n", + "plt.gca().invert_yaxis()\n", + "ax.tick_params(which=\"minor\", length=4, color=\"r\")\n", + "\n", + "# Set up colorbar\n", + "cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])\n", + "posn = ax.get_position()\n", + "\n", + "# Set position and size of colorbar position: [left, bottom, width, height]\n", + "cbar_ax.set_position([posn.x0 + posn.width + 0.005, posn.y0, 0.02, posn.height])\n", + "\n", + "ax.set_xlabel(\"Latitude\")\n", + "fig.colorbar(img, cax=cbar_ax, orientation=\"vertical\")" + ] + }, + { + "cell_type": "markdown", + "id": "ba0aa084-f95e-4951-81b0-3951e8ed0b29", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for a solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/basics_plot_7.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "a0beeca6-4697-4bc8-b42d-e43ccceecbef", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "- What differences do you see on the y-axis between the result from Plot 2 and Plot 3? " + ] + }, + { + "cell_type": "markdown", + "id": "1dd69767-20ac-49ff-9c1f-ade44886de56", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The plots are mirror images of each other because the y-axis has been flipped. Plot 2 has the highest pressures at the top of the y-axis while Plot 3 has the highest pressures at the bottom of the y-axis.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8bd94a75-58cb-4b5d-a806-45b64c26e727", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "- Where, vertically, do high pressures occur and what does this mean for where the ground would be located on these two figures ?" + ] + }, + { + "cell_type": "markdown", + "id": "4b1b7ab1-20ff-4745-bad7-cf50c9b807b8", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The highest pressures in the atmosphere occur at the ground where the most atmospheric mass is pressing downward. This means that Plot 2 is oriented so the location where the ground is is the top of the plot, or up. In Plot 3 the orientation is so that the ground would be at the bottom of the plot, or down. Thus Plot 3 is often more intuitive to read because it's oriented with the way we perceive the atmosphere.\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "84fc1ae8-74f3-4c4f-9171-18b5625f2d96", + "metadata": {}, + "source": [ + "### Plot 4: Complex ADF style\n", + "\n", + " - y-axis is decreasing with height\n", + " - y-axis is in log pressure" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50564cf7-948e-4b77-9537-707b7358bf71", + "metadata": {}, + "outputs": [], + "source": [ + "# Average over all dimensions except `lev` and `lat`.\n", + "d = ds_t.dims\n", + "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", + "\n", + "DS = ds_t.mean(dim=davgovr)\n", + "\n", + "# print(DS.lev.min(),DS.lev.max())\n", + "\n", + "lev = DS[\"lev\"]\n", + "lat = DS[\"lat\"]\n", + "mlev, mlat = np.meshgrid(lev, lat)\n", + "\n", + "# Generate zonal plot:\n", + "fig, ax = plt.subplots(1, figsize=(15, 7))\n", + "\n", + "# Create zonal plot with vertical levels\n", + "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n", + "\n", + "# Format axis and ticks\n", + "plt.yscale(\"log\")\n", + "plt.gca().invert_yaxis()\n", + "ax.tick_params(which=\"minor\", length=4, color=\"r\")\n", + "\n", + "# Set up colorbar\n", + "cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])\n", + "posn = ax.get_position()\n", + "\n", + "# Set position and size of colorbar position: [left, bottom, width, height]\n", + "cbar_ax.set_position([posn.x0 + posn.width + 0.005, posn.y0, 0.02, posn.height])\n", + "\n", + "ax.set_xlabel(\"Latitude\")\n", + "fig.colorbar(img, cax=cbar_ax, orientation=\"vertical\")" + ] + }, + { + "cell_type": "markdown", + "id": "5a8fb095-89fd-4112-9d4f-de5f4ee9f143", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for a solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cam/basics_plot_8.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "4d7c9ee8-9445-489f-9b07-3ab67e45398d", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "- What differences do you see on the y-axis between the result from Plot 3 and Plot 4? " + ] + }, + { + "cell_type": "markdown", + "id": "f931e791-2f0a-423f-b44b-644686657767", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The plots both have the y-axis oriented so that the highest pressures, or ground, is at the bottom of the plot. But the scale of the y-axis has changed so that in Plot 3 it is logarithmic.\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "6a6af568-90cf-4f8c-a3c8-5dcda30359fd", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "- What does that mean in terms of which part of the atmosphere are emphasized?" + ] + }, + { + "cell_type": "markdown", + "id": "0aa7131d-4337-4da2-8b8d-db9a69432142", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "With the logarithmic y-axis more of the upper atmosphere is shown on the figure. If you wanted to emphasize the boundary layer you might want to change the limits of the figure.\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c7c4e8c-4a4a-4f3e-a6d7-4b5e7725a2f7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/cam/cam.ipynb b/notebooks_copy/diagnostics/cam/cam.ipynb new file mode 100644 index 000000000..06092aaa0 --- /dev/null +++ b/notebooks_copy/diagnostics/cam/cam.ipynb @@ -0,0 +1,86 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Atmosphere" + ] + }, + { + "cell_type": "markdown", + "id": "30ca3a26-9666-433a-b9d6-852999d9455e", + "metadata": {}, + "source": [ + "## Basic Plotting\n", + "\n", + "### Learning Goals\n", + "\n", + "- Making a lat-lon plot\n", + "- Making a zonal plot\n", + "- Making a lat-height plot\n", + "\n", + "### Exercise 1: lat-lon plot\n", + "Plot global lat/lon of surface temperature\n", + "\n", + "### Exercise 2: Making a zonal plot\n", + "Plot zonal mean of short wave cloud forcing\n", + "\n", + "### Exercise 3: Making a lat-height plot\n", + "Plot temperature zonal mean with vertical levels\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c494a32c-cd81-4a0a-b290-eb0835919532", + "metadata": { + "tags": [] + }, + "source": [ + "_______________\n", + "## Advanced Plotting\n", + "\n", + "### Learning Goals\n", + "\n", + "- analysis of native output \n", + "\n", + "\n", + "### Exercise 1: CAM-SE output analysis\n", + "\n", + "Examples of simple analysis and plotting that can be done with CAM-SE output on the native cubed-sphere grid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d4d2795-ce87-424c-8b26-719932e22678", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/cice/advanced_cice.ipynb b/notebooks_copy/diagnostics/cice/advanced_cice.ipynb new file mode 100644 index 000000000..8dbb4b412 --- /dev/null +++ b/notebooks_copy/diagnostics/cice/advanced_cice.ipynb @@ -0,0 +1,910 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bb50f4e0-22b4-4eff-99ac-01733347aacc", + "metadata": {}, + "source": [ + "# Advanced Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "7eb5562a-1ee6-47e2-9266-f1da2f5cabc8", + "metadata": {}, + "source": [ + "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023a`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023a`." + ] + }, + { + "cell_type": "markdown", + "id": "7dfb6809-0426-42bf-9d73-0bcb5d5f9ea8", + "metadata": {}, + "source": [ + "_______________\n", + "This activity was developed primarily by David Clemens-Sewall." + ] + }, + { + "cell_type": "markdown", + "id": "bc842f8d-b0fe-41c2-be03-052eba873e72", + "metadata": {}, + "source": [ + "_______________\n", + "This notebook provides some additional examples of more advanced sea ice fields. Here we introduce the concept of the subgridscale ice thickness distribution (ITD). This means we have a fraction of ice in each grid cell that is binned into thickness categories." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "504d66c5-830c-43a2-a34a-abe8c62da585", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.path as mpath\n", + "from matplotlib.gridspec import GridSpec\n", + "import pop_tools\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "14fbb751-1804-4ff7-ad07-3c292e59e895", + "metadata": {}, + "source": [ + "For these exercises we will need to import multiple variables, below is an example of one way to do so." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3389a570-582d-4520-8fe7-4bdcd3d400e5", + "metadata": {}, + "outputs": [], + "source": [ + "monthly_output_path = \"/glade/campaign/cgd/cesm/CESM2-LE/ice/proc/tseries/month_1\"\n", + "run_name = \"b.e21.BHISTcmip6.f09_g17.LE2-1001.001\"\n", + "\n", + "var_names = ['aice',\n", + " 'aicen',\n", + " 'vsnon',\n", + " 'hs',\n", + " 'fsens',\n", + " 'fsens_ai',\n", + " ]\n", + "\n", + "da_list = []\n", + "\n", + "for var_name in var_names:\n", + " files = os.path.join(monthly_output_path, var_name,\n", + " run_name + \".cice.h.\" + var_name + \".*\")\n", + " ds_in = xr.open_mfdataset(files)\n", + " da_list.append(ds_in[var_name])\n", + " del ds_in\n", + "\n", + "ds = xr.merge(da_list)\n", + "\n", + "del da_list" + ] + }, + { + "cell_type": "markdown", + "id": "fe88ed04-642c-4fe2-87aa-cd4edc1e5b4d", + "metadata": {}, + "source": [ + "The next step is to read in some grid information for the `gx1v7` dipole grid used in POP and CICE. We will read in three main variables: `tarea`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Also, we will print the latitude array `TLAT` to see the metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24f47162-9166-46d4-b026-515b6f892c8f", + "metadata": {}, + "outputs": [], + "source": [ + "# get pop grid grid cell areas\n", + "grid = pop_tools.get_grid('POP_gx1v7')\n", + "\n", + "# convert tarea to m^2\n", + "with xr.set_options(keep_attrs=True):\n", + " grid['TAREA'] = grid['TAREA']/(1e4)\n", + "grid['TAREA'].attrs['units'] = 'm^2'" + ] + }, + { + "cell_type": "markdown", + "id": "785ff8b7-fdd0-4216-a059-038cbfe4e282", + "metadata": {}, + "source": [ + "We will merge in three main variables: `tarea`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Note that this overwrites the dataset object from above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da0d7049-59fc-4bda-90f4-211ed1ae387d", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.merge([ds.drop(['TLAT', 'TLON', 'ULAT', 'ULON']),\n", + " grid[['TLAT', 'TLONG', 'TAREA']].rename_dims({'nlat':'nj','nlon':'ni'})],\n", + " compat='identical', combine_attrs='no_conflicts')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6c35032-3dd2-43b6-988e-14f03e497c28", + "metadata": {}, + "outputs": [], + "source": [ + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "faf06e07-ee8a-4a61-a487-c0a445622e86", + "metadata": {}, + "source": [ + "_______________\n", + "## Example 1: Plot per-category ice area\n", + "\n", + "Compare the dataset in this notebook with `aice` in the basics notebook. Notice that in this case we have an additional category dimension `nc`. `aicen` is the per-category ice area fraction. We demonstrate plotting a per-category variable below. We also plot the full sea ice concentration in the final plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffb5f043-03ea-4483-90d2-26409072b9b5", + "metadata": {}, + "outputs": [], + "source": [ + "# make circular boundary for polar stereographic circular plots\n", + "theta = np.linspace(0, 2*np.pi, 100)\n", + "center, radius = [0.5, 0.5], 0.5\n", + "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", + "circle = mpath.Path(verts * radius + center)\n", + "\n", + "cmap = plt.cm.get_cmap('Blues_r') \n", + "\n", + "# create figure with subplots\n", + "fig, axs = plt.subplots(3, 2, figsize=(20,30),\n", + " subplot_kw={'projection':ccrs.NorthPolarStereo()})\n", + "axs = np.ravel(axs)\n", + "\n", + "# this creates a subplot for each ITD category\n", + "for i in ds.nc.values:\n", + " ax = axs[i]\n", + " ax.set_boundary(circle, transform=ax.transAxes)\n", + " ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + " ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + " this=ax.pcolormesh(ds['TLONG'],\n", + " ds['TLAT'],\n", + " ds['aicen'].sel({'time':'1850-02-01 00:00:00',\n", + " 'nc':i}).squeeze(),\n", + " cmap=cmap,vmax=1,vmin=0,\n", + " transform=ccrs.PlateCarree())\n", + " plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + " ax.set_title('Area Fraction Category ' + str(i+1))\n", + "\n", + "# gridcell mean aice in the final subplot\n", + "ax = axs[-1]\n", + "ax.set_boundary(circle, transform=ax.transAxes)\n", + "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + "this=ax.pcolormesh(ds['TLONG'],\n", + " ds['TLAT'],\n", + " ds['aice'].sel({'time':'1850-02-01 00:00:00'}).squeeze(),\n", + " cmap=cmap,vmax=1,vmin=0,\n", + " transform=ccrs.PlateCarree())\n", + "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + "ax.set_title('Sea Ice Concentration')" + ] + }, + { + "cell_type": "markdown", + "id": "71b64ff8-2c9a-4f63-9d63-cc9f5047642f", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution to final two panels
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/advanced_plot_1.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "6dded4c6-bb89-49fe-8c9f-ce46c26d5119", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "How do you get the grid cell total sea ice concentration? In which of the thickness categories is the concentration highest? How does that vary spatially? What does this indicate about the relative importance of different ice thicknesses in different Arctic regions?\n", + "\n", + "Note that the default CICE ice thickness categories are:\n", + "- Category 1: 0-0.64 m\n", + "- Category 2: 0.64-1.39 m\n", + "- Category 3: 1.39-2.47 m\n", + "- Category 4: 2.47-4.57 m\n", + "- Category 5: 4.57+ m" + ] + }, + { + "cell_type": "markdown", + "id": "5eab03af-1834-420d-87d3-6aea39398ef1", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "You would calculate the grid cell total sea ice concentration by summing up the concentration in each category. In the central Arctic the concentrations are highest in categories 2 and 3, while on the ice margins the concentrations are highest in category 1. The only location with any substantial thick ice (category 5) is the central Arctic. This indicates that different regions' sea ice are dominated by very different mean sea ice thickness. This will have implications for ice growth/melt and heat fluxes.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "0d50b7e3-6a20-432f-9c0e-2222b85604ed", + "metadata": {}, + "source": [ + "_______________\n", + "## Example 2: Plot per-category snow thickness\n", + "\n", + "Internally, the model actually stores the snow **volume** for each category, not the thickness. To get the thickness we need to divide `vsnon` by `aicen` (the per category area)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed4b81e5-c6b5-466d-9784-74bd7bbace8a", + "metadata": {}, + "outputs": [], + "source": [ + "ds['hsn'] = ds['vsnon'] / ds['aicen']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "889b2691-b6b3-4363-b3fa-e4d96e4309aa", + "metadata": {}, + "outputs": [], + "source": [ + "# Max snow depth for colorbars\n", + "hs_max = 0.5 \n", + "\n", + "# make circular boundary for polar stereographic circular plots\n", + "theta = np.linspace(0, 2*np.pi, 100)\n", + "center, radius = [0.5, 0.5], 0.5\n", + "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", + "circle = mpath.Path(verts * radius + center)\n", + "\n", + "cmap = plt.cm.get_cmap('Blues_r') \n", + "\n", + "\n", + "fig, axs = plt.subplots(3, 2, figsize=(20,30),\n", + " subplot_kw={'projection':ccrs.NorthPolarStereo()})\n", + "axs = np.ravel(axs)\n", + "\n", + "for i in ds.nc.values:\n", + " ax = axs[i]\n", + " ax.set_boundary(circle, transform=ax.transAxes)\n", + " ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + " ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + " this=ax.pcolormesh(ds['TLONG'],\n", + " ds['TLAT'],\n", + " ds['hsn'].sel({'time':'1850-02-01 00:00:00',\n", + " 'nc':i}).squeeze(),\n", + " cmap=cmap,vmax=hs_max,vmin=0,\n", + " transform=ccrs.PlateCarree())\n", + " plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + " ax.set_title('Snow Depth (m) Category ' + str(i+1))\n", + "\n", + "# gridcell mean snow volume in the final subplot\n", + "ax = axs[-1]\n", + "ax.set_boundary(circle, transform=ax.transAxes)\n", + "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + "this=ax.pcolormesh(ds['TLONG'],\n", + " ds['TLAT'],\n", + " ds['hs'].sel({'time':'1850-02-01 00:00:00'}).squeeze(),\n", + " cmap=cmap,vmax=hs_max,vmin=0,\n", + " transform=ccrs.PlateCarree())\n", + "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + "ax.set_title('Average Snow Depth (m)')" + ] + }, + { + "cell_type": "markdown", + "id": "5d21d343-ee9f-41ef-8672-5f10fe96b6e0", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution to final two panels
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/advanced_plot_2.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "d3e1e661-f909-4c25-bf61-96581e697b86", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "In addition to the per-category snow thickness values we plotted the grid cell mean snow thickness. How do these values compare?" + ] + }, + { + "cell_type": "markdown", + "id": "24792b73-508f-4501-976d-3bfef0508050", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The per-category snow thickness can be higher than the grid cell mean (see Category 4 and the grid cell mean). This means the thicker ice tends to have thicker snow, but if the overall concentration of the thick ice categories is small then the mean over the grid call can be lower.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e85aa3e0-6a08-4592-b616-2dc711b28230", + "metadata": {}, + "source": [ + "_______________\n", + "## Example 3: Ice area related tracer\n", + "\n", + "The default CICE outputs are averaged over the entire grid cell, including the open water. Thus if a grid cell happened to be half covered in 1-m-thick ice and half open water then `hi` would be 0.5 m. Some tracers are written out just for the ice-covered area of the grid cell. These are indicated by have `_ai` appended to the variable name.\n", + "\n", + "Below is an example of this for the sensible heat flux." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41e55cf3-f620-41f9-8b10-2f9d38c03fe4", + "metadata": {}, + "outputs": [], + "source": [ + "ds['fsens_diff'] = ds['fsens_ai'] - ds['fsens']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5055a66c-9826-4e9e-8429-bbd43ced1ab5", + "metadata": {}, + "outputs": [], + "source": [ + "# Min and max\n", + "mins = [-60, -60, -30, 0]\n", + "maxs = [60, 60, 30, 1]\n", + "\n", + "# make circular boundary for polar stereographic circular plots\n", + "theta = np.linspace(0, 2*np.pi, 100)\n", + "center, radius = [0.5, 0.5], 0.5\n", + "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", + "circle = mpath.Path(verts * radius + center)\n", + "\n", + "cmap = plt.cm.get_cmap('RdBu') \n", + "\n", + "vars_to_plt = ['fsens_ai', 'fsens', 'fsens_diff', 'aice']\n", + "\n", + "fig, axs = plt.subplots(2, 2, figsize=(20,20),\n", + " subplot_kw={'projection':ccrs.NorthPolarStereo()})\n", + "axs = np.ravel(axs)\n", + "\n", + "# set up the plots for sensible heat flux over the sea ice, gridcell mean sensible heat flux,\n", + "# difference, and the gridcell mean ice concentration aice\n", + "\n", + "for i in np.arange(4):\n", + " if i == 3:\n", + " cmap = plt.cm.get_cmap('Blues_r')\n", + " ax = axs[i]\n", + " ax.set_boundary(circle, transform=ax.transAxes)\n", + " ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + " ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + " this=ax.pcolormesh(ds['TLONG'],\n", + " ds['TLAT'],\n", + " ds[vars_to_plt[i]].sel({'time':'1850-02-01 00:00:00'}\n", + " ).squeeze(),\n", + " cmap=cmap,vmax=maxs[i],vmin=mins[i],\n", + " transform=ccrs.PlateCarree())\n", + " plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + " ax.set_title(vars_to_plt[i])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "71e4bcb8-ffa5-4c11-9e9f-de0bb2bc6ae2", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution to final two panels
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/advanced_plot_3.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "af18fd70-9b61-46d7-9512-65da395e6496", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Where are the differences in the fluxes most pronounced and why?" + ] + }, + { + "cell_type": "markdown", + "id": "4ae871ba-9383-4d6d-9c41-1b9c8bedadcf", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The differences are most pronounced near the ice edge where ice concentrations are lowest. This is because these areas have a lot of open water so the grid cell mean flux will be very different than that over just the ice covered portion. In the central Arctic most cells are nearly all ice covered, so the fluxes are not substantially different.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "097273a5-204d-438f-8f68-19d7f4b75e04", + "metadata": {}, + "source": [ + "_______________\n", + "## Example 4: Remapping a sea ice gridded field\n", + "\n", + "The sea ice and ocean grids usually have a transformation where the grid North Pole is moved into land to avoid instability problems with converging meridians (See [Orthogonal Grids - Murray 1996](https://doi.org/10.1006/jcph.1996.0136)). In the CESM we use the POP dipole and tripole grids and will soon be using the MOM6 tripole grids. While matplotlib can actually use the 2D latitude and longitude fields to make plots, it is sometimes necessary to remap or regrid the data onto a standard latitude-longitude grid. This can be done using the ESMF remapping tools [xESMF](https://xesmf.readthedocs.io/en/latest/) for python." + ] + }, + { + "cell_type": "markdown", + "id": "e8d2c0c2-b13b-41f1-9ff4-6324f8018f4b", + "metadata": {}, + "source": [ + "Let's first plot the raw field so the dipole grid can be visualized." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50a62138-19cc-4a3b-8a6e-011bc508dbb8", + "metadata": {}, + "outputs": [], + "source": [ + "aice = ds['aice']\n", + "\n", + "aice" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b3429a1-e2b6-419b-bd13-7ef651b5a5a0", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(12,8))\n", + "ax.set_facecolor('0.5') # Fill in model land by making the axis background whatever color you want\n", + "im1 = ax.pcolormesh(ds.coords['ni'],ds.coords['nj'],aice[0],cmap='Blues_r',vmin=0,vmax=1)\n", + "cbar = plt.colorbar(im1)\n", + "cbar.set_label('Sea Ice Concentration',fontsize=18)\n", + "plt.xticks([]);\n", + "plt.yticks([]);" + ] + }, + { + "cell_type": "markdown", + "id": "f83bb11b-dc76-468b-91dc-e751ac0ddbd5", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/advanced_plot_4.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "126ece37-f3be-45f4-90a8-030d9eb25921", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "What do you notice about the Northern Hemisphere? What is going on with Greenland? Do you notice anything odd in the Southern Hemisphere?" + ] + }, + { + "cell_type": "markdown", + "id": "e242c44d-3e3d-4704-aaa3-02c70eb77cec", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "See how the Northern Hemisphere looks weird? Greenland is stretched completely across the top edge of the plot. In contrast, the Southern Hemisphere looks normal.\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "fa913808-0583-480b-a087-23593d036389", + "metadata": {}, + "source": [ + "Next we will import the `xesmf` library and create a \"destination\" grid. This is just a simple one-degree by one-degree latitude longitude grid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ca157eb-230d-45c4-8116-654bd9bc2919", + "metadata": {}, + "outputs": [], + "source": [ + "import xesmf as xe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1392cd38-64bd-4139-a957-ad38e030dc2c", + "metadata": {}, + "outputs": [], + "source": [ + "# Setting up a target grid to only regrid the sea ice data\n", + "lat=np.arange(-90,90,1.0) \n", + "lon=np.arange(0,361,1.0)\n", + "#create a meshgrid (2D fields of lats and lons)\n", + "lon2d,lat2d=np.meshgrid(lon,lat) \n", + "#set up the target grid as an xarray Dataset\n", + "target_grid=xr.Dataset({'lat': (['y', 'x'], lat2d),'lon': (['y', 'x'], lon2d)})\n", + "\n", + "target_grid" + ] + }, + { + "cell_type": "markdown", + "id": "098c750b-9a23-43b6-a879-b3a0a6908b54", + "metadata": {}, + "source": [ + "Now we need to create a Dataset. For some reason the regridder does not accept a DataArray." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9048bc8e-5df6-468e-a38b-763e158d9aad", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the dataset and a variable `aice` with dimensions time, nj, and ni. Also, fill the\n", + "# lat and lon arrays from the POPgrid dataset.\n", + "\n", + "ds_siconc_ocean = xr.Dataset()\n", + "ds_siconc_ocean['aice'] = (('time','nj','ni'),ds['aice'].values)\n", + "ds_siconc_ocean.coords['lat'] = (('nj','ni'),grid['TLAT'].values)\n", + "ds_siconc_ocean.coords['lon'] = (('nj','ni'),grid['TLONG'].values)\n", + "sic_ocean = ds_siconc_ocean['aice']" + ] + }, + { + "cell_type": "markdown", + "id": "e7684c0a-19d4-45cc-95f0-045a9355290b", + "metadata": {}, + "source": [ + "Next we will use xesmf to regrid from the POP gx1v7 grid to the one degree by one degree regular grid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7dbe677b-bdde-464a-8e31-c3f8e651dedd", + "metadata": {}, + "outputs": [], + "source": [ + "#input grid, output grid, method, keyword arguments\n", + "#NOTE: This may throw a \"FutureWarning\", but feel free to ignore it.\n", + "regridder=xe.Regridder(sic_ocean[0,:,:], target_grid, 'nearest_s2d',periodic=True,reuse_weights=False)" + ] + }, + { + "cell_type": "markdown", + "id": "dba1a8bd-ac35-4bcf-b3ae-201575ec4f56", + "metadata": {}, + "source": [ + "Now that we have the mapping or regridder we can translate the Xarray Dataset from gx1v7 to a one-degree regular grid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f66e319-147a-44d4-8d07-6db65e6295b8", + "metadata": {}, + "outputs": [], + "source": [ + "sic_rg = regridder(sic_ocean)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a523793f-1e06-47b1-b960-98c20125c3ea", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the first slice of the new dataset.\n", + "fig, ax = plt.subplots(figsize=(12,8))\n", + "ax.set_facecolor('0.5') # Fill in model land by making the axis background whatever color you want\n", + "im4 = ax.pcolormesh(lon,lat,sic_rg[0,:,:],cmap='Blues_r',vmin=0,vmax=1)\n", + "cbar = plt.colorbar(im4)\n", + "cbar.set_label('Sea Ice Concentration [%]',fontsize=18)\n", + "plt.xticks([]);\n", + "plt.yticks([]);" + ] + }, + { + "cell_type": "markdown", + "id": "ecbf2b91-8b62-4fc1-9783-7818018e9095", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/advanced_plot_5.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e24f8ca8-53b3-4389-bf3b-cbb0a05d5920", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "What do you notice about the Northern Hemisphere now?" + ] + }, + { + "cell_type": "markdown", + "id": "f33b34d8-12d3-4029-8978-70e1125f0807", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "See how the Northern Hemisphere looks weird? Greenland is stretched completely across the top edge of the plot. In contrast, the Southern Hemisphere looks normal.\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "0e470b63-5cb9-4a26-8ed1-e83b78786a79", + "metadata": {}, + "source": [ + "This data could now be differenced against an observational dataset on a one-degree grid. Additionally, if you wanted to plot the model data using `contourf` instead of `pcolormesh` (which is what we used in the rest of these exercises)." + ] + }, + { + "cell_type": "markdown", + "id": "8653d261-e3c2-42f7-8782-e2d94e7f7358", + "metadata": {}, + "source": [ + "Now let's plot the original data with `pcolormesh`, the regridded data with `pcolormesh`, and the regridded data with `contourf` to see how this looks different." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26499fe9-e5dd-4446-a862-4d41c96adeba", + "metadata": {}, + "outputs": [], + "source": [ + "# make circular boundary for polar stereographic circular plots\n", + "theta = np.linspace(0, 2*np.pi, 100)\n", + "center, radius = [0.5, 0.5], 0.5\n", + "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", + "circle = mpath.Path(verts * radius + center)\n", + "\n", + "# define the colormap\n", + "cmap = plt.cm.get_cmap('Blues_r') \n", + "\n", + "# create the figure\n", + "fig = plt.figure(figsize=(20,20))\n", + "\n", + "### make first plot - native grid - using pcolormesh\n", + "ax = fig.add_subplot(1,3,1, projection=ccrs.NorthPolarStereo())\n", + "ax.set_boundary(circle, transform=ax.transAxes)\n", + "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + "\n", + "# sets the latitude / longitude boundaries of the plot\n", + "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + "\n", + "#Plot the first timeslice of aice\n", + "this=ax.pcolormesh(sic_ocean['lon'],\n", + " sic_ocean['lat'],\n", + " sic_ocean.isel(time=0).squeeze(),\n", + " cmap=cmap,vmax=1,vmin=0,\n", + " transform=ccrs.PlateCarree())\n", + "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + "plt.title('Native grid and pcolormesh',fontsize=20)\n", + "\n", + "### make second plot - regridded - using pcolormesh\n", + "ax = fig.add_subplot(1,3,2, projection=ccrs.NorthPolarStereo())\n", + "ax.set_boundary(circle, transform=ax.transAxes)\n", + "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + "\n", + "# sets the latitude / longitude boundaries of the plot\n", + "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + "\n", + "#Plot the first timeslice of aice\n", + "this=ax.pcolormesh(lon,\n", + " lat,\n", + " sic_rg.isel(time=0).squeeze(),\n", + " cmap=cmap,vmax=1,vmin=0,\n", + " transform=ccrs.PlateCarree())\n", + "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + "plt.title('Regridded and pcolormesh',fontsize=20)\n", + "\n", + "### make third plot - regridded - using contourf\n", + "ax = fig.add_subplot(1,3,3, projection=ccrs.NorthPolarStereo())\n", + "ax.set_boundary(circle, transform=ax.transAxes)\n", + "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + "\n", + "# sets the latitude / longitude boundaries of the plot\n", + "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + "\n", + "#Plot the first timeslice of aice\n", + "this=ax.contourf(lon,\n", + " lat,\n", + " sic_rg.isel(time=0).squeeze(),\n", + " cmap=cmap,levels=[0,0.2,0.4,0.6,0.8,1.0],\n", + " transform=ccrs.PlateCarree())\n", + "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + "plt.title('Regridded and pcolormesh',fontsize=20)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f48bde78-24ee-43b0-ae54-5beecc91735c", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/advanced_plot_6.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "21e7ca55-a6bf-4b76-aae8-09a09f71818c", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "How does the ice edge compare in the two `pcolormesh` plots? What's different in the regridded `pcolormesh` and `contourf` plots?" + ] + }, + { + "cell_type": "markdown", + "id": "c4d58cad-56b5-455e-8499-8d6385656d3d", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The regridded data is more coarse and so the filled gridcells using `pcolormesh` in the regridded plot (center) are larger than in the native grid (left). The `contourf` differences are most notable at the ice edge near Russia where you can see clear contour lines. You might have more luck trying the contours on a month in the summer instead of winter.\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02ca1b60-88e2-4898-b593-280c6025c6c8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023a", + "language": "python", + "name": "npl-2023a" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/cice/basics_cice.ipynb b/notebooks_copy/diagnostics/cice/basics_cice.ipynb new file mode 100644 index 000000000..15208a071 --- /dev/null +++ b/notebooks_copy/diagnostics/cice/basics_cice.ipynb @@ -0,0 +1,816 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "33070195-2d5f-4ee3-b6cb-a658209b1f4d", + "metadata": {}, + "source": [ + "# Basic Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "f837eedc-2e5c-44a7-b4a4-6be632371012", + "metadata": {}, + "source": [ + "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023a`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023a`." + ] + }, + { + "cell_type": "markdown", + "id": "4fc43611-93db-4fa6-9396-362101c0fc33", + "metadata": {}, + "source": [ + "_______________\n", + "This activity was developed primarily by David Bailey and Alice DuVivier." + ] + }, + { + "cell_type": "markdown", + "id": "55f2ff0c-2b84-4c10-bde3-bc23722cb0ab", + "metadata": {}, + "source": [ + "_______________\n", + "These are examples of typical variables and plots that we look at in our sea ice diagnostics package. The most current version of the sea ice diagnostics are in the CESM Postprocessing repository. More information can be found here: [CESM Postprocessing](https://github.com/NCAR/CESM_postprocessing)." + ] + }, + { + "cell_type": "markdown", + "id": "3d56ba02-c652-4a97-a466-d3633e53aeb1", + "metadata": {}, + "source": [ + "The first step is to import the libraries needed to plot the data. Here we will use `xarray` as a tool to read the netCDF file. We will use `numpy` for some basic math calculations. For plotting the data we will need `matplotlib`, `pop_tools`, `geocat` and `cartopy`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e7f10ae-61c8-45f1-9d5c-9be60632b556", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.path as mpath\n", + "from matplotlib.gridspec import GridSpec\n", + "import pop_tools\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import nc_time_axis\n", + "import cftime\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "98894413-8550-49ba-b5ff-c027900b253a", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 1: Plot Sea Ice Concentration on a polar projection." + ] + }, + { + "cell_type": "markdown", + "id": "0a9043ca-ec38-4bc9-84e9-54e375c8f4e6", + "metadata": {}, + "source": [ + "Here you will learn about plotting sea ice area and other variables on a polar projection." + ] + }, + { + "cell_type": "markdown", + "id": "4fa4bed0-8207-4cb6-ae96-2507b56f7aef", + "metadata": {}, + "source": [ + "The first step is to grab sea ice (CICE) history files from your CESM model run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da58ab48-2fc9-4bd8-8892-2af0782f1e59", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Set your username here:\n", + "username = \"PUT_USER_NAME_HERE\"\n", + "\n", + "# Here we point to the archive directory from your b.day2.1 simulation\n", + "monthly_output_path = f\"/glade/derecho/scratch/{username}/archive/b.day2.1/ice/hist\"\n", + "\n", + "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", + "# this provided simulation data instead:\n", + "#monthly_output_path = \"/glade/campaign/cesm/tutorial/tutorial_2023_archive/b.day2.1/ice/hist\"\n", + "\n", + "# Name of CESM run\n", + "run_name = \"b.day2.1\"\n", + "\n", + "# Create path to all files, including unix wild card for all dates\n", + "files = os.path.join(monthly_output_path, run_name + \".cice.h.*\")\n", + "\n", + "# read in files as an xarray dataset:\n", + "ds = xr.open_mfdataset(files)\n", + "\n", + "### For this analysis, choose which variable to keep. Start with `aice` and then later try `hi`\n", + "var_in = 'aice' # sea ice concentration\n", + "#var_in = 'hi' # sea ice thickness\n", + "\n", + "var_to_keep = ds[var_in]\n", + "\n", + "print(var_to_keep)" + ] + }, + { + "cell_type": "markdown", + "id": "ded5a0a7-9e8f-4e00-8f5e-383c68cdb201", + "metadata": {}, + "source": [ + "The next step is to read in some grid information for the `gx1v7` dipole grid used in POP and CICE. We will read in three main variables: `tarea`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Also, we will print the longitude array `TLONG` to see the metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19cf3c69-1a1d-467e-8071-e2be50d33150", + "metadata": {}, + "outputs": [], + "source": [ + "# get pop grid grid cell areas\n", + "grid = pop_tools.get_grid('POP_gx1v7')\n", + "\n", + "# convert tarea to m^2\n", + "with xr.set_options(keep_attrs=True):\n", + " grid['TAREA'] = grid['TAREA']/(1e4)\n", + "grid['TAREA'].attrs['units'] = 'm^2'\n", + "\n", + "grid\n", + "\n", + "grid['TLONG']" + ] + }, + { + "cell_type": "markdown", + "id": "b89ce1e7-cad5-4b14-a95b-58de981e73c7", + "metadata": {}, + "source": [ + "We will merge in three main variables: `TAREA`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Note that this overwrites the dataset object from above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e6d86e3-2876-4c79-9cc8-8c02f935deb9", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.merge([var_to_keep.drop(['TLAT', 'TLON', 'ULAT', 'ULON']),\n", + " grid[['TLAT', 'TLONG', 'TAREA']].rename_dims({'nlat':'nj','nlon':'ni'})],\n", + " compat='identical', combine_attrs='no_conflicts')\n", + "grid['TLAT']" + ] + }, + { + "cell_type": "markdown", + "id": "101ce439-4fd7-412b-9f1d-1e7f34060747", + "metadata": {}, + "source": [ + "The next step is to set up the northern hemisphere polar stereographic projection for plotting the sea ice variable. We start with sea ice concentration `aice`. We are using a \"rainbow\" colormap here and cutting off the plot with a circular boundary. Note that we are only plotting the first timeslice of the `aice` array here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28a0da20-6bad-45d7-a61a-43d55ea2acd2", + "metadata": {}, + "outputs": [], + "source": [ + "# make circular boundary for polar stereographic circular plots\n", + "theta = np.linspace(0, 2*np.pi, 100)\n", + "center, radius = [0.5, 0.5], 0.5\n", + "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", + "circle = mpath.Path(verts * radius + center)\n", + "\n", + "# define the colormap\n", + "cmap = plt.cm.get_cmap('rainbow') \n", + "\n", + "# set up the figure with a North Polar Stereographic projection\n", + "fig = plt.figure(figsize=(20,20))\n", + "ax = fig.add_subplot(1,2,1, projection=ccrs.NorthPolarStereo())\n", + "ax.set_boundary(circle, transform=ax.transAxes)\n", + "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", + "\n", + "# sets the latitude / longitude boundaries of the plot\n", + "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", + "\n", + "#Plot the first timeslice of aice\n", + "this=ax.pcolormesh(ds['TLONG'],\n", + " ds['TLAT'],\n", + " ds[var_in].isel(time=0).squeeze(),\n", + " cmap=cmap,vmax=1,vmin=0,\n", + " transform=ccrs.PlateCarree())\n", + "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", + "plt.title('Sea Ice Concentration',fontsize=20)" + ] + }, + { + "cell_type": "markdown", + "id": "0ed4273f-6855-40fd-b69f-03d78fcde11f", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/basics_plot_1.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "24071060-19f0-467c-9835-221ce0456333", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Why is the scale from 0-1 for `aice`?" + ] + }, + { + "cell_type": "markdown", + "id": "5c5c4859-c3de-4160-a2e2-cc6d8293173a", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The variable `aice` is actually ice fraction. To convert it to concentration you would need to multipy it by 100. In the rest of these notebooks we use the ice fraction fairly interchangeably with ice concentration. But if you are plotting these you should check your range and see if the maximum value is 1 or 100 because that's important for setting colorbar limits.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "032392b9-ebb9-4fc2-83a7-991d15248f15", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "What month did you plot above? How could you plot the September ice concentration? (Remember that the monthly files are written at the last timestep of the month, so the June files are written out on July 1 at 00Z)." + ] + }, + { + "cell_type": "markdown", + "id": "6090f64c-0763-46af-b345-ae4c6d4979e0", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "You can check the date plotted by using the following command.\n", + " \n", + "```python\n", + "ds['aice'].isel(time=0).time\n", + "\n", + "```\n", + "\n", + "You should see that the date is Feb.1 of year 1 at 00Z. Which means it is the January mean sea ice concentration. To plot the September mean sea ice concentration, use the timeslice `time=8`.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "90114326-683d-4c15-9a60-d3a49545cc7a", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "What do you think of that rainbow colormap? Try other colormaps like `viridis`, `plasma`, `Blues`. Note that if you add the string `_r` to the colormap name it will reverse the colormap. What is a more intutitive map for plotting sea ice concentration?" + ] + }, + { + "cell_type": "markdown", + "id": "bce3bc22-5c2d-4f6b-85c4-b742ff4e6584", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "We think the reversed Blues colormap is more intuitive.\n", + " \n", + "```python\n", + "cmap = plt.cm.get_cmap('Blues_r') \n", + "\n", + "```\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1d05d033-37b9-439f-8aa9-0d5727612a73", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Can you plot the annual mean over the data? What about computing the September mean over the data and plotting that?" + ] + }, + { + "cell_type": "markdown", + "id": "997afdf3-a145-4ca7-98d0-8e3ca3cdba5e", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "Try entering the following data to plot in the `pcolormesh` function.\n", + " \n", + "```python\n", + "ds['aice'].mean(dim='time').squeeze(),\n", + "\n", + "```\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f99f305c-be5a-44de-8c19-72551c10b51e", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "You have plotted the sea ice concentration (`aice`), now try plotting the thickness (`hi`). Note that the units of thickness are in meters, so think about how you should adjust the colorbar range to be sensible? Note that you will have to change the `var_in` at the top of this section." + ] + }, + { + "cell_type": "markdown", + "id": "58760c0a-ed62-40a0-b315-a018741de8cd", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "After selecting `var_in = hi`, run through the following cells. Then check different ranges for `vmax` that might make more sense for the sea ice thickness (e.g. `vmax=5`)\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "5ed91922-b419-4685-84e2-62c49a57a135", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "All of the previous plots were of the Northern Hemisphere. How would you plot the Southern Hemisphere? How might you want to consider the seasonality if you're looking at Antarctic sea ice?" + ] + }, + { + "cell_type": "markdown", + "id": "32e34c3e-b7b4-4da1-b297-f34cfe0a6a72", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "Use the following to plot the Southern Hemisphere\n", + " \n", + "```\n", + "ax = fig.add_subplot(1,2,1, projection=ccrs.SouthPolarStereo())\n", + "\n", + "ax.set_extent([0.005, 360, -90, -55], crs=ccrs.PlateCarree())\n", + "```\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "42b295a0-4675-4d4e-814f-5aac53b90973", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 2: Plot a time series of total sea ice area." + ] + }, + { + "cell_type": "markdown", + "id": "c0dd3647-5e9e-4d57-8cfb-4e0f073a269c", + "metadata": {}, + "source": [ + "The next few steps read in sea ice concentration or (`aice`) from one of the CESM2 large ensemble historical runs. Note this operation points to multiple files on the campaign file system, so we are using the `xarray` function `open_mfdataset` for a multifile dataset. We will also print the `aice` dataset to get an idea of the metadata and dimensions. Note that we could use the output from the tutorial simulations. However, those runs are very short and thus are not a very interesting timeseries. Instead we will use one of the CESM2 Large Ensemble historical simulations, as described in [Rodgers et al. 2021](https://doi.org/10.5194/esd-12-1393-2021)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3f591cc-dd67-4a3f-abe7-bfc740743db1", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# First remove the old 'ds' variable:\n", + "del ds\n", + "\n", + "### Here we point to the CESM2-LE datasets on campaign disk\n", + "\n", + "monthly_output_path = \"/glade/campaign/cgd/cesm/CESM2-LE/ice/proc/tseries/month_1\"\n", + "run_name = \"b.e21.BHISTcmip6.f09_g17.LE2-1001.001\"\n", + "\n", + "\n", + "\n", + "### For this we will use the sea ice concentration (aice) and ice thickness (hi)\n", + "\n", + "var_names = ['aice',\n", + " 'hi',\n", + " ]\n", + "\n", + "### This piece of code opens the files and combines them into a single xarray dataset\n", + "\n", + "da_list = []\n", + "\n", + "for var_name in var_names:\n", + " files = os.path.join(monthly_output_path, var_name,\n", + " run_name + \".cice.h.\" + var_name + \".*\")\n", + " ds_in = xr.open_mfdataset(files)\n", + " da_list.append(ds_in[var_name])\n", + " del ds_in\n", + "\n", + "ds = xr.merge(da_list)\n", + "\n", + "del da_list\n", + "\n", + "aice = ds['aice']\n", + "\n", + "print(aice)" + ] + }, + { + "cell_type": "markdown", + "id": "d38f2b64-72e7-4021-810b-8124062985b8", + "metadata": {}, + "source": [ + "The next step is to read in some grid information for the `gx1v7` dipole grid used in POP and CICE. We will read in three main variables: `TAREA`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Also, we will print the area array `TAREA` to see the metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "560ec8fb-7200-460e-a240-538dc223509d", + "metadata": {}, + "outputs": [], + "source": [ + "# get pop grid grid cell areas\n", + "grid = pop_tools.get_grid('POP_gx1v7')\n", + "\n", + "# convert tarea to m^2\n", + "with xr.set_options(keep_attrs=True):\n", + " grid['TAREA'] = grid['TAREA']/(1e4)\n", + "grid['TAREA'].attrs['units'] = 'm^2'\n", + "\n", + "grid\n", + "\n", + "grid['TAREA']" + ] + }, + { + "cell_type": "markdown", + "id": "2bb581ad-11b2-4682-be94-98c9eeedd11d", + "metadata": {}, + "source": [ + "We will merge in three main variables: `TAREA`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Note that this overwrites the dataset object from above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ddcac5a-2e22-470a-8243-a3219d0488be", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.merge([aice.drop(['TLAT', 'TLON', 'ULAT', 'ULON']),\n", + " grid[['TLAT', 'TLONG', 'TAREA']].rename_dims({'nlat':'nj','nlon':'ni'})],\n", + " compat='identical', combine_attrs='no_conflicts')\n", + "grid['TLAT']" + ] + }, + { + "cell_type": "markdown", + "id": "9aa682c3-d6ed-4188-885f-d34268deac50", + "metadata": {}, + "source": [ + "The next step is to compute the ice area in each grid cell by multiplying the grid cell areas by the ice area fraction. Then we subset to just grid cells in the Northern hemisphere (using `where`). Finally, we sum over all of the grid cells. We can do these operations in a single line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab1c8910-677e-4345-8f06-ec3f7a4a9f07", + "metadata": {}, + "outputs": [], + "source": [ + "ds_area = (ds.TAREA*ds.aice).where(ds.TLAT>0).sum(dim=['nj','ni'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d177fa4-70c3-4f58-8b2f-4b9193ee3cbe", + "metadata": {}, + "outputs": [], + "source": [ + "ds_area.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "8d7a8183-ab18-4fbc-a542-43d01a53f75f", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/basics_plot_2.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9efb6f20-f442-42b6-a838-c0af66a98ba6", + "metadata": {}, + "source": [ + "This plot looks very noisy! Why do you think this is? Hint: you have plotted how many years of monthly mean data? Let's try just plotting September instead.\n", + "\n", + "**Question:** Why do we use month 10 (`ds_area.time.dt.month.isin([10])`) to reference September data?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90d62f0a-e43b-4bad-a759-0133bc2860aa", + "metadata": {}, + "outputs": [], + "source": [ + "ds_area.sel(time=ds_area.time.dt.month.isin([10])).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "83148fbe-aa53-41fe-b8fd-aeb7c2a5dc0c", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/basics_plot_3.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "5ea1c8b6-19b9-441d-ae08-82db58ddb90a", + "metadata": {}, + "source": [ + "Now let's plot the observational data on top. The NSIDC Sea Ice Index will be the values we want. These are computed from satellite observations. More information can be found here: [Sea Ice Index](https://nsidc.org/data/seaice_index)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de2232e9-2705-4894-b98a-a93082ae7f79", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "##### Add the data values manually from the datafile.\n", + "##### Create an xarray object with the NSIDC values and the years from 1979 to 2022.\n", + "\n", + "seaice_index = [4.58,4.87,4.44,4.43,4.7,4.11,4.23,4.72,5.64,5.36,4.86,4.55,4.51,5.43,4.58,5.13,4.43,5.62,\\\n", + " 4.89,4.3,4.29,4.35,4.59,4.03,4.05,4.39,4.07,4.01,2.82,3.26,3.76,3.34,3.21,2.41,3.78,3.74,\\\n", + " 3.42,2.91,3.35,3.35,3.17,2.83,3.47,3.47]\n", + "\n", + "# Convert to m^2\n", + "seaice_index = np.array(seaice_index)\n", + "seaice_index *= 1e12\n", + "\n", + "nsidc_time = [cftime.datetime(y, 10, 15) for y in range(1979,2023)]\n", + "\n", + "nsidc_index = xr.DataArray(data=seaice_index,coords={\"time\":nsidc_time})\n", + "\n", + "nsidc_index\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00fc5097-af7d-4cf2-94ab-bb4e0a63960c", + "metadata": {}, + "outputs": [], + "source": [ + "ds_area.sel(time=ds_area.time.dt.month.isin([10])).plot()\n", + "nsidc_index.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "6193d89f-ce8a-48df-aaf1-cb291f360dcc", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/cice/basics_plot_4.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "eea0e9cc-1769-45ad-9461-c9d3d8c51264", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Why do you think the observed (orange) does not perfectly correspond to the model? " + ] + }, + { + "cell_type": "markdown", + "id": "f07628e0-6c6a-4f7d-89f2-e2f115e586a9", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The model is one of 100 ensemble realizations of the 20th century and evolves independently, meaning each simulation has different weather and that weather will almost certainly be different from the observations. It will not, and should not, exactly match the observations because the climate system is chaotic.\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "412e002b-6a7f-4604-93fa-dfa7718ab2e3", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "What do you think you'll see if you plot the March total area timeseries?" + ] + }, + { + "cell_type": "markdown", + "id": "ed26c32a-79e4-4842-8ca7-ca4ec6f68b2a", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "March total area.\n", + "```\n", + "ds_area.sel(time=ds_area.time.dt.month.isin([4])).plot()\n", + "```\n", + "This should be higher mean and have a negative, but smaller, trend than the September trend. \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c4f36db3-ca0f-4f6b-9b60-9e618222d0b8", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Try computing total ice volume and plotting it." + ] + }, + { + "cell_type": "markdown", + "id": "9e96388f-b421-4a3c-b719-c83908e36f30", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "To calculate ice volume, use the variable `hi` in place of `aice`. Note that volume is units of $m^3$. So, you might want to scale the volume by $1.0e-13$.\n", + " \n", + "```\n", + "ds_vol = (ds.TAREA*ds.hi).where(ds.TLAT>0).sum(dim=['nj','ni'])\n", + "``` \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "2ce5c6f9-4272-4a8c-9b7b-3e6a31a5a708", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "What does the Southern Hemisphere sea ice area and volume look like?" + ] + }, + { + "cell_type": "markdown", + "id": "7e921a48-2488-45f9-a7a4-ec860e7e21ec", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "To calculate the Southern Hemisphere total area or volume, set the following values so that you only include points where the latitude is *less than* zero.\n", + "```\n", + "ds_area = (ds.TAREA*ds.aice).where(ds.TLAT<0).sum(dim=['nj','ni'])\n", + "``` \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25cf9352-2ee8-4b2b-ae7f-d7edc764977a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023a", + "language": "python", + "name": "npl-2023a" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/cice/cice.ipynb b/notebooks_copy/diagnostics/cice/cice.ipynb new file mode 100644 index 000000000..f793a813b --- /dev/null +++ b/notebooks_copy/diagnostics/cice/cice.ipynb @@ -0,0 +1,160 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Sea Ice" + ] + }, + { + "cell_type": "markdown", + "id": "30ca3a26-9666-433a-b9d6-852999d9455e", + "metadata": {}, + "source": [ + "## Basic Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "### Learning Goals\n", + "\n", + "- Making polar projection plots\n", + "- Understanding sea ice variables such as concentration and thickness\n", + "- Masking and area averaging\n", + "- Computing total sea ice area or volume" + ] + }, + { + "cell_type": "markdown", + "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 1: Making polar projection plots of sea ice concentration.\n", + "\n", + "Here we will use some custom python tools to produce a polar projection plot of sea ice concentration. Some things to try include changing the colormap, plotting sea ice thickness instead of concentration, and changing hemispheres." + ] + }, + { + "cell_type": "markdown", + "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 2: Compute total sea ice area and plot versus time.\n", + "\n", + "Here will we learn how to compute the total sea ice area for the northern hemisphere. Then plot this versus time. Some exercises will be plotting the monthly mean sea ice area, plotting just September or March mean sea ice area, or computing sea ice volume and then plotting it.\n" + ] + }, + { + "cell_type": "markdown", + "id": "27dafcc2-beec-4d75-920f-a60f46fff27b", + "metadata": {}, + "source": [ + "_______________\n", + "## Advanced Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "c494a32c-cd81-4a0a-b290-eb0835919532", + "metadata": { + "tags": [] + }, + "source": [ + "### Learning Goals\n", + "\n", + "- Using sea ice thickness distribution (ITD) category information\n", + "- Using sea ice tracers\n", + "- Remapping sea ice fields." + ] + }, + { + "cell_type": "markdown", + "id": "850641b7-2a63-4a20-8a26-d18a400c6160", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 1: Plot per-category ice area\n", + "\n", + "Here you will plot a field that uses the subgridscale ice thickness distribution (ITD) categories." + ] + }, + { + "cell_type": "markdown", + "id": "9fc458d8-6ea2-461f-b4d3-6808accfca41", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 2: Plot per-category snow thickness\n", + "\n", + "Here is another example of an ITD related field, the snow thickness. Actual snow thickness is derived from the snow volume (per unit area)." + ] + }, + { + "cell_type": "markdown", + "id": "4a75149d-ef7b-4b89-91b5-4daec351d95c", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 3: Plot ice area related tracer\n", + "\n", + "In this exercise you will learn about a variable related to the ice covered area of a grid cell instead of the grid cell mean.\n" + ] + }, + { + "cell_type": "markdown", + "id": "82beac53-81f6-494f-ba15-b1718e14421b", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 4: Remapping or regridding sea ice fields\n", + "\n", + "The ocean and sea ice grids are different in that the North Pole is moved into land. In this exercise\n", + "you will learn how to \"remap\" or \"regrid\" a sea ice field onto a standard one degree by one degree grid." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5184b86-b584-451c-b37a-15d6f27be115", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/clm_ctsm/basics_clm.ipynb b/notebooks_copy/diagnostics/clm_ctsm/basics_clm.ipynb new file mode 100644 index 000000000..7acf2078c --- /dev/null +++ b/notebooks_copy/diagnostics/clm_ctsm/basics_clm.ipynb @@ -0,0 +1,926 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic Plotting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023a`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023a`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_______________\n", + "This activity was developed primarily by Will Wieder and Peter Lawrence." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_______________\n", + "## Global Visualizations\n", + "\n", + "These are examples of typical variables and plots that we look at in our land model diagnostics package. You can see examples of output from the [land model dagnostics package here](https://www2.cesm.ucar.edu/experiments/cesm2.0/land/diagnostics/clm_diag_PCKG.html). The most current version of the land model diagnostics are in the CESM Postprocessing. More information here: [CESM Postprocessing](https://github.com/NCAR/CESM_postprocessing).\n", + "\n", + "\n", + "## Notebook Objectives\n", + "1. Become familiar with Jupyter Notebooks\n", + "2. Begin getting acquainted with python packages and their utilities\n", + "3. Plot a map and timeseries of global results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "****\n", + "History files from CESM are saved in netcdf format (denoted with the `.nc` file extension), a file format commonly used for storing large, multi-dimensional scientific variables.\n", + "Netcdf files are platform independent and self-describing; each file includes metadata that describes the data, including: **variables**, **dimensions**, and **attributes**.\n", + "\n", + "The figure below provides a generic example of the data structure in a netcdf file. The dataset illustrated has two variables (temperature and pressure) that have three dimensions. Coordinate data (e.g., latitude, longitude, time) that describe the data are also included. \n", + "\n", + "*Note that each simulation file has multiple variables. Within each `h0` file, most of the variables have three dimensions (lat x lon x time), while a few soil variables (e.g., moisture, temperature) have 4 dimensions (lat x lon x time x depth).*\n", + "\n", + "![Netcdf](https://xarray.pydata.org/en/stable/_images/dataset-diagram.png)\n", + "****\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*We'll start by loading some packages*\n", + "\n", + "The first step is to import the libraries needed to plot the data. Here we will use `xarray` as a tool to read the netCDF file. We will use `numpy` for some basic math calculations. For plotting the data we will need `matplotlib` and `cartopy`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# python packages\n", + "import os\n", + "import xarray as xr\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# resources for plotting\n", + "import matplotlib.pyplot as plt\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cftime\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "NOTE: This example largely uses features of xarray and \n", + " matplotlib packages. We won't go into the basics \n", + " of python or features included in these packages, but there are lots of \n", + " resources to help get you started. Some of these are listed below. \n", + "\n", + "
\n", + "\n", + "- [NCAR python tutorial](https://ncar.github.io/python-tutorial/tutorials/yourfirst.html), which introduces python, the conda package manager, and more on github.\n", + "- [NCAR ESDS tutorial series](https://ncar.github.io/esds/blog/tag/python-tutorial-series/), features several recorded tutorials on a wide variety of topics.\n", + "- [Project Pythia](https://projectpythia.org/) links to lots of great resources!\n", + "- [GeoCAT examples](https://geocat-examples.readthedocs.io/en/latest/), with some nice plotting examples\n", + "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Reading and formatting data\n", + "\n", + "**Note**: the drop-down solutions, below, assume you used b.day2.1 output for plotting for this section" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Point to the data\n", + "The first step is to grab land (CTSM) history files from your CESM model run\n", + "\n", + "For this example we will use:\n", + "- reflected solar radiation (FSR), \n", + "- incident solar radiation (FSDS), and\n", + "- exposed leaf area index (ELAI)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set your username here:\n", + "username = \"PUT_USER_NAME_HERE\"\n", + "\n", + "# Here we point to the archive directory from your b.day2.1 simulation\n", + "monthly_output_path = f\"/glade/derecho/scratch/{username}/archive/b.day2.1/lnd/hist\"\n", + "\n", + "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", + "# this provided simulation data instead:\n", + "#monthly_output_path = \"/glade/campaign/cesm/tutorial/tutorial_2023_archive/b.day2.1/lnd/hist\"\n", + "\n", + "# Name of CESM run\n", + "run_name = \"b.day2.1\"\n", + "\n", + "# Create path to all files, including unix wild card for all dates\n", + "files = os.path.join(monthly_output_path, run_name + \".clm2.h0.*\")\n", + "\n", + "# read in files as an xarray dataset:\n", + "ds = xr.open_mfdataset(files)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NOTE: These are the raw history files that CTSM writes out. \n", + "\n", + "By default, they include grid cell averaged monthly means for different state and flux variables.\n", + "\n", + "
\n", + " TIP: If you want to look at other variables, the fields variable in the cell below is where you can modify what we're reading off of the CLM history files.\n", + "
\n", + "\n", + "#### Printing information about the dataset is helpful for understanding your data. \n", + "- *What dimensions do your data have?*\n", + "- *What are the coordinate variables?*\n", + "- *What variables are we looking at?*\n", + "- *Is there other helpful information, or are there attributes in the dataset we should be aware of?*\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print information about the dataset\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also print information about the variables in your dataset. The example below prints information about one of the data variables we read in. You can modify this cell to look at some of the other variables in the dataset.\n", + "\n", + "*What are the units, long name, and dimensions of your data?*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.FSDS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 Simple Calculations\n", + "To begin with we'll look at albedo that's simulated by the model. \n", + "\n", + "Albedo can be calculated in several different ways, but the calculations use solar radiation terms that are handled within CTSM.\n", + "\n", + "Here we'll look at 'all sky albedo', which is the ratio of reflected to incoming solar radiation (**FSR/FSDS**).\n", + "Other intereresting variables might include latent heat flux or gross primary productivity. \n", + "\n", + "We will add this as a new variable in the dataset and add appropriate metadata.\n", + "\n", + "*When doing calculations, it is important to avoid dividing by zero. Use the `.where` function for this purpose*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds['ASA'] = ds.FSR/ds.FSDS.where(ds.FSDS>0)\n", + "ds['ASA'].attrs['units'] = 'unitless'\n", + "ds['ASA'].attrs['long_name'] = 'All sky albedo'\n", + "ds['ASA']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## 2. Plotting\n", + "### 2.1 Easy plots using Xarray\n", + "To get a first look at the data, we can plot a month of data from the simulation, selecting the month using the `.isel` function.\n", + "\n", + "
\n", + " NOTE: The plotting function only works with 1D or 2D data. Our data are 3D (time, lat, lon), so we need to specify a specific value for the other variables. Below, we select a specific time using the isel, leaving the lat and lon dimensions to plot on the x and y axes.\n", + "
\n", + "\n", + "- We will plot all sky albedo (variable = `ASA`). Note that we select the variable by specifying our dataset, `ds`, and the variable. \n", + "- The plot is for the first year of data (`time=slice(0,12)`)\n", + "- This plotting function will plot `ASA` for each simulation in our dataset\n", + "\n", + "*More plotting examples are on the [xarray web site](https://docs.xarray.dev/en/latest/user-guide/plotting.html)*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.ASA.isel(time=slice(0,12)).plot(x='lon',y='lat',col=\"time\", col_wrap=6) ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_1.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Question:** \n", + "\n", + "Why don't you see the whole globe in some months?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Calculating differences\n", + "\n", + "We can calculate the differences between the beginning and end of the simulation to see the differences in albedo over the simulation period. The below code:\n", + "- Calculates a monthly climatology for the first and last full year of the simulation\n", + "- Defines the difference as a new variable, `dsDiff`\n", + "\n", + "We'll first plot maps of the difference in all sky albedo for each month" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dsIni = ds.isel(time=slice(0,12)).groupby('time.month').mean()\n", + "dsFin = ds.isel(time=slice(-16,-4)).groupby('time.month').mean()\n", + "dsDiff = dsFin-dsIni\n", + "dsDiff.ASA.plot(x='lon',y='lat',col=\"month\", col_wrap=6, robust=True) ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_2.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions:** \n", + "- How is albedo different at the end of the simulation, relative to the begining of simulation?\n", + "- Where are the differences the largest? \n", + "- Are the differences consistent throughout the year?\n", + "- **What's causing these albedo changes** in different regions?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Are the differences coming from incoming or reflected radiation? \n", + "To find out, we can plot each variable. First we will plot incoming radiation (the denominator in all-sky albedo)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dsDiff.FSDS.plot(x='lon',y='lat',col=\"month\", col_wrap=6, vmax=30.0,vmin=-30.0) ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_3.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions:**\n", + "- Do you see any dfferences? \n", + "- Try plotting reflected radiation (the numerator, FSR). What differences do you see? \n", + "\n", + "*Note that you might want to change the minimun (`vmin`) and maximum (`vmax`) colorbar values for the plot when you switch between variables*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Is exposed leaf area index (ELAI) contributing to the differences in albedo?\n", + "Plot the differences in ELAI below. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dsDiff.ELAI.plot(x='lon',y='lat',col=\"month\", col_wrap=6, robust=True) ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_4.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions:** \n", + "- What regions are LAI differences the greatest?\n", + "- What times of year is this true?\n", + "- Are the regions and times of largest differences the same as the differences in albedo? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Calculating Time Series\n", + "\n", + "**Note**: the drop-down solutions, below, assume you used CESM2 Large Ensemble output for plotting for this section\n", + "\n", + "\n", + "As above, the plotting function we use here requires data to be 1D or 2D. Therefore, to plot a time series we either need to select a single point or average over an area.\n", + "\n", + "#### 2.3.1 Time series at a single point\n", + "This example uses `.sel`, which functions similarly to the `.isel` function above, to select a single point in the Amazon. \n", + "\n", + "*What's the difference between `.sel` and `.isel`?*\n", + "- *`.sel` selects a value of a variable (e.g., latitude of -5)*\n", + "- *`.isel` selects an indexed point of a variable (e.g., the 6th point in the data vector)*\n", + "\n", + "In the below examples, we'll also use subplots to see multiple variables in several panels\n", + "\n", + "Also, the next few steps read in land model data from one of the CESM2 large ensemble historical runs. Note this operation points to multiple files on the campaign file system, so we are using the `xarray` function `open_mfdataset` for a multifile dataset. We will also print the dataset to get an idea of the metadata and dimensions. Note that we could use the output from the tutorial simulation. However, those runs are very short and thus are not a very interesting timeseries. Instead we can use one of the CESM2 Large Ensemble historical simulations (see [Rodgers et al. 2021](https://doi.org/10.5194/esd-12-1393-2021))." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#First remove the old datasets\n", + "del ds\n", + "del dsDiff\n", + "\n", + "### Here we point to the CESM2-LE datasets on campaign disk and \n", + "### Look at results from a single ensemble member\n", + "\n", + "monthly_output_path = \"/glade/campaign/cgd/cesm/CESM2-LE/lnd/proc/tseries/month_1\"\n", + "run_name = \"b.e21.BHISTcmip6.f09_g17.LE2-1001.001\"\n", + "\n", + "var_names = ['FSR','FSDS','ELAI']\n", + "\n", + "### This piece of code opens the files and combines them into a single xarray dataset\n", + "\n", + "da_list = []\n", + "\n", + "for var_name in var_names:\n", + " files = os.path.join(monthly_output_path, var_name,\n", + " run_name + \".clm2.h0.\" + var_name + \".*\")\n", + " ds_in = xr.open_mfdataset(files)\n", + " # keep history file attributes: This only needs to be done once\n", + " if var_name == 'FSR':\n", + " da_list.append(ds_in)\n", + " else: \n", + " da_list.append(ds_in[var_name])\n", + " del ds_in\n", + "\n", + "''' quick fix to adjust time vector for monthly data'''\n", + "def fix_time(ds): \n", + " nmonths = len(ds.time)\n", + " yr0 = ds['time.year'][0].values\n", + " ds['time'] =xr.cftime_range(str(yr0),periods=nmonths,freq='MS') \n", + "\n", + " return ds\n", + "\n", + "ds = fix_time(xr.merge(da_list))\n", + "\n", + "print('-- Your dataset is been opened --')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print information about the dataset\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Calculate Albedo and add to dataset\n", + "ds['ASA'] = ds.FSR/ds.FSDS.where(ds.FSDS>0)\n", + "ds['ASA'].attrs['units'] = 'unitless'\n", + "ds['ASA'].attrs['long_name'] = 'All sky albedo'\n", + "ds['ASA']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "point = ds.sel(lon=300, lat=-5, method='nearest')\n", + "point.ASA.plot(x='time') ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_5.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to the maps above, there are variations in albedo in the simulation at this location. Let's add other variables to explore why we see differences at this location. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10,6))\n", + "'''this first plot is the same as the one above'''\n", + "plt.subplot(221)\n", + "point.ASA.plot()\n", + "plt.xlabel(None)\n", + "\n", + "'''now we'll look for potential sources of the difference'''\n", + "plt.subplot(222)\n", + "point.ELAI.plot() \n", + "plt.xlabel(None)\n", + "\n", + "plt.subplot(223)\n", + "point.FSDS.plot() \n", + "plt.title(None)\n", + "\n", + "plt.subplot(224)\n", + "point.FSR.plot() \n", + "plt.title(None) ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_6.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Questions:** \n", + "- What variables show differences?\n", + "- What variables are similar?\n", + "- How do the differences and similarities help to explain the differences in albedo?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.3.2 Global time series\n", + "There are many reasons why we may want to calculate globally integrated time series for particular variables.\n", + "This requires weighting the values from each grid cell by the total land area of individual grid cells. The example below does this for our dataset. \n", + "\n", + "#### First calculate the land weights:\n", + "- land area `la` that is the product of land fraction (fraction of land area in each grid cell) and the total area of the grid cell (which changes by latitude). Units are the same as area.\n", + "- land weights `lw`, the fractional weight that each grid makes to the global total, is calculated as the land area of each grid cell divided by the global sum of the land area.\n", + "\n", + "The land weights are shown in the plot below. Note that these are larger near the equator, and smaller at the poles and along the coastline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "la = (ds.landfrac*ds.area).isel(time=0).drop(['time']) \n", + "la = la * 1e6 #converts from land area from km2 to m2 \n", + "la.attrs['units'] = 'm^2'\n", + "lw = la/la.sum()\n", + "lw.plot() ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_7.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Next, calculate and plot a global weighted sum\n", + "
\n", + "\n", + "NOTE: You will likely want to calculate global weighted sum for a variety of different variables. For variables that have area-based units (e.g. GPP, gC/m^2/s), you need to use the land area variable when calculating a global sum. Remember to pay attention to the units and apply any necessary conversions! Keep in mind that grid cell area is reported in km^2. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dsGlobalWgt = (ds * lw).sum(['lat','lon'])\n", + "\n", + "plt.figure(figsize=(12,5))\n", + "plotVars = ['ASA','FSDS','ELAI','FSR']\n", + "for i in range(len(plotVars)):\n", + " # First add metadata for plotting\n", + " dsGlobalWgt[plotVars[i]].attrs['long_name'] = ds[plotVars[i]].attrs['long_name']\n", + " dsGlobalWgt[plotVars[i]].attrs['units'] = ds[plotVars[i]].attrs['units']\n", + "\n", + " # then make plots\n", + " plt.subplot(2,2,(i+1))\n", + " dsGlobalWgt[plotVars[i]].plot()\n", + " \n", + " if i == 0:\n", + " plt.title('Weighted global sum',loc='left', fontsize='large', fontweight='bold')\n", + " \n", + " if i<2:\n", + " plt.xlabel(None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_8.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### 2.4 Calculate an annual weighted mean and create customized plots\n", + "Annual averages require a different kind of weighting: the number of days per month.\n", + "This example creates python functions that allow you to easily calculate annual averages and create customized plots. \n", + "\n", + "
\n", + "\n", + "Python functions: In python, creating a function allows us to use the same calculation numerous times instead of writing the same code repeatedly.\n", + "
\n", + "\n", + "\n", + "#### 2.4.1 Calculate monthly weights\n", + "The below code creates a function `weighted_annual_mean` to calculate monthly weights. Use this function any time you want to calculate weighted annual means." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# create a function that will calculate an annual mean weighted by days per month\n", + "def weighted_annual_mean(array):\n", + " mon_day = xr.DataArray(np.array([31,28,31,30,31,30,31,31,30,31,30,31]), dims=['month'])\n", + " mon_wgt = mon_day/mon_day.sum()\n", + " return (array.rolling(time=12, center=False) # rolling\n", + " .construct(\"month\") # construct the array\n", + " .isel(time=slice(11, None, 12)) # slice so that the first element is [1..12], second is [13..24]\n", + " .dot(mon_wgt, dims=[\"month\"]))\n", + "\n", + "# generate annual means\n", + "for i in range(len(plotVars)):\n", + " temp = weighted_annual_mean(\n", + " ds[plotVars[i]].chunk({\"time\": 12}))\n", + " \n", + " if i ==0:\n", + " dsAnn = temp.to_dataset(name=plotVars[i])\n", + " else:\n", + " dsAnn[plotVars[i]] = temp\n", + "\n", + "# Make a simple plot\n", + "dsAnn.isel(time=0).ELAI.plot(x='lon',y='lat',robust=True) ;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_9.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dsAnn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.4.2 Customized maps\n", + "Creating a function isn't necessary to plot maps, but this function, which uses python's `cartopy`, allows you to make several pretty maps in one figure.\n", + "\n", + "Additional examples and information are available on the [cartopy website](https://scitools.org.uk/cartopy/docs/v0.15/index.html)\n", + "\n", + "There are two code blocks below. The first block of code defines the function. The second code block creates the plot. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cartopy.feature as cfeature\n", + "from cartopy.util import add_cyclic_point\n", + "import copy\n", + "\n", + "# Generate a function for making panel plots of maps\n", + "## many of these features are not required, but provide additional control over plotting\n", + "def map_function(da, cb=0, cmap='viridis', panel=None, ax=None, \n", + " title=None, vmax=None, vmin=None, units=None,nbins=200):\n", + " '''a function to make one subplot'''\n", + " wrap_data, wrap_lon = add_cyclic_point(da.values, coord=da.lon)\n", + "\n", + " if ax is None: ax = plt.gca()\n", + "\n", + " # define the colormap, including the number of bins\n", + " cmap = copy.copy(plt.get_cmap(cmap,nbins))\n", + " im = ax.pcolormesh(wrap_lon,da.lat,wrap_data,\n", + " transform=ccrs.PlateCarree(),\n", + " vmax=vmax,vmin=vmin,cmap=cmap)\n", + "\n", + " # set the bounds of your plot\n", + " ax.set_extent([-180,180,-56,85], crs=ccrs.PlateCarree())\n", + "\n", + " # add title & panel labels\n", + " ax.set_title(title,loc='left', fontsize='large', fontweight='bold')\n", + " ax.annotate(panel, xy=(0.05, 0.90), xycoords=ax.transAxes,\n", + " ha='center', va='center',fontsize=16) \n", + "\n", + " # add plotting features\n", + " ax.coastlines()\n", + " ocean = ax.add_feature(\n", + " cfeature.NaturalEarthFeature('physical','ocean','110m', facecolor='white'))\n", + " \n", + " # control colorbars on each plot & their location\n", + " if cb == 1:\n", + " cbar = fig.colorbar(im, ax=ax,pad=0.02, fraction = 0.03, orientation='horizontal')\n", + " cbar.set_label(units,size=12,fontweight='bold')\n", + " if cb == 2:\n", + " cbar = fig.colorbar(im, ax=ax,pad=0.02, fraction = 0.05, orientation='vertical') \n", + " cbar.set_label(units,size=12)#,weight='bold')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now make the plot!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(13,6), constrained_layout=True,\n", + " subplot_kw=dict(projection=ccrs.Robinson()))\n", + "for index, ax in np.ndenumerate(axes):\n", + " if i == 0:\n", + " plotData = dsAnn.ELAI.isel(time=slice(-10,None)).mean('time')\n", + " map_function(plotData, ax=ax,cb=2,\n", + " panel='(a)', nbins=10,\n", + " vmax=5,vmin=0,\n", + " units='Final Annual ELAI')\n", + " if i == 1:\n", + " plotData = (dsAnn.ELAI.isel(time=slice(-10,None)).mean('time')- \\\n", + " dsAnn.ELAI.isel(time=slice(0,10)).mean('time'))\n", + " map_function(plotData, ax=ax,cb=2,panel='(b)',\n", + " units='Annual ELAI Change, Final-Initial',\n", + " cmap='bwr',nbins=7,\n", + " vmax=0.75,vmin=-0.75) \n", + " \n", + " i = i+1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_10.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extra credit challenge\n", + "If you have extra time & energy, try running through this notebook with other variables. Interesting options could include: \n", + "- Latent heat flux (the sum of `FCTR`+`FCEV`+`FGEV`) or \n", + "- Gross Primary Production (`GPP`) \n", + "\n", + "
\n", + "\n", + "HINT: pay attention to units for these challenges. \n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023a", + "language": "python", + "name": "npl-2023a" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks_copy/diagnostics/clm_ctsm/clm_ctsm.ipynb b/notebooks_copy/diagnostics/clm_ctsm/clm_ctsm.ipynb new file mode 100644 index 000000000..4774ffe0d --- /dev/null +++ b/notebooks_copy/diagnostics/clm_ctsm/clm_ctsm.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Land" + ] + }, + { + "cell_type": "markdown", + "id": "6a0eda7f-8348-4a1e-8ada-86d9a70c3af6", + "metadata": {}, + "source": [ + "This notebook is an introduction to analyzing CLM land model results from a global simulation. It uses results from the case you ran in the 1a Tutorial. We've prestaged model results from this simulation and another simulation using a different model configuration in a shared directory. This way, you can get started on analyzing simulations results before your simulations finish running and compare differences caused by model structure. " + ] + }, + { + "cell_type": "markdown", + "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", + "metadata": { + "tags": [] + }, + "source": [ + "## Learning Goals\n", + "\n", + "- Read in global CLM data\n", + "- Make basic spatial plots\n", + "- Make basic timeseries plots" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/diagnostics.ipynb b/notebooks_copy/diagnostics/diagnostics.ipynb new file mode 100644 index 000000000..3b9511709 --- /dev/null +++ b/notebooks_copy/diagnostics/diagnostics.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0959db1d-1790-4519-a34e-7026444af537", + "metadata": {}, + "source": [ + "# Diagnostics" + ] + }, + { + "cell_type": "markdown", + "id": "9fdd8253-f6b6-4969-b862-b7a8545ee46e", + "metadata": {}, + "source": [ + "_______________\n", + "These activities have been tested and updated by Jesse Nusbaumer and Alice DuVivier" + ] + }, + { + "cell_type": "markdown", + "id": "a7a70c8a-be92-4a7a-8d67-1b3941013b4f", + "metadata": { + "tags": [] + }, + "source": [ + "_______________\n", + "Once the CESM model has been run and the output data has been transfered to the short term archive directory the real job of understanding how the simulation ran and what it means from a scientific perspective begins. \n", + "\n", + "By this point you have run a number of simulations and have looked at model output using `ncview`. In this lab you will go beyond `ncview` by using Python plotting and analysis methods in Jupyterhub to produce additional diagnostic results. There is also a complete CESM diagnostics system currently under active development called [CUPiD](https://github.com/NCAR/CUPiD) that you will get to try out as well, although please note that it is still in its very early stages. Finally, please note that there are many other [CESM analysis tools](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html) and projects available as well, but not all will be covered here.\n", + "\n", + "To start running the Jupyter Notebooks provided for this tutorial, follow the steps below." + ] + }, + { + "cell_type": "markdown", + "id": "90a99498-75c0-46ce-8aa2-9c33ce5d7aed", + "metadata": {}, + "source": [ + "## Step 1. Download CESM Tutorial notebooks with Git Clone" + ] + }, + { + "cell_type": "markdown", + "id": "08427f89-a2c6-40d9-af61-45815753cd92", + "metadata": {}, + "source": [ + "
\n", + "We will use the main branch of the tutorial materials for use in this tutorial.

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f28bf659-f7cb-46e9-8594-c6da04977d68", + "metadata": {}, + "source": [ + "First we will change into the home directory and then we will use the `git clone` command to download the CESM Tutorial diagnostics notebooks.\n", + "\n", + "
\n", + "Change the current directory to the home directory:
\n", + "\n", + "```\n", + "cd \n", + "```\n", + "
\n", + " \n", + "Download the cesm code to your code workspace directory as `CESM-Tutorial`:
\n", + "```\n", + "git clone https://github.com/NCAR/CESM-Tutorial.git CESM-Tutorial\n", + "```\n", + "\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c582acd5-84df-4cf1-8d26-90bfd9aa6ebc", + "metadata": {}, + "source": [ + "## Step 2. Login to JupyterHub" + ] + }, + { + "cell_type": "markdown", + "id": "187dcf75-3527-44ac-9ecd-86f47a3d524e", + "metadata": {}, + "source": [ + "Go to the JupyterHub website ([https://jupyterhub.hpc.ucar.edu/](https://jupyterhub.hpc.ucar.edu/)) and click on the \"Production\" button." + ] + }, + { + "cell_type": "markdown", + "id": "823cbbdc-649f-47dd-8977-a525554e15cf", + "metadata": {}, + "source": [ + "![JupyterHub](../../images/diagnostics/login/Diagnostics_1.png)" + ] + }, + { + "cell_type": "markdown", + "id": "98b5aeea-43d8-4112-a1c5-ab6ebb00dc39", + "metadata": {}, + "source": [ + "This will take you to a page where you enter your username and password. Enter your username as you would for Derecho, but your password will be the following: \n", + "\n", + "**Derecho password,Duo Pin**\n", + "\n", + "Where \"Duo Pin\" is the six numbers you can find in your Duo app under the \"UCAR\" Account listing. **Please note that the comma ( , ) must also be there separating the password from the Duo Pin.**" + ] + }, + { + "cell_type": "markdown", + "id": "de2094b5-9ac8-428d-aec7-d9f16baea936", + "metadata": {}, + "source": [ + "![JupyterHub Login](../../images/diagnostics/login/Diagnostics_2.png)" + ] + }, + { + "cell_type": "markdown", + "id": "d226699a-6342-4887-a190-72b495d8dcc5", + "metadata": {}, + "source": [ + "![JupyterHub Login](../../images/diagnostics/login/Diagnostics_duo_pcode.png)" + ] + }, + { + "cell_type": "markdown", + "id": "8c34bbd4-c129-4481-9df9-b5839b582a21", + "metadata": {}, + "source": [ + "After you do this and click \"Sign In\" you will be taken to a Server landing page where you will need to start your sever on Jupyterhub." + ] + }, + { + "cell_type": "markdown", + "id": "99ec89f3-d470-469e-a7b0-a2bc4497678e", + "metadata": {}, + "source": [ + "![JupyterHub Server](../../images/diagnostics/login/Diagnostics_3.png)" + ] + }, + { + "cell_type": "markdown", + "id": "bc60d1e8-44bd-4cac-b0e1-53eac15eea26", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "bb66a956-7f58-4613-b1a6-7597549e25a4", + "metadata": {}, + "source": [ + " Select the \"Casper PBS Batch\" option in the drop down menu." + ] + }, + { + "cell_type": "markdown", + "id": "9c758769-6b83-4dc1-a03a-2958102a2d52", + "metadata": {}, + "source": [ + "![JupyterHub Casper PBS](../../images/diagnostics/login/Diagnostics_4.png)" + ] + }, + { + "cell_type": "markdown", + "id": "6d9f61c8-9f91-4f9b-b3f9-fb474534d73d", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "17233631-43b6-4dc1-8904-a4adaff7c47b", + "metadata": {}, + "source": [ + "After selecting \"Casper PBS Batch\" you need to make other choices to select the right resources. You should use the queue and project account keys specified below. You may also want to request `03:00:00` for your Wall Time so that your server is active for the entire hands-on activity session. Do not change any other selection for this tutorial. If you are doing further analysis of model experiments you may need to change these settings and can find more information in the [CISL Documentation about JupyterHub](https://arc.ucar.edu/knowledge_base/70549913).\n" + ] + }, + { + "cell_type": "markdown", + "id": "1a3a65c2-65ce-4927-87b2-d9472ce676ed", + "metadata": {}, + "source": [ + "
\n", + "For this tutorial you should use the Queue `casper` and Project Account `UESM0013`.

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b2b9d327-8f9e-486e-9ae3-94a17c3008c9", + "metadata": {}, + "source": [ + "![JupyterHub Selections](../../images/diagnostics/login/Diagnostics_5.png)" + ] + }, + { + "cell_type": "markdown", + "id": "4a3ffff9-689d-4329-9ebe-0d93c1ba9e8a", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "3fe7c826-800e-4718-9440-a224b97dec44", + "metadata": {}, + "source": [ + "You may have to wait a moment or two for your server to start up." + ] + }, + { + "cell_type": "markdown", + "id": "088f619d-9152-45c4-aa0c-73bfd0e33ec8", + "metadata": {}, + "source": [ + "![JupyterHub Casper PBS](../../images/diagnostics/login/Diagnostics_6.png)" + ] + }, + { + "cell_type": "markdown", + "id": "a08772d2-643c-40fa-9503-f1490c29de80", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "a66777ce-4356-4b95-b513-b33a604cbfdc", + "metadata": {}, + "source": [ + "Your JupyterHub session is now active and ready to run." + ] + }, + { + "cell_type": "markdown", + "id": "850d57a4-eab0-4da2-9f26-d1e36d1d6204", + "metadata": {}, + "source": [ + "## Step 3. Open a Diagnostics Notebook" + ] + }, + { + "cell_type": "markdown", + "id": "70792221-dc94-41ee-a9e2-e632b9eef1c1", + "metadata": {}, + "source": [ + "When your JupyterHub session opens you should be in your home directory on the NCAR HPC. In Step 1 you cloned the \"CESM-Tutorial\" repository, which has the notebooks you will run in this activity. " + ] + }, + { + "cell_type": "markdown", + "id": "b06d1cdb-4f85-4958-9bfe-9af1901c6539", + "metadata": {}, + "source": [ + "![JupyterHub Main Page](../../images/diagnostics/login/Diagnostics_7.png)" + ] + }, + { + "cell_type": "markdown", + "id": "8749817d-6a92-4c33-84f2-f09507c3fea9", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "995b7014-2dd2-4f05-bc63-a22affe3ff9c", + "metadata": {}, + "source": [ + "To get to the Diagnostics notebooks, double click the following sequence of folders:\n", + "1) CESM-Tutorial\n", + "2) notebooks\n", + "3) diagnostics\n", + "4) Click on the folder for the model component that you are interested in running (e.g. cam) \n", + "5) Click on the `basics.ipynb` notebook (see arrow on left, below)\n", + "\n", + "The final path in your browser url line for the following example should be:\n", + "`$USER/CESM-Tutorial/notebooks/diagnostics/cam/basics.ipynb`" + ] + }, + { + "cell_type": "markdown", + "id": "e4cb4eb8-eb39-4a5b-a94a-538e09ab637a", + "metadata": {}, + "source": [ + "![JupyterHub Open Notebook](../../images/diagnostics/login/Diagnostics_8.png)" + ] + }, + { + "cell_type": "markdown", + "id": "1077e225-5d39-4e54-8288-5ef69bd9b90b", + "metadata": {}, + "source": [ + "## Step 4. Check Your Notebook Kernel" + ] + }, + { + "cell_type": "markdown", + "id": "aef068ce-e240-4e9f-b008-5270534b6295", + "metadata": {}, + "source": [ + "Check your kernel (see arrow in upper right corner, above). It should be either `NPL 2023a` or `NPL 2023b`. You should use the specified kernel for any diagnostics you do during the tutorial as it is a default environment available on NCAR HPC and the notebooks here have been tested so that they work with that particular kernel for the analysis environment. We have set the default kernels and specifiy what they should be in each component notebook. However, if you need to change it click on the kernel button and select the correct kernel. " + ] + }, + { + "cell_type": "markdown", + "id": "784c789a-db86-4c80-8c92-d69dbeab7b48", + "metadata": {}, + "source": [ + "## Step 5. Run Jupyter Notebook Cells" + ] + }, + { + "cell_type": "markdown", + "id": "69174e22-0a81-47d6-b122-5b514154b31d", + "metadata": {}, + "source": [ + "To run a Jupyter cell\n", + "- Type your command into the cell\n", + "- To execute the command:\n", + " - Press **shift+return**\n", + "
\n", + " OR\n", + "
\n", + " - Select the cell then click the 'play' button at the top of the window (see red arrow, above)" + ] + }, + { + "cell_type": "markdown", + "id": "beaa3bc1-0ed5-4c73-901a-6fea974076e2", + "metadata": {}, + "source": [ + "All figures will be rendered in the Jupyter Notebook, so there is no need to open any other window for this portion of the lab activities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae3f94f8-c116-468f-b15f-66b090a6b39f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/pop/advanced_pop.ipynb b/notebooks_copy/diagnostics/pop/advanced_pop.ipynb new file mode 100644 index 000000000..fafddfde6 --- /dev/null +++ b/notebooks_copy/diagnostics/pop/advanced_pop.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2634ab1d-3022-4b34-b573-cb74e0f31088", + "metadata": {}, + "source": [ + "# Advanced Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "1522591e-1007-41e1-8599-8be707db5d84", + "metadata": {}, + "source": [ + "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023b`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023b`." + ] + }, + { + "cell_type": "markdown", + "id": "ff1b63b8-a5e8-445b-891e-7819f775dfd8", + "metadata": {}, + "source": [ + "_______________\n", + "This activity was developed primarily by Mauricio Rocha and Gustavo Marques." + ] + }, + { + "cell_type": "markdown", + "id": "677407b7-4f55-45c7-9193-123791b854da", + "metadata": {}, + "source": [ + "_______________\n", + "## Setting up the notebook\n", + "Here we load modules needed for our analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36365adf-9a62-4b34-ba16-73c6d89a8d32", + "metadata": {}, + "outputs": [], + "source": [ + "# loading modules\n", + "\n", + "# %load_ext watermark # this is so that in the end we can check which module versions we used\n", + "%load_ext autoreload\n", + "\n", + "import warnings \n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "import numpy as np \n", + "import pop_tools\n", + "import glob" + ] + }, + { + "cell_type": "markdown", + "id": "40c71af7-bb2d-47ec-8c5a-8a2724fab5c1", + "metadata": {}, + "source": [ + "### Get the data " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e1f41c1-1b64-429a-acd7-ffa4f5c50234", + "metadata": {}, + "outputs": [], + "source": [ + "# Set your username here:\n", + "username = \"PUT_USER_NAME_HERE\"\n", + "casename = 'b.day2.1'\n", + "\n", + "# Here we point to the archive directory from your b.day2.1 simulation\n", + "pth = f\"/glade/derecho/scratch/{username}/archive/{casename}/ocn/hist/\"\n", + "\n", + "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", + "# this provided simulation data instead:\n", + "#pth = f'/glade/campaign/cesm/tutorial/tutorial_2023_archive/{casename}/ocn/hist/'\n", + "\n", + "# Print path to screen\n", + "pth\n", + "flist = glob.glob(pth + casename + '.pop.h.00??-??.nc') \n", + "ds = xr.open_mfdataset(flist, compat='override', coords='minimal')" + ] + }, + { + "cell_type": "markdown", + "id": "8a27f988-20f9-457d-8947-371f6f2df74c", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 1" + ] + }, + { + "cell_type": "markdown", + "id": "655e97da-85b0-45ac-acda-f139e524f319", + "metadata": {}, + "source": [ + "Maximum mixed-layer depth for the winter months in the northern hemisphere (January, February, and March) and in the southern hemisphere (July, August, and September)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4a1832c-10f8-4ebe-b33d-4c28a22657fc", + "metadata": {}, + "outputs": [], + "source": [ + "# POP grid\n", + "pop_grid = pop_tools.get_grid('POP_gx1v7')\n", + "ds['TLONG'] = pop_grid.TLONG; ds['TLAT'] = pop_grid.TLAT\n", + "ds['ULONG'] = pop_grid.ULONG; ds['ULAT'] = pop_grid.ULAT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b5ee9b5-e146-4655-af65-91e10d80d189", + "metadata": {}, + "outputs": [], + "source": [ + "# July, August, and Septemper (JAS)\n", + "def is_jas(month):\n", + " return (month >= 7) & (month <= 9)\n", + "JAS = ds['XMXL'].sel(time=is_jas(ds['XMXL']['time.month'])).mean('time')\n", + "\n", + "# January, February, and March (JFM)\n", + "def is_jfm(month):\n", + " return (month >= 1) & (month <= 3)\n", + "JFM = ds['XMXL'].sel(time=is_jfm(ds['XMXL']['time.month'])).mean('time')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc5802e9-6cf5-4209-bbe3-a4c6461d932e", + "metadata": {}, + "outputs": [], + "source": [ + "# Find the latitude value closest to the equator\n", + "def find_nearest(array, value):\n", + " array = np.asarray(array)\n", + " idx = (np.abs(array - value)).argmin()\n", + " return array[idx]\n", + "eq=find_nearest(JAS['TLAT']['TLAT'][:,0], value=0)\n", + "print(eq)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a6d3ee5-8bba-4158-a209-b2b2e5105cd1", + "metadata": {}, + "outputs": [], + "source": [ + "# Find the index of this latitude\n", + "idx=np.where(JAS['TLAT']['TLAT'][:,0] == eq)[0]\n", + "print(idx)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1626984-8646-41b6-a5df-943562d8ccfa", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new array\n", + "winter=JFM.copy()\n", + "\n", + "# Since the variable winter already contains the data for the Northern Hemisphere, we will now add the data for the Southern Hemisphere\n", + "winter.loc[0:187,:]=JAS.loc[0:187,:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7d3bc83-0643-4cf6-9367-678f2e7823d5", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8,6));\n", + "ax = plt.axes(projection=ccrs.Robinson());\n", + "orig_map=plt.cm.get_cmap('turbo')\n", + "scale_color=orig_map.reversed()\n", + "cf=(winter*0.01).plot.pcolormesh(ax=ax, # Multiply by 0.01 to transform centimeters to meters\n", + " vmax=800,vmin=0,\n", + " transform=ccrs.PlateCarree(),\n", + " x='TLONG',\n", + " y='TLAT',\n", + " cmap=scale_color,\n", + " add_colorbar=False,\n", + " ) \n", + "ax.coastlines()\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n", + " linewidth=2, color='gray', alpha=0.5, linestyle='-')\n", + "cbar = plt.colorbar(cf, ax=ax, shrink=0.5, pad=0.1, ticks=np.arange(0,800,100), label='XMXL [m]')\n", + "plt.title('Maximum Mixed-Layer Depth for the Winter', fontsize=14)\n", + "#plt.savefig('advanced_plot_1.png', bbox_inches='tight')# uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "0b35d911-bd15-4825-8a6f-12c583f9c05e", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/advanced_plot_1.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "db016402-9b77-4145-b92e-a4503c03dcc3", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 2" + ] + }, + { + "cell_type": "markdown", + "id": "d326ce71-ed14-47b1-9095-a55f45e969cd", + "metadata": {}, + "source": [ + "Calculate the heat storage (HS) per area from the temperature tendency for the upper 2000m.\n", + "Equation: $$\\rm{HF = \\uprho_\\uptheta~C_p~\\int_{z_2}^{z_1}\\uptheta_{(z)}'~dz},$$\n", + "where:\n", + "* HF is heat storage ($\\rm{W~m^{-2}}$),\n", + "* $\\uprho_\\uptheta$ is the sea water density ($\\rm{kg~m^{-3}}$),\n", + "* $\\rm{C_p}$ is the sea water specific heat ($\\rm{J~kg^{-1}~^{\\circ}C^{-1}}$),\n", + "* $\\rm{dz}$ is the cell thickness (m),\n", + "* and $\\uptheta$' is the temperature tendency ($\\rm{^{\\circ}C^{-1}~s^{-1}}$). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "354537e8-6e2f-4cbd-87a3-c807545828ed", + "metadata": {}, + "outputs": [], + "source": [ + "ds_HS=ds['TEND_TEMP'].sel(z_t=slice(0,200000))*ds['dz'].sel(z_t=slice(0,200000))*0.01 # Select the depth and multiply by dz. Unit: oC.s-1.m \n", + "ds_HS=ds_HS.sum('z_t') # Sum in depth\n", + "ds_HS=ds_HS*1026 # Multiply it by the sea water density. Unit: oC.s-1.kg.m-2\n", + "ds_HS=ds_HS*3996 # Multiply it by the sea water heat specific. Unit: W.m-2 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "258d8431-27bb-4ce1-9afa-a7f9221e2d1c", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8,6))\n", + "ax = plt.axes(projection=ccrs.Robinson())\n", + "orig_map=plt.cm.get_cmap('RdBu')\n", + "scale_color=orig_map.reversed()\n", + "cf=ds_HS.mean('time').plot.pcolormesh(ax=ax,\n", + " transform=ccrs.PlateCarree(),\n", + " vmin=-50,\n", + " vmax=50,\n", + " x='TLONG',\n", + " y='TLAT',\n", + " cmap=scale_color,\n", + " add_colorbar=False,\n", + " ) \n", + "ax.coastlines()\n", + "ax.add_feature(cartopy.feature.LAND)\n", + "ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n", + " linewidth=2, color='gray', alpha=0.5, linestyle='-')\n", + "cbar = plt.colorbar(cf, ax=ax, shrink=0.5, pad=0.1, label='HS [W m$^{-2}$]')\n", + "plt.title('Heat Storage per area for the upper 2000 m', fontsize=14)\n", + "#plt.savefig('advanced_plot_2.png', bbox_inches='tight')# uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "a5eebe10-e058-4475-a69e-18a8faafaf7f", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/advanced_plot_2.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023b", + "language": "python", + "name": "npl-2023b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/pop/basics_pop.ipynb b/notebooks_copy/diagnostics/pop/basics_pop.ipynb new file mode 100644 index 000000000..4b6c8e8e6 --- /dev/null +++ b/notebooks_copy/diagnostics/pop/basics_pop.ipynb @@ -0,0 +1,1236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "989fe00b-ac80-497c-a432-36d0ae227636", + "metadata": {}, + "source": [ + "# Basic Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "e97a3589-e319-4477-9123-f2f7e4189dc9", + "metadata": {}, + "source": [ + "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023a`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023a`." + ] + }, + { + "cell_type": "markdown", + "id": "ed83e6c7-6801-4b43-af1c-32f6e3577f30", + "metadata": {}, + "source": [ + "_______________\n", + "This activity was developed primarily by Anna-Lena Deppenmeier and Gustavo Marques.\n" + ] + }, + { + "cell_type": "markdown", + "id": "cb535d76-8352-4736-b74f-4577b32a3aa9", + "metadata": { + "tags": [] + }, + "source": [ + "_______________\n", + "## Setting up the notebook\n", + "Here we load modules needed for our analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe59e1e9-a503-4c89-803e-01dee0fadd75", + "metadata": {}, + "outputs": [], + "source": [ + "# loading modules\n", + "\n", + "# %load_ext watermark # this is so that in the end we can check which module versions we used\n", + "%load_ext autoreload\n", + "\n", + "import warnings \n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import datetime\n", + "import glob\n", + "import os\n", + "import warnings\n", + "import dask\n", + "import dask_jobqueue\n", + "import distributed\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xarray as xr\n", + "import xgcm\n", + "from matplotlib import ticker, cm\n", + "import pop_tools\n", + "from cartopy import crs as ccrs, feature as cfeature\n", + "import cartopy" + ] + }, + { + "cell_type": "markdown", + "id": "7bb804be-f371-40a6-915d-803d3cc67151", + "metadata": {}, + "source": [ + "### Define some functions\n", + "These functions will be used more than once to read data and add a cyclic point. These could go in a package if you like." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cc3a601-4a26-4a26-9761-f5ba830e353d", + "metadata": {}, + "outputs": [], + "source": [ + "# define function to get you the data you want relatively quickly \n", + "\n", + "def read_dat(files, variables, pop=False):\n", + " def preprocess(ds):\n", + " return ds[variables].reset_coords(drop=True) # reset coords means they are reset as variables\n", + "\n", + " ds = xr.open_mfdataset(files, parallel=True, preprocess=preprocess,\n", + " chunks={'time':1, 'nlon': -1, 'nlat':-1},\n", + " combine='by_coords')\n", + " if pop==True:\n", + " file0 = xr.open_dataset(files[0])\n", + " ds.update(file0[['ULONG', 'ULAT', 'TLONG', 'TLAT']])\n", + " file0.close()\n", + "\n", + " ds\n", + " return ds\n", + "\n", + "# define function to be able to plot POP output properly on cartopy projections\n", + "def pop_add_cyclic(ds):\n", + " \n", + " nj = ds.TLAT.shape[0]\n", + " ni = ds.TLONG.shape[1]\n", + "\n", + " xL = int(ni/2 - 1)\n", + " xR = int(xL + ni)\n", + "\n", + " tlon = ds.TLONG.data\n", + " tlat = ds.TLAT.data\n", + " \n", + " tlon = np.where(np.greater_equal(tlon, min(tlon[:,0])), tlon-360., tlon) \n", + " lon = np.concatenate((tlon, tlon + 360.), 1)\n", + " lon = lon[:, xL:xR]\n", + "\n", + " if ni == 320:\n", + " lon[367:-3, 0] = lon[367:-3, 0] + 360. \n", + " lon = lon - 360.\n", + " \n", + " lon = np.hstack((lon, lon[:, 0:1] + 360.))\n", + " if ni == 320:\n", + " lon[367:, -1] = lon[367:, -1] - 360.\n", + "\n", + " #-- trick cartopy into doing the right thing:\n", + " # it gets confused when the cyclic coords are identical\n", + " lon[:, 0] = lon[:, 0] - 1e-8\n", + "\n", + " #-- periodicity\n", + " lat = np.concatenate((tlat, tlat), 1)\n", + " lat = lat[:, xL:xR]\n", + " lat = np.hstack((lat, lat[:,0:1]))\n", + "\n", + " TLAT = xr.DataArray(lat, dims=('nlat', 'nlon'))\n", + " TLONG = xr.DataArray(lon, dims=('nlat', 'nlon'))\n", + " \n", + " dso = xr.Dataset({'TLAT': TLAT, 'TLONG': TLONG})\n", + "\n", + " # copy vars\n", + " varlist = [v for v in ds.data_vars if v not in ['TLAT', 'TLONG']]\n", + " for v in varlist:\n", + " v_dims = ds[v].dims\n", + " if not ('nlat' in v_dims and 'nlon' in v_dims):\n", + " dso[v] = ds[v]\n", + " else:\n", + " # determine and sort other dimensions\n", + " other_dims = set(v_dims) - {'nlat', 'nlon'}\n", + " other_dims = tuple([d for d in v_dims if d in other_dims])\n", + " lon_dim = ds[v].dims.index('nlon')\n", + " field = ds[v].data\n", + " field = np.concatenate((field, field), lon_dim)\n", + " field = field[..., :, xL:xR]\n", + " field = np.concatenate((field, field[..., :, 0:1]), lon_dim) \n", + " dso[v] = xr.DataArray(field, dims=other_dims+('nlat', 'nlon'), \n", + " attrs=ds[v].attrs)\n", + "\n", + "\n", + " # copy coords\n", + " for v, da in ds.coords.items():\n", + " if not ('nlat' in da.dims and 'nlon' in da.dims):\n", + " dso = dso.assign_coords(**{v: da})\n", + " \n", + " \n", + " return dso" + ] + }, + { + "cell_type": "markdown", + "id": "657b093a-e719-4f68-92a4-907a3ecf89bb", + "metadata": {}, + "source": [ + "### Setting up the Dask cluster\n", + "Remember to: \n", + "- change the project number if doing this outside the tutorial\n", + "- potentially change the walltime depending on what you want to do\n", + "- check the memory if you are loading a different dataset with different needs\n", + "- check the number of cores if you are loading a different dataset with different needs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ca107e9-941f-4398-8df5-d6587064492f", + "metadata": {}, + "outputs": [], + "source": [ + "# Set your username here:\n", + "username = \"PUT_USER_NAME_HERE\"\n", + "\n", + "if \"client\" in locals():\n", + " client.close()\n", + " del client\n", + "if \"cluster\" in locals():\n", + " cluster.close()\n", + "\n", + "cluster = dask_jobqueue.PBSCluster(\n", + " cores=2, # The number of cores you want\n", + " memory=\"8GB\", # Amount of memory\n", + " processes=1, # How many processes\n", + " queue=\"casper\", # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)\n", + " log_directory=f\"/glade/derecho/scratch/{username}/dask/\", # Use your local directory\n", + " resource_spec=\"select=1:ncpus=1:mem=8GB\", # Specify resources\n", + " project=\"UESM0013\", # Input your project ID here\n", + " walltime=\"02:00:00\", # Amount of wall time\n", + ")\n", + "# cluster.adapt(maximum_jobs=24, minimum_jobs=2) # If you want to force everything to be quicker, increase the number of minimum jobs, \n", + "# # but sometimes then it will take a while until you get them assigned, so it's a trade-off\n", + "cluster.scale(12) # I changed this because currently dask is flaky, this might have to be adjusted during the tutorial\n", + "client = distributed.Client(cluster)\n", + "client" + ] + }, + { + "cell_type": "markdown", + "id": "884e8473-6d10-46e9-9a43-2175ae8631a0", + "metadata": {}, + "source": [ + "### Get the data \n", + "\n", + "**Note**: the drop-down solutions, below, assume you used b.day2.1 output for plotting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1a789cc-95ed-44f6-a566-215e6ed0668d", + "metadata": {}, + "outputs": [], + "source": [ + "# Set your casename here:\n", + "casename = 'b.day2.1'\n", + "\n", + "# Here we point to the archive directory from your b.day2.1 simulation\n", + "pth = f\"/glade/derecho/scratch/{username}/archive/{casename}/ocn/hist/\"\n", + "\n", + "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", + "# this provided simulation data instead:\n", + "#pth = f'/glade/campaign/cesm/tutorial/tutorial_2023_archive/{casename}/ocn/hist/'\n", + "\n", + "# Print path to screen\n", + "pth" + ] + }, + { + "cell_type": "markdown", + "id": "ad58f537-556f-406b-a223-a2474ae87361", + "metadata": {}, + "source": [ + "##### Details on files \n", + "- b.day2.1.pop.h.0001-01.nc : one timestep year ???? and month -?? for a number of 2D and 3D variables and constants\n", + "- b.day2.1.pop.h.nday1.0001-01-01.nc : daily timestep output for one month for SST, SST variance, SSS and (max) mixed layer depth\n", + "- b.day2.1.pop.h.once.nc : (background) mixing values\n", + "- b.day2.1.pop.hv.nc: viscosities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a0dd260-76ab-4de7-931d-1cc343d82384", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# how quick this is depends among other things on the availability of workers on casper\n", + "# you can check progress by clicking on the link for the cluster above which will show you the dask dashboard \n", + "flist = glob.glob(pth + casename + '.pop.h.00??-??.nc') #also might want to use just some years not all \n", + "ds_pop = read_dat(flist, ['TEMP', 'SHF'], pop=True)\n", + "ds_pop = ds_pop.sortby(ds_pop.time)\n", + "tlist = np.asarray([time.replace(year=time.year+1957) for time in ds_pop.time.values]) # this makes sure the time axis is useful\n", + "ds_pop['time'] = tlist\n", + "ds_pop[\"time\"] = ds_pop.indexes[\"time\"].to_datetimeindex()\n", + "ds_pop #print some meta-data to screen" + ] + }, + { + "cell_type": "markdown", + "id": "ec0ade1b-407c-4bae-b4a7-62fdee617961", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 1\n", + "Means of global Surface Heat Flux and Sea Surface Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5d20c79-d1d1-4c62-8300-fade14d8e588", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%%time\n", + "fig, ax = plt.subplots(1, 2, figsize=(12,3), sharex=True, sharey=True)\n", + "\n", + "ds_pop.SHF.mean('time').plot(robust=True, ax=ax[0])\n", + "ax[0].set_title(r'Surface Heat Flux [W/m$^2$]')\n", + "\n", + "ds_pop.TEMP.sel(z_t=0, method='nearest').mean('time').plot(robust=True, ax=ax[1], levels=np.arange(0,32,1))\n", + "ax[1].set_title(r'Sea Surface Temperature [$^{\\circ}$C]')\n", + "#plt.savefig('basics_plot_1.png', bbox_inches='tight') # uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "9b3d3721-a43b-4bde-9d15-7392173cc52b", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_1.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f8da7c30-27d9-4b43-b05e-afeeb94a2422", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Can you plot 50m ocean temperature instead of surface heat flux (SHF)?" + ] + }, + { + "cell_type": "markdown", + "id": "df7c4390-a6d0-4247-9ebc-10998086182d", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "First, try using Xarray's ```sel``` function to select temperature values at the POP z-axis (z_t) index closest to 50m (note that the values in z_t are in centimeters):\n", + " \n", + "```\n", + "ds_pop.TEMP.sel(z_t=5000, method='nearest').mean('time').plot(robust=True, ax=ax[0], levels=np.arange(0,32,1))\n", + "```\n", + "\n", + "What was the depth selected? \n", + " \n", + "```\n", + "ds_pop.TEMP.sel(z_t=5000, method='nearest').mean('time').z_t.values\n", + "```\n", + " \n", + "There is not a layer with the midpoint at 50m. There are layers with the midpoint at 45m and 55m. \n", + "\n", + "```\n", + "(ds_pop.z_t)/100\n", + "```\n", + "\n", + "To estimate the temperature at 50m, we can use Xarray's ```interp``` to interpolate the values along the z-axis. By default, this function uses a linear interpolation method:\n", + " \n", + "```\n", + "ds_pop.TEMP.interp(z_t=5000).mean('time').plot(robust=True, ax=ax[0], levels=np.arange(0,32,1))\n", + "``` \n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "0ff3c68f-99b3-4e51-afb9-d987bd918d22", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Can you plot sea surface height (SSH) instead of surface heat flux (SHF)?" + ] + }, + { + "cell_type": "markdown", + "id": "e307984c-ce16-4c4e-bda3-ba6d940f36a8", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "We didn't initially load SSH as a variable we kept, so you will need to do that above.\n", + " \n", + "```\n", + "ds_pop = read_dat(flist, ['TEMP', 'SHF','SSH'], pop=True)\n", + "```\n", + "\n", + "Always be aware of which variables might be in a file.\n", + " \n", + "Once you've loaded SSH in the dataset, then plot it instead of SHF as follows:\n", + "\n", + "```\n", + "ds_pop.SSH.mean('time').plot(robust=True, ax=ax[0])\n", + "ax[0].set_title(r'Sea Surface Height (cm)')\n", + "```\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b4652dc4-1383-4023-ad2e-ca28b2510687", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Can you plot standard deviations instead of means?" + ] + }, + { + "cell_type": "markdown", + "id": "36d0ea5a-ee50-4ca7-9d2b-8c9c3fff8c3f", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "Replace the `.mean` function with `.std` in the plotting call.\n", + " \n", + "```\n", + "ds_pop.SHF.std('time').plot(robust=True, ax=ax[0])\n", + "\n", + "ds_pop.TEMP.sel(z_t=0, method='nearest').std('time').plot(robust=True, ax=ax[1], levels=np.arange(0,32,1))\n", + "```\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "829160aa-36f6-46b1-8cad-860bd3735dba", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 2\n", + "\n", + "Let's make some nicer plots! Have you noticed the x and y axes of the plots above? They are indices rather than longitudes and latitudes. POP output is on a curvilinear grid which means that the grid is not regularly (evenly) spaced. ```TLAT``` and ```TLONG``` are 2D variables depending on these indices, let's have a look at how to make maps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8847b2d5-9290-422c-8772-40b0764937c5", + "metadata": {}, + "outputs": [], + "source": [ + "# learn what TLAT and TLONG look like \n", + "fig, ax = plt.subplots(1, 2, figsize=(12,3), sharex=True, sharey=True)\n", + "\n", + "ds_pop.TLAT.plot(ax=ax[0], levels=np.arange(-90,95,5))\n", + "ax[0].set_title('TLAT')\n", + "ds_pop.TLONG.plot(ax=ax[1], levels=np.arange(0,370,10))\n", + "ax[1].set_title('TLONG')" + ] + }, + { + "cell_type": "markdown", + "id": "88135e00-bfc9-4b81-88e6-712679ec3b29", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_2.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e1517969-1f19-4909-bf9a-f1b10e917700", + "metadata": {}, + "source": [ + "**Question**\n", + "\n", + "Can you see the irregularity in TLAT? What does the discontinuity in TLONG mean? " + ] + }, + { + "cell_type": "markdown", + "id": "b93cd75a-97ce-4d82-85d4-5b0c223d2ee9", + "metadata": {}, + "source": [ + "### 1. Make global maps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76c3a229-16cb-4001-833f-5f6ae18317c6", + "metadata": {}, + "outputs": [], + "source": [ + "# Add cyclic point\n", + "ds_pop_cyc = pop_add_cyclic(ds_pop)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "527f3cbe-1724-49ba-a539-f333af917d0d", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "# initiate the figure\n", + "fig = plt.figure(dpi=150, figsize=(12,3))\n", + "\n", + "# add the first subplot\n", + "ax_shf = plt.subplot(1, 2, 1, projection=ccrs.Robinson(central_longitude=300.0))\n", + "\n", + "pc = ax_shf.contourf(ds_pop_cyc.TLONG, ds_pop_cyc.TLAT, ds_pop_cyc.SHF.mean('time'),\n", + " transform=ccrs.PlateCarree(), cmap='RdYlBu_r', extend='both', levels=np.arange(-120,130,10))\n", + "\n", + "ax_shf.set_global() \n", + "\n", + "land = ax_shf.add_feature(\n", + " cartopy.feature.NaturalEarthFeature('physical', 'land', '110m',\n", + " linewidth=0.5,\n", + " edgecolor='black',\n", + " facecolor='darkgray'))\n", + "\n", + "shf_cbar = plt.colorbar(pc, shrink=0.55, ax=ax_shf);\n", + "shf_cbar.set_label(r'[W/m$^{2}$]')\n", + "\n", + "ax_shf.set_title('Surface Heat Flux')\n", + "\n", + "# add the second subplot\n", + "ax_sst = plt.subplot(1, 2, 2, projection=ccrs.Robinson(central_longitude=300.0))\n", + "\n", + "pc = ax_sst.contourf(ds_pop_cyc.TLONG, ds_pop_cyc.TLAT, ds_pop_cyc.TEMP.isel(z_t=0).mean('time'),\n", + " transform=ccrs.PlateCarree(), cmap='RdYlBu_r', extend='both', levels=np.arange(0,32,1))\n", + "\n", + "ax_sst.set_global() \n", + "\n", + "land = ax_sst.add_feature(\n", + " cartopy.feature.NaturalEarthFeature('physical', 'land', '110m',\n", + " linewidth=0.5,\n", + " edgecolor='black',\n", + " facecolor='darkgray'))\n", + "\n", + "sst_cbar = plt.colorbar(pc, shrink=0.55, ax=ax_sst);\n", + "sst_cbar.set_label(r'[$^{\\circ}$C]')\n", + "ax_sst.set_title('Sea Surface Temperature')\n", + "\n", + "#plt.savefig('basics_plot_3.png', bbox_inches='tight') # uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "97823b3e-49d7-4c8e-97e7-b957f93b533a", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_3.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9a562d62-1efb-45e0-ab7b-5408c893ddbb", + "metadata": {}, + "source": [ + "### 2. Make regional map over continental US" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a419e0b-bb58-4740-9877-35a358acbd0b", + "metadata": {}, + "outputs": [], + "source": [ + "# define the extent of the map\n", + "lonW = -140\n", + "lonE = -40\n", + "latS = 15\n", + "latN = 65\n", + "cLat = (latN + latS) / 2\n", + "cLon = (lonW + lonE) / 2\n", + "res = '110m'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dacb156-9942-43a3-9b57-1bd4fe6491aa", + "metadata": {}, + "outputs": [], + "source": [ + "# what does sea surface temperature around the US look like? (i.e. where would you like to go swimming..)\n", + "fig = plt.figure(figsize=(11, 8.5))\n", + "ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + "ax.set_title('')\n", + "gl = ax.gridlines(\n", + " draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n", + ")\n", + "ax.set_extent([lonW, lonE, latS, latN], crs=ccrs.PlateCarree())\n", + "ax.coastlines(resolution=res, color='black')\n", + "ax.add_feature(cfeature.STATES, linewidth=0.3, edgecolor='brown')\n", + "ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='blue');\n", + "tdat = ax.pcolormesh(ds_pop.TLONG, ds_pop.TLAT, ds_pop.TEMP.isel(z_t=0, time=10), cmap='RdYlBu_r')\n", + "plt.colorbar(tdat, ax=ax, shrink=0.5, pad=0.1)\n", + "#plt.savefig('basics_plot_4.png', bbox_inches='tight')# uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "72f9a1c9-7bf1-4dd7-b193-dcb5baf8bf77", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_4.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "cbda596b-5483-4221-b228-f87fbb0278cd", + "metadata": {}, + "source": [ + "### 3. Make regional map over the Pacific\n", + "\n", + "There's an awful lot of not-ocean over the continental US. Let's look at the Pacific instead." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39a879e8-1dc0-4c9f-afae-c98a33fa7778", + "metadata": {}, + "outputs": [], + "source": [ + "# define the extent of the map\n", + "lonW = -180\n", + "lonE = -60\n", + "latS = -30\n", + "latN = 30\n", + "cLat = (latN + latS) / 2\n", + "cLon = (lonW + lonE) / 2\n", + "res = '110m'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcce69d0-c184-4f2f-9b20-775c05489bdd", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(11, 8.5))\n", + "ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + "ax.set_title('SST')\n", + "gl = ax.gridlines(\n", + " draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n", + ")\n", + "ax.set_extent([lonW, lonE, latS, latN], crs=ccrs.PlateCarree())\n", + "ax.coastlines(resolution=res, color='black')\n", + "ax.add_feature(cfeature.STATES, linewidth=0.3, edgecolor='brown')\n", + "ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='blue');\n", + "tdat = ax.pcolormesh(ds_pop.TLONG, ds_pop.TLAT, ds_pop.TEMP.isel(z_t=0, time=10), cmap='RdYlBu_r', vmin=15, vmax=31)\n", + "cbar = plt.colorbar(tdat, ax=ax, shrink=0.5, pad=0.1, ticks=np.arange(15,35,5))\n", + "cbar.set_label(r'[$^{\\circ}$C]')\n", + "#plt.savefig('basics_plot_5.png', bbox_inches='tight')# uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "843c70eb-926e-4e86-92f9-3fd98aab069a", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_5.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "6fd00b8a-9894-4745-a4ca-21e9d9ade96a", + "metadata": {}, + "source": [ + "### 4. Plotting contours\n", + "\n", + "The figures above use `pcolormesh` to plot, but if you want to make filled contours using `contourf` you will need to make your dataset cyclical." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efe1c49f-f602-4dfb-bc10-12da1dc9300b", + "metadata": {}, + "outputs": [], + "source": [ + "#ds_pop_cyc = pop_add_cyclic(ds_pop)# uncomment this if you have not run this line before" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71d64e38-7de2-4db4-9759-5e850b0a3aca", + "metadata": {}, + "outputs": [], + "source": [ + "# define the extent of the map\n", + "lonW = -180\n", + "lonE = -60\n", + "latS = -30\n", + "latN = 30\n", + "cLat = (latN + latS) / 2\n", + "cLon = (lonW + lonE) / 2\n", + "res = '110m'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc6fd1db-1e65-4a23-984d-00e7406317cc", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=(11, 8.5))\n", + "ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + "ax.set_title('SST')\n", + "gl = ax.gridlines(\n", + " draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n", + ")\n", + "ax.set_extent([lonW, lonE, latS, latN], crs=ccrs.PlateCarree())\n", + "ax.coastlines(resolution=res, color='black')\n", + "ax.stock_img() # something else than the boarders for a change\n", + "tdat = ax.contourf(ds_pop_cyc.TLONG, ds_pop_cyc.TLAT, ds_pop_cyc.TEMP.isel(z_t=0, time=10), cmap='RdYlBu_r', levels=np.arange(10,31,1))\n", + "cbar = plt.colorbar(tdat, ax=ax, shrink=0.5, pad=0.1, ticks=np.arange(15,35,5))\n", + "cbar.set_label(r'[$^{\\circ}$C]')\n", + "#plt.savefig('basics_plot_6.png', bbox_inches='tight')# uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "aa52cb62-eec6-4390-8a12-76d34723d704", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_6.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c8b0288e-e6b4-4ef1-8624-b31e4eebff57", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Try looking at the Equatorial Atlantic Ocean or other region that interests you (Gulf of Mexico, Gulf of Maine, California Coast)." + ] + }, + { + "cell_type": "markdown", + "id": "c9d29a3b-d7c7-4d4c-aeaf-7eeedf48caa3", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "Before plotting the region, you'll need to modify the latitude/longitude bounds. Here are the bounds for the Equatorial Atlantic Ocean: \n", + "```\n", + "# define the extent of the map\n", + "lonW = -60\n", + "lonE = 20\n", + "latS = -30\n", + "latN = 30\n", + "cLat = (latN + latS) / 2\n", + "cLon = (lonW + lonE) / 2\n", + "res = '110m'\n", + "```\n", + "\n", + "You can play with these to look at other regions of interest to you.\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9f98a8bf-9012-4edd-8288-149579c13252", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Try plotting other variables like sea surface height (SSH) or 50m temperature." + ] + }, + { + "cell_type": "markdown", + "id": "12ba7997-bf17-48cb-8eec-5ea7c467b319", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "See hints in exercise 1. \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f5e01632-7866-4aa3-bdca-c292c5d66389", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 3\n" + ] + }, + { + "cell_type": "markdown", + "id": "8088c31c-46a0-44f9-b5c1-f435a0a712aa", + "metadata": {}, + "source": [ + "So far we've just looked at 2D ocean properties, primarily at the surface. But the ocean is deep and you might want to look at how a variable changes with depth. Here we'll plot a cross section of an ocean variable with depth and how it changes with time.\n", + "\n", + "The difficulty here is that you can't easily select your lat and lon location, you need to find the nlon and nlat index first. As you could see from the ```TLAT``` and ```TLONG``` plots above, they don't behave regularly, so this is a bit of a challenge. Let's start with the equator (which is a bit easier than high up north)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45ef4912-1b64-4dad-bc2d-e7ae0a92fda2", + "metadata": {}, + "outputs": [], + "source": [ + "# find the latitude that is the smallest, i.e. closest to the equator:\n", + "abs(ds_pop.TLAT).argmin(dim='nlat')" + ] + }, + { + "cell_type": "markdown", + "id": "53e0a032-1b39-4f76-88a1-e67b15bc4d7c", + "metadata": {}, + "source": [ + "**This shows you that the equator is not the same everywhere** \n", + "but it is within one index and so might be just on the south or north of the equator, you can choose either. (there is no latitude where lat=0, can you imagine why?)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3fb6625-488b-4681-a336-485eee29523d", + "metadata": {}, + "outputs": [], + "source": [ + "# so let's say \n", + "ind_eq = 180" + ] + }, + { + "cell_type": "markdown", + "id": "f0953a2a-ee77-4c2d-b5d4-e9dcd21c72c4", + "metadata": {}, + "source": [ + "Let's now find some location we might be interested in, say 140$^{\\circ}$W" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "402d8417-55d7-4cd4-af1a-728f0352a0b6", + "metadata": {}, + "outputs": [], + "source": [ + "ds_pop.TLONG.isel(nlat=ind_eq).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "574c504b-5c2b-49d4-8118-5a9cb6ddf57a", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_7.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "442fc568-4af4-49c3-98f0-83f4c32e3ef9", + "metadata": {}, + "outputs": [], + "source": [ + "# the longitude goes from 0-360, so if we want 140W which is -140 we would need to select 220\n", + "ind_140w = abs(ds_pop.TLONG.isel(nlat=ind_eq)-220).argmin()" + ] + }, + { + "cell_type": "markdown", + "id": "2d30bf12-3bbb-4be5-a138-30dc2692f30d", + "metadata": {}, + "source": [ + "### 1. First Plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "949a410b-c188-4bfa-ba3b-2be1b8839069", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(2, 1, figsize=(9,4.5))\n", + "\n", + "ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).plot(y='z_t', ax=ax[0], \n", + " ylim=(250e2,0), levels=np.arange(10,32,2), cmap='RdYlBu_r')\n", + "\n", + "ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).plot(y='z_t', ax=ax[1], \n", + " ylim=(5000e2,0), levels=np.arange(0,10.2,0.2), cmap='Blues')\n", + "\n", + "#plt.savefig('basics_plot_8.png', bbox_inches='tight')# uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "a0aaf85d-6350-410d-b158-e1c276ad6889", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_8.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "417f27d7-c58a-4c8f-980a-a531b0079aa9", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "What is the vertical dimension? Which side of the plot is the ocean surface vs. the ocean floor?" + ] + }, + { + "cell_type": "markdown", + "id": "4c2090a8-349a-4587-ba1d-525b20ec71ca", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "These plots have the surface of the ocean at the top of the plot. So it's oriented physically with how we percieve the world. You should be aware of how the y axis changes when plotting figures like this to make them more easily interpretable.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "d53404ea-6116-49a1-8173-71c57540f934", + "metadata": {}, + "source": [ + "### 2. Nicer axes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74211b70-e318-46b7-855c-4f7333e5fa97", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax_upper, ax_lower) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [1, 3]}, \n", + " figsize=(10,6), sharex=True)\n", + "\n", + "dat_upper = ax_upper.contourf(ds_pop.time, ds_pop.z_t/100, ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).T, \n", + " levels=np.arange(10,32,1), cmap='RdYlBu_r', extend='both')\n", + "ax_upper.set_ylim(300,0)\n", + "plt.colorbar(dat_upper, ax=ax_upper)\n", + "\n", + "dat_lower = ax_lower.contourf(ds_pop.time, ds_pop.z_t/100, ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).T, \n", + " levels=np.arange(0,10.5,0.5), cmap='Blues', \n", + " extend='both')\n", + "ax_lower.set_ylim(4000,300)\n", + "plt.colorbar(dat_lower, ax=ax_lower, shrink=0.7)\n", + "#plt.savefig('basics_plot_9.png', bbox_inches='tight')# uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "efe789d3-a585-4fb6-bd6e-0de87c9c86a1", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_9.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "15f7e5e0-a9f5-4f73-a334-8c9f39557fab", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "What happened to the vertical axis? Why does this make the plot easier to read?" + ] + }, + { + "cell_type": "markdown", + "id": "6a712296-7198-4a1e-82cc-37d679342bbd", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "The ocean is deep and there is often different rates of change in a variable over the the upper ocean compared to the deep ocean. So using different scales and plotting them separately can be useful for analysis. \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e6feb3cc-5731-42b4-8bdf-4b57f083c3e6", + "metadata": {}, + "source": [ + "_______________\n", + "## Exercise 4" + ] + }, + { + "cell_type": "markdown", + "id": "7ea55dc5-b495-49d1-9c48-d4cd54f2c792", + "metadata": {}, + "source": [ + "The previous exercise showed how to plot a vertical cross section of the ocean over time. But it can also be valuable to plot a profile of a variable with depth at a particular point either at one time, averaged over time, or a profile averaged over a set of points again at one time or averaged over time. \n", + "\n", + "Here we will plot an average profile of temperature with depth. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff0ca0b5-e110-4875-9860-05f841aabfb5", + "metadata": {}, + "outputs": [], + "source": [ + "ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64360723-834c-4d44-82b7-2f813c61c53c", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# let's load these calculated quantities so that we don't have to calculate them time and time again \n", + "t_0n140w_mean = ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).mean('time').load()\n", + "t_0n140w_std = ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).std('time').load()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b2438a1-8422-466c-809c-c7053c907473", + "metadata": {}, + "outputs": [], + "source": [ + "# plot the mean profile\n", + "t_0n140w_mean.plot(y='z_t', ylim=(300e2,0), label='mean')\n", + "plt.xlim(10,28)\n", + "plt.title('T at 0$^{\\circ}$N, 140$^{\\circ}$W')\n", + "\n", + "# let's add some error bars --> standard deviation \n", + "\n", + "plt.fill_betweenx(ds_pop.z_t, t_0n140w_mean-t_0n140w_std, t_0n140w_mean+t_0n140w_std, color='black', alpha=0.2, edgecolor=None, label='std')\n", + "\n", + "plt.legend()\n", + "#plt.savefig('basics_plot_10.png', bbox_inches='tight')# uncomment this to save your figure" + ] + }, + { + "cell_type": "markdown", + "id": "bddc4521-24ea-488d-9606-928c56429cc2", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "![plot example](../../../images/diagnostics/pop/basics_plot_10.png)\n", + "\n", + "*

Figure: Plotting solution.

*\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c6d5cf0f-bd2d-4ed9-9f87-305190c2b324", + "metadata": {}, + "source": [ + "**Question:**\n", + "\n", + "Why is there grey shading on the plot above? i.e. How many ensembles have we included? What else could be causing a spread in the output?" + ] + }, + { + "cell_type": "markdown", + "id": "5b8e9dd0-c17c-45b5-aeda-73e0da8b0c28", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + " Click here for hints \n", + "\n", + "Here we take an average of the profiles in one location over time. But you could average over ensembles or over a region at one time, and both would also provide information about the variability in this quantity over depth. \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4bb05d3e-2324-4705-a387-71715a454cd1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2023a", + "language": "python", + "name": "npl-2023a" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/diagnostics/pop/pop.ipynb b/notebooks_copy/diagnostics/pop/pop.ipynb new file mode 100644 index 000000000..b029792f8 --- /dev/null +++ b/notebooks_copy/diagnostics/pop/pop.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Ocean" + ] + }, + { + "cell_type": "markdown", + "id": "18a0c6f1-355a-4f2a-99ac-1fead4de8626", + "metadata": {}, + "source": [ + "## Basic Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "ce7f7f35-19aa-406c-9330-e973dbb576d0", + "metadata": { + "tags": [] + }, + "source": [ + "### Learning Goals\n", + "\n", + "- How to load and plot POP ocean model output\n", + "- How to deal with the curvilinear grid\n", + "- How to select a point for making profiles\n", + "- How to define useful functions" + ] + }, + { + "cell_type": "markdown", + "id": "ae93c512-ccde-43cf-9d73-822cddde7ef2", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 1: Making map plots\n", + "\n", + "Here we will produce a map plot of surface heat flux and sea surface temperature. Some things to try include plotting standard deviations instead of means, plotting another surface variable, or plotting temperature at a particular depth." + ] + }, + { + "cell_type": "markdown", + "id": "a0b48b29-16d6-4474-9d81-152f9849acd3", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 2: Making global and regional maps\n", + "\n", + "Here will we learn how to use the TLAT and TLONG coordinates in POP to plot nicer global or regional maps. Some things to try include plotting different regions or different variables.\n" + ] + }, + { + "cell_type": "markdown", + "id": "589f85ae-bbe7-4e57-8b4b-1eec5d1e326c", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 3: Making timeseries\n", + "\n", + "Here will we learn how to make a timeseries of temperature at a single point throughout the column.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5676b088-c81c-4a4c-8fc7-bce6f09dd32d", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 4: Making a profile\n", + "\n", + "Here we learn how to plot a temperature profile at a single point on the ocean grid." + ] + }, + { + "cell_type": "markdown", + "id": "782bebb4-f00e-4362-8738-736ab39a311d", + "metadata": {}, + "source": [ + "## Advanced Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "c9434bfe-c2c3-4e6a-ba83-de538a6f29ed", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 1: Plot winter time maximum mixed-layer depth\n", + "\n", + "Here we learn how to select winter months and plot the maximum mixed-layer depth for both hemispheres on a single map. " + ] + }, + { + "cell_type": "markdown", + "id": "714dba27-4bcf-40db-9779-9fe0c3dde5f3", + "metadata": {}, + "source": [ + "_______________\n", + "\n", + "### Exercise 2: Compute and plot ocean heat storage per area for the upper 2000m\n", + "\n", + "Here we learn how to calculate the heat storage per area from the temperature trend and plot the results on a global map." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/cesm_expts/clim_data_gateway.ipynb b/notebooks_copy/intro/cesm_expts/clim_data_gateway.ipynb new file mode 100644 index 000000000..3dbe209c5 --- /dev/null +++ b/notebooks_copy/intro/cesm_expts/clim_data_gateway.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Climate Data Gateway" + ] + }, + { + "cell_type": "markdown", + "id": "9d8e3ee1-e605-4894-afd2-b0a83420711a", + "metadata": {}, + "source": [ + "Publicly released CESM data is available via the Climate Data Gateway. Timeseries data in both CESM standard format and CMIP format are available. Registration is quick and easy, and NCAR accounts are not required for access." + ] + }, + { + "cell_type": "markdown", + "id": "4afe3049-6271-4659-b23d-b4627dc31e93", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Climate Data Gateway webpage](https://www.earthsystemgrid.org/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e33a15df-834d-4108-a342-905d66bce697", + "metadata": {}, + "source": [ + "![CDG](../../../images/intro/clim_data_gateway.png)\n", + "\n", + "*

Figure: Climate Data Gateway Homepage.

*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eeccd871-4162-4b13-a29d-cc9529557043", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/cesm_expts/clim_data_guide.ipynb b/notebooks_copy/intro/cesm_expts/clim_data_guide.ipynb new file mode 100644 index 000000000..e606d33f1 --- /dev/null +++ b/notebooks_copy/intro/cesm_expts/clim_data_guide.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Climate Data Guide" + ] + }, + { + "cell_type": "markdown", + "id": "43c2831e-6726-4718-8460-2a042109003b", + "metadata": {}, + "source": [ + "The Climate Data Guide describes observations used for Earth System Model evaluation. To date 150+ data sets have been profiled. Expert-user guidance by 45+ scientists is provided to users, including pros and cons of each dataset. There are also comparisons of common variables (e.g. precipitation, sea surface temperature, sea ice concentration, etc.)." + ] + }, + { + "cell_type": "markdown", + "id": "422d7b55-49d9-4059-90ff-eec47665f874", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Climate Data Guide webpage](http://climatedataguide.ucar.edu/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "16c243bf-a625-417d-929b-67a8171b2fab", + "metadata": {}, + "source": [ + "![CDG](../../../images/intro/clim_data_guide.png)\n", + "\n", + "*

Figure: Climate Data Guide Homepage.

*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7d1fc1d-4dc0-4718-92c9-30f38db2bb77", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/cesm_webpage.ipynb b/notebooks_copy/intro/cesm_webpage.ipynb new file mode 100644 index 000000000..b004a7fb1 --- /dev/null +++ b/notebooks_copy/intro/cesm_webpage.ipynb @@ -0,0 +1,81 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0086605a-f330-41bd-a79b-13339e0e5d58", + "metadata": { + "tags": [] + }, + "source": [ + "# CESM Webpage" + ] + }, + { + "cell_type": "markdown", + "id": "9546d840-c247-4952-a8f2-52a377e5e180", + "metadata": {}, + "source": [ + "The **CESM Web Page** is the first point of reference for all things CESM. Here we can explore the larger CESM project, delve deeper into individual working groups, models, research activities, model releases, supporting model data, tools, events and much much more.\n" + ] + }, + { + "cell_type": "markdown", + "id": "71155f04-3f3a-4aa2-9caa-96815767ef1c", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM project webpage](http://www.cesm.ucar.edu)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "59650279-f141-4093-b06d-b788d46ae33c", + "metadata": {}, + "source": [ + "**Some points to pay attention to:** \n", + "- CESM **``Project``** Information\n", + "- **``Working Groups``** Information\n", + "- **``Community Projects``** Information\n", + " - CESM2 Large Ensemble\n", + " - CESM2 Stratospheric Aerosol Injection (ARISE-SAI)\n", + "- What **``Version``** of the model should you use?\n", + "- **``Individual Model``** Pages https://www.cesm.ucar.edu/models/ \n", + "- **``Supported Model``** Releases – 2.1.X vs 2.2.X\n", + "- **``Experiments``**, expand a case for details, diagnostics plots for many experiments\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8bd790d-2ad5-445d-aaff-4bc8d5b23a29", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "toc-showcode": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/community_experiments.ipynb b/notebooks_copy/intro/community_experiments.ipynb new file mode 100644 index 000000000..7d661a29a --- /dev/null +++ b/notebooks_copy/intro/community_experiments.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Community Experiments" + ] + }, + { + "cell_type": "markdown", + "id": "59bb8a3f-74a5-4c7f-aaa7-15d22fe59b4a", + "metadata": {}, + "source": [ + "Information about community experiments performed with CESM version 2 and previous verisons of the model (CSM1, CCSM2, CCSM3, CESM1) are documented on an experiments webpage. " + ] + }, + { + "cell_type": "markdown", + "id": "c6540e1c-d489-401b-b227-2da4f69c50ec", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM experiments webpage](https://www.cesm.ucar.edu/experiments)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "387a41d7-3e1f-4272-9ba6-7940dc3c9f7f", + "metadata": {}, + "source": [ + "Explore this webpage and then click on the CESM2 Experiments, Data, and Diagnostic Output link for experiments from the most recent model version." + ] + }, + { + "cell_type": "markdown", + "id": "501487a2-94b7-4947-a273-208b0106c0fd", + "metadata": {}, + "source": [ + "![CESM Experiments](../../images/intro/CESM_exp_1.png)\n", + "\n", + "*

Figure: CESM experiments webpage.

*" + ] + }, + { + "cell_type": "markdown", + "id": "26b40b47-8dfc-4477-99f2-f54f859ea47c", + "metadata": {}, + "source": [ + "This page provides a searchable list of all the CESM2 experiments available to the public. \n", + "To interpret the casenames, it is helpful to remember CESM naming conventions that provide information about the experiment at a glance. Knowing the CESM case naming conventions will help you navigate CESM community experiments. Information about the CESM naming is available at:" + ] + }, + { + "cell_type": "markdown", + "id": "09e4bfeb-04af-427e-9636-f62f001b567e", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM case naming conventions](https://www.cesm.ucar.edu/models/cesm2/naming-conventions)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "ab79cd61-d4c9-4e40-92f2-9253756b2eda", + "metadata": {}, + "source": [ + "Click on the experiment that corresponds to the CESM2 CMIP6 preindustrial control experiment:\n", + "b.e21.B1850.f09_g17.MIP6-piControl.001" + ] + }, + { + "cell_type": "markdown", + "id": "50f1a8e8-90d0-44e7-8fcf-5dd2f0b09d3f", + "metadata": {}, + "source": [ + "![CESM Experiments](../../images/intro/CESM_exp_2.png)\n", + "\n", + "*

Figure: CESM2 CMIP6 PI control experiment.

*" + ] + }, + { + "cell_type": "markdown", + "id": "42e2aada-04d8-40ba-8ae2-3d501782dadb", + "metadata": {}, + "source": [ + "Information about this experiment drops down below the experiment name. There are three main sections of information:\n", + "\n", + "- The title of the experiment with active components listed.\n", + "- Links to the data and/or the NCAR HPC directory with data. (#1, green arrow)\n", + "- Links to the diagnostic component package output and the Climate Variability Diagnostics Package. (#2, blue arrow)\n", + "- Details about the run, including the resolution, compset, etc. (#3, red arrow)" + ] + }, + { + "cell_type": "markdown", + "id": "0d1ff550-d4c7-45be-a6d0-b1ea789d0d67", + "metadata": {}, + "source": [ + "![CESM Experiments](../../images/intro/CESM_exp_3.png)\n", + "\n", + "*

Figure: CESM2 CMIP6 PI control experiment.

*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "084b0a01-829d-4996-bea5-f0e56e2c6d04", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/components.ipynb b/notebooks_copy/intro/components.ipynb new file mode 100644 index 000000000..8abe5e901 --- /dev/null +++ b/notebooks_copy/intro/components.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0086605a-f330-41bd-a79b-13339e0e5d58", + "metadata": { + "tags": [] + }, + "source": [ + "# What is CESM ? \n", + "\n", + "CESM is a coupled Earth system model for simulating Earth’s climate system and connected component systems. The model is composed of **separate models** that simultaneously simulate the Earth’s atmosphere, ocean, land, river run-off, land-ice, and sea-ice. The model components that are coupled through the Common Infrastructure for Modeling the Earth known as **[CIME](https://esmci.github.io/cime/versions/master/html/index.html)**. \n" + ] + }, + { + "cell_type": "markdown", + "id": "bac52cc8-695c-49b9-9018-69503ad75f2f", + "metadata": {}, + "source": [ + "![Discuss Image](../../images/intro/CESM2.png)\n", + "*

Figure: CESM2 Structure

*" + ] + }, + { + "cell_type": "markdown", + "id": "cd1f097c-1b20-4905-9af1-1b7c3901cb79", + "metadata": {}, + "source": [ + "## Model Components\n", + "\n", + "Each model component has a page on the CESM website containing descriptions and documentation for active or prognostic models. \n", + "\n", + "You can explore each of the component models and CIME from the links below." + ] + }, + { + "cell_type": "markdown", + "id": "16519007-3902-4d9e-bdb4-380208c14e33", + "metadata": {}, + "source": [ + "
\n", + "Component Links:\n", + " \n", + "- ``Atmosphere`` \n", + "- ``Land`` \n", + "- ``Land Ice`` \n", + "- ``Ocean`` \n", + "- ``River Runoff`` \n", + "- ``Sea Ice`` \n", + "- ``Wave`` \n", + "- ``CIME`` \n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e00f9ea-6161-4090-ba36-7f4022e9e467", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "toc-showcode": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/coupling.ipynb b/notebooks_copy/intro/coupling.ipynb new file mode 100644 index 000000000..dabf2fed7 --- /dev/null +++ b/notebooks_copy/intro/coupling.ipynb @@ -0,0 +1,135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "163311c4-af4e-4ac5-bfa1-20fc25016f28", + "metadata": { + "tags": [] + }, + "source": [ + "# CIME (Advanced)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b247f4d7-ca36-4b62-bc4d-007c42ed9892", + "metadata": {}, + "source": [ + "The Combined Model Components form the coupled CESM through a shared common set of tools and infrastructure and coupling framework. Here we describe the Common Infrastructure for Modeling the Earth otherwise known as [CIME](https://esmci.github.io/cime/versions/master/html/index.html), as well as the current CESM2 and future coupling frameworks.\n" + ] + }, + { + "cell_type": "markdown", + "id": "e5ddb5ae-4b5f-4f55-8242-5b8e86c831c8", + "metadata": {}, + "source": [ + "## Common Infrastructure for Modeling the Earth (CIME)" + ] + }, + { + "cell_type": "markdown", + "id": "9dca3b1b-e249-4e6c-b34c-46efc6cdc144", + "metadata": {}, + "source": [ + "The Common Infrastructure for Modeling the Earth is a python-based framework that is shared between CESM2 and other modeling center efforts to ensure efficient \n", + "coordination of model development. The schema below shows some of the science benefits and organizations that are using the CIME infrastructure. \n", + "

\n" + ] + }, + { + "cell_type": "markdown", + "id": "d973f124-8754-4bf6-a089-2509ea267671", + "metadata": {}, + "source": [ + "![CESM directories and namelists](../../images/intro/CESM2_CIME_Infrastructure.png)\n", + "*

Figure: Overview of the CESM2 Common Infrastructure for Modeling the Earth (CIME)

*\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c689c94-e8c6-40a6-a2a0-0fa873d1f111", + "metadata": {}, + "source": [ + "## Model Coupling Toolkit (MCT)" + ] + }, + { + "cell_type": "markdown", + "id": "03cb0afa-459e-44f2-948b-452195c8589d", + "metadata": {}, + "source": [ + "The CESM2 coupling is performed through the Model Coupling Toolkit (MCT). Additionally, the MCT coupling framework allows data and stub components to \n", + "replace active or prognostic components providing flexible activation/deactivation of feedbacks. \n", + "The schematic below shows all of the components and their connections.\n", + "

\n" + ] + }, + { + "cell_type": "markdown", + "id": "9bc59881-d61d-49a0-8178-f5ffba630a41", + "metadata": {}, + "source": [ + "![CESM directories and namelists](../../images/intro/CESM2_MCT_Coupling.png)\n", + "*

Figure: Overview of the CESM2 Components and Model Coupling Toolkit (MCT)

*\n" + ] + }, + { + "cell_type": "markdown", + "id": "9c213a95-b901-4db3-b263-4bf2d3e54196", + "metadata": {}, + "source": [ + "## Earth System Modeling Framework (ESMF) Coupling" + ] + }, + { + "cell_type": "markdown", + "id": "70a62a59-021b-4798-9e77-7ff786f54e7e", + "metadata": {}, + "source": [ + "Coupling development beyond CESM2 will be changing to the Earth System Modeling Framework (ESMF) National Unified Operational Prediction Capability (NUOPC) framework. More details on NUOPC can be found at \n", + " https://earthsystemmodeling.org/nuopc/ . The schema below shows the new CESM configuration and coupling with NUOPC.\n", + "

" + ] + }, + { + "cell_type": "markdown", + "id": "d9569358-6f6b-4fcc-9909-11e295957bca", + "metadata": {}, + "source": [ + "\n", + "![CESM directories and namelists](../../images/intro/CESM_NUOPC_Coupling.png)\n", + "*

Figure: Overview of the new CESM Components and National Unified Operational Prediction Capability (NUOPC) framework

*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40abbd6b-70cb-427b-a468-9311191be2a7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "toc-showcode": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/getting_help.ipynb b/notebooks_copy/intro/getting_help.ipynb new file mode 100644 index 000000000..4c99fcfa7 --- /dev/null +++ b/notebooks_copy/intro/getting_help.ipynb @@ -0,0 +1,88 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Getting Help\n" + ] + }, + { + "cell_type": "markdown", + "id": "de6ee277-ec79-4c5f-9cbf-d2901dd20ba3", + "metadata": {}, + "source": [ + "As you gain experience using and running CESM, you may want additional sources of information to help with problem solving, designing modeling experiments, runtime issues, etc." + ] + }, + { + "cell_type": "markdown", + "id": "aa8da793-cfa0-472f-980e-6ec0c5478791", + "metadata": {}, + "source": [ + "## DiscussCESM Forums" + ] + }, + { + "cell_type": "markdown", + "id": "3dfb2ac3-aeb3-421a-a8ea-636ffe0a61b4", + "metadata": {}, + "source": [ + "The DiscussCESM or Bulletin Board forum is an excellent location to post questions or to search through previously posted and answered questions in regard to problems encounter while using CESM.\n", + "\n", + "Register as a forums user by entering your valid information in the registration form. You can subscribe to forums of interest -- especially the “Announcements” and “Known Problems” -- and this way we can communicate updates to you.\n" + ] + }, + { + "cell_type": "markdown", + "id": "153bdbd3-d587-4125-8f90-a69496f5f264", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[DiscussCESM Forum](https://bb.cgd.ucar.edu/cesm/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f5f4bfa4-9482-4e29-8a9b-b4082ae8f452", + "metadata": {}, + "source": [ + "![Discuss Image](../../images/intro/CESM2_Discuss.png)\n", + "*

Figure: CESM2 Discuss Bulletin Board

*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dedd34e-f4c7-452d-b31e-b80acc32609a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/getting_involved.ipynb b/notebooks_copy/intro/getting_involved.ipynb new file mode 100644 index 000000000..a0a4a6ffb --- /dev/null +++ b/notebooks_copy/intro/getting_involved.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Getting Involved\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d14afe8-0247-4446-a1d4-1a2859156d90", + "metadata": {}, + "source": [ + "## CESM Events" + ] + }, + { + "cell_type": "markdown", + "id": "ae8ef153-ef12-4306-97d7-9d1ce6cda746", + "metadata": {}, + "source": [ + "CESM has a long history of open, Community Events. A schedule of CESM Events is maintained on the CESM Website. CESM events include Community and Working Group Meetings, Tutorials, Seminars, and other related events. The first Community workshop was held in 1996 and continues annually today, and there are winter working-group specific meetings.\n" + ] + }, + { + "cell_type": "markdown", + "id": "d0a9d7fd-4003-4b94-8e42-2342a148909b", + "metadata": {}, + "source": [ + "
\n", + " \n", + "[CESM Events](https://www.cesm.ucar.edu/events)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9347c979-52dc-44f8-9d7f-57a74b722db4", + "metadata": {}, + "source": [ + "![CESM Events Image](../../images/basics/CESM2_Events.png)\n", + "*

Figure: CESM2 Events Page

*" + ] + }, + { + "cell_type": "markdown", + "id": "e231cf65-e68f-4767-a5f1-0a59ad02922c", + "metadata": {}, + "source": [ + "**We hope to see you at a future CESM workshop!**" + ] + }, + { + "cell_type": "markdown", + "id": "2fc2e42c-dcfa-45aa-a16b-c1846590f2f9", + "metadata": {}, + "source": [ + "## CESM Email Lists" + ] + }, + { + "cell_type": "markdown", + "id": "a459419f-4e90-4aac-a7f4-13ce597afd1c", + "metadata": {}, + "source": [ + "There are a number of email lists for the CESM community that you can join to receive updates for the whole CESM community or for particular working groups." + ] + }, + { + "cell_type": "markdown", + "id": "002db32d-d135-446c-9c01-c6807467ae32", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM Email lists](https://www.cesm.ucar.edu/about/faqs)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "47c38994-bf8c-4cf3-8112-9e6b871398de", + "metadata": {}, + "source": [ + "## CESM Github Alerts" + ] + }, + { + "cell_type": "markdown", + "id": "df28fe76-4efe-491c-8784-3c4f0fb2fd04", + "metadata": {}, + "source": [ + "If you create a github account and opt-in, you can watch CESM related repositories." + ] + }, + { + "cell_type": "markdown", + "id": "909d4342-cfab-40ea-90bc-bf9c713939c5", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM Github](https://github.com/ESCOMP/CESM)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "30eb47fb-19c2-4b0a-a0c6-ae412f018ef3", + "metadata": {}, + "source": [ + "![Discuss Image](../../images/intro/CESM_github.png)\n", + "*

Figure: CESM2 Github Page

*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dedd34e-f4c7-452d-b31e-b80acc32609a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/intro_overview.ipynb b/notebooks_copy/intro/intro_overview.ipynb new file mode 100644 index 000000000..5e4cc18d5 --- /dev/null +++ b/notebooks_copy/intro/intro_overview.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0959db1d-1790-4519-a34e-7026444af537", + "metadata": {}, + "source": [ + "# Introduction" + ] + }, + { + "cell_type": "markdown", + "id": "f0015117-d84f-471e-9bc5-ba87dc050baf", + "metadata": {}, + "source": [ + "Welcome to the world of the **Community Earth System Model (CESM)**. This introduction will discuss the structure of the CESM model, provide information about how to become more involved in the CESM community, and how to get help if required.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a108581-ab10-4779-b8a7-da44ccaf4495", + "metadata": {}, + "source": [ + "## Visualizing this documentation" + ] + }, + { + "cell_type": "markdown", + "id": "864d5c25-d84a-437a-b86f-65a5211db5e8", + "metadata": {}, + "source": [ + "Depending on your computer settings, you may see this documentation with a **white** or **black background**. To toggle between these settings click the sun or moon in the upper right of the page (see red arrow below). \n", + "\n", + "We have developed the documentation with the **white background** and recommend you use these settings. If you choose to use the dark background it is possible some of the visualizations will be more difficult to read." + ] + }, + { + "cell_type": "markdown", + "id": "1a9457c9-8717-4882-b6b2-311ce7ff899c", + "metadata": {}, + "source": [ + "![Light/Dark mode](../../images/intro/light_dark_mode.png)\n", + "\n", + "*

Figure: How to change between light/dark mode.

*" + ] + }, + { + "cell_type": "markdown", + "id": "7f68efa6-c770-4448-aa2a-13882b8fd5f4", + "metadata": {}, + "source": [ + "## Advanced Knowledge\n", + "\n", + "Some of the pages of this tutorial are marked as **Advanced**. Feel free to skip these as needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acd1bd32-84b7-4d4e-9688-605a832766e6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/intro/project.ipynb b/notebooks_copy/intro/project.ipynb new file mode 100644 index 000000000..b0ce1776a --- /dev/null +++ b/notebooks_copy/intro/project.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0086605a-f330-41bd-a79b-13339e0e5d58", + "metadata": { + "tags": [] + }, + "source": [ + "# CESM Working Groups" + ] + }, + { + "cell_type": "markdown", + "id": "cab53fe1-0293-48ad-b0e3-4b9869bc55fb", + "metadata": {}, + "source": [ + "The CESM project has a strong commitment to Community involvment in governance and development of the CESM. The CESM modeling effort is coordinated through **Working Groups** that are responsible for the science and development of individual CESM components or organized to coordinate other across CESM efforts. \n", + "\n", + "Each team takes responsibility for developing and continually improving its component of the CESM consistent with the CESM goal of a fully-coupled model and with the CESM design criteria. There are currently 12 Working Groups." + ] + }, + { + "cell_type": "markdown", + "id": "aa2d188a-e632-43f5-9f52-ea91411d2014", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Atmosphere Model Working Group (AMWG)](https://www.cesm.ucar.edu/working-groups/atmosphere)\n", + " \n", + "[Biogeochemistry Working Group (BGCWG)](https://www.cesm.ucar.edu/working-groups/biogeo)\n", + " \n", + "[Chemistry Climate Working Group (CCWG)](https://https://www.cesm.ucar.edu/working-groups/chemistry)\n", + " \n", + "[Climate Variability and Change Working Group (CVCWG)](https://www.cesm.ucar.edu/working-groups/climate)\n", + " \n", + "[Earth System Prediction Working Group (ESPWG)](https://www.cesm.ucar.edu/working-groups/earth-system)\n", + " \n", + "[Land Ice Working Group (LIWG)](https://www.cesm.ucar.edu/working-groups/land-ice)\n", + " \n", + "[Land Model Working Group (LMWG)](https://www.cesm.ucar.edu/working-groups/land)\n", + " \n", + "[Ocean Model Working Group (OMWG)](https://www.cesm.ucar.edu/working-groups/ocean)\n", + " \n", + "[Paleoclimate Working Group (PWG)](https://www.cesm.ucar.edu/working-groups/paleo)\n", + " \n", + "[Polar Climate Working Group (PCWG)](https://www.cesm.ucar.edu/working-groups/polar)\n", + " \n", + "[Software Engineering Working Group (SEWG)](https://www.cesm.ucar.edu/working-groups/software)\n", + " \n", + "[Whole Atmosphere Model Working Group (WAWG)](https://www.cesm.ucar.edu/working-groups/whole-atmosphere)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e0c8268d-cff5-4ae0-8f65-e5b7017d32fd", + "metadata": {}, + "source": [ + "Each Working Group is coordinated by Co-Chairs. The Working Groups decide their own development priorities and work schedules, consistent with the overall goals of CESM, and are subject to oversight by the CESM Scientific Steering \n", + "Committee (SSC)." + ] + }, + { + "cell_type": "markdown", + "id": "426af3d0-3705-4b5c-a265-3d60fac884d5", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Working Group Co-Chairs and Terms](https://www.cesm.ucar.edu/working-groups/co-chairs)\n", + " \n", + "[Scientific Steering Committee (SSC)](https://www.cesm.ucar.edu/management/ssc)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "09ca207c-608c-4933-b640-e8155e100b10", + "metadata": {}, + "source": [ + "**We welcome and strongly encourage anyone interested to join the relevant CESM working groups to participate more fully in the CESM project and model development.** \n", + "\n", + "**Check the webpage [\"Getting Involved\"](https://ncar.github.io/CESM-Tutorial/notebooks/intro/getting_involved.html) for more information.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8bd790d-2ad5-445d-aaff-4bc8d5b23a29", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "603ca3b9-3c17-4697-9bb3-02526745b130", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "toc-showcode": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml.ipynb b/notebooks_copy/modifications/xml.ipynb new file mode 100644 index 000000000..ae6bd61bb --- /dev/null +++ b/notebooks_copy/modifications/xml.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0959db1d-1790-4519-a34e-7026444af537", + "metadata": {}, + "source": [ + "# Simple XML Modifications" + ] + }, + { + "cell_type": "markdown", + "id": "09b1ddf9-0892-4a46-bfa6-11fa05eac358", + "metadata": {}, + "source": [ + "We use XML files to control case settings. This is what we cover in this chapter:\n", + "- The section *Overview* describes what XML files are and the primary files used in CESM.\n", + "- You will then learn how to query and change XML variables.\n", + "- Next, we describe some of the main XML variables used to control run length or run type. \n", + "- The section *Exercise Overview* offers opportunities to practice the concepts learned in this chapter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb5bff3c-1583-4653-8286-f45e4cc86b5f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/copying_cases.ipynb b/notebooks_copy/modifications/xml/copying_cases.ipynb new file mode 100644 index 000000000..6659aa2e0 --- /dev/null +++ b/notebooks_copy/modifications/xml/copying_cases.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Copying Cases \n", + "\n", + "This is a placeholder for this lesson." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/exercises.ipynb b/notebooks_copy/modifications/xml/exercises.ipynb new file mode 100644 index 000000000..9aa2008d9 --- /dev/null +++ b/notebooks_copy/modifications/xml/exercises.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bfc54be9-aad8-420b-83c1-874ecd9cdc1b", + "metadata": {}, + "source": [ + "# Exercise Overview" + ] + }, + { + "cell_type": "markdown", + "id": "eb20fd6b-c748-428f-a8dc-565b2c43dfe4", + "metadata": {}, + "source": [ + "We will do three exercises to help us better understand xml modifications.\n", + "\n", + "- In Exercise 1, we will practice runtime variable modifications.\n", + "- In Exercise 2, we will create a branch run and modify the ocean coupling frequency. \n", + "- In Exercise 3, we will create a hybrid run and modify the atmosphere physics timestep. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b57bbfc-2712-460c-9da6-9545fe3e9869", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/exercises/xml_length_exercise.ipynb b/notebooks_copy/modifications/xml/exercises/xml_length_exercise.ipynb new file mode 100644 index 000000000..99a9dbcf3 --- /dev/null +++ b/notebooks_copy/modifications/xml/exercises/xml_length_exercise.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55295ae2-df19-4226-9f54-2c052e4afadc", + "metadata": {}, + "source": [ + "# Exercise 1: Modify run length " + ] + }, + { + "cell_type": "markdown", + "id": "9b8d7632-491e-4851-b6dc-b28e6620c761", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Exercise: Modify the `env_run.xml` file

\n", + "**Part I**: \n", + "Create a new fully coupled **startup** case named \"b.day2.1\" from the 1850 climate conditions with a resolution of f19_g17. Increase the amount of standard output produced by the model for debugging. Run for 1 month.

\n", + " \n", + "**Part II**:\n", + "*Do part II only **after** Part I has finished running*. \n", + "Change the debug level back to 1. Expand Part I to produce a total of 38 months of model run. Remember that you have already run for 1 month. **You should submit this at the end of the day so that it will (hopefully) run overnight and be available the following day.** You can continue with Exercise 2-3 and submit the run later.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "bed2b80a-b00e-46c5-b39c-4d3fd7363c55", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Hint!\n", + " \n", + "\n", + "1. Which variable controls the amount of standard output for debugging? We can use partial query to find out:\n", + " \n", + "```\n", + " ./xmlquery -p DBUG\n", + "```\n", + "\n", + " \n", + "2. Use `xmlchange` to modify `env_run.xml` \n", + "\n", + "3. Make sure to update the walltime to match the run length.\n", + " \n", + "4. For Part II, how to tell the model that this run continues from the previously finished run of 1 month?\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "448ee61e-bbe3-4149-9029-58abf1247f3f", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution to Part I\n", + " \n", + "\n", + " \n", + "From the `SRCROOT` (`/glade/work/$USER/code/my_cesm_code`) directory, create your case:\n", + " \n", + "```\n", + " cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", + " ./create_newcase --case /glade/work/$USER/cases/b.day2.1 --res f19_g17 --compset B1850\n", + "```\n", + "
\n", + " \n", + "In your case directory, change debugging levels and set runtime variables:\n", + "\n", + "```\n", + " cd /glade/work/$USER/cases/b.day2.1\n", + " ./xmlchange INFO_DBUG=2,STOP_N=3,STOP_OPTION=nmonths\n", + " ./xmlchange --subgroup case.run JOB_WALLCLOCK_TIME=2:00:00\n", + "```\n", + "
\n", + " \n", + "Remember to manually update your README.case file to document your changes.\n", + "\n", + "\n", + "Carry on to setup, build and submit the run:\n", + " \n", + "```\n", + " ./case.setup \n", + " qcmd -- ./case.build\n", + " ./case.submit\n", + "```\n", + "\n", + "Remember that `qcmd` is used on derecho only.\n", + "\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f72d36bf-b3a6-4862-9479-169cbc2b01e7", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution to Part II\n", + " \n", + "\n", + "
\n", + "1. To continue the run, set `CONTINUE_RUN` to `TRUE`\n", + " \n", + "```\n", + " ./xmlchange CONTINUE_RUN=TRUE\n", + "```\n", + "\n", + "
\n", + "2. Keep `STOP_OPTION` as \"nmonths” \n", + " \n", + " \n", + "
\n", + "3. Set `STOP_N` to “37”\n", + " \n", + "```\n", + " ./xmlchange STOP_N=37\n", + "```\n", + "\n", + "
\n", + "4. Set INFO_DBUG to 1\n", + " \n", + "```\n", + " ./xmlchange INFO_DBUG=1\n", + "```\n", + "\n", + "
\n", + "5. Change wallclock time to use the maximum of the allowed wallclock on derecho:\n", + " \n", + "```\n", + " ./xmlchange --subgroup case.run JOB_WALLCLOCK_TIME=12:00:00\n", + " ./xmlchange --subgroup case.st_archive JOB_WALLCLOCK_TIME=6:00:00\n", + "```\n", + "\n", + "
\n", + "6. Submit (./case.submit) from your b.day2.1 case directory:\n", + "\n", + "```\n", + " ./case.submit\n", + "```\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a8dfc72-6b1a-4d20-8730-dd8515a2758c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/exercises/xml_runtype_exercise.ipynb b/notebooks_copy/modifications/xml/exercises/xml_runtype_exercise.ipynb new file mode 100644 index 000000000..7943d41ab --- /dev/null +++ b/notebooks_copy/modifications/xml/exercises/xml_runtype_exercise.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d8c91f63-5ac2-46e2-8051-a0ee087f62a5", + "metadata": {}, + "source": [ + "# Exercise 2: Modify run type " + ] + }, + { + "cell_type": "markdown", + "id": "7ca45013-1ec6-43e6-94e8-3006ac7f9be1", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Exercise: Modify the `env_run.xml` file

\n", + " \n", + "Branch from the end of Exercise 1 Part I to create a new case named \"b.day2.2\". \n", + "Double the ocean coupling frequency (`OCN_NCPL`). Include an initial file as output data. Run for 1 month, then restart for 1 month. \n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c289a358-fb18-4bb2-b087-33ea5d666aa6", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Hint!\n", + " \n", + "\n", + "1. For a branch run, we need to set several variables related to the reference case in `env_run.xml`. To find variables applied to reference case (Branch and Hybrid runs), use ./xmlquery -p REF.\n", + " \n", + "2. Ocean coupling frequency needs to be changed in `env_run.xml`, controlled by the variable `OCN_NCPL`. What is the default value of `OCN_NCPL`? Use `xmlquery` to find out! You may also take a look at the variable description in `env_run.xml` to learn more about the variable.\n", + " \n", + "3. Make sure to update the walltime to match the run length.\n", + " \n", + "4. Don't forget to put restart files into the run directory `$RUNDIR`.\n", + " \n", + "5. To include an initial file as output, we will need to modify the variable `inithist` in the atmospehre namelist `user_nl_cam`. We will learn more about this in the next chapter namelist modification. \n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "a7917422-93d0-473d-9950-68a39c35c41e", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution\n", + " \n", + "\n", + "
\n", + "1. Create your new case with the command:\n", + " \n", + "```\n", + " cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + " ./create_newcase --case /glade/work/$USER/cases/b.day2.2 --res f19_g17 --compset B1850\n", + "```\n", + "
\n", + "\n", + "
\n", + "2. In your case directory, change the run type to branch, and change the associated RUN_* variables:\n", + "\n", + "```\n", + " cd /glade/work/$USER/cases/b.day2.2\n", + " ./xmlchange RUN_TYPE=branch\n", + " ./xmlchange RUN_REFCASE=b.day2.1,RUN_REFDATE=0001-04-01,GET_REFCASE=FALSE\n", + "```\n", + "\n", + "
\n", + "3. Set run time variables: \n", + " \n", + "```\n", + " ./xmlchange STOP_OPTION=nmonths,STOP_N=1,RESUBMIT=1,JOB_WALLCLOCK_TIME=02:00:00\n", + "``` \n", + "\n", + "
\n", + "4. Double ocean coupling frequency in `env_run.xml`:\n", + " \n", + "```\n", + " ./xmlchange OCN_NCPL=48\n", + "```\n", + "\n", + "
\n", + "5. Set up the case, put the initial and restart files in the `$RUNDIR`:\n", + " \n", + "```\n", + " ./case.setup\n", + " cp /glade/derecho/scratch/$USER/archive/b.day2.1/rest/0001-04-01-00000/* /glade/derecho/scratch/$USER/b.day2.2/run/\n", + "```\n", + " \n", + "
\n", + "6. To include an initial file at the end of the run, add the following lines to file `user_nl_cam`:\n", + "\n", + "``` \n", + " inithist='ENDOFRUN'\n", + "```\n", + "\n", + "
\n", + "7. Run ./preview_namelists:\n", + "\n", + "```\n", + " ./preview_namelists\n", + "```\n", + " \n", + "
\n", + "6. Carry on to build and submit the run:\n", + " \n", + "```\n", + " qcmd -- ./case.build\n", + " ./case.submit\n", + "```\n", + "
\n", + "\n", + "Remember that `qcmd` is used on derecho only.\n", + " \n", + "After the job completes, go to the short term archive space and explore.\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30e887f9-f5fb-4cce-8f3b-9fdc7386f199", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/exercises/xml_timestep_exercise.ipynb b/notebooks_copy/modifications/xml/exercises/xml_timestep_exercise.ipynb new file mode 100644 index 000000000..f90017b0a --- /dev/null +++ b/notebooks_copy/modifications/xml/exercises/xml_timestep_exercise.ipynb @@ -0,0 +1,135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bd28c148-b907-489d-9892-068ed48b11d9", + "metadata": {}, + "source": [ + "# Exercise 3: Change physics timestep" + ] + }, + { + "cell_type": "markdown", + "id": "dddd8ba3-aa00-451c-938f-35b3927a016f", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Exercise: Change physics timestep in CAM and CLM

\n", + "\n", + "**Hybrid** start a fully coupled run under pre-Industrial conditions with a resolution of f19_g17. Use restart and initial files from Exercise 2. Change the physics timestep in the atmosphere and land to 1200 seconds (default is 1800). Run 5 days.\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "f2dacddc-4f8f-4fb5-9664-65ab2618f97f", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Hint!\n", + " \n", + "\n", + "The atmosphere physics timestep is determined by the base period of coupling `NCPL_BASE_PERIOD` and the coupling frequency `ATM_NCPL`. \n", + "Find out their default values using xmlquery.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "cb35e292-fcbb-45a6-9a28-f02a80df83a6", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution\n", + " \n", + "\n", + "
\n", + "1. From the script directory, create your case:\n", + " \n", + "```\n", + " cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + " ./create_newcase --case /glade/work/$USER/cases/b.day2.3 --res f19_g17 --compset B1850\n", + "```\n", + " \n", + "
\n", + "2. In your case directory, change run type and runtime variables:\n", + " \n", + "```\n", + " cd /glade/work/$USER/cases/b.day2.3\n", + " ./xmlchange RUN_TYPE=hybrid,RUN_REFCASE=b.day2.2,RUN_REFDATE=0001-06-01,GET_REFCASE=FALSE,JOB_WALLCLOCK_TIME=2:00:00\n", + "```\n", + " \n", + "
\n", + "3. Change the physics timestep for CAM: \n", + " \n", + "```\n", + " ./xmlchange ATM_NCPL=72\n", + "```\n", + "(Why `ATM_NCPL` = 72? Do the math).\n", + "\n", + "
\n", + "4. Set up the case and position your initial and restart files in the `$RUNDIR`:\n", + " \n", + "```\n", + " ./case.setup\n", + " cp /glade/derecho/scratch/$USER/archive/b.day2.2/rest/0001-06-01-00000/* /glade/derecho/scratch/$USER/b.day2.3/run/\n", + "```\n", + " \n", + "
\n", + "5. Continue to build and submit: \n", + " \n", + "```\n", + " qcmd -- ./case.build\n", + " ./case.submit\n", + "```\n", + " \n", + "Remember that `qcmd` is used on derecho only.\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd58b568-cd97-4a59-bd26-a6c13f3b0ba3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/model_control_files.ipynb b/notebooks_copy/modifications/xml/model_control_files.ipynb new file mode 100644 index 000000000..66cd0eafe --- /dev/null +++ b/notebooks_copy/modifications/xml/model_control_files.ipynb @@ -0,0 +1,435 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# How to Modify XML Files (*.xml)\n", + "\n", + "Now let's learn how to edit and search for variables in an `*.xml` file\n", + "\n", + "We will use **`xmlchange`** to modify xml variables and use **`xmlquery`** to search xml variables. Note that you _can_ change XML files manually, but using the `xmlchange` script prevents XML errors and ensures there is a record of your changes in the `CaseStatus` file.\n", + "\n", + "For the examples below, you can follow along by going to your `CASEROOT` directory b.day1.0 that you completed in the Basics section." + ] + }, + { + "cell_type": "markdown", + "id": "0f82a5d4-62f5-462f-b162-bf4854a5e8d0", + "metadata": {}, + "source": [ + "***\n" + ] + }, + { + "cell_type": "markdown", + "id": "37497e69-075a-4099-a65d-e1c5cf91086e", + "metadata": {}, + "source": [ + "## 1. Editing with **`xmlchange`**" + ] + }, + { + "cell_type": "markdown", + "id": "c1f5f422-194f-4bc9-85a1-5610a266b6c6", + "metadata": {}, + "source": [ + "When modifying an `*.xml` file, we highly recommend using the **`xmlchange`** tool. This is done using the syntax `./xmlchange VARIABLE=VALUE` in your case directory. " + ] + }, + { + "cell_type": "markdown", + "id": "057cc9f0-14fc-46f7-b3a5-9eca551dbe16", + "metadata": {}, + "source": [ + "The xml files can also be edited manually with your favorate text editor (vi, emacs, etc.), but users should take care not to introduce any formatting errors that could lead to incomplete environment variable settings. You can use your text editor to take a look at the xml files -- you will find descriptions of each variable, which can be very helpful in understanding what they control. " + ] + }, + { + "cell_type": "markdown", + "id": "2c4a7c72-2021-445f-80e4-84539d782e6f", + "metadata": {}, + "source": [ + "Here is an example for using `xmlchange`. If you want to change the length of your run to one month, edit `env_run.xml` via the `xmlchange` tool by typing on the command line: \n", + "\n", + "```\n", + "./xmlchange STOP_OPTION=nmonths,STOP_N=1\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "3221a454-5c83-40c0-ab4e-744a63fa9971", + "metadata": {}, + "source": [ + "
\n", + " \n", + " \n", + " \n", + " Tips!\n", + " \n", + " \n", + "When using xmlchange to modify multiple environment variables, use comma (and no space!) in between variables.
\n", + "You can always find help by typing ``./xmlchange --help``\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "2e2dbbae-0375-49d1-9655-49b2eb5df884", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + "Click here for example output of ``./xmlchange --help``
\n", + "\n", + "```\n", + "./xmlchange --help\n", + "```\n", + " \n", + "
\n", + " \n", + "Output:
\n", + "\n", + "```\n", + "usage: xmlchange [-h] [-d] [-v] [-s] [--caseroot CASEROOT] [--append]\n", + " [--subgroup SUBGROUP] [--id ID] [--val VAL] [--file FILE]\n", + " [--delimiter DELIMITER] [--dryrun] [--noecho] [-f]\n", + " [-loglevel LOGLEVEL]\n", + " [listofsettings]\n", + "\n", + "Allows changing variables in env_*xml files via a command-line interface.\n", + "\n", + "This provides two main benefits over editing the xml files by hand:\n", + " - Settings are checked immediately for validity\n", + " - Settings are echoed to the CaseStatus file, providing a \"paper trail\" of\n", + " changes made by the user.\n", + "\n", + "Examples:\n", + "\n", + " To set a single variable:\n", + " ./xmlchange REST_N=4\n", + "\n", + " To set multiple variables at once:\n", + " ./xmlchange REST_OPTION=ndays,REST_N=4\n", + "\n", + " Alternative syntax (no longer recommended, but supported for backwards\n", + " compatibility; only works for a single variable at a time):\n", + " ./xmlchange --id REST_N --val 4\n", + "\n", + " Several xml variables that have settings for each component have somewhat special treatment.\n", + " The variables that this currently applies to are:\n", + " NTASKS, NTHRDS, ROOTPE, PIO_TYPENAME, PIO_STRIDE, PIO_NUMTASKS\n", + " For example, to set the number of tasks for all components to 16, use:\n", + " ./xmlchange NTASKS=16\n", + " To set just the number of tasks for the atm component, use:\n", + " ./xmlchange NTASKS_ATM=16\n", + "\n", + " The CIME case xml variables are grouped together in xml elements .\n", + " This is done to associate together xml variables with common features.\n", + " Most variables are only associated with one group. However, in env_batch.xml,\n", + " there are also xml variables that are associated with each potential batch job.\n", + " For these variables, the '--subgroup' option may be used to specify a particular\n", + " group for which the variable's value will be adjusted.\n", + " \n", + "...\n", + "\n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "c1ce0178-094a-4f2d-9a8a-d9764b427a0a", + "metadata": {}, + "source": [ + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "bfcc9eca-86f5-4ad0-a481-3f8b481e5a6c", + "metadata": {}, + "source": [ + "
\n", + "Evaluate your understanding\n", + "\n", + "If you want to manually resubmit an initial case that previously had a `CONTINUE_RUN` value of `FALSE`, how do you change it to `TRUE`?\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bab2a8f3-4cee-4bba-a8fe-570ac306871d", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n", + "Click here for the solution
\n", + "\n", + "Use xmlchange to modify the variable value with the command:\n", + "``` \n", + " ./xmlchange CONTINUE_RUN=TRUE \n", + "```\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "b4cdce13-52a4-4dcc-bd19-93db33339402", + "metadata": {}, + "source": [ + "
\n", + "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "9dcd9f04-8a62-40bb-8901-c709edab0764", + "metadata": {}, + "source": [ + "## 2. Searching xml variables with **`xmlquery`**" + ] + }, + { + "cell_type": "markdown", + "id": "85e1421c-8386-4d7b-aa51-a0d0240d3a8b", + "metadata": {}, + "source": [ + "We recommend using the **`xmlquery`** tool in your case directory to query xml variables." + ] + }, + { + "cell_type": "markdown", + "id": "01d3cdf9-7eb5-4dc4-9745-1efb571f0efb", + "metadata": {}, + "source": [ + "For example, to find out the run type of your job, search for xml variable `RUN_TYPE`: \n", + "\n", + "```\n", + "./xmlquery RUN_TYPE\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "67864bd6-d229-4b28-b976-6e18883b042b", + "metadata": {}, + "source": [ + "
\n", + " \n", + " \n", + " \n", + " Tips!\n", + " \n", + "\n", + "You can always find help by typing `./xmlquery --help` \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b01a2599-923a-4a78-ae97-42904f679e52", + "metadata": {}, + "source": [ + "This will return the default `RUN_TYPE` value:\n", + ">RUN_TYPE: hybrid" + ] + }, + { + "cell_type": "markdown", + "id": "e83f2c2d-5a57-447a-add3-f0d37ec74bb3", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9adc30a9-549b-4a68-a25a-743fab747aa6", + "metadata": {}, + "source": [ + "
\n", + "Evaluate your understanding\n", + "\n", + "Let's check if the variables in the previous exercise are properly modified.\n", + " \n", + "Query for the value of `STOP_N`, `STOP_OPTION`, and `CONTINUE_RUN`.\n", + "\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2a664275-f370-4438-af52-7ed9c8526517", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution\n", + " \n", + " \n", + "Use `xmlquery` to search the variables with the command:\n", + "
\n", + "```\n", + " ./xmlquery STOP_N,STOP_OPTION,CONTINUE_RUN \n", + "```\n", + " \n", + "
\n", + "If you see: \n", + "\n", + "```\n", + ">STOP_N: 1 \n", + ">STOP_OPTION: nmonths \n", + ">CONTINUE_RUN: TRUE \n", + "```\n", + "
\n", + " Great! You have successfully queried these variables. \n", + "
\n", + "\n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "ba5e3006-7d70-4e3f-bdf9-351f766bd7ca", + "metadata": {}, + "source": [ + "
\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "markdown", + "id": "55f9f962-c8ae-4685-ae6d-9d4a75f1d5b5", + "metadata": {}, + "source": [ + "## 3. Use the subgroup functionality of **`xmlchange`**" + ] + }, + { + "cell_type": "markdown", + "id": "a3e50d72-dac9-42be-95a7-71e1d8759ea1", + "metadata": {}, + "source": [ + "Now let's first try to find out the wallclock time of your job, search for xml variable `JOB_WALLCLOCK_TIME`: \n", + "\n", + "```\n", + "./xmlquery JOB_WALLCLOCK_TIME\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "f190bd81-be55-481a-820a-522d7f77fb08", + "metadata": {}, + "source": [ + "
\n", + " \n", + " \n", + " \n", + " Tips!\n", + " \n", + " \n", + "If you are unsure about the full name of the xml variable, you can query variables with a partial match, using `--partial-match` or `-p`. \n", + " \n", + "For example: `./xmlquery -p WALLCLOCK`\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1279be11-3868-4aac-9132-042d7efd515b", + "metadata": {}, + "source": [ + "
\n", + "You will find that the query of wallclock time returns multiple instances:" + ] + }, + { + "cell_type": "markdown", + "id": "fea58693-2073-42e9-a219-568119f870f0", + "metadata": {}, + "source": [ + ">Results in group case.run
\n", + ">JOB_WALLCLOCK_TIME: 00:30:00\n", + "\n", + ">Results in group case.st_archive
\n", + ">JOB_WALLCLOCK_TIME: 00:30:00" + ] + }, + { + "cell_type": "markdown", + "id": "84969b59-8664-4104-a292-76c5e6d36d7f", + "metadata": {}, + "source": [ + "The variable `JOB-WALLCLOCK_TIME` has two instances in two different subgroups: `case.run` and `case.st_archive`.
\n", + "\n", + "Then, how do we specify which instance we want to modify?\n", + "\n", + "For variables that have multiple instances, we can use the **\"subgroups\"** functionality in `xmlchange`.\n", + "For example, if we want to change the default `JOB_WALLCLOCK_TIME` from 30 minutes to 1 hour for the short term archiver subgroup, type in the following command:\n", + "\n", + "```\n", + "./xmlchange --subgroup case.st_archive JOB_WALLCLOCK_TIME=01:00:00\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "be11b2ed-b3a5-49ea-a350-bc4fa7f2e609", + "metadata": {}, + "source": [ + "Try it yourself! Type in the command above and use `xmlquery` to check if your modifications are correctly applied. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1ed48db-f145-4e5c-a656-8249053833f7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/modify_run_type.ipynb b/notebooks_copy/modifications/xml/modify_run_type.ipynb new file mode 100644 index 000000000..90d3f43e5 --- /dev/null +++ b/notebooks_copy/modifications/xml/modify_run_type.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Modifying the Type of Run \n", + "\n", + "CESM has three types of initial runs:\n", + "- startup\n", + "- branch\n", + "- hybrid\n" + ] + }, + { + "cell_type": "markdown", + "id": "fb99d6cf-09ee-45da-9af8-4d404709378a", + "metadata": {}, + "source": [ + "\n", + "The xml variable `$RUN_TYPE` determines the initialization type. \n", + "\n", + "Note that the `RUN_TYPE` setting is ***only important for the initial run*** of a production run when the `$CONTINUE_RUN` variable is set to `FALSE`. After the initial run, the `$CONTINUE_RUN` variable is set to `TRUE`, and the model restarts exactly using input files in a case, date, and bit-for-bit continuous fashion. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9447be66-ca1f-4b05-b93c-4f1a0bb303ad", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/modify_run_type/hybrid_branch_restart.ipynb b/notebooks_copy/modifications/xml/modify_run_type/hybrid_branch_restart.ipynb new file mode 100644 index 000000000..6a1914580 --- /dev/null +++ b/notebooks_copy/modifications/xml/modify_run_type/hybrid_branch_restart.ipynb @@ -0,0 +1,311 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Hybrid, Branch and Startup \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "5ffbef19-73bc-43d9-806b-2b522129f838", + "metadata": {}, + "source": [ + "The CESM model can be initialized in one of three ways: startup, hybrid and branch. These are set with the variable **`$RUN_TYPE`** in `env_run.xml`. \n", + "\n", + "
\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "markdown", + "id": "5f390f8b-76ec-4a3a-8273-a1f2828b4ac2", + "metadata": {}, + "source": [ + "## 1. The three run types\n", + "\n", + "\n", + "#### - STARTUP\n", + "\n", + "- In a startup run (the default), all model components are initialized from the basic **default initial conditions**. The coupler does not need an initial file. \n", + "\n", + "\n", + "\n", + "#### - BRANCH\n", + "\n", + "- In a branch run, all components are initialized using **a consistent set of restart files from a previous run** (determined by the [`$RUN_REFCASE`](./run_variables.ipynb) and [`$RUN_REFDATE`](./run_variables.ipynb) variables in env_run.xml). \n", + "- The case name is generally changed for a branch run, although it does not have to be. \n", + "- In a branch run, setting [`$RUN_STARTDATE`](./run_variables.ipynb) is ignored because the model components obtain the start date from their restart datasets. Therefore, **the start date cannot be changed for a branch run**. This is the same mechanism that is used for performing a restart run (where `$CONTINUE_RUN` is set to TRUE in the env_run.xml file).\n", + "- Branch runs are typically used when sensitivity or parameter studies are required, or when settings for history file output streams need to be modified while still maintaining **bit-for-bit reproducibility**.\n", + "\n", + "\n", + "\n", + "\n", + "#### - HYBRID\n", + "\n", + "- A hybrid run indicates that CESM is initialized more like a startup, but uses initialization datasets from a previous case. This is somewhat analogous to a branch run with relaxed restart constraints. \n", + "- A hybrid run allows users to bring together combinations of initial/restart files from a previous case (specified by [`$RUN_REFCASE`](./run_variables.ipynb)) at a given model output date (specified by [`$RUN_REFDATE`](./run_variables.ipynb)). Unlike a branch run, the starting date of a hybrid run (specified by [`$RUN_STARTDATE`](./run_variables.ipynb)) can be modified relative to the reference case. \n", + "- In a hybrid run, the model does not continue in a bit-for-bit fashion with respect to the reference case. The resulting climate, however, should be continuous provided that no model source code or namelists are changed in the hybrid run. \n", + "- The atmosphere is initialized from initial condition files generated by a user-specified CESM simulation. The land, runoff, ocean and ice are initialized from restart files generated by a user-specified CESM simulation. No coupler file is needed\n", + "\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "62696cba-7150-4f3c-866e-d6ac374a96b8", + "metadata": {}, + "source": [ + "It is important to remember that the **`$RUN_TYPE` variable is only relevant when a run is initialized**. Let's again take a look at the [exercises](../run_length/changing_run_length.ipynb) we did in the chapter of [Changing run length](../run_length/changing_run_length.ipynb):\n" + ] + }, + { + "cell_type": "markdown", + "id": "823344b5-44a0-4f0d-a501-6b9ba8f9608e", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "id": "5e0f253a-2f0e-40c3-b97a-661b15608aa2", + "metadata": { + "tags": [] + }, + "source": [ + "
\n", + "Evaluate your understanding\n", + "\n", + "The tutorial version of the CESM model on derecho simulates ~10 model years per wallclock day. The maximum wallclock request is 12 hours.\n", + "If you want to run the model for 100 years, what values should be set for `STOP_OPTION`, `STOP_N` and `RESUBMIT`? \n", + "\n", + "We worked out that for a total of 100 years, we will need 20 submissions. We can set:\n", + "\n", + "```\n", + "STOP_OPTION = 'nyears', STOP_N = 5, RESUBMIT = 19\n", + "```\n", + "
\n", + " \n", + "Now, **if you want this run to be a hybrid run, what variables should you modify, and at which stage?**\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "44222574-7e88-431d-911d-6c96a9bfde2e", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Hint!\n", + " \n", + " \n", + "The `$RUN_TYPE` variable is only important at the initial run when `CONTINUE_RUN` is set to `FALSE`. \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1e2de2d0-90eb-4c6c-b5df-636dfc79d623", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution\n", + " \n", + "\n", + "Set `RUN_TYPE='hybrid'` in env_run.xml before submitting the initial run. \n", + "\n", + "Bonus question:
\n", + " \n", + "What happens if you set `run_type='branch'` after the 2nd resubmission when the `CONTINUE_RUN=TRUE`?
\n", + " \n", + "Answer: Nothing changes, because `RUN_TYPE` is only relevant for an initial run when `CONTINUE_RUN` is set to `FALSE`! \n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "7ef2ad7b-8e69-40cc-97df-165d0060ae45", + "metadata": {}, + "source": [ + "
\n", + "\n", + "One more!\n" + ] + }, + { + "cell_type": "markdown", + "id": "e38db72e-ab1a-4600-b5c5-bdec1586e645", + "metadata": {}, + "source": [ + "
\n", + "Evaluate your understanding\n", + "\n", + "\n", + "In the exercise above, if you have set `RUN_TYPE='hybrid'` and `RESUBMIT=19` before the initial run, what are the value of `CONTINUE_RUN`, `RESUBMIT`, and `RUN_TYPE` at the time of:\n", + "- the initial submission of 5 years\n", + "- the next submission of 5 years\n", + "- the 3rd run (2nd resubmission) of 5 years?\n", + " \n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "78e21916-454a-48ce-a423-be0cefbe6dae", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution \n", + " \n", + " \n", + "- **the initial submission of 5 years**: \n", + " \n", + " ``` \n", + " CONTINUE_RUN=FALSE, RESUBMIT=19, RUN_TYPE='hybrid' \n", + " ```\n", + " because when the run is first initialized, `CONTINUE_RUN=FALSE`
\n", + "
\n", + " \n", + "- **the next submission of 5 years**: \n", + " ```\n", + " CONTINUE_RUN=TRUE, RESUBMIT=18, RUN_TYPE='hybrid'\n", + " ```\n", + " because `RESUBMIT>0`, `CONTINUE_RUN` will automatically switch to `TRUE` after the initial run, but `RUN_TYPE` won't change.
\n", + "
\n", + " \n", + "- **the 3rd run (2nd resubmission) of 5 years**: \n", + " ```\n", + " CONTINUE_RUN=TRUE, RESUBMIT=17, RUN_TYPE='hybrid' \n", + " ```\n", + " `CONTINUE_RUN` stays to be `TRUE`, `RESUBMIT` decreases by 1, `RUN_TYPE` won't change.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "9ff63e65-0ae0-4d37-828f-cae55f5c1a7a", + "metadata": {}, + "source": [ + "
\n", + "\n", + "***\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "fca3acd9-afcf-4d36-a287-4b5220f366d7", + "metadata": {}, + "source": [ + "## 2. When to use branch vs. hybrid?" + ] + }, + { + "cell_type": "markdown", + "id": "28612407-0b64-4a29-a1eb-9495203ea223", + "metadata": {}, + "source": [ + "Branch and hybrid runs are useful if you have an experiment which only slightly differs from your control, but you want to make a slight modification, add history output, or start your simulation from a CESM spun-up initial state and maintaining an exact restart (which mimics what the model would do if it had kept running in the original setup).\n", + "\n", + "- **Use a hybrid run**: for most applications where you do NOT need bit for bit restart. You CAN specify a new start date for your model run.\n", + "\n", + "- **Use a branch run**: only for applications which require exact restart. It is typically used when sensitivity or parameter studies are required, or when settings for history file output streams need to be modified while still maintaining bit-for-bit reproducibility. You CANNOT specify a new start date for your model run. It will be assigned by the reference case ([`$RUN_REFDATE`](./run_variables.ipynb)). " + ] + }, + { + "cell_type": "markdown", + "id": "253bec5a-bd70-45a1-a63e-1a935a6caaba", + "metadata": {}, + "source": [ + "
\n", + "\n", + "***\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "1cee2835-49f8-4e36-92dc-7ccca26d1354", + "metadata": {}, + "source": [ + "## 3. Restart files for branch and hybrid runs" + ] + }, + { + "cell_type": "markdown", + "id": "656226b3-9959-4e47-b047-725c3ea64969", + "metadata": {}, + "source": [ + "For runs that are initialized as hybrid or branch runs, we will need restart/initial files from previous model runs (as specified by the variables, [`$RUN_REFCASE`](./run_variables.ipynb) and [`$RUN_REFDATE`](./run_variables.ipynb)). See the Chapter [Run variables related to run type](./run_variables.ipynb) for details. \n", + "\n", + "\n", + "Let's take a look back at the [Chapter on restart files](../run_length/restarting.ipynb) to refresh our memory!\n" + ] + }, + { + "cell_type": "markdown", + "id": "1dfd05c3-12d5-4980-b945-9fe7bcae452b", + "metadata": {}, + "source": [ + "It is important to note that these required files must be prestaged by the user to the case run directory (`$RUNDIR`) before the model run starts. This is normally done by just copying the contents of the relevant `$RUN_REFCASE/rest/$RUN_REFDATE.00000` directory.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71ae1b6e-8bf1-4b17-a00f-86f06f4577ff", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/modify_run_type/run_variables.ipynb b/notebooks_copy/modifications/xml/modify_run_type/run_variables.ipynb new file mode 100644 index 000000000..3ca13720a --- /dev/null +++ b/notebooks_copy/modifications/xml/modify_run_type/run_variables.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Variables Related to Run Type\n", + "\n", + "A summary of the xml variables pertaining to run type in `env_run.xml`. \n", + "\n", + "
\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "markdown", + "id": "a0652973-f463-4459-bcd1-0027c3cc2c9e", + "metadata": {}, + "source": [ + "- **`RUN_TYPE`** ==> Determines the model run initialization type. Only important for the initial run of a production run when the `$CONTINUE_RUN` variable is set to `FALSE`. Valid values are: 'startup', 'hybrid', 'branch'. \n", + "\n", + "- **`RUN_STARTDATE`** ==> Run start date (yyyy-mm-dd). Only used for startup or hybrid runs. \n", + "\n", + "- **`RUN_REFCASE`** ==> For hybrid or branch runs, name of the reference case you are starting from. \n", + "\n", + "- **`RUN_REFDATE`** ==> For hybrid or branch runs, date stamp (yyyy-mm-dd) of reference case you are starting from. \n", + "\n", + "- **`GET_REFCASE`** ==> Flag for automatically prestaging the refcase restart dataset. If `TRUE`, then the refcase data is prestaged into the executable directory. If `FALSE`, the user need to manually put the restart files into the `$RUNDIR`. " + ] + }, + { + "cell_type": "markdown", + "id": "26abab24-5108-4006-8f31-bf696f837aa4", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/other_xml_variables.ipynb b/notebooks_copy/modifications/xml/other_xml_variables.ipynb new file mode 100644 index 000000000..0e27b5636 --- /dev/null +++ b/notebooks_copy/modifications/xml/other_xml_variables.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8f61eb16-f182-4b94-9959-5fb517a5bc7e", + "metadata": {}, + "source": [ + "# Other Useful XML Variables " + ] + }, + { + "cell_type": "markdown", + "id": "b616dc75-7fd6-46db-8e96-27278c7ec731", + "metadata": {}, + "source": [ + "Here are some more xml variables in `env_run.xml` that you may encounter:\n", + "\n", + "- `INFO_DBUG` ==> sets the level of stdout (standard out) print statements. This variable is useful for debugging. If debugging, a higher value may be set. \n", + "- `DOUT_S` ==> turns on short-term archiving. `DOUT_S` is set to `TRUE` by default. \n", + "- `HIST_OPTION` ==> sets driver snapshot history file frequency. \n", + "- `CCSM_CO2_PPMV` ==> CO2 value to be propogated to the ocean component POP and the land component CLM, when `OCN_CO2_TYPE` and `CLM_CO2_TYPE` are set to `'constant'`. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d74f86e-c1dc-45a3-845f-2d0200fbedbd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/overview.ipynb b/notebooks_copy/modifications/xml/overview.ipynb new file mode 100644 index 000000000..37d127221 --- /dev/null +++ b/notebooks_copy/modifications/xml/overview.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": { + "tags": [] + }, + "source": [ + "# Overview \n", + "\n", + "What is an XML file, and how do we use it in CESM?" + ] + }, + { + "cell_type": "markdown", + "id": "55a68c69-196d-42c3-ae01-ad55f10daa6d", + "metadata": {}, + "source": [ + "***" + ] + }, + { + "cell_type": "markdown", + "id": "0637e559-0f3c-46b3-bc80-f7fe1081387a", + "metadata": {}, + "source": [ + "## 1. What is an XML file?\n", + "\n", + "XML (Extensible Markup Language) is a markup language that defines a syntax for encoding documents that both humans and machines could read. It is used to describe data in a structured way and defines how the document should be stored and transported." + ] + }, + { + "cell_type": "markdown", + "id": "027b5e63-e318-411e-b854-9109fc0491ce", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Additional information on XML files](https://www.howtogeek.com/357092/what-is-an-xml-file-and-how-do-i-open-one/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "fa35fe32-ea6a-4e3e-9a9a-070697bca22f", + "metadata": {}, + "source": [ + "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "57f433ea-3424-447f-b6e7-68673a40d24d", + "metadata": {}, + "source": [ + "## 2. CESM XML files and CESM environment variables" + ] + }, + { + "cell_type": "markdown", + "id": "0c67affb-108e-4005-8329-ccd19ef87eda", + "metadata": {}, + "source": [ + "CESM cases are customized, built and run largely through setting what CESM calls **\"environment variables\"**. These actually appear to the user as variables defined in xml files. \n", + "\n", + "\n", + "These files appear in the case directory (`$CASEROOT`) once a case is created and are named something like **`env_*.xml`**. We control how we compile and run the model with these files." + ] + }, + { + "cell_type": "markdown", + "id": "e58d2ab9-957f-4078-a7eb-5b7091c28616", + "metadata": {}, + "source": [ + "## XML file locations\n", + "\n", + "![CESM xml files](../../../images/modifications/CESM2_xml_files.png)\n", + "*

Figure: Overview of the XML file locations

*\n", + "For the current tutorial on derecho, the paths are:\n", + "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`" + ] + }, + { + "cell_type": "markdown", + "id": "cce5ecd7-e613-47bd-8c4b-1e547b2a75ae", + "metadata": {}, + "source": [ + "There are multiple **`env_*.xml`** files in the `$CASEROOT` directory:\n", + "\n", + "- `env_archive.xml`: specifies rules for short term archive script `case.st_archive`\n", + "- `env_batch.xml`: specifies batch specific settings used in `case.submit` script\n", + "- `env_build.xml`: specifies build information used in the `case.build` script\n", + "- `env_case.xml`: set by create_newcase and **cannot** be modified\n", + "- `env_mach_pes.xml`: specifies PE layout on NCAR HPC for components and used by `case.run` script \n", + "- `env_mach_specific.xml`: specifies machine specific information used in `case.build` script\n", + "- `env_run.xml`: sets run time information (such as length of run, number of submissions, …)\n" + ] + }, + { + "cell_type": "markdown", + "id": "58df9e9f-ef86-4a65-91c2-4b829b33a86e", + "metadata": {}, + "source": [ + "In this section, we will learn and practice **modifying CESM run length and run type using `env_run.xml`**!" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/physics_timestep.ipynb b/notebooks_copy/modifications/xml/physics_timestep.ipynb new file mode 100644 index 000000000..3262199d0 --- /dev/null +++ b/notebooks_copy/modifications/xml/physics_timestep.ipynb @@ -0,0 +1,69 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Physics Timestep\n", + "\n", + "**Where and When to Change Time Steps**:\n", + "\n", + "When the model crashes due to large, temporary instabilities, one method to overcome the problem is to change the time step.\n", + "This is typically done in either the atmosphere or ocean components. \n", + "\n", + "- For atmosphere and land component (CAM/CLM): change `ATM_NCPL` in `env_run.xml`.
\n", + "- For ocean (POP): change `dt_count` in POP namelist. \n", + "\n", + "
\n", + "\n", + "***\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "dc35cf0e-f3be-4a26-b2f0-0b716ca440f1", + "metadata": {}, + "source": [ + "- **For the atmosphere component (CAM)**, the physics time step is set by `ATM_NCPL` and `NCPL_BASE_PERIOD`in `env_run.xml`. `NCPL_BASE_PERIOD` specifies base period associated with coupling frequency, the default value is \"day\". The `ATM_NCPL` variable specifies the number of coupling intervals per `NCPL_BASE_PERIOD` between the atmosphere/land and the coupled system. Based on these two variables, the scripts will automatically compute the time step for the atmosphere and land and populate the namelist files accordingly. For example, if `NCPL_BASE_PERIOD` is set to \"day\", and `ATM_NCPL` is 48, then the timestep is 1800 seconds. \n", + "\n", + "- **For the land component (CLM)**, the physics ime step is the same as the CAM time step; this is automatically set with the CAM time step via `ATM_NCPL`. You cannot set this separately.\n", + "\n", + "- **For the ocean component (POP2)**, the physics time step is changed in the `user_nl_pop` file and is based on `$OCN_NCPL` (found in `env_run.xml`), `dt_count` , and `dt_option`. The default `dt_option` is `“steps_per_day”`.\n", + "\n", + "- **For the sea ice component (CICE)**, the physics time step is set by the coupling interval variable `$ICE_NCPL` found in env_run.xml. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d227999f-f3c0-4ae7-adbc-558cc7a6f287", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/run_length.ipynb b/notebooks_copy/modifications/xml/run_length.ipynb new file mode 100644 index 000000000..d727e50fc --- /dev/null +++ b/notebooks_copy/modifications/xml/run_length.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Controlling Run Length \n", + "\n", + "The length of your model run is controlled by several run time variables in the **`env_run.xml`** file. \n", + "These variables may be modified at the initialization of the model run and during the course of the model run. \n", + "\n", + "These variables comprise coupler namelist settings for the model stop time, model restart frequency, coupler history frequency and a flag to determine if the run should be flagged as a continuation run." + ] + }, + { + "cell_type": "markdown", + "id": "b69bbda8-c906-420f-a782-7da8f223c8bc", + "metadata": {}, + "source": [ + "We will learn about customizing runtime settings to control starting, stopping, restarting and continuing a model run, and practice how to specify the run length of your long simulations. \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/run_length/changing_run_length.ipynb b/notebooks_copy/modifications/xml/run_length/changing_run_length.ipynb new file mode 100644 index 000000000..06378c500 --- /dev/null +++ b/notebooks_copy/modifications/xml/run_length/changing_run_length.ipynb @@ -0,0 +1,232 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Changing Run Length\n", + "\n", + "How to set run length for long simulations using variables `$STOP_OPTION`, `$STOP_N` and `$RESUBMIT`?" + ] + }, + { + "cell_type": "markdown", + "id": "555d7d60-109c-45ed-94e2-441bf3a7a93a", + "metadata": {}, + "source": [ + "## 1. Number of submissions and run length" + ] + }, + { + "cell_type": "markdown", + "id": "2730b8e5-def6-4ae3-bde0-bb6f0d0f5e02", + "metadata": {}, + "source": [ + "Recall that we can use [STOP_N and STOP_OPTION](../starting_and_stopping.ipynb) to control the run length of each batch job submission. " + ] + }, + { + "cell_type": "markdown", + "id": "9641f6ad-7a95-4cf1-8178-26a33ca1ecfb", + "metadata": {}, + "source": [ + "A typical long model simulation (say you want to run the model for 100 years) is comprised of many job submissions. This is because we have limited batch wallclock time for each job submission. For example, on derecho, the regular queue wallclock limit is 12 hours. " + ] + }, + { + "cell_type": "markdown", + "id": "dbf6d5d2-ad44-4ff9-8d8b-b18ee368cb6f", + "metadata": {}, + "source": [ + "We can specify **the number of times to resubmit the run using the `$RESUBMIT`** variable in **`env_run.xml`** to complete the long run. " + ] + }, + { + "cell_type": "markdown", + "id": "3db7b888-74cf-40dd-9931-e9e312029cf8", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "id": "58076e6d-4ca6-442d-bd7a-6695e00e6b3d", + "metadata": {}, + "source": [ + "
\n", + "Evaluate your understanding\n", + "\n", + "The tutorial version of the CESM model on derecho simulates ~10 model years per wallclock day. The maximum wallclock request is 12 hours.\n", + "If you want to run the model for 100 years, what values should be set for `STOP_OPTION`, `STOP_N` and `RESUBMIT`?\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "2bbcde94-569d-40cd-bece-f98e511a5c3b", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Hint!\n", + " \n", + " \n", + "- How to set `STOP_N` and `STOP_OPTION` for **each submission**, given the wallclock limit?\n", + " \n", + "- How many times to resubmit the job to reach the 100 years?\n", + " \n", + "- The number of total submission = the initial submission + the number of resubmission.\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "caa0ef50-df17-4841-beaa-485c4b5be2c8", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution \n", + " \n", + "Assume we want to use the full 12 hours for each job submission.\n", + " \n", + "The model runs 10 years / wallclock day, which means that 12 hours would give us 5 years per job submission. \n", + " \n", + "For a total of 100 years, we will need 20 submissions. \n", + "\n", + "``` \n", + "STOP_OPTION='nyears', STOP_N=5, RESUBMIT=19\n", + "``` \n", + "so that initial run of 5 years + (19 resubmits x 5 years per job) = 100 years. \n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "6149a7df-1f01-477a-b06a-f7d9196c1060", + "metadata": {}, + "source": [ + "***\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "0bfe03b7-bd83-4c43-a586-3692bc728dbb", + "metadata": {}, + "source": [ + "## 2. RESUBMIT and CONTINUE_RUN" + ] + }, + { + "cell_type": "markdown", + "id": "ba756e82-bbb4-46e5-a603-ff17507f3f8b", + "metadata": {}, + "source": [ + "In the exercise above, the first submission is the initial run, where `CONTINUE_RUN` is by default set to `FALSE`. When you want to continue the run after running the first 5 years, you will need to tell the model to continue by setting `CONTINUE_RUN=TRUE`. " + ] + }, + { + "cell_type": "markdown", + "id": "9af5ecdc-778b-49aa-8847-002f10194841", + "metadata": {}, + "source": [ + "If you have set **`RESUBMIT>0`, your script will automatically change `CONTINUE_RUN=TRUE`** after completion of the first submission for all subsequent submissions into the queue. \n" + ] + }, + { + "cell_type": "markdown", + "id": "fd397d89-a0d5-4a1d-859a-d2cf574af152", + "metadata": {}, + "source": [ + "
\n", + "Evaluate your understanding\n", + "\n", + "In the previous exercise, if we have set `RESUBMIT`=19 before the initial run, what are the value of `CONTINUE_RUN` and `RESUBMIT` at the time of:\n", + "- the initial submission of 5 years\n", + "- the next submission of 5 years\n", + "- the 3rd run (2nd resubmission) of 5 years?\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "fb184cde-0045-4b17-8530-5eb0d1ade77b", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution \n", + " \n", + " \n", + "- **the initial submission of 5 years**: \n", + " ```\n", + " CONTINUE_RUN=FALSE, RESUBMIT=19 \n", + " ```\n", + " because when the run is first initialized, `CONTINUE_RUN=FALSE`.\n", + "
\n", + "- **the next submission of 5 years**: \n", + " ```\n", + " CONTINUE_RUN=TRUE, RESUBMIT=18 \n", + " ```\n", + " because `RESUBMIT>0`, `CONTINUE_RUN` will automatically switch to `TRUE` after completing the initial run.\n", + "
\n", + " \n", + "- **the 3rd run (2nd resubmission) of 5 years**: \n", + " ```\n", + " CONTINUE_RUN=TRUE, RESUBMIT=17\n", + " ```\n", + " because `CONTINUE_RUN` stays to be `TRUE`, `RESUBMIT` decreases by 1.\n", + " \n", + " \n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "610c09ea-94e9-41ef-95b9-bfc537d81da1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/run_length/number_of_submissions.ipynb b/notebooks_copy/modifications/xml/run_length/number_of_submissions.ipynb new file mode 100644 index 000000000..4387df7be --- /dev/null +++ b/notebooks_copy/modifications/xml/run_length/number_of_submissions.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Controlling the Number of Submissions \n", + "\n", + "and continue_run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e59cdc0f-21dc-430d-bdb9-1f9c9f2d7bcb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/run_length/restarting.ipynb b/notebooks_copy/modifications/xml/run_length/restarting.ipynb new file mode 100644 index 000000000..80ea1367e --- /dev/null +++ b/notebooks_copy/modifications/xml/run_length/restarting.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Restarting a Run\n", + "\n", + "How to continue a run.\n", + "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "6dfbe426-d5b3-47fd-a4bd-faf6fb107f5f", + "metadata": {}, + "source": [ + "## 1. Restart files" + ] + }, + { + "cell_type": "markdown", + "id": "8746a209-be0f-4c0d-ad1c-168da2a7e4b6", + "metadata": {}, + "source": [ + "Restart files are written by each model component at intervals dictated by the driver via the setting of the `env_run.xml` variables, **`$REST_OPTION`** and **`$REST_N`**. The default values for these two variables are set to be the same as `$STOP_OPTION` and `$STOP_N`. In most cases, we do not modify these two variables.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b514409e-4215-4627-a783-28fafc852211", + "metadata": {}, + "source": [ + "\n", + "Restart files allow the model to stop and then start again with **bit-for-bit exact** capability (i.e. the model output is exactly the same as if it had never been stopped). The driver coordinates the writing of restart files as well as the time evolution of the model. All components receive restart and stop information from the driver and write restarts or stop as specified by the driver.\n" + ] + }, + { + "cell_type": "markdown", + "id": "bfc93f04-70d4-4cb1-8e90-a9878cdca329", + "metadata": {}, + "source": [ + "Whenever a component writes a restart file, it also writes a restart pointer file of the form, **`rpointer.$component`** (i.e. `rpointer.atm`). The restart pointer file contains the restart filename that was just written by the component. Upon a restart, each component reads its restart pointer file to determine the filename(s) to read in order to continue the model run. As examples, the following pointer files will be created for a component set using full active model components.\n", + "\n", + "- rpointer.atm\n", + "- rpointer.drv\n", + "- rpointer.ice\n", + "- rpointer.lnd\n", + "- rpointer.rof\n", + "- rpointer.cism\n", + "- rpointer.ocn.ovf\n", + "- rpointer.ocn.restart\n" + ] + }, + { + "cell_type": "markdown", + "id": "93d66937-95c1-44cc-bbd3-39a798efc378", + "metadata": {}, + "source": [ + "
\n", + " \n", + " \n", + " \n", + " Tips!\n", + " \n", + " \n", + "1. Try using xmlquery to check the values of `REST_OPTION` and `REST_N`.\n", + "What do you find?
\n", + "2. Take a look at the restart files and restart pointer files in your archive directory (`$DOUT_S_ROOT/rest/yyyy-mm-dd-ssss/`) or run directory (`$RUNDIR`). What do they look like?\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "aca8e554-0df7-4de2-9059-9e1b2e33a590", + "metadata": {}, + "source": [ + "***\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "6d6522b2-c30c-4b92-b2f4-c89ce0158dcf", + "metadata": {}, + "source": [ + "## 2. Continue a run" + ] + }, + { + "cell_type": "markdown", + "id": "1c0eb950-6238-4f37-a875-19950279ab4a", + "metadata": {}, + "source": [ + "\n", + "Recall that the flag variable `$CONTINUE_RUN` controls whether a model run is **initialized (FALSE)** or **continues a run (TRUE)**." + ] + }, + { + "cell_type": "markdown", + "id": "a3ff7ec3-941e-447f-b027-3f45fb3b8bb1", + "metadata": {}, + "source": [ + "In the case of our 1-month test run, we submited our initial job with `CONTINUE_RUN = FALSE` (because it was just initialized) and your `RUN_TYPE` (to startup, branch or hybrid). If the run has been finished and everything looks good, and we want to continue the run for another month, what do we do?\n", + "\n", + "We will need to use `xmlchange` to change `CONTINUE_RUN = TRUE` and submit the run again to carry on running the model. The model will use the restart files to continue our run with a bit-for-bit match, as if it had never been stopped. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "a1b40ec3-fdd5-45ea-aa09-74620e62b35c", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "id": "65d2fb52-18b5-43ae-87b5-866ec0c730ef", + "metadata": {}, + "source": [ + "
\n", + "Evaluate your understanding\n", + "\n", + "If we do not modify `CONTINUE_RUN=TRUE` and leave it as `FALSE`, what would happen after we submit the run again?\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "12239227-29af-4738-851b-b1eae99932e4", + "metadata": {}, + "source": [ + "
\n", + " \n", + "
\n", + " \n", + " \n", + " Click here for the solution \n", + " \n", + "\n", + "The model will run the previous month once again instead of carrying on to the next month!\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "43a8d53a-d66d-4c4a-a9fd-2313f4c23dfd", + "metadata": {}, + "source": [ + "The `$CONTINUE_RUN` flag will be automatically set to `TRUE` when the variable `RESUBMIT>0`. Learn more in the chapter [Changing Run Length](./changing_run_length.ipynb)." + ] + }, + { + "cell_type": "markdown", + "id": "707b12c1-e970-4097-99c3-f6fe3b1914c8", + "metadata": {}, + "source": [ + "
\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "markdown", + "id": "2a816fcb-9242-4d6b-98b4-b4e62fab08eb", + "metadata": {}, + "source": [ + "## 3. Backing up to a previous restart" + ] + }, + { + "cell_type": "markdown", + "id": "c2e232e3-37ae-42e7-bdf2-953ef4cc86cb", + "metadata": {}, + "source": [ + "If a run encounters problems and crashes, it is extremely useful to back up to a previous restart. \n", + "\n", + "You will need to find the latest restart files in the `$DOUT_S_ROOT/rest/yyyy-mm-dd-ssss/` directory that was created and copy the contents of that directory into your run directory (`$RUNDIR`). You can then continue the run and these restarts will be used. \n", + "\n", + "It is important to make sure the new `rpointer.*` files overwrite the previous `rpointer.*` files that were in `$RUNDIR`, or the job may not restart in the correct place.\n", + "\n", + "Occasionally, when a run has problems restarting, it is because the rpointer files are out of sync with the restart files. The rpointer files are text files and can easily be edited to match the correct dates of the restart and history files. All the restart files should have the same date." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32d9c126-14c2-41d1-8bce-a895fdf407dd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/run_length/starting_and_stopping.ipynb b/notebooks_copy/modifications/xml/run_length/starting_and_stopping.ipynb new file mode 100644 index 000000000..8b78f684a --- /dev/null +++ b/notebooks_copy/modifications/xml/run_length/starting_and_stopping.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Starting and Stopping \n", + "\n", + "To start a model run, we will need to submit `$CASE.run` to the batch queue. In addition, we will also need to specify how long we want to run the model in `env_run.xml`. \n", + "\n", + "\n", + "We will learn how to modify the runtime variables in `env_run.xml` to initialize, stop and restart a CESM model run." + ] + }, + { + "cell_type": "markdown", + "id": "ec38ef4d-2a68-44e8-a731-c55e1a2d940a", + "metadata": {}, + "source": [ + "
\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "markdown", + "id": "344c4fb8-da46-44c1-8a64-6808e7b3a814", + "metadata": {}, + "source": [ + "## 1. \"Initial run\" and \"continue run\"\n", + "\n", + "When a CESM model run is first initialized, it is called an **initial run**. \n", + "\n", + "The variable **`$CONTINUE_RUN`** is a flag indicating if the current model run is an **initial run** or a **continue run**. \n", + "- For an intial run, `CONTINUE_RUN` must be set to *`FALSE`*, \n", + "- If the model continues a run, `CONTINUE_RUN` is set to *`TRUE`*. \n", + "\n", + "Your initial CESM run can be initialized in one of three run types: startup, branch,or hybrid. We will get into more details about the **run types** in the Chapter [Modifying Run Types](../modify_run_type/hybrid_branch_restart.ipynb). \n", + "\n", + "For now, we need to know that **setting run types is only important for an initial run when `CONTINUE_RUN` is set to `FALSE`**. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "37195fbd-8a4f-45f8-81b6-7042bcbf5f08", + "metadata": {}, + "source": [ + "
\n", + "\n", + "****\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "fa6734b1-bb5e-4fc3-96b7-df867adbbf04", + "metadata": {}, + "source": [ + "## 2. Set runtime limits with **`STOP_OPTION`** and **`STOP_N`**" + ] + }, + { + "cell_type": "markdown", + "id": "68eef81e-bd50-43e1-9961-15d0712d17d3", + "metadata": {}, + "source": [ + "Run length options can be set using **`$STOP_OPTION`** and **`$STOP_N`** variables in env_run.xml. \n", + "\n", + "- `STOP_OPTION` : sets the run length time interval type, i.e. nmonths, ndays, nyears.\n", + "- `STOP_N` : sets the number of intervals (set by `STOP_OPTION`) to run the model during each submission within the specified wallclock time. " + ] + }, + { + "cell_type": "markdown", + "id": "da97a05c-ef45-4473-8f05-032aed8dfcf9", + "metadata": {}, + "source": [ + "
\n", + "For example, if you want to run a simulation for 6 months during the job submssion, you will need to set:\n", + "\n", + "```\n", + "./xmlchange STOP_N=6\n", + "./xmlchange STOP_OPTION='nmonths'\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "c5947ac5-c8d6-4abb-9ea7-bce711f48ddd", + "metadata": {}, + "source": [ + "
\n", + "\n", + "`STOP_N` and `STOP_OPTION` control the length of the run **per job submission**. They should be set based on the job queue limit and model throughput. You can find more information on this topic in the chapter [Using timing files](./timing_files.ipynb).\n", + "\n", + "
\n", + "\n", + "A typical simulation is comprised of many job submissions. This is because you can only stay in the computer queue for a limited time. We will learn more about completing long simulations in the chapter [Changing run length](./changing_run_length.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c99fbe29-f232-4534-8476-d12bbbc41ffe", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/modifications/xml/run_length/timing_files.ipynb b/notebooks_copy/modifications/xml/run_length/timing_files.ipynb new file mode 100644 index 000000000..6608d8f37 --- /dev/null +++ b/notebooks_copy/modifications/xml/run_length/timing_files.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Using Timing Files \n", + "\n", + "Model timing files contain a summary of various timing information for the run. It is helpful to check the timings after the run to verify that the model is running efficiently. \n", + "\n", + "
\n", + "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "78615f64-047d-421d-9436-925533cf6827", + "metadata": {}, + "source": [ + "## 1. What are timing files and where are they located?\n", + "\n", + "A summary timing output file is produced after every CESM run. This file is placed in `$CASEROOT/timing/cesm_timing.$CASE.$date`, where $date is a datestamp set by CESM at runtime, and contains a summary of various timing information.\n", + "\n", + "The first section in the timing output, \"TIMING PROFILE\", summarizes general timing information for the run. The total run time and cost is given in several metrics including pe-hrs per simulated year (**cost**), simulated years per wall day (**thoughput**), seconds, and seconds per model day. This provides general summary information quickly in several units for analysis and comparison with other runs. The total run time for each component is also provided, as is the time for initialization of the model. These times are the aggregate over the total run and do not take into account any temporal or processor load imbalances.\n", + "\n", + "
\n", + "\n", + "***\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "40e1c00d-ab11-427c-aa03-c6e0f366dceb", + "metadata": { + "tags": [] + }, + "source": [ + "## 2. Use timing files to determine runtime variables\n", + "\n", + "Here is an example of cost information shown in a timing file:\n", + "\n", + "```\n", + "Overall Metrics: \n", + "Model Cost: 327.14 pe-hrs/simulated_year (scale= 0.50)\n", + "Model Throughput: 4.70 simulated_years/day\n", + "```\n", + "\n", + "The model throughput is the estimated number of model years that you can run in a wallclock day. Based on this, you can maximize `$CASE.run` queue limit and change `STOP_OPTION` and `STOP_N` in `env_run.xml`.\n", + "\n", + "For example, say a model’s throughput is 4.7 simulated_years/day. On derecho, the maximum runtime limit is 12 hours. 4.7 model years/24 hours * 12 hours = 2.34 years. On the massively parallel computers, there is always some variability in how long it will take a job to run. On some machines, you may need to leave as much as 20% buffer time in your run to guarantee that jobs finish reliably before the time limit. For that reason we will set our model to run only 2 model year/job. \n", + "\n", + "Continuing to assume that the run is on derecho, we can set:\n", + "\n", + "```\n", + "./xmlchange STOP_OPTION='nyears'\n", + "./xmlchange STOP_N=2\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c03243b7-4aca-40d2-bdab-02312b58e016", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/documentation.ipynb b/notebooks_copy/namelist/documentation.ipynb new file mode 100644 index 000000000..2dd739a60 --- /dev/null +++ b/notebooks_copy/namelist/documentation.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Namelist Variables Documentation\n", + "\n", + "\n", + "
\n", + "\n", + "Complete documentation about the namelist variables can be found on the [CESM webpage.](https://www2.cesm.ucar.edu/models/cesm2/settings/current/)\n", + "\n", + "
\n", + "\n", + "For instance, if you want to change the **_output frequency_** in CAM, go to the website and type the word ``frequency`` in the search box as illustrated in *Figure 1*. " + ] + }, + { + "cell_type": "markdown", + "id": "017b45ad-dcff-42b2-8623-06033b8e11d8", + "metadata": {}, + "source": [ + "___\n", + "![Search namelist variables](../../images/namelist/namelist_var_search.png)\n", + "\n", + "*

Figure 1: Search for a namelist variable.

*\n", + "___" + ] + }, + { + "cell_type": "markdown", + "id": "42e1dd6f-c704-4f69-965a-a015d3d0baed", + "metadata": {}, + "source": [ + "This will return the attributes of the namelist variable ``nhtfrq``, as illustrated in *Figure 2*. ``nhtfrq`` is the variable you need in order to change the output frequency of a history file. \n", + "\n", + "___\n", + "![Attribute of a namelist variable](../../images/namelist/nhtfrq.png)\n", + "\n", + "*

Figure 2: Attribute of a namelist variable. Here the variable: ``nhtfrq``

*" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/exercises/exercise_cam_output.ipynb b/notebooks_copy/namelist/exercises/exercise_cam_output.ipynb new file mode 100644 index 000000000..fc37494bc --- /dev/null +++ b/notebooks_copy/namelist/exercises/exercise_cam_output.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Modify CAM output" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Customize your CAM history files

\n", + " \n", + "Create a case called `b1850_high_freq` using the compset ``B1850`` at ``f19_g17`` resolution. \n", + " \n", + "In addition to the monthly history file ``h0``, output:\n", + "- ``h1`` file with **_instantaneous_** values of T, Q, U and V every **_24 hour_**.\n", + "- ``h2`` file with **_time-average_** values of T, Q, U and V every **_3 hour_**.\n", + "\n", + "Set your namelist so that you output: \n", + "- a **_single_** ``h1`` file with all the daily output for the month. \n", + "- **_multiple_** ``h2`` files, one for every day of the month.\n", + "\n", + "Your goal is to produce:\n", + "- one ``h1`` file with 31 timesteps and \n", + "- thirty-one ``h2`` files with 8 timesteps each.\n", + "\n", + "Set the run length to **_1 month_** and make a 1-month run.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "\n", + " Click here for hints \n", + " \n", + "**# How do I compile?**\n", + "\n", + "You can compile with the command:\n", + "```\n", + "qcmd -- ./case.build\n", + "```\n", + "\n", + "**# How do I control the output?**\n", + "\n", + "Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", + "\n", + "Look at the online documentation for these variables.\n", + "\n", + "**# How do I check my solution?**\n", + "\n", + "When your run is completed, go to the archive directory and navigate to the subdirectory `atm/hist`\n", + "\n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/b1850_high_freq\n", + "cd atm/hist\n", + "```\n", + "\n", + "(1) Check that your archive directory contains the files:\n", + "\n", + "- ``h0`` files\n", + "```\n", + "b1850_high_freq.cam.h0.0001-01.nc\n", + "```\n", + "- ``h1`` files\n", + "```\n", + "b1850_high_freq.cam.h1.0001-01-01-00000.nc\n", + "b1850_high_freq.cam.h1.0001-02-01-00000.nc\n", + "```\n", + "- ``h2`` files\n", + "```\n", + "b1850_high_freq.cam.h2.0001-01-01-00000.nc\n", + "…\n", + "b1850_high_freq.cam.h2.0001-01-31-00000.nc\n", + "b1850_high_freq.cam.h2.0001-02-01-00000.nc\n", + "```\n", + "\n", + "(2) Compare the contents of the ``h1`` and ``h2`` files using ``ncdump``.\n", + "\n", + "```\n", + "ncdump -h b1850_high_freq.cam.h1.0001-01-01-00000.nc\n", + "ncdump -h b1850_high_freq.cam.h2.0001-01-01-00000.nc\n", + "```\n", + "\n", + "(3) Check the number of timesteps in the ``h1`` and the ``h2`` files.\n", + "Look at the sizes of the files. \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "**# Create a new case**\n", + "\n", + "Create a new case `b1850_high_freq` with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case ~/cases/b1850_high_freq --compset B1850 --res f19_g17 \n", + "```\n", + "\n", + "**# Setup**\n", + " \n", + "Invoke case.setup with the command:\n", + " \n", + "``` \n", + "cd ~/cases/b1850_high_freq \n", + "./case.setup\n", + "```\n", + "\n", + "**# Customize namelists**\n", + " \n", + "Edit the file `user_nl_cam` and add the lines:\n", + "```\n", + " nhtfrq = 0, -24, -3\n", + " mfilt = 1, 31, 8 \n", + " fincl2 = 'T:I','Q:I','U:I','V:I'\n", + " fincl3 = 'T','Q','U','V'\n", + "```\n", + " \n", + "**# Set run length**\n", + " \n", + "Change the `run length`:\n", + "``` \n", + "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change `job queue` and `account number`.
\n", + "For instance, to run in the queue `regular` and the project number ``UESM0013`` (You should use the project number given for this tutorial), use the command:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "**# Build and submit**\n", + " \n", + "Build the model and submit your job:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "\n", + "____\n", + " \n", + "**# Look at your solution**\n", + "\n", + "When the run is completed, look into the archive directory for: \n", + "`b1850_high_freq`. \n", + " \n", + "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/b1850_high_freq/atm/hist\n", + "ls \n", + "```\n", + "\n", + "(2) Compare the contents of the ``h1`` and ``h2`` files using ``ncdump``.\n", + "Look at the variables attributes. What is the difference between the 2 files ? \n", + "\n", + "The file with the instantaneous output ``h1`` have no cell_methods attribute while the average output ``h2`` has a attribute: \n", + "```\n", + "cell_methods = \"time: mean\"\n", + "```\n", + "\n", + "For instance for the field Q.\n", + "\n", + "In the instantaneous file ``b1850_high_freq.cam.h1.0001-01-01-00000.nc``\n", + "```\n", + "float Q(time, lev, lat, lon) ;\n", + " Q:mdims = 1 ;\n", + " Q:units = \"kg/kg\" ;\n", + " Q:mixing_ratio = \"wet\" ;\n", + " Q:long_name = \"Specific humidity\" ;\n", + "```\n", + "In the time-averaged file ``b1850_high_freq.cam.h2.0001-01-01-00000.nc``\n", + "```\n", + "float Q(time, lev, lat, lon) ;\n", + " Q:mdims = 1 ;\n", + " Q:units = \"kg/kg\" ;\n", + " Q:mixing_ratio = \"wet\" ;\n", + " Q:long_name = \"Specific humidity\" ;\n", + " Q:cell_methods = \"time: mean\" ;\n", + "```\n", + "(3) Check the number of timesteps in the h1 and the h2 files. \n", + "\n", + "- ``h1`` contains 31 time samples. \n", + "In the netcdf file, \n", + "```\n", + "time = UNLIMITED ; // (31 currently)\n", + "```\n", + "- ``h2`` contains 8 time samples \n", + "In the netcdf file, \n", + "```\n", + "time = UNLIMITED ; // (8 currently)\n", + "```\n", + "- Check the size of the files\n", + "```\n", + "du –ks –h /glade/derecho/scratch/$USER/archive/b1850_high_freq/atm/hist/*\n", + "```\n", + "```\n", + "234M b1850_high_freq.cam.h0.0001-01.nc\n", + "\n", + "210M b1850_high_freq.cam.h1.0001-01-01-00000.nc\n", + "7.0M b1850_high_freq.cam.h1.0001-02-01-00000.nc\n", + "\n", + "55M b1850_high_freq.cam.h2.0001-01-01-00000.nc\n", + "55M b1850_high_freq.cam.h2.0001-01-02-00000.nc\n", + "...\n", + "55M b1850_high_freq.cam.h2.0001-01-31-00000.nc\n", + "7.0M b1850_high_freq.cam.h2.0001-02-01-00000.nc\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/exercises/exercise_tuning_parameter.ipynb b/notebooks_copy/namelist/exercises/exercise_tuning_parameter.ipynb new file mode 100644 index 000000000..0776f4af4 --- /dev/null +++ b/notebooks_copy/namelist/exercises/exercise_tuning_parameter.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Change a tuning parameter in CAM" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Modify a parameter, dcs

\n", + " \n", + "\n", + "In the tuning lecture, we talked about the parameter dcs:\n", + "http://www.cesm.ucar.edu/events/tutorials/2019/files/Specialized-hannay.pdf\n", + "\n", + "Create a case called `b1850_dcs` using the compset ``B1850`` at ``f19_g17`` resolution. \n", + "\n", + "Locate where to change the parameter **Dcs** and change from the default value:
\n", + "dcs = 200.D-6
\n", + "to
\n", + "dcs = 500.D-6
\n", + "\n", + "Make a **_1-month_** run.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + " \n", + " Click here for hints\n", + " \n", + "\n", + "- The trick is going to locate where to change `dcs` \n", + " \n", + "- Compare to this run to the first run you did today: `b1850_high_freq`.\n", + "- You can use `ncdiff` and `ncview` to look at the difference between the 2 runs. For instance:\n", + "```\n", + "ncdiff /glade/derecho/scratch/$USER/archive/b1850_dcs/atm/hist/b1850_dcs.cam.h0.0001-01.nc /glade/derecho/scratch/$USER/archive/b1850_high_freq/atm/hist/b1850_high_freq.cam.h0.0001-01.nc diff.nc\n", + "\n", + "ncview diff.nc\n", + "```\n", + "- Analyze how this does this affect the `LWCF` ?\n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + "\n", + "**# Create a new case**\n", + " \n", + "Create a new case `b1850_dcs`with the command:\n", + "```\n", + "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case ~/cases/b1850_dcs --compset B1850 --res f19_g17 \n", + "```\n", + "\n", + "**# Setup**\n", + "\n", + "Invoke case.setup with the command:\n", + "``` \n", + "cd ~/cases/b1850_dcs\n", + "./case.setup\n", + "```\n", + "\n", + "**# Customize namelists**\n", + " \n", + "Locate the variable to modify dcs. (Locating variables requires a learning curve. A good place to start is to look at the `Variable namelist documentation`. \n", + " \n", + "The variable is: `micro_mg_dcs`.
\n", + "By default this variable is set to:\n", + "`micro_mg_dcs = 200 microns`\n", + "You want to set it to:\n", + "`micro_mg_dcs = 500 microns`\n", + "\n", + " \n", + "Edit the file `user_nl_cam` and add the lines:\n", + "```\n", + "micro_mg_dcs = 500.D-6 \n", + "```\n", + "\n", + "**# Set run length**\n", + " \n", + "Change the `run length`:\n", + "``` \n", + "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change `job queue` and `account number`.
\n", + "For instance, to run in the queue `regular` and the project number `UESM0013`, use the command:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "\n", + "**# Build and submit**\n", + " \n", + "Build the model and submit your job:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "\n", + "____\n", + " \n", + "**# Look at your solution**\n", + " \n", + "- When the run is completed, compare this run to the first run: \n", + "`b1850_high_freq`. \n", + " \n", + "- Create a file with the difference in `LWCF` between `b1850_dcs` and `b1850_high_freq`.\n", + " \n", + "- You can use `ncdiff` and `ncview` to look at the difference between the 2 runs. \n", + "\n", + "- For instance, use `ncdiff` to create a file with the difference between the 2 runs:\n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/b1850_dcs \n", + "ncdiff /glade/derecho/scratch/$USER/archive/b1850_dcs/atm/hist/b1850_dcs.cam.h0.0001-01.nc /glade/derecho/scratch/$USER/archive/b1850_high_freq/atm/hist/b1850_high_freq.cam.h0.0001-01.nc diff.nc\n", + "``` \n", + "\n", + "- Use `ncview` to look at the file you created.\n", + "```\n", + "ncview diff.nc\n", + "```\n", + "\n", + "**#How does this affect the LWCF ?** \n", + "\n", + "Dcs = Threshold diameter to convert cloud ice particles to snow\n", + "\n", + "*

\n", + " ![dcs representation](../../../images/namelist/dcs.png) \n", + " Figure: Representation of Dcs.

*\n", + " \n", + "We increased Dcs from 200 microns to 500 microns. We should have more ice cloud and the LWCF should be larger. \n", + "\n", + " \n", + "The field `LWCF` difference looks like \n", + "*

\n", + " ![ncview diff LWCF](../../../images/namelist/ncview_diff_dcs.png) \n", + " Figure: Difference of LWCF for micro_mg_dcs = 200.D-6->500.D-6

*\n", + " \n", + "We can see that the change in Dcs affects the tropics where LWCF is large. However, 1-month run is too short to look at robust statistics. \n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "850528d8-e923-4a97-b49a-171ad511026f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/exercises_overview.ipynb b/notebooks_copy/namelist/exercises_overview.ipynb new file mode 100644 index 000000000..d4cfde859 --- /dev/null +++ b/notebooks_copy/namelist/exercises_overview.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Exercise Overview \n", + "\n", + "## Learning Goals\n", + "\n", + "- Student will practice doing namelist changes \n", + "- Student will edit `user_nl_*` files, run the model and check the results\n", + "\n", + "\n", + "## Exercise overview:\n", + "\n", + "Do at last one exercise:\n", + "- Output high frequency data in CAM.\n", + "- Change a tuning parameter in CAM.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84583445-5875-44e3-91a1-14fe6db10093", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/namelist.ipynb b/notebooks_copy/namelist/namelist.ipynb new file mode 100644 index 000000000..2a891d5cd --- /dev/null +++ b/notebooks_copy/namelist/namelist.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0959db1d-1790-4519-a34e-7026444af537", + "metadata": {}, + "source": [ + "# Namelist Modifications" + ] + }, + { + "cell_type": "markdown", + "id": "285a23b4-2906-4422-a8a8-987546de6f0c", + "metadata": {}, + "source": [ + "There are several ways in which the model can be modified using namelist settings. This is what we cover in this chapter:\n", + "- The section **_Overview_** describes the steps to modify the namelists and provides a visual representation of the directories and files that need to be modified.\n", + "- The section **_Documentation_** explains how to find information about the namelist variables on the CESM webpage.\n", + "- The section **_Customizing output_** describes how to customize the output history files, such as changing the output frequency or adding variables to a file.\n", + "- The section **_Exercises_** offers opportunities to practice the concepts learned in this chapter. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7cf2d79-da13-416d-8345-385bc1977b79", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/output.ipynb b/notebooks_copy/namelist/output.ipynb new file mode 100644 index 000000000..cbf82331f --- /dev/null +++ b/notebooks_copy/namelist/output.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Customizing Output\n" + ] + }, + { + "cell_type": "markdown", + "id": "34b38707-9fbb-4e2a-bb99-5d3ddac76dc0", + "metadata": {}, + "source": [ + "If you don't add anything to the namelist, CESM will generate its **``default output``**. This is what was done in previous chapters of this tutorial. Typically, the model outputs monthly means (and in some cases, higher frequency outputs) of a default set of variables. Depending on your needs, you may want to output variables at a different frequency, or add more variables, etc ... \n", + "\n", + "In this chapter **``Customizing output``**, we will cover how to output at a different frequency, add history files, or add variables to the history files.\n", + "\n", + "In the sections below, we provide the basics of customizing the output for the different components (atm, lnd, ice, and pop). The **``Customize CAM output``** is the most **_comprehensive_**, so we recommend **_at least reviewing that section_**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd23aec3-cb79-432b-96e1-dcad3c071a90", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4115c790-a2b3-41c1-8291-db1a9de90ef3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/output/output_cam.ipynb b/notebooks_copy/namelist/output/output_cam.ipynb new file mode 100644 index 000000000..48cdc13f2 --- /dev/null +++ b/notebooks_copy/namelist/output/output_cam.ipynb @@ -0,0 +1,253 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Customize CAM output\n", + "\n", + "In this section, we will cover:\n", + "- how to change the **output frequency**\n", + "- how to control the **number of time samples** written to a history file\n", + "- how to output **extra variables**\n", + "- how to output **extra history files**\n", + "\n", + "This can be achieved with 3 namelist variables:\n", + "- **``nhtfrq``**: sets the output frequency\n", + "- **``mfilt``**: maximum number of time samples written to a history file\n", + "- **``fincl``**: add variables to the history file\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "3245bdc2-3932-4582-aec1-30991cabd916", + "metadata": {}, + "source": [ + "## Customizing CAM output frequency: **``nhtfrq``**\n", + "The default history file from CAM is a monthly average. \n", + "We can change the **output frequency** with the namelist variable **``nhtfrq``**\n", + "- If **``nhtfrq=0``**, the file will be a **monthly** average\n", + "- If **``nhtfrq>0``**, frequency is input as number of **timesteps**.\n", + "- If **``nhtfrq<0``**, frequency is input as number of **hours**.\n", + "\n", + "For instance to change the history file from monthly average to daily average, we set the namelist variable:\n", + "\n", + "```\n", + " nhtfrq = -24 \n", + "```\n", + "\n", + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "The default history file in CAM is a monthly average. How do you modify the output to output 3-hourly data ? \n", + "\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "Click here for the solution
\n", + "Add the following line to user_nl_cam:
\n", + "\n", + "```\n", + " nhtfrq = -3\n", + "```\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "42e1dd6f-c704-4f69-965a-a015d3d0baed", + "metadata": {}, + "source": [ + "## Customizing CAM history files: **``mfilt``**\n", + "To control the **number of time samples** in the history file, we use the variable **``mfilt``**.
\n", + "This variable specifies is the maximum number of time samples written to a history file.\n", + "\n", + "For example, if we want to have 10 time samples on each history file, we can set the namelist variable as follows:\n", + "```\n", + " mfilt = 10\n", + "```\n", + "\n", + "\n", + "To output daily data for a 1-year run in a single file, we can use the following values:\n", + "```\n", + " nhtfrq = -24 \n", + " mfilt = 365\n", + "```\n", + "If we want to output daily data with only 1 time sample per file, we can set the variables as follows:\n", + "```\n", + " nhtfrq = -24\n", + " mfilt = 1\n", + "```\n", + "\n", + "NB: we cannot change mfilt for monthly frequency. For monthly frequency, we always have: \n", + "```mfilt = 1``` \n", + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "What is the setting to generate 3 hourly data for a month-long simulation that will create a file every day? \n", + "
\n", + "\n", + "
\n", + "
\n", + "\n", + "Click here for the solution
\n", + "\n", + "Modify the length of the run in `env_run.xml` with the command:\n", + "``` \n", + " ./xmlchange STOP_N=nmonths, STOP_OPTION=1\n", + "```\n", + "\n", + "Add the following line to `user_nl_cam` to output 3 hourly data and to create one file per day:\n", + "``` \n", + " nhtfrq = -3\n", + " mfilt = 8\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b35a3197-e561-4a00-909d-9f3d80808676", + "metadata": {}, + "source": [ + "\n", + "## Add extra variables and history files: **``fincl``**\n", + "\n", + "You can output up to 10 history files: **``h0``**, **``h1``**, …, **``h9``**. \n", + "- The **``h0``** file contains the default variables or fields. (These are the variables you get by default without doing any modification to the namelist or code).\n", + "\n", + "- For the files **``h1``** to **``h9``**, the user must specify the variables to output. \n", + "\n", + "To control the list of fields in the history files, use the namelist variable **``fincl``**\n", + "- **``fincl1``** controls the fields in **``h0``** \n", + "- **``fincl2``** controls the fields in **``h1``** \n", + "- ...\n", + "- **``fincl10``** controls the fields in **``h9``** \n", + "\n", + "For example, to add the field `PRECT` to the `h0` file, use the line:\n", + "```\n", + " fincl1 = 'PRECT' \n", + "```\n", + "\n", + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "What is the namelist setting to add:\n", + "- a hourly history `h1` with the variables U, V, T \n", + "- and a daily history `h2` with the variable PRECT \n", + "- and output 10 time samples in `h1` and `h2` ? \n", + "
\n", + "\n", + "
\n", + "
\n", + "\n", + "Click here for the solution
\n", + "\n", + "Add the following lines to `user_nl_cam`:\n", + "``` \n", + " nhtfrq = 0, -1, -24 \n", + " mfilt = 1, 10, 10 \n", + " fincl2 = 'U', 'V', 'T' \n", + " fincl3 = 'PRECT'\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "55e82e97-06da-48a1-8b71-29324759a2c3", + "metadata": {}, + "source": [ + "\n", + "## Averaging flag for the **``fincl``** variables \n", + "\n", + "Using a `:` following a field gives the averaging flag for the output field. \n", + "\n", + "Valid flags are: \n", + "- **``A``** ==> Average\n", + "- **``B``** ==> GMT 00:00:00 average\n", + "- **``I``** ==> Instantaneous\n", + "- **``M``** ==> Minimum\n", + "- **``X``** ==> Maximum\n", + "- **``L``** ==> Local-time\n", + "- **``S``** ==> Standard deviation\n", + "\n", + "For instance, the line:\n", + "```\n", + " fincl1 = 'PRECT:M' \n", + "```\n", + "is used to add the minimum of ``PRECT`` to the file ``h0``\n", + "\n", + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "What happens if we set in `user_nl_cam`:\n", + "``` \n", + " fincl2 = 'T:I','Q:I','U:I','V:I'\n", + " nhtfrq = 0, -3 \n", + " mfilt = 1, 8\n", + "```\n", + "\n", + "
\n", + "\n", + "
\n", + "
\n", + "\n", + "Click here for the solution
\n", + "\n", + "In addition to the monthly history file `h0`, \n", + "- we will also output the file `h1` with instantaneous values of T, Q, U, and V. \n", + "- These variables will be output every 3 hours, resulting in 8 time samples in each `h1` file. \n", + "- A new file will be created every day.\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8da564c-942e-4bf1-9522-26061784161a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/output/output_cice.ipynb b/notebooks_copy/namelist/output/output_cice.ipynb new file mode 100644 index 000000000..209f9ac1b --- /dev/null +++ b/notebooks_copy/namelist/output/output_cice.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Customize CICE output\n", + "\n", + "The CICE output can changed with the namelist variables\n", + "\n", + "- **``histfreq``**: Frequency of output written to history files ('1', 'm', 'd', 'y', …)\n", + "- **``histfreq_n``**: Frequency history data is written to history files\n", + "- **``hist_avg``**: if false => instantaneous values, if true => time-averages\n", + "\n", + "\n", + "Here is how to set ``user_nl_cice`` to output an extra history file with daily values (leaving the primary history file as monthly): \n", + "```\n", + " histfreq = 'm','d','x','x','x'\n", + " histfreq_n = 1,1,1,1,1 \n", + "```\n", + "\n", + "See: http://www.cesm.ucar.edu/models/cesm2/settings/current/cice_nml.html\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd6df83b-1a5d-4878-96b5-715e12c4c0a9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/output/output_clm.ipynb b/notebooks_copy/namelist/output/output_clm.ipynb new file mode 100644 index 000000000..b630fbc2b --- /dev/null +++ b/notebooks_copy/namelist/output/output_clm.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": { + "tags": [] + }, + "source": [ + "# Customize CLM output\n", + "\n", + "In this section, we will cover:\n", + "- how to change the **output frequency**\n", + "- how to control the **number of time samples** written to a history file\n", + "- how to output **extra variables**\n", + "- how to output **extra history files**\n", + "\n", + "This can be achieved with 3 namelist variables:\n", + "- **``hist_nhtfrq:``**: output frequency of the history file \n", + "- **``hist_mfilt``**: number of samples on each history file\n", + "- **``hist_fincl``**: adding variables and auxiliary history files" + ] + }, + { + "cell_type": "markdown", + "id": "3245bdc2-3932-4582-aec1-30991cabd916", + "metadata": {}, + "source": [ + "## Customizing CLM output frequency: **``hist_nhtfrq``**\n", + "We can change the output frequency with the namelist variable **``hist_nhtfrq``**\n", + "- If **``hist_nhtfrq=0``**, the file will be a **monthly** average\n", + "- If **``hist_nhtfrq>0``**, frequency is input as number of **timesteps**.\n", + "- If **``hist_nhtfrq<0``**, frequency is input as number of **hours**.\n", + "\n", + "For instance, \n", + "\n", + "```\n", + " hist_nhtfrq = -24\n", + "```\n", + "means **daily output**\n", + "\n", + "```\n", + " hist_nhtfrq = 24\n", + "```\n", + "means output every **24 timesteps**\n", + "\n", + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "How do you modify the CLM output to output 3-hourly data ? \n", + "\n", + "
\n", + "\n", + "
\n", + " \n", + "
\n", + "Click here for the solution
\n", + "\n", + "Add the following line to user_nl_clm:
\n", + "```\n", + " hist_nhtfrq = -3\n", + "```\n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "b35a3197-e561-4a00-909d-9f3d80808676", + "metadata": {}, + "source": [ + "\n", + "## Add extra variables and history files: **``hist_fincl``**\n", + "\n", + "You can output up to 10 history files:**``h0``**, **``h1``**, …, **``h9``**. \n", + "\n", + "To control the list of fields in the history files, use the namelist variable **``hist_fincl``**\n", + "- **``hist_fincl1``** controls the fields in **``h0``** \n", + "- **``hist_fincl2``** controls the fields in **``h1``** \n", + "- ...\n", + "- **``hist_fincl10``** controls the fields in **``h9``** \n", + "\n", + "For example, to add the field 'TG' to the ``h0`` file, use the line:\n", + "```\n", + " hist_fincl1 = 'TG' \n", + "```\n", + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "What is the namelist setting to add a monthly history h1 with the variables 'TG', 'TV' and a daily history h2 with the variables 'TG', 'TV'. Output 10 time samples in h1 and h2 ? \n", + "
\n", + "\n", + "
\n", + "
\n", + "\n", + "Click here for the solution
\n", + "\n", + "Modify env_run.xml with the command:\n", + "``` \n", + " ./xmlchange STOP_N=nmonths, STOP_OPTION=1\n", + "```\n", + "\n", + "Add the following lines to user_nl_cam:\n", + "``` \n", + " hist_nhtfrq = 0, 0, -24 \n", + " hist_mfilt = 1, 10, 10 \n", + " hist_fincl2 = 'TG', 'TV' \n", + " hist_fincl3 = 'TG', 'TV' \n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "42e1dd6f-c704-4f69-965a-a015d3d0baed", + "metadata": { + "jp-MarkdownHeadingCollapsed": true, + "tags": [] + }, + "source": [ + "## Customizing CLM history files: **``hist_mfilt``**\n", + "To control the number of time samples in the history file, we use the variable **``hist_mfilt``**.
\n", + "This variable specifies is the maximum number of time samples written to a history file.\n", + "\n", + "For example, if we want to have **10 time samples** on each history file, we can set the namelist variable as follows:\n", + "```\n", + " hist_mfilt = 10\n", + "```\n", + "\n", + "\n", + "To output **daily** data in a single file for a 1-year run, we can use the following values:\n", + "```\n", + " hist_nhtfrq = -24 \n", + " hist_mfilt = 365\n", + "```\n", + "If we want to output **daily** data with only **1 time sample** per file, we can set the variables as follows:\n", + "```\n", + " hist_nhtfrq = -24\n", + " hist_mfilt = 1\n", + "```\n", + "\n", + "NB: we cannot change mfilt for monthly frequency. \u000b", + "\tFor monthly frequency, we always have: \n", + "```hist_mfilt = 1``` \n", + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "What is the setting to generate CLM 3-hourly data for a month-long simulation that will create a file every day?\n", + "\n", + "
\n", + "\n", + "
\n", + "
\n", + "\n", + "Click here for the solution
\n", + "\n", + "Modify env_run.xml with the command:\n", + "```\n", + " ./xmlchange STOP_N=nmonths, STOP_OPTION=1\n", + "```\n", + "\n", + "Add the following line to user_nl_cam:\n", + "``` \n", + " hist_nhtfrq = -3\n", + " hist_mfilt = 8\n", + "```\n", + "\n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "55e82e97-06da-48a1-8b71-29324759a2c3", + "metadata": {}, + "source": [ + "\n", + "## Averaging flag for the **``fincl``** variables \n", + "\n", + "Using a \":\" following a field gives the averaging flag for the output field. \n", + "\n", + "Valid flags are: \n", + "- **``A``** ==> Average\n", + "- **``I``** ==> Instantaneous\n", + "- **``M``** ==> Minimum\n", + "- **``X``** ==> Maximum\n", + "- **``SUM``** ==> Sum\n", + "\n", + "For instance, the line:\n", + "```\n", + " hist_fincl1 = 'TLAI:M' \n", + "```\n", + "is used to add the minimum of ``TLAI`` to the file ``h0``\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "2bbd917e-8123-4bb1-a9cb-3c37d80c1fd2", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Evaluate your understanding\n", + "\n", + "What happens if we set in user_nl_clm:\n", + "```\n", + " hist_fincl2 = 'TG', 'TV' \n", + " hist_fincl3 = 'TG', 'TV' \n", + " hist_fincl4 = 'TG', 'TV'\n", + " hist_fincl5 = 'TG', 'TV' \n", + " hist_nhtfrq = 0, -24, -6, -1, 1 \n", + "```\n", + "\n", + "
\n", + "\n", + "
\n", + "
\n", + "\n", + "Click here for the solution
\n", + "\n", + "In addition to the monthly history file h0, we output 4 extra history files with daily, six-hourly, hourly, and every time-step values of TG and TV (leaving the primary history ``h0`` files as monthly). \n", + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02582628-9da7-4271-b330-e11c2e3ceb0f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/output/output_pop.ipynb b/notebooks_copy/namelist/output/output_pop.ipynb new file mode 100644 index 000000000..f2f18b954 --- /dev/null +++ b/notebooks_copy/namelist/output/output_pop.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Customize POP output\n", + "\n", + "POP output is controlled by POP's tavg_nml namelist, which variables such as\n", + "\n", + "- **``tavg_freq``**: frequency at which the model fields are written\n", + "- **``tavg_freq_opt``**: units of time for 'tavg_freq’ ('nmonth’, 'nhour’, 'once’,…)\n", + "- **``tavg_file_freq``**:frequency at which the model files are written\n", + "- **``tavg_file_freq_opt``**: units of time for 'tavg_file_freq’ ('nmonth’, 'nhour’, …)\n", + "\n", + "These are arrays, with a value for each output stream.\n", + "A convenient way to change the first stream to be daily averages bundled into monthly\n", + "files is to add the following lines to ``user_nl_pop``: \n", + "```\n", + "tavg_freq_opt(1) = 'nday' \n", + "tavg_freq(1) = 1 \n", + "tavg_file_freq_opt(1) = 'nmonth' \n", + "tavg_file_freq(1) = 1 \n", + "```\n", + "\n", + "See: https://www.cesm.ucar.edu/models/cesm2/settings/current/pop2_nml.html\n", + "\n", + "\n", + "CAUTION: Note that changing tavg_nml variables via ``user_nl_pop`` is non-standard.\n", + "While the above is convenient, attempting more complicated changes can lead to unexpected model behavior.\n", + "For full flexibility, use the **workaround** explained in ``user_nl_pop``.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6857dadc-8cbd-4a2e-af21-14b0be4600ee", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/namelist/overview.ipynb b/notebooks_copy/namelist/overview.ipynb new file mode 100644 index 000000000..392f4e40f --- /dev/null +++ b/notebooks_copy/namelist/overview.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f5e23fcc-d36b-4ca2-a596-9fb3c88c82e8", + "metadata": { + "tags": [] + }, + "source": [ + "# Overview \n", + "\n", + "There are a number of ways that the model can be modified via namelist settings. These values control the way the code is run. \n", + "\n", + "The **namelist variables** include settings to control over output, tune the model for various quantities, and many other options. For instance, the output frequency in the different components can be controlled via the namelists. \n", + "\n", + "
\n", + "\n", + "The **steps** to modify the **namelists** are:\n", + "\n", + "In ``$CASEROOT`` \n", + "- edit the ``user_nl_xxx`` files.\n", + "- generate the namelists by running ``./preview_namelists``. \n", + " \n", + " \n", + "This results in the creation of component namelists, the ``*_in`` files (i.e. `atm_in`, `lnd_in`, and so on). \n", + "\n", + "The ``*_in`` files are located in ``$CASEROOT/CaseDocs/`` and in ``$RUNDIR``.\n", + " \n", + "
\n", + "\n", + "\n", + "An overview of the CESM directories and the location of the namelist files is showed in *Figure 1*\n" + ] + }, + { + "cell_type": "markdown", + "id": "301171cd-e6da-4574-ab96-02d82d5fc95f", + "metadata": {}, + "source": [ + "___\n", + "![CESM directories and namelists](../../images/namelist/CESM_directories_and_namelists.png)\n", + "

Figure 1: Overview of the CESM directories and the namelist files.

\n", + "\n", + "___" + ] + }, + { + "cell_type": "markdown", + "id": "f0a5cc0d-f3cf-4508-9751-ff6866fa3f20", + "metadata": {}, + "source": [ + "\n", + "The ``*_in`` files **should not be directly edited**. Any manual changes of the ``*_in`` files will be overwritten when you compile or submit the run. \n", + "\n", + "Note that step ``./preview_namelists`` is **optional** as the script *preview_namelists* is also called when you build the model. \n", + "\n", + "Note that you cannot change the namelist variables: \n", + "- after the run is submitted \n", + "- or when `CONTINUE_RUN=TRUE`. \n" + ] + }, + { + "cell_type": "markdown", + "id": "c55ea30e-7dc5-4694-82d6-90952257c06a", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Complete documentation about the namelist variables can be found on the [CESM webpage.](https://www2.cesm.ucar.edu/models/cesm2/settings/current/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e30b4f4-1e35-4f70-af22-a81489b56018", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/prereqs/exercises.ipynb b/notebooks_copy/prereqs/exercises.ipynb new file mode 100644 index 000000000..9d071b53d --- /dev/null +++ b/notebooks_copy/prereqs/exercises.ipynb @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "082bb7b7-5657-4601-8615-4dd2058a4625", + "metadata": { + "tags": [] + }, + "source": [ + "# Exercise Overview\n", + "\n", + "This exercise will introduce you to the derecho computing system. Specifically, in this exercise we will:\n", + "\n", + "- Step 1. Login to the derecho supercomputer.\n", + "- Step 2. Examine the derecho glade file system.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d9ff1113-8a6d-42b5-8ca7-d3bef493d7fb", + "metadata": {}, + "source": [ + "## Step 1: derecho Login" + ] + }, + { + "cell_type": "markdown", + "id": "f4a25131-701e-4d13-aad7-461d1302b84b", + "metadata": {}, + "source": [ + "From your tutorial terminal window prompt get online.
\n", + "\n", + "
\n", + "Login to derecho:

\n", + "\n", + "```\n", + "ssh –XY username@derecho.hpc.ucar.edu\n", + "```\n", + "\n", + "
\n", + "\n", + "Where `username` is replaced with your tutorial username. Your derecho login will be used through out the tutorial. If you have difficulty getting this to work please ask for help. \n", + "- If you are in the in-person tutorial at NCAR, there will be helpers available.\n", + "- If you are doing this outside of the tutorial then you can contact the user support from the NCAR Computational and Information Systems Laboratory (CISL)." + ] + }, + { + "cell_type": "markdown", + "id": "74512322-32da-400c-8aea-ff65658233db", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL User Support](https://ithelp.ucar.edu/plugins/servlet/desk/site/rc)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "6250a6b0-e65e-43c1-b36d-ebec14345076", + "metadata": {}, + "source": [ + "## Step 2: Look Around derecho File Systems" + ] + }, + { + "cell_type": "markdown", + "id": "ff5b81e1-292a-4bc4-8415-57098366cf80", + "metadata": {}, + "source": [ + "Now that you are on the derecho login nodes you can start to explore the file system. The following simple exercises will start that process.\n", + "\n", + "
\n", + "Show your current directory:
\n", + "\n", + "```\n", + "pwd\n", + "```\n", + "
\n", + "\n", + "List all the directories on the glade file system:
\n", + "\n", + "```\n", + "ls /glade\n", + "```\n", + "
\n", + " \n", + "List the contents of the Input Data directory:
\n", + "\n", + "```\n", + "ls /glade/campaign/cesm/cesmdata/cseg/inputdata/\n", + "```\n", + "\n", + "
\n", + "\n", + "\n" + ] + }, + { + "cell_type": "raw", + "id": "b32ec2e7-06f8-4f26-905f-6d495c4cf299", + "metadata": {}, + "source": [ + "Feel free to look around more of the NCAR HPC system. In the Resources chapter, you will find a section about the [NCAR Supercomputer](https://ncar.github.io/CESM-Tutorial/notebooks/resources/ncar_hpc.html) with links that may be particularly relevant to you." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d338e513-cb0b-45fd-b65a-1e7a66d95971", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/prereqs/prereqs_overview.ipynb b/notebooks_copy/prereqs/prereqs_overview.ipynb new file mode 100644 index 000000000..2b43ba3bf --- /dev/null +++ b/notebooks_copy/prereqs/prereqs_overview.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Prerequisites for Success\n" + ] + }, + { + "cell_type": "markdown", + "id": "4651282a-0c4f-449a-bf36-74af04e7281e", + "metadata": {}, + "source": [ + "NOTE: Although the materials and notebooks in this tutorial are published as a Jupyter Book, **you will need to use a terminal window to actually run the commands presented in this tutorial.**\n", + "\n", + "To successfully complete this tutorial you will:\n", + "- Utilize a **terminal** window\n", + "- Issue commands at the **command line** using **UNIX**\n", + "- Need to be able to access and run on the **NCAR HPC systems** (HPC = High Performance Computing)\n", + "- Use **JupyterHub** for some basic diagnostic plotting\n", + "\n", + "The links below provide more information about these topics. **If you are not familiar with any of the four topics above, we strongly recommend that you read through the following sections.**" + ] + }, + { + "cell_type": "markdown", + "id": "24e22d53-7b0b-4fb8-82ad-360e75e64ac0", + "metadata": {}, + "source": [ + "___\n", + "## Terminal windows" + ] + }, + { + "cell_type": "markdown", + "id": "14f5fe74-cc59-4ccc-af62-e31650e1372c", + "metadata": {}, + "source": [ + "For most of these tutorial exercises, we assume that you will use the online documentation in tandem with an open terminal window. The commands described in these materials will be entered at the command line in your terminal window.\n", + "\n", + "If you are unfamiliar with opening terminal windows, please review the information at the link below. " + ] + }, + { + "cell_type": "markdown", + "id": "01790d14-4535-4e0a-a7da-38ac467717de", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Terminals](https://ncar.github.io/CESM-Tutorial/notebooks/resources/terminals.html#terminal-windows)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "37406e6f-57c2-4493-86a9-52bc2d5eb3f8", + "metadata": {}, + "source": [ + "___\n", + "## UNIX" + ] + }, + { + "cell_type": "markdown", + "id": "6572f442-e01a-4809-a90d-c251ff79f8af", + "metadata": {}, + "source": [ + "The commands described in these materials will be entered in a UNIX environment. If you are unfamiliar with navigating a UNIX environment, we suggest you use the link below to learn more about UNIX. " + ] + }, + { + "cell_type": "markdown", + "id": "e664eb92-e98d-4c3d-afbe-ea517b71439b", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[UNIX](https://ncar.github.io/CESM-Tutorial/notebooks/resources/unix.html#unix)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "16324ba6-5c27-41c2-8e62-dc2ccc817527", + "metadata": {}, + "source": [ + "___\n", + "## Login to NCAR High Performance Computing (HPC)" + ] + }, + { + "cell_type": "markdown", + "id": "3b3e13f6-45e2-4dc8-bd2a-b550ddb11270", + "metadata": {}, + "source": [ + "When logging into the NCAR HPC machines, we recommend using X11 forwarding. X11 forwarding is an SSH protocol that allows users to run graphical applications on a remote server and interact with them using their local display and I/O devices. It is frequently used by CESM developers for securely interacting with NCAR HPC remote machines.\n", + "\n", + "To enable X11 forwarding when logging into the NCAR HPC, simply add the `-XY` option to your `SSH` command. " + ] + }, + { + "cell_type": "markdown", + "id": "ddb0d0fb-1fd9-4814-bb20-af81d2baae5a", + "metadata": {}, + "source": [ + "```\n", + "ssh -XY username@derecho.hpc.ucar.edu \n", + "```\n", + "OR \n", + "```\n", + "ssh -XY username@casper.hpc.ucar.edu\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "510846c7-4018-420e-a899-96e1b9e92902", + "metadata": {}, + "source": [ + "More information about logging into the NCAR HPC follows below.\n", + "\n", + "
\n", + "\n", + "[NCAR High Performance Computing (HPC)](https://ncar.github.io/CESM-Tutorial/notebooks/resources/ncar_hpc.html)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "3c9cc169-489e-427a-865a-539468cb060d", + "metadata": {}, + "source": [ + "____\n", + "## Using JupyterHub" + ] + }, + { + "cell_type": "markdown", + "id": "4a799266-780e-4281-8955-e20e522cdb4d", + "metadata": {}, + "source": [ + "The diagnostics section of this tutorial will use JupyterHub to run Jupyter Notebooks. You will need to login to the NCAR JupyterHub system to run the notebooks successfully.\n", + "\n", + "NOTE: To run a Jupyter cell\n", + "- Type your command into the cell\n", + "- To execute the command:\n", + " - Press **shift+return**\n", + "
\n", + " OR\n", + "
\n", + " - Select the cell then click the 'play' button at the top of the window\n", + "\n", + "To learn more about JupyterHub, please follow the link below." + ] + }, + { + "cell_type": "markdown", + "id": "7db0afc5-0462-45db-ac81-1ee18c34c716", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation about JupyterHub](https://arc.ucar.edu/knowledge_base/70549913)\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "088d2dc6-ae4f-4023-93d4-494aeaf8ba44", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/fortran.ipynb b/notebooks_copy/resources/fortran.ipynb new file mode 100644 index 000000000..ac5770092 --- /dev/null +++ b/notebooks_copy/resources/fortran.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Fortran" + ] + }, + { + "cell_type": "markdown", + "id": "386d6593-bcbf-4c9a-8e64-9c0ee687de47", + "metadata": {}, + "source": [ + "The CESM model is primarily written in Fortran. If you plan to modify the CESM code in any way, we strongly recommend you be able to navigate the Fortran code." + ] + }, + { + "cell_type": "markdown", + "id": "db9ec999-e1b8-43dc-b72c-77223226e1af", + "metadata": {}, + "source": [ + "The 2-unit module listed below, from The COMET Program, introduces users to scientific programming structures and methods and the history, basic syntax and functionality of Fortran. " + ] + }, + { + "cell_type": "markdown", + "id": "d65da730-a8df-47ad-9e9d-68f283637fef", + "metadata": {}, + "source": [ + "Recommendations for Module Use:\n", + "\n", + "- Users who have little to no programming experience should take both units in full.\n", + "- Users that do have some programming experience, such as a web-based language like HTML, but have limited experience in scientific programming, such as a C-based languages, should start with unit 1 and may find they can complete it quickly. While unit 2, covering Fortran and CESM Fortran examples, should be taken in full.\n", + "- Users that have significant scientific programming experience (C-based languages, for example) can safely start in unit 2, which covers Fortran and CESM Fortran examples.\n", + "- Users that have significant programming experience in Fortran can likely begin at the \"Fortran and CESM\" subsection (near bottom of table of contents) in unit 2." + ] + }, + { + "cell_type": "markdown", + "id": "6924d6f2-f210-4704-8084-415c81a2122a", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Fortran COMET module](https://www.meted.ucar.edu/ucar/fortran)\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/github.ipynb b/notebooks_copy/resources/github.ipynb new file mode 100644 index 000000000..860bf0e73 --- /dev/null +++ b/notebooks_copy/resources/github.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Github" + ] + }, + { + "cell_type": "markdown", + "id": "ecbb8df1-076e-43a9-a409-73fea1a4475d", + "metadata": {}, + "source": [ + "The CESM project is managed through the GitHub online software development platform. The CESM Project is hosted under the Earth System Community Modeling Portal (ESCOMP) which is managed by the University Corporation for Atmospheric Research (UCAR). The ESCOMP portal and the CESM project are publicly available and do not require a Github account for read and download access. You can find information, track conversations, create issues, and learn lots about what's going on with CESM development through the CESM GitHub repository and the associated wiki.\n", + "\n", + "**Note: In the tutorial, where git commands are needed we explicitly detail what commands to use. You will not need to use Github for other activities in this Tutorial. We do however suggest that in your own time you become familar with Github, register as a Github user, and then become an active member of the CESM user and development community. When you make a free account in GitHub to access numerous repositories for different software code development at NCAR and beyond. Github, ESCOMP, and the CESM project are vast and you can get lost in this material for hours, days, weeks or even years. Don't go too far off the path in this tutorial as we are expecting that you will focus on learning other derecho and CESM code download tasks.**" + ] + }, + { + "cell_type": "markdown", + "id": "ee473b49-9e16-44b2-a432-235d453094f9", + "metadata": {}, + "source": [ + "## What are Git and GitHub?" + ] + }, + { + "cell_type": "markdown", + "id": "4332ee98-964a-4123-8cd1-56a64af8aa58", + "metadata": {}, + "source": [ + "Git is an open-source version control software to track your changes in the source code. \n", + "\n", + "GitHub is an online software development platform. GitHub provides a centralized online service to host the source code and version control using Git. GitHub is used for storing, tracking, and collaborating on software projects. It makes it easy for developers to share code files and collaborate with fellow developers on open-source projects. GitHub also serves as a social networking site where developers can openly network, collaborate, and pitch their work. GitHub allows software developers to create remote, publicly available repositories on the cloud for free. A repository, or \"repo\" for short, is a coding project’s files and the revision history for each file. " + ] + }, + { + "cell_type": "markdown", + "id": "230ba41c-1c66-482b-b57d-3e14cf326dd2", + "metadata": {}, + "source": [ + "![Github Image](../../images/resources/CESM2_github_main.png)\n", + "*

Figure: Top Level Github Header

*" + ] + }, + { + "cell_type": "markdown", + "id": "2db5b08a-8c5f-4a92-8691-fb9581340d82", + "metadata": {}, + "source": [ + "Learning all the tricks and features of Git and GitHub takes some time to figure out. Below are some links to GitHub tutorials that can help you become more familiar with this powerful tool." + ] + }, + { + "cell_type": "markdown", + "id": "6a271fd9-ef98-4883-bcbd-4d8e83d43a8d", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[GitHub tutorial from software carpentry](https://swcarpentry.github.io/git-novice/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "babdf239-9e83-4f00-b2a6-033f4e1f53b3", + "metadata": {}, + "source": [ + "## ESCOMP" + ] + }, + { + "cell_type": "markdown", + "id": "fcde532b-ea6e-49e2-ab45-52ed8b0f486e", + "metadata": {}, + "source": [ + "The ESCOMP github organization is managed by UCAR and NCAR to organize and manage large, community-oriented earth system modeling projects of broad interest. As shown below there are many projects beyond CESM managed under the ESCOMP organization. The component models (see CAM and CTSM in figure below) that make up CESM are also hosted under the ESCOMP organization." + ] + }, + { + "cell_type": "markdown", + "id": "852d2da7-1b6d-4a7f-b4b1-9d07222de7d9", + "metadata": {}, + "source": [ + "![ESCOMP Image](../../images/resources/CESM2_github_ESCOMP.png)\n", + "*

Figure: ESCOMP Github Header

*" + ] + }, + { + "cell_type": "markdown", + "id": "1306dc86-1d29-48f0-9138-9587b7ff7051", + "metadata": {}, + "source": [ + "## CESM and GitHub" + ] + }, + { + "cell_type": "markdown", + "id": "c37b8978-46bd-47ec-bf45-f403fb53b4de", + "metadata": {}, + "source": [ + "The CESM Project has been hosted on Github since CESM2.0 under the ESCOMP organization. The CESM Project contains all development and release code for all subsequent versions of CESM. The CESM GitHub repository has the tools for managing the exeternal components that make up a CESM model tag. Additionally, the documentation at this repository provides information about software requirements to run CESM, developer guidelines, and a Quikstart guide to accessing CESM through GitHub." + ] + }, + { + "cell_type": "markdown", + "id": "c4e62e1f-7ae6-4420-8bfc-445559d188d3", + "metadata": {}, + "source": [ + "![CESM Project Image](../../images/resources/CESM2_github_CESM.png)\n", + "*

Figure: CESM Project Github Header

*\n" + ] + }, + { + "cell_type": "markdown", + "id": "56c7b16d-5875-463c-88a8-36206fe5ec6a", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CESM GitHub repository](https://github.com/ESCOMP/CESM)\n", + " \n", + "[CESM and GitHub Quickstart guide](https://escomp.github.io/CESM/versions/master/html/index.html)\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdb5e70e-85ca-4e84-9381-4b1b8ab63692", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/ncar_hpc.ipynb b/notebooks_copy/resources/ncar_hpc.ipynb new file mode 100644 index 000000000..d0adfd4ef --- /dev/null +++ b/notebooks_copy/resources/ncar_hpc.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "da307274-8069-4cc8-b333-cb156373cda3", + "metadata": {}, + "source": [ + "# NCAR Supercomputer\n", + "\n", + "Once you have an account and the necessary software, you can log in and run jobs on the NCAR supercomputer. The NCAR supercomputer is also referenced to the NCAR High Performance Computing (HPC). " + ] + }, + { + "cell_type": "markdown", + "id": "28823b60-6956-417c-a48c-3b88668b0968", + "metadata": {}, + "source": [ + "___\n", + "## Logging in on an NCAR system" + ] + }, + { + "cell_type": "markdown", + "id": "3b3e13f6-45e2-4dc8-bd2a-b550ddb11270", + "metadata": {}, + "source": [ + "To log in, start your terminal or Secure Shell client and run an `ssh` command as shown here:\n" + ] + }, + { + "cell_type": "markdown", + "id": "ddb0d0fb-1fd9-4814-bb20-af81d2baae5a", + "metadata": {}, + "source": [ + "```\n", + "ssh -XY username@derecho.hpc.ucar.edu \n", + "```\n", + "OR \n", + "```\n", + "ssh -XY username@casper.hpc.ucar.edu\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "311d9679-8944-49c0-936e-8e8ddcc3ba37", + "metadata": {}, + "source": [ + "\n", + "When you log in to the NCAR HPC machines, we recommend you use `X11` forwarding. `X11` forwarding is an SSH protocol that enables users to run graphical applications on a remote server and interact with them using their local display and I/O devices. It is frequently used by CESM developers for securely interacting with NCAR HPC remote machines.\n", + "\n", + "You can do this by adding the `-XY` option when logging into the machines with `SSH`. More information about logging into the NCAR HPC follows below." + ] + }, + { + "cell_type": "markdown", + "id": "510846c7-4018-420e-a899-96e1b9e92902", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation on Using X11](https://arc.ucar.edu/knowledge_base/72581213#QuickstartonDerecho-LogginginonanNCARsystem)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e731c34f-4386-4869-bdb7-a6e479f07a63", + "metadata": {}, + "source": [ + "___\n", + "## Running on derecho" + ] + }, + { + "cell_type": "markdown", + "id": "202de099-dc2d-47d2-99fb-b6f1b87d654c", + "metadata": {}, + "source": [ + "This tutorial material is designed for use with the derecho HPC system. All of your CESM experiment builds and jobs will be run on derecho.\n", + "\n", + "Please see the link below for more information about the derecho system, including a quick start guide with information about logging into derecho from a terminal, setting up your environment, submitting jobs, etc. " + ] + }, + { + "cell_type": "markdown", + "id": "235c1c69-da82-4cff-95bf-02c1bb2db897", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation on Using derecho HPC](https://arc.ucar.edu/knowledge_base/74317833)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c7e2b935-85ea-47f1-b8f0-654d54714484", + "metadata": {}, + "source": [ + "#### Porting" + ] + }, + { + "cell_type": "markdown", + "id": "a47bc7ae-4bd8-4e55-87e8-27019a689099", + "metadata": {}, + "source": [ + "This tutorial assumes that you are using NCAR HPC assets. In order to run the CESM on a **_different_** computing platform, you will first need to port the CESM code to that environment/machine. We provide information about how to port the model code in the [porting section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/porting.html)." + ] + }, + { + "cell_type": "markdown", + "id": "8a831d36-6629-4cd6-a247-2a8293aa2561", + "metadata": {}, + "source": [ + "___\n", + "## Using Casper" + ] + }, + { + "cell_type": "markdown", + "id": "2f8ad62c-89c5-49c3-9751-219303bce4c7", + "metadata": {}, + "source": [ + "The Casper cluster is a system of specialized data analysis and visualization resources. Casper is not used for building or running CESM, but it is used for CESM data analysis. We will use Casper for the diagnostics section of the lab activities. To utilize the **_additional_** analysis tools described in the [analysis tools section](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html) you will also want to use Casper." + ] + }, + { + "cell_type": "markdown", + "id": "aee2a810-f356-4c55-a19a-c0a32dfd504b", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation on Using Casper Cluster](https://arc.ucar.edu/knowledge_base/70549550)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "603c963b-4d65-4cd6-94a3-ea5ae932578e", + "metadata": {}, + "source": [ + "___\n", + "## Using JupyterHub" + ] + }, + { + "cell_type": "markdown", + "id": "a9423bae-be8c-4848-916c-926f86645da6", + "metadata": {}, + "source": [ + "The diagnostics section of this tutorial will use JupyterHub to run Jupyter Notebooks. You will need to login to the NCAR JupyterHub system to run the notebooks successfully.\n", + "\n", + "NOTE: To run a Jupyter cell\n", + "- Type your command into the cell\n", + "- To execute the command:\n", + " - Press **shift+return**\n", + "
\n", + " OR\n", + "
\n", + " - Select the cell then click the 'play' button at the top of the window\n", + "\n", + "To learn more about JupyterHub, please follow the link below." + ] + }, + { + "cell_type": "markdown", + "id": "6ae8710f-8dd2-4ed6-893f-d2de5b863b94", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation about JupyterHub](https://arc.ucar.edu/knowledge_base/70549913)\n", + " \n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/ncar_hpc_login.ipynb b/notebooks_copy/resources/ncar_hpc_login.ipynb new file mode 100644 index 000000000..2e4e93dab --- /dev/null +++ b/notebooks_copy/resources/ncar_hpc_login.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1e8e7890-9733-47bf-9ff6-91613f4738f1", + "metadata": {}, + "source": [ + "# NCAR Supercomputer\n", + "\n", + "Once you have an account and the necessary software, you can log in and run jobs on the NCAR supercomputer. The NCAR supercomputer is also referenced to the NCAR High Performance Computing (HPC). " + ] + }, + { + "cell_type": "markdown", + "id": "28823b60-6956-417c-a48c-3b88668b0968", + "metadata": {}, + "source": [ + "___\n", + "## Logging in on an NCAR system¶" + ] + }, + { + "cell_type": "markdown", + "id": "3b3e13f6-45e2-4dc8-bd2a-b550ddb11270", + "metadata": {}, + "source": [ + "To log in, start your terminal or Secure Shell client and run an `ssh` command as shown here:\n" + ] + }, + { + "cell_type": "markdown", + "id": "ddb0d0fb-1fd9-4814-bb20-af81d2baae5a", + "metadata": {}, + "source": [ + "```\n", + "ssh -XY username@derecho.hpc.ucar.edu \n", + "```\n", + "OR \n", + "```\n", + "ssh -XY username@casper.hpc.ucar.edu\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "311d9679-8944-49c0-936e-8e8ddcc3ba37", + "metadata": {}, + "source": [ + "\n", + "When you log in to the NCAR HPC machines, we recommend you use `X11` forwarding. `X11` forwarding is an SSH protocol that enables users to run graphical applications on a remote server and interact with them using their local display and I/O devices. It is frequently used by CESM developers for securely interacting with NCAR HPC remote machines.\n", + "\n", + "You can do this by adding the `-XY` option when logging into the machines with `SSH`. More information about logging into the NCAR HPC follows below." + ] + }, + { + "cell_type": "markdown", + "id": "510846c7-4018-420e-a899-96e1b9e92902", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation on Using X11](https://arc.ucar.edu/knowledge_base/72581213#QuickstartonDerecho-LogginginonanNCARsystem)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e731c34f-4386-4869-bdb7-a6e479f07a63", + "metadata": {}, + "source": [ + "___\n", + "## Running on derecho" + ] + }, + { + "cell_type": "markdown", + "id": "202de099-dc2d-47d2-99fb-b6f1b87d654c", + "metadata": {}, + "source": [ + "This tutorial material is designed for use with the derecho HPC system. All of your CESM experiment builds and jobs will be run on derecho.\n", + "\n", + "Please see the link below for more information about the derecho system, including a quick start guide with information about logging into derecho from a terminal, setting up your environment, submitting jobs, etc. " + ] + }, + { + "cell_type": "markdown", + "id": "235c1c69-da82-4cff-95bf-02c1bb2db897", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation on Using derecho HPC](https://arc.ucar.edu/knowledge_base/74317833)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c7e2b935-85ea-47f1-b8f0-654d54714484", + "metadata": {}, + "source": [ + "#### Porting" + ] + }, + { + "cell_type": "markdown", + "id": "a47bc7ae-4bd8-4e55-87e8-27019a689099", + "metadata": {}, + "source": [ + "This tutorial assumes that you are using NCAR HPC assets. In order to run the CESM on a **_different_** computing platform, you will first need to port the CESM code to that environment/machine. We provide information about how to port the model code in the [porting section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/porting.html)." + ] + }, + { + "cell_type": "markdown", + "id": "8a831d36-6629-4cd6-a247-2a8293aa2561", + "metadata": {}, + "source": [ + "___\n", + "## Using Casper" + ] + }, + { + "cell_type": "markdown", + "id": "2f8ad62c-89c5-49c3-9751-219303bce4c7", + "metadata": {}, + "source": [ + "The Casper cluster is a system of specialized data analysis and visualization resources. Casper is not used for building or running CESM, but it is used for CESM data analysis. We will use Casper for the diagnostics section of the lab activities. To utilize the **_additional_** analysis tools described in the [analysis tools section](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html) you will also want to use Casper." + ] + }, + { + "cell_type": "markdown", + "id": "aee2a810-f356-4c55-a19a-c0a32dfd504b", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation on Using Casper Cluster](https://arc.ucar.edu/knowledge_base/70549550)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "603c963b-4d65-4cd6-94a3-ea5ae932578e", + "metadata": {}, + "source": [ + "___\n", + "## Using JupyterHub" + ] + }, + { + "cell_type": "markdown", + "id": "a9423bae-be8c-4848-916c-926f86645da6", + "metadata": {}, + "source": [ + "The diagnostics section of this tutorial will use JupyterHub to run Jupyter Notebooks. You will need to login to the NCAR JupyterHub system to run the notebooks successfully.\n", + "\n", + "NOTE: To run a Jupyter cell\n", + "- Type your command into the cell\n", + "- To execute the command:\n", + " - Press **shift+return**\n", + "
\n", + " OR\n", + "
\n", + " - Select the cell then click the 'play' button at the top of the window\n", + "\n", + "To learn more about JupyterHub, please follow the link below." + ] + }, + { + "cell_type": "markdown", + "id": "6ae8710f-8dd2-4ed6-893f-d2de5b863b94", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL Documentation about JupyterHub](https://arc.ucar.edu/knowledge_base/70549913)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "144caca2-989b-40a5-b6c0-c8a0b7a470da", + "metadata": {}, + "source": [ + "___\n", + "## Modules on NCAR HPC" + ] + }, + { + "cell_type": "markdown", + "id": "52805170-9397-48b3-8bdf-a2c76fb45583", + "metadata": {}, + "source": [ + "NCAR maintains a number of different programs on the HPC systems that can be added to your user environment by executing the ``module load`` command. " + ] + }, + { + "cell_type": "markdown", + "id": "a551ace3-3b1a-4130-8084-3034842e0259", + "metadata": {}, + "source": [ + "We will use a few modules throughout this tutorial to visualize data, so you need to be able to check which modules you have loaded and to load necessary modules." + ] + }, + { + "cell_type": "markdown", + "id": "f6edec76-66ba-42f0-b3b2-00a4d90f15fd", + "metadata": {}, + "source": [ + "We provide basic instructions below on how to check and load Modules, but to learn more about Modules on NCAR HPC, please follow the link below." + ] + }, + { + "cell_type": "markdown", + "id": "b2aef333-82a9-4f3d-8afa-2f5c3df4ef4c", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL documentation on modules](https://arc.ucar.edu/knowledge_base/72581272)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "33d4014c-bc0f-4ab0-aaf3-cf6cf7572f22", + "metadata": {}, + "source": [ + "### Checking what modules are loaded in your environment\n", + "\n", + "To check what modules you have loaded in your environment, use the following at the command line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fe867e0-f0df-4d87-a9d6-169294ec222c", + "metadata": {}, + "outputs": [], + "source": [ + "module list" + ] + }, + { + "cell_type": "markdown", + "id": "2a5ec941-4f4a-4a99-a3a0-6e514c448805", + "metadata": {}, + "source": [ + "### Checking what modules are available\n", + "\n", + "You can see which modules are available on the HPC resources by using the following at the command line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b974222c-a270-4075-b9b9-7be7b4550942", + "metadata": {}, + "outputs": [], + "source": [ + "module avail" + ] + }, + { + "cell_type": "markdown", + "id": "0d2cfc8e-2085-4b72-b917-9e98a1586e13", + "metadata": {}, + "source": [ + "The command returns a list to the terminal window of the modules available to load to your environment." + ] + }, + { + "cell_type": "markdown", + "id": "ec74810f-c422-468d-b30e-ddb9c41d878f", + "metadata": {}, + "source": [ + "![Modules](../../images/prereqs/modules.png)\n", + "\n", + "*

Figure: Some modules available on NCAR HPC resources. D: Default Module. L: Module is loaded.

*" + ] + }, + { + "cell_type": "markdown", + "id": "c523021e-9328-4e0b-8c40-15bcf2ebab75", + "metadata": {}, + "source": [ + "The modules highlighted in blue are text editors discussed further in the [text editors section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/text_editors.html).\n", + "\n", + "The modules highlighted in red are netCDF tools that will be discussed further in the [netCDF section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html).\n", + "\n", + "Note that there are many other analysis software tools available to load into your NCAR HPC environment. These include: idl, julia, matlab, R, python, ncl, etc. While some of these tools are discussed in the [analysis tools section](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html), we do not highlight them all here and encourage you to further explore your environment and needs." + ] + }, + { + "cell_type": "markdown", + "id": "65848ac8-1cad-47bf-b680-8b129ce4e25d", + "metadata": {}, + "source": [ + "### Loading a new module to your environment\n", + "\n", + "You can load one of the available modules (here we show loading ncview) using the following at the command line: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad9db6c8-91a9-4c3e-84b4-6e6fb65de213", + "metadata": {}, + "outputs": [], + "source": [ + "module load ncview" + ] + }, + { + "cell_type": "markdown", + "id": "a1e13c85-65d1-4904-a07b-6cba1249ddfd", + "metadata": {}, + "source": [ + "
\n", + "NOTE: We will provide a file that will load necessary modules for those attending the in person tutorial. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "3c9cc169-489e-427a-865a-539468cb060d", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a1cb820-6efd-4eda-9581-a7067ff4f27d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/ncar_hpc_module.ipynb b/notebooks_copy/resources/ncar_hpc_module.ipynb new file mode 100644 index 000000000..02eac9de8 --- /dev/null +++ b/notebooks_copy/resources/ncar_hpc_module.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "144caca2-989b-40a5-b6c0-c8a0b7a470da", + "metadata": {}, + "source": [ + "# Modules on NCAR HPC" + ] + }, + { + "cell_type": "markdown", + "id": "52805170-9397-48b3-8bdf-a2c76fb45583", + "metadata": {}, + "source": [ + "NCAR maintains a number of different programs on the HPC systems that can be added to your user environment by executing the ``module load`` command. " + ] + }, + { + "cell_type": "markdown", + "id": "a551ace3-3b1a-4130-8084-3034842e0259", + "metadata": {}, + "source": [ + "We will use a few modules throughout this tutorial to visualize data, so you need to be able to check which modules you have loaded and to load necessary modules." + ] + }, + { + "cell_type": "markdown", + "id": "f6edec76-66ba-42f0-b3b2-00a4d90f15fd", + "metadata": {}, + "source": [ + "We provide basic instructions below on how to check and load Modules, but to learn more about Modules on NCAR HPC, please follow the link below." + ] + }, + { + "cell_type": "markdown", + "id": "b2aef333-82a9-4f3d-8afa-2f5c3df4ef4c", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CISL documentation on modules](https://arc.ucar.edu/knowledge_base/72581272)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "33d4014c-bc0f-4ab0-aaf3-cf6cf7572f22", + "metadata": {}, + "source": [ + "### Checking what modules are loaded in your environment\n", + "\n", + "To check what modules you have loaded in your environment, use the following at the command line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fe867e0-f0df-4d87-a9d6-169294ec222c", + "metadata": {}, + "outputs": [], + "source": [ + "module list" + ] + }, + { + "cell_type": "markdown", + "id": "2a5ec941-4f4a-4a99-a3a0-6e514c448805", + "metadata": {}, + "source": [ + "### Checking what modules are available\n", + "\n", + "You can see which modules are available on the HPC resources by using the following at the command line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b974222c-a270-4075-b9b9-7be7b4550942", + "metadata": {}, + "outputs": [], + "source": [ + "module avail" + ] + }, + { + "cell_type": "markdown", + "id": "0d2cfc8e-2085-4b72-b917-9e98a1586e13", + "metadata": {}, + "source": [ + "The command returns a list to the terminal window of the modules available to load to your environment." + ] + }, + { + "cell_type": "markdown", + "id": "ec74810f-c422-468d-b30e-ddb9c41d878f", + "metadata": {}, + "source": [ + "![Modules](../../images/prereqs/modules.png)\n", + "\n", + "*

Figure: Some modules available on NCAR HPC resources. D: Default Module. L: Module is loaded.

*" + ] + }, + { + "cell_type": "markdown", + "id": "c523021e-9328-4e0b-8c40-15bcf2ebab75", + "metadata": {}, + "source": [ + "The modules highlighted in blue are text editors discussed further in the [text editors section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/text_editors.html).\n", + "\n", + "The modules highlighted in red are netCDF tools that will be discussed further in the [netCDF section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html).\n", + "\n", + "Note that there are many other analysis software tools available to load into your NCAR HPC environment. These include: idl, julia, matlab, R, python, ncl, etc. While some of these tools are discussed in the [analysis tools section](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html), we do not highlight them all here and encourage you to further explore your environment and needs." + ] + }, + { + "cell_type": "markdown", + "id": "65848ac8-1cad-47bf-b680-8b129ce4e25d", + "metadata": {}, + "source": [ + "### Loading a new module to your environment\n", + "\n", + "You can load one of the available modules (here we show loading ncview) using the following at the command line: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad9db6c8-91a9-4c3e-84b4-6e6fb65de213", + "metadata": {}, + "outputs": [], + "source": [ + "module load ncview" + ] + }, + { + "cell_type": "markdown", + "id": "a1e13c85-65d1-4904-a07b-6cba1249ddfd", + "metadata": {}, + "source": [ + "
\n", + "NOTE: We will provide a file that will load necessary modules for those attending the in person tutorial. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "3c9cc169-489e-427a-865a-539468cb060d", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a1cb820-6efd-4eda-9581-a7067ff4f27d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/netcdf.ipynb b/notebooks_copy/resources/netcdf.ipynb new file mode 100644 index 000000000..5bbacda9a --- /dev/null +++ b/notebooks_copy/resources/netcdf.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# NetCDF files" + ] + }, + { + "cell_type": "markdown", + "id": "e70f8ee6-91c8-4543-830f-4196163a7131", + "metadata": {}, + "source": [ + "All CESM model output is in netCDF (*.nc) data format. Below, you will find some useful information for how to navigate and manipulate CESM netCDF files." + ] + }, + { + "cell_type": "markdown", + "id": "59bb8a3f-74a5-4c7f-aaa7-15d22fe59b4a", + "metadata": {}, + "source": [ + "## NetCDF\n", + "\n", + "NetCDF stands for “network Common Data Form”. NetCDF files are array-oriented and a community standard for sharing scientific model data.\n", + "\n", + "Some benefits of netCDF files include that they:\n", + "- Are self describing and generally include substantial metadata to assist with comprehension of the data.\n", + "- Supported by a range of languages (Fortran, C, Matlab, ferret, GrADS, NCL, IDL, python).\n", + "- Appendable, so data can be added to a file.\n", + "- Viewable with tools like ncview and panopoly." + ] + }, + { + "cell_type": "markdown", + "id": "5b9c0340-90be-4957-82a7-5d911287a35f", + "metadata": {}, + "source": [ + "The files are saved in netcdf format (denoted with the `.nc` file extension), a file format commonly used for storing large, multi-dimensional scientific variables.\n", + "Netcdf files are platform independent and self-describing; each file includes metadata that describes the data, including: **variables**, **dimensions**, and **attributes**.\n", + "\n", + "The figure below provides a generic example of the data structure in a netcdf file. The dataset illustrated has two variables (temperature and pressure) that have three dimensions. Coordinate data (e.g., latitude, longitude, time) that describe the data are also included. \n", + "\n", + "![Netcdf](https://xarray.pydata.org/en/stable/_images/dataset-diagram.png)" + ] + }, + { + "cell_type": "markdown", + "id": "c6540e1c-d489-401b-b227-2da4f69c50ec", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[netCDF information](https://www.unidata.ucar.edu/software/netcdf/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "eeaa2e1e-68fa-4b72-9415-d12448484719", + "metadata": {}, + "source": [ + "## ncdump\n", + "\n", + "Sometimes you want to see what information a netCDF file without fully opening it. `ncdump` is a command line netCDF utility that allows the user to dump the contents of the netCDF file to the terminal screen or to a file in a human-readable format. \n", + "\n", + "**NOTE:** `ncdump` **is installed by default as part of the netCDF libraries.**" + ] + }, + { + "cell_type": "markdown", + "id": "bf4014fd-c02a-485d-a648-a01d7bb6f204", + "metadata": {}, + "source": [ + "To view the header of a netCDF file, use the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78c32209-629f-4d7e-afe2-64cb6ff4b04c", + "metadata": {}, + "outputs": [], + "source": [ + "ncdump -h file.nc" + ] + }, + { + "cell_type": "markdown", + "id": "177838bf-0241-4265-8418-fcfbdcff7dd0", + "metadata": {}, + "source": [ + "At the command line, you get the following information from the `ncdump -h` command. It includes the dimensions in the file, a list of variables, and global attributes that can be very useful." + ] + }, + { + "cell_type": "markdown", + "id": "c7cdea85-fb03-440b-a65a-53ca69483e02", + "metadata": {}, + "source": [ + "![ncdump](../../images/resources/ncdump_example.png)\n", + "\n", + "*

Figure: Example output from the ncdump -h command.

*" + ] + }, + { + "cell_type": "markdown", + "id": "a06d67d2-b5e5-477a-871b-16dcad514357", + "metadata": {}, + "source": [ + "To view the contents of a variable, use the following command:\n", + "* Note that you must verify the variable exists in the file by first doing a the command above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74324a3f-7129-4b6b-96e8-d3d831247d68", + "metadata": {}, + "outputs": [], + "source": [ + "ncdump -v PSL file.nc" + ] + }, + { + "cell_type": "markdown", + "id": "73922a90-4f0e-488a-a1e5-81d07141a216", + "metadata": {}, + "source": [ + "To view the netCDF file type, use the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1aa78eba-bdab-48fe-a563-6dae2747af2f", + "metadata": {}, + "outputs": [], + "source": [ + "ncdump –k file.nc" + ] + }, + { + "cell_type": "markdown", + "id": "86ba17a2-a119-40d7-bda2-1c0b40694596", + "metadata": {}, + "source": [ + "To print readable date-time strings, use the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e52ddeec-7fe3-49fb-ac4c-e243f5263b72", + "metadata": {}, + "outputs": [], + "source": [ + "ncdump –t –v time file.nc" + ] + }, + { + "cell_type": "markdown", + "id": "b9478866-10ee-4b8f-a80f-7e58f840872c", + "metadata": {}, + "source": [ + "## ncview\n", + "\n", + "`ncview` is a graphical interface which allows the user to quickly view the variables inside a NetCDF file. `ncview` also allows the user to interactively visualize a selected variable aross a selected range (time, spatial).\n", + "\n", + "**NOTE:** `ncview` **must be loaded as a module in your HPC environment.** See section (LINK)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95c41eb1-01cc-4f24-90fb-ca85ed0da51f", + "metadata": {}, + "outputs": [], + "source": [ + "ncview file.nc" + ] + }, + { + "cell_type": "markdown", + "id": "f31e7620-b199-4fd7-8b9c-715b199c0436", + "metadata": {}, + "source": [ + "![ncview](../../images/resources/ncview.png)\n", + "\n", + "*

Figure: Example output from ncview command.

*" + ] + }, + { + "cell_type": "markdown", + "id": "674a8519-2434-4d87-a418-feb10005b83a", + "metadata": {}, + "source": [ + "## netCDF Operators (NCO) \n", + "\n", + "NCO is a suite of programs designed to perform certain “operations” on netCDF files, i.e., things like averaging, concatenating, subsetting, or metadata manipulation.Command-line operations are extremely useful for processing model data given that modelers often work in a UNIX-type environment.\n", + "\n", + "We will describe a few key types of NCO operators below, but there are many other NCO operators beyond these that could be of use. We recommend visiting the NCO page to get full documentation.\n", + "\n", + "**NOTE:** `nco` **must be loaded as a module in your HPC environment.** See section (LINK)." + ] + }, + { + "cell_type": "markdown", + "id": "fab944fa-04c3-4843-af5b-2763970dd0fd", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[NCO github page](https://github.com/nco/nco)\n", + "\n", + "[NCO sourceforge page](http://nco.sourceforge.net)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "de438002-157e-4f2f-8f3f-d0f2c3d01b41", + "metadata": {}, + "source": [ + "`ncra` is a netCDF record averager that averages across the record dimesion (i.e. time) for the files specified. The example below shows the command for how to average across two files from different months." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "604168a3-5268-43f6-a002-3f4906b9caf9", + "metadata": {}, + "outputs": [], + "source": [ + "ncra file1.nc file2.nc avgfile.nc" + ] + }, + { + "cell_type": "markdown", + "id": "575d65ac-82aa-4d13-9702-9c63ce84321e", + "metadata": {}, + "source": [ + "`ncrcat` is a netCDF record concatenator that combines the files specified across the record dimesion (i.e. time). The example below shows the command for how to concatenate two files from different months into a single file that will have two timesteps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88a35e2d-64e0-4c9a-9764-7a4587d25a28", + "metadata": {}, + "outputs": [], + "source": [ + "ncrcat file1.nc file2.nc out12.nc" + ] + }, + { + "cell_type": "markdown", + "id": "8b756d23-71bb-45f4-ba63-83b8ee50f781", + "metadata": {}, + "source": [ + "`ncdiff` is a netCDF differener that will reveal differences between files. The example below shows the command for how to create a file showing differences between two files from different months." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca1c37fc-ab49-4769-aa39-e4804b01dd83", + "metadata": {}, + "outputs": [], + "source": [ + "ncdiff file1.nc file2.nc diff.nc" + ] + }, + { + "cell_type": "markdown", + "id": "91216ea2-7278-4887-8a6f-f9a2e1e4eedb", + "metadata": {}, + "source": [ + "`ncks` stands for netCDF kitchen sink and has numerous functions that may be useful. The examples below show \n", + "1) how to use NCO operator options to subset only some specified variables from the input file because the `-v` option operates only on the variables listed.\n", + "2) how to use NCO operator options to subset only a given latitude and longitude range because the `-d` option operates on the dimensions. Note that real numbers indicate coordinate values while integers indicate array indexes. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d9ba792-79e3-4bdd-9823-816110b5d0a1", + "metadata": {}, + "outputs": [], + "source": [ + "ncks –v T,U,PS file_in.nc file_out.nc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c549782-7aba-4de7-816f-5dcea3264e81", + "metadata": {}, + "outputs": [], + "source": [ + "ncks -d lon,0.,180. -d lat,0,63 file_in.nc file_out.nc" + ] + }, + { + "cell_type": "markdown", + "id": "6b81015d-05e5-44e9-9c3f-6ea31e8eb44f", + "metadata": {}, + "source": [ + "## Climate Data Operators (CDO) \n", + "\n", + "CDO are a suite of command line operators to manipulate and analyze climate and NWP model data. CDO are similar to NCO. The CDO library inclues over 600 command line operators that do a variety of tasks including: detrending, EOF analysis, metadata modification, statistical analysis, etc. \n", + "\n", + "**NOTE:** `cdo` **must be loaded as a module in your HPC environment.** See section (LINK)." + ] + }, + { + "cell_type": "markdown", + "id": "cd5527e1-d0c7-4108-81a9-357a82110f82", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CDO github page](https://github.com/AZed/cdo)\n", + "\n", + "[CDO project page](https://code.mpimet.mpg.de/projects/cdo/)\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Bash", + "language": "bash", + "name": "bash" + }, + "language_info": { + "codemirror_mode": "shell", + "file_extension": ".sh", + "mimetype": "text/x-sh", + "name": "bash" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/porting.ipynb b/notebooks_copy/resources/porting.ipynb new file mode 100644 index 000000000..c84e59b20 --- /dev/null +++ b/notebooks_copy/resources/porting.ipynb @@ -0,0 +1,84 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Porting" + ] + }, + { + "cell_type": "markdown", + "id": "fe00d177-b2e2-4067-b1fe-95b0a1d45f3d", + "metadata": {}, + "source": [ + "Moving CESM to a new machine that has not been previously setup requires knowledge of both the CESM model and the target machine architecture and software. Porting details can also be found in specialized lecture slides from previous CESM tutorials.
" + ] + }, + { + "cell_type": "markdown", + "id": "52b78297-4829-477b-8018-6d80512389a5", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[CIME documentation (see part 2)](http://esmci.github.io/cime/)\n", + "\n", + "[Porting CESM2](http://www.cesm.ucar.edu/models/cesm2/linux_cluster/)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "080839b4-514f-4072-88a6-bd514424cf6e", + "metadata": {}, + "source": [ + "![Porting Image](../../images/resources/CESM2_Porting.png)\n", + "*

Figure: Porting CESM2 to the CGD Cluster Hobart

*" + ] + }, + { + "cell_type": "markdown", + "id": "f1ec0337-eadf-46e3-80b5-2085854b9742", + "metadata": {}, + "source": [ + "# Take-away Points\n", + "- On supported machines - no porting is necessary\n", + "- On new machines - porting needs to be done\n", + "- Documentation from CIME and CESM Website\n", + "- Thursday afternoon lecture on Porting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2de6735-4420-4b9d-bae1-3384a34de010", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/profile.ipynb b/notebooks_copy/resources/profile.ipynb new file mode 100644 index 000000000..328c5f7c3 --- /dev/null +++ b/notebooks_copy/resources/profile.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b2cd974a-dfbb-4937-8ed0-ab87ab60f1ef", + "metadata": {}, + "source": [ + "# Tutorial specific instructions" + ] + }, + { + "cell_type": "markdown", + "id": "4a6f76fe-9658-443c-89a7-9ede364803e5", + "metadata": {}, + "source": [ + "### Tutorial Project Account\n", + "\n", + "
\n", + "You should have access to project account UESM0013 and use this for your simulations.\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9db9d54e-ef78-44ef-a003-4ddd8a416317", + "metadata": {}, + "source": [ + "### Special Queues\n", + "\n", + "
\n", + "We have a special queue every day for running to ensure you get through the derecho queues quickly and get your jobs run. These are only active for portions of each day during our lab sessions and change for each session. So you should be sure you are using the correct reservation queue. \n", + " \n", + "Schedule for Tutorial Reservation Queues\n", + "```\n", + "Mon 14:00 - 17:00 derecho R???????\n", + "Tue 10:30 - 12:00 derecho R???????\n", + " 14:00 - 17:00 derecho R???????\n", + "Wed 10:30 - 12:00 derecho R???????\n", + " 14:00 - 17:00 derecho R???????\n", + "Thu 13:30 - 17:00 casper R???????\n", + "Fri 13:30 - 17:00 derecho R??????? \n", + "```\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e127deda-73f0-42fc-9c96-e49bb1bb0f8b", + "metadata": {}, + "source": [ + "### Using these tutorial-specific codes" + ] + }, + { + "cell_type": "markdown", + "id": "ec3ec832-823f-48ce-b142-05c69dcc72b1", + "metadata": {}, + "source": [ + "In theory, you _should_ be able to automatically use this account and the reservation queues if you correctly set up your NCAR HPC environment using your `.profile` or `.tcshrc` files, as described below. If you need to change the values for a run, you can change the account and reservation queue in your case directory using the following command:\n", + "\n", + "```\n", + "./xmlchange --force JOB_QUEUE=R???????\n", + "./xmlchange PROJECT=UESM0013\n", + "```\n", + "\n", + "For instance, on Wednesday\n", + "\n", + "- If (Wed 10:30 - 12:00) then\n", + "\n", + "```\n", + "./xmlchange JOB_QUEUE=R??????? --force\n", + "``` \n", + "\n", + "- If (Wed 14:00 - 17:00) then\n", + "\n", + "```\n", + "./xmlchange JOB_QUEUE=R??????? --force\n", + "```\n", + "\n", + "\n", + "- If (Wed at another time) then\n", + "```\n", + "./xmlchange JOB_QUEUE=regular\n", + "```\n", + "\n", + "If you do use these commands, make sure for the `JOB_QUEUE` you are using the **correct** reservation code for the relevant day _and_ time of that lab session. You should not have to set the `PROJECT` more than once during the tutorial if your profile is set up, as described below, but we include it for your reference in case you are having trouble." + ] + }, + { + "cell_type": "markdown", + "id": "0c07b51d-f999-4737-9739-3ea989f20802", + "metadata": {}, + "source": [ + "## Setting up your NCAR HPC environment" + ] + }, + { + "cell_type": "markdown", + "id": "9d6954eb-3505-4a58-b6fa-bea7047c63cf", + "metadata": {}, + "source": [ + "Every time you log onto the NCAR HPC you want to ensure you have the correct modules loaded." + ] + }, + { + "cell_type": "markdown", + "id": "7440f1b8-06af-435a-b1db-d444b99bf846", + "metadata": {}, + "source": [ + "
\n", + "NOTE: The next section describes how to use a file that loads the modules necessary for those attending the in-person tutorial. Once you set up your environment you should not have to do it again for the remainder of the tutorial. Every time you login to NCAR HPC these files will automatically be sourced. However, if you intend to use NCAR HPC after the tutorial you may need to modify your `.profile` or `.tcshrc` file to load the correct modules, project numbers, or queues. \n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8e6da3c0-4cbf-42e3-b599-9ef4d1b4a72c", + "metadata": {}, + "source": [ + "___\n", + "\n", + "### SHELL environment " + ] + }, + { + "cell_type": "markdown", + "id": "c04124a8-5e76-4104-8d62-7bd0a329ce58", + "metadata": {}, + "source": [ + "To determine what shell environment you are using on the NCAR HPC, type the following command:" + ] + }, + { + "cell_type": "markdown", + "id": "9f8488f1-72e7-4594-be66-f875c51cc4e1", + "metadata": {}, + "source": [ + "
\n", + "\n", + "```\n", + "echo $SHELL\n", + "```\n", + "\n", + " \n", + "If the command returns, you are a bash user\n", + "```/bin/bash```\n", + "\n", + " \n", + "If the command returns, you are a cshell user\n", + "```/bin/tcsh```\n", + "\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "91a8addf-1e89-43b9-a364-1e1aa151454d", + "metadata": {}, + "source": [ + "**NOTE:** All new NCAR HPC accounts default to bash. " + ] + }, + { + "cell_type": "markdown", + "id": "8c4f6e24-577b-4736-a251-69b51553d704", + "metadata": {}, + "source": [ + "___\n", + "\n", + "### Bash users" + ] + }, + { + "cell_type": "markdown", + "id": "a82c1d5f-eddc-4504-bd66-febd2f3179f8", + "metadata": {}, + "source": [ + "**NOTE:** You may already have a `.profile` file in your home directory. \n", + "- If you have an existing `.profile` file and do not wish to overwrite its contents, please save your original `.profile` file to a new filename before copying the tutorial .profile file into your home directory. \n", + "- Alternatively, you could copy the contents of the tutorial profile file into your existing `.profile` file using a text editor. (see special instructions below)" + ] + }, + { + "cell_type": "markdown", + "id": "870dc6c7-98f5-4e3a-b226-2f2437d5321a", + "metadata": {}, + "source": [ + "#### One-time setup\n", + "To set up your environment for the tutorial, follow the following three steps:" + ] + }, + { + "cell_type": "markdown", + "id": "9a2baee0-804e-4586-b8fc-a3636c528484", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Copy over the `.profile` file into your home directory: \n", + " \n", + "```\n", + "cp /glade/campaign/cesm/development/cross-wg/profile ~/.profile\n", + "```\n", + "
\n", + "\n", + "Source the file:\n", + "\n", + "```\n", + "source ~/.profile\n", + "```\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf95578e-40e5-4c40-8884-0b80e29eb858", + "metadata": {}, + "source": [ + "\n", + "#### Before each practical\n", + "Do the following step before **every practical**. This is because we are getting a different special queue every day. \n", + "\n", + "
\n", + "\n", + "Source the file:
\n", + "\n", + "```\n", + "source ~/.profile\n", + "```\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "4daab2a7-58c1-41f0-b51b-c9e8a573574b", + "metadata": {}, + "source": [ + "___\n", + "\n", + "### Tcsh users" + ] + }, + { + "cell_type": "markdown", + "id": "42432d90-eaef-4205-b4fe-39cf3c7b5881", + "metadata": {}, + "source": [ + "**NOTE:** You should already have a `.tcshrc` file in your home directory. If you do not wish to overwrite the existing `.tcshrc` file, please save this file to a new name OR copy the contents of the `/glade/campaign/cesm/development/cross-wg/tcshrc` file to your `.tcshrc` file using a text editor." + ] + }, + { + "cell_type": "markdown", + "id": "0722df3e-2497-492c-8301-7d54e7485c05", + "metadata": {}, + "source": [ + "#### One-time setup\n", + "\n", + "To set up your environment for the tutorial, follow the following three steps:" + ] + }, + { + "cell_type": "markdown", + "id": "a77636dd-1651-431a-a72e-f862947b37a6", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
\n", + "\n", + "Copy over the `.tcshrc` file to your home directory:\n", + "```\n", + "cp /glade/campaign/cesm/development/cross-wg/tcshrc ~/.tcshrc\n", + "```\n", + " \n", + "
\n", + "\n", + "Source the file:\n", + "\n", + "```\n", + "source ~/.tcshrc\n", + "```\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "637d5219-104b-4e53-ab41-b3969b275527", + "metadata": {}, + "source": [ + "\n", + "#### Before each practical\n", + "\n", + "Do the following step before **every practical**. This is because we are getting a different special queue every day. \n", + "\n", + "
\n", + " \n", + "Source the file:
\n", + "\n", + "```\n", + "source ~/.tcshrc\n", + "``` \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "5edccc8b-0080-4837-aa70-d50360cb2c6c", + "metadata": {}, + "source": [ + "___\n", + "\n", + "### If you have any trouble\n", + "\n", + "**Please ask for help if you are having trouble with this step!**\n", + "\n", + "The modules necessary for this tutorial and loaded through these files are:\n", + "- ncarenv/1.3\n", + "- intel\n", + "- ncl\n", + "- nco\n", + "- ncview\n", + "- netcdf\n" + ] + }, + { + "cell_type": "markdown", + "id": "71cae660-0d1a-4193-8333-909310c49dac", + "metadata": {}, + "source": [ + "____\n", + "\n", + "### Special instructions (if you already had an account on derecho)\n", + "\n", + "If you already have a `.profile` or `.tcsh` and you do not wish to overwrite its contents, you need to add the following into your existing `.profile` or `.tcsh` using a text editor.\n", + "\n", + "#### Bash:\n", + "\n", + "
\n", + "\n", + "Add to your .profile: \n", + "\n", + "```\n", + "export PROJECT=UESM0013 #<- This should never change over the course of the tutorial\n", + "export TUTORIAL_QUEUE=R??????? #<- This will be different for every practical. \n", + " #<- Look at the schedule below:\n", + "```\n", + "\n", + "Source the file:
\n", + "\n", + "```\n", + "source ~/.profile\n", + "```\n", + "
\n", + "\n", + "#### C-shell:\n", + "\n", + "
\n", + "\n", + "Add to your .tcshrc: \n", + "```\n", + "setenv PROJECT UESM0013 #<-This should never change over the course of the tutorial\n", + "setenv TUTORIAL_QUEUE R??????? #<-This will be different for every practical \n", + " #<- Look at the schedule below:\n", + "```\n", + " \n", + "Source the file:
\n", + "\n", + "```\n", + "source ~/.tcshrc\n", + "``` \n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b77ea28-df80-4c20-a123-ec1e09a1475a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "toc-showcode": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/resources_overview.ipynb b/notebooks_copy/resources/resources_overview.ipynb new file mode 100644 index 000000000..f9ad4a385 --- /dev/null +++ b/notebooks_copy/resources/resources_overview.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0959db1d-1790-4519-a34e-7026444af537", + "metadata": {}, + "source": [ + "# Resources" + ] + }, + { + "cell_type": "markdown", + "id": "43fca83b-54e5-4950-a9b3-bbfbbcb01c77", + "metadata": {}, + "source": [ + "While there is no way for this tutorial to provide every bit of information or resources needed to successfully learn CESM, this section provides some resources that you may find particularly helpful." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6dc4950b-9954-4019-9c0b-46a3e3b0b3aa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/terminals.ipynb b/notebooks_copy/resources/terminals.ipynb new file mode 100644 index 000000000..ed38fc238 --- /dev/null +++ b/notebooks_copy/resources/terminals.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Terminal Windows" + ] + }, + { + "cell_type": "markdown", + "id": "93b754e9-2569-4779-890f-f3f069fd9faa", + "metadata": {}, + "source": [ + "For local or remote execution of a program, a terminal is a window in a graphical interface that is used to display a command line. In the UNIX environment, the terminal window is widely used by developers to perform myriad maintenance operations in local and remote computers. \n", + "\n", + "Most CESM users and developers use terminal windows to run the model. Users open a terminal window and access the NCAR HPC resources remotely. This way the user can be located anywhere and access the HPC assets located in Cheyenne, WY." + ] + }, + { + "cell_type": "markdown", + "id": "c3c5cecc-b0ef-4bd2-bb50-5f92efeb12c7", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Logging into NCAR HPC](https://ncar.github.io/CESM-Tutorial/notebooks/resources/ncar_hpc.html#logging-in-on-an-ncar-system)\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "75f18a09-8ff0-4944-b19f-a51d6473e645", + "metadata": {}, + "source": [ + "## Logging into a terminal from a Mac" + ] + }, + { + "cell_type": "markdown", + "id": "fa3a2300-0e3a-4b87-9456-0c741acb9e96", + "metadata": {}, + "source": [ + "Go to the search spotlight (magnifying glass icon) and type in \"terminal\". \n", + "\n", + "- `Terminal` is automatically built into the MacOS and clicking on it will open a terminal window.\n", + "\n", + "- `iTerm` is software that was installed by the user on their laptop and is another option for opening a terminal. (*Recommended*)" + ] + }, + { + "cell_type": "markdown", + "id": "d4ad6ecc-155e-40f8-965f-a223c119b85c", + "metadata": {}, + "source": [ + "![mac terminal](../../images/resources/term_mac.png)\n", + "\n", + "*

Figure: Finding terminal on a mac.

*" + ] + }, + { + "cell_type": "markdown", + "id": "6b6d2dbf-f7e1-40a1-a81a-a147189a133a", + "metadata": {}, + "source": [ + "## Logging into a terminal from a PC" + ] + }, + { + "cell_type": "markdown", + "id": "1a24b174-9a10-4863-91b2-7a0408cc9d03", + "metadata": {}, + "source": [ + "Go to the start menu and type in \"terminal\". \n", + "\n", + "- `Terminal` will open a terminal window.\n", + "\n", + "- `Command Prompt` will open a terminal window." + ] + }, + { + "cell_type": "markdown", + "id": "705e15f9-ae66-45a6-a9e3-c212cbc83ba1", + "metadata": {}, + "source": [ + "![pc terminal](../../images/resources/term_pc.png)\n", + "\n", + "*

Figure: Finding terminal on a PC.

*" + ] + }, + { + "cell_type": "markdown", + "id": "99e35425-03b2-40ed-a1ff-2d19655bbdad", + "metadata": {}, + "source": [ + "## Logging into a terminal from JupyterHub" + ] + }, + { + "cell_type": "markdown", + "id": "8db0641a-0960-4e2e-9596-cb97f37b84ca", + "metadata": {}, + "source": [ + "If you have a JupyterHub session open you can open a terminal window as well. \n", + "\n", + "- Click on the + symbol in the upper left for a New Launcher\n", + "- Click on the Terminal icon\n", + "\n", + "Note: some terminal functions (e.g. X11 forwarding) do not work in a terminal opened through JupyterHub." + ] + }, + { + "cell_type": "markdown", + "id": "750465fe-e05d-4d1c-b33c-2c82db333f67", + "metadata": {}, + "source": [ + "![JH terminal](../../images/resources/term_jupyter_1.png)\n", + "\n", + "*

Figure: Opening a terminal using JupyterHub.

*" + ] + }, + { + "cell_type": "markdown", + "id": "d80a918d-4cbc-47aa-b10c-a37f255e6f0f", + "metadata": {}, + "source": [ + "![JH terminal](../../images/resources/term_jupyter_2.png)\n", + "\n", + "*

Figure: Terminal interface in JupyterHub.

*" + ] + }, + { + "cell_type": "markdown", + "id": "9004d0e1-c472-4e9a-9115-0a77fd5d3ee4", + "metadata": {}, + "source": [ + "## Logging into a terminal via fastX" + ] + }, + { + "cell_type": "markdown", + "id": "ee17bae0-38d6-4556-94d0-5aada3603a77", + "metadata": {}, + "source": [ + "CISL has provided an option to log into casper via the fastX software. Detailed instructions are provided [here](https://arc.ucar.edu/knowledge_base/72581391)\n", + "\n", + "- Note that you will not be able to use the first option \"Using FastX via web browser\" as this requires being on the internal UCAR network or VPN. \n", + "\n", + "- If you are able to install the [FastX desktop client](https://arc.ucar.edu/knowledge_base/72581391#UsingFastXforremotedesktops-UsingtheFastXdesktopclient), we would recommend that option\n", + "\n", + "- Otherwise, you can create an [ssh tunnel via a terminal window and then start the browser client](https://arc.ucar.edu/knowledge_base/72581391#UsingFastXforremotedesktops-UsingFastXviawebbrowserandsshtunnel)\n" + ] + }, + { + "cell_type": "markdown", + "id": "da9e1c05-6abb-41ca-ac87-fb7e8b5f463c", + "metadata": {}, + "source": [ + "## Using PuTTY and Xmg" + ] + }, + { + "cell_type": "markdown", + "id": "6b212673-a637-4073-9be5-817d32696560", + "metadata": {}, + "source": [ + "For older Windows laptops, it may require the use of PuTTY and Xmg. These might be installed on your machine already, but if not you can download them. Here is a [nice guide](https://laptops.eng.uci.edu/engineering-software/using-linux/how-to-configure-putty-xming-on-your-laptop) from UC-Irvine.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c94441cd-66f7-4fe9-8ddf-2123048149b9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/text_editors.ipynb b/notebooks_copy/resources/text_editors.ipynb new file mode 100644 index 000000000..2f8e95c80 --- /dev/null +++ b/notebooks_copy/resources/text_editors.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Text Editors " + ] + }, + { + "cell_type": "markdown", + "id": "d0122319-defa-49ca-a794-91cfc36fb332", + "metadata": {}, + "source": [ + "You will need to edit files directly through the command line when using UNIX. There are quite a few text editors available for UNIX. Below we provide some information about some of the favorite text editors from CESM developers. This list is not comprehensive, so if you have another editor you prefer you can use that." + ] + }, + { + "cell_type": "markdown", + "id": "947bea15-69c9-47e9-815c-f07d89327bab", + "metadata": {}, + "source": [ + "## Vi" + ] + }, + { + "cell_type": "markdown", + "id": "2692e830-0863-4bef-98f7-7796a3bece8e", + "metadata": {}, + "source": [ + "Vi is a text editor included with most UNIX systems, even embedded ones. It was designed to be simple yet powerful for text manipulation. It's a modal text editor, which means has both an insert and command mode. Vi doesn't provide a list of keyboard shortcuts on the screen. \n", + "\n", + "**NOTE:** `vi` **does not need to be loaded in your environment to be used.**" + ] + }, + { + "cell_type": "markdown", + "id": "244ecc7f-b49b-4fba-ad36-9e816701fbb9", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Vi documentation](http://linuxfocus.org/~guido/vi/)\n", + " \n", + "[Vi Introduction](https://www.howtogeek.com/102468/a-beginners-guide-to-editing-text-files-with-vi/)\n", + " \n", + "[Vi cheatsheet](https://www.atmos.albany.edu/daes/atmclasses/atm350/vi_cheat_sheet.pdf)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "da6fee1f-f259-4922-a667-bf903d3536dd", + "metadata": {}, + "source": [ + "## Vim" + ] + }, + { + "cell_type": "markdown", + "id": "9460aa4b-741d-4052-a6a7-82730d5837b7", + "metadata": {}, + "source": [ + "Vim (Vi IMproved) is a clone of Vi but offers more features than Vi. It's free and open source and can be used both at the command-line or in a graphical user interface (GUI).\n", + "\n", + "**NOTE:** `vim` **does not need to be loaded in your environment to be used.**" + ] + }, + { + "cell_type": "markdown", + "id": "bcd11fc0-e9e4-4534-b79c-3963b6b98f7f", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Vi documentation](https://www.vim.org/docs.php)\n", + " \n", + "[Vi cheatsheet](https://vim.rtorr.com/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "234894b0-4983-4d0e-aca1-30c466521c50", + "metadata": {}, + "source": [ + "## Nano" + ] + }, + { + "cell_type": "markdown", + "id": "5e62b82c-dbf8-4724-8e82-869f944f0ba4", + "metadata": {}, + "source": [ + "Nano is a text editor that uses a command line interface. It emulates the Pico text editor and in addition to basic text editing, nanco has features such as an interactive search-and-replace, undo/redo, etc.\n", + "\n", + "**NOTE:** `nano` **must be loaded as a module in your HPC environment.** See section (LINK)." + ] + }, + { + "cell_type": "markdown", + "id": "cf16bd44-45f4-4bb4-927b-eafc24d9aeb1", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Nano documentation](https://www.nano-editor.org/docs.php)\n", + "\n", + "[Nano Introduction](https://www.howtogeek.com/42980/the-beginners-guide-to-nano-the-linux-command-line-text-editor/)\n", + " \n", + "[Nano cheatsheet](https://www.nano-editor.org/dist/latest/cheatsheet.html)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "69a56d2b-7508-4df0-9dc1-33f1f0887127", + "metadata": {}, + "source": [ + "## Emacs" + ] + }, + { + "cell_type": "markdown", + "id": "342a8b59-a009-4974-9878-66e78c46effc", + "metadata": {}, + "source": [ + "Emacs is an extensible, customizable, and self-documenting editor. Emacs opens a GUI to your computer and requires X11 forwarding. Once open, it can be used similar to a normal document including scrolling.\n", + "\n", + "**NOTE:** `emacs` **does not need to be loaded in your environment to be used. While it will work from the terminal command line, it does not work in the Jupyter environment.**" + ] + }, + { + "cell_type": "markdown", + "id": "0761f906-a868-491e-a89d-4d4a6f6f7bcf", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[Emacs documentation](https://www.gnu.org/software/emacs/manual/html_node/emacs/index.html)\n", + "\n", + "[Emacs Introduction](https://www.gnu.org/software/emacs/tour/)\n", + " \n", + "[Emacs cheatsheet](https://www.gnu.org/software/emacs/refcards/pdf/refcard.pdf)\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/tutorial_specific.ipynb b/notebooks_copy/resources/tutorial_specific.ipynb new file mode 100644 index 000000000..328c5f7c3 --- /dev/null +++ b/notebooks_copy/resources/tutorial_specific.ipynb @@ -0,0 +1,413 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b2cd974a-dfbb-4937-8ed0-ab87ab60f1ef", + "metadata": {}, + "source": [ + "# Tutorial specific instructions" + ] + }, + { + "cell_type": "markdown", + "id": "4a6f76fe-9658-443c-89a7-9ede364803e5", + "metadata": {}, + "source": [ + "### Tutorial Project Account\n", + "\n", + "
\n", + "You should have access to project account UESM0013 and use this for your simulations.\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "9db9d54e-ef78-44ef-a003-4ddd8a416317", + "metadata": {}, + "source": [ + "### Special Queues\n", + "\n", + "
\n", + "We have a special queue every day for running to ensure you get through the derecho queues quickly and get your jobs run. These are only active for portions of each day during our lab sessions and change for each session. So you should be sure you are using the correct reservation queue. \n", + " \n", + "Schedule for Tutorial Reservation Queues\n", + "```\n", + "Mon 14:00 - 17:00 derecho R???????\n", + "Tue 10:30 - 12:00 derecho R???????\n", + " 14:00 - 17:00 derecho R???????\n", + "Wed 10:30 - 12:00 derecho R???????\n", + " 14:00 - 17:00 derecho R???????\n", + "Thu 13:30 - 17:00 casper R???????\n", + "Fri 13:30 - 17:00 derecho R??????? \n", + "```\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "e127deda-73f0-42fc-9c96-e49bb1bb0f8b", + "metadata": {}, + "source": [ + "### Using these tutorial-specific codes" + ] + }, + { + "cell_type": "markdown", + "id": "ec3ec832-823f-48ce-b142-05c69dcc72b1", + "metadata": {}, + "source": [ + "In theory, you _should_ be able to automatically use this account and the reservation queues if you correctly set up your NCAR HPC environment using your `.profile` or `.tcshrc` files, as described below. If you need to change the values for a run, you can change the account and reservation queue in your case directory using the following command:\n", + "\n", + "```\n", + "./xmlchange --force JOB_QUEUE=R???????\n", + "./xmlchange PROJECT=UESM0013\n", + "```\n", + "\n", + "For instance, on Wednesday\n", + "\n", + "- If (Wed 10:30 - 12:00) then\n", + "\n", + "```\n", + "./xmlchange JOB_QUEUE=R??????? --force\n", + "``` \n", + "\n", + "- If (Wed 14:00 - 17:00) then\n", + "\n", + "```\n", + "./xmlchange JOB_QUEUE=R??????? --force\n", + "```\n", + "\n", + "\n", + "- If (Wed at another time) then\n", + "```\n", + "./xmlchange JOB_QUEUE=regular\n", + "```\n", + "\n", + "If you do use these commands, make sure for the `JOB_QUEUE` you are using the **correct** reservation code for the relevant day _and_ time of that lab session. You should not have to set the `PROJECT` more than once during the tutorial if your profile is set up, as described below, but we include it for your reference in case you are having trouble." + ] + }, + { + "cell_type": "markdown", + "id": "0c07b51d-f999-4737-9739-3ea989f20802", + "metadata": {}, + "source": [ + "## Setting up your NCAR HPC environment" + ] + }, + { + "cell_type": "markdown", + "id": "9d6954eb-3505-4a58-b6fa-bea7047c63cf", + "metadata": {}, + "source": [ + "Every time you log onto the NCAR HPC you want to ensure you have the correct modules loaded." + ] + }, + { + "cell_type": "markdown", + "id": "7440f1b8-06af-435a-b1db-d444b99bf846", + "metadata": {}, + "source": [ + "
\n", + "NOTE: The next section describes how to use a file that loads the modules necessary for those attending the in-person tutorial. Once you set up your environment you should not have to do it again for the remainder of the tutorial. Every time you login to NCAR HPC these files will automatically be sourced. However, if you intend to use NCAR HPC after the tutorial you may need to modify your `.profile` or `.tcshrc` file to load the correct modules, project numbers, or queues. \n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "8e6da3c0-4cbf-42e3-b599-9ef4d1b4a72c", + "metadata": {}, + "source": [ + "___\n", + "\n", + "### SHELL environment " + ] + }, + { + "cell_type": "markdown", + "id": "c04124a8-5e76-4104-8d62-7bd0a329ce58", + "metadata": {}, + "source": [ + "To determine what shell environment you are using on the NCAR HPC, type the following command:" + ] + }, + { + "cell_type": "markdown", + "id": "9f8488f1-72e7-4594-be66-f875c51cc4e1", + "metadata": {}, + "source": [ + "
\n", + "\n", + "```\n", + "echo $SHELL\n", + "```\n", + "\n", + " \n", + "If the command returns, you are a bash user\n", + "```/bin/bash```\n", + "\n", + " \n", + "If the command returns, you are a cshell user\n", + "```/bin/tcsh```\n", + "\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "91a8addf-1e89-43b9-a364-1e1aa151454d", + "metadata": {}, + "source": [ + "**NOTE:** All new NCAR HPC accounts default to bash. " + ] + }, + { + "cell_type": "markdown", + "id": "8c4f6e24-577b-4736-a251-69b51553d704", + "metadata": {}, + "source": [ + "___\n", + "\n", + "### Bash users" + ] + }, + { + "cell_type": "markdown", + "id": "a82c1d5f-eddc-4504-bd66-febd2f3179f8", + "metadata": {}, + "source": [ + "**NOTE:** You may already have a `.profile` file in your home directory. \n", + "- If you have an existing `.profile` file and do not wish to overwrite its contents, please save your original `.profile` file to a new filename before copying the tutorial .profile file into your home directory. \n", + "- Alternatively, you could copy the contents of the tutorial profile file into your existing `.profile` file using a text editor. (see special instructions below)" + ] + }, + { + "cell_type": "markdown", + "id": "870dc6c7-98f5-4e3a-b226-2f2437d5321a", + "metadata": {}, + "source": [ + "#### One-time setup\n", + "To set up your environment for the tutorial, follow the following three steps:" + ] + }, + { + "cell_type": "markdown", + "id": "9a2baee0-804e-4586-b8fc-a3636c528484", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Copy over the `.profile` file into your home directory: \n", + " \n", + "```\n", + "cp /glade/campaign/cesm/development/cross-wg/profile ~/.profile\n", + "```\n", + "
\n", + "\n", + "Source the file:\n", + "\n", + "```\n", + "source ~/.profile\n", + "```\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf95578e-40e5-4c40-8884-0b80e29eb858", + "metadata": {}, + "source": [ + "\n", + "#### Before each practical\n", + "Do the following step before **every practical**. This is because we are getting a different special queue every day. \n", + "\n", + "
\n", + "\n", + "Source the file:
\n", + "\n", + "```\n", + "source ~/.profile\n", + "```\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "4daab2a7-58c1-41f0-b51b-c9e8a573574b", + "metadata": {}, + "source": [ + "___\n", + "\n", + "### Tcsh users" + ] + }, + { + "cell_type": "markdown", + "id": "42432d90-eaef-4205-b4fe-39cf3c7b5881", + "metadata": {}, + "source": [ + "**NOTE:** You should already have a `.tcshrc` file in your home directory. If you do not wish to overwrite the existing `.tcshrc` file, please save this file to a new name OR copy the contents of the `/glade/campaign/cesm/development/cross-wg/tcshrc` file to your `.tcshrc` file using a text editor." + ] + }, + { + "cell_type": "markdown", + "id": "0722df3e-2497-492c-8301-7d54e7485c05", + "metadata": {}, + "source": [ + "#### One-time setup\n", + "\n", + "To set up your environment for the tutorial, follow the following three steps:" + ] + }, + { + "cell_type": "markdown", + "id": "a77636dd-1651-431a-a72e-f862947b37a6", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
\n", + "\n", + "Copy over the `.tcshrc` file to your home directory:\n", + "```\n", + "cp /glade/campaign/cesm/development/cross-wg/tcshrc ~/.tcshrc\n", + "```\n", + " \n", + "
\n", + "\n", + "Source the file:\n", + "\n", + "```\n", + "source ~/.tcshrc\n", + "```\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "637d5219-104b-4e53-ab41-b3969b275527", + "metadata": {}, + "source": [ + "\n", + "#### Before each practical\n", + "\n", + "Do the following step before **every practical**. This is because we are getting a different special queue every day. \n", + "\n", + "
\n", + " \n", + "Source the file:
\n", + "\n", + "```\n", + "source ~/.tcshrc\n", + "``` \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "5edccc8b-0080-4837-aa70-d50360cb2c6c", + "metadata": {}, + "source": [ + "___\n", + "\n", + "### If you have any trouble\n", + "\n", + "**Please ask for help if you are having trouble with this step!**\n", + "\n", + "The modules necessary for this tutorial and loaded through these files are:\n", + "- ncarenv/1.3\n", + "- intel\n", + "- ncl\n", + "- nco\n", + "- ncview\n", + "- netcdf\n" + ] + }, + { + "cell_type": "markdown", + "id": "71cae660-0d1a-4193-8333-909310c49dac", + "metadata": {}, + "source": [ + "____\n", + "\n", + "### Special instructions (if you already had an account on derecho)\n", + "\n", + "If you already have a `.profile` or `.tcsh` and you do not wish to overwrite its contents, you need to add the following into your existing `.profile` or `.tcsh` using a text editor.\n", + "\n", + "#### Bash:\n", + "\n", + "
\n", + "\n", + "Add to your .profile: \n", + "\n", + "```\n", + "export PROJECT=UESM0013 #<- This should never change over the course of the tutorial\n", + "export TUTORIAL_QUEUE=R??????? #<- This will be different for every practical. \n", + " #<- Look at the schedule below:\n", + "```\n", + "\n", + "Source the file:
\n", + "\n", + "```\n", + "source ~/.profile\n", + "```\n", + "
\n", + "\n", + "#### C-shell:\n", + "\n", + "
\n", + "\n", + "Add to your .tcshrc: \n", + "```\n", + "setenv PROJECT UESM0013 #<-This should never change over the course of the tutorial\n", + "setenv TUTORIAL_QUEUE R??????? #<-This will be different for every practical \n", + " #<- Look at the schedule below:\n", + "```\n", + " \n", + "Source the file:
\n", + "\n", + "```\n", + "source ~/.tcshrc\n", + "``` \n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b77ea28-df80-4c20-a123-ec1e09a1475a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "toc-showcode": false + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/unix-cheatsheet.ipynb b/notebooks_copy/resources/unix-cheatsheet.ipynb new file mode 100644 index 000000000..0ddd8e275 --- /dev/null +++ b/notebooks_copy/resources/unix-cheatsheet.ipynb @@ -0,0 +1,114 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# UNIX cheatsheet\n", + "\n", + "This Unix commands cheat sheet summarizes a few basic commands you will need during the tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "76bc874f-270c-4a05-8fe9-eac874cb0db7", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "805c8473-8106-4ba6-9d8e-c27cee7fcd7d", + "metadata": {}, + "source": [ + "You can also find a lot of resources online. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96f010ce-2005-4a69-9081-7ced219e66ea", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/resources/unix.ipynb b/notebooks_copy/resources/unix.ipynb new file mode 100644 index 000000000..195769a04 --- /dev/null +++ b/notebooks_copy/resources/unix.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# UNIX" + ] + }, + { + "cell_type": "markdown", + "id": "011c91f1-75f0-496e-a329-45ed70297e86", + "metadata": {}, + "source": [ + "Most CESM users and developers use the UNIX environment to set up CESM experiments and do preliminary analysis of their CESM model output data. We strongly suggest those using CESM become familiar with working in a command line environment." + ] + }, + { + "cell_type": "markdown", + "id": "31e68033-a4f3-4866-8048-f2f57900f98b", + "metadata": {}, + "source": [ + "## Some Unix Basics\n", + "\n", + "The module listed below, from The COMET Program, will help those unfamiliar with UNIX. You can learn about the basics of UNIX file structures, how to navigate in a UNIX environment, and you'll get to practice creating, storing and searching for files.\n", + "\n", + "The expected length is 15-30 minutes for users with some UNIX experience, and 30-60 minutes for novices.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b343574d-dae5-4653-87fd-db6d0748b338", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[UNIX module from COMET](https://www.meted.ucar.edu/ucar/unix/)\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "ef5eda98-33cc-460d-be37-fc080c09a1a2", + "metadata": {}, + "source": [ + "## Unix cheatsheet\n", + "\n", + "This Unix commands cheat sheet summarizes a few basic commands you will need during the tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "399759d6-00b3-41f2-b483-541b84032ce3", + "metadata": {}, + "source": [ + "
\n", + "\n", + "[UNIX cheatsheet](https://ncar.github.io/CESM-Tutorial/notebooks/resources/unix-cheatsheet.html)\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66239ceb-8b7c-4d1b-9d6d-56a8c2ace41c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/sourcemods/add_fields_cam.ipynb b/notebooks_copy/sourcemods/add_fields_cam.ipynb new file mode 100644 index 000000000..f1b3eb835 --- /dev/null +++ b/notebooks_copy/sourcemods/add_fields_cam.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Source Modification Example\n", + "\n", + "A common source code modification that you may want to do is to output a **new variable** that is not defined in CESM. \n", + "\n", + "As an example, we will add a new variable to CAM: the atmopsheric **temperature at 750hPa**.\n", + "\n", + "## Adding a variable to CAM\n", + "CAM has a history field that corresponds to the **temperature at 500hPa** and a number of other pressure levels, but not at **750hPa**. Suppose you wanted to output the **temperature at 750hPa**. The following two\n", + "calls are required to add an output variable:\n", + "```\n", + "call addfld(’T750’,...) (Add a field to the master field list)\n", + "call outfld(’T750’,...) (Collect values for this field and write to history file)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "08800317-afcb-42b5-a181-38003b2323aa", + "metadata": {}, + "source": [ + "Each of these are now described in more detail:\n", + "\n", + "## ``addfld``\n", + "The sub-routine ``addfld`` adds a field to the master list with the following syntax:\n", + "```\n", + "addfld(fname,type,avgflag,units,long name)\n", + "```\n", + "where\n", + "\n", + "- **``fname``** = field name\n", + "- **``type``** = the type of field. The entry for a single level field would be “horiz only” and the entry\n", + "for a 3D field would be “(/ ’lev’ /)”.\n", + "- **``avgflag``** = Averaging flag, A = average, I=instantaneous\n", + "- **``units``** = the units of the field\n", + "- **``long name``** = Field full name\n", + "\n", + "Appropriate values of these parameters for the output of T750 are:\n", + "\n", + "```\n", + "call addfld(’T750’,horiz only, ’A’, ’K’, ’Temperature at 750hPa pressure surface’)\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "5fe6a0f0-e750-4d0c-bd8a-4966286a4764", + "metadata": {}, + "source": [ + "\n", + "## ``outfld``\n", + "The subroutine ``outfld`` accumulates (or takes the minimum or maximum of, as appropriate) the field into the history buffer for the appropriate history tape with the following syntax\n", + "```\n", + " outfld(fname, field, idim, c)\n", + "```\n", + "\n", + "with\n", + "- **``fname``** = Field name\n", + "- **``field``** = array containing field values\n", + "- **``idim``** = longitude dimension of field array\n", + "- **``c``** = chunk (physics) or latitude (dynamics) index.\n", + "\n", + "For example:\n", + "\n", + "```\n", + " call outfld(’T750’,t750, pcols, lchnk)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "c1978926-c9f9-4405-8574-12d39e3f8ed8", + "metadata": {}, + "source": [ + "\n", + "## ``add_default`` \n", + "Another useful subroutine is the subroutine ``add_default``. The subroutine ``add_default`` Add a field to the list of default fields on history file with the following syntax\n", + "```\n", + " subroutine add_default (fname, tindex, flag)\n", + "```\n", + "where\n", + "- **``fname``** = Field name\n", + "- **``tindex``** = history tape index\n", + "- **``flag``** = Averaging flag: A = average (default). I = instantaneous\n", + "\n", + "\n", + "For example:\n", + "\n", + "```\n", + " call add_default ('T500', 1, ' ')\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/sourcemods/exercises/exercise_add_field.ipynb b/notebooks_copy/sourcemods/exercises/exercise_add_field.ipynb new file mode 100644 index 000000000..56ba26650 --- /dev/null +++ b/notebooks_copy/sourcemods/exercises/exercise_add_field.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Add output variable in CAM" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Add an additional output variable

\n", + " \n", + "Create a case called `b1850_T750` using the compset `B1850` at `f19_g17` resolution. \n", + "- Add an output field for the temperature at 750 mbar. \n", + "- Output daily values of `T750` and `T500` in the `h1` history file. \n", + "- Set the namelist to output a single `h1` for the run. \n", + "- Make a 1-month run. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4e74779-4778-446a-be7e-50e0c1ee125e", + "metadata": {}, + "source": [ + "
\n", + "
\n", + " \n", + " Click here for hints\n", + " \n", + " \n", + "**Tip to add `T750`**
\n", + "- Use `T500` as a template for your changes.\n", + "\n", + "- Find the subroutine containing `T750`. \n", + "For instance, in the CESMROOT, use the command: \n", + "\n", + "```\n", + " grep –r T500 * \n", + "```\n", + " \n", + " \n", + "**Tip to check your solution `T750`**
\n", + "- When the run is completed, go to your archive directory:\n", + "- check the fields `T750` and `T500` are in the file `h1`\n", + "- create a file with the difference between `T750-T500`\n", + "- For instance, you can use ``ncap2`` \n", + "```\n", + " ncap2 -s ’T750_minus_T500=T750-T500' b1850_T750.cam.h1.0001-01-01-00000.nc T750-T500.nc\n", + "```\n", + "- Look at the difference with ``ncview``. \n", + "```\n", + " ncview T750-T500.nc\n", + "```\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "fbfacd19-3e46-4ef4-9111-7bb1b4669b2f", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "**# Create a new case**\n", + " \n", + "Create a new case `b1850_T750` with the command:\n", + "```\n", + " cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + "./create_newcase --case ~/cases/b1850_T750 --compset B1850 --res f19_g17 \n", + "```\n", + "\n", + "**# Setup**\n", + "\n", + "Invoke case.setup with the command:\n", + "``` \n", + "cd ~/cases/b1850_T750\n", + "./case.setup\n", + "```\n", + "\n", + "**# Make Source Modifications** \n", + "Use `T500` as a template for your changes. For that purpose, locate the file where `T500` is computed and copy it into\n", + " `SourceMods/src.atm`:\n", + " \n", + "``` \n", + "cp /glade/campaign/cesm/development/cross-wg/cesm2.1_tutorial2022/components/cam/src/physics/cam/cam_diagnostics.F90 SourceMods/src.cam\n", + "```\n", + "\n", + "Now, let's use `T500` as a template for your changes and add the relevant lines for `T750`: edit the file `SourceMods/src.cam/cam_diagnostics.F90` to add the following.\n", + " \n", + "**First change**\n", + " \n", + "Under the lines: \n", + "```\n", + "!++ add a variable for T500 \n", + "call addfld ('T500', horiz_only, 'A', 'K','Temperature at 500 mbar pressure surface') \n", + "```\n", + "add the lines:\n", + "\n", + "```\n", + "!++ add a variable for T750 \n", + "call addfld ('T750', horiz_only, 'A', 'K','Temperature at 750 mbar pressure surface') \n", + "```\n", + " \n", + "**Second change**\n", + "Under the lines: \n", + "```\n", + "!++ add a variable for T500 \n", + "if (hist_fld_active('T500')) then \n", + " call vertinterp(ncol, pcols, pver, state%pmid, 50000._r8, state%t, p_surf, & \n", + " extrapolate='T', ps=state%ps, phis=state%phis)\n", + " call outfld('T500 ', p_surf, pcols, lchnk )\n", + "end if\n", + "```\n", + "add the lines:\n", + " \n", + "``` \n", + "!++ add a variable for T750 \n", + "if (hist_fld_active('T750')) then \n", + " call vertinterp(ncol, pcols, pver, state%pmid, 75000._r8, state%t, p_surf, & \n", + " extrapolate='T', ps=state%ps, phis=state%phis)\n", + " call outfld('T750 ', p_surf, pcols, lchnk )\n", + "end if\n", + "```\n", + "\n", + "**# Customize namelists**\n", + "Edit the file `user_nl_cam` and add the lines:\n", + "```\n", + " nhtfrq = 0, -24\n", + " mfilt = 1, 31\n", + " fincl2 = 'T750', 'T500'\n", + "```\n", + " \n", + " \n", + "**# Set run length**\n", + "\n", + "Set the **run length** to 1 month:\n", + "``` \n", + "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change `job queue` and `account number`.
\n", + "For instance, to run in the queue `regular` and the project number `UESM0013`, use the command:\n", + "\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "**# Build and submit**\n", + " \n", + "Build the model and submit your job:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "____\n", + " \n", + "**# Look at your solution**\n", + " \n", + "When the run is completed, check the fields `T750` and `T500` are in the file `h1`:\n", + "``` \n", + "cd /glade/derecho/scratch/$USER/archive/b1850_T750/atm/hist/ \n", + "ncdump -h b1850_T750.cam.h1.0001-01-01-00000.nc\n", + "```\n", + " \n", + "The file should contain:\n", + "```\n", + "float T500(time, lat, lon) ;\n", + " T500:units = \"K\" ;\n", + " T500:long_name = \"Temperature at 500 mbar pressure surface\" ;\n", + " T500:cell_methods = \"time: mean\" ;\n", + "float T750(time, lat, lon) ;\n", + " T750:units = \"K\" ;\n", + " T750:long_name = \"Temperature at 750 mbar pressure surface\" ;\n", + " T750:cell_methods = \"time: mean\" ;\n", + "```\n", + "If you don't see these variables, check you correctly set the `user_nl_cam`.\n", + " \n", + "Create a file with the difference between `T750-T500`:\n", + "``` \n", + "cd /glade/derecho/scratch/$USER/archive/b1850_T750/atm/hist/ \n", + "ncap2 -s 'T750_minus_T500=T750-T500' b1850_T750.cam.h1.0001-01-01-00000.nc T750-T500.nc \n", + "```\n", + "\n", + "Look at the difference between `T750-T500` with `ncview`: \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/b1850_T750/atm/hist/ \n", + "ncview T750-T500.nc\n", + "```\n", + " \n", + "The field `T750-T500` looks like:\n", + "\n", + "*

\n", + " ![ncview T750-T500](../../../images/sourcemods/ncview_T750-T500.png) \n", + " Figure: Overview of the CESM directories and the SourceMods directories.

*\n", + " \n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99077aae-e99c-479f-b7a9-b1af1fd4e62f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/sourcemods/exercises/exercise_rain_threshold.ipynb b/notebooks_copy/sourcemods/exercises/exercise_rain_threshold.ipynb new file mode 100644 index 000000000..007546c7c --- /dev/null +++ b/notebooks_copy/sourcemods/exercises/exercise_rain_threshold.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "be068a4c-5d8d-442f-aa26-65712f572c65", + "metadata": {}, + "source": [ + "# Modify the `rain_threshold` in CLM\n", + "\n", + "In the below exercise, we will change the rain threshold for stress deciduous vegetation, which includes C3 grasses. The rain threshold is the amount of rain required to initiate leaf onset. Reaching the rain threshold is one of several requirements for stress deciduous vegetation leaf onset. If you are interested, you can find more information about the stress deciduous phenology representation in the CLM Technical Note.\n", + "\n", + "The current value of rain_threshold is 20mm as specified in the code:\n", + "\n", + "```\n", + "rain_threshold = 20._r8 \n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "571a03a6-51cb-425a-b9c2-d674b097f5b8", + "metadata": {}, + "source": [ + "\n", + "\n", + "
\n", + "Exercise: Add an additional output variable

\n", + " \n", + "Create a case called `b1850_rain_threshold` using the compset `B1850` at `f19_g17` resolution. \n", + "- Change the rain_threshold from 20mm to 1mm. \n", + "- Output daily values of ??? in the `h1` history file. \n", + "- Make a 1-month run. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4e74779-4778-446a-be7e-50e0c1ee125e", + "metadata": {}, + "source": [ + "
\n", + "
\n", + " \n", + " Click here for hints\n", + " \n", + " \n", + "**Tip to add `T750`**
\n", + "- Use `T500`as a template for your changes.\n", + "\n", + "- Find the subroutine containing `T750`. \n", + "For instance, in the CESMROOT, use the command: \n", + "\n", + "```\n", + " grep –r T500 * \n", + "```\n", + " \n", + " \n", + "**Tip to check your solution `T750`**
\n", + "- When the run is completed, go to your archive directory:\n", + "- check the fields `T750` and `T500` are in the file `h1`\n", + "- create a file with the difference between `T750-T500`\n", + "- For instance, you can use ``ncap2`` \n", + "```\n", + " ncap2 -s ’T750_minus_T500=T750-T500' b1850_T750.cam.h1.0001-01-01-00000.nc T750-T500.nc\n", + "```\n", + "- Look at the difference with ``ncview``. \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "fbfacd19-3e46-4ef4-9111-7bb1b4669b2f", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "Create a new case `b1850_rain_threshold` with the command:\n", + "```\n", + "cd /glade/campaign/cesm/development/cross-wg/cesm2.1_tutorial2022/cime/scripts\n", + "./create_newcase --case ~/cases/b1850_rain_threshold --compset B1850 --res f19_g17 \n", + "```\n", + "\n", + "Case `setup`:\n", + "``` \n", + "cd ~/cases/b1850_rain_threshold\n", + "./case.setup\n", + "```\n", + "\n", + " \n", + "Locate the file where `rain_threshold` is defined and copy it into\n", + " `SourceMods/src.lnd`:\n", + "``` \n", + "cp /glade/campaign/cesm/development/cross-wg/cesm2.1_tutorial2022/components/clm/src/biogeochem/CNPhenologyMod.F90 SourceMods/src.clm\n", + "```\n", + " \n", + "Edit the file `SourceMods/src.clm/CNPhenologyMod.F90 ` and add the lines:\n", + "```\n", + " ! specify rain threshold for leaf onset \n", + " rain_threshold = 20._r8\n", + "```\n", + "to \n", + "```\n", + " ! specify rain threshold for leaf onset \n", + " rain_threshold = 1._r8\n", + "```\n", + " \n", + " \n", + "Change the `run length`:\n", + "``` \n", + "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", + "```\n", + "\n", + "If needed, change `job queue` and `account number` \n", + "For instance:\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "Build and submit:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + " \n", + "When the run is completed, check the fields `T750` and `T500` are in the file `h1`:\n", + "``` \n", + "cd /glade/derecho/scratch/$USER/archive/b1850_rain_threshold/atm/hist/ \n", + "ncdump -h bb1850_rain_threshold.cam.h1.0001-01-01-00000.nc\n", + "```\n", + " \n", + "The file should contain:\n", + "```\n", + "float T500(time, lat, lon) ;\n", + " T500:units = \"K\" ;\n", + " T500:long_name = \"Temperature at 500 mbar pressure surface\" ;\n", + " T500:cell_methods = \"time: mean\" ;\n", + "float T750(time, lat, lon) ;\n", + " T750:units = \"K\" ;\n", + " T750:long_name = \"Temperature at 750 mbar pressure surface\" ;\n", + " T750:cell_methods = \"time: mean\" ;\n", + "```\n", + "If you don't see these variables, check you correctly set the `user_nl_cam`.\n", + " \n", + "Create a file with the difference between `T750-T500`:\n", + "``` \n", + "cd /glade/derecho/scratch/$USER/archive/b1850_rain_threshold/atm/hist/ \n", + "ncap2 -s ‘T750_minus_T500=T750-T500' b1850_T750.cam.h1.0001-01-01-00000.nc T750-T500.nc \n", + "```\n", + "\n", + "Look at the difference between `T750-T500` with `ncview`: \n", + "```\n", + "cd /glade/derecho/scratch/$USER/archive/b1850_rain_threshold/atm/hist/ \n", + "ncview T750-T500.nc\n", + "```\n", + " \n", + "The field `T750-T500` looks like:\n", + "\n", + "*

\n", + " ![ncview T750-T500](../../../images/sourcemods/ncview_T750-T500.png) \n", + " Figure: Overview of the CESM directories and the SourceMods directories.

*\n", + " \n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "831a1859-4cd1-48aa-b1d2-616216d2f9cb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/sourcemods/exercises_overview.ipynb b/notebooks_copy/sourcemods/exercises_overview.ipynb new file mode 100644 index 000000000..537a72a47 --- /dev/null +++ b/notebooks_copy/sourcemods/exercises_overview.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Exercise Overview \n", + "\n", + "## Learning Goals\n", + "\n", + "- Student will practice how to do a code modification in CESM. \n", + "\n", + "\n", + "## Exercise overview:\n", + "\n", + "Do at least one exercise:\n", + "- Add a new variable in CAM\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/sourcemods/overview.ipynb b/notebooks_copy/sourcemods/overview.ipynb new file mode 100644 index 000000000..f4168b24c --- /dev/null +++ b/notebooks_copy/sourcemods/overview.ipynb @@ -0,0 +1,73 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Overview\n", + "\n", + "If you want to make modifications to the CESM code, the best practise is to copy the relevant sub-routine into the relevant sub-directory within the `SourceMods` directory in your case directory. \n", + "\n", + "For example, if you wanted to modify a CAM subroutine, in the case called case01, you would copy that subroutine to the following location\n", + "\n", + "```\n", + " case01/SourceMods/src.cam\n", + "```\n", + "and modify it there.\n", + "\n", + "*Figure 1* shows the location of the `SourceMods` directory and sub-directories.\n", + "\n", + "
\n", + "\n", + "The **steps** when making a **source code modification** are:\n", + "\n", + "- In the **CESM code**, find the subroutine you want to modify.\n", + "- Copy this subrouting to the relevant **SourceMods** directory within your **case directory**\n", + "- Make your **modifications**\n", + "- **Compile** and **run** the model \n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "017b45ad-dcff-42b2-8623-06033b8e11d8", + "metadata": {}, + "source": [ + "![CESM directories and namelists](../../images/sourcemods/CESM_directories_and_SourceMods.png)\n", + "\n", + "*

Figure: Overview of the CESM directories and the SourceMods directories.

*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14f20e06-e171-4891-96de-759c5f5182a9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/sourcemods/sourcemods.ipynb b/notebooks_copy/sourcemods/sourcemods.ipynb new file mode 100644 index 000000000..d9f0b8b21 --- /dev/null +++ b/notebooks_copy/sourcemods/sourcemods.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0959db1d-1790-4519-a34e-7026444af537", + "metadata": {}, + "source": [ + "# Source Modifications\n", + "\n", + "The goal of this chapter is to understand how to modify the CESM code. This is referred to as **_source modifications_**.\n", + "\n", + "- The section **_Overview_** describes the steps for making source modifications and provides a visual representation of the directories and files that need to be modified.\n", + "\n", + "- The section **_Source Modification Example_** provides detailed instructions on how to add a new variable to CAM: the atmospheric temperature at 750hPa.\n", + "\n", + "- The section **_Exercises_** offers opportunities to practice the concepts learned in this chapter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35b1a8e6-f30e-4e15-b519-a87d23bfffc6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/troubleshooting/debugging_flag.ipynb b/notebooks_copy/troubleshooting/debugging_flag.ipynb new file mode 100644 index 000000000..25c651135 --- /dev/null +++ b/notebooks_copy/troubleshooting/debugging_flag.ipynb @@ -0,0 +1,52 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "afbbe9ba-517d-4db4-9c7c-0b3daaf0e7ac", + "metadata": {}, + "source": [ + "# Adding debugging info\n", + "\n", + "If you cannot find the reason of the crash in the **log** files, there are two ways to add more debugging information. \n", + "- Increase the value of the run-time xml variable ``INFO_DBUG`` (This **does NOT require rebuilding**): \n", + "```\n", + "./xmlchange INFO_DBUG=2. \n", + "```\n", + "This adds more information to the ``cpl.log`` file that can be useful if you can’t tell what component is aborting the run, or where bad coupling fields are originating.\n", + "\n", + "- Try rebuilding and rerunning with the variable DEBUG set to TRUE (This ** requires rebuilding**): \n", + "```\n", + "./xmlchange DEBUG=TRUE.\n", + "```\n", + "This adds various runtime checks that trap conditions such as out-of-bounds array indexing, divide by 0, and other floating point exceptions.\n", + "Before running, you must rebuild run \n", + "```\n", + "./case.build --clean-all\n", + "qcmd -- ./case.build.\n", + "```\n", + "Note that the model will run **significantly slower** in ``DEBUG mode``, so this may not be feasible if the model has to run a long time before producing the error. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/troubleshooting/exercises/troubleshooting_cam.ipynb b/notebooks_copy/troubleshooting/exercises/troubleshooting_cam.ipynb new file mode 100644 index 000000000..36df98d3e --- /dev/null +++ b/notebooks_copy/troubleshooting/exercises/troubleshooting_cam.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Debugging CAM" + ] + }, + { + "cell_type": "markdown", + "id": "b90d4773-7ca0-4131-ab07-517608a3e976", + "metadata": {}, + "source": [ + "\n", + "
\n", + "Exercise: Add an additional output variable

\n", + " \n", + "Create a case called ``b1850_high_freq_bugfixing`` using the compset ``B1850`` at ``f19_g17`` resolution. \n", + "Set the run length to ``1 month``. \n", + " \n", + "Now in addition to the default monthly output, add the following output:\n", + "- an ``h1`` file containing daily averages of ``T2M`` and set your namelist so that there is one file per day for this daily averaged output.\n", + "\n", + "Set up, build and submit your case. \n", + "\n", + "NB: Your goal is to make the model crashes. And then to troubleshoot why it crashed. \n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "e4e74779-4778-446a-be7e-50e0c1ee125e", + "metadata": {}, + "source": [ + "
\n", + "
\n", + " \n", + " Click here for hints\n", + " \n", + "\n", + "**Tip to add a ``h1`` file** \n", + "\n", + "For more information about how to add a ``h1`` file, check the section about namelist modifications. \n", + "\n", + "If you don't have time to check the section immediately, the way to add an ``h1`` file with daily averages of ``T2M`` and create one file per day for this daily averaged output is:\n", + "\n", + "add the following lines in ``user nl cam``\n", + " \n", + "```\n", + " fincl2 = ’T2M:A’\n", + " nhtfrq = 0,-24\n", + " mfilt = 1,1\n", + "```\n", + "\n", + "**Tip to for troubleshooting** \n", + "\n", + "Check the derecho queue and wait until your run doesn't show in the queue anymore. \n", + "\n", + "When your run is not in the queue anymore:\n", + "- Go to the ``archive`` directory: can you see the history files in the archive directory? The answer should be no. Why? \n", + "- Go to the ``run`` directory: Is there any evidence of history files or restart files being created by the run? The answer, again, should be no. This is because we have tricked you, with a bug.\n", + "\n", + "Look at the log files in the ``RUNDIR`` to try to understand why the run crashed. \n", + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "fbfacd19-3e46-4ef4-9111-7bb1b4669b2f", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "Click here for the solution
\n", + " \n", + "**# Create a new case**\n", + " \n", + "Create a new case ``b1850_high_freq_bugfixing`` with the command:\n", + "```\n", + " cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", + " ./create_newcase --case ~/cases/b1850_high_freq_bugfixing --compset B1850 --res f19_g17 \n", + "```\n", + "\n", + "**# Setup**\n", + "\n", + "Invoke case.setup with the command:\n", + "``` \n", + " cd ~/cases/b1850_high_freq_bugfixing\n", + " ./case.setup\n", + "```\n", + "\n", + "**# Customize namelists**\n", + " \n", + "Add the daily output of ``T2M`` by editing the file ``user_nl_cam`` and adding the lines:\n", + "```\n", + " fincl2 = 'T2M:A'\n", + " nhtfrq = 0,-24\n", + " mfilt = 1,1\n", + "```\n", + "\n", + "**# Set run length**\n", + "\n", + "Set the **run length** to 1 month:\n", + "``` \n", + "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", + "```\n", + "\n", + "**# Change the job queue and account number**\n", + "\n", + "If needed, change `job queue` and `account number`.
\n", + "For instance, to run in the queue `regular` and the project number ``UESM0013`` (you should use the project number given for this tutorial), use the command:\n", + "\n", + "``` \n", + "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", + "```\n", + "\n", + "**# Build and submit**\n", + " \n", + "Build the model and submit your job:\n", + "```\n", + "qcmd -- ./case.build\n", + "./case.submit\n", + "```\n", + "\n", + "____\n", + " \n", + "**# Look at what happened**\n", + "\n", + "**Your run should crash !!!**. This is normal. The goal of the exercise is to troubleshooting. \n", + "\n", + "What you should find in your run directory is three log files. \n", + "- One for the coupler ``cpl.log.*``,\n", + "- one for CAM ``atm.log.*`` \n", + "- and one for CESM ``cesm.log.*``.\n", + "\n", + "Somewhere in these log files is information about what has gone wrong, but it is often not entirely\n", + "straightforward to find. \n", + "- Often at the bottom of the log file, there are errors that are not relative to your problem because\n", + "they are just demonstrating that individual processes are exiting. \n", + "- Often the relevant error lies above this and can sometimes be found by searching for the first occurrence of ERROR or ABORT or cesm.exe. \n", + "\n", + "In this case, searching for the first occurrence of ERROR in ``cesm.log.*`` gives us some relevant information. We find\n", + "```\n", + "ERROR: FLDLST: 1 errors found, see log\n", + "```\n", + "This tells us is that something has gone wrong with the list of output variables that we have asked for.\n", + "\n", + "More information can then be found in the CAM log file ``atm.log.*``.\n", + "Look at the very end of that file and you should see\n", + "```\n", + "FLDLST: T2M in fincl(1, 2) not found\n", + "ERROR: FLDLST: 1 errors found, see log\n", + "```\n", + "This tells us that ``T2M`` is not a valid history variable for CAM. That’s because the correct variable\n", + "for near surface temperature is ``TREFHT``. ``T2M`` is not a CAM\n", + "history field and this has caused CESM to crash\n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "848cf254-9146-4f58-ad1b-2d46325e5067", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/troubleshooting/exercises_overview.ipynb b/notebooks_copy/troubleshooting/exercises_overview.ipynb new file mode 100644 index 000000000..eb628ef5b --- /dev/null +++ b/notebooks_copy/troubleshooting/exercises_overview.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# Exercise Overview \n", + "\n", + "## Learning Goals\n", + "- The goal of the exercise is to make the model crashes. \n", + "- And then to troubleshoot why it crashed.\n", + "\n", + "## Exercise\n", + "- Do the exercise: Debugging CAM. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/troubleshooting/log_files.ipynb b/notebooks_copy/troubleshooting/log_files.ipynb new file mode 100644 index 000000000..440851204 --- /dev/null +++ b/notebooks_copy/troubleshooting/log_files.ipynb @@ -0,0 +1,62 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", + "metadata": {}, + "source": [ + "# The log files\n", + "\n", + "The log files are files in the format ``$model.log.*`` \n", + "- When the model is running, it produces the log files in the **run directory**: ``RUNDIR``. \n", + "- When the run completes successfully, the model moves the log files into the **archive** directory: ``DOUT_S_ROOT``\n", + "- When the model fails, the log files remains in the run directory ``RUNDIR``\n", + "\n", + "![CESM directories and namelists](../../images/troubleshooting/CESM_directories_and_log_files.png)\n", + "\n", + "*

Figure: Overview of the CESM directories and the log files.

*\n" + ] + }, + { + "cell_type": "markdown", + "id": "017b45ad-dcff-42b2-8623-06033b8e11d8", + "metadata": {}, + "source": [ + "## What to do when a run fails? \n", + "\n", + "First, check the latest ``cpl.log.*``, which will often tell you when the model failed. If a run completed successfully, the last several lines of the ``cpl.log.*`` file will have a string like ``SUCCESSFUL TERMINATION OF CESM``. \n", + "If you don't see this message, it means the run has failed. \n", + "\n", + "Check these things first when a job fails:\n", + "- Did the model time out?\n", + "- Was a disk quota limit hit?\n", + "- Did a machine go down?\n", + "- Did a file system become full?\n", + "If any of those things happened, take appropriate corrective action and resubmit the job.\n", + "\n", + "If it is not clear that any of the above caused a case to fail, check the rest of the component log files ``$model.log.*`` for error messages. It takes a bit of practice to interpret message errors. We will look at an example in this chapter exercices. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks_copy/troubleshooting/troubleshooting.ipynb b/notebooks_copy/troubleshooting/troubleshooting.ipynb new file mode 100644 index 000000000..6f0ca89a5 --- /dev/null +++ b/notebooks_copy/troubleshooting/troubleshooting.ipynb @@ -0,0 +1,44 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0959db1d-1790-4519-a34e-7026444af537", + "metadata": {}, + "source": [ + "# Troubleshooting runtime errors\n", + "\n", + "There are several places to look for information if a job fails. \n", + "- The logs files will probably give you a hint. \n", + "- It is also possible to run with more debugging info. \n", + "\n", + "\n", + "
\n", + "\n", + "More information about troubleshooting can be found in the [CIME documentation](https://esmci.github.io/cime/versions/master/html/users_guide/troubleshooting.html).\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 388d5a6a214cfd7c7db6e2bfecd8fc52f3f1f258 Mon Sep 17 00:00:00 2001 From: Cecile Hannay Date: Mon, 22 Jul 2024 14:28:23 -0600 Subject: [PATCH 2/5] basics exercises --- notebooks/basics/exercises_overview.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/basics/exercises_overview.ipynb b/notebooks/basics/exercises_overview.ipynb index 93c3f11be..c19cacae1 100644 --- a/notebooks/basics/exercises_overview.ipynb +++ b/notebooks/basics/exercises_overview.ipynb @@ -18,7 +18,7 @@ "By then end of this exercise you will have:\n", "- created, setup, updated, built, run, and examined the output of a standard B1850 control case\n", "\n", - "**You should not proceed in working on this exercise until you have successfully completed the [one-time set up exercise](https://ncar.github.io/CESM-Tutorial/notebooks/basics/basics_overview.html). You must have successfully downloaded CESM using git and have a `cases` directory to begin this exercise.**\n" + "**You should not proceed in working on this exercise until you have successfully [set up your workspaces](https://ncar.github.io/CESM-Tutorial/notebooks/basics/cesm_workspaces.html) and [downloaded the CESM code](https://ncar.github.io/CESM-Tutorial/notebooks/basics/code/git_download_cesm.html). You must have have a `cases` directory and have successfully downloaded CESM using git to begin this exercise.**\n" ] }, { From 7c82e1236ad7aa68759f43cf8410a14d6b39c776 Mon Sep 17 00:00:00 2001 From: Cecile Hannay Date: Mon, 22 Jul 2024 14:45:51 -0600 Subject: [PATCH 3/5] rename basics exercises --- .../basics/cesm_workflow/case_build.ipynb | 32 +++++++++---------- .../basics/cesm_workflow/case_setup.ipynb | 2 +- .../basics/cesm_workflow/case_submit.ipynb | 8 ++--- .../basics/cesm_workflow/create_newcase.ipynb | 2 +- .../exercises/examine_history_B1850.ipynb | 28 ++++++++-------- notebooks_copy/basics/exercises/extra.ipynb | 2 +- .../basics/exercises/first_B1850.ipynb | 12 +++---- .../basics/exercises/second_B1850.ipynb | 12 +++---- .../xml/model_control_files.ipynb | 2 +- 9 files changed, 50 insertions(+), 50 deletions(-) diff --git a/notebooks_copy/basics/cesm_workflow/case_build.ipynb b/notebooks_copy/basics/cesm_workflow/case_build.ipynb index d9832e576..d733d9f6e 100644 --- a/notebooks_copy/basics/cesm_workflow/case_build.ipynb +++ b/notebooks_copy/basics/cesm_workflow/case_build.ipynb @@ -93,11 +93,11 @@ "
\n", " \n", "```\n", - "Building case in directory /glade/work/$USER/cases/b.day1.0\n", + "Building case in directory /glade/work/$USER/cases/b1850.basics\n", "sharedlib_only is False\n", "model_only is False\n", "Generating component namelists as part of build\n", - "- Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + "- Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", @@ -121,7 +121,7 @@ "Creating component namelists\n", "Calling /glade/u/home/$USER/my_cesm_code/components/cam//cime_config/buildnml\n", "...calling cam buildcpp to set build time options\n", - "CAM namelist copy: file1 /glade/work/$USER/cases/b.day1.0/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b.day1.0/run/atm_in\n", + "CAM namelist copy: file1 /glade/work/$USER/cases/b1850.basics/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b1850.basics/run/atm_in\n", "Calling /glade/u/home/$USER/my_cesm_code/components/clm//cime_config/buildnml\n", "Calling /glade/u/home/$USER/my_cesm_code/components/cice//cime_config/buildnml\n", "...buildnml calling cice buildcpp to set build time options\n", @@ -146,30 +146,30 @@ "Calling /glade/u/home/$USER/my_cesm_code/cime/src/components/stub_comps/sesp/cime_config/buildnml\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/drivers/mct/cime_config/buildnml\n", "Finished creating component namelists\n", - "Building gptl with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/gptl.bldlog.240606-134217\n", + "Building gptl with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/gptl.bldlog.240606-134217\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.gptl\n", "Component gptl build complete with 2 warnings\n", - "Building mct with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/mct.bldlog.240606-134217\n", + "Building mct with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/mct.bldlog.240606-134217\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.mct\n", "Component mct build complete with 55 warnings\n", - "Building pio with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/pio.bldlog.240606-134217\n", + "Building pio with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/pio.bldlog.240606-134217\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.pio\n", "Component pio build complete with 28 warnings\n", - "Building csm_share with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/csm_share.bldlog.240606-134217\n", + "Building csm_share with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/csm_share.bldlog.240606-134217\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.csm_share\n", "Component csm_share build complete with 85 warnings\n", "- Building clm4_5/clm5_0 Library\n", - "Building lnd with output to /glade/derecho/scratch/$USER/b.day1.0/bld/lnd.bldlog.240606-134217\n", + "Building lnd with output to /glade/derecho/scratch/$USER/b1850.basics/bld/lnd.bldlog.240606-134217\n", "\n", "Component lnd build complete with 292 warnings\n", "clm built in 109.479993 seconds\n", - "Building atm with output to /glade/derecho/scratch/$USER/b.day1.0/bld/atm.bldlog.240606-134217\n", - "Building ice with output to /glade/derecho/scratch/$USER/b.day1.0/bld/ice.bldlog.240606-134217\n", - "Building ocn with output to /glade/derecho/scratch/$USER/b.day1.0/bld/ocn.bldlog.240606-134217\n", - "Building rof with output to /glade/derecho/scratch/$USER/b.day1.0/bld/rof.bldlog.240606-134217\n", - "Building glc with output to /glade/derecho/scratch/$USER/b.day1.0/bld/glc.bldlog.240606-134217\n", - "Building wav with output to /glade/derecho/scratch/$USER/b.day1.0/bld/wav.bldlog.240606-134217\n", - "Building esp with output to /glade/derecho/scratch/$USER/b.day1.0/bld/esp.bldlog.240606-134217\n", + "Building atm with output to /glade/derecho/scratch/$USER/b1850.basics/bld/atm.bldlog.240606-134217\n", + "Building ice with output to /glade/derecho/scratch/$USER/b1850.basics/bld/ice.bldlog.240606-134217\n", + "Building ocn with output to /glade/derecho/scratch/$USER/b1850.basics/bld/ocn.bldlog.240606-134217\n", + "Building rof with output to /glade/derecho/scratch/$USER/b1850.basics/bld/rof.bldlog.240606-134217\n", + "Building glc with output to /glade/derecho/scratch/$USER/b1850.basics/bld/glc.bldlog.240606-134217\n", + "Building wav with output to /glade/derecho/scratch/$USER/b1850.basics/bld/wav.bldlog.240606-134217\n", + "Building esp with output to /glade/derecho/scratch/$USER/b1850.basics/bld/esp.bldlog.240606-134217\n", "Component esp build complete with 1 warnings\n", "sesp built in 4.413109 seconds\n", "Component wav build complete with 35 warnings\n", @@ -184,7 +184,7 @@ "cam built in 105.142718 seconds\n", "Component glc build complete with 150 warnings\n", "cism built in 133.173952 seconds\n", - "Building cesm with output to /glade/derecho/scratch/$USER/b.day1.0/bld/cesm.bldlog.240606-134217\n", + "Building cesm with output to /glade/derecho/scratch/$USER/b1850.basics/bld/cesm.bldlog.240606-134217\n", "Component cesm exe build complete with 27 warnings\n", "Time spent not building: 130.716460 sec\n", "Time spent building: 389.249592 sec\n", diff --git a/notebooks_copy/basics/cesm_workflow/case_setup.ipynb b/notebooks_copy/basics/cesm_workflow/case_setup.ipynb index 93c5935fd..937f9b1a1 100644 --- a/notebooks_copy/basics/cesm_workflow/case_setup.ipynb +++ b/notebooks_copy/basics/cesm_workflow/case_setup.ipynb @@ -95,7 +95,7 @@ "
\n", " \n", "```\n", - "/glade/work/$USER/cases/b.day1.0/env_mach_specific.xml already exists, delete to replace\n", + "/glade/work/$USER/cases/b1850.basics/env_mach_specific.xml already exists, delete to replace\n", "job is case.run USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", "Creating batch scripts\n", "Writing case.run script from input template /glade/work/$USERrun\n", diff --git a/notebooks_copy/basics/cesm_workflow/case_submit.ipynb b/notebooks_copy/basics/cesm_workflow/case_submit.ipynb index 4d66a87a2..b7b98320c 100644 --- a/notebooks_copy/basics/cesm_workflow/case_submit.ipynb +++ b/notebooks_copy/basics/cesm_workflow/case_submit.ipynb @@ -95,7 +95,7 @@ "
\n", " \n", "```\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", @@ -107,7 +107,7 @@ "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", "Creating component namelists\n", " Calling /glade/u/home/$USER/my_cesm_code/components/cam//cime_config/buildnml\n", - "CAM namelist copy: file1 /glade/work/$USER/cases/b.day1.0/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b.day1.0/run/atm_in \n", + "CAM namelist copy: file1 /glade/work/$USER/cases/b1850.basics/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b1850.basics/run/atm_in \n", " Calling /glade/u/home/$USER/my_cesm_code/components/clm//cime_config/buildnml\n", " Calling /glade/u/home/$USER/my_cesm_code/components/cice//cime_config/buildnml\n", " Calling /glade/u/home/$USER/my_cesm_code/components/pop//cime_config/buildnml\n", @@ -139,7 +139,7 @@ "Loading input file list: 'Buildconf/cism.input_data_list'\n", "Loading input file list: 'Buildconf/mosart.input_data_list'\n", "Loading input file list: 'Buildconf/cam.input_data_list'\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", @@ -149,7 +149,7 @@ "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", diff --git a/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb b/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb index 648ec7edc..0511ca94b 100644 --- a/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb +++ b/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb @@ -130,7 +130,7 @@ "Batch_system_type is pbs\n", "job is case.run USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", "job is case.st_archive USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", - " Creating Case directory /glade/work/$USER/cases/b.day1.0\n", + " Creating Case directory /glade/work/$USER/cases/b1850.basics\n", "\n", "```\n", "\n", diff --git a/notebooks_copy/basics/exercises/examine_history_B1850.ipynb b/notebooks_copy/basics/exercises/examine_history_B1850.ipynb index c31b7875e..edc0a54f2 100644 --- a/notebooks_copy/basics/exercises/examine_history_B1850.ipynb +++ b/notebooks_copy/basics/exercises/examine_history_B1850.ipynb @@ -15,8 +15,8 @@ "source": [ "Having successfully completed your first month of the new CESM B1850 case you will now examine the history files that have been transfered to the Archive directory. You will be using the NetCDF viewer ncview to look at the monthly average values for the single month. For this exercise you will be looking at the output from the Community Atmospheric Model (CAM) component of CESM, however the ncview tool can be applied to other components such as the Community Land Model (CLM). In this exercise we will:\n", "\n", - "- Step 1. Explore the b.day1.0 Archive directory.\n", - "- Step 2. Open the b.day1.0 cam h0 in ncview.\n", + "- Step 1. Explore the b1850.basics Archive directory.\n", + "- Step 2. Open the b1850.basics cam h0 in ncview.\n", "- Step 3. Examine Average Monthly Surface Temperature.\n", "- Step 4. Examine Average Monthly Precipitation." ] @@ -26,7 +26,7 @@ "id": "736b22e8-4b55-4c33-bff9-1ab1a026931c", "metadata": {}, "source": [ - "## Step 1. Explore the b.day1.0 Archive directory" + "## Step 1. Explore the b1850.basics Archive directory" ] }, { @@ -43,10 +43,10 @@ "metadata": {}, "source": [ "
\n", - "Change into the b.day1.0 short term archive directory:

\n", + "Change into the b1850.basics short term archive directory:

\n", "\n", "```\n", - "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", "```\n", "
\n", "\n", @@ -84,7 +84,7 @@ "id": "b7a357a4-fec5-44df-8a2d-db27dd57d9fe", "metadata": {}, "source": [ - "You will now use `ncview` to look at the b.day1.0 case CAM monthly average values for the single month you ran.\n", + "You will now use `ncview` to look at the b1850.basics case CAM monthly average values for the single month you ran.\n", "\n", "If you are unfamiliar with `ncview` you can learn more at [this link](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#ncview). \n", "\n", @@ -121,11 +121,11 @@ "```\n", "
\n", " \n", - "There should be a single file, b.day1.0.cam.h0.0001-01.nc.
\n", + "There should be a single file, b1850.basics.cam.h0.0001-01.nc.
\n", "\n", "Open the file using `ncview`:
\n", "```\n", - "ncview b.day1.0.cam.h0.0001-01.nc &\n", + "ncview b1850.basics.cam.h0.0001-01.nc &\n", "```\n", "
" ] @@ -156,12 +156,12 @@ "id": "0b5d30af-109a-40a8-a49f-01ac21734dfb", "metadata": {}, "source": [ - "Using ncview with the b.day1.0 January 0001 CAM monthly average values file loaded, examine the Surface Temperature variable. Update the color palette used to plot values and change the size of the plot.\n", + "Using ncview with the b1850.basics January 0001 CAM monthly average values file loaded, examine the Surface Temperature variable. Update the color palette used to plot values and change the size of the plot.\n", "\n", "
\n", "Select the Surface Temperature Variable:

\n", "\n", - "Click on the **(244) 2d vars** button shown above. This will pull up a list of all 2d vars on the b.day1.0 January 0001 CAM history file. This is a long list but it is in alphabetical order. You are looking for the `TS` variable. \n", + "Click on the **(244) 2d vars** button shown above. This will pull up a list of all 2d vars on the b1850.basics January 0001 CAM history file. This is a long list but it is in alphabetical order. You are looking for the `TS` variable. \n", "
\n", "\n", "Change the color selection from detail to bright:
\n", @@ -177,7 +177,7 @@ "
\n", "\n", "![NCView TS](../../../images/basics/NCVIEW_Jan0001_TS.png)\n", - "*

Figure: NCView b.day1.0 TS Jan 0001

*\n", + "*

Figure: NCView b1850.basics TS Jan 0001

*\n", "\n", "The Surface Temperature variable is in Kelvin. A value of 273.15K represents 0°C and 32°F. There are many options to better view and explore data with ncview. Some of these will come up over the next few days of the tutorial." ] @@ -195,7 +195,7 @@ "id": "25c204c4-d1f2-4af8-93c1-d9296b5cba9c", "metadata": {}, "source": [ - "Using ncview with the b.day1.0 January 0001 CAM monthly average values file loaded, examine the the two variables representing Convective Precipitation and Large Scale Precipitation.\n", + "Using ncview with the b1850.basics January 0001 CAM monthly average values file loaded, examine the the two variables representing Convective Precipitation and Large Scale Precipitation.\n", "\n", "
\n", "Exercise: Select the Convective Precipitation Variable:

\n", @@ -206,7 +206,7 @@ "
\n", "\n", "![NCView PRECC](../../../images/basics/NCVIEW_Jan0001_PRECC.png)\n", - "*

Figure: NCView b.day1.0 PRECC Jan 0001

*\n", + "*

Figure: NCView b1850.basics PRECC Jan 0001

*\n", "\n", "
\n", "Exercise: Select the Convective Precipitation Variable:

\n", @@ -219,7 +219,7 @@ "
\n", "\n", "![NCView PRECL](../../../images/basics/NCVIEW_Jan0001_PRECL.png)\n", - "*

Figure: NCView b.day1.0 PRECL Jan 0001

*\n", + "*

Figure: NCView b1850.basics PRECL Jan 0001

*\n", "\n", "Precipitation in the model is recorded in m/s. To convert to mm/day we can multiply the values by 1000 mm in a meter and 86,400 seconds in a day. Can you see the differences in Convective and Large Scale Precipitation?" ] diff --git a/notebooks_copy/basics/exercises/extra.ipynb b/notebooks_copy/basics/exercises/extra.ipynb index fcb8145f4..1cfb184b4 100644 --- a/notebooks_copy/basics/exercises/extra.ipynb +++ b/notebooks_copy/basics/exercises/extra.ipynb @@ -32,7 +32,7 @@ "
\n", " \n", "```\n", - "cd /glade/work/$USER/cases/b.day1.0/\n", + "cd /glade/work/$USER/cases/b1850.basics/\n", "```\n", "
" ] diff --git a/notebooks_copy/basics/exercises/first_B1850.ipynb b/notebooks_copy/basics/exercises/first_B1850.ipynb index 2c7a44637..99894df55 100644 --- a/notebooks_copy/basics/exercises/first_B1850.ipynb +++ b/notebooks_copy/basics/exercises/first_B1850.ipynb @@ -17,7 +17,7 @@ "
\n", "Exercise: Run a basic, out-of-the-box (that is, no user changes are implemented) CESM experiment

\n", " \n", - "Create a case called **b.day1.0** using the compset ``B1850`` at ``f19_g17`` resolution. \n", + "Create a case called **b1850.basics** using the compset ``B1850`` at ``f19_g17`` resolution. \n", "\n", "Check the status of the run after submitting.\n", "\n", @@ -84,15 +84,15 @@ "
\n", "Click here for the solution
\n", " \n", - "Create a new case b.day1.0 with the command:\n", + "Create a new case b1850.basics with the command:\n", "```\n", "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", - "./create_newcase --case /glade/work/$USER/cases/b.day1.0 --res f19_g17 --compset B1850\n", + "./create_newcase --case /glade/work/$USER/cases/b1850.basics --res f19_g17 --compset B1850\n", "```\n", "\n", "Case setup:\n", "``` \n", - "cd /glade/work/$USER/cases/b.day1.0 \n", + "cd /glade/work/$USER/cases/b1850.basics \n", "./case.setup\n", "```\n", " \n", @@ -112,11 +112,11 @@ "``` \n", "\n", "When the run is completed, look into the archive directory for: \n", - "b.day1.0. \n", + "b1850.basics. \n", " \n", "Check that your archive directory on derecho (The path will be different on other machines): \n", "```\n", - "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", "ls \n", "```\n", "\n", diff --git a/notebooks_copy/basics/exercises/second_B1850.ipynb b/notebooks_copy/basics/exercises/second_B1850.ipynb index be5f5cb55..12a2fcdd9 100644 --- a/notebooks_copy/basics/exercises/second_B1850.ipynb +++ b/notebooks_copy/basics/exercises/second_B1850.ipynb @@ -19,7 +19,7 @@ "\n", "The default value for a new case run length is 5 days. The run length is set by the XML variables STOP_OPTION and STOP_N. As the 5 days is too short to produce a monthly history file we will change these to run for a single month. In the next section you will learn more about `xml` files, but for now we will just provide the commands.\n", " \n", - "From your `CASE` directory, extend the run b.day1.0 with the command:\n", + "From your `CASE` directory, extend the run b1850.basics with the command:\n", "```\n", "./xmlchange STOP_OPTION=nmonths\n", "./xmlchange STOP_N=1\n", @@ -54,7 +54,7 @@ "**List the contents of the Run directory**\n", "\n", "```\n", - "ls -lt /glade/derecho/scratch/$USER/b.day1.0/run\n", + "ls -lt /glade/derecho/scratch/$USER/b1850.basics/run\n", "```\n", " \n", "
\n", @@ -71,9 +71,9 @@ "
\n", "Click here for the solution
\n", " \n", - "Go to your b.day1.0 case directory:\n", + "Go to your b1850.basics case directory:\n", "```\n", - "cd /glade/work/$USER/cases/b.day1.0\n", + "cd /glade/work/$USER/cases/b1850.basics\n", "```\n", "\n", "Changing xml files:\n", @@ -96,11 +96,11 @@ " \n", " \n", "When the run is completed, look into the archive directory for: \n", - "b.day1.0. \n", + "b1850.basics. \n", " \n", "Check that your archive directory on derecho (The path will be different on other machines): \n", "```\n", - "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", "ls \n", "```\n", "\n", diff --git a/notebooks_copy/modifications/xml/model_control_files.ipynb b/notebooks_copy/modifications/xml/model_control_files.ipynb index 66cd0eafe..6580967b1 100644 --- a/notebooks_copy/modifications/xml/model_control_files.ipynb +++ b/notebooks_copy/modifications/xml/model_control_files.ipynb @@ -11,7 +11,7 @@ "\n", "We will use **`xmlchange`** to modify xml variables and use **`xmlquery`** to search xml variables. Note that you _can_ change XML files manually, but using the `xmlchange` script prevents XML errors and ensures there is a record of your changes in the `CaseStatus` file.\n", "\n", - "For the examples below, you can follow along by going to your `CASEROOT` directory b.day1.0 that you completed in the Basics section." + "For the examples below, you can follow along by going to your `CASEROOT` directory b1850.basics that you completed in the Basics section." ] }, { From 64b4276c4699a6b72e06836d02a2890106993d88 Mon Sep 17 00:00:00 2001 From: Cecile Hannay Date: Mon, 22 Jul 2024 14:49:04 -0600 Subject: [PATCH 4/5] rename basics exercises --- .../basics/cesm_workflow/case_build.ipynb | 32 +++++++++---------- .../basics/cesm_workflow/case_setup.ipynb | 2 +- .../basics/cesm_workflow/case_submit.ipynb | 8 ++--- .../basics/cesm_workflow/create_newcase.ipynb | 2 +- .../exercises/examine_history_B1850.ipynb | 28 ++++++++-------- notebooks/basics/exercises/extra.ipynb | 2 +- notebooks/basics/exercises/first_B1850.ipynb | 12 +++---- notebooks/basics/exercises/second_B1850.ipynb | 12 +++---- .../xml/model_control_files.ipynb | 2 +- 9 files changed, 50 insertions(+), 50 deletions(-) diff --git a/notebooks/basics/cesm_workflow/case_build.ipynb b/notebooks/basics/cesm_workflow/case_build.ipynb index d9832e576..d733d9f6e 100644 --- a/notebooks/basics/cesm_workflow/case_build.ipynb +++ b/notebooks/basics/cesm_workflow/case_build.ipynb @@ -93,11 +93,11 @@ "
\n", " \n", "```\n", - "Building case in directory /glade/work/$USER/cases/b.day1.0\n", + "Building case in directory /glade/work/$USER/cases/b1850.basics\n", "sharedlib_only is False\n", "model_only is False\n", "Generating component namelists as part of build\n", - "- Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + "- Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", @@ -121,7 +121,7 @@ "Creating component namelists\n", "Calling /glade/u/home/$USER/my_cesm_code/components/cam//cime_config/buildnml\n", "...calling cam buildcpp to set build time options\n", - "CAM namelist copy: file1 /glade/work/$USER/cases/b.day1.0/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b.day1.0/run/atm_in\n", + "CAM namelist copy: file1 /glade/work/$USER/cases/b1850.basics/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b1850.basics/run/atm_in\n", "Calling /glade/u/home/$USER/my_cesm_code/components/clm//cime_config/buildnml\n", "Calling /glade/u/home/$USER/my_cesm_code/components/cice//cime_config/buildnml\n", "...buildnml calling cice buildcpp to set build time options\n", @@ -146,30 +146,30 @@ "Calling /glade/u/home/$USER/my_cesm_code/cime/src/components/stub_comps/sesp/cime_config/buildnml\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/drivers/mct/cime_config/buildnml\n", "Finished creating component namelists\n", - "Building gptl with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/gptl.bldlog.240606-134217\n", + "Building gptl with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/gptl.bldlog.240606-134217\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.gptl\n", "Component gptl build complete with 2 warnings\n", - "Building mct with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/mct.bldlog.240606-134217\n", + "Building mct with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/mct.bldlog.240606-134217\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.mct\n", "Component mct build complete with 55 warnings\n", - "Building pio with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/pio.bldlog.240606-134217\n", + "Building pio with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/pio.bldlog.240606-134217\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.pio\n", "Component pio build complete with 28 warnings\n", - "Building csm_share with output to file /glade/derecho/scratch/$USER/b.day1.0/bld/csm_share.bldlog.240606-134217\n", + "Building csm_share with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/csm_share.bldlog.240606-134217\n", "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.csm_share\n", "Component csm_share build complete with 85 warnings\n", "- Building clm4_5/clm5_0 Library\n", - "Building lnd with output to /glade/derecho/scratch/$USER/b.day1.0/bld/lnd.bldlog.240606-134217\n", + "Building lnd with output to /glade/derecho/scratch/$USER/b1850.basics/bld/lnd.bldlog.240606-134217\n", "\n", "Component lnd build complete with 292 warnings\n", "clm built in 109.479993 seconds\n", - "Building atm with output to /glade/derecho/scratch/$USER/b.day1.0/bld/atm.bldlog.240606-134217\n", - "Building ice with output to /glade/derecho/scratch/$USER/b.day1.0/bld/ice.bldlog.240606-134217\n", - "Building ocn with output to /glade/derecho/scratch/$USER/b.day1.0/bld/ocn.bldlog.240606-134217\n", - "Building rof with output to /glade/derecho/scratch/$USER/b.day1.0/bld/rof.bldlog.240606-134217\n", - "Building glc with output to /glade/derecho/scratch/$USER/b.day1.0/bld/glc.bldlog.240606-134217\n", - "Building wav with output to /glade/derecho/scratch/$USER/b.day1.0/bld/wav.bldlog.240606-134217\n", - "Building esp with output to /glade/derecho/scratch/$USER/b.day1.0/bld/esp.bldlog.240606-134217\n", + "Building atm with output to /glade/derecho/scratch/$USER/b1850.basics/bld/atm.bldlog.240606-134217\n", + "Building ice with output to /glade/derecho/scratch/$USER/b1850.basics/bld/ice.bldlog.240606-134217\n", + "Building ocn with output to /glade/derecho/scratch/$USER/b1850.basics/bld/ocn.bldlog.240606-134217\n", + "Building rof with output to /glade/derecho/scratch/$USER/b1850.basics/bld/rof.bldlog.240606-134217\n", + "Building glc with output to /glade/derecho/scratch/$USER/b1850.basics/bld/glc.bldlog.240606-134217\n", + "Building wav with output to /glade/derecho/scratch/$USER/b1850.basics/bld/wav.bldlog.240606-134217\n", + "Building esp with output to /glade/derecho/scratch/$USER/b1850.basics/bld/esp.bldlog.240606-134217\n", "Component esp build complete with 1 warnings\n", "sesp built in 4.413109 seconds\n", "Component wav build complete with 35 warnings\n", @@ -184,7 +184,7 @@ "cam built in 105.142718 seconds\n", "Component glc build complete with 150 warnings\n", "cism built in 133.173952 seconds\n", - "Building cesm with output to /glade/derecho/scratch/$USER/b.day1.0/bld/cesm.bldlog.240606-134217\n", + "Building cesm with output to /glade/derecho/scratch/$USER/b1850.basics/bld/cesm.bldlog.240606-134217\n", "Component cesm exe build complete with 27 warnings\n", "Time spent not building: 130.716460 sec\n", "Time spent building: 389.249592 sec\n", diff --git a/notebooks/basics/cesm_workflow/case_setup.ipynb b/notebooks/basics/cesm_workflow/case_setup.ipynb index 93c5935fd..937f9b1a1 100644 --- a/notebooks/basics/cesm_workflow/case_setup.ipynb +++ b/notebooks/basics/cesm_workflow/case_setup.ipynb @@ -95,7 +95,7 @@ "
\n", " \n", "```\n", - "/glade/work/$USER/cases/b.day1.0/env_mach_specific.xml already exists, delete to replace\n", + "/glade/work/$USER/cases/b1850.basics/env_mach_specific.xml already exists, delete to replace\n", "job is case.run USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", "Creating batch scripts\n", "Writing case.run script from input template /glade/work/$USERrun\n", diff --git a/notebooks/basics/cesm_workflow/case_submit.ipynb b/notebooks/basics/cesm_workflow/case_submit.ipynb index 4d66a87a2..b7b98320c 100644 --- a/notebooks/basics/cesm_workflow/case_submit.ipynb +++ b/notebooks/basics/cesm_workflow/case_submit.ipynb @@ -95,7 +95,7 @@ "
\n", " \n", "```\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", @@ -107,7 +107,7 @@ "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", "Creating component namelists\n", " Calling /glade/u/home/$USER/my_cesm_code/components/cam//cime_config/buildnml\n", - "CAM namelist copy: file1 /glade/work/$USER/cases/b.day1.0/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b.day1.0/run/atm_in \n", + "CAM namelist copy: file1 /glade/work/$USER/cases/b1850.basics/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b1850.basics/run/atm_in \n", " Calling /glade/u/home/$USER/my_cesm_code/components/clm//cime_config/buildnml\n", " Calling /glade/u/home/$USER/my_cesm_code/components/cice//cime_config/buildnml\n", " Calling /glade/u/home/$USER/my_cesm_code/components/pop//cime_config/buildnml\n", @@ -139,7 +139,7 @@ "Loading input file list: 'Buildconf/cism.input_data_list'\n", "Loading input file list: 'Buildconf/mosart.input_data_list'\n", "Loading input file list: 'Buildconf/cam.input_data_list'\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", @@ -149,7 +149,7 @@ "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b.day1.0/run\n", + " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", diff --git a/notebooks/basics/cesm_workflow/create_newcase.ipynb b/notebooks/basics/cesm_workflow/create_newcase.ipynb index cd9985017..6c263ed0e 100644 --- a/notebooks/basics/cesm_workflow/create_newcase.ipynb +++ b/notebooks/basics/cesm_workflow/create_newcase.ipynb @@ -131,7 +131,7 @@ "Batch_system_type is pbs\n", "job is case.run USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", "job is case.st_archive USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", - " Creating Case directory /glade/work/$USER/cases/b.day1.0\n", + " Creating Case directory /glade/work/$USER/cases/b1850.basics\n", "\n", "```\n", "\n", diff --git a/notebooks/basics/exercises/examine_history_B1850.ipynb b/notebooks/basics/exercises/examine_history_B1850.ipynb index c31b7875e..edc0a54f2 100644 --- a/notebooks/basics/exercises/examine_history_B1850.ipynb +++ b/notebooks/basics/exercises/examine_history_B1850.ipynb @@ -15,8 +15,8 @@ "source": [ "Having successfully completed your first month of the new CESM B1850 case you will now examine the history files that have been transfered to the Archive directory. You will be using the NetCDF viewer ncview to look at the monthly average values for the single month. For this exercise you will be looking at the output from the Community Atmospheric Model (CAM) component of CESM, however the ncview tool can be applied to other components such as the Community Land Model (CLM). In this exercise we will:\n", "\n", - "- Step 1. Explore the b.day1.0 Archive directory.\n", - "- Step 2. Open the b.day1.0 cam h0 in ncview.\n", + "- Step 1. Explore the b1850.basics Archive directory.\n", + "- Step 2. Open the b1850.basics cam h0 in ncview.\n", "- Step 3. Examine Average Monthly Surface Temperature.\n", "- Step 4. Examine Average Monthly Precipitation." ] @@ -26,7 +26,7 @@ "id": "736b22e8-4b55-4c33-bff9-1ab1a026931c", "metadata": {}, "source": [ - "## Step 1. Explore the b.day1.0 Archive directory" + "## Step 1. Explore the b1850.basics Archive directory" ] }, { @@ -43,10 +43,10 @@ "metadata": {}, "source": [ "
\n", - "Change into the b.day1.0 short term archive directory:

\n", + "Change into the b1850.basics short term archive directory:

\n", "\n", "```\n", - "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", "```\n", "
\n", "\n", @@ -84,7 +84,7 @@ "id": "b7a357a4-fec5-44df-8a2d-db27dd57d9fe", "metadata": {}, "source": [ - "You will now use `ncview` to look at the b.day1.0 case CAM monthly average values for the single month you ran.\n", + "You will now use `ncview` to look at the b1850.basics case CAM monthly average values for the single month you ran.\n", "\n", "If you are unfamiliar with `ncview` you can learn more at [this link](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#ncview). \n", "\n", @@ -121,11 +121,11 @@ "```\n", "
\n", " \n", - "There should be a single file, b.day1.0.cam.h0.0001-01.nc.
\n", + "There should be a single file, b1850.basics.cam.h0.0001-01.nc.
\n", "\n", "Open the file using `ncview`:
\n", "```\n", - "ncview b.day1.0.cam.h0.0001-01.nc &\n", + "ncview b1850.basics.cam.h0.0001-01.nc &\n", "```\n", "
" ] @@ -156,12 +156,12 @@ "id": "0b5d30af-109a-40a8-a49f-01ac21734dfb", "metadata": {}, "source": [ - "Using ncview with the b.day1.0 January 0001 CAM monthly average values file loaded, examine the Surface Temperature variable. Update the color palette used to plot values and change the size of the plot.\n", + "Using ncview with the b1850.basics January 0001 CAM monthly average values file loaded, examine the Surface Temperature variable. Update the color palette used to plot values and change the size of the plot.\n", "\n", "
\n", "Select the Surface Temperature Variable:

\n", "\n", - "Click on the **(244) 2d vars** button shown above. This will pull up a list of all 2d vars on the b.day1.0 January 0001 CAM history file. This is a long list but it is in alphabetical order. You are looking for the `TS` variable. \n", + "Click on the **(244) 2d vars** button shown above. This will pull up a list of all 2d vars on the b1850.basics January 0001 CAM history file. This is a long list but it is in alphabetical order. You are looking for the `TS` variable. \n", "
\n", "\n", "Change the color selection from detail to bright:
\n", @@ -177,7 +177,7 @@ "
\n", "\n", "![NCView TS](../../../images/basics/NCVIEW_Jan0001_TS.png)\n", - "*

Figure: NCView b.day1.0 TS Jan 0001

*\n", + "*

Figure: NCView b1850.basics TS Jan 0001

*\n", "\n", "The Surface Temperature variable is in Kelvin. A value of 273.15K represents 0°C and 32°F. There are many options to better view and explore data with ncview. Some of these will come up over the next few days of the tutorial." ] @@ -195,7 +195,7 @@ "id": "25c204c4-d1f2-4af8-93c1-d9296b5cba9c", "metadata": {}, "source": [ - "Using ncview with the b.day1.0 January 0001 CAM monthly average values file loaded, examine the the two variables representing Convective Precipitation and Large Scale Precipitation.\n", + "Using ncview with the b1850.basics January 0001 CAM monthly average values file loaded, examine the the two variables representing Convective Precipitation and Large Scale Precipitation.\n", "\n", "
\n", "Exercise: Select the Convective Precipitation Variable:

\n", @@ -206,7 +206,7 @@ "
\n", "\n", "![NCView PRECC](../../../images/basics/NCVIEW_Jan0001_PRECC.png)\n", - "*

Figure: NCView b.day1.0 PRECC Jan 0001

*\n", + "*

Figure: NCView b1850.basics PRECC Jan 0001

*\n", "\n", "
\n", "Exercise: Select the Convective Precipitation Variable:

\n", @@ -219,7 +219,7 @@ "
\n", "\n", "![NCView PRECL](../../../images/basics/NCVIEW_Jan0001_PRECL.png)\n", - "*

Figure: NCView b.day1.0 PRECL Jan 0001

*\n", + "*

Figure: NCView b1850.basics PRECL Jan 0001

*\n", "\n", "Precipitation in the model is recorded in m/s. To convert to mm/day we can multiply the values by 1000 mm in a meter and 86,400 seconds in a day. Can you see the differences in Convective and Large Scale Precipitation?" ] diff --git a/notebooks/basics/exercises/extra.ipynb b/notebooks/basics/exercises/extra.ipynb index fcb8145f4..1cfb184b4 100644 --- a/notebooks/basics/exercises/extra.ipynb +++ b/notebooks/basics/exercises/extra.ipynb @@ -32,7 +32,7 @@ "
\n", " \n", "```\n", - "cd /glade/work/$USER/cases/b.day1.0/\n", + "cd /glade/work/$USER/cases/b1850.basics/\n", "```\n", "
" ] diff --git a/notebooks/basics/exercises/first_B1850.ipynb b/notebooks/basics/exercises/first_B1850.ipynb index 2c7a44637..99894df55 100644 --- a/notebooks/basics/exercises/first_B1850.ipynb +++ b/notebooks/basics/exercises/first_B1850.ipynb @@ -17,7 +17,7 @@ "
\n", "Exercise: Run a basic, out-of-the-box (that is, no user changes are implemented) CESM experiment

\n", " \n", - "Create a case called **b.day1.0** using the compset ``B1850`` at ``f19_g17`` resolution. \n", + "Create a case called **b1850.basics** using the compset ``B1850`` at ``f19_g17`` resolution. \n", "\n", "Check the status of the run after submitting.\n", "\n", @@ -84,15 +84,15 @@ "
\n", "Click here for the solution
\n", " \n", - "Create a new case b.day1.0 with the command:\n", + "Create a new case b1850.basics with the command:\n", "```\n", "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", - "./create_newcase --case /glade/work/$USER/cases/b.day1.0 --res f19_g17 --compset B1850\n", + "./create_newcase --case /glade/work/$USER/cases/b1850.basics --res f19_g17 --compset B1850\n", "```\n", "\n", "Case setup:\n", "``` \n", - "cd /glade/work/$USER/cases/b.day1.0 \n", + "cd /glade/work/$USER/cases/b1850.basics \n", "./case.setup\n", "```\n", " \n", @@ -112,11 +112,11 @@ "``` \n", "\n", "When the run is completed, look into the archive directory for: \n", - "b.day1.0. \n", + "b1850.basics. \n", " \n", "Check that your archive directory on derecho (The path will be different on other machines): \n", "```\n", - "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", "ls \n", "```\n", "\n", diff --git a/notebooks/basics/exercises/second_B1850.ipynb b/notebooks/basics/exercises/second_B1850.ipynb index be5f5cb55..12a2fcdd9 100644 --- a/notebooks/basics/exercises/second_B1850.ipynb +++ b/notebooks/basics/exercises/second_B1850.ipynb @@ -19,7 +19,7 @@ "\n", "The default value for a new case run length is 5 days. The run length is set by the XML variables STOP_OPTION and STOP_N. As the 5 days is too short to produce a monthly history file we will change these to run for a single month. In the next section you will learn more about `xml` files, but for now we will just provide the commands.\n", " \n", - "From your `CASE` directory, extend the run b.day1.0 with the command:\n", + "From your `CASE` directory, extend the run b1850.basics with the command:\n", "```\n", "./xmlchange STOP_OPTION=nmonths\n", "./xmlchange STOP_N=1\n", @@ -54,7 +54,7 @@ "**List the contents of the Run directory**\n", "\n", "```\n", - "ls -lt /glade/derecho/scratch/$USER/b.day1.0/run\n", + "ls -lt /glade/derecho/scratch/$USER/b1850.basics/run\n", "```\n", " \n", "
\n", @@ -71,9 +71,9 @@ "
\n", "Click here for the solution
\n", " \n", - "Go to your b.day1.0 case directory:\n", + "Go to your b1850.basics case directory:\n", "```\n", - "cd /glade/work/$USER/cases/b.day1.0\n", + "cd /glade/work/$USER/cases/b1850.basics\n", "```\n", "\n", "Changing xml files:\n", @@ -96,11 +96,11 @@ " \n", " \n", "When the run is completed, look into the archive directory for: \n", - "b.day1.0. \n", + "b1850.basics. \n", " \n", "Check that your archive directory on derecho (The path will be different on other machines): \n", "```\n", - "cd /glade/derecho/scratch/$USER/archive/b.day1.0\n", + "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", "ls \n", "```\n", "\n", diff --git a/notebooks/modifications/xml/model_control_files.ipynb b/notebooks/modifications/xml/model_control_files.ipynb index 66cd0eafe..6580967b1 100644 --- a/notebooks/modifications/xml/model_control_files.ipynb +++ b/notebooks/modifications/xml/model_control_files.ipynb @@ -11,7 +11,7 @@ "\n", "We will use **`xmlchange`** to modify xml variables and use **`xmlquery`** to search xml variables. Note that you _can_ change XML files manually, but using the `xmlchange` script prevents XML errors and ensures there is a record of your changes in the `CaseStatus` file.\n", "\n", - "For the examples below, you can follow along by going to your `CASEROOT` directory b.day1.0 that you completed in the Basics section." + "For the examples below, you can follow along by going to your `CASEROOT` directory b1850.basics that you completed in the Basics section." ] }, { From 32dd1ebf3ff5638e77e25d5b8774b3d172b6b860 Mon Sep 17 00:00:00 2001 From: Cecile Hannay Date: Mon, 22 Jul 2024 14:51:45 -0600 Subject: [PATCH 5/5] remove notebooks_copy --- notebooks_copy/basics/basics_overview.ipynb | 109 -- notebooks_copy/basics/cesm_workflow.ipynb | 92 -- .../basics/cesm_workflow/case_build.ipynb | 258 ---- .../basics/cesm_workflow/case_setup.ipynb | 154 -- .../basics/cesm_workflow/case_submit.ipynb | 222 --- .../checking_jobs_and_status.ipynb | 201 --- .../basics/cesm_workflow/create_clone.ipynb | 109 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"b2cd974a-dfbb-4937-8ed0-ab87ab60f1ef", - "metadata": {}, - "source": [ - "# Basics" - ] - }, - { - "cell_type": "markdown", - "id": "9db9d54e-ef78-44ef-a003-4ddd8a416317", - "metadata": {}, - "source": [ - "The CESM Model is developed around a structured workflow. The first step of this workflow is to set up your workspace, which is usually a one-time task. After setting up your workspace, a basic CESM simulation can be run with only four commands. This section will cover these steps and enable you to complete your first CESM run." - ] - }, - { - "cell_type": "markdown", - "id": "e2abf5cb-c507-408e-8f2a-2fd12934873d", - "metadata": {}, - "source": [ - "## Workflow Elements" - ] - }, - { - "cell_type": "markdown", - "id": "acfe09a5-e3ee-457a-a328-bde665cfe761", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "
\n", - "\n", - "- Set up workspace (one time setup)\n", - " - **``Download``** the CESM code\n", - " - **``Create or Locate ``** an **``Input Data``** Root Directory\n", - " - Possibly **``Porting``** if not on a setup machine\n", - "- Creating, Running and Reviewing a Case\n", - " - **``Create``** a new case\n", - " - **``Invoke``** `case.setup`\n", - " - **``Build``** the executable with `case.build`\n", - " - **``Run``** the model with `case.submit`\n", - " - **``Review``** output data\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "0c07b51d-f999-4737-9739-3ea989f20802", - "metadata": {}, - "source": [ - "## Setting up your environment" - ] - }, - { - "cell_type": "markdown", - "id": "9d6954eb-3505-4a58-b6fa-bea7047c63cf", - "metadata": {}, - "source": [ - "Every time you log onto the NCAR HPC you want to ensure you have the correct modules loaded. Please refer to the [NCAR HPC environment](https://ncar.github.io/CESM-Tutorial/notebooks/resources/profile.html#setting-up-your-ncar-hpc-environment) section to make sure you are setting up your environment properly. \n", - "\n", - "**YOU SHOULD ENSURE YOU CHECK THIS EVERY TIME YOU ARE BUILDING A NEW CASE**\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "523165e7-728b-4305-b9f7-4e6ee530a27d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "715674b2-a858-49ae-b044-7f7fc61a750c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/cesm_workflow.ipynb b/notebooks_copy/basics/cesm_workflow.ipynb deleted file mode 100644 index b31e36592..000000000 --- a/notebooks_copy/basics/cesm_workflow.ipynb +++ /dev/null @@ -1,92 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CESM Workflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CESM has been designed to be easy to use. Once you have downloaded the CESM code, a CESM `case` can be run with a set of 4 commands. \n", - "\n", - "*Note: In CESM jargon, a case refers to a specific instance of a model simulation.*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "1) Create a new case using `create_newcase`\n", - "2) Set up the case by invoking `case.setup`\n", - "3) Build the executable using `case.build`\n", - "4) Run your case using `case.submit`\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following sections will go into more detail about these 4 commands and also provide information about how to check your job status or create a clone of a previous case." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### More information" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "More detailed information on the CESM2 Workflow is documented in the following links and can be read in detail at a later time." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "\n", - "[CESM2 release](https://escomp.github.io/CESM/release-cesm2/)\n", - " \n", - "[CIME](https://esmci.github.io/cime/versions/master/html/index.html)\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks_copy/basics/cesm_workflow/case_build.ipynb b/notebooks_copy/basics/cesm_workflow/case_build.ipynb deleted file mode 100644 index d733d9f6e..000000000 --- a/notebooks_copy/basics/cesm_workflow/case_build.ipynb +++ /dev/null @@ -1,258 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f9dac1fe-df62-4560-adf5-2779d9ed8f2d", - "metadata": { - "tags": [] - }, - "source": [ - "# Case Build\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "4f42d8e1-ac3a-4f2d-b9fb-5664e1625fb4", - "metadata": {}, - "source": [ - "After a new case is setup, the tool that builds the new case by compiling the code is `case.build`. This tool is located in the `$CASEROOT` directory.\n", - "\n", - "Running this script results in the following actions:\n", - "- Checks and creates final component namelists\n", - "- Builds individual model component libraries\n", - "- Builds the final CESM model executable" - ] - }, - { - "cell_type": "markdown", - "id": "0d8b2ab4-7976-48b7-81e4-e2ae0f28cd97", - "metadata": {}, - "source": [ - "![CESM case.build](../../../images/basics/CESM2_case_build.png)\n", - "*

Figure: Detailed view of the location of case.build

*\n", - "\n", - "\n", - "For the current tutorial on derecho, the paths are:\n", - "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", - "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", - "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", - "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` " - ] - }, - { - "cell_type": "markdown", - "id": "1892841c-9c0c-4baf-b73e-413b74798e59", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "35ab1731-c0c2-4a39-a6e1-31f86d6f8cdd", - "metadata": {}, - "source": [ - "## Command Syntax" - ] - }, - { - "cell_type": "markdown", - "id": "f2581dbe-4077-43c1-b2bb-1d26c1af544e", - "metadata": {}, - "source": [ - "
\n", - "\n", - "You should still be in the `CASEROOT` directory after running `case.setup`\n", - "```\n", - "cd /glade/work/$USER/cases/CASE\n", - "```\n", - " \n", - "
\n", - " \n", - "Example `case.build` command:
\n", - " \n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - " \n", - "
\n", - "\n", - "**NOTE:** Do not enter the example above at the command line. You will create your first case in the Exercise at the end of this section.
" - ] - }, - { - "cell_type": "markdown", - "id": "8835e60d-6412-4b21-8b50-f122ce6b167d", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - " \n", - "```\n", - "Building case in directory /glade/work/$USER/cases/b1850.basics\n", - "sharedlib_only is False\n", - "model_only is False\n", - "Generating component namelists as part of build\n", - "- Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.rof\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.atm\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.tavg.5\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.pop.ro.0301-01-01-00000\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.mosart.r.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cpl.r.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.pop.r.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cam.r.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cice.r.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cam.rs.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.clm2.r.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cam.i.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.cism.r.0301-01-01-00000.nc\n", - "Staging file /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/b.e20.B1850.f19_g17.release_cesm2_1_0.020.ww3.r.0301-01-01-00000\n", - "Creating component namelists\n", - "Calling /glade/u/home/$USER/my_cesm_code/components/cam//cime_config/buildnml\n", - "...calling cam buildcpp to set build time options\n", - "CAM namelist copy: file1 /glade/work/$USER/cases/b1850.basics/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b1850.basics/run/atm_in\n", - "Calling /glade/u/home/$USER/my_cesm_code/components/clm//cime_config/buildnml\n", - "Calling /glade/u/home/$USER/my_cesm_code/components/cice//cime_config/buildnml\n", - "...buildnml calling cice buildcpp to set build time options\n", - "Calling /glade/u/home/$USER/my_cesm_code/components/pop//cime_config/buildnml\n", - "... buildnml: calling pop buildcpp to set build time options\n", - "given is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2666.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2667.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2668.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2675.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2676.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2677.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2678.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2679.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2680.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2681.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2682.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2683.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2684.\n", - "Calling /glade/u/home/$USER/my_cesm_code/components/mosart//cime_config/buildnml\n", - "Running /glade/u/home/$USER/my_cesm_code/components/cism//cime_config/buildnml\n", - "Calling /glade/u/home/$USER/my_cesm_code/components/ww3//cime_config/buildnml\n", - "Calling /glade/u/home/$USER/my_cesm_code/cime/src/components/stub_comps/sesp/cime_config/buildnml\n", - "Calling /glade/u/home/$USER/my_cesm_code/cime/src/drivers/mct/cime_config/buildnml\n", - "Finished creating component namelists\n", - "Building gptl with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/gptl.bldlog.240606-134217\n", - "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.gptl\n", - "Component gptl build complete with 2 warnings\n", - "Building mct with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/mct.bldlog.240606-134217\n", - "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.mct\n", - "Component mct build complete with 55 warnings\n", - "Building pio with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/pio.bldlog.240606-134217\n", - "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.pio\n", - "Component pio build complete with 28 warnings\n", - "Building csm_share with output to file /glade/derecho/scratch/$USER/b1850.basics/bld/csm_share.bldlog.240606-134217\n", - "Calling /glade/u/home/$USER/my_cesm_code/cime/src/build_scripts/buildlib.csm_share\n", - "Component csm_share build complete with 85 warnings\n", - "- Building clm4_5/clm5_0 Library\n", - "Building lnd with output to /glade/derecho/scratch/$USER/b1850.basics/bld/lnd.bldlog.240606-134217\n", - "\n", - "Component lnd build complete with 292 warnings\n", - "clm built in 109.479993 seconds\n", - "Building atm with output to /glade/derecho/scratch/$USER/b1850.basics/bld/atm.bldlog.240606-134217\n", - "Building ice with output to /glade/derecho/scratch/$USER/b1850.basics/bld/ice.bldlog.240606-134217\n", - "Building ocn with output to /glade/derecho/scratch/$USER/b1850.basics/bld/ocn.bldlog.240606-134217\n", - "Building rof with output to /glade/derecho/scratch/$USER/b1850.basics/bld/rof.bldlog.240606-134217\n", - "Building glc with output to /glade/derecho/scratch/$USER/b1850.basics/bld/glc.bldlog.240606-134217\n", - "Building wav with output to /glade/derecho/scratch/$USER/b1850.basics/bld/wav.bldlog.240606-134217\n", - "Building esp with output to /glade/derecho/scratch/$USER/b1850.basics/bld/esp.bldlog.240606-134217\n", - "Component esp build complete with 1 warnings\n", - "sesp built in 4.413109 seconds\n", - "Component wav build complete with 35 warnings\n", - "ww built in 33.126202 seconds\n", - "Component rof build complete with 14 warnings\n", - "mosart built in 33.278138 seconds\n", - "Component ice build complete with 74 warnings\n", - "cice built in 51.958399 seconds\n", - "Component ocn build complete with 207 warnings\n", - "pop built in 86.837194 seconds\n", - "Component atm build complete with 528 warnings\n", - "cam built in 105.142718 seconds\n", - "Component glc build complete with 150 warnings\n", - "cism built in 133.173952 seconds\n", - "Building cesm with output to /glade/derecho/scratch/$USER/b1850.basics/bld/cesm.bldlog.240606-134217\n", - "Component cesm exe build complete with 27 warnings\n", - "Time spent not building: 130.716460 sec\n", - "Time spent building: 389.249592 sec\n", - "MODEL BUILD HAS FINISHED SUCCESSFULLY\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7e677c5f-2b85-459b-ad9e-2f4b42904589", - "metadata": {}, - "source": [ - "**Notes:**\n", - "- Notice the `./` before any command run in the CASEROOT. \n", - "- On NCAR HPC machines you must call the `case.build` using `qcmd` because it compiles the model on a compute node rather than a login node. This reduces the load on login nodes and prevents a timeout while you are building the model.\n", - "- The output tells you if it was successful at the end: `MODEL BUILD HAS FINISHED SUCCESSFULLY`" - ] - }, - { - "cell_type": "markdown", - "id": "f6edbb1c-0ca4-49f9-b238-974c1c19de89", - "metadata": {}, - "source": [ - "## Tips" - ] - }, - { - "cell_type": "markdown", - "id": "9b750904-678a-40d2-b5fa-e87c52d5c8eb", - "metadata": {}, - "source": [ - "- To completely rebuild a case, run `./case.build --clean-all` first before building the model.\n", - "- If you want to make any source code modifications in `SourceMods`, do this before building the model. We will cover SourceMods in a later section.\n", - "- If you want to make any modifications to `env_build.xml`, do this before building the model.\n", - "- If any input data is missing the build will abort but provide a list of missing files. To acquire missing data run `./check_input_data --download`. This will download the required data and put it in the `inputdata` directory defined by the XML variable `DIN_LOC_ROOT`. After you have done these steps you can re-run the `case.build` script." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48056ea8-6b71-4485-ad55-d91c8ade8ba0", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/cesm_workflow/case_setup.ipynb b/notebooks_copy/basics/cesm_workflow/case_setup.ipynb deleted file mode 100644 index 937f9b1a1..000000000 --- a/notebooks_copy/basics/cesm_workflow/case_setup.ipynb +++ /dev/null @@ -1,154 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f9dac1fe-df62-4560-adf5-2779d9ed8f2d", - "metadata": {}, - "source": [ - "# Case Setup\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "69d76269-97de-4fed-9816-22f66e13866e", - "metadata": {}, - "source": [ - "After a new case is created, the tool that sets up the new case is `case.setup`. This tool is located in the `$CASEROOT` directory.\n", - "\n", - "After running `case.setup` the following actions occur:\n", - "- The `$RUNDIR` and `$EXEROOT` directories are created as shown on the figure." - ] - }, - { - "cell_type": "markdown", - "id": "93aec491-6938-4d83-adb0-c059c8c8b87e", - "metadata": {}, - "source": [ - "![CESM case.setup](../../../images/basics/CESM2_case_setup.png)\n", - "*

Figure: Detailed view of the location of case.setup

*\n", - "\n", - "\n", - "For the current tutorial on derecho, the paths are:\n", - "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", - "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", - "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", - "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` " - ] - }, - { - "cell_type": "markdown", - "id": "831f1ad8-627d-4c5f-a692-02e16ce0112e", - "metadata": {}, - "source": [ - "Note that other files and directories are created at `case.setup`. This will be covered in more detail in later sections (i.e. [Namelist modifications](https://ncar.github.io/CESM-Tutorial/notebooks/namelist/namelist.html))\n", - "- `user_nl_xxx` files, where the user can customize component namelist files\n", - "- The `case.run` and `case.st_archive` scripts and `Macros.make` file\n", - "- Hidden files like `env_mach_specific.*`\n", - "- The `CaseDocs` directory, which should not be edited" - ] - }, - { - "cell_type": "markdown", - "id": "07a1da51-7e51-4f3d-b63c-d8492fd1f171", - "metadata": {}, - "source": [ - "## Command Syntax" - ] - }, - { - "cell_type": "markdown", - "id": "d630ea67-4c16-495d-8bdd-3d5fb254b530", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Go to the `$CASEROOT` directory \n", - "```\n", - "cd /glade/work/$USER/cases/CASE\n", - "```\n", - " \n", - "
\n", - " \n", - "Example `case.setup` command:
\n", - " \n", - "```\n", - "./case.setup\n", - "```\n", - " \n", - "
\n", - " \n", - "NOTE: Do not enter the example above at the command line. You will create your first case in the Exercise at the end of this section.
" - ] - }, - { - "cell_type": "markdown", - "id": "b68a6c25-55ee-42a6-bff2-93004c84abd5", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - " \n", - "```\n", - "/glade/work/$USER/cases/b1850.basics/env_mach_specific.xml already exists, delete to replace\n", - "job is case.run USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", - "Creating batch scripts\n", - "Writing case.run script from input template /glade/work/$USERrun\n", - "Writing case.st_archive script from input template /glade/work/$USER/code/my_cesm_code/cime/config/cesm/machines/template.st_archive\n", - "Creating file case.st_archive\n", - "Creating user_nl_xxx files for components and cpl\n", - "If an old case build already exists, might want to run 'case.build --clean' before building\n", - "You can now run './preview_run' to get more info on how your case will be run\n", - "\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f12bff84-04f0-4d28-8da9-af706f874933", - "metadata": {}, - "source": [ - "**Notes:**\n", - "- Notice the `./` before any command run in the CASEROOT. \n", - "- You can run the script name followed by the `--h` or `--help` argument to see help documentation for that script and a list of all command line arguments for that script.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0bae850c-b342-41d3-9237-12e4c5ef3fc0", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/cesm_workflow/case_submit.ipynb b/notebooks_copy/basics/cesm_workflow/case_submit.ipynb deleted file mode 100644 index b7b98320c..000000000 --- a/notebooks_copy/basics/cesm_workflow/case_submit.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "683d9e77-d700-4f75-b3f0-eb813657351b", - "metadata": { - "tags": [] - }, - "source": [ - "# Case Submit\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "ae2803f9-270d-4dd6-803a-e4158cbf7b86", - "metadata": {}, - "source": [ - "After a new case is built, the tool that submits the experiment to start it running is `case.submit`. This tool is located in the `$CASEROOT` directory.\n", - "\n", - "Running this script results in the following actions:\n", - "- Checking archive and run options\n", - "- Checking in namelists that need to be rebuilt\n", - "- Checking input data\n", - "- Submitting the `case.run` script to the NCAR HPC batch job scheduler\n", - "- Submitting the `case.st_archive` script to archive the model output. This step is dependent on the successful completion of `case.run`" - ] - }, - { - "cell_type": "markdown", - "id": "fd09cd4e-8e74-4867-aaf7-bb480d679e3d", - "metadata": {}, - "source": [ - "![CESM case.submit](../../../images/basics/CESM2_case_submit.png)\n", - "*

Figure: Detailed view of the location of case.submit

*\n", - "\n", - "For the current tutorial on derecho, the paths are:\n", - "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", - "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", - "- `$CIME_OUTPUT_ROOT` = `/glade/derecho/scratch/$USER`\n", - "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", - "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` " - ] - }, - { - "cell_type": "markdown", - "id": "579cb4c8-03f1-4c9f-913f-de2ffeccfbc5", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "d8e6525b-31d9-450d-a3b9-e3b435fc231a", - "metadata": {}, - "source": [ - "## Command Syntax" - ] - }, - { - "cell_type": "markdown", - "id": "12524d65-0037-4652-a77d-3bfea021869e", - "metadata": {}, - "source": [ - "
\n", - "\n", - "You should still be in the `CASEROOT` directory after running `case.build`\n", - "```\n", - "cd /glade/work/$USER/cases/CASE\n", - "```\n", - " \n", - "
\n", - " \n", - "Example `case.submit` command:
\n", - " \n", - "```\n", - "./case.submit\n", - "```\n", - " \n", - "
\n", - "\n", - "NOTE: Do not enter the example above at the command line. You will create your first case in the Exercise at the end of this section.
" - ] - }, - { - "cell_type": "markdown", - "id": "630dfd32-c4fc-40a9-812c-4c62ff6c4862", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - " \n", - "```\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.rof\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.atm\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.tavg.5\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", - "Creating component namelists\n", - " Calling /glade/u/home/$USER/my_cesm_code/components/cam//cime_config/buildnml\n", - "CAM namelist copy: file1 /glade/work/$USER/cases/b1850.basics/Buildconf/camconf/atm_in file2 /glade/derecho/scratch/$USER/b1850.basics/run/atm_in \n", - " Calling /glade/u/home/$USER/my_cesm_code/components/clm//cime_config/buildnml\n", - " Calling /glade/u/home/$USER/my_cesm_code/components/cice//cime_config/buildnml\n", - " Calling /glade/u/home/$USER/my_cesm_code/components/pop//cime_config/buildnml\n", - " given is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2666.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2667.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2668.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2675.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2676.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2677.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2678.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2679.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2680.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2681.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2682.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2683.\n", - "when is deprecated at /glade/u/home/$USER/my_cesm_code/components/pop/bld/build-namelist line 2684.\n", - " Calling /glade/u/home/$USER/my_cesm_code/components/mosart//cime_config/buildnml\n", - " Running /glade/u/home/$USER/my_cesm_code/components/cism//cime_config/buildnml \n", - " Calling /glade/u/home/$USER/my_cesm_code/components/ww3//cime_config/buildnml\n", - " Calling /glade/u/home/$USER/my_cesm_code/cime/src/components/stub_comps/sesp/cime_config/buildnml\n", - " Calling /glade/u/home/$USER/my_cesm_code/cime/src/drivers/mct/cime_config/buildnml\n", - "Finished creating component namelists\n", - "Checking that inputdata is available as part of case submission\n", - "Loading input file list: 'Buildconf/clm.input_data_list'\n", - "Loading input file list: 'Buildconf/cpl.input_data_list'\n", - "Loading input file list: 'Buildconf/pop.input_data_list'\n", - "Loading input file list: 'Buildconf/ww3.input_data_list'\n", - "Loading input file list: 'Buildconf/cice.input_data_list'\n", - "Loading input file list: 'Buildconf/cism.input_data_list'\n", - "Loading input file list: 'Buildconf/mosart.input_data_list'\n", - "Loading input file list: 'Buildconf/cam.input_data_list'\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.rof\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.atm\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.tavg.5\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", - " - Prestaging REFCASE (/glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01) to /glade/derecho/scratch/$USER/b1850.basics/run\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.restart\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ice\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.lnd\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.rof\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.atm\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.tavg.5\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.glc\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.ocn.ovf\n", - "Copy rpointer /glade/campaign/cesm/cesmdata/inputdata/cesm2_init/b.e20.B1850.f19_g17.release_cesm2_1_0.020/0301-01-01/rpointer.drv\n", - "Creating component namelists\n", - "Finished creating component namelists\n", - "Check case OK\n", - "submit_jobs case.run\n", - "Submit job case.run\n", - "Submitting job script qsub -q main -l walltime=12:00:00 -A $PROJECT -l job_priority=regular -v ARGS_FOR_SCRIPT='--resubmit' .case.run\n", - "Submitted job id is 4743615.desched1\n", - "Submit job case.st_archive\n", - "Submitting job script qsub -q main -l walltime=0:20:00 -A $PROJECT -l job_priority=regular -W depend=afterok:4743615.desched1 -v ARGS_FOR_SCRIPT='--resubmit' case.st_archive\n", - "Submitted job id is 4743616.desched1\n", - "Submitted job case.run with id 4743615.desched1\n", - "Submitted job case.st_archive with id 4743616.desched1\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "138df653-4e57-4668-ab27-cabe6806076a", - "metadata": {}, - "source": [ - "**Notes:**\n", - "- `case.submit` coordinates all the tasks of running CESM.\n", - "- `case.run` is submitted to the batch job scheduler by `case.submit`. Do not try to submit `case.run` separately.\n", - "- CESM runs in the Build/Run Directory.\n", - "- `case.st_archive` moves the history files to the archive directory runs only after `case.run` completes successfully. If a model run was unsuccessful the output remains in the Run Directory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2463ac2-7ada-48bd-8662-9e4115f7e8f5", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/cesm_workflow/checking_jobs_and_status.ipynb b/notebooks_copy/basics/cesm_workflow/checking_jobs_and_status.ipynb deleted file mode 100644 index fe99cc33c..000000000 --- a/notebooks_copy/basics/cesm_workflow/checking_jobs_and_status.ipynb +++ /dev/null @@ -1,201 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Checking Your Run\n" - ] - }, - { - "cell_type": "markdown", - "id": "169d4dc6-f655-4d60-b8c8-46bab0524c91", - "metadata": {}, - "source": [ - "## Batch Job Scheduler Monitoring" - ] - }, - { - "cell_type": "markdown", - "id": "e70a2387-d1d3-41b7-b0df-4811d2246566", - "metadata": {}, - "source": [ - "Once CESM has been submitted the status of the run on derecho can be followed through the qstat command. Qstat accesses the information in the Batch Job Scheduler to see the status of all jobs running on derecho. To simplify the list down the -u option can be specified for a particular user.\n", - "\n", - "
\n", - "Result of running qstat with an active job running:

\n", - "\n", - "```\n", - "qstat -u $USER\n", - "```\n", - "
\n", - " \n", - "Output:
\n", - "\n", - "```\n", - " Req'd Req'd Elap\n", - "Job ID Username Queue Jobname SessID NDS TSK Memory Time S Time\n", - "--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----\n", - "4743615.desche* $USER cpu run.b.day* -- 6 768 1410g 12:00 Q -- \n", - "4743616.desche* $USER cpu st_archiv* -- 1 1 235gb 00:20 H -- \n", - "```\n", - "\n", - "
\n", - "\n", - "Note, there may be a slight delay from submitting CESM until the jobs appear in the queue. Once the jobs have completed they will disappear from the qstat command." - ] - }, - { - "cell_type": "markdown", - "id": "5e079b70-b679-44ff-b13f-0089b0a9e3b1", - "metadata": {}, - "source": [ - "## Monitoring Files in the Run and Archive Directories\n" - ] - }, - { - "cell_type": "markdown", - "id": "a213054b-be89-464b-a596-b6e67fad52db", - "metadata": {}, - "source": [ - "\n", - "As the CESM job runs it will update files in Build / Run Directory. \n", - "Once the job has completed successfully the archiver will transfer history files to the Archive Directory.\n", - "One way to keep track of the progress of the run is to monitor the files in these directories.\n", - "- Check the files in the `$RUNDIR` as the model is running and once it is finished\n", - "- Check the files in the `$DOUT_S_ROOT` after `case.st_archive` runs." - ] - }, - { - "cell_type": "markdown", - "id": "6d99a83a-9b78-4b02-9aa0-e8dbf1cb8d0b", - "metadata": {}, - "source": [ - "## CaseStatus file" - ] - }, - { - "cell_type": "markdown", - "id": "c93c592b-bda0-45a5-97a0-747c600807e1", - "metadata": {}, - "source": [ - "\n", - "All activities for the case are recorded in the CaseStatus file in the CASEROOT directory. By looking through the file the successful or otherwise outcome of each step of the run can be tracked.\n", - "\n", - "
\n", - "Status of the case recorded in the `$CASEROOT` CaseStatus file:

\n", - "\n", - "```\n", - "cd /glade/work/$USER/cases/CASE\n", - "more CaseStatus\n", - "```\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "462ea73c-e73c-49cd-a74c-1261ebda445d", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - " \n", - "```\n", - "2024-06-06 13:41:19: case.setup starting \n", - " ---------------------------------------------------\n", - "2024-06-06 13:41:22: case.setup success \n", - " ---------------------------------------------------\n", - "2024-06-06 13:42:17: case.build starting \n", - " ---------------------------------------------------\n", - "CESM version is release-cesm2.1.5\n", - "Processing externals description file : Externals.cfg (/glade/u/home/$USER/my_cesm_code)\n", - "Processing externals description file : Externals_CAM.cfg (/glade/u/home/$USER/my_cesm_code/components/cam)\n", - "Processing externals description file : Externals_CISM.cfg (/glade/u/home/$USER/my_cesm_code/components/cism)\n", - "Processing externals description file : Externals_CLM.cfg (/glade/u/home/$USER/my_cesm_code/components/clm)\n", - "Processing externals description file : Externals_POP.cfg (/glade/u/home/$USER/my_cesm_code/components/pop)\n", - "Checking local status of required & optional components: cam, chem_proc, carma, clubb, cosp2, cice, cime, cism, source_cism, clm, \n", - "fates, mosart, pop, cvmix, marbl, rtm, ww3, \n", - " ./cime\n", - " clean sandbox, on cime5.6.49\n", - " ./components/cam\n", - " clean sandbox, on cam_cesm2_1_rel_60\n", - " ./components/cam/chem_proc\n", - " clean sandbox, on tools/proc_atm/chem_proc/release_tags/chem_proc5_0_03_rel\n", - " ./components/cam/src/physics/carma/base\n", - " clean sandbox, on carma/release_tags/carma3_49_rel\n", - " ./components/cam/src/physics/clubb\n", - " clean sandbox, on vendor_clubb_r8099_n03\n", - " ./components/cam/src/physics/cosp2/src\n", - " clean sandbox, on v2.1.4cesm\n", - " ./components/cice\n", - " clean sandbox, on cice5_cesm2_1_1_20231220\n", - " ./components/cism\n", - " clean sandbox, on cism-release-cesm2.1.2_04\n", - " ./components/cism/source_cism\n", - " clean sandbox, on release-cism2.1.04\n", - " ./components/clm\n", - " clean sandbox, on release-clm5.0.37\n", - " ./components/clm/src/fates\n", - " clean sandbox, on sci.1.30.0_api.8.0.0\n", - " ./components/mosart\n", - " clean sandbox, on release-cesm2.0.04\n", - " ./components/pop\n", - " clean sandbox, on pop2_cesm2_1_rel_n15\n", - " ./components/pop/externals/CVMix\n", - " clean sandbox, on v0.93-beta\n", - " ./components/pop/externals/MARBL\n", - " clean sandbox, on cesm2.1-n00\n", - " ./components/rtm\n", - " clean sandbox, on release-cesm2.0.04\n", - " ./components/ww3\n", - " clean sandbox, on ww3_181001\n", - "2024-06-06 13:50:57: case.build success \n", - " ---------------------------------------------------\n", - "2024-06-06 13:53:31: case.submit starting \n", - " ---------------------------------------------------\n", - "2024-06-06 13:53:46: case.submit success case.run:4743615.desched1, case.st_archive:4743616.desched1\n", - " ---------------------------------------------------\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1189f3e-7266-492a-aa42-664a968227e3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/cesm_workflow/create_clone.ipynb b/notebooks_copy/basics/cesm_workflow/create_clone.ipynb deleted file mode 100644 index e53a335a3..000000000 --- a/notebooks_copy/basics/cesm_workflow/create_clone.ipynb +++ /dev/null @@ -1,109 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Create Clone" - ] - }, - { - "cell_type": "markdown", - "id": "0848a34c-67f8-4727-b8e5-8e9246927d2b", - "metadata": {}, - "source": [ - "The tool that clones a new case based on an existing case is `create_clone`. This tool is located in the `$SRCROOT` directory under the `cime/scripts` directory." - ] - }, - { - "cell_type": "markdown", - "id": "aa0ffc44-9f53-4b02-bdaf-4035ff768cdd", - "metadata": {}, - "source": [ - "Things that **are** copied into the new case are:\n", - "- Most `env_*.xml` settings (not all!)\n", - "- user_nl_xxx files\n", - "- Macros\n", - "- SourceMods\n", - "- Batch system files\n", - "- README.case\n", - "\n", - "Things that **are not** copied into the new case are:\n", - "- Logs\n", - "- Timing files" - ] - }, - { - "cell_type": "markdown", - "id": "76ae1e16-f343-4149-a82b-a75f5bb94fa9", - "metadata": {}, - "source": [ - "## Command Syntax" - ] - }, - { - "cell_type": "markdown", - "id": "404c5149-3a66-4677-9dc5-19f4f2d0a08b", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Go to the `cime/scripts` directory in the CESM code\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "```\n", - " \n", - "
\n", - " \n", - "Example of basic `./create_clone` syntax to create CASE2:
\n", - " \n", - "```\n", - "./create_clone --clone /glade/work/$USER/cases/CASE1 --case /glade/work/$USER/cases/CASE2\n", - "```\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "d5d86794-b242-4f85-9b9c-4e1fcf235d46", - "metadata": {}, - "source": [ - "**Notes:**\n", - "- Do not use `cp` or `cp -R` to copy case directories to new experiments. \n", - "- Document changes in CaseStatus and README.case to keep track of the changes you make.\n", - "- If you are making many cases with only a small changes, you can script the steps into a python or shell script of your choice to make the process more automated and less prone to human error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "839f45aa-3c6b-4170-b913-a6eb9da8feb4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb b/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb deleted file mode 100644 index 0511ca94b..000000000 --- a/notebooks_copy/basics/cesm_workflow/create_newcase.ipynb +++ /dev/null @@ -1,533 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Create New Case" - ] - }, - { - "cell_type": "markdown", - "id": "efcaf0cc-2b3f-49de-8c4e-953464c295ef", - "metadata": {}, - "source": [ - "The tool that generates a new case is `create_newcase`. This tool is located in the `$SRCROOT` directory under the `cime/scripts` directory. \n", - "\n", - "After running `create_newcase`, a `$CASEROOT` directory is created. The directory `$CASEROOT` contains the scripts needed for the following steps (`case.setup`, `case.build` and `case.submit`)" - ] - }, - { - "cell_type": "markdown", - "id": "25ef9ed6-7dea-493f-8ef5-3f1de625a31c", - "metadata": {}, - "source": [ - "![CESM create_newcase](../../../images/basics/CESM2_create_newcase.png)\n", - "*

Figure: Detailed view of the location of create_newcase

*\n", - "\n", - "For the current tutorial on derecho, the paths are:\n", - "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", - "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "af956bea-9166-48bf-99df-02f744bd792d", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "557d4028-7176-4e2a-8a90-7febf2de37ef", - "metadata": {}, - "source": [ - "## Command Syntax" - ] - }, - { - "cell_type": "markdown", - "id": "40696acf-8c56-4c1b-8fab-f9d1b1d3b2d1", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Go to the `cime/scripts` directory in the CESM code
\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "```\n", - " \n", - "
\n", - " \n", - "Example of basic `./create_newcase` syntax:
\n", - " \n", - "```\n", - "./create_newcase --case /glade/work/$USER/cases/CASE --res RES --compset COMPSET\n", - "```\n", - " \n", - "
\n", - "\n", - "NOTE: Do not enter the example above at the command line. You will create your first case in the Exercise at the end of this section.
" - ] - }, - { - "cell_type": "markdown", - "id": "3923b808-2496-4f4d-b5a1-8b4cd44466ba", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - " \n", - "```\n", - "Compset longname is 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - "Compset specification file is /glade/work/$USER/code/cesm2.1.1/cime/../cime_config/config_compsets.xml\n", - "Compset forcing is 1850\n", - "Com forcing is Biogeochemistry intercomponent with diagnostic CO2\n", - "ATM component is CAM cam6 physics:\n", - "LND component is clm5.0:BGC (vert. resol. CN and methane) with prognostic crop:\n", - "ICE component is Sea ICE (cice) model version 5\n", - "OCN component is POP2 EcosystemAbiotic DIC/DIC14\n", - "ROF component is MOSART: MOdel for Scale Adaptive River Transport\n", - "GLC component is cism2 (default, higher-order, can run in parallel):cism ice evolution turned off (this is the standard configuration unless you're explicitly interested in ice evolution):\n", - "WAV component is Wave Watch\n", - "ESP component is \n", - "Pes specification file is /glade/work/$USER/code/cesm2.1.1/cime/../cime_config/config_pes.xml\n", - "Compset specific settings: name is RUN_STARTDATE and value is 0001-01-01\n", - "Compset specific settings: name is RUN_REFDATE and value is 0301-01-01\n", - "Compset specific settings: name is RUN_TYPE and value is hybrid\n", - "Compset specific settings: name is RUN_REFCASE and value is b.e20.B1850.f19_g17.release_cesm2_1_0.020\n", - "Compset specific settings: name is CLM_NAMELIST_OPTS and value is use_init_interp=.true.\n", - "Machine is derecho\n", - "Pes setting: grid match is a%1.9x2.5.+l%1.9x2.5.+oi%gx1 \n", - "Pes setting: machine match is derecho \n", - "Pes setting: compset_match is CAM.+CLM.+CICE.+POP.+ \n", - "Pes setting: grid is a%1.9x2.5_l%1.9x2.5_oi%gx1v7_r%r05_g%gland4_w%ww3a_m%gx1v7 \n", - "Pes setting: compset is 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD \n", - "Pes setting: tasks is {'NTASKS_ATM': 288, 'NTASKS_ICE': 108, 'NTASKS_CPL': 288, 'NTASKS_LND': 144, 'NTASKS_WAV': 36, 'NTASKS_ROF': 40, 'NTASKS_OCN': 288, 'NTASKS_GLC': 36} \n", - "Pes setting: threads is {'NTHRDS_ICE': 1, 'NTHRDS_ATM': 1, 'NTHRDS_ROF': 1, 'NTHRDS_LND': 1, 'NTHRDS_WAV': 1, 'NTHRDS_OCN': 1, 'NTHRDS_CPL': 1, 'NTHRDS_GLC': 1} \n", - "Pes setting: rootpe is {'ROOTPE_OCN': 288, 'ROOTPE_LND': 0, 'ROOTPE_ATM': 0, 'ROOTPE_ICE': 144, 'ROOTPE_WAV': 252, 'ROOTPE_CPL': 0, 'ROOTPE_ROF': 0, 'ROOTPE_GLC': 0} \n", - "Pes setting: pstrid is {} \n", - "Pes other settings: {}\n", - "Pes comments: about 12ypd expected\n", - " Compset is: 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD \n", - " Grid is: a%1.9x2.5_l%1.9x2.5_oi%gx1v7_r%r05_g%gland4_w%ww3a_m%gx1v7 \n", - " Components in compset are: ['cam', 'clm', 'cice', 'pop', 'mosart', 'cism', 'ww3', 'sesp', 'drv', 'dart'] \n", - "\n", - "This is a CESM scientifically supported compset at this resolution.\n", - "\n", - "Using project from .cesm_proj: P93300641\n", - "No charge_account info available, using value from PROJECT\n", - "Using project from .cime/config: P93300641\n", - "cesm model version found: cesm2.1_tutorial2022\n", - "Batch_system_type is pbs\n", - "job is case.run USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", - "job is case.st_archive USER_REQUESTED_WALLTIME None USER_REQUESTED_QUEUE None WALLTIME_FORMAT %H:%M:%S\n", - " Creating Case directory /glade/work/$USER/cases/b1850.basics\n", - "\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "87ae84f9-449b-4051-9dc9-9c53bcb8ac57", - "metadata": {}, - "source": [ - "**Notes:**\n", - "- For all user scripts you can run the script name followed by the `--h` or `--help` argument to see help documentation for that script and a list of all command line arguments for that script.\n", - "- Double dashes `--` are required with command line arguments\n" - ] - }, - { - "cell_type": "markdown", - "id": "d7064891-5a5a-41f2-9b3b-d4acb231a2ee", - "metadata": {}, - "source": [ - "## Command Arguments" - ] - }, - { - "cell_type": "markdown", - "id": "b89a4484-e32c-413a-bfec-4051d666a8a8", - "metadata": {}, - "source": [ - "![Create New Case Command Image](../../../images/basics/CESM2_Create_Newcase_Command.png)" - ] - }, - { - "cell_type": "markdown", - "id": "122cb2ea-b34d-472b-8790-9d3950d8eea4", - "metadata": {}, - "source": [ - "### Casename" - ] - }, - { - "cell_type": "markdown", - "id": "0ea36beb-fe98-4289-a15e-37c21ff2f4ad", - "metadata": {}, - "source": [ - "`--case` is the argument that specifies the **name** and **location** of the case being created. In the example above, the casename is `CASE` and the location of the case is `/glade/work/$USER/cases/`. Note that if a path preceding the casename is not specified, then the case is created as a subdirectory of the `$CIMEROOT/scripts` directory.\n" - ] - }, - { - "cell_type": "markdown", - "id": "bb90ccd2-b4d4-4ab7-96ec-749a596f25ae", - "metadata": {}, - "source": [ - "- The path `/glade/work/$USER/cases/CASE` is your workspace `CASEROOT`, as described in the [workspaces section](https://ncar.github.io/CESM-Tutorial/notebooks/basics/cesm_workspaces.html#workspace-overview)." - ] - }, - { - "cell_type": "markdown", - "id": "8f080dc7-3bfb-4e61-b112-af418f52018a", - "metadata": {}, - "source": [ - "In this tutorial we will use fairly simple names for the cases you create, build, and run. However, most CESM experiments have much longer casenames that provide information about the experiment at a glance. Knowing the CESM case naming conventions will help you navigate CESM community experiments. Information about the CESM naming is available at:" - ] - }, - { - "cell_type": "markdown", - "id": "2216d55d-6705-4d1d-9734-eddfc225420d", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM case naming conventions](https://www.cesm.ucar.edu/models/cesm2/naming-conventions)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "83b752d0-34c8-42d0-ba5b-efee17ab3386", - "metadata": {}, - "source": [ - "### Resolution" - ] - }, - { - "cell_type": "markdown", - "id": "6e3f9220-e60f-456b-93a2-09c4d52f5e22", - "metadata": {}, - "source": [ - "`--res` is the argument that specifies the **model resolution or grid**. In the example above, the resolution is `RES`. Each model resolution can be specified with the `--res` argument by its alias or its full long name. However, the long name conventions are generally quite lengthy, so it is useful if you become familiar with using the alias version. Because the atmosphere and land share a grid and the ocean and sea ice share a grid only two grids are specified by the alias using the following format: `atm/lnd_ocn/ice`." - ] - }, - { - "cell_type": "markdown", - "id": "4c69afd3-925c-42c8-a54e-017336ef03c6", - "metadata": {}, - "source": [ - "![Create New Case Resolution Image](../../../images/basics/CESM2_Create_Newcase_Resolution.png)" - ] - }, - { - "cell_type": "markdown", - "id": "8143f8da-726e-4db9-99af-ce75e19c4810", - "metadata": {}, - "source": [ - "#### CESM2 Supported Grid Definitions" - ] - }, - { - "cell_type": "markdown", - "id": "83f7274d-95fe-4be3-bf08-a6964eebef10", - "metadata": {}, - "source": [ - " Link for CESM Supported Grids: \n", - "
\n", - "[http://www.cesm.ucar.edu/models/cesm2/config/grids.html](http://www.cesm.ucar.edu/models/cesm2/config/grids.html)\n" - ] - }, - { - "cell_type": "markdown", - "id": "0ffff371-8ee8-45c8-b452-b225a4464b21", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Cime Tool for listing configurations:

\n", - "\n", - "```\n", - "CIMEROOT/scripts/query_config --grids --long\n", - "```\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "885ef623-f80d-4173-a8f8-cf4232217549", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - " \n", - "```\n", - " =========================================\n", - " GRID naming convention\n", - " =========================================\n", - " The notation for the grid longname is\n", - " a%name_l%name_oi%name_r%name_m%mask_g%name_w%name\n", - " where\n", - " a% => atm, l% => lnd, oi% => ocn/ice, r% => river, m% => mask, g% => glc, w% => wav\n", - "\n", - " Supported out of the box grid configurations are given via alias specification in\n", - " the file \"config_grids.xml\". Each grid alias can also be associated with the\n", - " following optional attributes\n", - "\n", - " compset (Regular expression for compset matches that are required for this grid)\n", - " not_compset (Regular expression for compset matches that are not permitted this grid)\n", - "\n", - " Using the alias and the optional \"compset\" and \"not_compset\" attributes a grid longname is created\n", - " Note that the mask is for information only - and is not an attribute of the grid\n", - " By default, if the mask is not specified below, it will be set to the ocnice grid\n", - " And if there is no ocnice grid (such as for single column, the mask is null since it does not mean anything)\n", - " \n", - " -------------------------------------------------------------\n", - " default component grids:\n", - "\n", - " component compset value \n", - " -------------------------------------------------------------\n", - " atm SATM null \n", - " lnd SLND null \n", - " ocnice SOCN null \n", - " rof SROF null \n", - " rof DWAV rx1 \n", - " rof RTM r05 \n", - " rof MOSART r05 \n", - " rof DROF rx1 \n", - " rof DROF%CPLHIST r05 \n", - " rof XROF r05 \n", - " glc SGLC null \n", - " glc CISM1 gland5UM \n", - " glc CISM2 gland4 \n", - " glc XGLC gland4 \n", - " wav SWAV null \n", - " wav DWAV ww3a \n", - " wav WW3 ww3a \n", - " wav XWAV ww3a \n", - " -------------------------------------------------------------\n", - "\n", - " alias: g16_g16 (only for compsets that are DATM.+DROF )\n", - " non-default grids are: atm:gx1v6 lnd:gx1v6 ocnice:gx1v6 \n", - " \n", - " gx1v6 is displaced Greenland pole v6 1-deg grid: with domain file(s): \n", - " $DIN_LOC_ROOT/share/domains/domain.ocn.gx1v6.090206.nc (only for grid match: atm|lnd)\n", - " $DIN_LOC_ROOT/share/domains/domain.ocn.gx1v6.090206.nc (only for grid match: ocnice) \n", - "\n", - " alias: g17_g17 (only for compsets that are DATM.+DROF )\n", - " non-default grids are: atm:gx1v7 lnd:gx1v7 ocnice:gx1v7 \n", - " \n", - " gx1v7 is displaced Greenland pole 1-deg grid with Caspian as a land feature: with domain file(s): \n", - " $DIN_LOC_ROOT/share/domains/domain.ocn.gx1v7.151008.nc (only for grid match: atm|lnd)\n", - " $DIN_LOC_ROOT/share/domains/domain.ocn.gx1v7.151008.nc (only for grid match: ocnice) \n", - "\n", - "...\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "82faa8c6-88d6-41ba-a063-52939e664391", - "metadata": {}, - "source": [ - "### Compset" - ] - }, - { - "cell_type": "markdown", - "id": "0ccf426a-57d0-4447-9eb7-c53c18544148", - "metadata": {}, - "source": [ - "`--compset` is the argument that specifies the **component set**. In the example above, the compset is `COMPSET`. The compset specifies the component models and if they are active or not, forcing scenarios, and physics options for the models. As above, the `--compset` can be specified by its alias or its full long name. However, the long name conventions are generally quite lengthy, so it is useful if you become familiar with using the alias version." - ] - }, - { - "cell_type": "markdown", - "id": "160dd746-6ec4-4ea2-85f1-92bbbdd5647d", - "metadata": {}, - "source": [ - "![Create New Case Compset Image](../../../images/basics/CESM2_Create_Newcase_Compset.png)" - ] - }, - { - "cell_type": "markdown", - "id": "ee1cde34-5742-48c3-964b-6362d32288d0", - "metadata": {}, - "source": [ - "**Note**:\n", - "- Compsets are defined by different model components (active or data) and cime.\n", - "- Some compsets are scientifically supported and/or tested, while some are only defined. The CESM project will not provide comprehensive support for compsets that are only defined.\n", - "- Compsets determine which grid is required." - ] - }, - { - "cell_type": "markdown", - "id": "0d522ed3-098c-4194-be32-f219be984248", - "metadata": {}, - "source": [ - "#### CESM2 Supported Compsets" - ] - }, - { - "cell_type": "markdown", - "id": "e8b7943e-0be5-4feb-9b3f-cb8d4fe4aac0", - "metadata": {}, - "source": [ - " Link for CESM2 Supported Compsets: \n", - "
\n", - "[http://www.cesm.ucar.edu/models/cesm2/config/compsets.html](http://www.cesm.ucar.edu/models/cesm2/config/compsets.html)" - ] - }, - { - "cell_type": "markdown", - "id": "e91dff67-7992-48d2-afba-c0acb5528fb8", - "metadata": {}, - "source": [ - "
\n", - "Cime Tool for listing configurations:

\n", - "\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./query_config --compsets\n", - "```\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "65d58f32-51ff-4dfa-a52d-edc182e6cae4", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - " \n", - "```\n", - "Active component: allactive\n", - " --------------------------------------\n", - " Compset Alias: Compset Long Name \n", - " --------------------------------------\n", - " B1850 : 1850_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " B1850cmip6 : 1850_CAM60_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BMH6kcmip6 : midH_CAM60_CLM50%BGC_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BLIG127kcmip6 : 127ka_CAM60_CLM50%BGC_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BCO2x4cmip6 : 1850_CAM60%4xCO2_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " B1PCTcmip6 : 1850_CAM60%1PCT_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BW1850 : 1850_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BW1850cmip6 : 1850_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWsc1850 : 1850_CAM60%WCSC_CLM50%BGC-CROP_CICE_POP2%ECO_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWsc1850smyle : 1850_CAM60%WCSC%SMYLE_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BWscHIST : HIST_CAM60%WCSC_CLM50%BGC-CROP_CICE_POP2%ECO_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWCO2x4cmip6 : 1850_CAM60%WCTS%4xCO2_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BW1PCTcmip6 : 1850_CAM60%WCTS%1PCT_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWmaCO2x4cmip6 : 1850_CAM60%WCCM%4xCO2_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWma1PCTcmip6 : 1850_CAM60%WCCM%1PCT_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP126cmip6 : SSP126_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP245cmip6 : SSP245_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP370cmip6 : SSP370_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP585cmip6 : SSP585_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP534oscmip6 : SSP534_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP126 : SSP126_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP245 : SSP245_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP370 : SSP370_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP585 : SSP585_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP534os : SSP534_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWHIST : HIST_CAM60%WCTS_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP585extcmip6 : SSP585EXT_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWSSP534osextcmip6 : SSP534EXT_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWHISTcmip6 : HIST_CAM60%WCTS_CLM50%BGC-CROP-CMIP6WACCMDECK_CICE%CMIP6_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWma1850 : 1850_CAM60%WCCM_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BWmaHIST : HIST_CAM60%WCCM_CLM50%BGC-CROP_CICE_POP2%ECO%NDEP_MOSART_CISM2%NOEVOLVE_WW3\n", - " BHIST : HIST_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BHISTcmip6 : HIST_CAM60_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BHISTsmbb : HIST_CAM60%SMBB_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BSSP370smbb : SSP370_CAM60%SMBB_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BSSP370smbbext : SSP370EXT_CAM60%SMBB_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BSSP245smbb : SSP245_CAM60%SMBB_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BC5L45BGC : 2000_CAM50_CLM45%BGC_CICE_POP2_MOSART_SGLC_SWAV\n", - " B1850L45BGCR : 1850_CAM60_CLM45%BGC_CICE_POP2_RTM_SGLC_SWAV\n", - " B1850C5L45BGC : 1850_CAM50_CLM45%BGC_CICE_POP2_MOSART_SGLC_SWAV\n", - " BSSP585 : SSP585_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BSSP126 : SSP126_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BSSP245 : SSP245_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BSSP370 : SSP370_CAM60_CLM50%BGC-CROP_CICE_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - " BSSP585cmip6 : SSP585_CAM60_CLM50%BGC-CROP-CMIP6DECK_CICE%CMIP6_POP2%ECO%ABIO-DIC_MOSART_CISM2%NOEVOLVE_WW3_BGC%BDRD\n", - "\n", - "...\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "61581a4e-86f4-44e4-92d9-d29aefa9ed8b", - "metadata": {}, - "source": [ - "### Machines" - ] - }, - { - "cell_type": "markdown", - "id": "5e4e5466-8955-4f61-84f7-6228b1aac32b", - "metadata": {}, - "source": [ - "The argument `--mach` is not required on CESM supported machines, but is required on other machines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94b79d8f-2da5-4a3f-b9b9-0cbb45896c0e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/cesm_workflow/model_output.ipynb b/notebooks_copy/basics/cesm_workflow/model_output.ipynb deleted file mode 100644 index feebad7b9..000000000 --- a/notebooks_copy/basics/cesm_workflow/model_output.ipynb +++ /dev/null @@ -1,78 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "683d9e77-d700-4f75-b3f0-eb813657351b", - "metadata": { - "tags": [] - }, - "source": [ - "# Model Output\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "d8e6525b-31d9-450d-a3b9-e3b435fc231a", - "metadata": {}, - "source": [ - "If the model run is successful, the CESM netcdf output history files are automatically moved to a short term archive (`$DOUT_S_ROOT`) by `case.st_archive`." - ] - }, - { - "cell_type": "markdown", - "id": "d62c7e4b-86e0-40ec-8dfa-5744c3884bc0", - "metadata": {}, - "source": [ - "![Case Submit Image](../../../images/basics/CESM2_short_term_archive.png)\n", - "\n", - "\n", - "For the current tutorial on derecho, the paths are:\n", - "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", - "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", - "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", - "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` \n", - "- `$DOUT_S_ROOT` =`/glade/derecho/scratch/$USER/archive/$CASE` " - ] - }, - { - "cell_type": "markdown", - "id": "9be4ca8e-8483-48b1-80e7-821b8627eead", - "metadata": {}, - "source": [ - "**Notes:**\n", - "- If a model run was unsuccessful the output remains in the Run Directory (`$RUNDIR`) and the short term archive is not created.\n", - "- Both `$RUNDIR` and `$DOUT_S_ROOT` are in the NCAR HPC scratch space. This space is scrubbed and files deleted after a number of days. Thus, it is a good idea to move your model output files from the short term archive to a more permanent location as soon as you are able." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b026c623-617e-42d1-a3d1-4f6fce55bd5f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/cesm_workspaces.ipynb b/notebooks_copy/basics/cesm_workspaces.ipynb deleted file mode 100644 index 2c00a02dc..000000000 --- a/notebooks_copy/basics/cesm_workspaces.ipynb +++ /dev/null @@ -1,180 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Workspaces" - ] - }, - { - "cell_type": "markdown", - "id": "15d62f62-74a4-4936-8283-4d0226dcedab", - "metadata": {}, - "source": [ - "Utilizing the CESM model requires knowledge of the locations of the different parts of the model. The CESM model consists of five main directory tree areas that make up the **CESM Workspace**. \n", - "\n", - "One of the key elements of the Tutorial will be learning where each of these workspace elements are located and how to use them to setup, build, run and analyze the output of model simulations.\n", - "\n", - "\n", - "There are 5 main directories (see figure, below) you will need to navigate:\n", - "1) The path to your **CESM source code**. This is referred to as `$SRCROOT` (or `$CESMCODE`) and contains the `$CIMEROOT`.\n", - "2) The path to the **input data**, referred to as `$CESMDATA`.\n", - "3) The path to your experiment **case directories**. This is referred to as `$CASEROOT`.\n", - "4) The path to your **build** and **run directories**, referred to as the `$EXEROOT` and `$RUNDIR`, respectively.\n", - "5) The path to your **archived model output**, referred to as `$DOUT_S_ROOT`.\n", - "\n", - "We will investigate these in more detail in the remainder of the Tutorial.\n", - "\n", - "**Note on Terminology**
\n", - "- Paths are the directions to the location of different pieces of your workspace.\n", - "- Roots are saved paths that point to a specific piece of the model." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6b1d1b2e-9c93-4e47-8f4d-d51b96cdab7b", - "metadata": {}, - "source": [ - "## Workspace Overview\n", - "\n", - "![CESM workspace](../../images/basics/CESM2_Workspaces_Detail.png)\n", - "*

Figure: Overview of the CESM2 Workspace Directories

*\n", - "\n", - "For the current tutorial on derecho, the paths are:\n", - "- `$SRCROOT` = `/glade/work/$USER/code/my_cesm_code`\n", - "- `$DIN_LOC_ROOT` = `/glade/campaign/cesm/cesmdata/cseg/inputdata`\n", - "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`\n", - "- `$CIME_OUTPUT_ROOT` = `/glade/derecho/scratch/$USER`\n", - "- `$EXEROOT` =`/glade/derecho/scratch/$USER/$CASE/bld` \n", - "- `$RUNDIR` =`/glade/derecho/scratch/$USER/$CASE/run` \n", - "- `$DOUT_S_ROOT` =`/glade/derecho/scratch/$USER/archive/$CASE` \n" - ] - }, - { - "cell_type": "markdown", - "id": "40962c56-fb4d-4862-95a1-8d5c98b82720", - "metadata": {}, - "source": [ - "Each of these directories contains sub-directories. You will need to learn to navigate between these directories." - ] - }, - { - "cell_type": "markdown", - "id": "19acc3aa-fb98-44af-966f-16ca49710135", - "metadata": {}, - "source": [ - "____\n", - "\n", - "## Setting your workspaces\n", - "\n", - "You will need to create a directory for the CESM Code and a directory when you will keep all your cases. \n", - "In the CESM jargon, a `case` refers to a specific instance of a model simulation." - ] - }, - { - "cell_type": "markdown", - "id": "1c6bda59-5302-4454-88bb-dfe5c4bd3209", - "metadata": {}, - "source": [ - "- ### Create CESM Code Directory\n", - "Create a workspace location where you can put your CESM code on the derecho glade file system. In all exercises in this tutorial `$USER` is a placeholder and you should use your NCAR HPC login name when completing the exercise.\n" - ] - }, - { - "cell_type": "markdown", - "id": "b281cda4-e1c0-458b-98df-a1cf34c45a72", - "metadata": {}, - "source": [ - "
\n", - "\n", - "```\n", - "cd /glade/work/$USER\n", - "mkdir code\n", - "```\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "76f14c71-ffae-4d67-b496-d6b075d4652d", - "metadata": {}, - "source": [ - "- ### Create Cases Workspace Location\n" - ] - }, - { - "cell_type": "markdown", - "id": "667ca15d-2ef7-40b1-be89-3abac566d472", - "metadata": {}, - "source": [ - "Create a workspace location where all your cases from this tutorial will be located on the derecho glade file system." - ] - }, - { - "cell_type": "markdown", - "id": "37b8b7c6-9152-47d8-af76-7814006a5718", - "metadata": {}, - "source": [ - "
\n", - "\n", - "```\n", - "cd /glade/work/$USER\n", - "mkdir cases\n", - "```\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "3a779263-cb21-443e-a3d8-0bd4a0e267e8", - "metadata": {}, - "source": [ - "- ### Other Workspace Locations\n" - ] - }, - { - "cell_type": "markdown", - "id": "4e972986-5267-43a8-917b-1e36c7117323", - "metadata": {}, - "source": [ - "The `INPUTDATA` directory does not need to be set up directly on NCAR HPC as these data are in a common directory.\n", - "\n", - "We will not directly set up your `Build/Run` or `Archive` workspace locations. These directories will be automatically created when you create a CESM case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0ddf488-72e4-40f8-a797-cc2d1b958208", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/code/cesm_code_explore.ipynb b/notebooks_copy/basics/code/cesm_code_explore.ipynb deleted file mode 100644 index fe18a4f87..000000000 --- a/notebooks_copy/basics/code/cesm_code_explore.ipynb +++ /dev/null @@ -1,347 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Explore CESM Code\n" - ] - }, - { - "cell_type": "markdown", - "id": "285f4d12-6411-4f57-aa81-09ca1106a380", - "metadata": {}, - "source": [ - "Having downloaded the CESM Code component to your workspace we are now going to briefly locate and explore the different code directories within it. To get some familiarity with this area we are going to use the terminal window to explore the directories illustrated below this top level location. Specifically in this exercise we will:\n", - "\n", - "- Step 1. Locate your CESM Code area on the glade file system.\n", - "- Step 2. Examine the CIME Directory area.\n", - "- Step 3. Examine the Model Components area.\n", - "- Step 4. Look at the Community Atmosphere Model (CAM) Component Model.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "8a064b52-fc3a-4efd-93b8-ac7d96538e36", - "metadata": {}, - "source": [ - "![CESM directories](../../../images/basics/CESM2_Workspaces_Code.png)\n", - "*

Figure: Detailed view of the CESM2 Code Directory

*\n", - "\n", - "For the current tutorial on derecho, the paths are: \n", - "- `$SRCROOT = /glade/work/$USER/code/my_cesm_code` " - ] - }, - { - "cell_type": "markdown", - "id": "44f8925f-4703-44d9-baa7-5ac59da0ede1", - "metadata": {}, - "source": [ - "## Step 1. Locate Your Code Workspace Area\n", - "\n", - "Returning to your terminal window we will now locate your `$SRCROOT` location on the NCAR HPC file system.\n", - "\n", - "
\n", - "Locate your current directory:
\n", - "\n", - "```\n", - "pwd\n", - "```\n", - "
\n", - " \n", - "Expected output:
\n", - "\n", - "```\n", - "/glade/work/$USER/code/my_cesm_code\n", - "```\n", - "
\n", - "\n", - " \n", - "If you have not done anything since the last exercise then you will already be in the `$SRCROOT` location. If your output doesn't match the path above, then change the current directory to your Workspace Code directory:
\n", - "\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code\n", - "```\n", - "
\n", - " \n", - "List the contents of the Workspace Code directory with the ls long option:
\n", - "\n", - "```\n", - "ls -l\n", - "```\n", - "
\n", - " \n", - "Expected output:
\n", - "\n", - "```\n", - "total 2119\n", - "-rw-r--r-- 1 $USER ncar 2116708 Jun 3 15:46 ChangeLog\n", - "-rw-r--r-- 1 $USER ncar 2433 Jun 3 15:46 ChangeLog_template\n", - "drwxr-xr-x 10 $USER ncar 4096 Jun 4 09:32 cime\n", - "drwxr-xr-x 4 $USER ncar 4096 Jun 3 15:46 cime_config\n", - "drwxr-xr-x 10 $USER ncar 4096 Jun 4 09:32 components\n", - "-rwxr-xr-x 1 $USER ncar 2475 Jun 3 15:46 describe_version\n", - "drwxr-xr-x 3 $USER ncar 4096 Jun 3 15:46 doc\n", - "-rw-r--r-- 1 $USER ncar 1409 Jun 3 15:46 Externals.cfg\n", - "-rw-r--r-- 1 $USER ncar 5498 Jun 3 15:46 LICENSE.txt\n", - "drwxr-xr-x 5 $USER ncar 4096 Jun 3 15:46 manage_externals\n", - "-rw-r--r-- 1 $USER ncar 8904 Jun 3 15:46 README.rst\n", - "```\n", - "
\n", - "\n", - " \n", - "
\n", - "\n", - "**If the output of these commands does not match for your CESM Workspace then you will need to ask for help in configuring them. This area will be used for the remainder of the Tutorial so it is important to get this step right.**" - ] - }, - { - "cell_type": "markdown", - "id": "d3d8846b-cfa4-4b89-b262-39892ebdb52a", - "metadata": {}, - "source": [ - "## Step 2. Examine the CIME Directory\n", - "\n", - "In your terminal window we will now move to the CIME directory and examine the contents. Remember that CIME is the Common Infrastructure for Modeling the Earth. It is a python-based framework that integrates the various components of the CESM into a single modeling framework. The CIME Scripts directory will be the location where you will start the Workflow of creating a new case through the rest of the Tutorial.\n", - "\n", - "
\n", - "Change into the CIME Directory:
\n", - "\n", - "```\n", - "cd cime\n", - "```\n", - "
\n", - " \n", - "List the contents of the CIME Directory:
\n", - "\n", - "```\n", - "ls -l\n", - "```\n", - "
\n", - " \n", - "Expected output:
\n", - "\n", - "```\n", - "total 648\n", - "-rw-r--r-- 1 $USER ncar 618865 Jun 4 09:32 ChangeLog\n", - "-rw-r--r-- 1 $USER ncar 421 Jun 4 09:32 ChangeLog_template\n", - "-rw-r--r-- 1 $USER ncar 4000 Jun 4 09:32 CMakeLists.txt\n", - "drwxr-xr-x 5 $USER ncar 4096 Jun 4 09:32 config\n", - "-rw-r--r-- 1 $USER ncar 4670 Jun 4 09:32 CONTRIBUTING.md\n", - "drwxr-xr-x 3 $USER ncar 4096 Jun 4 09:32 doc\n", - "-rw-r--r-- 1 $USER ncar 444 Jun 4 09:32 index.html\n", - "-rw-r--r-- 1 $USER ncar 2461 Jun 4 09:32 LICENSE.TXT\n", - "-rw-r--r-- 1 $USER ncar 1747 Jun 4 09:32 README.md\n", - "drwxr-xr-x 7 $USER ncar 4096 Jun 4 09:32 scripts\n", - "drwxr-xr-x 8 $USER ncar 4096 Jun 4 09:32 src\n", - "drwxr-xr-x 7 $USER ncar 4096 Jun 4 09:32 tools\n", - "drwxr-xr-x 3 $USER ncar 4096 Jun 4 09:32 utils\n", - "```\n", - "
\n", - " \n", - "Change into the CIME Scripts directory:
\n", - "\n", - "```\n", - "cd scripts\n", - "```\n", - "
\n", - " \n", - "List the contents of the CIME Scripts directory:
\n", - "\n", - "```\n", - "ls -l\n", - "```\n", - "
\n", - " \n", - "Expected output:
\n", - "\n", - "```\n", - "total 101\n", - "-rwxr-xr-x 1 $USER ncar 5128 Jun 4 09:32 create_clone\n", - "-rwxr-xr-x 1 $USER ncar 11117 Jun 4 09:32 create_newcase\n", - "-rwxr-xr-x 1 $USER ncar 29859 Jun 4 09:32 create_test\n", - "drwxr-xr-x 2 $USER ncar 4096 Jun 4 09:32 data_assimilation\n", - "drwxr-xr-x 4 $USER ncar 4096 Jun 4 09:32 fortran_unit_testing\n", - "drwxr-xr-x 3 $USER ncar 4096 Jun 4 09:32 lib\n", - "-rwxr-xr-x 1 $USER ncar 14663 Jun 4 09:32 query_config\n", - "-rwxr-xr-x 1 $USER ncar 9067 Jun 4 09:32 query_testlists\n", - "drwxr-xr-x 5 $USER ncar 4096 Jun 4 09:32 tests\n", - "drwxr-xr-x 3 $USER ncar 4096 Jun 4 09:32 Tools\n", - "```\n", - "
\n", - "\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "37af6f2c-34ce-4de6-a13e-593e61a90f52", - "metadata": {}, - "source": [ - "## Step 3. Examine the CESM Components Area\n", - "\n", - "In your terminal window we will now move to the Components Directory and examine the contents of the individual sub models. \n", - "\n", - "![CESM directories](../../../images/basics/CESM2_Code_Components_List.png)\n", - "*

Figure: CESM2 Code Components

*\n", - "\n", - "
\n", - "Change back to the Code Workspace Location:
\n", - "\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code\n", - "```\n", - "OR\n", - "Alternatively, you can use the `cd ../..` to move up two levels in the directory hierarchy.\n", - "```\n", - "cd ../..\n", - "```\n", - "\n", - "
\n", - " \n", - "Change into the Components area of the Code Workspace:
\n", - "\n", - "```\n", - "cd components\n", - "```\n", - "
\n", - "\n", - "List the contents of the Components Directory:
\n", - "\n", - "```\n", - "ls -l\n", - "```\n", - "
\n", - " \n", - "Expected Output:
\n", - "\n", - "```\n", - "total 8\n", - "drwxr-xr-x 12 $USER ncar 4096 Jun 4 09:34 cam\n", - "drwxr-xr-x 7 $USER ncar 4096 Jun 4 09:32 cice\n", - "drwxr-xr-x 14 $USER ncar 4096 Jun 4 09:32 cism\n", - "drwxr-xr-x 12 $USER ncar 4096 Jun 4 09:31 clm\n", - "drwxr-xr-x 6 $USER ncar 4096 Jun 4 09:31 mosart\n", - "drwxr-xr-x 15 $USER ncar 4096 Jun 4 09:32 pop\n", - "drwxr-xr-x 6 $USER ncar 4096 Jun 4 09:32 rtm\n", - "drwxr-xr-x 6 $USER ncar 4096 Jun 4 09:31 ww3\n", - "```\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "aa9fb22c-de5f-41d7-ac25-07ba585ad2a9", - "metadata": {}, - "source": [ - "## Step 4. Look at the Community Atmosphere Model (CAM) Component Model\n", - "\n", - "As a final task in this exercise we are going to have a quick look at the Community Atmosphere Model (CAM) component model. At this point we are just exploring the Workspace. \n", - "\n", - "
\n", - "Change into the CAM Directory of the Components area:
\n", - "\n", - "```\n", - "cd cam\n", - "```\n", - "
\n", - "\n", - "List the contents of the CAM Directory:
\n", - "\n", - "```\n", - "ls -l\n", - "```\n", - "
\n", - " \n", - "Expected Output:
\n", - "\n", - "```\n", - "total 26\n", - "drwxr-xr-x 6 $USER ncar 4096 Jun 4 09:32 bld\n", - "drwxr-xr-x 10 $USER ncar 4096 Jun 4 09:34 chem_proc\n", - "drwxr-xr-x 4 $USER ncar 4096 Jun 4 09:32 cime_config\n", - "-rw-r--r-- 1 $USER ncar 7501 Jun 4 09:32 CODE_OF_CONDUCT.md\n", - "drwxr-xr-x 2 $USER ncar 4096 Jun 4 09:32 doc\n", - "-rw-r--r-- 1 $USER ncar 685 Jun 4 09:32 Externals_CAM.cfg\n", - "-rw-r--r-- 1 $USER ncar 1155 Jun 4 09:32 Externals.cfg\n", - "drwxr-xr-x 5 $USER ncar 4096 Jun 4 09:32 manage_externals\n", - "-rw-r--r-- 1 $USER ncar 1925 Jun 4 09:32 README_EXTERNALS\n", - "-rw-r--r-- 1 $USER ncar 282 Jun 4 09:32 README.md\n", - "drwxr-xr-x 11 $USER ncar 4096 Jun 4 09:32 src\n", - "drwxr-xr-x 4 $USER ncar 4096 Jun 4 09:32 test\n", - "drwxr-xr-x 11 $USER ncar 4096 Jun 4 09:32 tools\n", - "```\n", - "\n", - "
\n", - "\n", - "**Congratulations, you have now completed the CESM code exploration exercises!!**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50898f29-f9dd-470c-9918-a05df415b4a8", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "80d816ff-6217-4a2b-a9d0-4e5ae5e7ab21", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab049642-5bfe-4d46-92c7-c3c2ec6570fc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18a9a811-0883-410d-bfb1-d3eb16e3fd75", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5191d62a-76ee-4f90-b46b-f70ec0af9909", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/code/git_download_cesm.ipynb b/notebooks_copy/basics/code/git_download_cesm.ipynb deleted file mode 100644 index dd90e7451..000000000 --- a/notebooks_copy/basics/code/git_download_cesm.ipynb +++ /dev/null @@ -1,355 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Download CESM" - ] - }, - { - "cell_type": "markdown", - "id": "9d121236-47a7-48ef-8b67-40ef3b827178", - "metadata": {}, - "source": [ - "You are going to download the CESM code from github into your own personal CESM workspace. Follow the three steps, below, to setup your CESM Code workspace.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "458e826c-60cb-4c5a-980f-b89f47cd06d1", - "metadata": {}, - "source": [ - "
\n", - "We will use the CESM release version `release-cesm2.1.5` for use in this tutorial.

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c029d6bc-bd76-471a-8126-e888616e70d0", - "metadata": {}, - "source": [ - "Downloading the CESM code is an important step as this will be the code workspace we will be using throughout the rest of the Tutorial. **If you have problems here please ask for help**." - ] - }, - { - "cell_type": "markdown", - "id": "40257a4e-aa6d-4f12-b825-b9986cdf4daf", - "metadata": {}, - "source": [ - "## Step 1: Create CESM Code Directory" - ] - }, - { - "cell_type": "markdown", - "id": "986d1b83-f863-4294-b65b-5c9c65a8ac58", - "metadata": {}, - "source": [ - "\n", - "Create a workspace location where you can put your CESM code on the derecho glade file system. In all exercises in this tutorial `$USER` is a placeholder and you should use your NCAR HPC login name when completing the exercise." - ] - }, - { - "cell_type": "markdown", - "id": "d95a81b5-16b9-4291-9490-4058f98dcb3f", - "metadata": {}, - "source": [ - "
\n", - "\n", - "```\n", - "cd /glade/work/$USER\n", - "mkdir code\n", - "```\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e980a755-b914-46eb-b217-3fe15781aeb3", - "metadata": {}, - "source": [ - "## Step 2. Download CESM Code with Git Clone" - ] - }, - { - "cell_type": "markdown", - "id": "76ef78ca-bb75-4340-996f-04f95233c84f", - "metadata": {}, - "source": [ - "First we will change into the `Code` Workspace location then we will use the `git clone` command to download the CESM code.\n", - "\n", - "
\n", - "Change the current directory to the code workspace directory:
\n", - "\n", - "```\n", - "cd /glade/work/$USER/code\n", - "```\n", - "
\n", - " \n", - "Download the cesm code to your code workspace directory as `my_cesm_code`:
\n", - "```\n", - "git clone https://github.com/ESCOMP/CESM.git my_cesm_code\n", - "cd my_cesm_code\n", - "git checkout release-cesm2.1.5\n", - "```\n", - " \n", - "Note that while we named the downloaded code `my_cesm_code`, you can name it anything you want and for future downloads outside this tutorial it can be helpful to label the code for the release you are using for your experiments.\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b9211e68-8991-4f08-a6fd-ebd009cafe75", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - " \n", - "```\n", - "Note: switching to 'release-cesm2.1.5'.\n", - "\n", - "You are in 'detached HEAD' state. You can look around, make experimental\n", - "changes and commit them, and you can discard any commits you make in this\n", - "state without impacting any branches by switching back to a branch.\n", - "\n", - "If you want to create a new branch to retain commits you create, you may\n", - "do so (now or later) by using -c with the switch command. Example:\n", - "\n", - " git switch -c \n", - "\n", - "Or undo this operation with:\n", - "\n", - " git switch -\n", - "\n", - "Turn off this advice by setting config variable advice.detachedHead to false\n", - "\n", - "HEAD is now at 7a6c5b0 Update for cesm2.1.5-rc.01\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "97b726d5-99e6-450d-b494-fd9ddc917284", - "metadata": {}, - "source": [ - "### Versions" - ] - }, - { - "cell_type": "markdown", - "id": "cdeef63a-d107-46d2-a89b-d233c8ed13c2", - "metadata": {}, - "source": [ - "One thing you will need to determine is what version of the CESM code you should use. Some things to consider when making that decision include: \n", - "- What versions of the code are supported scientifically or technically?\n", - "- Things you may want to consider are what sort of model features do you need and what versions of the model are these available in? \n", - "- If you want to compare your sensitivity experiment against a community experiment (e.g. CESM2-LE) then you should use the same version of the code.\n", - "\n", - "\n", - "More information can be found on the [CESM Models website](https://www.cesm.ucar.edu/models)." - ] - }, - { - "cell_type": "markdown", - "id": "d5db54cd-0018-4187-afa7-6da675ea55ba", - "metadata": {}, - "source": [ - "
\n", - " \n", - "If you want to check which other release versions are available, you can use the command:\n", - "```\n", - "git tag --list 'release-cesm2*' \n", - "``` \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "195d4a4b-514b-446e-9d2b-4c7aeae60aa2", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - " \n", - "```\n", - "release-cesm2.0.0\n", - "release-cesm2.0.1\n", - "release-cesm2.1.0\n", - "release-cesm2.1.1\n", - "release-cesm2.1.2\n", - "release-cesm2.1.3\n", - "release-cesm2.1.4\n", - "release-cesm2.1.5\n", - "release-cesm2.2.0\n", - "release-cesm2.2.1\n", - "release-cesm2.2.2\n", - "\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "88bd0742-e990-4fcf-9b40-5c8b21a9ec43", - "metadata": {}, - "source": [ - "## Step 3. Download the Component Models with checkout_externals" - ] - }, - { - "cell_type": "markdown", - "id": "615c0e7b-5e6c-4352-b1a2-3f8ac03b4b6b", - "metadata": {}, - "source": [ - "\n", - "The CESM code downloaded with the branch tain the previous step does not include all of the code and support for the component models. These are developed and maintained in other Github repositories. To include all of the component models into your code workspace you need to download them as an additional step. The checkout_externals tool is located in manage_externals directory in the `my_cesm_code` code directory.\n", - "\n", - "
\n", - "Exercise: Run the `checkout_externals` command to download all the component models:
\n", - "\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code\n", - "./manage_externals/checkout_externals\n", - "```\n", - "
\n", - "*Note: If you get a message about accepting a certificate permanently or temporarily, accept the certificate permanently. If you do not get this message, do not worry, you are still on track!\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "8341952a-2c3b-4892-a7ea-6b0407ecb78c", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for example output \n", - "\n", - "
\n", - "\n", - " where `$USER` will be replaced by your user name\n", - "```\n", - "Processing externals description file : Externals.cfg (/glade/work/$USER/code/my_cesm_code)\n", - "Checking local status of required & optional components: cam, cice, cime, cism, clm, mosart, pop, rtm, ww3, \n", - "Checking out externals: cime, cam, Processing externals description file : Externals_CAM.cfg (/glade/work/$USER/code/my_cesm_code/components/cam)\n", - "Checking out externals: chem_proc, carma, clubb, cosp2, \n", - "cice, cism, Processing externals description file : Externals_CISM.cfg (/glade/work/$USER/code/my_cesm_code/components/cism)\n", - "Checking out externals: source_cism, \n", - "clm, Processing externals description file : Externals_CLM.cfg (/glade/work/$USER/code/my_cesm_code/components/clm)\n", - "Checking out externals: fates, ptclm, \n", - "mosart, pop, Processing externals description file : Externals_POP.cfg (/glade/work/$USER/code/my_cesm_code/components/pop)\n", - "Checking out externals: cvmix, marbl, \n", - "rtm, ww3, \n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "74d14003-78cc-48e6-bcbc-622f0150dfed", - "metadata": {}, - "source": [ - "- The path `/glade/work/$USER/code/my_cesm_code` is your workspace `$SRCROOT`, as described in the [workspaces section](https://ncar.github.io/CESM-Tutorial/notebooks/basics/cesm_workspaces.html#workspace-overview)." - ] - }, - { - "cell_type": "markdown", - "id": "ff11dd07-faa2-42a2-8692-83d0895bc9f3", - "metadata": {}, - "source": [ - "**Congratulations, you have now downloaded the CESM code component of your workspace!!**" - ] - }, - { - "cell_type": "markdown", - "id": "a9a323ca-575d-4d80-ae5d-6e5402cccc75", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "You downloaded the CESM code with the instructions above. In which directory is the CESM code? How do you navigate to that directory?\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "4e18eab9-75d1-4bf0-8d60-454f0cb6ce7d", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - "Click here for the solution
\n", - "If you download CESM with the instructions above, the CESM code is located in:\n", - "\n", - "```\n", - "/glade/work/$USER/code/my_cesm_code\n", - "```\n", - "\n", - "To navigate to that directory, use the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code\n", - "```\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9630dee5-2e36-4c2f-81b6-f8abd736a73f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/code_overview.ipynb b/notebooks_copy/basics/code_overview.ipynb deleted file mode 100644 index b22cf4e8c..000000000 --- a/notebooks_copy/basics/code_overview.ipynb +++ /dev/null @@ -1,80 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8c2d8431-0e66-4597-96bb-858874da9235", - "metadata": {}, - "source": [ - "# CESM code" - ] - }, - { - "cell_type": "markdown", - "id": "56f53601-ba7c-41b0-8d9e-d56f67689734", - "metadata": {}, - "source": [ - "Before you can start running the CESM model you must first download CESM from Github and set up your Workspace. This exercise is estimated to take around 30-60 minutes, depending on your previous experience with NCAR HPC and Github." - ] - }, - { - "cell_type": "markdown", - "id": "9dcb3c4e-df54-4e99-892c-8ce99ea35410", - "metadata": {}, - "source": [ - "## Learning Objectives" - ] - }, - { - "cell_type": "markdown", - "id": "f7a8cc2d-e2eb-4f06-a173-9ca19a1dc383", - "metadata": {}, - "source": [ - "
\n", - " \n", - "- Download CESM to your workspace with Git commands.\n", - "- Explore your CESM workspace for CESM Code.\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "89b36bf1-182b-4460-af55-6f05b7a4a086", - "metadata": {}, - "source": [ - "If you are unfamiliar with logging into NCAR HPC resources, please go back to this [section](https://ncar.github.io/CESM-Tutorial/notebooks/prereqs/prereqs_overview.html#ncar-high-performance-computing-hpc) to learn more about logging into derecho.\n", - "\n", - "Github and Git software are used as development and management tools for supporting and distributing CESM. This tutorial is not intended to be a Github or Git software tutorial, so we will provide the basic git commands needed to download CESM. If you want to learn more about using these tools go to this [section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/github.html#github).\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "729b1035-407c-4a7a-8da2-97402230fc75", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/exercises/examine_history_B1850.ipynb b/notebooks_copy/basics/exercises/examine_history_B1850.ipynb deleted file mode 100644 index edc0a54f2..000000000 --- a/notebooks_copy/basics/exercises/examine_history_B1850.ipynb +++ /dev/null @@ -1,257 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Examine History Files\n" - ] - }, - { - "cell_type": "markdown", - "id": "0f737e0e-3ba8-40e4-8a82-8484830e002a", - "metadata": {}, - "source": [ - "Having successfully completed your first month of the new CESM B1850 case you will now examine the history files that have been transfered to the Archive directory. You will be using the NetCDF viewer ncview to look at the monthly average values for the single month. For this exercise you will be looking at the output from the Community Atmospheric Model (CAM) component of CESM, however the ncview tool can be applied to other components such as the Community Land Model (CLM). In this exercise we will:\n", - "\n", - "- Step 1. Explore the b1850.basics Archive directory.\n", - "- Step 2. Open the b1850.basics cam h0 in ncview.\n", - "- Step 3. Examine Average Monthly Surface Temperature.\n", - "- Step 4. Examine Average Monthly Precipitation." - ] - }, - { - "cell_type": "markdown", - "id": "736b22e8-4b55-4c33-bff9-1ab1a026931c", - "metadata": {}, - "source": [ - "## Step 1. Explore the b1850.basics Archive directory" - ] - }, - { - "cell_type": "markdown", - "id": "0c778f29-eacc-4e41-b729-1cb6e45cc65f", - "metadata": {}, - "source": [ - "The short term archive directory has a range of model component directories along with the restart (rest) and logs directories. First change directory into the archive directory and then list the contents of the restart and logs directories. " - ] - }, - { - "cell_type": "markdown", - "id": "27929215-8026-42f7-acf7-84c259f944e2", - "metadata": {}, - "source": [ - "
\n", - "Change into the b1850.basics short term archive directory:

\n", - "\n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", - "```\n", - "
\n", - "\n", - "List the logs directory:
\n", - "\n", - "```\n", - "ls -l logs\n", - "```\n", - "
\n", - " \n", - "This is where all the log files from the model run are kept.
\n", - "\n", - "List the logs directory:
\n", - "\n", - "```\n", - "ls -l rest/0001-02-01-00000\n", - "```\n", - "
\n", - "\n", - "These are the files that CESM will use to restart the model.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f1fad1f2-8f0a-4555-a40b-c226877d9c06", - "metadata": {}, - "source": [ - "## Step 2. Open the CAM h0 monthly history file" - ] - }, - { - "cell_type": "markdown", - "id": "b7a357a4-fec5-44df-8a2d-db27dd57d9fe", - "metadata": {}, - "source": [ - "You will now use `ncview` to look at the b1850.basics case CAM monthly average values for the single month you ran.\n", - "\n", - "If you are unfamiliar with `ncview` you can learn more at [this link](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#ncview). \n", - "\n", - "**Note**: you must load the `ncview` module to your environment. If you loaded the tutorial `.profile` or `.tcshrc` file it should already be loaded. Check that it is in your environment.\n", - "\n", - "```\n", - "module list\n", - "```\n", - "\n", - "If you don't see `ncview` in the list of modules, you can load it by doing:\n", - "\n", - "```\n", - "module load ncview\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "e4952d0c-ce4b-4274-9850-1f6e9e8fe7b6", - "metadata": {}, - "source": [ - "
\n", - "Change into the CAM history directory:

\n", - "\n", - "```\n", - "cd atm/hist\n", - "```\n", - "
\n", - "\n", - "List the files in the directory:
\n", - "\n", - "```\n", - "ls -l\n", - "```\n", - "
\n", - " \n", - "There should be a single file, b1850.basics.cam.h0.0001-01.nc.
\n", - "\n", - "Open the file using `ncview`:
\n", - "```\n", - "ncview b1850.basics.cam.h0.0001-01.nc &\n", - "```\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "3c1fc1c9-910a-40e6-9a5e-77306885925d", - "metadata": {}, - "source": [ - "If your X11 environment is correctly set up then the ncview tool should appear as shown below on the left. Note the ` &` at the end of the command. This puts the command into the background allowing other tasks to be run from the derecho terminal window. The view on the right shows the ncview application once the Surface Temperature variable has been selected.\n", - "\n", - "
\n", - "\n", - "![NCView App Window](../../../images/basics/NCVIEW_App_Window.png)\n", - "*

Figure: NCView App Window

*" - ] - }, - { - "cell_type": "markdown", - "id": "24a515d6-95e5-48be-b7c4-b335ec0ad88a", - "metadata": {}, - "source": [ - "## Step 3. Examine Average Monthly Surface Temperature" - ] - }, - { - "cell_type": "markdown", - "id": "0b5d30af-109a-40a8-a49f-01ac21734dfb", - "metadata": {}, - "source": [ - "Using ncview with the b1850.basics January 0001 CAM monthly average values file loaded, examine the Surface Temperature variable. Update the color palette used to plot values and change the size of the plot.\n", - "\n", - "
\n", - "Select the Surface Temperature Variable:

\n", - "\n", - "Click on the **(244) 2d vars** button shown above. This will pull up a list of all 2d vars on the b1850.basics January 0001 CAM history file. This is a long list but it is in alphabetical order. You are looking for the `TS` variable. \n", - "
\n", - "\n", - "Change the color selection from detail to bright:
\n", - "\n", - "Click on the color button marked with **detail**. This will change the color selection to **3gauss**. Clicking again will change this to **ssec** and finally **bright**.\n", - " \n", - "
\n", - " \n", - "Increase the map plot size:
\n", - "\n", - "Click on the magnify button marked with **M X3**. This will change the magnification to \"M X4\". Clicking again will change this to \"M X5\" and finally \"M X6\"\n", - " \n", - "
\n", - "\n", - "![NCView TS](../../../images/basics/NCVIEW_Jan0001_TS.png)\n", - "*

Figure: NCView b1850.basics TS Jan 0001

*\n", - "\n", - "The Surface Temperature variable is in Kelvin. A value of 273.15K represents 0°C and 32°F. There are many options to better view and explore data with ncview. Some of these will come up over the next few days of the tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "14ba2172-2188-4bca-b861-deb6bb45731a", - "metadata": {}, - "source": [ - "## Step 4. Examine Average Monthly Precipitation" - ] - }, - { - "cell_type": "markdown", - "id": "25c204c4-d1f2-4af8-93c1-d9296b5cba9c", - "metadata": {}, - "source": [ - "Using ncview with the b1850.basics January 0001 CAM monthly average values file loaded, examine the the two variables representing Convective Precipitation and Large Scale Precipitation.\n", - "\n", - "
\n", - "Exercise: Select the Convective Precipitation Variable:

\n", - "\n", - "Click on the \"(244) 2d vars\" button shown above. Find and select the PRECC variable. \n", - "
\n", - "\n", - "
\n", - "\n", - "![NCView PRECC](../../../images/basics/NCVIEW_Jan0001_PRECC.png)\n", - "*

Figure: NCView b1850.basics PRECC Jan 0001

*\n", - "\n", - "
\n", - "Exercise: Select the Convective Precipitation Variable:

\n", - "\n", - "Click on the \"(244) 2d vars\" button shown above. Find and select the PRECL variable. \n", - " \n", - "
\n", - "\n", - " \n", - "
\n", - "\n", - "![NCView PRECL](../../../images/basics/NCVIEW_Jan0001_PRECL.png)\n", - "*

Figure: NCView b1850.basics PRECL Jan 0001

*\n", - "\n", - "Precipitation in the model is recorded in m/s. To convert to mm/day we can multiply the values by 1000 mm in a meter and 86,400 seconds in a day. Can you see the differences in Convective and Large Scale Precipitation?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aefd9392-3406-466a-939e-3382755675ad", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/exercises/extra.ipynb b/notebooks_copy/basics/exercises/extra.ipynb deleted file mode 100644 index 1cfb184b4..000000000 --- a/notebooks_copy/basics/exercises/extra.ipynb +++ /dev/null @@ -1,205 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Explore Further\n" - ] - }, - { - "cell_type": "markdown", - "id": "0f737e0e-3ba8-40e4-8a82-8484830e002a", - "metadata": {}, - "source": [ - "The focus of this exercise has been to get the model running. We will learn more about how to modify runs in this section, and we will cover details in further sections of this tutorial. For now, if you have extra time, try the following actions to better familiarize yourself with the CESM code." - ] - }, - { - "cell_type": "markdown", - "id": "b86bda30-feb3-4cc4-aa4e-90bde99a8d1d", - "metadata": {}, - "source": [ - "## Explore the `CASE` directory" - ] - }, - { - "cell_type": "markdown", - "id": "19338922-d825-435d-bc21-4b6bf78c2ad9", - "metadata": {}, - "source": [ - "
\n", - " \n", - "```\n", - "cd /glade/work/$USER/cases/b1850.basics/\n", - "```\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b349d98b-d239-4760-bc52-54300f47d6c9", - "metadata": { - "tags": [] - }, - "source": [ - "Look around your case directory to become familiar with the structure. Note there are scripts (e.g. case.setup, case.submit, etc.), xml files, etc. We will delve into these in more detail in future sections of the tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "33e05339-2796-427a-867d-5891dba3803e", - "metadata": { - "tags": [] - }, - "source": [ - "### Check the CaseStatus file" - ] - }, - { - "cell_type": "markdown", - "id": "be1faf19-5e5b-48fa-ad77-89cdc01a6fc5", - "metadata": {}, - "source": [ - "All activities for the case are recorded in the CaseStatus file. By looking through the file the successful or otherwise outcome of each step of the run can be tracked." - ] - }, - { - "cell_type": "markdown", - "id": "972ea62f-d33d-49c4-a615-bb4efd603946", - "metadata": {}, - "source": [ - "
\n", - " \n", - "```\n", - "more CaseStatus \n", - "```\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e8455397-9f25-4108-ba7d-5636dd6f14df", - "metadata": {}, - "source": [ - "### Check the Job Queue and Project" - ] - }, - { - "cell_type": "markdown", - "id": "67bef6ac-3a5a-4594-9fb4-bb0aadfe9fb8", - "metadata": {}, - "source": [ - "
\n", - " \n", - "```\n", - "./xmlquery JOB_QUEUE\n", - "./xmlquery PROJECT\n", - "```\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "bc61ce2c-016d-42a1-807f-d9429b60c8b2", - "metadata": { - "tags": [] - }, - "source": [ - "## Explore the `cime/scripts` directory" - ] - }, - { - "cell_type": "markdown", - "id": "6d59eac7-637a-4ab5-aa13-e58c76402ffb", - "metadata": {}, - "source": [ - "
\n", - " \n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", - "```\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "0bf92c97-a6f3-42d1-81dc-3afb5df626e6", - "metadata": {}, - "source": [ - "### Check the CESM compset definitions" - ] - }, - { - "cell_type": "markdown", - "id": "0fea9ec7-4dfe-4901-be41-2815b8d0408c", - "metadata": {}, - "source": [ - "
\n", - " \n", - "```\n", - "./query_config --compsets | more\n", - "```\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b20b8542-59e8-4f7f-9a47-daf1d324794f", - "metadata": {}, - "source": [ - "Note the ` | more` after the command sends the output of the query_config script to the `more` tool so you can scroll through it more easily. You can use the spacebar to move to the next page and the q key to quit out of the tool." - ] - }, - { - "cell_type": "markdown", - "id": "ca441693-1d6a-47d9-add7-cba4754fe18a", - "metadata": {}, - "source": [ - "### Check the CESM grid resolutions" - ] - }, - { - "cell_type": "markdown", - "id": "8407b1f3-5e5f-4b85-aee5-b2b197b8c6b1", - "metadata": {}, - "source": [ - "
\n", - " \n", - "```\n", - "./query_config --grids --long | more\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cff6ac2f-b73d-4585-805d-79267555b707", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/exercises/first_B1850.ipynb b/notebooks_copy/basics/exercises/first_B1850.ipynb deleted file mode 100644 index 99894df55..000000000 --- a/notebooks_copy/basics/exercises/first_B1850.ipynb +++ /dev/null @@ -1,180 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7013ccab-46dc-4a38-8039-7ec464251982", - "metadata": {}, - "source": [ - "# Your first CESM run" - ] - }, - { - "cell_type": "markdown", - "id": "30c6953e-8197-4fea-b398-5a090db1a762", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Run a basic, out-of-the-box (that is, no user changes are implemented) CESM experiment

\n", - " \n", - "Create a case called **b1850.basics** using the compset ``B1850`` at ``f19_g17`` resolution. \n", - "\n", - "Check the status of the run after submitting.\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "73e4cb02-1717-420c-8a28-f56c0078956c", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - " \n", - "**Is my workspace set up?**\n", - "\n", - "Did you already create a `cases` directory where you will put all new cases\n", - "```\n", - "cd /glade/work/$USER\n", - "mkdir cases\n", - "```\n", - " \n", - "**Did you create a new case?**\n", - "\n", - "You can create a new case with the command:\n", - "```\n", - "./create_newcase --case /glade/work/$USER/cases/CASE --res RES --compset COMPSET\n", - "```\n", - "Make sure you use the correct information for the CASE, RES, and COMPSET inputs.\n", - "\n", - "**Did you set up the case?**\n", - "\n", - "You can invoke case set up as follows:\n", - "```\n", - "./case.setup\n", - "```\n", - "**How do I compile?**\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "**How do I check my job status?**\n", - "\n", - "You can check job status using the command:\n", - "```\n", - "qstat -u $USER\n", - "```\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f7c918d3-6475-463d-a10e-2badb6728cb5", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Create a new case b1850.basics with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", - "./create_newcase --case /glade/work/$USER/cases/b1850.basics --res f19_g17 --compset B1850\n", - "```\n", - "\n", - "Case setup:\n", - "``` \n", - "cd /glade/work/$USER/cases/b1850.basics \n", - "./case.setup\n", - "```\n", - " \n", - "Do not make any other changes since we are running an out-of-the-box simulation.\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - " \n", - "Remember that `qcmd` is used on derecho only.\n", - "\n", - "Check the status:\n", - "```\n", - "qstat -u $USER\n", - "``` \n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "b1850.basics. \n", - " \n", - "Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", - "ls \n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "9c66385f-055c-4b74-b6fe-6fc189c02690", - "metadata": { - "tags": [] - }, - "source": [ - "## Test your understanding" - ] - }, - { - "cell_type": "markdown", - "id": "8218314c-f299-4171-b0fe-bac655c2d4f5", - "metadata": { - "tags": [] - }, - "source": [ - "- Did your run complete successfully?\n", - "- What sorts of files did you get as output?\n", - "- How long did the run go for?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a7b563b-7afd-4699-954b-8a3cc1e7bc4e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/exercises/review_questions.ipynb b/notebooks_copy/basics/exercises/review_questions.ipynb deleted file mode 100644 index 5742e8c53..000000000 --- a/notebooks_copy/basics/exercises/review_questions.ipynb +++ /dev/null @@ -1,269 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Review Questions\n", - "\n", - "If time allows here are some review questions to go back over the material in the [Introduction](https://ncar.github.io/CESM-Tutorial/notebooks/intro.html#introduction) and [Basics](https://ncar.github.io/CESM-Tutorial/notebooks/basics.html#basics) sections." - ] - }, - { - "cell_type": "markdown", - "id": "400791b9-2296-448b-a246-3eddd1ee828f", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Question 1: What is the CESM Workspace? How many separate directory areas are there?

\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "41bfb932-b6bc-4bce-adb9-6bb568abc0c5", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "The CESM model consists of five main directory tree areas that make up the CESM Workspace. The individual directories represent different elements including the source code, your experiment cases, the area where the model runs, the location of input data, and the location where the output data from the model will be archived for later use.\n", - " \n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "f1d27e05-48e4-476f-8714-abd94ff8b38c", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Question 2: Where is the CESM Code Development Managed?

\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c081edba-611f-4f08-88c5-57d4ce94ea5b", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "The CESM Project is hosted on Github under under the Earth System Community Modeling Portal (ESCOMP) which is managed by the University Corporation for Atmospheric Research (UCAR). Code can be directly downloaded from Github using Git commands.\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "9b6dd2e6-d422-4460-acf5-08843d505872", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Question 3: Where do you find the create_newcase command? What does it do?

\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "740a99b4-14df-4167-9dfd-75b464a91ef3", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "The create_newcase script is located in the CIME Scripts Directory. The create_newcase script creates a new case in your CASE Directory. The command takes the new casename, compset, and resolution as values.\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7fe2eb40-6373-4a71-a488-a3b98b8e1dc8", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Question 4: What are the three commands required to start running a new case?

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7c13abe3-e7f2-4371-a43a-f3a82856f40a", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "After create_newcase you need to change to the new case directory where you need to run case.setup, case.build and case.submit. As an additional step you can run the script preview_namelists.\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "30282de3-e5e8-4886-88c8-409c74ca9506", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Question 5: How can you monitor your CESM submission while it is running?

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "4392489e-96c9-44d7-8897-a6560891c6e9", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "There are three main ways to monitor your CESM job while it is running. The first is with the qstat command, the second is with the build/run directory files, and the last is with the archive directory files. \n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "1a83b705-7035-4614-a13b-1ecf153a5f1c", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Question 6: What is the main way to understand how a CESM simulation ran?

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "197fefcd-c57a-431b-b517-5e2aacc4948c", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "The main output from your CESM job is the history files. These can be specified to output the status of the component models at various levels of detail and for different time averaging periods. \n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "69723db1-9640-4744-94e1-78f327581c0f", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Question 7: What is an XML file? What is it used for in the Case Directory?

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "70243d30-f462-4254-957e-e40ef33118ad", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "The XML files are Markup Language files that contain variables used by the CESM scripts. In the Case Directory there are seven xml files:\n", - " \n", - "- env_archive.xml specifies rules for short-term archival script case.st_archive\n", - "- env_batch.xml set by create_newcase to define batch specific settings used by script case.submit\n", - "- env_build.xml specifies build information used by script case.build\n", - "- env_case.xml set by create_newcase and cannot be modified\n", - "- env_mach_pes.xml specifies PE layout of components used by script case.run\n", - "- env_mach_specific.xml specifies machine specific information used by script case.build\n", - "- env_run.xml sets run time information (such as length of run, frequency of\n", - "restarts, …) User interacts with this file most frequently\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "12cb104f-bc23-4464-b531-a89934b9bb54", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Question 8: What are the main ways to investigate and modify XML values in the Case Directory?

\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "b030bacf-4dfb-42aa-8c56-8dc199840fd2", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "While the values in XML files can be modified directly with an editor, CESM provides tools for querying and changing values. The xmlquery and xmlchange tools ensure that the values found and updated are valid for the files they belong to and that any changes made are recorded in the CaseStatus file in the CASE Directory.\n", - " \n", - "
\n", - "
\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/exercises/second_B1850.ipynb b/notebooks_copy/basics/exercises/second_B1850.ipynb deleted file mode 100644 index 12a2fcdd9..000000000 --- a/notebooks_copy/basics/exercises/second_B1850.ipynb +++ /dev/null @@ -1,163 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7013ccab-46dc-4a38-8039-7ec464251982", - "metadata": {}, - "source": [ - "# Extending your run" - ] - }, - { - "cell_type": "markdown", - "id": "30c6953e-8197-4fea-b398-5a090db1a762", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Run the same case, but for longer

\n", - "\n", - "The default value for a new case run length is 5 days. The run length is set by the XML variables STOP_OPTION and STOP_N. As the 5 days is too short to produce a monthly history file we will change these to run for a single month. In the next section you will learn more about `xml` files, but for now we will just provide the commands.\n", - " \n", - "From your `CASE` directory, extend the run b1850.basics with the command:\n", - "```\n", - "./xmlchange STOP_OPTION=nmonths\n", - "./xmlchange STOP_N=1\n", - "```\n", - "\n", - "After you have changed these variables, submit the job again and check its status.\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "73e4cb02-1717-420c-8a28-f56c0078956c", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - " \n", - "**Submit:**\n", - "```\n", - "./case.submit\n", - "```\n", - "\n", - "**Check the Status:**\n", - "```\n", - "qstat -u $USER\n", - "``` \n", - " \n", - "**List the contents of the Run directory**\n", - "\n", - "```\n", - "ls -lt /glade/derecho/scratch/$USER/b1850.basics/run\n", - "```\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f7c918d3-6475-463d-a10e-2badb6728cb5", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Go to your b1850.basics case directory:\n", - "```\n", - "cd /glade/work/$USER/cases/b1850.basics\n", - "```\n", - "\n", - "Changing xml files:\n", - "``` \n", - "./xmlchange STOP_OPTION=nmonths\n", - "./xmlchange STOP_N=1\n", - "```\n", - " \n", - "Do not make any other changes since we are running an out-of-the-box simulation.\n", - "\n", - "Submit:\n", - "```\n", - "./case.submit\n", - "```\n", - "\n", - "Check the job status:\n", - "```\n", - "./qstat -u $USER\n", - "``` \n", - " \n", - " \n", - "When the run is completed, look into the archive directory for: \n", - "b1850.basics. \n", - " \n", - "Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/b1850.basics\n", - "ls \n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "9c66385f-055c-4b74-b6fe-6fc189c02690", - "metadata": { - "tags": [] - }, - "source": [ - "## Test your understanding" - ] - }, - { - "cell_type": "markdown", - "id": "8218314c-f299-4171-b0fe-bac655c2d4f5", - "metadata": { - "tags": [] - }, - "source": [ - "- Did your run complete successfully?\n", - "- What sorts of files did you get as output and how are these different from before?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a7b563b-7afd-4699-954b-8a3cc1e7bc4e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/basics/exercises_overview.ipynb b/notebooks_copy/basics/exercises_overview.ipynb deleted file mode 100644 index 93c3f11be..000000000 --- a/notebooks_copy/basics/exercises_overview.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8c2d8431-0e66-4597-96bb-858874da9235", - "metadata": {}, - "source": [ - "# Exercise Overview \n", - "\n", - "\n", - "## Learning Goals:\n", - "\n", - "- The student will successfully set up and run their first (short) CESM simulation.\n", - "- This lab exercise is designed to build off the one-time setup section and uses the CESM workflow information presented. \n", - "\n", - "## Exercise overview:\n", - "\n", - "By then end of this exercise you will have:\n", - "- created, setup, updated, built, run, and examined the output of a standard B1850 control case\n", - "\n", - "**You should not proceed in working on this exercise until you have successfully completed the [one-time set up exercise](https://ncar.github.io/CESM-Tutorial/notebooks/basics/basics_overview.html). You must have successfully downloaded CESM using git and have a `cases` directory to begin this exercise.**\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e062147c-f8f3-4a21-877e-b1c5755c6cad", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/bgc/bgc.ipynb b/notebooks_copy/challenge/bgc/bgc.ipynb deleted file mode 100644 index 6e85a9dda..000000000 --- a/notebooks_copy/challenge/bgc/bgc.ipynb +++ /dev/null @@ -1,213 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Biogeochemistry" - ] - }, - { - "cell_type": "markdown", - "id": "c0590b2a-ae3f-48b2-b287-8fa40eae213d", - "metadata": {}, - "source": [ - "## Biogeochemistry in CESM" - ] - }, - { - "cell_type": "markdown", - "id": "34516fd8-ed12-44c0-ba57-b684c1c279cf", - "metadata": {}, - "source": [ - "Biogeochemistry features have been available since CESM1.0 and are on by default in CESM2.0. \n", - "\n", - "Three components of CESM have biogeochemistry features:\n", - "\n", - "**CAM**\n", - "- CO$_{2}$ constituents in CAM that use the land and ocean fluxes of CO$_{2}$ as surface boundary conditions.\n", - "- CAM passes CO$_{2}$ to the coupler for land and ocean flux computations. \n", - "- The CO$_{2}$ consitutients are coupled with radiation computations.\n", - "\n", - "**POP**\n", - "- Includes the MARBL ecosystem library in CESM2.0\n", - "\n", - "**CLM**\n", - "- Features covered elsewhere [LINK]" - ] - }, - { - "cell_type": "markdown", - "id": "f6f495f2-b20f-441e-877e-b0f5e9ec5755", - "metadata": {}, - "source": [ - "## Coupled BGC Compsets" - ] - }, - { - "cell_type": "markdown", - "id": "ef4e3af3-cc84-4dda-b483-daf8bf2b1b30", - "metadata": {}, - "source": [ - "**Terminology**\n", - "- BGC CO$_{2}$: what is used by surface components\n", - "- RAD CO$_{2}$: what is used by the atmospheric radiative code\n", - "- Diagnostic CO$_{2}$: prescribed atmospheric CO$_{2}$ concentrations. (E.g. constant, read from file, 1% ramp, etc.)\n", - "- Prognostic CO$_{2}$: predicted atmospheric CO$_{2}$ concentrations. Atmospheric consitutent is computed from surface CO$_{2}$ fluxes.\n", - "\n", - "\n", - "**B1850, BHIST**\n", - "- Compset long name has BCG%BDRD\n", - "- Coupled model, BGC & RAD CO$_{2}$ are diagnostic\n", - "\n", - "**B1850_BPRP, BHIST_BPRP**\n", - "- Shortnames introduced in CESM2.1.1\n", - "- Compset long name has BGC%BPRP\n", - "- Coupled model, BGC & RAD CO$_{2}$ are prognostic" - ] - }, - { - "cell_type": "markdown", - "id": "0a62ca85-30f9-409d-86d4-8bc2c1da2bcd", - "metadata": {}, - "source": [ - "## Ocean Specific BGC Compsets" - ] - }, - { - "cell_type": "markdown", - "id": "64720908-da20-491c-9c03-dea85f6f770f", - "metadata": {}, - "source": [ - "**C1850ECO**\n", - "- Ocean alone, 1850 aerosols, normal year forcing\n", - "\n", - "**G1850ECO**\n", - "- Ocean and sea ice, 1850 aerosols, normal year forcing\n", - "\n", - "**G1850ECOIAF**\n", - "- Ocean and sea ice, 1850 aerosols, interannually varying forcing\n", - "\n", - "These can be found by using the following command in the same directory as `create_newcase`\n", - "```\n", - "./query_config --compsets pop\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "acd0ff2e-fceb-4245-a8fb-0b7ff86aeb8c", - "metadata": {}, - "source": [ - "## BGC Initial Conditions" - ] - }, - { - "cell_type": "markdown", - "id": "56ffb1dc-1d35-4859-85f7-b3a47e9b567d", - "metadata": {}, - "source": [ - "**Coupled BDRD compsets**\n", - "- BDRD is default RUN_REFCASE set for f09_g17 or f19_g17 resolution experiments\n", - "- Provided initial conditions were spun-up with f09_g17 resolution model. *Note that the carbon cycle is not as well balanced in f19_g17 resolution experiments as it is with f09_g17 resolution.*\n", - "\n", - "**Coupled BPRP compsets**\n", - "- PBRP is the default RUN_REFCASE set for f09_g17\n", - "\n", - "**Ocean alone or Ocean-ice compsets**\n", - "- Initial conditions are provided but are not spun up." - ] - }, - { - "cell_type": "markdown", - "id": "d52161c7-d87e-455d-848f-6be823d6d110", - "metadata": {}, - "source": [ - "## BGC env*xml variables" - ] - }, - { - "cell_type": "markdown", - "id": "644683f9-5b69-4756-8643-84af9fdf7e6c", - "metadata": {}, - "source": [ - "**CCSM_BGC**\n", - "- Controls which CO$_{2}$ fields are exchanged between CESM components.\n", - "\n", - "![bgc_settings](../../../images/challenge/bgc_table_1.png)\n", - "\n", - "*

Figure: CCSM_BGC settings.

*\n", - " - CO2A: land only or ocean only runs.\n", - " - CO2B: atmosphere-land runs. Ocean and fossil fuel CO$_{2}$ fluxes are read for m a file.\n", - " - CO2C: fully coupled runs.\n", - "\n", - "**CCSM_CO2_PPMV**\n", - "- Constant CO$_{2}$ reference value used in some configurations.\n", - "\n", - "**OCN_CO2_TYPE or LND_CO2_TYPE**\n", - "- Controls CO$_{2}$ used by ocean and land components.\n", - "- Constant, prognistic, diagnostic.\n", - "\n", - "**OCN_TRACER_MODULES**\n", - "- Controls which ocean tracers are used.\n", - "- Ocean ecosystem model is called ecosys." - ] - }, - { - "cell_type": "markdown", - "id": "387d8807-75a6-40d2-97b2-eb20a7f7930a", - "metadata": {}, - "source": [ - "## BGC units and sign conventions" - ] - }, - { - "cell_type": "markdown", - "id": "255b3eff-e3f2-4a8d-9733-f4ba018588c7", - "metadata": {}, - "source": [ - "![bgc_units](../../../images/challenge/bgc_table_2.png)\n", - "\n", - "*

Figure: BGC units and sign conventions.

*" - ] - }, - { - "cell_type": "markdown", - "id": "a150934b-18e6-48da-98f4-b1a464e74343", - "metadata": {}, - "source": [ - "- CAM variables CO2, CO2_LND, CO2_OCN, CO2_FF all have units of (kg CO2)/(kg dry air). Note that these are not typical units for carbon cycle modelers. To convert to ppmv multipy by 1.0e6*28.966/44.0 . 28.966 is the molecular weight of dry air and 44.0 is the molecular weight of CO$_{2}$." - ] - }, - { - "cell_type": "markdown", - "id": "7e7c19a6-9a75-42a3-8338-59ffb4061acb", - "metadata": {}, - "source": [ - "* Same quantity in different CESM component output has: different names, different units, different sign conventions (for fluxes)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/bgc/bgc_exercise_1.ipynb b/notebooks_copy/challenge/bgc/bgc_exercise_1.ipynb deleted file mode 100644 index e3e910b22..000000000 --- a/notebooks_copy/challenge/bgc/bgc_exercise_1.ipynb +++ /dev/null @@ -1,81 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 1: Set up a BGC case" - ] - }, - { - "cell_type": "markdown", - "id": "f4238578-b8ac-4e94-aa01-2aac2c42a9dc", - "metadata": {}, - "source": [ - "We will create two different cases and compare the case directories. We want to check if the differences make sense. You will **NOT** run the model in this challenge exercise." - ] - }, - { - "cell_type": "markdown", - "id": "6457c1d2-0530-435d-ae27-d0f1eeabe583", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Set up a BGC control case

\n", - " \n", - "Create a case called **b_1850** using the compset ``B1850`` at ``f19_g17`` resolution. \n", - "\n", - "Create a case called **b_1850_bprp** using the compset ``B1850_BPRP`` at ``f19_g17`` resolution.\n", - "- What happens when you run create_newcase? Follow the instructions to get this working, but don't do it for real unless you know what you're doing.\n", - "\n", - "Run case.setup and preview_namelists for each case.\n", - "- What changes occur when the carbon cycle is made prognostic?\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "5efef9de-221c-411d-b209-23e53668a101", - "metadata": {}, - "source": [ - "## POP BGC Specific Output" - ] - }, - { - "cell_type": "markdown", - "id": "5d25903c-cd61-42fe-9631-04517ac35103", - "metadata": {}, - "source": [ - "**ocn/hist/$CASE.pop.h.ecosys.nday1.????-??-??.nc**\n", - "- selected ocean ecosys variables at daily resolution\n", - "- surface flux related, productivity, and functional group vertical integrals.\n", - "\n", - "**ocn/hist/$CASE.pop.h.ecosys.nyear1.????.nc**\n", - "- selected three dimensional ocean ecosys tracer budget terms." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam-chem_waccm/cam-chem_waccm.ipynb b/notebooks_copy/challenge/cam-chem_waccm/cam-chem_waccm.ipynb deleted file mode 100644 index 8d2b202b4..000000000 --- a/notebooks_copy/challenge/cam-chem_waccm/cam-chem_waccm.ipynb +++ /dev/null @@ -1,130 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "curious-gibraltar", - "metadata": {}, - "source": [ - "# Atmospheric chemistry" - ] - }, - { - "cell_type": "markdown", - "id": "available-federation", - "metadata": { - "tags": [] - }, - "source": [ - "## Learning Goals\n", - "\n", - "- Student will learn about different chemistry (CAM-chem/WACCM) compsets and how to run one.\n", - "- Student will learn how to modify chemistry and change output settings.\n", - "- Student will learn how to change emissions.\n" - ] - }, - { - "cell_type": "markdown", - "id": "brave-copyright", - "metadata": {}, - "source": [ - "## CAM-chem and WACCM component sets\n", - "\n", - "The CESM2 components can be combined in numerous ways to carry out various scientific or software experiments. A particular mix of components, along with component-specific configuration and/or namelist settings, is called a component set or ``compset``. \n", - "\n", - "**WACCM** (Whole Atmosphere Community Climate Model) and **CAM-chem** (Community Atmosphere Model with chemistry) are atmospheric components of CESM, like CAM. They can be run as ``F`` and ``B`` compsets, as well as with nudged meteorology (for example towards reanalysis). They can be run using various dycores and resolutions.\n", - "In contrast to CAM, WACCM and CAM-chem run with different possibilities of chemistry complexity, whereby CAM uses simplified chemistry. CAM and CAM-chem use the same vertical resolution and model top.\n", - "Different chemical mechanisms for CAM-chem with more or less complexity have been developed based on the MOZART-TS1 and TS2 (currently the most comprehensive) chemical mechanism.\n", - "WACCM has a model top at about 150km and therefore covers the Troposphere, Stratosphere, Mesosphere and Lower-Thermosphere. The most comprehensive WACCM mechanism is therefore called the TSMLT chemical mechanism.\n", - "WACCM can also run with Middle Atmosphere (MA) chemistry (e.g., FWmaHIST, which includes very simplified chemistry in the troposphere, and with Specified Chemistry (e.g., FWscHIST), similar to the CAM chemistry.\n" - ] - }, - { - "cell_type": "markdown", - "id": "weekly-brass", - "metadata": {}, - "source": [ - "## CAM-chem and WACCM tested F component sets\n", - "\n", - "In CESM2.2 only the CAM-chem F compsets have been scientifically tested and no PI-control or transient history simulation has been scientifically validated. In contrast, WACCM has been used for CMIP6 simulations.\n", - "Two different nudging approaches exist using CAM-chem and WACCM, both using MERRA2 or GEOS5 meteorological analysis fields. The ``nudged`` approach is the recommended approach that uses meteorological fields (U, V, T) on CESM model levels.\n", - "The older specified dynamics (SD) approach requires the model to run on the vertical MERRA2 grids.\n", - "\n", - "An overview of different compsets can be found in the diagrams below.\n", - "\n", - "\n", - "![CAMChem tested F compsets](../../../images/challenge/CAMchem_compsets.png)\n", - "

Figure 1: CAM-chem tested F compsets.

\n", - "\n", - "![WACCM scientifically supported F compsets](../../../images/challenge/WACCM_compsets1.png)\n", - "

Figure 2: WACCM scientifically supported F compsets.

\n", - "\n", - "![WACCM tested F compsets](../../../images/challenge/WACCM_compsets2.png)\n", - "

Figure 3: WACCM tested F compsets.

" - ] - }, - { - "cell_type": "markdown", - "id": "unsigned-cooling", - "metadata": {}, - "source": [ - "## Overview of the Challenge Exercises for CAM-chem/WACCM\n", - "Start running one control case and choose between two options:\n", - "\n", - "a) Run a CAM-chem compset with TS1 chemistry, historical SSTs and 0.9x1.25 degrees horizontal resolution
\n", - "b) Run a WACCM with TSMLT1 chemistry, historical SSTs and 0.9x1.25 degrees horizontal resolution\n", - "\n", - "Once a control case has been configured and run, perform two test cases - one changing chemistry and one changing emissions." - ] - }, - { - "cell_type": "markdown", - "id": "adolescent-plaintiff", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "\n", - "\n", - "### Finding more information CAM-chem and WACCM\n", - "\n", - "More explanation (and some more options) can be found in the docs:
\n", - "https://wiki.ucar.edu/display/camchem/Home
\n", - "https://www.cesm.ucar.edu/models/cesm2/config/compsets.html
\n", - "https://ncar.github.io/CAM/doc/build/html/users_guide/atmospheric-configurations.html\n", - "\n", - "**Discussion board:**
\n", - "CAM-chem: https://bb.cgd.ucar.edu/cesm/forums/cam-chem.154/
\n", - "WACCM: https://bb.cgd.ucar.edu/cesm/forums/waccm.155/
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "equal-ethics", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam-chem_waccm/exercise_1.ipynb b/notebooks_copy/challenge/cam-chem_waccm/exercise_1.ipynb deleted file mode 100644 index 718e5881d..000000000 --- a/notebooks_copy/challenge/cam-chem_waccm/exercise_1.ipynb +++ /dev/null @@ -1,264 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "arctic-workstation", - "metadata": {}, - "source": [ - "# 1: Control case: running with chemistry" - ] - }, - { - "cell_type": "markdown", - "id": "urban-jason", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise 1: Run a present-day compset that includes chemistry

\n", - " \n", - "Create, configure, build and run a case for one of these following configurations: \n", - "- **CAM-chem:** ``f.e21.FCHIST.f09_f09_mg17.tutorial.test1`` with the ``FCHIST`` compset; or \n", - "- **WACCM:** ``f.e21.FWHIST.f09_f09_mg17.tutorial.test1`` with the ``FWHIST`` compset. \n", - " \n", - "Run a simulation for 5 days, with daily instantaneous output of the variables: ‘PS’,’Z3’,’T’,’U’,’V’,’O3’.\n", - "You are also welcome to output your own additional variables.\n", - "Remember, F compsets have data ocean and data sea ice. \n", - "Check your model output in your run directory.\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "animal-crash", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**How do I output daily instantaneous variables?**\n", - "\n", - "- Modify history stream 2 with namelist variable ``fincl2``\n", - "- Use namelist variables: ``nhtfrq``, ``mfilt``\n", - "- For more information, look at the chapter:
\n", - " **NAMELIST MODIFICATIONS** -> [Changing CAM namelist options](https://ncar.github.io/CAM/doc/build/html/users_guide/customizing-compsets.html#changing-cam-namelist-options)\n", - "- The default set up for these namelist variables can be found in ``CaseDocs/atm_in``\n", - "\n", - "**What is the resolution for FCHIST and FWHIST?**\n", - "\n", - "- Use resolution: f09_f09_mg17\n", - " \n", - "**Important! Check the newly generated namelist prior run**\n", - "\n", - "``ls CaseDocs/*``\n", - " \n", - "``atm_in`` \n", - "``atm_modelio.nml`` \n", - "``chem_mech.doc`` \n", - "``chem_mech.in`` \n", - "``cism.config`` \n", - "``cism_in`` \n", - "``cpl_modelio.nml`` \n", - "``docn_in ice_in`` \n", - "``docn.streams.txt.prescribed``\n", - "``drv_flds_in``\n", - "``drv_in`` \n", - "``esp_modelio.nml`` \n", - "``glc_modelio.nml``\n", - "``ice_modelio.nml``\n", - "``lnd_in``\n", - "``lnd_modelio.nml``\n", - "``mosart_in``\n", - "``ocn_modelio.nml``\n", - "``rof_modelio.nml``\n", - "``seq_maps.rc``\n", - "``wav_modelio.nml``\n", - " \n", - "- atm_in: atmospheric namelist variables\n", - "- chem_mech.in: chemical mechanism file\n", - "- drv_flds_in: dry deposition variables, MEGAN variables (if used)\n", - "- lnd_in: land namelist variables\n", - "- …\n", - "\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "synthetic-reproduction", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " \n", - "**# Set environment variables**\n", - " \n", - "Use the following commands
\n", - "for **CAM-chem**:\n", - "```\n", - "set CASENAME = f.e21.FCHIST.f09_f09_mg17.tutorial.test1\n", - "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET = FCHIST\n", - "set RESOLUTION = f09_f09_mg17\n", - "```\n", - "\n", - "or for **WACCM**\n", - " \n", - "```\n", - "set CASENAME = f.e21.FWHIST.f09_f09_mg17.tutorial.test1\n", - "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET = FWHIST\n", - "set RESOLUTION = f09_f09_mg17\n", - "```\n", - "\n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET\n", - "```\n", - "You may need to add ``--project \"UESM0013\"`` when creating a new case.\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "**If needed**, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the tutorial project number ``UESM0013``\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "From within the case directory, invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - " \n", - "**# Build**\n", - " \n", - "Perform an initial build within the case directory\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "namelists (``atm_in``, ``ice_in``, ``lnd_in``, ``docn_in``) will appear in the CaseDocs\n", - "subdirectory, as well as in your $rundir\n", - "\n", - "**# Customize namelists**\n", - "\n", - "Edit the file ``user_nl_cam``. First open the ``CaseDocs/atm_in`` and find the lines for avgflag_pertap, mfilt, nhtfrq and copy and paste them into ``user_nl_cam``. Then make changes to those lines in ``user_nl_cam`` and add a line to write out a second history file, ``fincl2``. The namelist lines should look something like:\n", - "```\n", - "&cam_history_nl\n", - " avgflag_pertape = 'A','I','A','A','A','A','A','A','I'\n", - " mfilt = 1,30,365,240,240,480,365,73,30\n", - " nhtfrq = 0,-24,-24,-3,-1,1,-24,-120,-240\n", - " fincl2 = 'PS','Z3','T','U','V','O3'\n", - "/\n", - "``` \n", - " \n", - "You can do this with a text editor.\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "\n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Submit:**\n", - "```\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the run files and history files. \n", - " \n", - "(1) Find your model output in your run directory (``$run_dir``) after finished:\n", - "\n", - "```\n", - "ls /glade/derecho/scratch/$USER/$CASENAME/run\n", - "```\n", - "\n", - "![rundir example](../../../images/challenge/chemistry-rundir-example.png)\n", - "\n", - " \n", - "(2) Check that your archive directory on derecho (the path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "(3) Look at the contents of the ``h1`` files using ``ncdump``.\n", - "\n", - "```\n", - "ncdump –h f.e21.FCHIST.f09_f09_mg17.tutorial.test1.cam.h1.1995-01-01-00000.nc\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "northern-agency", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam-chem_waccm/exercise_2.ipynb b/notebooks_copy/challenge/cam-chem_waccm/exercise_2.ipynb deleted file mode 100644 index 52681d6c2..000000000 --- a/notebooks_copy/challenge/cam-chem_waccm/exercise_2.ipynb +++ /dev/null @@ -1,282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "prepared-adaptation", - "metadata": {}, - "source": [ - "# 2: Test case: changing the chemistry" - ] - }, - { - "cell_type": "markdown", - "id": "sound-recovery", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise 2: Building the model with new chemistry

\n", - " \n", - "Create, configure, build a new case as done in exercise 1, for one of these following configurations: \n", - "- **CAM-chem:** ``f.e21.FCHIST.f09_f09_mg17.tutorial.test2`` with the ``FCHIST`` compset; or \n", - "- **WACCM:** ``f.e21.FWHIST.f09_f09_mg17.tutorial.test2`` with the ``FWHIST`` compset. \n", - "\n", - "Set up this simulation to run for 5 days, with daily instantaneous output of the variables: ‘PS’,’Z3’,’T’,’U’,’V’,’O3’. \n", - " \n", - "**To change the chemistry**, copy your chemistry preprocessor file chem_mech.in from CaseDocs to your case directory as my_chem_mech.in and change reaction rate for the O1D reaction with O2 to 1.65e-12. Point the simulation to your new mechanism file using ``CAM_CONFIG_OPTS`` and re-build and submit. Once the simulation is complete, compare the 5th day output to the results from exercise 1.\n", - "\n", - "\n", - "**Extra information:**\n", - " \n", - "The chemistry preprocessor generates CAM Fortran source code to solve chemistry. The input is an ASCII file listing chemical reactions and rates. The chemistry preprocessor input file used in your previous run is in your\n", - "``$CASEROOT/CaseDocs/chem_mech.in``\n", - "\n", - "An example of a simple chemistry mechanism file content can be seen below:\n", - "![chemistry mechanism example](../../../images/challenge/chemistry-mechanism-example.png)\n", - "\n", - "Additional input files for default chemical mechanisms are in each source code subdirectory for mechanisms under:\n", - "``$SRCROOT/components/cam/src/chemistry/pp_*``\n", - "(i.e. ``pp_waccm_tsmlt_mam4``). You do not need to change these additional files for this exercise.\n", - "\n", - "Below is an overview of the different types of reactions and rates that can be defined in the mechanism file.\n", - "![chemistry preprocessor options example](../../../images/challenge/chemistry-preprocessor-info.png)\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "daily-acting", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**Creating a new chemistry input file**\n", - "``` \n", - "less CaseDocs/chem_mech.in\n", - "cp CaseDocs/chem_mech.in my_chem_mech.in\n", - "```\n", - "Open your copied new chemistry mechanism file with a text editor and change the reaction rate:\n", - " \n", - "![chemistry ozone reaction](../../../images/challenge/chemistry-ozone-reaction.png)\n", - "\n", - "**How to tell CESM to read your new chemistry mechanism file**\n", - "\n", - "- First look at the ``CAM_CONFIG_OPTS`` default setting for CAM-chem
\n", - "``./xmlquery CAM_CONFIG_OPTS``
\n", - "``CAM_CONFIG_OPTS: -phys cam6 -chem trop_strat_mam4_vbs -age_of_air_trcs``
\n", - "(note this will be different for WACCM)\n", - " \n", - "- Use xmlchange to append a pointer to your user mechanism:
\n", - "``--usr_mech_infile `pwd`/my_chem_mech.in``\n", - " \n", - "- Check the ``CAM_CONFIG_OPTS`` has been changed
\n", - "``./xmlquery CAM_CONFIG_OPTS``
\n", - "``CAM_CONFIG_OPTS: -phys cam6 -chem trop_strat_mam4_vbs -age_of_air_trcs --usr_mech_infile /glade/u/home/$USER/cases/$CASENAME/my_chem_mech.in``\n", - " \n", - "- You will need to reset the case setup, and rebuild before submitting\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "political-wages", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution \n", - "
\n", - " \n", - "**# Set environment variables**
\n", - "Use the commands
\n", - "for **CAM-chem**\n", - "```\n", - "set CASENAME = f.e21.FCHIST.f09_f09_mg17.tutorial.test2\n", - "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET = FCHIST\n", - "set RESOLUTION = f09_f09_mg17\n", - "```\n", - "\n", - "or for **WACCM** \n", - "```\n", - "set CASENAME = f.e21.FWHIST.f09_f09_mg17.tutorial.test2\n", - "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET = FWHIST\n", - "set RESOLUTION = f09_f09_mg17\n", - "```\n", - "\n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET\n", - "```\n", - "You may need to add ``--project \"UESM0013\"`` when creating a new case.\n", - " \n", - "**# Change the job queue and account number**\n", - "\n", - "**If needed**, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the project number ``UESM0013``\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "From within the case directory, invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - " \n", - "**# Build**\n", - " \n", - "Perform an initial build within the case directory\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "namelists (``atm_in``, ``ice_in``, ``lnd_in``, ``docn_in``) will appear in the CaseDocs\n", - "subdirectory, as well as in your $rundir\n", - "\n", - "**# Customize namelists**\n", - "\n", - "As before, edit the file ``user_nl_cam``. First open the ``CaseDocs/atm_in`` and find the lines for avgflag_pertap, mfilt, nhtfrq and copy and paste them into ``user_nl_cam``. Then make changes to those lines in ``user_nl_cam`` and add a line to write out a second history file, ``fincl2``. The namelist lines should look something like:\n", - "```\n", - "&cam_history_nl\n", - " avgflag_pertape = 'A','I','A','A','A','A','A','A','I'\n", - " mfilt = 1,30,365,240,240,480,365,73,30\n", - " nhtfrq = 0,-24,-24,-3,-1,1,-24,-120,-240\n", - " fincl2 = 'PS','Z3','T','U','V','O3'\n", - "/\n", - "``` \n", - "You can do this with a text editor.\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "**# Change the chemistry**\n", - " \n", - "Copy the default mechanism to your own user defined file \n", - "``` \n", - "cp CaseDocs/chem_mech.in my_chem_mech.in\n", - "```\n", - "\n", - "Open your mechanism file with a text editor (e.g. nedit) and change the reaction rate for O1D with O2\n", - "```\n", - "nedit my_chem_mech.in &\n", - "```\n", - " In CAM-chem\n", - "![chemistry ozone reaction](../../../images/challenge/chemistry-ozone-reaction.png)\n", - "\n", - " In WACCM:\n", - "![chemistry ozone reaction](../../../images/challenge/chemistry-ozone-reaction_orig.png)\n", - "\n", - "\n", - "Append a pointer in the to the user mechanism:\n", - "```\n", - "./xmlchange --append CAM_CONFIG_OPTS=\"--usr_mech_infile `pwd`/my_chem_mech.in\"\n", - "```\n", - "\n", - "**# Re-build the model**\n", - "```\n", - "./case.setup --reset\n", - "./case.build --clean\n", - "qcmd -- ./case.build\n", - "```\n", - " \n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Submit:**\n", - "```\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the run files and history files. \n", - " \n", - "(1) Find your model output in your run directory (``$run_dir``) after finished:\n", - "\n", - "```\n", - "ls /glade/derecho/scratch/$USER/$CASENAME/run\n", - "```\n", - " \n", - "(2) Check that your archive directory on derecho (the path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "(3) Look at the contents of the ``h1`` files using ``ncdump``.\n", - "\n", - "```\n", - "ncdump –h f.e21.FCHIST.f09_f09_mg17.tutorial.test2.cam.h1.1995-01-01-00000.nc\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "alpha-politics", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam-chem_waccm/exercise_3.ipynb b/notebooks_copy/challenge/cam-chem_waccm/exercise_3.ipynb deleted file mode 100644 index 3fc955e6e..000000000 --- a/notebooks_copy/challenge/cam-chem_waccm/exercise_3.ipynb +++ /dev/null @@ -1,365 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "expensive-beatles", - "metadata": {}, - "source": [ - "# 3: Test case: changing the emissions" - ] - }, - { - "cell_type": "markdown", - "id": "brave-confirmation", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise 3: Using different emissions

\n", - " \n", - "Create, configure, build a new case as done in exercise 1 or 2, for one of these following configurations: \n", - "- **CAM-chem:** ``f.e21.FCHIST.f09_f09_mg17.tutorial.test3`` with the ``FCHIST`` compset; or \n", - "- **WACCM:** ``f.e21.FWHIST.f09_f09_mg17.tutorial.test3`` with the ``FWHIST`` compset. \n", - "\n", - "Set up this simulation to run for 5 days, with daily instantaneous output of the variables: ‘PS’,’Z3’,’T’,’U’,’V’,’O3’. \n", - " \n", - "Change the namelist variables to use daily fire emissions from QFED. This will also require a change of model STARTDATE. Once the simulation is complete, compare the 5th day output to the previous results.\n", - " \n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "checked-arrest", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**Pointing to new emission files**\n", - "\n", - "Point to new emission files in your ``user_nl_cam`` file. Note that you will also need to include pointers to emissions that you are not changing.\n", - "\n", - "The location of qfed fire emissions on glade is described on the wiki: https://wiki.ucar.edu/display/camchem/Emission+Inventories\n", - " \n", - "**Make sure your model start date is covered by the emissions files**\n", - "For this model set up will also need to double check the following are available:\n", - "- The SST file covers your selected dates (``env_run.xml`` file)\n", - "- Lower boundary condition file must include your selected dates (``atm_in`` file)\n", - "- Consider that the initialization file will be for the default 1995. If you don't change your initialization file, you will need to run your own model spin up.\n", - "\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "three-taiwan", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " \n", - "**# Set environment variables**\n", - "Use the commands:\n", - " \n", - "**CAM-chem**\n", - "```\n", - "set CASENAME = f.e21.FCHIST.f09_f09_mg17.tutorial.test3\n", - "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET = FCHIST\n", - "set RESOLUTION = f09_f09_mg17\n", - "```\n", - "\n", - "or for **WACCM**\n", - " \n", - "```\n", - "set CASENAME = f.e21.FWHIST.f09_f09_mg17.tutorial.test3\n", - "set CASEDIR = /glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR = /glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET = FWHIST\n", - "set RESOLUTION = f09_f09_mg17\n", - "```\n", - "\n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET\n", - "```\n", - "You may need to add ``--project \"UESM0013\"`` when creating a new case.\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "**If needed**, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the project number ``UESM0013``\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "From within the case directory, invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - " \n", - "**# Change the dates to cover emission files**
\n", - "Here, we choose 2010\n", - "```\n", - "./xmlchange RUN_STARTDATE=2010-01-01\n", - "```\n", - "\n", - "Make sure the sea surface temperature and ice (SSTICE) data file covers your dates of interest\n", - "```\n", - "./xmlquery SSTICE_DATA_FILENAME\n", - "```\n", - " \n", - "should return: \n", - "```\n", - "SSTICE_DATA_FILENAME: /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/sst/sst_HadOIBl_bc_0.9x1.25_1850_2017_c180507.nc\n", - "```\n", - "\n", - " \n", - "**# Build**\n", - " \n", - "Perform an initial build within the case directory\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "namelists (``atm_in``, ``ice_in``, ``lnd_in``, ``docn_in``) will appear in the CaseDocs\n", - "subdirectory, as well as in your $rundir\n", - "\n", - "**# Customize namelists**\n", - "\n", - "As before, edit the file ``user_nl_cam``. First open the ``CaseDocs/atm_in`` and find the lines for avgflag_pertap, mfilt, nhtfrq and copy and paste them into ``user_nl_cam``. Then make changes to those lines in ``user_nl_cam`` and add a line to write out a second history file, ``fincl2``. The namelist lines should look something like:\n", - "```\n", - "&cam_history_nl\n", - " avgflag_pertape = 'A','I','A','A','A','A','A','A','I'\n", - " mfilt = 1,30,365,240,240,480,365,73,30\n", - " nhtfrq = 0,-24,-24,-3,-1,1,-24,-120,-240\n", - " fincl2 = 'PS','Z3','T','U','V','O3'\n", - "/\n", - "``` \n", - "You can do this with a text editor.\n", - "\n", - "**# Add in different emissions**\n", - "Also add in the namelist pointers to emission files, replacing the fire emissions (every file with 'bb' in the name) with qfed files found at ``/glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/``\n", - "\n", - "```\n", - "&chem_inparm\n", - " srf_emis_specifier\t\t= 'BENZENE -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_BENZENE_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'BENZENE -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_BENZENE_0.9x1.25_mol_2000_2022.nc',\n", - " 'BIGALK -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_BIGALK_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'BIGALK -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_BIGALK_0.9x1.25_mol_2000_2022.nc',\n", - " 'BIGENE -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_BIGENE_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'BIGENE -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_BIGENE_0.9x1.25_mol_2000_2022.nc',\n", - " 'C2H2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H2_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'C2H2 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C2H2_0.9x1.25_mol_2000_2022.nc',\n", - " 'C2H4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'C2H4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C2H4_0.9x1.25_mol_2000_2022.nc',\n", - " 'C2H4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H4_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", - " 'C2H5OH -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H5OH_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'C2H5OH -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C2H5OH_0.9x1.25_mol_2000_2022.nc',\n", - " 'C2H6 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H6_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'C2H6 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C2H6_0.9x1.25_mol_2000_2022.nc',\n", - " 'C2H6 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C2H6_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", - " 'C3H6 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C3H6_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'C3H6 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C3H6_0.9x1.25_mol_2000_2022.nc',\n", - " 'C3H6 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C3H6_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", - " 'C3H8 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C3H8_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'C3H8 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_C3H8_0.9x1.25_mol_2000_2022.nc',\n", - " 'C3H8 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_C3H8_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", - " 'CH2O -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH2O_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'CH2O -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH2O_0.9x1.25_mol_2000_2022.nc',\n", - " 'CH3CHO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3CHO_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'CH3CHO -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3CHO_0.9x1.25_mol_2000_2022.nc',\n", - " 'CH3CN -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3CN_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'CH3CN -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3CN_0.9x1.25_mol_2000_2022.nc',\n", - " 'CH3COCH3 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3COCH3_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'CH3COCH3 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3COCH3_0.9x1.25_mol_2000_2022.nc',\n", - " 'CH3COCHO -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3COCHO_0.9x1.25_mol_2000_2022.nc',\n", - " 'CH3COOH -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3COOH_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'CH3COOH -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3COOH_0.9x1.25_mol_2000_2022.nc',\n", - " 'CH3OH -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CH3OH_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'CH3OH -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CH3OH_0.9x1.25_mol_2000_2022.nc',\n", - " 'CO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CO_anthro_surface_1750-2015_0.9x1.25_c20180504.nc',\n", - " 'CO -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_CO_0.9x1.25_mol_2000_2022.nc',\n", - " 'CO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_CO_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", - " 'E90 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions_E90global_surface_1750-2100_0.9x1.25_c20170322.nc',\n", - " 'GLYALD -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_GLYALD_0.9x1.25_mol_2000_2022.nc',\n", - " 'HCN -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_HCN_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'HCN -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_HCN_0.9x1.25_mol_2000_2022.nc',\n", - " 'HCOOH -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_HCOOH_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'HCOOH -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_HCOOH_0.9x1.25_mol_2000_2022.nc',\n", - " 'ISOP -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_ISOP_0.9x1.25_mol_2000_2022.nc',\n", - " 'IVOC -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_IVOC_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'IVOC -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_IVOC_0.9x1.25_mol_2000_2022.nc',\n", - " 'MEK -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_MEK_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'MEK -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_MEK_0.9x1.25_mol_2000_2022.nc',\n", - " 'MTERP -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_TERPENES_0.9x1.25_mol_2000_2022.nc ',\n", - " 'NH3 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_NH3_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'NH3 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_NH3_0.9x1.25_mol_2000_2022.nc ',\n", - " 'NH3 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_NH3_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", - " 'NO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_NO_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'NO -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_NO_0.9x1.25_mol_2000_2022.nc ',\n", - " 'NO -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_NO_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", - " 'SVOC -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_SVOC_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'SVOC -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_SVOC_0.9x1.25_mol_2000_2022.nc ',\n", - " 'TOLUENE -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_TOLUENE_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'TOLUENE -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_TOLUENE_0.9x1.25_mol_2000_2022.nc ',\n", - " 'XYLENES -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_XYLENES_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'XYLENES -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_XYLENES_0.9x1.25_mol_2000_2022.nc ',\n", - " 'bc_a4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_bc_a4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'bc_a4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_bc_a4_0.9x1.25_mol_2000_2022.nc ',\n", - " 'DMS -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_DMS_0.9x1.25_mol_2000_2022.nc ',\n", - " 'DMS -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_DMS_other_surface_1750-2015_0.9x1.25_c20170322.nc',\n", - " 'num_a1 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_num_so4_a1_0.9x1.25_mol_2000_2022.nc ',\n", - " 'num_a1 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_num_so4_a1_anthro-ag-ship_surface_1750-2015_0.9x1.25_c20170616.nc',\n", - " 'num_a2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_num_so4_a2_anthro-res_surface_1750-2015_0.9x1.25_c20170616.nc',\n", - " 'num_a4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_num_bc_a4_0.9x1.25_mol_2000_2022.nc ',\n", - " 'num_a4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_num_bc_a4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'num_a4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_num_pom_a4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'num_a4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_num_pom_a4_0.9x1.25_mol_2000_2022.nc ',\n", - " 'pom_a4 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_pom_a4_anthro_surface_1750-2015_0.9x1.25_c20170608.nc',\n", - " 'pom_a4 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_pom_a4_0.9x1.25_mol_2000_2022.nc ',\n", - " 'SO2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_SO2_anthro-ag-ship-res_surface_1750-2015_0.9x1.25_c20170616.nc',\n", - " 'SO2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_SO2_anthro-ene_surface_1750-2015_0.9x1.25_c20170616.nc',\n", - " 'SO2 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_SO2_0.9x1.25_mol_2000_2022.nc',\n", - " 'so4_a1 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_so4_a1_anthro-ag-ship_surface_1750-2015_0.9x1.25_c20170616.nc',\n", - " 'so4_a1 -> /glade/campaign/cesm/acom/MUSICA/emissions/qfed2.5_finn/f09/qfed.emis_so4_a1_0.9x1.25_mol_2000_2022.nc ',\n", - " 'so4_a2 -> /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/chem/emis/CMIP6_emissions_1750_2015/emissions-cmip6_so4_a2_anthro-res_surface_1750-2015_0.9x1.25_c20170616.nc'\n", - "/\n", - "```\n", - "\n", - "**# Lower boundary condition file check**\n", - "\n", - "Make sure the LBC files cover your dates of interest. In ``CaseDocs/atm_in`` you should see:\n", - "```\n", - " flbc_file = '/glade/campaign/cesm/cesmdata/cseg/inputdata/atm/waccm/lb/LBC_1750-2014_CMIP6_0p5degLat_c170126.nc'\n", - "```\n", - "\n", - "**# Initialization files for your new startdate**\n", - "\n", - "Note: Below are initialization files for 2010. For scientifically valid simulations you need to point to an initialization file for the start date you choose. If one is not available, you will need to spin up the model.\n", - "\n", - "For **CAM-chem**:\n", - "atmospheric initialization files are redefined in ``user_nl_cam``\n", - "\n", - "```\n", - "&cam_initfiles_nl\n", - " ncdata = '/glade/campaign/cesm/acom/acom-climate/tilmes/inputdata/init/camchem/FCnudged_MAM4_f09.carma_trop_strat09.aqchem.2001_2020.cam.i.2010-01-01-00000.nc'\n", - "/\n", - "```\n", - "\n", - "\n", - "For **WACCM**:\n", - "atmospheric initialization files are redefined in ``user_nl_cam``\n", - "\n", - "```\n", - "&cam_initfiles_nl\n", - " ncdata = '/glade/campaign/cesm/acom/acom-climate/tilmes/inputdata/init/waccm/f.e21.FWHISTBgcCrop.f09_f09_mg17.CMIP6-AMIP-WACCM.001.cam.i.2010-01-01-00000.nc'\n", - "/\n", - "``` \n", - "\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "\n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Submit:**\n", - "```\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the run files and history files. \n", - " \n", - "(1) Find your model output in your run directory (``$run_dir``) after finished:\n", - "\n", - "```\n", - "ls /glade/derecho/scratch/$USER/$CASENAME/run\n", - "```\n", - " \n", - "(2) Check that your archive directory on derecho (the path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "(3) Look at the contents of the ``h1`` files using ``ncdump``.\n", - "\n", - "```\n", - "ncdump –h f.e21.FCHIST.f09_f09_mg17.tutorial.test3.cam.h1.2010-01-01-00000.nc\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "affected-donna", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam-chem_waccm/visualization.ipynb b/notebooks_copy/challenge/cam-chem_waccm/visualization.ipynb deleted file mode 100644 index 7cab6a1ac..000000000 --- a/notebooks_copy/challenge/cam-chem_waccm/visualization.ipynb +++ /dev/null @@ -1,87 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "straight-opposition", - "metadata": {}, - "source": [ - "# 4: Visualization option with GEOV" - ] - }, - { - "cell_type": "markdown", - "id": "industrial-enhancement", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Visualization with GEOV

\n", - " \n", - "CESM history files are in standard netCDF format, and may be analyzed with standard\n", - "analysis tools, including Matlab, Python, IDL, NCL, and NCO and ncview.\n", - " \n", - "GEOV is an IDL-based viewer with a graphical user interface (GUI) for geophysical history files created by NCAR's CAM, WACCM and MOZART models. GEOV can be downloaded from the WACCM webpage
\n", - "http://www.cesm.ucar.edu/working_groups/Whole-Atmosphere/code-release.html
\n", - "It is useful for quick viewing and comparisons.\n", - " \n", - "**#To use GEOV on derecho/casper:**\n", - " \n", - "Add the idl module:
\n", - "``module load idl``
\n", - " \n", - "Then go to the location of GEOV and start it\n", - "```\n", - "cd /glade/u/home/fvitt/geov4.8e\n", - "idl geov\n", - "```\n", - " \n", - "Then you can use the GEOV interface to find files and explore the model output. Some examples of using the GEOV interface are below:\n", - "\n", - "![GEOV opening](../../../images/challenge/geov-openingfile.png)\n", - "

Figure 1: Example using GEOV to browse to a file to open.

\n", - " \n", - "![GEOV 2 files](../../../images/challenge/geov-2files.png)\n", - "

Figure 2: Example using GEOV to plot surface layer ozone for two files.

\n", - " \n", - "![GEOV difference](../../../images/challenge/geov-difference.png)\n", - "

Figure 3: Example using GEOV to look at zonal average difference in model ozone (left).

\n", - " \n", - " \n", - " \n", - "If you quit GEOV, you will need to type ``exit`` to also exit IDL.\n", - "\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "weekly-kennedy", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam/cam.ipynb b/notebooks_copy/challenge/cam/cam.ipynb deleted file mode 100644 index e6ae0430b..000000000 --- a/notebooks_copy/challenge/cam/cam.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Atmosphere" - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "## Learning Goals\n", - "\n", - "- Student will learn what a F compset is, the types of forcing available to run one, and how to run one.\n", - "- Student will learn how to modify datasets using nco operators." - ] - }, - { - "cell_type": "markdown", - "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", - "metadata": {}, - "source": [ - "## What is a F compset ?\n", - "\n", - "The CESM2 components can be combined in numerous ways to carry out various scientific or software experiments. A particular mix of components, along with component-specific configuration and/or namelist settings is called a component set or ``compset``. \n", - "\n", - "In the previous chapter, we have run experiments with the ``B`` compset. In this chapter we will run the experiments with the ``F`` compset. \n", - "The ``F`` compsets use prescribed ocean (observed sea-surface temperature data) and prescribed sea-ice (observed sea-ice thickness and area)\n", - "\n", - "- ``F2000climo`` uses climatological forcings from around year 2000\n", - "- ``F2010climo`` uses climatological forcings from around year 2010\n", - "- ``FHIST`` use time varying forcing\n", - "\n", - "![B compsets versus F compsets](../../../images/challenge/Fcompset.png)\n", - "\n", - "*

Figure: Differences between a F and a B compset.

*" - ] - }, - { - "cell_type": "markdown", - "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", - "metadata": {}, - "source": [ - "## Overview of CAM Challenge Exercises\n", - "Start running a control case with the compset ``F2000climo``\n", - "\n", - "Then choose one or more exercises to try:\n", - "- Use historical SSTs/forcings instead of fixed (compset change)\n", - "- Try running starting 1850 with spun-up pre-industrial model\n", - "- Increase orographic height over the western US (dataset change)\n", - "- Modify sea surface temperature in the tropics \n", - "- Increase the triggering threshold for deep convection over land (code change--simple)\n", - "- Add a (fake) physics parameterization (code change--advanced)\n", - "Compare your test exercise(s) to your control\n" - ] - }, - { - "cell_type": "markdown", - "id": "f01e969b-ab1a-466a-9cf0-5da2a137c35f", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "\n", - "\n", - "### Finding more information about compsets\n", - "\n", - "If you want to run a different configuration from what you’ve learned here, it is important to learn how to find and/or modify a compset.\n", - "\n", - "The tool `query compsets` allows you to find more information about the available. This tool is located in the same directory as `create_newcase` in `/glade/work/$USER/code/my_cesm_code/cime/scripts/`:\n", - "\n", - "The command is:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./query_config --compsets\n", - "```\n", - "\n", - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "Using the tool above and/or web searches below, find a CESM compset with an active atmosphere version cam6.0, that uses historical forcing data including sea surface temperatures. If you find several candidates, look at the components option and/or webpage to decide. Is it scientifically validated? For what resolutions?\n", - "
\n", - "\n", - "
\n", - " \n", - "
\n", - "Click here for the solution
\n", - "\n", - "The command gives a list of all the compsets available, and what components are included. \n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./query_config --compsets\n", - "```\n", - "This can show exactly what you ran before, and what is possible. To narrow down your choices:\n", - "```\n", - " ./query_config --compsets | grep -i hist\n", - "```\n", - "
\n", - "
\n", - "\n", - "\n", - "If you want to build your own, you can see all your options, perhaps modify one of the above with changes\u000b", - "\n", - "```\n", - "./query_config --components\n", - "```\n", - "More explanation about `compsets` can be found in the docs?|:\n", - "https://www.cesm.ucar.edu/models/cesm2/config/compsets.html\n", - "\n", - "\n", - "There are a number of atmospheric models which can run within CESM. While CAM is the basic atmospheric model within CESM, there are several models with significant extensions to CAM which may also be run within CESM. The available atmospheric models in CESM2 are:\n", - "\n", - "- CAM: Community Atmosphere Model\n", - "- CAM-chem: Community Atmosphere Model with Chemistry\n", - "- WACCM: Whole Atmosphere Community Climate Model\n", - "- WACCM-X: Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension\n", - "\n", - "https://ncar.github.io/CAM/doc/build/html/users_guide/atmospheric-configurations.html\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam/exercise_1.ipynb b/notebooks_copy/challenge/cam/exercise_1.ipynb deleted file mode 100644 index c24986518..000000000 --- a/notebooks_copy/challenge/cam/exercise_1.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 1: Control case: F2000climo" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Customize your CAM history files

\n", - " \n", - "Create, configure, build and run a case called ``f2000_control`` with the ``F2000climo`` compset (data ocean and climatological forcings from around year 2000). \n", - "\n", - "Run for 5 days, with 3-hourly instantaneous output of the variables: TS, PS, Z500, U850, U200, T850, T500, T200, CLDLOW, PRECT, LHFLX, SHFLX, FLNT, FLNS.\n", - "You are also welcome to output your own variables\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**How do I output 3 hourly instantaneous variables?**\n", - "\n", - "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", - "- For more information, look at the chapter:
\n", - "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", - "\n", - "**What is the resolution for F2000climo?**\n", - "\n", - "- Use resolution: f09_f09_mg17\n", - "\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " \n", - "**# Set environment variables** \n", - "\n", - "Set environment variables with the commands:\n", - " \n", - "**For tcsh users** \n", - " \n", - "```\n", - "set CASENAME=f2000_control\n", - "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET=F2000climo\n", - "set RESOLUTION=f19_f19_mg17\n", - "```\n", - "**For bash users** \n", - " \n", - "```\n", - "export CASENAME=f2000_control\n", - "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "export COMPSET=F2000climo\n", - "export RESOLUTION=f19_f19_mg17\n", - "```\n", - "\n", - "**# Make a case directory**\n", - "\n", - "If needed create a directory `cases` into your home directory:\n", - " \n", - "```\n", - "mkdir /glade/u/home/$USER/cases/\n", - "```\n", - " \n", - "\n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET\n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "Invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - "\n", - "**# Customize namelists**\n", - "\n", - "Edit the file ``user_nl_cam`` and add the lines:\n", - "```\n", - "nhtfrq(2) = -3\n", - "mfilt(2) = 240\n", - "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", - "```\n", - "You can do this with a text editor. Alternatively, you can use the echo command:\n", - "```\n", - "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", - "echo \"mfilt(2) = 240\">> user_nl_cam\n", - "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", - "echo \"\">> user_nl_cam \n", - "```\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "\n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Build and submit**:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the history files into the archive directory. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", - "\n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ncdump -h f2000_control.cam.h1.0001-01-01-00000.nc\n", - "```\n", - "\n", - "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", - "```\n", - " float FLNS(time, lat, lon) ;\n", - " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNS:units = \"W/m2\" ;\n", - " FLNS:long_name = \"Net longwave flux at surface\" ;\n", - " float FLNT(time, lat, lon) ;\n", - " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNT:units = \"W/m2\" ;\n", - " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", - " float LHFLX(time, lat, lon) ;\n", - " LHFLX:units = \"W/m2\" ;\n", - " LHFLX:long_name = \"Surface latent heat flux\" ;\n", - " float PRECT(time, lat, lon) ;\n", - " PRECT:units = \"m/s\" ;\n", - " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", - " float PS(time, lat, lon) ;\n", - " PS:units = \"Pa\" ;\n", - " PS:long_name = \"Surface pressure\" ;\n", - " float SHFLX(time, lat, lon) ;\n", - " SHFLX:units = \"W/m2\" ;\n", - " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", - " float T850(time, lat, lon) ;\n", - " T850:units = \"K\" ;\n", - " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", - " float TS(time, lat, lon) ;\n", - " TS:units = \"K\" ;\n", - " TS:long_name = \"Surface temperature (radiative)\" ;\n", - " float U850(time, lat, lon) ;\n", - " U850:units = \"m/s\" ;\n", - " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", - "```\n", - "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "472131c7-88f9-4863-a2bc-d7364333542d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam/exercise_2.ipynb b/notebooks_copy/challenge/cam/exercise_2.ipynb deleted file mode 100644 index 473bad878..000000000 --- a/notebooks_copy/challenge/cam/exercise_2.ipynb +++ /dev/null @@ -1,249 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 2: Historical compset: FHIST" - ] - }, - { - "cell_type": "markdown", - "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", - "metadata": {}, - "source": [ - "CAM is capable of running over historical periods with time-varying sea surface temperatures (SSTs) as well as anthropogenic and natural forcings. This is called the AMIP protocol.\n", - "\n", - "\n", - "
\n", - "\n", - "For more information about the [AMIP protocol](https://pcmdi.llnl.gov/mips/amip/) and the [HadSST data sets](https://climatedataguide.ucar.edu/climate-data/sst-data-hadisst-v11)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Customize your CAM history files

\n", - " \n", - "Create, configure, build and run a case called ``fhist`` with the compset ``FHIST`` at the resolution ``f09_f09_mg17`` using the same history file output as in the control.\n", - "Run for 5 days. \n", - "\n", - "- How can you check that there is a difference between the set up of this and your control?\n", - "- How can you check that it is running the way you intended: using ssts & ghg forcings from time-varying, historical files?\n", - "- What year is the model running?\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**How do I output 3 hourly instantaneous variables?**\n", - "\n", - "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", - "- For more information, look at the chapter:
\n", - "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", - "\n", - "**I am getting an error: ``This compset and grid combination is untested in CESM``**\n", - "\n", - "- overide this error by adding ``--run-unsupported`` to the create_newcase command\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " \n", - "**# Set environment variables** \n", - " \n", - "Set environment variables with the commands:\n", - "\n", - "**tcsh user**\n", - "```\n", - "set CASENAME=fhist\n", - "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET=FHIST\n", - "set RESOLUTION=f19_f19_mg17\n", - "```\n", - "**bash user**\n", - "```\n", - "export CASENAME=fhist\n", - "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "export COMPSET=FHIST\n", - "export RESOLUTION=f19_f19_mg17\n", - "``` \n", - " \n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET --run-unsupported\n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "Invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - "\n", - "**# Customize namelists**\n", - "\n", - "Edit the file ``user_nl_cam`` and add the lines:\n", - "```\n", - "nhtfrq(2) = -3\n", - "mfilt(2) = 240\n", - "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", - "```\n", - "You can do this with a text editor. Alternatively, you can use the echo command:\n", - "```\n", - "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", - "echo \"mfilt(2) = 240\">> user_nl_cam\n", - "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", - "echo \"\">> user_nl_cam \n", - "```\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "\n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Build and submit**:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the history files into the archive directory. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "Note that by default the starting year for a `FHIST` compset is `1950` instead of the year `0001` with a `F2000climo` compset.\n", - "\n", - "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", - " \n", - "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", - "```\n", - " float FLNS(time, lat, lon) ;\n", - " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNS:units = \"W/m2\" ;\n", - " FLNS:long_name = \"Net longwave flux at surface\" ;\n", - " float FLNT(time, lat, lon) ;\n", - " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNT:units = \"W/m2\" ;\n", - " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", - " float LHFLX(time, lat, lon) ;\n", - " LHFLX:units = \"W/m2\" ;\n", - " LHFLX:long_name = \"Surface latent heat flux\" ;\n", - " float PRECT(time, lat, lon) ;\n", - " PRECT:units = \"m/s\" ;\n", - " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", - " float PS(time, lat, lon) ;\n", - " PS:units = \"Pa\" ;\n", - " PS:long_name = \"Surface pressure\" ;\n", - " float SHFLX(time, lat, lon) ;\n", - " SHFLX:units = \"W/m2\" ;\n", - " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", - " float T850(time, lat, lon) ;\n", - " T850:units = \"K\" ;\n", - " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", - " float TS(time, lat, lon) ;\n", - " TS:units = \"K\" ;\n", - " TS:long_name = \"Surface temperature (radiative)\" ;\n", - " float U850(time, lat, lon) ;\n", - " U850:units = \"m/s\" ;\n", - " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", - "```\n", - "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", - "\n", - "
\n", - "
\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam/exercise_3.ipynb b/notebooks_copy/challenge/cam/exercise_3.ipynb deleted file mode 100644 index 511ee4664..000000000 --- a/notebooks_copy/challenge/cam/exercise_3.ipynb +++ /dev/null @@ -1,264 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 3: Starting FHIST from spunup state in 1850" - ] - }, - { - "cell_type": "markdown", - "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", - "metadata": {}, - "source": [ - "By default the FHIST starts from a default initial condition in 1979, but you can start from another year and use initial conditions coming from a previous run. " - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Customize your CAM history files

\n", - " \n", - "Create, configure, build and run a case called `fhist.1850` with the compset FHIST at the resolution `f09_f09_mg17` using the same history file output as in the control. \n", - "- Use initialization datasets from a previous case.
For instance, use the data from the run: `b.e21.B1850.f19_g17.CMIP6-piControl-2deg.001` at year `321`\n", - "- Start model in `1850`. \n", - "- Run for 5 days. \n", - "\n", - "Check your solution:\n", - "- How can you check that there is a difference between the set up of this and your control?\n", - "- How can you check that it is running the way you intended: using ssts & ghg forcings from time-varying, historical files?\n", - "- What year is the model running?\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - " \n", - "**How do I start my run using initialization datasets from a previous run?**\n", - "\n", - "- Look at chapter `Modifify the run type`. In that chapter, you learned different ways to start up the model, and how to use initial conditions/restart files from a different case to start up a hybrid case. Use that method for the current exercise. \n", - "\n", - "- Data from spun-up model runs can be found in inputdata (DIN_LOC_ROOT) . \n", - " \n", - "**How do I start my run in 1850?**\n", - "\n", - "- Look at the description of the xml variable `RUN_STARTDATE`\n", - " \n", - " \n", - "**How do I output 3 hourly instantaneous variables?**\n", - "\n", - "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", - "- For more information, look at the chapter:
\n", - "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", - "\n", - "**I am getting an error: ``This compset and grid combination is untested in CESM``**\n", - "\n", - "- overide this error by adding ``--run-unsupported`` to the create_newcase command\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " \n", - "**# Set environment variables** \n", - "\n", - "Set environment variables with the commands:\n", - "\n", - "**tcsh**\n", - "```\n", - "set CASENAME=fhist.1850\n", - "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET=FHIST\n", - "set RESOLUTION=f19_f19_mg17\n", - "```\n", - "**bash**\n", - "```\n", - "export CASENAME=fhist.1850\n", - "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "export COMPSET=FHIST\n", - "export RESOLUTION=f19_f19_mg17\n", - "```\n", - " \n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET --run-unsupported\n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the project number ``UESM0013``\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "Invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - "\n", - "**# Customize namelists**\n", - "\n", - "Edit the file ``user_nl_cam`` and add the lines:\n", - "```\n", - "nhtfrq(2) = -3\n", - "mfilt(2) = 240\n", - "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", - "```\n", - "You can do this with a text editor. Alternatively, you can use the echo command:\n", - "```\n", - "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", - "echo \"mfilt(2) = 240\">> user_nl_cam\n", - "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", - "echo \"\">> user_nl_cam \n", - "```\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "**# change starting date and ref case**\n", - "\n", - "``` \n", - "./xmlchange RUN_STARTDATE=1850-01-01 \n", - "./xmlchange RUN_REFCASE=b.e21.B1850.f19_g17.CMIP6-piControl-2deg.001\n", - "./xmlchange RUN_REFDATE=0321-01-01\n", - "```\n", - "\n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Build and submit**:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the history files into the archive directory. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "Notice that the start year is `1850`.\n", - "\n", - "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", - "\n", - "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", - "```\n", - " float FLNS(time, lat, lon) ;\n", - " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNS:units = \"W/m2\" ;\n", - " FLNS:long_name = \"Net longwave flux at surface\" ;\n", - " float FLNT(time, lat, lon) ;\n", - " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNT:units = \"W/m2\" ;\n", - " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", - " float LHFLX(time, lat, lon) ;\n", - " LHFLX:units = \"W/m2\" ;\n", - " LHFLX:long_name = \"Surface latent heat flux\" ;\n", - " float PRECT(time, lat, lon) ;\n", - " PRECT:units = \"m/s\" ;\n", - " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", - " float PS(time, lat, lon) ;\n", - " PS:units = \"Pa\" ;\n", - " PS:long_name = \"Surface pressure\" ;\n", - " float SHFLX(time, lat, lon) ;\n", - " SHFLX:units = \"W/m2\" ;\n", - " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", - " float T850(time, lat, lon) ;\n", - " T850:units = \"K\" ;\n", - " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", - " float TS(time, lat, lon) ;\n", - " TS:units = \"K\" ;\n", - " TS:long_name = \"Surface temperature (radiative)\" ;\n", - " float U850(time, lat, lon) ;\n", - " U850:units = \"m/s\" ;\n", - " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", - "```\n", - "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", - "\n", - "
\n", - "
\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam/exercise_4.ipynb b/notebooks_copy/challenge/cam/exercise_4.ipynb deleted file mode 100644 index da445e6be..000000000 --- a/notebooks_copy/challenge/cam/exercise_4.ipynb +++ /dev/null @@ -1,291 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 4: Increase orographic height over the western US" - ] - }, - { - "cell_type": "markdown", - "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", - "metadata": {}, - "source": [ - "Change input boundary datasets by increasing surface geopotential height by 50% in the western USA" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Increase orographic height over the western US

\n", - "\n", - "Create a case similar to control case but change input boundary datasets by increasing surface geopotential height by 50% in the western USA.\n", - "\n", - "\n", - "![Increase orographic height over the western US](../../../images/challenge/cam_topo_diff.png)\n", - "\n", - "*

Figure: Increase orographic height over the western US.

*\n", - " \n", - "Create, configure, build and run a case called ``f2000.topo`` with the compset ``F2000climo`` at the resolution ``f09_f09_mg17`` using the same history file output as in the control. Change the topography input datasets\n", - "Run for 5 days. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**How do I output 3 hourly instantaneous variables?**\n", - "\n", - "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", - "- For more information, look at the chapter:
\n", - "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", - "\n", - "\n", - "**Where do I change the topography dataset?**\n", - "\n", - "- Look at the namelist definition for the variable `bnd_topo` \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "\n", - "**# Set environment variables** \n", - " \n", - "Set environment variables with the commands:\n", - "\n", - "**tcsh user**\n", - "```\n", - "set CASENAME=f2000.topo\n", - "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET=F2000climo\n", - "set RESOLUTION=f19_f19_mg17\n", - "```\n", - "**bash user**\n", - "```\n", - "export CASENAME=f2000.topo\n", - "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "export COMPSET=F2000climo\n", - "export RESOLUTION=f19_f19_mg17\n", - "```\n", - " \n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET \n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "Invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - "\n", - "**# Modify orography**\n", - "\n", - "Copy the topography file into your case directory and modify it over there. \n", - "\n", - "Here we use the `nco` operators but feel free to use any other tool that you familiar with.\n", - "```\n", - "cd $CASEDIR\n", - "cp \\\n", - "/glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/topo/fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.nc .\n", - "\n", - "ncap2 -O -s 'lat2d[lat,lon]=lat ; lon2d[lat,lon]=lon' \\\n", - "-s 'omask=(lat2d >= 30. && lat2d <= 50.) && (lon2d >= 235. && lon2d <= 260.)' \\\n", - "-s 'PHIS=(PHIS*(1.+omask*0.5))' \\\n", - "fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.nc \\\n", - "fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.topo50.nc\n", - "``` \n", - "\n", - "**# Visualize your modification to orography**\n", - "\n", - "You could create a file `diff_topo.nc` with the differences of topography `ncdiff`and then visualize the that file with `ncview`. You also welcome to use your own tools to visualize your mods to orography.\n", - "\n", - "- Create a file `diff_topo.nc` with the differences in topographic:\n", - "```\n", - "ncdiff \\\n", - "fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.nc \\\n", - "fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.topo50.nc \\\n", - "diff_topo.nc\n", - "```\n", - "\n", - "- Look at the `diff_topo.nc` with `ncview`:\n", - "```\n", - "ncview diff_topo.nc\n", - "```\n", - "\n", - "![Increase orographic height over the western US](../../../images/challenge/cam_topo_diff.png)\n", - "\n", - "*

Figure: View the increase in orographic height over the western US with ncview.

*\n", - "\n", - "\n", - "**# Customize namelists**\n", - "\n", - "Edit the file ``user_nl_cam`` and add the lines:\n", - "```\n", - "nhtfrq(2) = -3\n", - "mfilt(2) = 240\n", - "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", - "bnd_topo = '$CASEDIR/fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.topo50.nc'\n", - "```\n", - "You can do this with a text editor. Alternatively, you can use the echo command:\n", - "```\n", - "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", - "echo \"mfilt(2) = 240\">> user_nl_cam\n", - "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", - "echo \"bnd_topo = '$CASEDIR/fv_1.9x2.5_nc3000_Nsw084_Nrs016_Co120_Fi001_ZR_GRNL_031819.topo50.nc' \" >> user_nl_cam\n", - "echo \"\">> user_nl_cam \n", - "```\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Build and submit**:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the history files into the archive directory. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", - "\n", - "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", - "```\n", - " float FLNS(time, lat, lon) ;\n", - " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNS:units = \"W/m2\" ;\n", - " FLNS:long_name = \"Net longwave flux at surface\" ;\n", - " float FLNT(time, lat, lon) ;\n", - " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNT:units = \"W/m2\" ;\n", - " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", - " float LHFLX(time, lat, lon) ;\n", - " LHFLX:units = \"W/m2\" ;\n", - " LHFLX:long_name = \"Surface latent heat flux\" ;\n", - " float PRECT(time, lat, lon) ;\n", - " PRECT:units = \"m/s\" ;\n", - " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", - " float PS(time, lat, lon) ;\n", - " PS:units = \"Pa\" ;\n", - " PS:long_name = \"Surface pressure\" ;\n", - " float SHFLX(time, lat, lon) ;\n", - " SHFLX:units = \"W/m2\" ;\n", - " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", - " float T850(time, lat, lon) ;\n", - " T850:units = \"K\" ;\n", - " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", - " float TS(time, lat, lon) ;\n", - " TS:units = \"K\" ;\n", - " TS:long_name = \"Surface temperature (radiative)\" ;\n", - " float U850(time, lat, lon) ;\n", - " U850:units = \"m/s\" ;\n", - " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", - "```\n", - "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam/exercise_5.ipynb b/notebooks_copy/challenge/cam/exercise_5.ipynb deleted file mode 100644 index d1174970d..000000000 --- a/notebooks_copy/challenge/cam/exercise_5.ipynb +++ /dev/null @@ -1,302 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 5: Modify sea surface temperature in the tropics" - ] - }, - { - "cell_type": "markdown", - "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", - "metadata": {}, - "source": [ - "Change input boundary datasets (Sea Surface Temperature) by increasing its value by 2K in the tropical Central Pacific. " - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Modify sea surface temperature in the tropics

\n", - "\n", - "Create a case similar to control case but change input boundary datasets (Sea Surface Temperature) by increasing its value by 2K in the tropical Central Pacific. \n", - "\n", - "\n", - "![Increase orographic height over the western US](../../../images/challenge/cam_SST_diff.png)\n", - "\n", - "*

Figure: Increase orographic height over the western US.

*\n", - " \n", - "Create, configure, build and run a case called ``f2000.sst`` with the compset ``F2000climo`` at the resolution ``f09_f09_mg17`` using the same history file output as in the control. Change the SST input datasets\n", - "Run for 5 days. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**How do I output 3 hourly instantaneous variables?**\n", - "\n", - "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", - "- For more information, look at the chapter:
\n", - "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", - "\n", - "\n", - "**Where do I change the SST dataset?**\n", - "\n", - "- Look at the xml variable `SSTICE_DATA_FILENAME` \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "\n", - "**# Set environment variables** \n", - " \n", - "Set environment variables with the commands:\n", - "\n", - "**tcsh user**\n", - "```\n", - "set CASENAME=f2000.sst\n", - "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET=F2000climo\n", - "set RESOLUTION=f19_f19_mg17\n", - "```\n", - "\n", - "\n", - "**bash user**\n", - "```\n", - "export CASENAME=f2000.sst\n", - "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "export COMPSET=F2000climo\n", - "export RESOLUTION=f19_f19_mg17\n", - "```\n", - " \n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET \n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "Invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - "\n", - "**# Modify orography**\n", - "\n", - "Copy the SST file into your case directory and modify it over there. \n", - "\n", - "Here we use the `nco` operators but feel free to use any other tool that you familiar with.\n", - "```\n", - "cd $CASEDIR\n", - "cp /glade/campaign/cesm/cesmdata/cseg/inputdata/atm/cam/sst/sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc .\n", - "\n", - "ncap2 -O -s 'lat2d[lat,lon]=lat ; lon2d[lat,lon]=lon' \\\n", - " -s 'omask=(lat2d >= -10. && lat2d <= 10.) && (lon2d >= 180. && lon2d <= 240.)'\\\n", - " -s 'SST_cpl=(SST_cpl+omask*2.)' sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc \\\n", - " sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc.warmtcp.nc\n", - "``` \n", - "\n", - "**# Visualize your modification to orography**\n", - "\n", - "You could create a file `diff_sst.nc` with the differences of SST `ncdiff`and then visualize the that file with `ncview`. You also welcome to use your own tools to visualize your mods to orography.\n", - "\n", - "- Create a file `diff_sst.nc` with the differences in SSTs:\n", - "```\n", - "ncdiff \\\n", - "sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc \\\n", - "sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc.warmtcp.nc \\\n", - "diff_sst.nc\n", - "```\n", - "\n", - "- Look at the `diff_sst.nc` with `ncview`:\n", - "```\n", - "ncview diff_sst.nc\n", - "```\n", - "\n", - "![Increase orographic height over the western US](../../../images/challenge/cam_SST_diff.png)\n", - "\n", - "*

Figure: View the increase in orographic height over the western US with ncview.

*\n", - "\n", - "**# Point to the new SST file**\n", - "\n", - "```\n", - "./xmlchange SSTICE_DATA_FILENAME=\"$CASEDIR/sst_HadOIBl_bc_1.9x2.5_2000climo_c180511.nc.warmtcp.nc\"\n", - "```\n", - "\n", - "**# Customize namelists**\n", - "\n", - "Edit the file ``user_nl_cam`` and add the lines:\n", - "```\n", - "nhtfrq(2) = -3\n", - "mfilt(2) = 240\n", - "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", - "```\n", - "You can do this with a text editor. Alternatively, you can use the echo command:\n", - "```\n", - "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", - "echo \"mfilt(2) = 240\">> user_nl_cam\n", - "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", - "echo \"\">> user_nl_cam \n", - "```\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Build and submit**:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the history files into the archive directory. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", - "\n", - "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", - "```\n", - " float FLNS(time, lat, lon) ;\n", - " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNS:units = \"W/m2\" ;\n", - " FLNS:long_name = \"Net longwave flux at surface\" ;\n", - " float FLNT(time, lat, lon) ;\n", - " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNT:units = \"W/m2\" ;\n", - " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", - " float LHFLX(time, lat, lon) ;\n", - " LHFLX:units = \"W/m2\" ;\n", - " LHFLX:long_name = \"Surface latent heat flux\" ;\n", - " float PRECT(time, lat, lon) ;\n", - " PRECT:units = \"m/s\" ;\n", - " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", - " float PS(time, lat, lon) ;\n", - " PS:units = \"Pa\" ;\n", - " PS:long_name = \"Surface pressure\" ;\n", - " float SHFLX(time, lat, lon) ;\n", - " SHFLX:units = \"W/m2\" ;\n", - " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", - " float T850(time, lat, lon) ;\n", - " T850:units = \"K\" ;\n", - " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", - " float TS(time, lat, lon) ;\n", - " TS:units = \"K\" ;\n", - " TS:long_name = \"Surface temperature (radiative)\" ;\n", - " float U850(time, lat, lon) ;\n", - " U850:units = \"m/s\" ;\n", - " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", - "```\n", - "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54e459f8-1ebc-4746-b667-4fd6bd2ce867", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cam/exercise_6.ipynb b/notebooks_copy/challenge/cam/exercise_6.ipynb deleted file mode 100644 index 8f21ceced..000000000 --- a/notebooks_copy/challenge/cam/exercise_6.ipynb +++ /dev/null @@ -1,295 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 6: Adjust threshold for deep convection over land" - ] - }, - { - "cell_type": "markdown", - "id": "ef3504b5-0dc7-4cc8-b874-bb4bdf7eab96", - "metadata": {}, - "source": [ - "The purpose of this exercise is to add a code change to the deep convection to delay the initiation of convection. This is done by increasing the minimum required convective available potential energy (CAPE) to initiate convection over land. " - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Effects of delaying the initiation of convection

\n", - "\n", - "Create a case similar to control case but add code to delay the initiation of convection over land by increasing the minimum required convective available potential energy (CAPE) to initiate convection. \n", - "\n", - "\n", - "![Increase orographic height over the western US](../../../images/challenge/cape.png)\n", - "\n", - "*

Figure: CAPE.

*\n", - " \n", - "Create, configure, build and run a case called ``f2000.cape`` with the compset ``F2000climo`` at the resolution ``f09_f09_mg17`` using the same history file output as in the control. \n", - "Run for 5 days. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**How do I output 3 hourly instantaneous variables?**\n", - "\n", - "- Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", - "- For more information, look at the chapter:
\n", - "**NAMELIST MODIFICATIONS** -> **Customize CAM output**\n", - "\n", - "\n", - "**Where do I change threshold for CAPE?**\n", - "\n", - "- Look for the file `zm_conv.F90` into the cesm code\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "\n", - "**# Set environment variables** \n", - " \n", - "Set environment variables with the commands:\n", - "\n", - "**Tcsh user**\n", - "```\n", - "set CASENAME=f2000.cape\n", - "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET=F2000climo\n", - "set RESOLUTION=f19_f19_mg17\n", - "```\n", - "**bash user**\n", - "```\n", - "export CASENAME=f2000.cape\n", - "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "export COMPSET=F2000climo\n", - "export RESOLUTION=f19_f19_mg17\n", - "```\n", - " \n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET \n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change ``job queue`` and ``account number``.
\n", - "For instance, to run in the queue ``regular`` and the project number ``UESM0013``. You should use the project number given for this tutorial.\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "Invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - "
\n", - "\n", - "**# Modify code**\n", - "\n", - "- copy `zm_conv.F90` into the `SourceMods/src.cam` directory and modify it\n", - "```\n", - "cp /glade/work/$USER/code/my_cesm_code/components/cam/src/physics/cam/zm_conv.F90 SourceMods/src.cam\n", - "```\n", - "\n", - "- In the subroutine `zm_convr`\n", - "\n", - "Replace the code:\n", - "```\n", - "if (cape(i) > capelmt) then \n", - " lengath = lengath + 1 \n", - " ideep(lengath) = i \n", - " end if\n", - "```\n", - "by\n", - "```\n", - "if (landfrac(i) > 0.5_r8) then\n", - " capelmt_mask = 10._r8*capelmt\n", - "else\n", - " capelmt_mask = capelmt\n", - "end if\n", - "\n", - "if (cape(i) > capelmt_mask) then \n", - " lengath = lengath + 1 \n", - " deep(lengath) = i \n", - "end if\n", - "\n", - "write(iulog,*) 'HELLO WORLD'\n", - "\n", - "```\n", - "\n", - "- Near the top of subroutine `zm_convr`:\n", - "\n", - "Add the line\n", - "```\n", - "real(r8) :: capelmt_mask\n", - "```\n", - "right after:\n", - "```\n", - "real(r8) pblt(pcols) \t\n", - "```\n", - "\n", - "**# Customize namelists**\n", - "\n", - "Edit the file ``user_nl_cam`` and add the lines:\n", - "```\n", - "nhtfrq(2) = -3\n", - "mfilt(2) = 240\n", - "fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\n", - "```\n", - "You can do this with a text editor. Alternatively, you can use the echo command:\n", - "```\n", - "echo \"nhtfrq(2) = -3\">> user_nl_cam \n", - "echo \"mfilt(2) = 240\">> user_nl_cam\n", - "echo \"fincl2 = 'TS:I','PS:I', 'U850:I','T850:I','PRECT:I','LHFLX:I','SHFLX:I','FLNT:I','FLNS:I'\">> user_nl_cam\n", - "echo \"\">> user_nl_cam \n", - "```\n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "**# Set run length**\n", - "\n", - "If needed, change the ``run length``. If you want to run 5 days, you don't have to do this, as 5 days is the default. \n", - "``` \n", - "./xmlchange STOP_N=5,STOP_OPTION=ndays\n", - "```\n", - "\n", - "\n", - "**# Build and submit**:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the history files into the archive directory. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is only 5-day, there should be no monthly file (``h0``)\n", - "\n", - "(2) Look at the contents of the ``h1`` files using ``ncdump``.\n", - "\n", - "- The file should contain the instantaneous output in the file ``h1`` for the variables:\n", - "```\n", - " float FLNS(time, lat, lon) ;\n", - " FLNS:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNS:units = \"W/m2\" ;\n", - " FLNS:long_name = \"Net longwave flux at surface\" ;\n", - " float FLNT(time, lat, lon) ;\n", - " FLNT:Sampling_Sequence = \"rad_lwsw\" ;\n", - " FLNT:units = \"W/m2\" ;\n", - " FLNT:long_name = \"Net longwave flux at top of model\" ;\n", - " float LHFLX(time, lat, lon) ;\n", - " LHFLX:units = \"W/m2\" ;\n", - " LHFLX:long_name = \"Surface latent heat flux\" ;\n", - " float PRECT(time, lat, lon) ;\n", - " PRECT:units = \"m/s\" ;\n", - " PRECT:long_name = \"Total (convective and large-scale) precipitation rate (liq + ice)\" ;\n", - " float PS(time, lat, lon) ;\n", - " PS:units = \"Pa\" ;\n", - " PS:long_name = \"Surface pressure\" ;\n", - " float SHFLX(time, lat, lon) ;\n", - " SHFLX:units = \"W/m2\" ;\n", - " SHFLX:long_name = \"Surface sensible heat flux\" ;\n", - " float T850(time, lat, lon) ;\n", - " T850:units = \"K\" ;\n", - " T850:long_name = \"Temperature at 850 mbar pressure surface\" ;\n", - " float TS(time, lat, lon) ;\n", - " TS:units = \"K\" ;\n", - " TS:long_name = \"Surface temperature (radiative)\" ;\n", - " float U850(time, lat, lon) ;\n", - " U850:units = \"m/s\" ;\n", - " U850:long_name = \"Zonal wind at 850 mbar pressure surface\" ;\n", - "```\n", - "Note that these variables have no ``cell_methods`` attribute becasue the output is instantaneous. \n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc170590-11bc-40fa-a209-4ed1d1f64849", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/challenge.ipynb b/notebooks_copy/challenge/challenge.ipynb deleted file mode 100644 index 0cd561b2d..000000000 --- a/notebooks_copy/challenge/challenge.ipynb +++ /dev/null @@ -1,45 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0959db1d-1790-4519-a34e-7026444af537", - "metadata": {}, - "source": [ - "# Challenge Exercises" - ] - }, - { - "cell_type": "markdown", - "id": "33e420f8-dca3-47ed-9999-17da427c6443", - "metadata": {}, - "source": [ - "This section of the CESM tutorial is designed to test your understanding of the CESM model that you have learned about in previous sections. \n", - "\n", - "We provide challenge exercises for the individual model components for you to test yourself. \n", - "\n", - "Feel free to try all the challenge exercises or just the one(s) that are relevant for the CESM components of interest to you." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cice/cice.ipynb b/notebooks_copy/challenge/cice/cice.ipynb deleted file mode 100644 index 3f01f39f1..000000000 --- a/notebooks_copy/challenge/cice/cice.ipynb +++ /dev/null @@ -1,218 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Sea Ice\n", - "\n", - "The sea ice component of CESM is CICE. This is developed by the [CICE Consortium](https://github.com/CICE-Consortium/CICE). Note that CESM2 uses CICE version 5. In version 6 of CICE, the vertical thermodynamics was separated into a submodule known as Icepack. CICE6 will be the sea ice component in CESM3. \n", - "\n", - "It can be useful for people interested in sea ice science to run simulations with only active sea ice and ocean components and atmospheric forcing. In this exercise, you will learn how to run one of these ice-ocean simulations.\n", - "\n", - "This exercise was created by David Bailey and Alice DuVivier." - ] - }, - { - "cell_type": "markdown", - "id": "ea051c12-642e-4194-8291-2c1924d2e1e8", - "metadata": {}, - "source": [ - "## Learning Goals" - ] - }, - { - "cell_type": "markdown", - "id": "346cbd7b-3b8e-41f0-b120-b369ab20f6cc", - "metadata": {}, - "source": [ - "- Student will learn what a G compset is, the types of forcing available to run one, and how to run one.\n", - "- Student will learn how to make a namelist modification that changes snow albedo and compare results with a control experiment.\n", - "- Student will learn how to make a source code modification that changes the conductivity through snow (ksno) and compare results with a control experiment.\n" - ] - }, - { - "cell_type": "markdown", - "id": "47c3af32-d018-441e-bcb0-c74e86ef4272", - "metadata": {}, - "source": [ - "## Exercise Details" - ] - }, - { - "cell_type": "markdown", - "id": "df216c41-3ec4-43e0-b7bd-be2b1e4294bd", - "metadata": {}, - "source": [ - "- This exercise uses the same code base as the rest of the tutorial. \n", - "- You will be using the G compset at the T62_g37 resolution.\n", - "- You will run a control simulation and two experimental simulations. Each simulation will be run for one year. \n", - "- You will use simple, command line netcdf tools to evaluate how the experiments differ from the control simulation." - ] - }, - { - "cell_type": "markdown", - "id": "fd2ff959-6ec0-4534-8713-f5dfdb13b955", - "metadata": {}, - "source": [ - "## Useful CICE references" - ] - }, - { - "cell_type": "markdown", - "id": "baa2afd1-4289-4baf-8bdf-83de7592d76b", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM CICE User's Guide](https://www.cesm.ucar.edu/models/cesm2/sea-ice)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "523878ee-2bfa-4363-a8ec-5ff151b15d57", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "\n", - "[CESM CICE Discussion Forum](https://bb.cgd.ucar.edu/cesm/forums/cesm-cice.137/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "cb8a0e10-a920-41dd-8bc4-5c03395d231e", - "metadata": {}, - "source": [ - "## What is a G case?" - ] - }, - { - "cell_type": "markdown", - "id": "69988cae-fd26-4fda-8fee-6ab52e79f42d", - "metadata": {}, - "source": [ - "The G compset has active and coupled ocean and sea-ice components. The G compset requires boundary forcing from the atmosphere. The G compset is forced with atmospheric data that does not change interactively as the ocean and sea-ice evolve in time. The land and land ice are not active during a G compset experiment run and the runoff is specified. " - ] - }, - { - "cell_type": "markdown", - "id": "68ca54e2-d8ad-41bc-be8f-31a85eec6e65", - "metadata": {}, - "source": [ - "![gcase](../../../images/challenge/gcase.png)\n", - "\n", - "*

Figure: G Compset definition.

*" - ] - }, - { - "cell_type": "markdown", - "id": "c93817fd-8031-4917-bf45-eb0f442578f9", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Compset Definitions](https://www2.cesm.ucar.edu/models/cesm2/config/compsets.html)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "5cbebcff-7d58-4d34-8613-6c1613ab438e", - "metadata": {}, - "source": [ - "## G Compset forcing data" - ] - }, - { - "cell_type": "markdown", - "id": "6deea789-4c3f-475d-9fc9-a1153fb3061b", - "metadata": {}, - "source": [ - "There are two types of temporal forcing for G compsets:\n", - "- Normal Year Forcing (NYF) is 12 months of atmospheric data (like a climatology) that repeats every year. NYF is the default forcing.\n", - "- Interannual varying forcing (GIAF) is forcing that varies by year over the time period (1948-2017). \n", - "\n", - "There are two datasets that can be used for G compsets:\n", - "- JRA55-do atmospheric data \\([Tsujino et al. 2018](https://doi.org/10.1016/j.ocemod.2018.07.002)\\)\n", - "- Coordinated Ocean-ice Reference Experiments (CORE) version 2 atmospheric data \\([Large and Yeager 2009](http://doi.org/10.1007/s00382-008-0441-3)\\).\n", - "\n", - "In these exercises we will use the CORE NYF." - ] - }, - { - "cell_type": "markdown", - "id": "da3d9215-30c1-4152-abc0-ccb1e709cde3", - "metadata": {}, - "source": [ - "## Post processing and viewing your output\n", - "\n", - "You will use [ncview](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#ncview) and [NCO operator](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#netcdf-operators-nco) tools to evaluate how the experiments differ from the control simulation.\n", - "\n", - "These modules should already be loaded into your environment, so verify that they are in your environment and it not load them using the NCAR HPC [modules](https://ncar.github.io/CESM-Tutorial/notebooks/tools/unix/modules.html).\n", - "\n", - "```\n", - "module list\n", - "```\n", - "\n", - "If they are not listed, then you can load the modules:\n", - "\n", - "```\n", - "module load ncview\n", - "module load nco\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "id": "828e538d-3558-4747-9b61-3b29da04e294", - "metadata": {}, - "source": [ - "1) You can create an annual average of the first year's data for each simulationg using the `ncra` (netCDF averager) command from the netCDF operators package \\([NCO](https://nco.sourceforge.net/)\\). \n", - "```\n", - "ncra $OUTPUT_DIR/*.cice.h.*nc $CASENAME.cice.h.0001.nc\n", - "```\n", - "\n", - "2) Create a file that contains differences between each of the experiments and the control simulation\n", - "```\n", - "ncdiff $CASENAME.cice.h.0001.nc $CONTROLCASE.cice.h.0001.nc $CASENAME_diff.nc\n", - "```\n", - "\n", - "3) Examine variables within each annual mean and the difference files using `ncview`\n", - "```\n", - "ncview $CASENAME_diff.nc\n", - "```\n", - "\n", - "4) You can also look at other monthly-mean outputs or component log files." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cice/cice_exercise_1.ipynb b/notebooks_copy/challenge/cice/cice_exercise_1.ipynb deleted file mode 100644 index 41990385a..000000000 --- a/notebooks_copy/challenge/cice/cice_exercise_1.ipynb +++ /dev/null @@ -1,212 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 1: Control case" - ] - }, - { - "cell_type": "markdown", - "id": "e2bfe3d7-3e5f-46ea-a30c-65fc1743c69e", - "metadata": {}, - "source": [ - "**NOTE:** Building the control case for the CICE challenge exercises is idential to building the control case in the POP challenge exercises. If you have already completed the POP challenge exercises you can skip this step." - ] - }, - { - "cell_type": "markdown", - "id": "6457c1d2-0530-435d-ae27-d0f1eeabe583", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Run a control case

\n", - " \n", - "Create a case called **g_control** using the compset `G` at `T62_g37` resolution. \n", - " \n", - "Set the run length to **1 year**. \n", - "\n", - "Build and run the model. Since this is a control case, we want to build it \"out of the box\" without any modifications. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "
\n", - " \n", - "**How do I compile?**\n", - "
\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "
\n", - " \n", - "**How do I control the output?**\n", - "
\n", - "\n", - "Use namelist variables: `histfreq`, `histfreq_n`, and `f_var`. \n", - "\n", - "```\n", - "histfreq = 'm','d','x','x','x'\n", - "histfreq_n = 1,1,1,1,1\n", - "\n", - "f_aice = 'mdxxx'\n", - "f_hi = 'mdxxx'\n", - "```\n", - "
\n", - "\n", - "[CICE History](https://cesmcice.readthedocs.io/en/latest/users_guide/ice_history.html#history-files)\n", - "
\n", - "\n", - "**How do I check my solution?**\n", - "
\n", - "\n", - "When your run is completed, go to the archive directory. \n", - "\n", - "(1) Check that your archive directory contains the files:\n", - "\n", - "- `h` files\n", - "```\n", - "g_control.cice.h.0001-01.nc\n", - "```\n", - "- `h1` files\n", - "```\n", - "g_control.cice.h1.0001-01-01.nc\n", - "g_control.cice.h1.0001-01-02.nc\n", - "```\n", - "
\n", - "\n", - "(2) Compare the contents of the `h` and `h1` files using `ncdump`.\n", - "```\n", - "ncdump -h g_control.cice.h.0001-01.nc\n", - "ncdump -h g_control.cice.h1.0001-01-01.nc\n", - "```\n", - "
\n", - "\n", - "Look at the sizes of the files. \n", - "```\n", - "ls -l g_control.cice.h.0001-01.nc\n", - "ls -l g_control.cice.h1.0001*.nc\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Create a new case g_control with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case /glade/work/$USER/cases/g_control --compset G --res T62_g37 \n", - "```\n", - "
\n", - "\n", - "Case setup:\n", - "``` \n", - "cd ~/cases/g_control \n", - "./case.setup\n", - "```\n", - "
\n", - "\n", - "Change the run length:\n", - "``` \n", - "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", - "```\n", - "
\n", - "\n", - "If needed, change job queue \n", - "and account number. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "
\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "
\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "g_control. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/g_control/ice/hist\n", - "\n", - "ls \n", - "```\n", - "
\n", - "\n", - "(2) Compare the contents of the `h` and `h1` files using `ncdump`.\n", - "\n", - "(3) Check the number of timesteps / files for the `h` and the `h1` files. \n", - "\n", - "- `h1` has 31 time samples / files.\n", - " \n", - "- Check the size of the files\n", - "```\n", - "du –ks –h /glade/derecho/scratch/$USER/archive/g_control/ice/hist/*\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17cf7e19-1211-45f2-97cd-fe2badc69dac", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cice/cice_exercise_2.ipynb b/notebooks_copy/challenge/cice/cice_exercise_2.ipynb deleted file mode 100644 index d46c199c4..000000000 --- a/notebooks_copy/challenge/cice/cice_exercise_2.ipynb +++ /dev/null @@ -1,261 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 2: Tune the albedo" - ] - }, - { - "cell_type": "markdown", - "id": "0037b73f-f174-48e7-8e4f-0744d7d23fe0", - "metadata": {}, - "source": [ - "One of the most common changes to the cice model involves changing the albedo of the snow on the surface of the sea ice using the delta-Eddington radiation scheme. To tune the albedo, you must actually change the inherent optical properties of the snow, bare ice, or ponds.\n", - "\n", - "Here we will experiment with changing the snow properties using the `r_snw` parameter. \n", - "\n", - "`r_snw` specifies the number of **standard deviations** away from the base optical properties of the shortwave radiative transfer code. `r_snw` is used to determine the non-melting snow grain radius using the following equation:" - ] - }, - { - "cell_type": "markdown", - "id": "d49c7e65-cc71-4838-91a9-0bbc27393e24", - "metadata": {}, - "source": [ - "$$\n", - " {rsnw}_{nonmelt} = 500 - r_{snw} * 250\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "737f543b-8c17-42a2-98a0-da3b5de9368e", - "metadata": {}, - "source": [ - "This is in microns $(\\mu m)$ and `rsnw_nonmelt` has a minimum value of 100 and a maximum value of `rsnw_mlt`. As can be seen, when `r_snw` is larger then there is a lower value of `rsnw_nonmelt`. When `rsnw_nonmelt` is lower then the albedos are higher and vice versa. This is because smaller grains lead to higher albedos. Hence the sign of `r_snw` is positive for higher albedos and negative for lower albedos." - ] - }, - { - "cell_type": "markdown", - "id": "67bee263-c16d-43d3-989b-03959c0b31dd", - "metadata": {}, - "source": [ - "The figure below shows how this works. The x-axis is temperature and the y-axis is effective snow grain radius." - ] - }, - { - "cell_type": "markdown", - "id": "1ce8051e-1439-4c94-8d10-07712dd9c9d3", - "metadata": {}, - "source": [ - "![rsnw](../../../images/challenge/cice_rsnw2.png)\n", - "\n", - "*

Figure: r_snw parameter plot.

*" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Change snow albedo

\n", - " \n", - "Create a case called **g_snowalbedo** by cloning the control experiment case. \n", - " \n", - "Verify that the run length is set to **1 year**. \n", - "\n", - "In user_nl_cice make the following modifications:`r_snw = 2.0`.\n", - "\n", - "Build and run the model for one year. \n", - "\n", - "Provide info about how to compare the simulations using ncview/ncdiff, etc.\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", - "metadata": {}, - "source": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - " \n", - "**How do I compile?**\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "
\n", - "\n", - "**How do I control the output?**\n", - "\n", - "Use namelist variables: `histfreq`,`histfreq_n`, and `f_var`.\n", - "\n", - "Look at the online documentation for these variables.\n", - "\n", - "**How do I check my solution?**\n", - "\n", - "When your run is completed, go to the archive directory. \n", - "\n", - "(1) Check that your archive directory contains the files:\n", - "\n", - "- `h` files\n", - "```\n", - "g_snowalbedo.cice.h.0001-01.nc\n", - "```\n", - "- `h1` files\n", - "```\n", - "g_snowalbedo.cice.h1.0001-01-01-00000.nc\n", - "g_snowalbedo.cice.h1.0001-02-01-00000.nc\n", - "```\n", - "
\n", - "\n", - "(2) Compare the contents of the `h` and `h1` files using `ncdump`.\n", - "\n", - "```\n", - "ncdump -h g_snowalbedo.cice.h.0001-01-01-00000.nc\n", - "ncdump -h g_snowalbedo.cice.h1.0001-01-01-00000.nc\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Clone a new case g_snowalbedo from your control experiment with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_clone --case /glade/work/$USER/cases/g_snowalbedo --clone /glade/work/$USER/cases/g_control\n", - "```\n", - "
\n", - "\n", - "Case setup:\n", - "``` \n", - "cd /glade/work/$USER/cases/g_snowalbedo\n", - "./case.setup\n", - "```\n", - "
\n", - "\n", - "Verify that the run length is 1 year:\n", - "``` \n", - "./xmlquery STOP_N\n", - "./xmlquery STOP_OPTION\n", - "```\n", - "
\n", - "\n", - "Edit the file user_nl_cice and add the lines:\n", - "```\n", - " r_snw = 2.0\n", - "```\n", - "
\n", - "\n", - "If needed, change job queue \n", - "and account number. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "
\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "
\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "g_snowalbedo. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/g_snowalbedo/ice/hist\n", - "ls \n", - "```\n", - "
\n", - "\n", - "(2) Compare to control run:\n", - "```\n", - "ncdiff g_snowalbedo.cice.h.0001-01.nc /glade/derecho/scratch/$USER/archive/g_control/ice/hist/g_control.cice.h.0001-01.nc g_diff.nc\n", - "\n", - "ncview g_diff.nc\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "ff6921a7-bb56-4617-8e13-1d9169c9dd8f", - "metadata": { - "tags": [] - }, - "source": [ - "## Test your understanding" - ] - }, - { - "cell_type": "markdown", - "id": "e46ea9f9-937c-48f8-bbec-7b16dc7b2266", - "metadata": { - "tags": [] - }, - "source": [ - "- What changes do you see from the control case with an increased snow albedo? Try other values of `r_snw`.\n", - "- What time of year did you start your run and which season do you expect to see the biggest impact for shortwave changes?\n", - "- How did changes in the Arctic vs. the Antarctic compare?\n", - "- Are the modified `r_snw` values physically realistic? Why or why not? Why do you think this parameter is sometimes used to tune ESMs?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57e44486-5057-4394-9e00-46ed9c2623d9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cice/cice_exercise_3.ipynb b/notebooks_copy/challenge/cice/cice_exercise_3.ipynb deleted file mode 100644 index 6318ca3b7..000000000 --- a/notebooks_copy/challenge/cice/cice_exercise_3.ipynb +++ /dev/null @@ -1,241 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 3: Modify the snow conductivity" - ] - }, - { - "cell_type": "markdown", - "id": "63f3e4ed-fcf6-4b6e-bdfd-2eccb16b640d", - "metadata": {}, - "source": [ - "One of the more sensitive sea ice parameters is the snow thermal conductivity or `ksno` parameter \\([Urrego-Blanco et al. 2016](https://doi.org/10.1002/2015JC011558)\\). Thermal conductivity determines the ability of the snow to conduct heat through it.\n", - "\n", - "$$\n", - "F_{cond} = - k_{sno} * dT / dz\n", - "$$\n", - "\n", - "Here you will see how this impacts a simulation by multiplying this value by a factor of three. In this version of the CICE model, this is actually a hard coded parameter in a Fortran module. It was found that this parameter was important enough that it was included in the namelist in future versions of CICE.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "ee251754-9068-46b7-a36c-78784d7fc6c9", - "metadata": {}, - "source": [ - "
\n", - "Exercise 3: Something with source code

\n", - " \n", - "Create a case (clone) called **g_ksno** using the compset `G` at `T62_g37` resolution. \n", - " \n", - "Set the run length to **1 year**. \n", - "\n", - "Go to the main source directory under /glade/work/$USER/code/my_cesm_code/components/cice/src and search for the variable `ksno`.\n", - " \n", - "Copy the source module from the main CESM code directory into $CASE/SourceMods/src.cice.\n", - "\n", - "Edit the fortran code in SourceMods/src.cice.\n", - " \n", - "Find the variable \"ksno\" and change this to 0.9.\n", - " \n", - "Build and run the model for one year. \n", - "\n", - "Provide info about how to compare the simulations using ncview/ncdiff, etc.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "604924a5-bf6e-4102-a019-8f50cbf35c35", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - " \n", - "**How do I compile?**\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "
\n", - "\n", - "**How do I control the output?**\n", - "\n", - "Use namelist variables: `histfreq`,`histfreq_n`, and `f_var`.\n", - "\n", - "Look at the online documentation for these variables.\n", - "\n", - "**How do I check my solution?**\n", - "\n", - "When your run is completed, go to the archive directory. \n", - "\n", - "(1) Check that your archive directory contains the files:\n", - "\n", - "- `h` files\n", - "```\n", - "g_ksno.cice.h.0001-01.nc\n", - "```\n", - "- `h1` files\n", - "```\n", - "g_ksno.cice.h1.0001-01-01-00000.nc\n", - "g_ksno.cice.h1.0001-02-01-00000.nc\n", - "```\n", - "
\n", - "\n", - "(2) Compare the contents of the `h` and `h1` files using `ncdump`.\n", - "\n", - "```\n", - "ncdump -h g_ksno.cice.h.0001-01-01-00000.nc\n", - "ncdump -h g_ksno.cice.h1.0001-01-01-00000.nc\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "031b65a2-ab0f-4784-bab3-1b4824e6e029", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " Clone a new case g_ksno from your control experiment with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_clone --case /glade/work/$USER/cases/g_ksno --clone /glade/work/$USER/cases/g_control\n", - "```\n", - "
\n", - "\n", - "Case setup:\n", - "``` \n", - "cd /glade/work/$USER/cases/g_ksno\n", - "./case.setup\n", - "```\n", - "
\n", - "\n", - "Verify that the run length is 1 year:\n", - "``` \n", - "./xmlquery STOP_N\n", - "./xmlquery STOP_OPTION\n", - "```\n", - "
\n", - "\n", - "Copy the file from the $CODEROOT directory:\n", - "```\n", - "cp /glade/work/$USER/code/my_cesm_code/components/cice/src/drivers/cesm/ice_constants.F90 /glade/work/$USER/cases/g_ksno/SourceMods/src.cice\n", - "\n", - "vi /glade/work/$USER/cases/g_ksno/SourceMods/src.cice/ice_constants.F90\n", - "```\n", - "
\n", - "\n", - "Change the following line to a value of `0.90_dbl_kind`:\n", - "```\n", - " ksno = 0.30_dbl_kind ,&! thermal conductivity of snow (W/m/deg)\n", - "```\n", - "
\n", - "\n", - "If needed, change job queue \n", - "and account number. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "
\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "
\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "g_ksno. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/g_ksno/ice/hist\n", - "ls \n", - "```\n", - "
\n", - "\n", - "(2) Compare to control run:\n", - "```\n", - "ncdiff g_ksno.cice.h.0001-01.nc /glade/derecho/scratch/$USER/archive/g_control/ice/hist/g_control.cice.h.0001-01.nc g_diff.nc\n", - "\n", - "ncview g_diff.nc\n", - "``` \n", - " \n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "928576e7-dc1e-4b85-88e7-1b6e9b09f0d8", - "metadata": { - "tags": [] - }, - "source": [ - "## Test your understanding" - ] - }, - { - "cell_type": "markdown", - "id": "617f643f-50e3-4867-996a-0a72080d1d6e", - "metadata": { - "tags": [] - }, - "source": [ - "- What changes do you see in the ice state from the control case with increased thermal conductivity? Are these changes in line with what you expect?\n", - "- How did changes in the Arctic vs. the Antarctic compare?\n", - "- How does the magnitude of the changes compare to the snow albedo changes done in exercise 2? Do these results agree with those of [Urrego-Blanco et al. 2016](https://doi.org/10.1002/2015JC011558)?\n", - "- Are the modified `ksno` values physically realistic? Why or why not? Could you imagine this being used as a tuning parameter for ESMs?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8e311bb4-822f-4150-ad15-0572864365dc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cism/cism.ipynb b/notebooks_copy/challenge/cism/cism.ipynb deleted file mode 100755 index 72044ffdb..000000000 --- a/notebooks_copy/challenge/cism/cism.ipynb +++ /dev/null @@ -1,152 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cd674360-c487-4160-92f8-67a283d8104d", - "metadata": {}, - "source": [ - "# Land Ice\n", - "\n", - "The land ice component of CESM is the Community Ice Sheet Model (CISM). Running CESM with a fully evolving ice sheet and 2-way coupling is relatively new and is not the default CESM fully-coupled setup. \n", - "\n", - "However, it can be useful for people interested in land ice science to investigate the impact of atmospheric forcing on the ice sheet. In this exercise, you will learn how to evolve CISM in an uncoupled configuration using an existing CESM forcing dataset.\n", - "\n", - "This exercise was created by Gunter Leguy and Kate Thayer-Calder." - ] - }, - { - "cell_type": "markdown", - "id": "e220126d-bde0-4f98-a9ed-889932480f54", - "metadata": {}, - "source": [ - "## Learning Goals" - ] - }, - { - "cell_type": "markdown", - "id": "edf500e6-fbd0-42ca-88ea-e0e94ceb5813", - "metadata": {}, - "source": [ - "- Student will learn what a T compset is, the types of forcing available to run one, and how to run one.\n", - "- Student will learn how to make XML variable modifications to force CISM with forcing data from an existing run.\n", - "- Student will learn how to look at 2D and time series CISM history datasets.\n", - "- Student will learn how to compute the sea level change due ice sheet evolution. " - ] - }, - { - "cell_type": "markdown", - "id": "3caefecd-731a-496d-9245-1586cdec05ba", - "metadata": { - "tags": [] - }, - "source": [ - "## Exercise Details" - ] - }, - { - "cell_type": "markdown", - "id": "01b99858-0566-4d36-a5b5-0f0d7550d6a0", - "metadata": {}, - "source": [ - "- This exercise uses the same codebase as the rest of the tutorial. \n", - "- You will be using the T compset at the f19_g17_gl4 resolution.\n", - "- You will run an experimental simulation for 86 years. \n", - "- You will then use jupyter notebooks to look at your simulation." - ] - }, - { - "cell_type": "markdown", - "id": "cefcfbc6-e5c7-4323-b292-5bba542ddabb", - "metadata": {}, - "source": [ - "## Useful CISM references" - ] - }, - { - "cell_type": "markdown", - "id": "2b884ee2-d45d-4b76-ab55-f69be8acbe64", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM CISM User's Guide](https://www.cesm.ucar.edu/models/cesm2/land-ice)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "1c520b9a-5e96-4aee-927d-5b9124a45ac7", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "\n", - "[CESM CISM Discussion Forum](https://bb.cgd.ucar.edu/cesm/forums/cism.135/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "d720b227-8b14-4bcf-8b99-04acf4e2a94b", - "metadata": {}, - "source": [ - "## What is a T case?" - ] - }, - { - "cell_type": "markdown", - "id": "3b6cd7b4-1086-4397-bb58-f9ab8f475e5f", - "metadata": {}, - "source": [ - "The CESM T compset runs only the ice sheet component (here CISM). Within a T compset case, CISM can run in Evolve or Non-Evolve mode and is forced by output from a previous CESM run. A \"plain\" T compset (like T1850) runs CISM in Non-Evolve mode and a \"TG\" compset (like T1850G) runs CISM in Evolve mode. All other components of a T compset are stubs. Before running a T compset, you must have coupler history files from a previous run that included CLM (version 4.5 or later). You can run with either existing forcing data or with your own forcing data.\\\n", - "Typically, coupler history files for CISM are yearly averages downscaled onto the CISM grid. For now, downscaled variables include surface temperature and glacier ice flux available at each elevation class. (In the future, ocean data will also be available.)" - ] - }, - { - "cell_type": "markdown", - "id": "e0126170-58d9-4f81-82d7-c5b1406a196a", - "metadata": {}, - "source": [ - "![Tcase](../../images/challenge/Tcompset.png)\n", - "\n", - "*

Figure: TG compset definition.

*" - ] - }, - { - "cell_type": "markdown", - "id": "94de8f70-6391-4201-83fe-3a61c514750a", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[T Compset definition](https://www2.cesm.ucar.edu/models/cesm2/config/compsets.html)\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "CMIP6 2019.10", - "language": "python", - "name": "cmip6-201910" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cism/cism_exercise_1.ipynb b/notebooks_copy/challenge/cism/cism_exercise_1.ipynb deleted file mode 100755 index b0e19e070..000000000 --- a/notebooks_copy/challenge/cism/cism_exercise_1.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a76577f4-b549-4ae4-afd4-5e58e83375be", - "metadata": {}, - "source": [ - "# CISM Challenge Exercise" - ] - }, - { - "cell_type": "markdown", - "id": "bcd30c0c-7ae2-47ef-b228-b32b5bdbb0a0", - "metadata": {}, - "source": [ - "In this challenge, you will set up and run a T compset forced with existing output from a B compset simulation (fully coupled climate simulation with no evolving ice sheet). The experiment will force the Greenland ice sheet (GrIS) with atmospheric forcing spanning 2015-2100 that were created from a 2 degree fully-coupled SSP5-8.5 scenario experiment. \n", - "\n", - "**WARNING**\\\n", - "The forcing data you will use in this exercise has not been scientifically validated yet, or ever been looked at carefully. You are entering the world of research that we deal with daily at NCAR :)." - ] - }, - { - "cell_type": "markdown", - "id": "f3d1fec9-fad1-48ab-ba75-ac1c0177aeb4", - "metadata": {}, - "source": [ - "
\n", - "Step 1: Create your T compset experiment

\n", - " \n", - "Create a case called **T_GrIS_SSP585_2015_2100** using the compset ``T1850G`` at ``f19_g17_gl4`` resolution. \n", - "\n", - "**Note1:** The 2 deg grid we are using in this exercise is not scientifically validated. \n", - "**Note2:** In the new version of CESM, the compset names will be different and T1850G is replaced by T1850Gg (evolving GrIS) and the grid resolution f19_g17_gl4 will be replaced by f19_g17_gris4. \n", - "**Note3:** The grid resolution for this case and the forcing data are both f19_g17_gl4. For a T compset, the forcing data and experiment must be at the same grid resolution.\n", - "\n", - "Step 2: Make the changes to the case XML variables

\n", - "\n", - "**Note1:** The run length is **86 years**. \\\n", - "**Note2:** The forcing data for this case can be found here: **/glade/u/home/gunterl/workshops/CESM-Tutorial/data/cpl_SSP585**\n", - "\n", - "Step 3: Build and run the model

\n", - " \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "860bdf2f-c173-4e46-859c-dc45c9ca60a0", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "\n", - "**How to I create a case a case that is not scientifically supported?**\n", - "\n", - "You need to add the option \n", - "```\n", - "--run-unsupported\n", - "```\n", - "at the end of your call to **./create_newcase**\n", - " \n", - " \n", - "**How do I make changes to case xml variables?**\n", - "\n", - "Make your changes to xml variables using the command:\n", - "```\n", - "./xmlchange\n", - "```\n", - " \n", - " \n", - "**How do I compile?**\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "cdaee5ee-b575-4484-a78e-23a88c140ffd", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Set up your tutorial run queue if you have not done this yet. For csh:\n", - "```\n", - "setenv TUTORIAL_QUEUE regular\n", - "```\n", - "And for bash:\n", - "```\n", - "export TUTORIAL_QUEUE=\"regular\"\n", - "```\n", - " \n", - " \n", - "Create a new case T_GrIS_SSP585_2015_2100 with the command:\n", - "```\n", - "cd /glade/campaign/cesm/development/cross-wg/tutorial/cesm2.1_tutorial2022/cime/scripts\n", - "./create_newcase --case ~/cases/T_GrIS_SSP585_2015_2100 --compset T1850G --res f19_g17_gl4 --run-unsupported\n", - "```\n", - "\n", - "Case setup:\n", - "``` \n", - "cd ~/cases/T_GrIS_SSP585_2015_2100\n", - "./case.setup\n", - "```\n", - " \n", - "Change the run length:\n", - "``` \n", - "./xmlchange STOP_N=86,STOP_OPTION=nyears\n", - "```\n", - "\n", - "If you are completing this exercise outside of the tutorial, change job queue \n", - "and account number. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "Modify the environment variables:\n", - "```\n", - "./xmlchange DLND_CPLHIST_DIR=/glade/u/home/gunterl/workshops/CESM-Tutorial/data/cpl_SSP585\n", - "./xmlchange DLND_CPLHIST_CASE=b.e21.BSSP585cmip6.f19_g17.CMIP6-SSP5-8.5.001\n", - "./xmlchange DLND_CPLHIST_YR_START=2015\n", - "./xmlchange DLND_CPLHIST_YR_END=2100\n", - "./xmlchange RUN_STARTDATE=2015-01-01\n", - "./xmlchange DLND_CPLHIST_YR_ALIGN=2015\n", - "```\n", - "\n", - "Confirm that you have set up the paths and file names correctly by running:\n", - "```\n", - "./preview_namelists\n", - "and examine the generated file, CaseDocs/dlnd.streams.txt.sno.cplhist.\n", - "```\n", - "\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "T_GrIS_SSP585_2015_2100. \n", - " \n", - "(1) Check your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/T_GrIS_SSP585_2015_2100/glc/hist\n", - "ls \n", - "```\n", - " \n", - "(2) Take a look at the contents of a file using ``ncdump``.\n", - "```\n", - "ncdump -h T_GrIS_SSP585_2015_2100.cism.h.2016-01-01-00000.nc\n", - "```\n", - "\n", - "You will notice that CISM outputs are written yearly (by default) even though the time step is 0.1 year. Changes in ice sheet variables that are typically looked at (such as changes in ice mass or ice thickness) will be captured at this output frequency. \n", - "\n", - "\n", - "
\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "CMIP6 2019.10", - "language": "python", - "name": "cmip6-201910" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cism/cism_exercise_2.ipynb b/notebooks_copy/challenge/cism/cism_exercise_2.ipynb deleted file mode 100644 index 8ac04912c..000000000 --- a/notebooks_copy/challenge/cism/cism_exercise_2.ipynb +++ /dev/null @@ -1,649 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b94bf6b6-ccac-40e8-ad8f-1b5d993ec48d", - "metadata": {}, - "source": [ - "# Looking at the simulation\n", - "\n", - "Here we will plot a few diagnostics to see what happens to the ice sheet during the simulation. We will focus on comparing the last time slice to the first time slice.\n", - "\n", - "\n", - "First, and for convenience, we will rename the first output file corresponding to year 2015." - ] - }, - { - "cell_type": "markdown", - "id": "49386dc4-979d-4919-b72d-f8a7b6757b9e", - "metadata": {}, - "source": [ - "
\n", - " \n", - " Find your history output on derecho (descibed above as well):

\n", - "``cd /glade/derecho/scratch/$USER/archive/T_GrIS_SSP585_2015_2100/glc/hist``
\n", - "``ls``
\n", - " \n", - " Rename the initial_hist file to the start date and time:

\n", - "\n", - "``\n", - "mv T_GrIS_SSP585_2015_2100.cism.initial_hist.2015-01-01-00000.nc T_GrIS_SSP585_2015_2100.cism.h.2015-01-01-00000.nc\n", - "``\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "916e2e9b-fd7e-4fc3-83f9-4f69c052a16b", - "metadata": {}, - "source": [ - "### Loading packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "e78178bd-6f9f-420c-9a5d-1da6201d6fab", - "metadata": {}, - "outputs": [], - "source": [ - "#import xarray as xr \n", - "import numpy as np \n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt \n", - "import matplotlib.colors as mplc\n", - "import matplotlib.cm as mcm\n", - "from netCDF4 import Dataset\n", - "import netCDF4\n", - "\n", - "# to display figures in notebook after executing the code.\n", - "%matplotlib inline " - ] - }, - { - "cell_type": "markdown", - "id": "071fa04b-c689-4c76-8da6-eed739ca6398", - "metadata": {}, - "source": [ - "### Set your user name " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "988024ed-5662-4f7c-bc78-670f27710d4f", - "metadata": {}, - "outputs": [], - "source": [ - "User = ''" - ] - }, - { - "cell_type": "markdown", - "id": "177b05f1-ec2b-4086-9f26-2234c19d18c6", - "metadata": {}, - "source": [ - "### Set the 2 years you would like to compare" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "74fd7507-61c7-4b62-8f93-2a555c58c9ca", - "metadata": {}, - "outputs": [], - "source": [ - "year1 = '2016'\n", - "year2 = '2101'" - ] - }, - { - "cell_type": "markdown", - "id": "e57243eb-3968-4a6d-9e6a-b477507507ed", - "metadata": {}, - "source": [ - "### Defining the path and filenames " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "12cf918c-9dee-4595-84da-a1522ea9e1a6", - "metadata": {}, - "outputs": [], - "source": [ - "# Defining the path\n", - "path_to_file = '/glade/derecho/scratch/' + User + '/archive/T_GrIS_SSP585_2015_2100/glc/hist/'\n", - "\n", - "# Defining the files to compare\n", - "file1 = path_to_file + 'T_GrIS_SSP585_2015_2100.cism.h.' + year1 + '-01-01-00000.nc'\n", - "file2 = path_to_file + 'T_GrIS_SSP585_2015_2100.cism.h.' + year2 + '-01-01-00000.nc'\n", - "\n", - "# Loading the data\n", - "ncfile = Dataset(file1,'r')\n", - "thk1 = np.squeeze(ncfile.variables[\"thk\"][0,:,:]) # ice thickness\n", - "artm1 = np.squeeze(ncfile.variables[\"artm\"][0,:,:]) # air temperature\n", - "smb1 = np.squeeze(ncfile.variables[\"smb\"][0,:,:])/1000. # surface mass balance, switching the units from mm/yr w.e. to m/yr w.e\n", - "ncfile.close()\n", - "\n", - "ncfile = Dataset(file2,'r')\n", - "thk2 = np.squeeze(ncfile.variables[\"thk\"][0,:,:])\n", - "artm2 = np.squeeze(ncfile.variables[\"artm\"][0,:,:])\n", - "smb2 = np.squeeze(ncfile.variables[\"smb\"][0,:,:])/1000. # switching the units from mm/yr w.e. to m/yr w.e\n", - "ncfile.close()\n", - "\n", - "# Computing the differences in the different variables\n", - "diff_thk = thk2 - thk1 # thickness difference\n", - "diff_artm = artm2 - artm1 # air temperature difference\n", - "diff_smb = smb2 - smb1 # SMB difference" - ] - }, - { - "cell_type": "markdown", - "id": "918d524b-c3f9-40cd-84dd-e1924ff15ea9", - "metadata": {}, - "source": [ - "## Looking at 2D plots\n", - "\n", - "**Note:**\n", - "In these plots, you can adjust the range of the colorbars by adjusting the \"vmin\" and \"vmax\" values of each subplot." - ] - }, - { - "cell_type": "markdown", - "id": "8630fc1d-8656-4b52-b51c-c00f1ca43d51", - "metadata": {}, - "source": [ - "### Air temperature" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cbb7265a-095b-4bf5-b700-523fef9fe1a9", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "labelsize=15\n", - "\n", - "my_cmap = mcm.get_cmap('Spectral_r')\n", - "\n", - "\n", - "fig, ax = plt.subplots(1, 3, sharey=True, figsize=[21, 9])\n", - "\n", - "\n", - "# Plotting air temperature for year1\n", - "\n", - "vmin = -20\n", - "vmax = 20\n", - "\n", - "last_panel0 = ax[0].imshow(artm1, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title0 = 'Air temperature (deg C), ' + year1 \n", - "ax[0].set_title(title0, fontsize=labelsize)\n", - "\n", - "fig.subplots_adjust(right=0.8)\n", - "\n", - "pos = ax[0].get_position()\n", - "cax = fig.add_axes([0.32, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "cbar = fig.colorbar(last_panel0,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "cbar.ax.get_yaxis().labelpad = 15\n", - "\n", - "\n", - "\n", - "# Plotting air temperature for year2\n", - "\n", - "last_panel1 = ax[1].imshow(artm2, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title1 = 'Air temperature (deg C), ' + year2 \n", - "\n", - "ax[1].set_title(title1, fontsize=labelsize)\n", - "\n", - "pos = ax[1].get_position()\n", - "cax = fig.add_axes([0.56, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "cbar = fig.colorbar(last_panel1,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "cbar.ax.get_yaxis().labelpad = 15\n", - "\n", - "\n", - "\n", - "# Plotting the difference in air temperature\n", - "\n", - "\n", - "vmin = -15\n", - "vmax = 15\n", - "\n", - "last_panel2 = ax[2].imshow(diff_artm, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title2 = 'Difference (deg C), ' + year2 + '-' + year1 \n", - "ax[2].set_title(title2, fontsize=labelsize)\n", - "pos = ax[2].get_position()\n", - "cax = fig.add_axes([0.8, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "cbar = fig.colorbar(last_panel2,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "\n", - "\n", - "for i in range(len(ax)):\n", - " ax[i].invert_yaxis()\n", - " ax[i].set_xlabel('')\n", - " ax[i].set_ylabel('')\n", - " ax[i].set_xticklabels('')\n", - " ax[i].set_yticklabels('')\n", - " ax[i].set_xticks([])\n", - " ax[i].set_yticks([])\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "e352d623-3525-4b67-b86c-23e25ceb4608", - "metadata": {}, - "source": [ - "### Surface mass balance" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "053ef542-d289-4dd7-8881-8a1a6b324ac0", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "labelsize=15\n", - "\n", - "my_cmap = mcm.get_cmap('Spectral_r')\n", - "\n", - "\n", - "fig, ax = plt.subplots(1, 3, sharey=True, figsize=[21, 9])\n", - "\n", - "\n", - "# Plotting SMB for year1\n", - "\n", - "vmin = -4\n", - "vmax = 4\n", - "\n", - "last_panel0 = ax[0].imshow(smb1, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title0 = 'SMB (m/yr w.e), ' + year1 \n", - "\n", - "ax[0].set_title(title0, fontsize=labelsize)\n", - "\n", - "\n", - "fig.subplots_adjust(right=0.8)\n", - "\n", - "pos = ax[0].get_position()\n", - "cax = fig.add_axes([0.32, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "\n", - "cbar = fig.colorbar(last_panel0,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "\n", - "cbar.ax.get_yaxis().labelpad = 15\n", - "\n", - "\n", - "\n", - "# Plotting SMB for year2\n", - "\n", - "last_panel1 = ax[1].imshow(smb2, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title1 = 'SMB (m/yr w.e), ' + year2 \n", - "\n", - "ax[1].set_title(title1, fontsize=labelsize)\n", - "pos = ax[1].get_position()\n", - "cax = fig.add_axes([0.56, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "cbar = fig.colorbar(last_panel1,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "cbar.ax.get_yaxis().labelpad = 15\n", - "\n", - "\n", - "\n", - "# Plotting the difference in SMB\n", - "\n", - "\n", - "vmin = -3\n", - "vmax = 3\n", - "\n", - "last_panel2 = ax[2].imshow(diff_smb, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title2 = 'Difference (m/yr w.e), ' + year2 + '-' + year1 \n", - "\n", - "ax[2].set_title(title2, fontsize=labelsize)\n", - "\n", - "\n", - "pos = ax[2].get_position()\n", - "cax = fig.add_axes([0.8, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "cbar = fig.colorbar(last_panel2,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "\n", - "\n", - "for i in range(len(ax)):\n", - " ax[i].invert_yaxis()\n", - " ax[i].set_xlabel('')\n", - " ax[i].set_ylabel('')\n", - " ax[i].set_xticklabels('')\n", - " ax[i].set_yticklabels('')\n", - " ax[i].set_xticks([])\n", - " ax[i].set_yticks([])\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "e003a818-7fe2-49e3-b973-fa90e169acfd", - "metadata": {}, - "source": [ - "### Ice Thickness" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fe0a05e5-13e7-450f-a8f1-6f7be9a90c12", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "labelsize=15\n", - "my_cmap = mcm.get_cmap('Spectral_r')\n", - "\n", - "\n", - "fig, ax = plt.subplots(1, 3, sharey=True, figsize=[21, 9])\n", - "\n", - "# Plotting thickness for year1\n", - "\n", - "vmin = 0\n", - "vmax = 3500\n", - "\n", - "last_panel0 = ax[0].imshow(thk1, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title0 = 'Ice thickness (m), ' + year1 \n", - "ax[0].set_title(title0, fontsize=labelsize)\n", - "\n", - "fig.subplots_adjust(right=0.8)\n", - "\n", - "pos = ax[0].get_position()\n", - "cax = fig.add_axes([0.32, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "\n", - "cbar = fig.colorbar(last_panel0,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "\n", - "cbar.ax.get_yaxis().labelpad = 15\n", - "\n", - "\n", - "\n", - "# Plotting thickness for year2\n", - "\n", - "last_panel1 = ax[1].imshow(thk2, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title1 = 'Ice thickness (m), ' + year2 \n", - "\n", - "ax[1].set_title(title1, fontsize=labelsize)\n", - "\n", - "pos = ax[1].get_position()\n", - "cax = fig.add_axes([0.56, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "cbar = fig.colorbar(last_panel1,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "cbar.ax.get_yaxis().labelpad = 15\n", - "\n", - "\n", - "\n", - "# Plotting the difference in ice thickness\n", - "\n", - "vmin = -300\n", - "vmax = 300\n", - "\n", - "last_panel2 = ax[2].imshow(diff_thk, vmin=vmin, vmax=vmax,\n", - " cmap=my_cmap)\n", - "\n", - "title2 = 'Difference (m), ' + year2 + '-' + year1 \n", - "\n", - "ax[2].set_title(title2, fontsize=labelsize)\n", - "pos = ax[2].get_position()\n", - "cax = fig.add_axes([0.8, pos.y0, 0.015, pos.y1 - pos.y0])\n", - "cbar = fig.colorbar(last_panel2,cax=cax)\n", - "cbar.ax.tick_params(labelsize=labelsize)\n", - "\n", - "\n", - "for i in range(len(ax)):\n", - " ax[i].invert_yaxis()\n", - " ax[i].set_xlabel('')\n", - " ax[i].set_ylabel('')\n", - " ax[i].set_xticklabels('')\n", - " ax[i].set_yticklabels('')\n", - " ax[i].set_xticks([])\n", - " ax[i].set_yticks([])\n" - ] - }, - { - "cell_type": "markdown", - "id": "38529a7f-2e31-4853-83b2-02218e85290e", - "metadata": {}, - "source": [ - "## Looking at time series\n", - "Each output history file contains a few scalars that give information about the state of the ice sheet. Here we are going to look at:\n", - "- the grounded ice area\n", - "- the ice mass\n", - "- the ice mass above flotation" - ] - }, - { - "cell_type": "markdown", - "id": "e8014b04-4075-4eb0-b01c-157383637c7b", - "metadata": {}, - "source": [ - "
\n", - " Combining all time for each scalar in a single time series file

\n", - "\n", - "While we could create an array using python for each scalar by looping through every single file, it is convenient to extract them in their own file using nco. \n", - "To do this we will use the command \"ncrcat\"\n", - " \n", - "On derecho: \n", - "```\n", - "module load nco\n", - "```\n", - " \n", - "**For mass**\n", - "```\n", - "ncrcat -v imass T_GrIS_SSP585_2015_2100.cism.h.*.nc mass.nc\n", - "```\n", - "\n", - "**For grounded ice area**\n", - "```\n", - "ncrcat -v iareag T_GrIS_SSP585_2015_2100.cism.h.*.nc area_ground.nc\n", - "```\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b3bfbfe2-c8f1-4bf0-88ed-a967843ad2db", - "metadata": {}, - "source": [ - "Now we can look at the time series evolution" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8935ff8e-37ef-4238-b8cd-35841f0565d2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Loading the variables\n", - "file_mass = path_to_file + \"mass.nc\"\n", - "file_areag = path_to_file + \"area_ground.nc\"\n", - "\n", - "ncfile = Dataset(file_mass,'r')\n", - "mass = ncfile.variables[\"imass\"][:]\n", - "time_mass = ncfile.variables[\"time\"][:]\n", - "ncfile.close()\n", - "\n", - "ncfile = Dataset(file_areag,'r')\n", - "area = ncfile.variables[\"iareag\"][:]\n", - "time_area = ncfile.variables[\"time\"][:]\n", - "ncfile.close()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bf483b28-980c-4311-bb32-2eacb1b4eea2", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Ice area evolution (m2)')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "labelsize=15\n", - "\n", - "timemin = 2015\n", - "timemax = 2101\n", - "\n", - "color = 'black'\n", - "line = '-'\n", - "\n", - "plt.figure(figsize=(12,5))\n", - "\n", - "# Plotting the Ice mass evolution time series\n", - "\n", - "plt.subplot(121)\n", - "plt.plot(time_mass, mass, line, ms=3, mfc=color, color=color)\n", - "plt.xlim([timemin, timemax])\n", - "plt.xlabel('Time (yr)', multialignment='center',fontsize=labelsize)\n", - "plt.title('Ice mass evolution (kg)',fontsize=labelsize)\n", - "\n", - "\n", - "# Plotting the Ice area evolution time series\n", - "\n", - "plt.subplot(122)\n", - "plt.plot(time_area, area, line, ms=3, mfc=color, color=color)\n", - "plt.xlim([timemin, timemax])\n", - "plt.xlabel('Time (yr)', multialignment='center',fontsize=labelsize)\n", - "plt.title('Ice area evolution (m2)',fontsize=labelsize)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff2ee88c-52ec-4dc2-b9df-5cfd71984df4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "CMIP6 2019.10", - "language": "python", - "name": "cmip6-201910" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/cism/cism_exercise_3.ipynb b/notebooks_copy/challenge/cism/cism_exercise_3.ipynb deleted file mode 100644 index 5c6e688ee..000000000 --- a/notebooks_copy/challenge/cism/cism_exercise_3.ipynb +++ /dev/null @@ -1,219 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "69f9f86e-e033-441c-99d0-3e5bd35214b2", - "metadata": {}, - "source": [ - "# Computing ice sheet related sea level change from a CISM simulation" - ] - }, - { - "cell_type": "markdown", - "id": "b2cc6d1d-7ae7-4c31-ac57-862997b422dd", - "metadata": {}, - "source": [ - "Global sea level varies as a function of primarily 3 component changes:\n", - "- Ocean thermal expension\n", - "- Glacier\n", - "- Ice sheets\n", - "\n", - "**Note 1:** Here we are talking about **global** and not local changes. Local impact, on different time scales, needs to include the effect of tides and winds among other parameters.\\\n", - "**Note 2:** In our case, we will look at the sea level changes from the Greenland ice sheet only, as that is the only actively evolving ice sheet in our simulation.\\\n", - "**Note 3:** Articles have been published (e.g. [Goelzer et al. (2020)](https://tc.copernicus.org/articles/14/833/2020/)) arguing that the computation of sea level change from stand alone ice sheet model output needs to correct for other factors (e.g. bedrock elevation changes, density correction due to the small difference between ice and fresh water density). In this exercise, we will only be accounting for the changes in total ice volume above floatation. The other correcting factors account for about 10% of the total sea level change magnitude (in general). " - ] - }, - { - "cell_type": "markdown", - "id": "2f6f9e38-9ac8-4a9d-b632-6ec56bf11cfa", - "metadata": {}, - "source": [ - "
\n", - "Step 1: Understanding ice sheet sea level contribution

\n", - "\n", - "\n", - "Only looking at the change of mass of an ice sheet is not enough to compute sea level contribution. In fact, any ice already in the ocean (including the bits still attached to the ice sheet) is already displacing sea water, hence contributing to sea level. What we need to compute is the change of mass that is not displacing sea water, or in other words, the change of mass above floatation.

\n", - " \n", - " \n", - "Step 2: Knowing the conversion
\n", - "\n", - "Because ice, fresh water, and ocean water have different densities, it takes about **360 Gt** of ice to raise the mean global sea level (MGSL) by **1 mm**. \n", - "A good description on how this number of 360 Gt was obtained can be found here:\\\n", - "[Calculating glacier ice volumes and sea level equivalents](https://www.antarcticglaciers.org/wp-content/plugins/antarcticglaciers-pdf/download.php?p=7657#:~:text=In%20the%20same%20way%2C%201,sea%20levels%20by%201%20mm.)\n", - " \n", - "
\n", - "Step 3: Compute the contribution of the GrIS to sea level change from this experiment.

\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "bae11c14-9fe4-4f5a-95b7-ab47b05edf8f", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - " \n", - "**(1) Compute the change in mass above flotation**\n", - "\n", - "Luckily, this scalar is part of the CISM history output variables and is called **imass_above_flotation**\n", - " \n", - "**(2) Create a time series for mass above flotation**\n", - " \n", - "You can follow similar steps as done previously for ice mass.\n", - "\n", - "**(3) Load the time series data and use the conversion number above to finalize your computation**\n", - "\n", - "**(4) Plot your results**\n", - " \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "5005a00b-2a07-4cef-86b3-4e264539b6e7", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "\n", - "Create a time series for imass_above_flotation scalars in a single file:\n", - "\n", - "```\n", - "ncrcat -v imass_above_flotation T_GrIS_SSP585_2015_2100.cism.h.*.nc mass_above_flotation.nc \n", - "```\n", - "\n", - "Copy and execute the text bellow in a new jupyter window\n", - "\n", - "```\n", - "# Defining the conversion constant\n", - "mm_equiv = 360.e12 # converting Gt to kg \n", - "\n", - "# Reading in the mass above flotation data\n", - "User = 'username'\n", - "path_to_file = '/glade/derecho/scratch/' + User + '/archive/T_GrIS_SSP585_2015_2100/glc/hist/'\n", - "file_mass_above_flotation = path_to_file + \"mass_above_flotation.nc\"\n", - "ncfile = Dataset(file_mass_above_flotation,'r')\n", - "mass_af = ncfile.variables[\"imass_above_flotation\"][:]\n", - "time_maf = ncfile.variables[\"time\"][:]\n", - "ncfile.close()\n", - "\n", - "\n", - "# Computing the overal sea level contribution from the GrIS during this experiment\n", - "slc = -(mass_af[:] - mass_af[0])/mm_equiv\n", - "print('The GrIS contribution of global mean sea level is ',slc[-1],'mm')\n", - "\n", - "\n", - "# Plotting the time series of see level change\n", - "sizefontx = 13\n", - "sizefonty = 13\n", - "sizefonttitle = 13\n", - "color = 'black'\n", - "line = '-'\n", - "\n", - "\n", - "timemin = 2015\n", - "timemax = 2101\n", - "\n", - "plt.figure(figsize=(7,5))\n", - "\n", - "plt.plot(time_maf, slc, line, ms=3, mfc=color, color=color)\n", - "plt.xlim([timemin, timemax])\n", - "plt.xlabel('Time (yr)', multialignment='center',fontsize=sizefontx)\n", - "plt.ylabel('Sea level change (mm)', multialignment='center',fontsize=sizefonty)\n", - "plt.title('GrIS contribution to sea level change',fontsize=sizefonttitle)\n", - "\n", - "\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "7e76b55a-c994-4475-ad9b-09ae72e9663d", - "metadata": {}, - "source": [ - "
\n", - "Food for thought

\n", - " \n", - "\n", - "(1) Based on the sea level change figure, the Greenland ice sheet has been accumulating more ice than it has lost leading to a sea level sink. What do you think about these results?\n", - "\n", - "(2) Do you have any suspicions about the forcing used in this experiment? If so, what are they?\n", - " \n", - "(3) What else could you be suspicious about?\n", - "\n", - "(4) What would you do to validate these results?\n", - "\n", - "\n", - "Note:
these questions are quite challenging. Contact Gunter Leguy (gunterl@ucar.edu) for further discussion.\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "1f57aa63-503b-418d-a955-04fbd8c1bedc", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for some answers
\n", - " \n", - "\n", - "Question 1\n", - "The current observations show a positive Greenland ice sheet (GrIS) contribution to sea level change of about 1 mm per year over the past decade. For this reason, it is surprising to see it contribute negatively in this set of experiments especially under a strong warming scenario and these results are highly suspicious. \n", - "\n", - "Question 2\n", - "Looking at the SMB forcing, the values are negative along the coasts and positive in the interior of the ice sheet (especially in the south). Looking at the CISM thickness difference plot between the end and beginning of the simulation, the ice thickness evolution correlates closely with the changes in SMB. It is difficult (without further analyzes) to draw any conclusions about the forcing. One can always suspect whether we set the experiment properly. Also, this experiment has not yet been analyzed and one can suspect that the 2 deg version of the CESM model could lead to unexpected climate simulations. \n", - " \n", - "Question 3\n", - "In this exercise, we have not talked about how the GrIS was initialized and then used in CESM. Initialization is one of the greatest uncertainties in ice sheet modeling. Here, the model was spun-up using the SMB and air temperature of the RACMO2.3 regional climate model. This way we could invert for the basal physics using observed ice thickness and bed topography. However, there are some biases between RACMO results and the CESM simulated SMB and air temperature which will impact the ice sheet evolution. Ideally, a simulation would correct for these biases which is not the case here. \n", - " \n", - "Question 4\n", - "At least a couple of steps should be considered to validate these results:\n", - "1. Look at the biases between the observed fields used to initialize CISM and compare them to those in CESM at the beginning of the simulation.\n", - "2. Compare the forcing of the FV2 (2 deg model) output to the FV1 (1 deg model) output and look for potential biases due to the CESM model resolution. \n", - "\n", - "
\n", - "
\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "CMIP6 2019.10", - "language": "python", - "name": "cmip6-201910" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/clm_ctsm/clm_ctsm.ipynb b/notebooks_copy/challenge/clm_ctsm/clm_ctsm.ipynb deleted file mode 100644 index fd254f236..000000000 --- a/notebooks_copy/challenge/clm_ctsm/clm_ctsm.ipynb +++ /dev/null @@ -1,196 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Land\n", - "\n", - "The land component of CESM is the Community Land Model (CLM), also refered to as the Community Terrestrial Systems Model (CTSM).\n", - "\n", - "It can be useful for people interested in land science to run simulations with only an active land components and atmospheric forcing. In this exercise, you will learn how to run one of these land-only simulations.\n", - "\n", - "This exercise was created by Peter Lawrence, Erik Kluzek and Alice DuVivier." - ] - }, - { - "cell_type": "markdown", - "id": "ea051c12-642e-4194-8291-2c1924d2e1e8", - "metadata": {}, - "source": [ - "## Learning Goals" - ] - }, - { - "cell_type": "markdown", - "id": "346cbd7b-3b8e-41f0-b120-b369ab20f6cc", - "metadata": {}, - "source": [ - "- Student will learn what a I compset is, the types of forcing available to run one, and how to run one.\n", - "- Student will learn how to run a case with satellite phenology and one with prognostic crops and compare the two experiments.\n", - "- Learn what inputs CLM needs and what they look like.\n" - ] - }, - { - "cell_type": "markdown", - "id": "47c3af32-d018-441e-bcb0-c74e86ef4272", - "metadata": {}, - "source": [ - "## Exercise Details" - ] - }, - { - "cell_type": "markdown", - "id": "df216c41-3ec4-43e0-b7bd-be2b1e4294bd", - "metadata": {}, - "source": [ - "- This exercise uses the same code base as the rest of the tutorial. \n", - "- You will be using the I2000Clm50Sp and IHistClm50BgcCrop compsets at the f09_g17_gl4 resolution.\n", - "- You will run a control simulation and two experimental simulations. \n", - "- You will modify netcdf input files. \n", - "- You will use simple, command line netcdf tools to evaluate how the experiments differ from the control simulation." - ] - }, - { - "cell_type": "markdown", - "id": "fd2ff959-6ec0-4534-8713-f5dfdb13b955", - "metadata": {}, - "source": [ - "## Useful CLM references" - ] - }, - { - "cell_type": "markdown", - "id": "baa2afd1-4289-4baf-8bdf-83de7592d76b", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM CLM/CTSM User's Guide](https://escomp.github.io/ctsm-docs/versions/master/html/users_guide/index.html)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "523878ee-2bfa-4363-a8ec-5ff151b15d57", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "\n", - "[CESM CLM/CTSM Discussion Forum](https://bb.cgd.ucar.edu/cesm/forums/ctsm-clm-mosart-rtm.134/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "cb8a0e10-a920-41dd-8bc4-5c03395d231e", - "metadata": {}, - "source": [ - "## What is an I case?" - ] - }, - { - "cell_type": "markdown", - "id": "69988cae-fd26-4fda-8fee-6ab52e79f42d", - "metadata": {}, - "source": [ - "The I compset has active clm with a data atmosphere. The sea ice, ocean, atmosphere, and wave models are not active. There are two types of options for I compsets:\n", - "- SP: Satellite Phenology\n", - "- BGC: Biogeochemistry \n", - "\n", - "We will use the Satellite Phenology option for our control case." - ] - }, - { - "cell_type": "markdown", - "id": "68ca54e2-d8ad-41bc-be8f-31a85eec6e65", - "metadata": {}, - "source": [ - "![icase](../../../images/challenge/i2000.png)\n", - "\n", - "*

Figure: I2000 compset definition.

*" - ] - }, - { - "cell_type": "markdown", - "id": "c93817fd-8031-4917-bf45-eb0f442578f9", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[I Compset definition](https://www2.cesm.ucar.edu/models/cesm2/config/compsets.html)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "da3d9215-30c1-4152-abc0-ccb1e709cde3", - "metadata": {}, - "source": [ - "## Post processing and viewing your output\n", - "\n", - "You will use [ncview](https://ncar.github.io/CESM-Tutorial/notebooks/tools/netcdf_tools.html#ncview) and [NCO operator](https://ncar.github.io/CESM-Tutorial/notebooks/tools/netcdf_tools.html#netcdf-operators-nco) tools to evaluate how the experiments differ from the control simulation.\n", - "\n", - "These tools will need to be loaded into your environment using the NCAR HPC [modules](https://ncar.github.io/CESM-Tutorial/notebooks/tools/unix/modules.html)." - ] - }, - { - "cell_type": "markdown", - "id": "828e538d-3558-4747-9b61-3b29da04e294", - "metadata": {}, - "source": [ - "1) You can create an annual average of the first year's data for each simulationg using the `ncra` (netCDF averager) command from the netCDF operators package \\([NCO](https://nco.sourceforge.net/)\\). \n", - "```\n", - "ncra $OUTPUT_DIR/*.clm.*nc $CASENAME.cln.h.0001.nc\n", - "```\n", - "\n", - "2) Create a file that contains differences between each of the experiments and the control simulation\n", - "```\n", - "ncdiff i.day5.b.clm2.XXX.nc /glade/derecho/scratch/$user/archive/i.day5.a/lnd/hist/i.day5.a.clm2.XXX.nc i_diff.nc\n", - "```\n", - "\n", - "3) Examine variables within each annual mean and the difference files using `ncview`\n", - "```\n", - "ncview i_diff.nc\n", - "```\n", - "\n", - "4) You can also look at other monthly-mean outputs or component log files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0a882d4-0c0e-4d2e-b59f-df2a648fa93d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "CMIP6 2019.10", - "language": "python", - "name": "cmip6-201910" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/clm_ctsm/clm_exercise_1.ipynb b/notebooks_copy/challenge/clm_ctsm/clm_exercise_1.ipynb deleted file mode 100644 index 666c7ff50..000000000 --- a/notebooks_copy/challenge/clm_ctsm/clm_exercise_1.ipynb +++ /dev/null @@ -1,123 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 1: Control case" - ] - }, - { - "cell_type": "markdown", - "id": "6457c1d2-0530-435d-ae27-d0f1eeabe583", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Run a control case

\n", - " \n", - "Create a case called **i.day5.a** using the compset `I2000Clm50Sp` at `f09_g17_gl4` resolution. \n", - " \n", - "Set the run length to **5 days**. \n", - "\n", - "Build and run the model. Since this is a control case, we want to build it \"out of the box\" without any modifications. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Create a new case i.day5.a with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", - "./create_newcase --case ~/cases/i.day5.a --compset I2000Clm50Sp --res f09_g17_gl4 --run-unsupported\n", - "```\n", - "
\n", - "\n", - "Case setup:\n", - "``` \n", - "cd ~/cases/i.day5.a \n", - "./case.setup\n", - "```\n", - "
\n", - "\n", - "Change the clm namelist using user_nl_clm by adding the following lines:\n", - "``` \n", - "hist_nhtfrq = -24\n", - "hist_mfilt = 6\n", - "```\n", - "
\n", - " \n", - "Check the namelist by running:\n", - "``` \n", - "./preview_namelists\n", - "```\n", - "
\n", - "\n", - "If needed, change job queue, account number, or wallclock time. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013,JOB_WALLCLOCK_TIME=0:15:00\n", - "```\n", - "
\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "
\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "i.day5.a. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/i.day5.a/lnd/hist\n", - "\n", - "ls \n", - "```\n", - "
\n", - "\n", - "(2) Look at the output using ncview\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17cf7e19-1211-45f2-97cd-fe2badc69dac", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "CMIP6 2019.10", - "language": "python", - "name": "cmip6-201910" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/clm_ctsm/clm_exercise_2.ipynb b/notebooks_copy/challenge/clm_ctsm/clm_exercise_2.ipynb deleted file mode 100644 index d5651c9b9..000000000 --- a/notebooks_copy/challenge/clm_ctsm/clm_exercise_2.ipynb +++ /dev/null @@ -1,188 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 2: Use the BGC model" - ] - }, - { - "cell_type": "markdown", - "id": "0037b73f-f174-48e7-8e4f-0744d7d23fe0", - "metadata": {}, - "source": [ - "We can use a different I compset: IHistClm50BgcCrop. This experiment is a 20th century transient run using GSWP3v1 and the biogeochemistry model including crops." - ] - }, - { - "cell_type": "markdown", - "id": "bdd131c8-d1ec-4568-81dd-701f8bdbe6cb", - "metadata": {}, - "source": [ - "![icase](../../../images/challenge/ihist.png)\n", - "\n", - "*

Figure: IHIST compset definition.

*" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Run an experimental case with prognostic BGC

\n", - " \n", - "Create a case called **i.day5.b** using the compset `IHistClm50BgcCrop` at `f09_g17_gl4` resolution. \n", - " \n", - "Set the run length to **5 days**. \n", - "\n", - "Build and run the model.\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "\n", - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Create a new case i.day5.b :\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", - "./create_newcase --case ~/cases/i.day5.b --compset IHistClm50BgcCrop --res f09_g17_gl4 --run-unsupported\n", - "```\n", - "
\n", - "\n", - "Case setup:\n", - "``` \n", - "cd ~/cases/i.day5.b\n", - "./case.setup\n", - "```\n", - "
\n", - "\n", - "Note differences between this case and the control case:\n", - "``` \n", - "diff env_run.xml ../i.day5.a/env_run.xml\n", - "```\n", - "
\n", - "\n", - "Change the clm namelist using user_nl_clm by adding the following lines:\n", - "``` \n", - "hist_nhtfrq = -24\n", - "hist_mfilt = 6\n", - "```\n", - "
\n", - " \n", - "Check the namelist by running:\n", - "``` \n", - "./preview_namelists\n", - "```\n", - "
\n", - "\n", - "If needed, change job queue, account number, or wallclock time. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013,JOB_WALLCLOCK_TIME=0:15:00\n", - "```\n", - "
\n", - "\n", - "Build case:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "
\n", - " \n", - "Compare the namelists from the two experiments:\n", - "```\n", - "diff CaseDocs/lnd_in ../i.day5.a/CaseDocs/lnd_in\n", - "```\n", - "
\n", - " \n", - "Submit case:\n", - "```\n", - "./case.submit\n", - "```\n", - "
\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "i.day5.b. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/i.day5.b/lnd/hist\n", - "\n", - "ls \n", - "```\n", - "
\n", - "\n", - " \n", - "(2) Compare to control run:\n", - "```\n", - "ncdiff -v TLAI i.day5.b.clm2.XXX.nc /glade/derecho/scratch/$USER/archive/i.day5.a/lnd/hist/i.day5.a.clm2.XXX.nc i_diff.nc\n", - "\n", - "ncview i_diff.nc\n", - "```\n", - "\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "d69c456a-fdc6-4625-bbcc-ed32ab6ae8e8", - "metadata": { - "tags": [] - }, - "source": [ - "## Test your understanding" - ] - }, - { - "cell_type": "markdown", - "id": "b3f28ecf-02b8-4cc0-bdc4-c36c8fc9e7aa", - "metadata": {}, - "source": [ - "- What changes do you see from the control case with the prognostic BGC?\n", - "- ... OTHERS?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b5a8cee-0ca9-4076-a731-e6ec200b70d4", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "CMIP6 2019.10", - "language": "python", - "name": "cmip6-201910" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/clm_ctsm/clm_exercise_3.ipynb b/notebooks_copy/challenge/clm_ctsm/clm_exercise_3.ipynb deleted file mode 100644 index 0b1e84c14..000000000 --- a/notebooks_copy/challenge/clm_ctsm/clm_exercise_3.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 3: Modify input data" - ] - }, - { - "cell_type": "markdown", - "id": "0037b73f-f174-48e7-8e4f-0744d7d23fe0", - "metadata": {}, - "source": [ - "We can modify the input to CLM by changing one of the plant functional type properties. We will then compare these results with the control experiment.\n", - "\n", - "Note that you will need to change a netcdf file for this exercise. Because netcdf are in binary format you will need a type of script or interperter to read the file and write it out again. (e.g. ferret, IDL, NCL, NCO, Perl, Python, Matlab, Yorick). Below in the solution we will show how to do this using NCO.\n", - "\n", - "NOTE: For any tasks other than setting up, building, submitting cases you should probably do these tasks on the Data Visualization Cluster - casper, and not on the derecho login nodes." - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Run an experimental case

\n", - " \n", - "Create a case called **i.day5.a_pft** using the compset `I2000Clm50Sp` at `f09_g17_gl4` resolution. \n", - "\n", - "Look at variable “rholvis” in the forcing file using ncview or ncdump –v rholvis. This is the visible leaf reflectance for every pft. Modify the rholvis parameter to .\n", - "`/glade/campaign/cesm/cesmdata/cseg/inputdata/lnd/clm2/paramdata/clm5_params.c171117.nc`\n", - " \n", - "Set the run length to **5 days**. \n", - "\n", - "Build and run the model.\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "\n", - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Create a clone from the control experiment i.day5.a_pft :\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", - "./create_clone --case ~/cases/i.day5.a_pft --clone ~/cases/i.day5.a\n", - "```\n", - "
\n", - "\n", - "Modify the rholvis parameter in the physiology file:\n", - "``` \n", - "cd /glade/derecho/scratch/$USER\n", - "cp /glade/campaign/cesm/cesmdata/cseg/inputdata/lnd/clm2/paramdata/clm5_params.c171117.nc .\n", - "chmod u+w clm5_params.c171117.nc\n", - "cp clm5_params.c171117.nc clm5_params.c171117.new.nc\n", - "ncap2 -A -v -s 'rholvis(4)=0.4' clm5_params.c171117.nc clm5_params.c171117.new.nc\n", - "```\n", - "
\n", - "\n", - "Check the new rholvis parameter to be sure the modification worked:\n", - "``` \n", - "ncdump -v rholvis clm5_params.c171117.new.nc\n", - "# and compare it to the original file\n", - "ncdiff clm5_params.c171117.nc clm5_params.c171117.new.nc ncdiff.nc\n", - "ncdump -v rholvis ncdiff.nc\n", - "```\n", - "
\n", - " \n", - "Case setup:\n", - "``` \n", - "cd ~/cases/i.day5.a_pft\n", - "./case.setup\n", - "```\n", - "
\n", - "\n", - "Change the clm namelist using user_nl_clm to point at the modified file. Add the following line:\n", - "``` \n", - "paramfile = '/glade/derecho/scratch/$USER/clm5_params.c171117.new.nc' \n", - "```\n", - "
\n", - " \n", - "Check the namelist by running:\n", - "``` \n", - "./preview_namelists\n", - "```\n", - "
\n", - "\n", - "If needed, change job queue, account number, or wallclock time. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013,JOB_WALLCLOCK_TIME=0:15:00\n", - "```\n", - "
\n", - "\n", - "Build case:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "
\n", - " \n", - "Compare the namelists from the two experiments:\n", - "```\n", - "diff CaseDocs/lnd_in ../i.day5.a/CaseDocs/lnd_in\n", - "```\n", - "
\n", - " \n", - "Submit case:\n", - "```\n", - "./case.submit\n", - "```\n", - "
\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "i.day5.a. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/i.day5.a_pft/lnd/hist\n", - "\n", - "ls \n", - "```\n", - "
\n", - "\n", - "(2) Compare to control run:\n", - "```\n", - "ncdiff i.day5.a_pft.clm2.XXX.nc /glade/derecho/scratch/$USER/archive/i.day5.a/lnd/hist/i.day5.a.clm2.XXX.nc i_diff.nc\n", - "\n", - "ncview i_diff.nc\n", - "```\n", - "\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b3ffd3cc-676e-4e7c-9ff4-cd65d4745397", - "metadata": { - "tags": [] - }, - "source": [ - "## Test your understanding" - ] - }, - { - "cell_type": "markdown", - "id": "03ac3664-5360-45d7-a3ad-0797a839a1d3", - "metadata": {}, - "source": [ - "- How did rholvis change (increase/decrease)? Given this, what do you expect the model response to be?\n", - "- What changes do you see from the control case with the modified rholvis parameter?\n", - "- ... OTHERS? " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27f332b2-5799-43a8-9060-50315ebdf6dc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "CMIP6 2019.10", - "language": "python", - "name": "cmip6-201910" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/paleo/exercise_1.ipynb b/notebooks_copy/challenge/paleo/exercise_1.ipynb deleted file mode 100644 index cca93f3b8..000000000 --- a/notebooks_copy/challenge/paleo/exercise_1.ipynb +++ /dev/null @@ -1,219 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 1: Preindustrial control case\n" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Run a preindustrial control simulation

\n", - " \n", - "Create, configure, build and run a fully coupled preindustrial case called ``b.e21.B1850.f19_g17.piControl.001`` following [CESM naming conventions](https://www.cesm.ucar.edu/models/cesm2/naming-conventions). \n", - "\n", - "Run for 1 year. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**What is the compset for fully coupled preindustrial?**\n", - "\n", - "- ``B1850`` \n", - "\n", - "**What is the resolution for B1850?**\n", - "\n", - "- Use resolution f19_g17 for fast throughput \n", - "\n", - "**Which XML variable should you change to tell the model to run for one year?**\n", - "\n", - "- Use ``STOP_OPTION`` and ``STOP_N`` \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " \n", - "**# Set environment variables** \n", - "\n", - "Set environment variables with the commands:\n", - " \n", - "**For tcsh users** \n", - " \n", - "```\n", - "set CASENAME=b.e21.B1850.f19_g17.piControl.001\n", - "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET=B1850\n", - "set RESOLUTION=f19_g17\n", - "set PROJECT=UESM0013\n", - "```\n", - "\n", - "Note: You should use the project number given for this tutorial.\n", - "\n", - "**For bash users** \n", - " \n", - "```\n", - "export CASENAME=b.e21.B1850.f19_g17.piControl.001\n", - "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "export COMPSET=B1850\n", - "export RESOLUTION=f19_g17\n", - "export PROJECT=UESM0013\n", - "```\n", - "\n", - "Note: You should use the project number given for this tutorial.\n", - "\n", - "**# Make a case directory**\n", - "\n", - "If needed create a directory `cases` into your home directory:\n", - " \n", - "```\n", - "mkdir /glade/u/home/$USER/cases/\n", - "```\n", - " \n", - "\n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET --project $PROJECT\n", - "```\n", - "\n", - "**# Change the job queue**\n", - "\n", - "If needed, change ``job queue``.
\n", - "For instance, to run in the queue ``main``.\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=main\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "Invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "\n", - "**# Set run length**\n", - "\n", - "```\n", - "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", - "```\n", - "\n", - "**# Build and submit**\n", - "\n", - "```\n", - "qcmd -A $PROJECT -- ./case.build\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "**# Check on your run**\n", - "\n", - "\n", - "After submitting the job, use ``qstat -u $USER`` to check the status of your job. \n", - "It may take ~16 minutes to finish the one-year simulation. \n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the history files into the archive directory. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is one-year, there should be 12 monthly files (``h0``) for each model component. \n", - "\n", - "\n", - "Success! Now let's look back into the past... \n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "472131c7-88f9-4863-a2bc-d7364333542d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/paleo/exercise_2.ipynb b/notebooks_copy/challenge/paleo/exercise_2.ipynb deleted file mode 100644 index 94549c8a6..000000000 --- a/notebooks_copy/challenge/paleo/exercise_2.ipynb +++ /dev/null @@ -1,316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 2: mid-Holocene case \n", - "\n", - "The Holocene Epoch started ~11,700 before present (11.7 ka BP) and is the current period of geologic time. \n", - "\n", - "Although humans were already well established before the Holocene, this period of time is also referred to as the Anthropocene Epoch because its primary characteristic is the global changes caused by human activity.\n", - "\n", - "The Holocene is an interglacial period, marked by receding ice sheets and rising greenhouse gases that were accompanied by changes in the Earth's orbit around the Sun. \n", - "\n", - "Today, we will use CESM to investigate influence of Holocene orbital forcing on climate. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Run a mid-Holocene simulation with orbital forcing

\n", - " \n", - "Create, configure, build and run a fully coupled mid-Holocene (~6 ka BP) case called ``b.e21.B1850.f19_g17.midHolocene.001`` following [CESM naming conventions](https://www.cesm.ucar.edu/models/cesm2/naming-conventions). \n", - "\n", - "Run for 1 year. \n", - "\n", - "Compare and visualize differences between preindustrial and mid-Holocene runs using NCO and Ncview. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "
\n", - "\n", - "**What is the compset for fully coupled mid-Holocene run?**\n", - "\n", - "- Use ``B1850`` and modify preindustrial orbital configuration (no mid-Holocene compset available) \n", - "\n", - "**What is the resolution for B1850?**\n", - "\n", - "- Use resolution ``f19_g17`` for fast throughput \n", - "\n", - "**What was the orbital configuration 6 ka BP?**\n", - "\n", - "- According to Table 1 of [Otto-Bliesner et al., (2017)](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://gmd.copernicus.org/articles/10/3979/2017/gmd-10-3979-2017.pdf), Eccentricity = 0.018682, Obliquity (degrees) = 24.105, Perihelion = 0.87 (for simplicity, we don't consider the other forcings here, i.e., CO2) \n", - "\n", - "**How to modify orbital configuration in CESM world?**\n", - "\n", - "- Edit ``user_nl_cpl`` \n", - "- orb_mode = 'fixed_parameters' \n", - "- orb_eccen = 0.018682 \n", - "- orb_obliq = 24.105 \n", - "- orb_mvelp = 0.87 \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " \n", - "**# Set environment variables** \n", - "\n", - "Set environment variables with the commands:\n", - " \n", - "**For tcsh users** \n", - " \n", - "```\n", - "set CASENAME=b.e21.B1850.f19_g17.midHolocene.001\n", - "set CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "set RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "set COMPSET=B1850\n", - "set RESOLUTION=f19_g17\n", - "set PROJECT=UESM0013\n", - "```\n", - "\n", - "You should use the project number given for this tutorial.\n", - "\n", - "**For bash users** \n", - " \n", - "```\n", - "export CASENAME=b.e21.B1850.f19_g17.midHolocene.001\n", - "export CASEDIR=/glade/u/home/$USER/cases/$CASENAME\n", - "export RUNDIR=/glade/derecho/scratch/$USER/$CASENAME/run\n", - "export COMPSET=B1850\n", - "export RESOLUTION=f19_g17\n", - "export PROJECT=UESM0013\n", - "```\n", - "\n", - "You should use the project number given for this tutorial.\n", - "\n", - "**# Make a case directory**\n", - "\n", - "If needed create a directory `cases` into your home directory:\n", - " \n", - "```\n", - "mkdir /glade/u/home/$USER/cases/\n", - "```\n", - " \n", - "\n", - "**# Create a new case**\n", - "\n", - "Create a new case with the command ``create_newcase``:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case $CASEDIR --res $RESOLUTION --compset $COMPSET --project $PROJECT\n", - "```\n", - "\n", - "**# Change the job queue**\n", - "\n", - "If needed, change ``job queue``.
\n", - "For instance, to run in the queue ``main``.\n", - "``` \n", - "cd $CASEDIR\n", - "./xmlchange JOB_QUEUE=main\n", - "```\n", - "This step can be redone at anytime in the process. \n", - "\n", - "**# Setup**\n", - "\n", - "Invoke ``case.setup`` with the command:\n", - "``` \n", - "cd $CASEDIR\n", - "./case.setup \n", - "``` \n", - "\n", - "You build the namelists with the command:\n", - "```\n", - "./preview_namelists\n", - "```\n", - "This step is optional as the script ``preview_namelists`` is automatically called by ``case.build`` and ``case.submit``. But it is nice to check that your changes made their way into:\n", - "```\n", - "$CASEDIR/CaseDocs/atm_in\n", - "```\n", - "\n", - "\n", - "**# Set run length**\n", - "\n", - "```\n", - "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", - "```\n", - "\n", - "\n", - "**# Add the following to user_nl_cpl**\n", - "\n", - "```\n", - "orb_mode = 'fixed_parameters' \n", - " orb_eccen = 0.018682\n", - " orb_obliq = 24.105\n", - " orb_mvelp = 0.87\n", - "```\n", - "\n", - "\n", - "**# Build and submit**\n", - "\n", - "```\n", - "qcmd -A $PROJECT -- ./case.build\n", - "./case.submit\n", - "```\n", - "------------\n", - "\n", - "\n", - "**# Validate your simulation setup**\n", - "\n", - "\n", - "(1) If you want to check the log file, ``cpl.log.xxx``, in the Run Directory (when model is still running) or in your Storage Directory (when the simulation and archiving have finished). \n", - "```\n", - "less /glade/derecho/scratch/$USER/$CASENAME/run/cpl.log.* \n", - "less /glade/derecho/scratch/$USER/archive/$CASENAME/logs/cpl.log.*.gz\n", - "```\n", - "(2) Type ``/orb_params`` to search, you should see the following\n", - "```\n", - " (shr_orb_params) Calculate characteristics of the orbit:\n", - " (shr_orb_params) Calculate orbit for year: -4050\n", - " (shr_orb_params) ------ Computed Orbital Parameters ------\n", - " (shr_orb_params) Eccentricity = 1.868182E-02\n", - " (shr_orb_params) Obliquity (deg) = 2.410538E+01\n", - " (shr_orb_params) Obliquity (rad) = 4.207183E-01\n", - " (shr_orb_params) Long of perh(deg) = 8.696128E-01\n", - " (shr_orb_params) Long of perh(rad) = 3.156770E+00\n", - " (shr_orb_params) Long at v.e.(rad) = -5.751115E-04\n", - "```\n", - "\n", - "**# Check your solution**\n", - "\n", - "When the run is completed, look at the history files into the archive directory. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/$CASENAME/atm/hist\n", - "ls \n", - "```\n", - "\n", - "As your run is one-year, there should be 12 monthly files (``h0``) for each model component. \n", - "\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here to visualize results \n", - "
\n", - "\n", - "**# Use Ncview to visualize solar insolation**\n", - "\n", - "Earth's orbital configuration influences incoming solar insolation.\n", - "Take a look at the ``SOLIN`` CAM variable for August in the pre-industrial and mid-Holocene runs.\n", - "``` \n", - "module load ncview\n", - "cd /glade/derecho/scratch/$USER/archive\n", - "ncview b.e21.B1850.f19_g17.piControl.001/atm/hist/b.e21.B1850.f19_g17.piControl.001.cam.h0.0001-08.nc b.e21.B1850.f19_g17.midHolocene.001/atm/hist/b.e21.B1850.f19_g17.midHolocene.001.cam.h0.0001-08.nc\n", - "```\n", - "\n", - "Using the right arrow button in the Ncview window, you can toggle between pre-industrial and mid-Holocene August ``SOLIN`` and other variables. \n", - "\n", - "\n", - "A few side notes on comparing pre-industrial and mid-Holocene runs: \n", - "- Changes in Earth's orbit alter the length of months or seasons over time, this is referred to as the 'paleo calendar effect' \n", - "- This means that the modern fixed-length definition of months do not apply when the Earth traversed different portions of its orbit \n", - "- Tools exist to adjust monthly CESM output to account for the 'paleo calendar effect' \n", - "- See [Bartlein & Shafer et al. (2019)](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://gmd.copernicus.org/articles/12/3889/2019/gmd-12-3889-2019.pdf) for more information \n", - "- For simplicity, we assume in this exercise that the definition of months is the same for the pre-industrial and mid-Holocene \n", - "\n", - "Now, let's take a look at the differences between the two cases more clearly using NCO. \n", - "\n", - "``` \n", - "module load nco\n", - "cd /glade/derecho/scratch/$USER/archive\n", - "ncdiff b.e21.B1850.f19_g17.piControl.001/atm/hist/b.e21.B1850.f19_g17.midHolocene.001.cam.h0.0001-08.nc b.e21.B1850.f19_g17.piControl.001/atm/hist/b.e21.B1850.f19_g17.piControl.001.cam.h0.0001-08.nc diff_MH-PI_0001-08.nc \n", - "ncview diff_MH-PI_0001-08.nc \n", - "```\n", - "\n", - "Note: Running ncdiff this way will place ``diff_MH-PI_0001-08.nc`` in your archive directory. You may use ``mv`` to move ``diff_MH-PI_0001-08.nc`` to another directory. \n", - "\n", - "**# Questions for reflection:**\n", - "- Which orbital parameters are different at the middle Holocene (6 ka BP)? \n", - "- How does the orbital parameter impact the top-of-atmosphere shortwave radiation (solar insolation) during summertime in the Northern Hemisphere? \n", - "- Do the results look correct? You can compare your results with Figure 3b of [Otto-Bliesner et al., (2017)](chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://gmd.copernicus.org/articles/10/3979/2017/gmd-10-3979-2017.pdf) \n", - "- What other aspects of climate are different between the mid-Holocene and pre-industrial runs?", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "472131c7-88f9-4863-a2bc-d7364333542d", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/paleo/paleo.ipynb b/notebooks_copy/challenge/paleo/paleo.ipynb deleted file mode 100644 index ec64c4fa4..000000000 --- a/notebooks_copy/challenge/paleo/paleo.ipynb +++ /dev/null @@ -1,88 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Paleo \n", - "Paleoclimatology is the study of ancient climate variability and change, before the availability of instrumental records. \n", - "\n", - "Paleoclimatology relies on a combination of physical, biological, and chemical proxies of past environmental and climate change, such as glacial ice, tree rings, sediments, corals, and cave mineral deposits. \n", - "\n", - "CESM is widely used for paleoclimate studies. \n", - "\n", - "CESM simulations of past climates are a tool to better understand and interpret proxy reconstructions and to evaluate CESM skill in simulating out-of-sample climate states. \n" - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "## Learning Goals" - ] - }, - { - "cell_type": "markdown", - "id": "346cbd7b-3b8e-41f0-b120-b369ab20f6cc", - "metadata": {}, - "source": [ - "- Student will learn how to modify Earth's orbital configuration in CESM for a simple paleoclimate experiment. \n", - "- Student will learn how to validate that the orbital modification is implemented properly. \n", - "- Student will learn how to quickly compare differences in paleo and preindustrial CESM runs using NCO and Ncview. \n" - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "## Exercise Details" - ] - }, - { - "cell_type": "markdown", - "id": "346cbd7b-3b8e-41f0-b120-b369ab20f6cc", - "metadata": {}, - "source": [ - "- This exercise uses the same code base as the rest of the tutorial. \n", - "- You will be using the B1850 compset at the f19_g17 resolution. \n", - "- You will run a preindustrial control simulation and a simple mid-Holocene simulation. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/pop/pop.ipynb b/notebooks_copy/challenge/pop/pop.ipynb deleted file mode 100644 index 65afedd26..000000000 --- a/notebooks_copy/challenge/pop/pop.ipynb +++ /dev/null @@ -1,204 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Ocean\n", - "\n", - "The ocean component of CESM is the Parallel Ocean Program (POP). \n", - "\n", - "It can be useful for people interested in ocean science to run simulations with only active sea ice and ocean components and atmospheric forcing. In this exercise, you will learn how to run one of these ice-ocean simulations.\n", - "\n", - "This exercise was created by Gustavo Marques." - ] - }, - { - "cell_type": "markdown", - "id": "45a57a9d-99e1-48c2-a365-b09f3aa40ec0", - "metadata": {}, - "source": [ - "## Learning Goals" - ] - }, - { - "cell_type": "markdown", - "id": "a39c7159-f7ee-4515-920f-68a8d345e392", - "metadata": {}, - "source": [ - "- Student will learn what a G compset is, the types of forcing available to run one, and how to run one.\n", - "- Student will learn how to make a namelist modification that turns off the overflow parameterization and compare results with a control experiment.\n", - "- Student will learn how to make a source code modification that changes zonal wind stress and compare results with a control experiment.\n", - "- Student will learn what a G1850ECO compset is and compare it to the G compset." - ] - }, - { - "cell_type": "markdown", - "id": "6bcc23d6-04c4-49b2-a809-15badc7b5ff9", - "metadata": {}, - "source": [ - "## Exercise Details" - ] - }, - { - "cell_type": "markdown", - "id": "59f7b9fd-7a3d-4b54-b874-61ddc264b102", - "metadata": {}, - "source": [ - "- This exercise uses the same code base as the rest of the tutorial. \n", - "- You will be using the G compset at the T62_g37 resolution.\n", - "- You will run a control simulation and three experimental simulations. Each simulation will be run for one year. \n", - "- You will then use 'ncview' \\([http://meteora.ucsd.edu/~pierce/ncview_home_page.html](http://meteora.ucsd.edu/~pierce/ncview_home_page.html)\\) to evaluate how the experiments differ from the control simulation." - ] - }, - { - "cell_type": "markdown", - "id": "f1ed4850-1e61-4b03-b036-69ecaa06f23f", - "metadata": {}, - "source": [ - "## Useful POP references" - ] - }, - { - "cell_type": "markdown", - "id": "27190b16-2c11-40a1-94fc-09fe0fbb1a57", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM POP User's Guide](https://www.cesm.ucar.edu/models/pop)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9f4fecc3-e03e-4d35-aecb-7daa16a9acb0", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "\n", - "[CESM POP Discussion Forum](https://bb.cgd.ucar.edu/cesm/forums/pop.136/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c082b63d-a408-4b01-8fe8-c446d25a1c91", - "metadata": {}, - "source": [ - "## What is a G case?" - ] - }, - { - "cell_type": "markdown", - "id": "9ad378a2-89e1-4afe-ad88-e0c0759b9864", - "metadata": {}, - "source": [ - "The G compset has active and coupled ocean and sea-ice components. The G compset requires boundary forcing from the atmosphere. The G compset is forced with atmospheric data that does not change interactively as the ocean and sea-ice evolve in time. The land and land ice are not active during a G compset experiment and the runoff is specified. " - ] - }, - { - "cell_type": "markdown", - "id": "3ce9e152-c915-4e18-8199-040a26cf68c5", - "metadata": {}, - "source": [ - "![gcase](../../../images/challenge/gcase.png)\n", - "\n", - "*

Figure: G compset definition.

*" - ] - }, - { - "cell_type": "markdown", - "id": "346ef398-2703-4990-9387-d9006e75c5e6", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Component Set Definitions](https://www2.cesm.ucar.edu/models/cesm2/config/compsets.html)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "cecd306b-bc35-48e2-8b47-fec1362616cc", - "metadata": {}, - "source": [ - "## G Compset forcing data" - ] - }, - { - "cell_type": "markdown", - "id": "b6e0b74a-4578-40b3-8af1-920e6bacffc4", - "metadata": {}, - "source": [ - "There are two types of temporal forcing for G compsets:\n", - "- Normal Year Forcing (NYF) is 12 months of atmospheric data (like a climatology) that repeats every year. NYF is the default forcing.\n", - "- Interannual varying forcing (GIAF) is forcing that varies by year over the time period (1948-2017). \n", - "\n", - "There are two datasets that can be used for G compsets:\n", - "- JRA55-do atmospheric data \\([Tsujino et al. 2018](https://doi.org/10.1016/j.ocemod.2018.07.002)\\)\n", - "- Coordinated Ocean-ice Reference Experiments (CORE) version 2 atmospheric data \\([Large and Yeager 2009](http://doi.org/10.1007/s00382-008-0441-3)\\).\n", - "\n", - "In these exercises we will use the CORE NYF." - ] - }, - { - "cell_type": "markdown", - "id": "e77543f2-6f2a-4d29-8919-827a2d7f96e6", - "metadata": {}, - "source": [ - "## Post processing and viewing your output" - ] - }, - { - "cell_type": "markdown", - "id": "221e2616-682c-44e5-835d-0fce3603555d", - "metadata": {}, - "source": [ - "1) You can create an annual average of the first year's data for each simulationg using the `ncra` (netCDF averager) command from the netCDF operators package \\([NCO](https://nco.sourceforge.net/)\\). \n", - "```\n", - "ncra $OUTPUT_DIR/*.pop.h.0001*nc $CASENAME.pop.h.0001.nc\n", - "```\n", - "\n", - "2) Create a file that contains differences between each of the experiments and the control simulation\n", - "```\n", - "ncdiff $CASENAME.pop.h.0001.nc $CONTROLCASE.pop.h.0001.nc $CASENAME_diff.nc\n", - "```\n", - "\n", - "3) Examine variables within each annual mean and the difference files using `ncview`\n", - "```\n", - "ncview $CASENAME_diff.nc\n", - "```\n", - "\n", - "4) You can also look at other monthly-mean outputs or component log files." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/pop/pop_exercise_1.ipynb b/notebooks_copy/challenge/pop/pop_exercise_1.ipynb deleted file mode 100644 index 56334d731..000000000 --- a/notebooks_copy/challenge/pop/pop_exercise_1.ipynb +++ /dev/null @@ -1,170 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 1: Control case" - ] - }, - { - "cell_type": "markdown", - "id": "0bdbbd2b-8255-44f3-8c8c-da725d26f845", - "metadata": {}, - "source": [ - "**NOTE:** Building the control case for the POP challenge exercises is idential to building the control case in the CICE challenge exercises. If you have already completed the CICE challenge exercises you can skip this step." - ] - }, - { - "cell_type": "markdown", - "id": "6457c1d2-0530-435d-ae27-d0f1eeabe583", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Run a control case

\n", - " \n", - "Create a case called **g_control** using the compset ``G`` at ``T62_g37`` resolution. \n", - " \n", - "Set the run length to **1 year**. \n", - "\n", - "Build and run the model. Since this is a control case, we want to build it \"out of the box\" without any modifications. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - " \n", - "**How do I compile?**\n", - "\n", - "You can compile with the command:\n", - " \n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "\n", - "**How do I control the output?**\n", - "\n", - "Check the following links:\n", - "\n", - "* https://www2.cesm.ucar.edu/models/cesm1.2/pop2/doc/faq/#output_tavg_add1\n", - "* https://www2.cesm.ucar.edu/models/cesm1.2/pop2/doc/faq/#output_tavg_add2\n", - "\n", - "**How do I check my solution?**\n", - "\n", - "When your run is completed, go to the archive directory. \n", - "\n", - "(1) Check that your archive directory contains files *pop.h.*, *pop.h.nday1*, etc\n", - "\n", - "\n", - "(2) Compare the contents of the ``h`` and ``h.nday1`` files using ``ncdump``.\n", - "\n", - "```\n", - "ncdump -h gpop.pop.h.0001-01-01-00000.nc\n", - "ncdump -h gpop.pop.h.nday1.0001-01-01-00000.nc\n", - "```\n", - "\n", - "(3) Look at the sizes of the files. \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Create a new case g_control with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case /glade/work/$USER/cases/g_control --compset G --res T62_g37 \n", - "```\n", - "
\n", - "\n", - "Case setup:\n", - "``` \n", - "cd ~/cases/g_control \n", - "./case.setup\n", - "```\n", - "
\n", - "\n", - "Change the run length:\n", - "``` \n", - "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", - "```\n", - "
\n", - "\n", - "If needed, change job queue \n", - "and account number. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "
\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "
\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "g_control. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/g_control/ocn/hist\n", - "\n", - "ls \n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dabace0e-c3f2-4c88-b77d-4b28828c0944", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/pop/pop_exercise_2.ipynb b/notebooks_copy/challenge/pop/pop_exercise_2.ipynb deleted file mode 100644 index 2acba9fec..000000000 --- a/notebooks_copy/challenge/pop/pop_exercise_2.ipynb +++ /dev/null @@ -1,175 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 2: Turn off parameterization" - ] - }, - { - "cell_type": "markdown", - "id": "33cdee65-f03f-4c72-adfe-b5ce02416d12", - "metadata": {}, - "source": [ - "Oceanic overflows are dense currents originating in semienclosed basins or continental shelves. They contribute to the formation of abyssal waters and play a crucial role in large-scale ocean circulation. When these dense currents flow down the continental slope, they undergo intense mixing with the surrounding (ambient) ocean waters, causing significant changes in their density and transport (see figure below). However, these mixing processes occur on scales that are smaller than what ocean climate models can accurately capture, leading to poor simulations of deep waters and deep western boundary currents. To improve the representation of overflows some ocean climate models rely on overflow paramterizations, such as the one developed for the POP model (check [this](https://echorock.cgd.ucar.edu/staff/gokhan/OFP_Tech_Note.pdf) report for additional information). \n", - "\n", - "![overflows](../../../images/challenge/overflows.png)\n", - "\n", - "\n", - "*

Figure: Physical processes acting in overflows (from [Legg et al., 2009](https://doi-org.cuucar.idm.oclc.org/10.1175/2008BAMS2667.1))

*" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Turn off overflow parameterization

\n", - " \n", - "Create a case called **g_overflows** by cloning the control experiment case. \n", - " \n", - "Verify that the run length is set to **1 year**. \n", - "\n", - "In user_nl_pop make the following modifications:``overflows_on = .false.`` and ``overflows_interactive = .false.``\n", - "\n", - "Build and run the model for one year. \n", - "\n", - "Compare the simulations using ncview/ncdiff, etc.\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - " \n", - "**How do I compile and run?**\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "\n", - "You can run with the command:\n", - "```\n", - "./case.submit\n", - "```\n", - " \n", - "**How do I check the lenght of the run?**\n", - "\n", - "Use ```xmlquery``` to search for the variables that control the run length\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Clone a new case g_overflows from your control experiment with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_clone --case /glade/work/$USER/cases/g_overflows --clone /glade/work/$USER/cases/g_control\n", - "```\n", - "\n", - "Case setup:\n", - "``` \n", - "cd /glade/work/$USER/cases/g_overflows\n", - "./case.setup\n", - "```\n", - "\n", - "Verify that the run length is 1 year:\n", - "``` \n", - "./xmlquery STOP_N\n", - "./xmlquery STOP_OPTION\n", - "```\n", - " \n", - "Edit the file user_nl_pop and add the lines:\n", - "```\n", - " overflows_on = .false.\n", - " overflows_interactive = .false.\n", - "```\n", - "\n", - "If needed, change job queue \n", - "and account number. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "g_overflows. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/g_overflows/ocn/hist\n", - "ls \n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "f19ab341-b76b-462b-9bc9-49d4793ed409", - "metadata": {}, - "source": [ - "## Test your understanding" - ] - }, - { - "cell_type": "markdown", - "id": "31d67bb4-3e04-459e-a6ac-866ee9224776", - "metadata": {}, - "source": [ - "- What variables do you expect to change when you turn off the overflow parameterization?\n", - "- What variables show a difference between this experiment and the control difference? How different are they?" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/pop/pop_exercise_3.ipynb b/notebooks_copy/challenge/pop/pop_exercise_3.ipynb deleted file mode 100644 index b71259f2d..000000000 --- a/notebooks_copy/challenge/pop/pop_exercise_3.ipynb +++ /dev/null @@ -1,175 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 3: Modify wind stress" - ] - }, - { - "cell_type": "markdown", - "id": "33cdee65-f03f-4c72-adfe-b5ce02416d12", - "metadata": {}, - "source": [ - "Wind stress plays a critical role in driving ocean currents and is a key factor in shaping the overall patterns of large-scale ocean circulation and, consequentialy, the climate. Further details on how wind stress affects the ocean circulation are discussed in [this](https://doi-org.cuucar.idm.oclc.org/10.1006/rwos.2001.0110) manuscript." - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Increase zonal wind stress

\n", - " \n", - "Create a case called **g_windstress** by cloning the control experiment case. \n", - " \n", - "Verify that the run length is set to **1 year**. \n", - "\n", - "Modify the subroutine rotate_wind_stress in forcing_coupled.F90 to increase the first (x) component of the wind stress by 25%.\n", - "\n", - "Build and run the model for one year. \n", - "\n", - "Compare the simulations using ncview/ncdiff, etc.\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - " \n", - "**How do I compile and run?**\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "\n", - "You can run with the command:\n", - "```\n", - "./case.submit\n", - "```\n", - " \n", - "**How do I check the lenght of the run?**\n", - "\n", - "Use ```xmlquery``` to search for the variables that control the run length\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Clone a new case g_windstress from your control experiment with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_clone --case /glade/work/$USER/cases/g_windstress --clone /glade/work/$USER/cases/g_control\n", - "```\n", - "\n", - "Case setup:\n", - "``` \n", - "cd /glade/work/$USER/cases/g_windstress\n", - "./case.setup\n", - "```\n", - "\n", - "Verify that the run length is 1 year:\n", - "``` \n", - "./xmlquery STOP_N\n", - "./xmlquery STOP_OPTION\n", - "```\n", - " \n", - "Copy the forcing_coupled.F90 file from the control case to the ocean SourceMods.\n", - "``` \n", - "cp /glade/work/$USER/code/my_cesm_code/components/pop/source/forcing_coupled.F90 /glade/work/$USER/cases/g_windstress/SourceMods/src.pop\n", - "``` \n", - " \n", - "Edit the file forcing_coupled.F90 in the rotate_wind_stress routine after ```SMFT(:,:,1,:)``` is defined:\n", - " \n", - "```\n", - " SMFT(:,:,1,:) = SMFT(:,:,1,:) * 1.25\n", - "```\n", - "\n", - "If needed, change job queue \n", - "and account number. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "g_windstress. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$user/archive/g_windstress/ocn/hist\n", - "ls \n", - "```\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "286e2e7f-ccea-4c5e-acc5-5f9867341102", - "metadata": {}, - "source": [ - "## Test your understanding" - ] - }, - { - "cell_type": "markdown", - "id": "63f2688d-9857-4a49-93bf-2b3117ec0d13", - "metadata": {}, - "source": [ - "- What are the impacts of increased zonal wind stress? \n", - "- Where do you thinkt he impacts would be largest in the ocean?\n", - "- How do you think the changes would compare if you increased meridional wind stress?" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/challenge/pop/pop_exercise_4.ipynb b/notebooks_copy/challenge/pop/pop_exercise_4.ipynb deleted file mode 100644 index cd2bc2a6f..000000000 --- a/notebooks_copy/challenge/pop/pop_exercise_4.ipynb +++ /dev/null @@ -1,161 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# 4: Turn on the ecosystem" - ] - }, - { - "cell_type": "markdown", - "id": "72423b27-32ee-492a-a023-ffd418e2d6ea", - "metadata": {}, - "source": [ - "You can also explore setting up a similar case but using the ``G1850ECO`` component set. Note how this differs from the previous ``G`` component set we used in Exercise 1. " - ] - }, - { - "cell_type": "markdown", - "id": "8f13d092-c9d8-4e47-93b2-caf3cb8335d6", - "metadata": {}, - "source": [ - "![gcase](../../../images/challenge/gecocase.png)\n", - "\n", - "*

Figure: G1850ECO compset definition.

*" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "
\n", - "Exercise: Run a control case

\n", - " \n", - "Create a case called **g_eco1850** using the compset ``G1850ECO`` at ``T62_g37`` resolution. \n", - " \n", - "Set the run length to **1 year**. \n", - "\n", - "Build and run the model. Since this is a control case, we want to build it \"out of the box\" without any modifications. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e2e33a95-e93c-4aca-86d7-1a830cc0562c", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - " \n", - "**How do I compile and run?**\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "\n", - "You can run with the command:\n", - "```\n", - "./case.submit\n", - "```\n", - " \n", - "**How do I check the lenght of the run?**\n", - "\n", - "Use ```xmlquery``` to search for the variables that control the run length\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f639e182-f48a-431c-a594-9c34323417eb", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - " \n", - "Create a new case G1850ECO with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case /glade/work/$USER/cases/G1850ECO --compset G1850ECO --res T62_g37\n", - "```\n", - "\n", - "Case setup:\n", - "``` \n", - "cd /glade/work/$USER/cases/G1850ECO \n", - "./case.setup\n", - "```\n", - " \n", - "Change the run length:\n", - "``` \n", - "./xmlchange STOP_N=1,STOP_OPTION=nyears\n", - "```\n", - "\n", - "If needed, change job queue \n", - "and account number. \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "G1850ECO. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/G1850ECO/ocn/hist\n", - "ls \n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dce7f4af-243c-47fd-b4d6-c37832aa80fd", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/additional/additional.ipynb b/notebooks_copy/diagnostics/additional/additional.ipynb deleted file mode 100644 index 6c1317db0..000000000 --- a/notebooks_copy/diagnostics/additional/additional.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Additional Topics" - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "This section provides other information about how to use CESM output including:\n", - "- The difference between timeseries and history files\n", - "- The Climate Variabilty and Diagnostics Package (CVDP) (**In progress**)\n", - "- Links to different analysis tools and resources used by CESM developers and users" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/additional/adf.ipynb b/notebooks_copy/diagnostics/additional/adf.ipynb deleted file mode 100644 index 8056d9f6d..000000000 --- a/notebooks_copy/diagnostics/additional/adf.ipynb +++ /dev/null @@ -1,77 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# ADF" - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "## Learning Goals\n", - "\n", - "- Enter learning goals here." - ] - }, - { - "cell_type": "markdown", - "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "## Subsection 1\n", - "\n", - "Info here" - ] - }, - { - "cell_type": "markdown", - "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "## Subsection 2\n", - "\n", - "Info here\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/additional/analysis_tools.ipynb b/notebooks_copy/diagnostics/additional/analysis_tools.ipynb deleted file mode 100644 index 0e551fc31..000000000 --- a/notebooks_copy/diagnostics/additional/analysis_tools.ipynb +++ /dev/null @@ -1,335 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# CESM analysis tools" - ] - }, - { - "cell_type": "markdown", - "id": "55b5588e-6e74-4bd7-a278-877611c4e87b", - "metadata": {}, - "source": [ - "We have provided some information about tools the CESM users and developers use for analysis of model simulations below. This list is not comprehensive and is intended to provide you with information to start your searches." - ] - }, - { - "cell_type": "markdown", - "id": "6a4f8751-c312-49b5-a578-604b7f39099a", - "metadata": {}, - "source": [ - "## Analysis Software" - ] - }, - { - "cell_type": "markdown", - "id": "d31bdb0d-5afe-4b38-b304-34c3179ac6dc", - "metadata": {}, - "source": [ - "Many data analysis and visualization software packages are freely available for use on CISL-managed resources. These packages include some developed and supported by NCAR and CISL. Some of these resources are open source while others require licences.\n", - "\n", - "Some of these packages include:\n", - "- Numerous python packages\n", - "- Interactive Data Language (IDL) \n", - "- MATLAB\n", - "- NCAR Command Language (NCL)" - ] - }, - { - "cell_type": "markdown", - "id": "5bd4569e-601e-47d9-ad24-ae2da7087b7e", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Data Analysis Website](https://arc.ucar.edu/knowledge_base/70550011)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "cc1d09f3-dc55-46b2-912e-deee2147e45d", - "metadata": {}, - "source": [ - "## Python" - ] - }, - { - "cell_type": "markdown", - "id": "0275672f-9536-4bb6-bfff-4a2eb9bc3630", - "metadata": {}, - "source": [ - "Python is an open source, general-purpose programming language. \n", - "\n", - "Python is known for:\n", - "- having a wide range of applications and packages available. There is a huge user base and rougly ~1 gazillion online tutorials. \n", - "- active development in packages related to the geosciences.\n", - "\n", - "Python is becoming the dominant language for CESM developers and users, so most of the active development of tools for the CESM project at large are done in this language. We provide more detailed information below about some of the tools available for python users on NCAR computing assets." - ] - }, - { - "cell_type": "markdown", - "id": "8ce79e09-e9e2-4bf0-ad12-4aede3b2d072", - "metadata": {}, - "source": [ - "### Jupyter Hub" - ] - }, - { - "cell_type": "markdown", - "id": "f82c8f41-42a1-40ce-86bc-5138a9940d08", - "metadata": {}, - "source": [ - "The JupyterHub deployment that CISL manages allows \"push-button\" access to NCAR's supercomputing resource cluster of nodes used for data analysis and visualization, machine learning, and deep learning.\n", - "\n", - "JupyterHub gives users the ability to create, save, and share Jupyter Notebooks through the JupyterLab interface and to run interactive, web-based analysis, visualization and compute jobs on derecho and casper.\n", - "\n", - "Information about getting started with JupyterHub on NCAR computing resources, environments, and documentation is avaiable at the website below." - ] - }, - { - "cell_type": "markdown", - "id": "4b1cc783-53e5-45db-8954-385863b1a778", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Jupyter Hub Website](https://arc.ucar.edu/knowledge_base/70549913)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "71c323bc-a8e3-406d-a08b-92030f436863", - "metadata": {}, - "source": [ - "### Earth System Data Science initiative (ESDS)" - ] - }, - { - "cell_type": "markdown", - "id": "2d21da94-244c-42c0-970f-8daec7bacf61", - "metadata": {}, - "source": [ - "ESDS is an NCAR initiative that seeks to foster a collaborative, open, inclusive community for Earth Science data analysis. ESDS promotes deeper collaboration centered on analytics, improving our capacity to deliver impactful, actionable, reproducible science and serve the university community by transforming how geoscientists synthesize and extract information from large, diverse data sets.\n", - "\n", - "More information, including FAQs and a blog with examples can be found at the website below. " - ] - }, - { - "cell_type": "markdown", - "id": "a6813683-9206-492d-b477-2aee1abe4f17", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[ESDS Website](https://ncar.github.io/esds/about/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f4055e68-100a-4ae7-8ee3-47cefcd43d73", - "metadata": {}, - "source": [ - "### Project Pythia" - ] - }, - { - "cell_type": "markdown", - "id": "f25bcd2d-a7d5-46fe-b5fb-05c14eb6a19b", - "metadata": {}, - "source": [ - "If you are new to Python and its application to the geosciences, then starting with Project Pythia is a good first step. Project Pythia is the education working group for Pangeo and is an educational resource for the entire geoscience community. Together these initiatives are helping geoscientists make sense of huge volumes of numerical scientific data using tools that facilitate open, reproducible science, and building an inclusive community of practice around these goals." - ] - }, - { - "cell_type": "markdown", - "id": "552bc788-8a39-4ce9-b7ab-8d8f283614e7", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Project Pythia Website](https://projectpythia.org/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "efe8a781-b9e6-4ad2-a590-75ceab387fb4", - "metadata": {}, - "source": [ - "### GeoCAT" - ] - }, - { - "cell_type": "markdown", - "id": "4583d068-c9c4-44ed-8ca3-67352c4fd414", - "metadata": {}, - "source": [ - "The Geoscience Community Analysis Toolkit (GeoCAT) is a software engineering effort at NCAR. GeoCAT aims to create scalable data analysis and visualization tools for Earth System Science data to serve the geosciences community in the scientific Python ecosystem. GeoCAT tools are built upon the cornerstone technologies in the Pangeo stack such as Xarray, Dask, and Jupyter Notebooks. In addition, some of the functionalities in the GeoCAT stack are inspired/reimplemented from NCL." - ] - }, - { - "cell_type": "markdown", - "id": "207a8b16-9f0b-447f-b027-1a47ba747d52", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[GeoCAT Website](https://geocat.ucar.edu/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "56d3130e-8f95-4a5e-a797-0b33d538141a", - "metadata": {}, - "source": [ - "### MetPy" - ] - }, - { - "cell_type": "markdown", - "id": "76b9dc6d-74d0-4444-96d4-e60db58f8257", - "metadata": {}, - "source": [ - "MetPy is a collection of tools in Python for reading, visualizing, and performing calculations with weather data. The website below has information about getting started as well as examples and a reference guide." - ] - }, - { - "cell_type": "markdown", - "id": "53f2c96c-f36a-4080-99b9-e7e2fb1d899d", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[MetPy Website](https://unidata.github.io/MetPy/latest/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "7787ed12-bce7-4e49-ac3f-2229de327823", - "metadata": {}, - "source": [ - "## NCAR Command Language (NCL)" - ] - }, - { - "cell_type": "markdown", - "id": "c83303a5-24e5-4758-aeed-0cf3672c665e", - "metadata": {}, - "source": [ - "NCL is an open source tool developed at NCAR that is free to download and use. It can be run at the command line in interactive mode or as a batch mode. While once a widely used language for CESM developers and users, NCL is now in a maintenence stage and is no longer in development. Much of the active development is now being done with python tools.\n", - "\n", - "NCL is known for:\n", - "- easy input/output use with netCDF, Grib, Grib2, shapefiles, ascii, and binary files. \n", - "- good graphics that are very flexible.\n", - "- functions tailored to the geosciences community.\n", - "- a central website with 1000+ examples. There are also mini language and processing manuals." - ] - }, - { - "cell_type": "markdown", - "id": "7b185d5f-dcdb-4275-99b4-67bf6e5dcc2b", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[NCL Website](https://www.ncl.ucar.edu/get_started.shtml)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "7e2bcaf7-f5d0-4d20-b2ed-840841a02972", - "metadata": {}, - "source": [ - "## Panopoly" - ] - }, - { - "cell_type": "markdown", - "id": "3f774a9f-c4c0-4411-97df-99afe765259f", - "metadata": {}, - "source": [ - "Panopoly is a graphic user interface (GUI) application that allows the user to quickly view data in a number of file formats. Panopoly is similar to ncview, but it's more powerful. Panopoly works with files in netCDF, HDF, or GRIB format (among others). It also allows the user to perform simple calculations, apply masks, and quickly create spatial or line plots.\n", - "\n", - "The Panopoly website provies more documentation, including How-To's and demonstration videos." - ] - }, - { - "cell_type": "markdown", - "id": "483efa17-9787-4bb4-8348-2d2ebadf1dbd", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Panopoly Website](http://www.giss.nasa.gov/tools/panoply/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "124c346f-9a8a-4589-b74a-e04194a3e473", - "metadata": {}, - "source": [ - "## Image Magick" - ] - }, - { - "cell_type": "markdown", - "id": "38488672-40e0-4383-b00c-e3128cdc5304", - "metadata": {}, - "source": [ - "ImageMagick is a free suite of software that that can be used to display, manipulate, or compare images. It works with a wide range of file types (ps, pdf, png, gif, jpg, etc.). It can also be used to create movies. You can also alter an image at the command line. There are many options available when converting images, and more information can be found at the website below." - ] - }, - { - "cell_type": "markdown", - "id": "23b653c8-95d6-4a28-a2cc-4d6656c3ceec", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Image Magick Website](https://imagemagick.org/index.php)\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/additional/cvdp.ipynb b/notebooks_copy/diagnostics/additional/cvdp.ipynb deleted file mode 100644 index 13e608e76..000000000 --- a/notebooks_copy/diagnostics/additional/cvdp.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# CVDP" - ] - }, - { - "cell_type": "markdown", - "id": "8f46aef7-947e-498e-90b1-a4ed6b077f6d", - "metadata": {}, - "source": [ - "The climate variability and diagnostics package (CVDP) was developed by NCAR scientists to document the major modes of climate variability in models and observations.\n", - "\n", - "More info here:\n", - "https://www.cesm.ucar.edu/projects/cvdp\n", - "\n", - "If you use CVDP results in oral or written form, please cite the following paper:\n", - "https://doi.org/10.1002/2014EO490002\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "f5d5800f-8a0c-4cdf-930b-48be1cb40796", - "metadata": {}, - "source": [ - "**Additional documentation in progress.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "def586e4-6553-48b9-b05c-2308dad9181c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/additional/large_ensembles.ipynb b/notebooks_copy/diagnostics/additional/large_ensembles.ipynb deleted file mode 100644 index 6b12beb9b..000000000 --- a/notebooks_copy/diagnostics/additional/large_ensembles.ipynb +++ /dev/null @@ -1,77 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Large Ensembles" - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "## Learning Goals\n", - "\n", - "- Enter learning goals here." - ] - }, - { - "cell_type": "markdown", - "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "## Subsection 1\n", - "\n", - "Info here" - ] - }, - { - "cell_type": "markdown", - "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "## Subsection 2\n", - "\n", - "Info here\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/additional/postprocessing.ipynb b/notebooks_copy/diagnostics/additional/postprocessing.ipynb deleted file mode 100644 index ff5b3e1b1..000000000 --- a/notebooks_copy/diagnostics/additional/postprocessing.ipynb +++ /dev/null @@ -1,93 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Postprocessing data" - ] - }, - { - "cell_type": "markdown", - "id": "5bd5c142-f778-4570-8edc-cc760139f30e", - "metadata": {}, - "source": [ - "A wide range of tools exist for postprocessing and analyzing data with techniques and methods exist. One of the first things you have to decide is how to store your files.\n", - "\n", - "In the diagnostics notebooks we have have provided examples of how to use both history files and timeseries files, described below." - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "## History vs. Timeseries files\n", - "\n", - "When you run the CESM model the default output is history files, or files for a single timestep that include all variables for a given component and time frequency. However, most CESM community experiment data will be provided as timeseries files, or files that are a single variable over many timesteps. It is important you understand how to use both types of files, and for you to know that for some tasks (e.g. debugging) you should be using history files instead of timeseries files. However, it is much more efficient to store timeseries files because the overall size is smaller once the files have been processed into timeseries format.\n", - "\n", - "CESM does not currently have a supported tool for the community to create timeseries files. We can recommend you investigate using [NCO tools](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html#netcdf-operators-nco) in coordination with scripts. We hope to have a better way to create timeseries files by community members soon.\n" - ] - }, - { - "cell_type": "markdown", - "id": "784300ed-3d93-4365-8776-adcce4d4eb1f", - "metadata": {}, - "source": [ - "Until then, we'll give you a sheepish grin." - ] - }, - { - "cell_type": "markdown", - "id": "74d0734c-6b19-4c82-96cc-670dcbbea861", - "metadata": {}, - "source": [ - "![sheep](../../../images/diagnostics/file_types/sheepish1.png)\n", - "\n", - "*

Figure: Baaaa.

*" - ] - }, - { - "cell_type": "markdown", - "id": "1775714f-10bd-4e6f-aa07-1c5b2325584a", - "metadata": {}, - "source": [ - "![sheep](../../../images/diagnostics/file_types/sheepish2.png)\n", - "\n", - "*

Figure: Baaaa.

*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/additional/uxarray.ipynb b/notebooks_copy/diagnostics/additional/uxarray.ipynb deleted file mode 100644 index d98462609..000000000 --- a/notebooks_copy/diagnostics/additional/uxarray.ipynb +++ /dev/null @@ -1,77 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# uxarray" - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "## Learning Goals\n", - "\n", - "- Enter learning goals here." - ] - }, - { - "cell_type": "markdown", - "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "## Subsection 1\n", - "\n", - "Info here" - ] - }, - { - "cell_type": "markdown", - "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "## Subsection 2\n", - "\n", - "Info here\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/cam/advanced_cam.ipynb b/notebooks_copy/diagnostics/cam/advanced_cam.ipynb deleted file mode 100644 index 9838c6ed7..000000000 --- a/notebooks_copy/diagnostics/cam/advanced_cam.ipynb +++ /dev/null @@ -1,261 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Advanced Plotting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023b`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023b`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_______________\n", - "This activity was developed primarily by Cecile Hannay and Jesse Nusbaumer." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "## Exercise 1: CAM-SE output analysis\n", - "\n", - "Examples of simple analysis and plotting that can be done with CAM-SE output on the native cubed-sphere grid." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import xarray as xr\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import cartopy.crs as ccrs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def make_map(data, lon, lat,):\n", - " \"\"\"This function plots data on a Mollweide projection map.\n", - "\n", - " The data is transformed to the projection using Cartopy's `transform_points` method.\n", - "\n", - " The plot is made by triangulation of the points, producing output very similar to `pcolormesh`,\n", - " but with triangles instead of rectangles used to make the image.\n", - " \"\"\"\n", - " dataproj = ccrs.PlateCarree() # assumes data is lat/lon\n", - " plotproj = ccrs.Mollweide() # output projection \n", - " # set up figure / axes object, set to be global, add coastlines\n", - " fig, ax = plt.subplots(figsize=(6,3), subplot_kw={'projection':plotproj})\n", - " ax.set_global()\n", - " ax.coastlines(linewidth=0.2)\n", - " # this figures out the transformation between (lon,lat) and the specified projection\n", - " tcoords = plotproj.transform_points(dataproj, lon.values, lat.values) # working with the projection\n", - " xi=tcoords[:,0] != np.inf # there can be bad points set to infinity, but we'll ignore them\n", - " assert xi.shape[0] == tcoords.shape[0], f\"Something wrong with shapes should be the same: {xi.shape = }, {tcoords.shape = }\"\n", - " tc=tcoords[xi,:]\n", - " datai=data.values[xi] # convert to numpy array, then subset\n", - " # Use tripcolor --> triangluates the data to make the plot\n", - " # rasterized=True reduces the file size (necessary for high-resolution for reasonable file size)\n", - " # keep output as \"img\" to make specifying colorbar easy\n", - " img = ax.tripcolor(tc[:,0],tc[:,1], datai, shading='gouraud', rasterized=True)\n", - " cbar = fig.colorbar(img, ax=ax, shrink=0.4)\n", - " return fig, ax" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Input data\n", - "\n", - "In the following cell, specify the data source.\n", - "\n", - "`location_of_hfiles` is a path object that points to the directory where data files should be.\n", - "`search_pattern` specifies what pattern to look for inside that directory.\n", - "\n", - "**SIMPLIFICATION** If you want to just provide a path to a file, simply specify it by commenting (with `#`) the lines above \"# WE need lat and lon\", and replace with:\n", - "```\n", - "fil = \"/path/to/your/data/file.nc\"\n", - "ds = xr.open_dataset(fil)\n", - "```\n", - "\n", - "## Parameters\n", - "Specify the name of the variable to be analyzed with `variable_name`.\n", - "\n", - "To change the units of the variable, specify `scale_factor` and provide the new units string as `units`. Otherwise, just set `scale_factor` and `units`:\n", - "\n", - "```\n", - "scale_factor = 1\n", - "units = ds[\"variable_name\"].attrs[\"units\"]\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "location_of_hfiles = Path(\"/glade/campaign/cesm/tutorial/tutorial_2023_archive/cam-se/\")\n", - "search_pattern = \"f.cam6_3_112.FMTHIST_v0c.ne30.non-ogw-ubcT-effgw0.7_taubgnd2.5.001.cam.h3.2003-01-01-00000.nc\"\n", - "\n", - "fils = sorted(location_of_hfiles.glob(search_pattern))\n", - "if len(fils) == 1:\n", - " ds = xr.open_dataset(fils[0])\n", - "else:\n", - " print(f\"Just so you konw, there are {len(fils)} files about to be loaded.\")\n", - " ds = xr.open_mfdataset(fils)\n", - "\n", - "# We need lat and lon:\n", - "lat = ds['lat']\n", - "lon = ds['lon']\n", - "\n", - "# Choose what variables to plot,\n", - "# in this example we are going to combine the\n", - "# convective and stratiform precipitation into\n", - "# a single, total precipitation variable\n", - "convective_precip_name = \"PRECC\"\n", - "stratiform_precip_name = \"PRECL\"\n", - "\n", - "# If needed, select scale factor and new units:\n", - "scale_factor = 86400. * 1000. # m/s -> mm/day\n", - "units = \"mm/day\"\n", - "\n", - "cp_data = scale_factor * ds[convective_precip_name]\n", - "st_data = scale_factor * ds[stratiform_precip_name]\n", - "cp_data.attrs['units'] = units\n", - "st_data.attrs['units'] = units\n", - "\n", - "# Sum the two precip variables to get total precip\n", - "data = cp_data + st_data\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# temporal averaging\n", - "# simplest case, just average over time:\n", - "data_avg = data.mean(dim='time')\n", - "data_avg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#\n", - "# Global average\n", - "#\n", - "data_global_average = data_avg.weighted(ds['area']).mean()\n", - "print(f\"The area-weighted average of the time-mean data is: {data_global_average.item()}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#\n", - "# Regional average using a (logical) rectangle\n", - "#\n", - "west_lon = 110.0\n", - "east_lon = 200.0\n", - "south_lat = -30.0\n", - "north_lat = 30.0\n", - "\n", - "# To reduce to the region, we need to know which indices of ncol dimension are inside the boundary\n", - "\n", - "region_inds = np.argwhere(((lat > south_lat)&(lat < north_lat)&(lon>west_lon)&(lon \n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/advanced_plot_1.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks_copy/diagnostics/cam/basics_cam.ipynb b/notebooks_copy/diagnostics/cam/basics_cam.ipynb deleted file mode 100644 index 8b0dd52e5..000000000 --- a/notebooks_copy/diagnostics/cam/basics_cam.ipynb +++ /dev/null @@ -1,972 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "33070195-2d5f-4ee3-b6cb-a658209b1f4d", - "metadata": {}, - "source": [ - "# Basic Plotting\n" - ] - }, - { - "cell_type": "markdown", - "id": "8f2a265a-5258-47df-9da2-86afb69cf660", - "metadata": {}, - "source": [ - "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023b`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023b`." - ] - }, - { - "cell_type": "markdown", - "id": "4ef5d835-37b7-4a4a-84c1-8053093f1500", - "metadata": {}, - "source": [ - "_______________\n", - "This activity was developed primarily by Cecile Hannay and Jesse Nusbaumer." - ] - }, - { - "cell_type": "markdown", - "id": "b35face4-1542-4ab0-8f04-8220a00b0086", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "For the atmospheric data, we will look at common variables in the atmospheric diagnostics. This notebook covers 3 basic plotting examples:\n", - "\n", - "Exercise 1: Global lat/lon of surface temperature\n", - "\n", - "Exercise 2: Zonal mean of short wave cloud forcing\n", - "\n", - "Exercise 3: Temperature zonal mean with vertical levels\n", - "\n", - "Some of the plotting in these examples are based on the AMWG Diagnostics Framework (ADF) and some are natively from the `xarray` functionality. `xarray` will be used for the data I/O, analysis, and some plotting, `matplotlib` and `cartopy` will aid in plotting, and `numpy` for calculations" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "72e6f251-c6ff-4478-adab-783ac1795e34", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "import cftime\n", - "import matplotlib as mpl\n", - "import matplotlib.path as mpath\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import xarray as xr\n", - "from matplotlib.gridspec import GridSpec\n", - "from matplotlib.lines import Line2D\n", - "from mpl_toolkits.axes_grid1 import make_axes_locatable" - ] - }, - { - "cell_type": "markdown", - "id": "3d56ba02-c652-4a97-a466-d3633e53aeb1", - "metadata": {}, - "source": [ - "The first step is to grab an atmosphere (CAM) history file from your CESM model run" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "20d4eec8-5332-44a5-bd5c-910048c5d95f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b.day2.1/atm/hist/b.day2.1.cam.h0.0003-07.nc'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Set your username here:\n", - "username = \"PUT_USER_NAME_HERE\"\n", - "\n", - "# Here we point to the archive directory from your b.day2.1 simulation\n", - "monthly_output_path = f\"/glade/derecho/scratch/{username}/archive/b.day2.1/atm/hist\"\n", - "\n", - "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", - "# this provided simulation data instead:\n", - "#monthly_output_path = \"/glade/campaign/cesm/tutorial/tutorial_2023_archive/b.day2.1/atm/hist\"\n", - "\n", - "# Name of history file to plot\n", - "file_name = \"b.day2.1.cam.h0.0003-07.nc\"\n", - "\n", - "files = os.path.join(monthly_output_path, file_name)\n", - "files" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fa978aa8-bf89-4971-a59b-ae2fafb9e0a5", - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b.day2.1/atm/hist/b.day2.1.cam.h0.0003-07.nc'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n", - "\u001b[0;31mKeyError\u001b[0m: [, ('/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b.day2.1/atm/hist/b.day2.1.cam.h0.0003-07.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'da467520-dd8b-4048-a691-3445d0f0f93e']", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m ds\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/api.py:566\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 554\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m 555\u001b[0m decode_cf,\n\u001b[1;32m 556\u001b[0m open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 562\u001b[0m decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m 563\u001b[0m )\n\u001b[1;32m 565\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 566\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 567\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 568\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 569\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 570\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 571\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 572\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m 573\u001b[0m backend_ds,\n\u001b[1;32m 574\u001b[0m filename_or_obj,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 585\u001b[0m )\n\u001b[1;32m 586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:590\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m( \u001b[38;5;66;03m# type: ignore[override] # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 571\u001b[0m filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 587\u001b[0m autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 588\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m 589\u001b[0m filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 590\u001b[0m store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 592\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 594\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 595\u001b[0m \u001b[43m \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 596\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 597\u001b[0m \u001b[43m \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 598\u001b[0m \u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 599\u001b[0m \u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 600\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 602\u001b[0m store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m 603\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:391\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 385\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m 386\u001b[0m clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m 387\u001b[0m )\n\u001b[1;32m 388\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m 389\u001b[0m netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m 390\u001b[0m )\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:338\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 338\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m 340\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:400\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 400\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/netCDF4_.py:394\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 394\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m 395\u001b[0m ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/contextlib.py:135\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 198\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m file\n", - "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2023b/lib/python3.10/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 215\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 216\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 219\u001b[0m \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", - "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2464\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2027\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/glade/derecho/scratch/PUT_USER_NAME_HERE/archive/b.day2.1/atm/hist/b.day2.1.cam.h0.0003-07.nc'" - ] - } - ], - "source": [ - "ds = xr.open_dataset(files)\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "39d93758-aedb-42ab-810c-257a37dae487", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 1: Make a lat-lon plot of TS\n", - "\n", - "To highlight plotting the variables from the CESM atmosphere (CAM) file, the first example will plot a simple global lat/lon plot of surface temperature `TS`" - ] - }, - { - "cell_type": "markdown", - "id": "1e6b83ad", - "metadata": {}, - "source": [ - "### Grab data from first time stamp\n", - "\n", - "NOTE: This dataset has only one time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "feb6d347-cbcb-407c-bb4d-6d89c69fcf32", - "metadata": {}, - "outputs": [], - "source": [ - "ts_0 = ds.TS.sel({\"time\": ds.TS.time.values[0]}).squeeze()\n", - "ts_0" - ] - }, - { - "cell_type": "markdown", - "id": "cf1a965c", - "metadata": {}, - "source": [ - "The next step is to set up the map. Since we are plotting a global lat/lon, we will use the \"Plate Carree\" projection. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b183aba-6eae-48ad-9545-f3c918fb99ff", - "metadata": {}, - "outputs": [], - "source": [ - "# define the colormap\n", - "cmap = mpl.colormaps[\"jet\"]\n", - "\n", - "# set up the figure with a Plate Carree projection\n", - "fig = plt.figure(figsize=(15, 10))\n", - "\n", - "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", - "\n", - "# Plot the first timeslice of TS\n", - "img = ax.pcolormesh(ds.lon, ds.lat, ts_0, cmap=cmap, transform=ccrs.PlateCarree())\n", - "\n", - "plt.title(\"Surface Temperature\", fontsize=20)\n", - "\n", - "# Set up colorbar\n", - "plt.colorbar(img, orientation=\"vertical\", fraction=0.0242, pad=0.01)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ccd1f942-a16d-490b-98e6-5f57e92a1cce", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/basics_plot_1.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "1bedaea9-73cf-4011-b928-e8c2de752737", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "The colorbar limits are set automatically by `pcolormesh`. How could you change the arguments for the `pcolormesh` function to set the plotting limits?" - ] - }, - { - "cell_type": "markdown", - "id": "e18f7699-44a7-46f9-9503-1d97fb9418f7", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "Choose a maximum temperature of 300K and minimum temperature of 225K.\n", - " \n", - "```python\n", - "img = ax.pcolormesh(ds.lon, ds.lat, ts_0, vmax=300, vmin=225, cmap=cmap, transform=ccrs.PlateCarree())\n", - "\n", - "```\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "20dc7be8", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "How could we change the central longitude?" - ] - }, - { - "cell_type": "markdown", - "id": "26a14d27-a027-4774-87bf-63712f1f88b6", - "metadata": {}, - "source": [ - "\n", - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "The default central longitude is 0. Try setting it to 180. Then try other values from 0-360.\n", - "\n", - "```python\n", - "ax = fig.add_subplot(1,1,1, projection=ccrs.PlateCarree(central_longitude=180))\n", - "```\n", - " \n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "c632aed7", - "metadata": {}, - "source": [ - "A second quick example is for `xarray`'s built-in plotting which uses the `matplotlib` and `cartopy` in the background. `xarray` makes creating a basic plot fairly simple." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f48694b-1eae-4b88-87b3-9aec97ed7ffe", - "metadata": {}, - "outputs": [], - "source": [ - "# Xarray native plotting\n", - "\n", - "# Set up figure and axis\n", - "fig, ax = plt.subplots(1, figsize=(20, 10))\n", - "\n", - "# Plot the data straight from the xarray dataset\n", - "ts_0.plot.contourf(cmap=\"jet\", levels=np.arange(220, 321, 5))\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c47469f3-3b57-41c9-9bb2-f56a295bf027", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/basics_plot_2.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "8d12294c-691b-491f-bf88-be4c267a5c73", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "42b295a0-4675-4d4e-814f-5aac53b90973", - "metadata": {}, - "source": [ - "## Exercise 2: Zonal plot of SWCF\n", - "\n", - "The second example will plot the short wave cloud forcing (`SWCF`) zonally." - ] - }, - { - "cell_type": "markdown", - "id": "765089ca", - "metadata": {}, - "source": [ - "Grab the variable data and mean over the the lon value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "801cf760-f576-4cea-9dd6-0dadfc1788a1", - "metadata": {}, - "outputs": [], - "source": [ - "ds_swcf = ds.SWCF\n", - "\n", - "# Get all the dataset dimensions\n", - "d = ds_swcf.dims\n", - "\n", - "# Grab all dimensions to mean the lon values from\n", - "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", - "\n", - "# Make new dataset of zonal mean values\n", - "ds_swcf_zonal = ds_swcf.mean(dim=davgovr)\n", - "\n", - "# print some values of new zonally-averaged SWCF variable\n", - "ds_swcf_zonal" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b92a7bdd-b2dc-4a2c-a6e9-9b75e361492a", - "metadata": {}, - "outputs": [], - "source": [ - "# Create figure and axis\n", - "fig, ax = plt.subplots(1, figsize=(12, 7))\n", - "\n", - "# Set Main title for subplots:\n", - "plt.title(\"Short Wave Cloud Forcing\", fontsize=20)\n", - "\n", - "# Plot value on y-axis and latitude on the x-axis\n", - "ax.plot(ds_swcf_zonal.lat, ds_swcf_zonal, c=\"goldenrod\")\n", - "\n", - "ax.set_xlim(\n", - " [max([ds_swcf_zonal.lat.min(), -90.0]), min([ds_swcf_zonal.lat.max(), 90.0])]\n", - ")\n", - "\n", - "ax.set_xlabel(\"Latitude\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "47ccd4c3-3c40-4b61-9ac1-3df14249eed1", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/basics_plot_3.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "a4cd24f4", - "metadata": {}, - "source": [ - "**Question**\n", - "\n", - "What code could you add to set a legend for the plot line?" - ] - }, - { - "cell_type": "markdown", - "id": "afcc9ee3-0312-4440-b720-a547382a960a", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "enter the following lines after the `ax.set_xlabel` command and before the `plt.show()` command. \n", - " \n", - "```python\n", - "label = ds_swcf.time.values[0].strftime() # -> '0001-02-01 00:00:00'\n", - "line = Line2D([0], [0], label=label,\n", - " color=\"blue\")\n", - " \n", - "fig.legend(handles=[line],bbox_to_anchor=(-0.15, 0.15, 1.05, .102),loc=\"right\",\n", - " borderaxespad=0.0,fontsize=16,frameon=False)\n", - "```\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "dbe1163c-0ec0-4f9e-94df-4163078c8803", - "metadata": {}, - "source": [ - "**Question**\n", - "\n", - "What else could you label the line legend, if anything?\n" - ] - }, - { - "cell_type": "markdown", - "id": "a5cc83e2-3385-472f-864a-88307c6195c2", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "Try setting the label value to something else like your simulation run name, the season or month, etc.\n", - " \n", - "```\n", - "label = 'Season '\n", - "```\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "d7018dc2-c95b-4643-b078-d5033a6f236e", - "metadata": {}, - "source": [ - "**Question**\n", - "\n", - "How can you change the color on the plot and the legend?" - ] - }, - { - "cell_type": "markdown", - "id": "3bfc85e8-831b-4ead-a5e9-aa25ce2194ff", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "To change colors, try a different named color (e.g. \"red\" or \"goldenrod\"). You should make sure both the plot and the legend match or your plot won't make sense.\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "55a42e67-2609-4f07-8485-bc8f5035e14a", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for a solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/basics_plot_4.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "4d50620e-407d-44ef-bce5-e76c076bcbcd", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "49402bdd-648e-4021-af0e-9280067b7d76", - "metadata": {}, - "source": [ - "## Exercise 3: Plot of zonal T\n", - "\n", - "This example will plot the 3D zonal mean of temperature `T` with the pressure as the y-variable and latitude as the x-variable. We are showing you four different ways to make the same plot since each is valuable for plots with pressure on the y-axis and how to format those to get more information from the data.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "619d661b-84c9-4a5f-8366-36d8023f082e", - "metadata": {}, - "outputs": [], - "source": [ - "# Remove all dimensions of length one\n", - "ds_t = ds.T.squeeze()\n", - "ds_t" - ] - }, - { - "cell_type": "markdown", - "id": "3eee4a65-9094-41e6-b905-b6623d62bde4", - "metadata": {}, - "source": [ - "### Plot 1: Natively via xarray\n", - "\n", - "This plot uses the xarray native grids for the plot\n", - "\n", - " - y-axis is increasing with height\n", - " - y-axis is not in log pressure" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5d7c3b5c-96ec-4da6-af39-7c4460827f71", - "metadata": {}, - "outputs": [], - "source": [ - "# Average over all dimensions except `lev` and `lat`.\n", - "d = ds_t.dims\n", - "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", - "\n", - "DS = ds_t.mean(dim=davgovr)\n", - "DS.transpose(\"lat\", \"lev\", ...)\n", - "fig, ax = plt.subplots(1, figsize=(20, 10))\n", - "DS.plot.contourf(ax=ax, y=\"lev\", cmap=\"Spectral_r\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5324e5a3-c6b4-4213-bf11-6184338a63e2", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for a solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/basics_plot_5.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9b8a57a1-0e26-4740-8182-bf4da973c911", - "metadata": {}, - "source": [ - "### Plot 2: Quick ADF style\n", - "\n", - "This plot uses the quick style of the AMWG Diagnostics Framework (ADF) packages.\n", - "\n", - " - y-axis is increasing with height\n", - " - y-axis is not in log pressure" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2440adfe-60ab-4feb-832c-496423057404", - "metadata": {}, - "outputs": [], - "source": [ - "# Average over all dimensions except `lev` and `lat`.\n", - "d = ds_t.dims\n", - "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", - "\n", - "DS = ds_t.mean(dim=davgovr)\n", - "\n", - "lev = DS[\"lev\"]\n", - "lat = DS[\"lat\"]\n", - "mlev, mlat = np.meshgrid(lev, lat)\n", - "\n", - "# Generate zonal plot:\n", - "fig, ax = plt.subplots(1, figsize=(15, 7))\n", - "\n", - "# Create zonal plot with vertical levels\n", - "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n", - "\n", - "# Format axis and ticks\n", - "ax.set_xlabel(\"Latitude\")\n", - "fig.colorbar(img, ax=ax, location=\"right\")" - ] - }, - { - "cell_type": "markdown", - "id": "bd0ac9e7-4a8e-498a-be2b-3a7ddd8cca25", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for a solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/basics_plot_6.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "98f31b9b-db5d-4e2a-81f9-df146980dec0", - "metadata": {}, - "source": [ - "**Questions**\n", - "\n", - "- What differences do you see between the ADF quick plot and the xarray native plot? \n", - "- Why might you want to use one or the other?\n", - "- Where, vertically, do high pressures occur and what does this mean for where the ground would be located on this plot?" - ] - }, - { - "cell_type": "markdown", - "id": "96e8b966-f242-4e43-bb51-9deaa77a6bc6", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "Notice that the colorbar and axes labels are different between the two plots. Also note that the y-axis is going from lower to higher pressure values, but in the real atmosphere the highest pressures are near the surface (so the plot is inverted relative to the actual height of the layers above the Earth's surface). \n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e866346a-2d31-4df9-955c-b7a2ae00b888", - "metadata": {}, - "source": [ - "### Plot 3: ADF style with reversed y-axis\n", - "\n", - " - y-axis is decreasing with height\n", - " - y-axis is not in log pressure" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c08d64d9-40d9-4064-9b06-2b8489d916a0", - "metadata": {}, - "outputs": [], - "source": [ - "# Average over all dimensions except `lev` and `lat`.\n", - "d = ds_t.dims\n", - "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", - "\n", - "DS = ds_t.mean(dim=davgovr)\n", - "\n", - "# print(DS.lev.min(),DS.lev.max())\n", - "\n", - "lev = DS[\"lev\"]\n", - "lat = DS[\"lat\"]\n", - "mlev, mlat = np.meshgrid(lev, lat)\n", - "\n", - "# Generate zonal plot:\n", - "fig, ax = plt.subplots(1, figsize=(15, 7))\n", - "\n", - "# Create zonal plot with vertical levels\n", - "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n", - "\n", - "# Format axis and ticks\n", - "plt.gca().invert_yaxis()\n", - "ax.tick_params(which=\"minor\", length=4, color=\"r\")\n", - "\n", - "# Set up colorbar\n", - "cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])\n", - "posn = ax.get_position()\n", - "\n", - "# Set position and size of colorbar position: [left, bottom, width, height]\n", - "cbar_ax.set_position([posn.x0 + posn.width + 0.005, posn.y0, 0.02, posn.height])\n", - "\n", - "ax.set_xlabel(\"Latitude\")\n", - "fig.colorbar(img, cax=cbar_ax, orientation=\"vertical\")" - ] - }, - { - "cell_type": "markdown", - "id": "ba0aa084-f95e-4951-81b0-3951e8ed0b29", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for a solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/basics_plot_7.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "a0beeca6-4697-4bc8-b42d-e43ccceecbef", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "- What differences do you see on the y-axis between the result from Plot 2 and Plot 3? " - ] - }, - { - "cell_type": "markdown", - "id": "1dd69767-20ac-49ff-9c1f-ade44886de56", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The plots are mirror images of each other because the y-axis has been flipped. Plot 2 has the highest pressures at the top of the y-axis while Plot 3 has the highest pressures at the bottom of the y-axis.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "8bd94a75-58cb-4b5d-a806-45b64c26e727", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "- Where, vertically, do high pressures occur and what does this mean for where the ground would be located on these two figures ?" - ] - }, - { - "cell_type": "markdown", - "id": "4b1b7ab1-20ff-4745-bad7-cf50c9b807b8", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The highest pressures in the atmosphere occur at the ground where the most atmospheric mass is pressing downward. This means that Plot 2 is oriented so the location where the ground is is the top of the plot, or up. In Plot 3 the orientation is so that the ground would be at the bottom of the plot, or down. Thus Plot 3 is often more intuitive to read because it's oriented with the way we perceive the atmosphere.\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "84fc1ae8-74f3-4c4f-9171-18b5625f2d96", - "metadata": {}, - "source": [ - "### Plot 4: Complex ADF style\n", - "\n", - " - y-axis is decreasing with height\n", - " - y-axis is in log pressure" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50564cf7-948e-4b77-9537-707b7358bf71", - "metadata": {}, - "outputs": [], - "source": [ - "# Average over all dimensions except `lev` and `lat`.\n", - "d = ds_t.dims\n", - "davgovr = [dim for dim in d if dim not in (\"lev\", \"lat\")]\n", - "\n", - "DS = ds_t.mean(dim=davgovr)\n", - "\n", - "# print(DS.lev.min(),DS.lev.max())\n", - "\n", - "lev = DS[\"lev\"]\n", - "lat = DS[\"lat\"]\n", - "mlev, mlat = np.meshgrid(lev, lat)\n", - "\n", - "# Generate zonal plot:\n", - "fig, ax = plt.subplots(1, figsize=(15, 7))\n", - "\n", - "# Create zonal plot with vertical levels\n", - "img = ax.contourf(mlat, mlev, DS.transpose(\"lat\", \"lev\"), cmap=\"Spectral_r\")\n", - "\n", - "# Format axis and ticks\n", - "plt.yscale(\"log\")\n", - "plt.gca().invert_yaxis()\n", - "ax.tick_params(which=\"minor\", length=4, color=\"r\")\n", - "\n", - "# Set up colorbar\n", - "cbar_ax = fig.add_axes([0, 0, 0.1, 0.1])\n", - "posn = ax.get_position()\n", - "\n", - "# Set position and size of colorbar position: [left, bottom, width, height]\n", - "cbar_ax.set_position([posn.x0 + posn.width + 0.005, posn.y0, 0.02, posn.height])\n", - "\n", - "ax.set_xlabel(\"Latitude\")\n", - "fig.colorbar(img, cax=cbar_ax, orientation=\"vertical\")" - ] - }, - { - "cell_type": "markdown", - "id": "5a8fb095-89fd-4112-9d4f-de5f4ee9f143", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for a solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cam/basics_plot_8.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "4d7c9ee8-9445-489f-9b07-3ab67e45398d", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "- What differences do you see on the y-axis between the result from Plot 3 and Plot 4? " - ] - }, - { - "cell_type": "markdown", - "id": "f931e791-2f0a-423f-b44b-644686657767", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The plots both have the y-axis oriented so that the highest pressures, or ground, is at the bottom of the plot. But the scale of the y-axis has changed so that in Plot 3 it is logarithmic.\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6a6af568-90cf-4f8c-a3c8-5dcda30359fd", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "- What does that mean in terms of which part of the atmosphere are emphasized?" - ] - }, - { - "cell_type": "markdown", - "id": "0aa7131d-4337-4da2-8b8d-db9a69432142", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "With the logarithmic y-axis more of the upper atmosphere is shown on the figure. If you wanted to emphasize the boundary layer you might want to change the limits of the figure.\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c7c4e8c-4a4a-4f3e-a6d7-4b5e7725a2f7", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/cam/cam.ipynb b/notebooks_copy/diagnostics/cam/cam.ipynb deleted file mode 100644 index 06092aaa0..000000000 --- a/notebooks_copy/diagnostics/cam/cam.ipynb +++ /dev/null @@ -1,86 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Atmosphere" - ] - }, - { - "cell_type": "markdown", - "id": "30ca3a26-9666-433a-b9d6-852999d9455e", - "metadata": {}, - "source": [ - "## Basic Plotting\n", - "\n", - "### Learning Goals\n", - "\n", - "- Making a lat-lon plot\n", - "- Making a zonal plot\n", - "- Making a lat-height plot\n", - "\n", - "### Exercise 1: lat-lon plot\n", - "Plot global lat/lon of surface temperature\n", - "\n", - "### Exercise 2: Making a zonal plot\n", - "Plot zonal mean of short wave cloud forcing\n", - "\n", - "### Exercise 3: Making a lat-height plot\n", - "Plot temperature zonal mean with vertical levels\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "c494a32c-cd81-4a0a-b290-eb0835919532", - "metadata": { - "tags": [] - }, - "source": [ - "_______________\n", - "## Advanced Plotting\n", - "\n", - "### Learning Goals\n", - "\n", - "- analysis of native output \n", - "\n", - "\n", - "### Exercise 1: CAM-SE output analysis\n", - "\n", - "Examples of simple analysis and plotting that can be done with CAM-SE output on the native cubed-sphere grid." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d4d2795-ce87-424c-8b26-719932e22678", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/cice/advanced_cice.ipynb b/notebooks_copy/diagnostics/cice/advanced_cice.ipynb deleted file mode 100644 index 8dbb4b412..000000000 --- a/notebooks_copy/diagnostics/cice/advanced_cice.ipynb +++ /dev/null @@ -1,910 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bb50f4e0-22b4-4eff-99ac-01733347aacc", - "metadata": {}, - "source": [ - "# Advanced Plotting" - ] - }, - { - "cell_type": "markdown", - "id": "7eb5562a-1ee6-47e2-9266-f1da2f5cabc8", - "metadata": {}, - "source": [ - "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023a`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023a`." - ] - }, - { - "cell_type": "markdown", - "id": "7dfb6809-0426-42bf-9d73-0bcb5d5f9ea8", - "metadata": {}, - "source": [ - "_______________\n", - "This activity was developed primarily by David Clemens-Sewall." - ] - }, - { - "cell_type": "markdown", - "id": "bc842f8d-b0fe-41c2-be03-052eba873e72", - "metadata": {}, - "source": [ - "_______________\n", - "This notebook provides some additional examples of more advanced sea ice fields. Here we introduce the concept of the subgridscale ice thickness distribution (ITD). This means we have a fraction of ice in each grid cell that is binned into thickness categories." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "504d66c5-830c-43a2-a34a-abe8c62da585", - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.path as mpath\n", - "from matplotlib.gridspec import GridSpec\n", - "import pop_tools\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "id": "14fbb751-1804-4ff7-ad07-3c292e59e895", - "metadata": {}, - "source": [ - "For these exercises we will need to import multiple variables, below is an example of one way to do so." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3389a570-582d-4520-8fe7-4bdcd3d400e5", - "metadata": {}, - "outputs": [], - "source": [ - "monthly_output_path = \"/glade/campaign/cgd/cesm/CESM2-LE/ice/proc/tseries/month_1\"\n", - "run_name = \"b.e21.BHISTcmip6.f09_g17.LE2-1001.001\"\n", - "\n", - "var_names = ['aice',\n", - " 'aicen',\n", - " 'vsnon',\n", - " 'hs',\n", - " 'fsens',\n", - " 'fsens_ai',\n", - " ]\n", - "\n", - "da_list = []\n", - "\n", - "for var_name in var_names:\n", - " files = os.path.join(monthly_output_path, var_name,\n", - " run_name + \".cice.h.\" + var_name + \".*\")\n", - " ds_in = xr.open_mfdataset(files)\n", - " da_list.append(ds_in[var_name])\n", - " del ds_in\n", - "\n", - "ds = xr.merge(da_list)\n", - "\n", - "del da_list" - ] - }, - { - "cell_type": "markdown", - "id": "fe88ed04-642c-4fe2-87aa-cd4edc1e5b4d", - "metadata": {}, - "source": [ - "The next step is to read in some grid information for the `gx1v7` dipole grid used in POP and CICE. We will read in three main variables: `tarea`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Also, we will print the latitude array `TLAT` to see the metadata." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24f47162-9166-46d4-b026-515b6f892c8f", - "metadata": {}, - "outputs": [], - "source": [ - "# get pop grid grid cell areas\n", - "grid = pop_tools.get_grid('POP_gx1v7')\n", - "\n", - "# convert tarea to m^2\n", - "with xr.set_options(keep_attrs=True):\n", - " grid['TAREA'] = grid['TAREA']/(1e4)\n", - "grid['TAREA'].attrs['units'] = 'm^2'" - ] - }, - { - "cell_type": "markdown", - "id": "785ff8b7-fdd0-4216-a059-038cbfe4e282", - "metadata": {}, - "source": [ - "We will merge in three main variables: `tarea`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Note that this overwrites the dataset object from above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da0d7049-59fc-4bda-90f4-211ed1ae387d", - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.merge([ds.drop(['TLAT', 'TLON', 'ULAT', 'ULON']),\n", - " grid[['TLAT', 'TLONG', 'TAREA']].rename_dims({'nlat':'nj','nlon':'ni'})],\n", - " compat='identical', combine_attrs='no_conflicts')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6c35032-3dd2-43b6-988e-14f03e497c28", - "metadata": {}, - "outputs": [], - "source": [ - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "faf06e07-ee8a-4a61-a487-c0a445622e86", - "metadata": {}, - "source": [ - "_______________\n", - "## Example 1: Plot per-category ice area\n", - "\n", - "Compare the dataset in this notebook with `aice` in the basics notebook. Notice that in this case we have an additional category dimension `nc`. `aicen` is the per-category ice area fraction. We demonstrate plotting a per-category variable below. We also plot the full sea ice concentration in the final plot." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffb5f043-03ea-4483-90d2-26409072b9b5", - "metadata": {}, - "outputs": [], - "source": [ - "# make circular boundary for polar stereographic circular plots\n", - "theta = np.linspace(0, 2*np.pi, 100)\n", - "center, radius = [0.5, 0.5], 0.5\n", - "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", - "circle = mpath.Path(verts * radius + center)\n", - "\n", - "cmap = plt.cm.get_cmap('Blues_r') \n", - "\n", - "# create figure with subplots\n", - "fig, axs = plt.subplots(3, 2, figsize=(20,30),\n", - " subplot_kw={'projection':ccrs.NorthPolarStereo()})\n", - "axs = np.ravel(axs)\n", - "\n", - "# this creates a subplot for each ITD category\n", - "for i in ds.nc.values:\n", - " ax = axs[i]\n", - " ax.set_boundary(circle, transform=ax.transAxes)\n", - " ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - " ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - " this=ax.pcolormesh(ds['TLONG'],\n", - " ds['TLAT'],\n", - " ds['aicen'].sel({'time':'1850-02-01 00:00:00',\n", - " 'nc':i}).squeeze(),\n", - " cmap=cmap,vmax=1,vmin=0,\n", - " transform=ccrs.PlateCarree())\n", - " plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - " ax.set_title('Area Fraction Category ' + str(i+1))\n", - "\n", - "# gridcell mean aice in the final subplot\n", - "ax = axs[-1]\n", - "ax.set_boundary(circle, transform=ax.transAxes)\n", - "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - "this=ax.pcolormesh(ds['TLONG'],\n", - " ds['TLAT'],\n", - " ds['aice'].sel({'time':'1850-02-01 00:00:00'}).squeeze(),\n", - " cmap=cmap,vmax=1,vmin=0,\n", - " transform=ccrs.PlateCarree())\n", - "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - "ax.set_title('Sea Ice Concentration')" - ] - }, - { - "cell_type": "markdown", - "id": "71b64ff8-2c9a-4f63-9d63-cc9f5047642f", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution to final two panels
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/advanced_plot_1.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6dded4c6-bb89-49fe-8c9f-ce46c26d5119", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "How do you get the grid cell total sea ice concentration? In which of the thickness categories is the concentration highest? How does that vary spatially? What does this indicate about the relative importance of different ice thicknesses in different Arctic regions?\n", - "\n", - "Note that the default CICE ice thickness categories are:\n", - "- Category 1: 0-0.64 m\n", - "- Category 2: 0.64-1.39 m\n", - "- Category 3: 1.39-2.47 m\n", - "- Category 4: 2.47-4.57 m\n", - "- Category 5: 4.57+ m" - ] - }, - { - "cell_type": "markdown", - "id": "5eab03af-1834-420d-87d3-6aea39398ef1", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "You would calculate the grid cell total sea ice concentration by summing up the concentration in each category. In the central Arctic the concentrations are highest in categories 2 and 3, while on the ice margins the concentrations are highest in category 1. The only location with any substantial thick ice (category 5) is the central Arctic. This indicates that different regions' sea ice are dominated by very different mean sea ice thickness. This will have implications for ice growth/melt and heat fluxes.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "0d50b7e3-6a20-432f-9c0e-2222b85604ed", - "metadata": {}, - "source": [ - "_______________\n", - "## Example 2: Plot per-category snow thickness\n", - "\n", - "Internally, the model actually stores the snow **volume** for each category, not the thickness. To get the thickness we need to divide `vsnon` by `aicen` (the per category area)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed4b81e5-c6b5-466d-9784-74bd7bbace8a", - "metadata": {}, - "outputs": [], - "source": [ - "ds['hsn'] = ds['vsnon'] / ds['aicen']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "889b2691-b6b3-4363-b3fa-e4d96e4309aa", - "metadata": {}, - "outputs": [], - "source": [ - "# Max snow depth for colorbars\n", - "hs_max = 0.5 \n", - "\n", - "# make circular boundary for polar stereographic circular plots\n", - "theta = np.linspace(0, 2*np.pi, 100)\n", - "center, radius = [0.5, 0.5], 0.5\n", - "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", - "circle = mpath.Path(verts * radius + center)\n", - "\n", - "cmap = plt.cm.get_cmap('Blues_r') \n", - "\n", - "\n", - "fig, axs = plt.subplots(3, 2, figsize=(20,30),\n", - " subplot_kw={'projection':ccrs.NorthPolarStereo()})\n", - "axs = np.ravel(axs)\n", - "\n", - "for i in ds.nc.values:\n", - " ax = axs[i]\n", - " ax.set_boundary(circle, transform=ax.transAxes)\n", - " ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - " ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - " this=ax.pcolormesh(ds['TLONG'],\n", - " ds['TLAT'],\n", - " ds['hsn'].sel({'time':'1850-02-01 00:00:00',\n", - " 'nc':i}).squeeze(),\n", - " cmap=cmap,vmax=hs_max,vmin=0,\n", - " transform=ccrs.PlateCarree())\n", - " plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - " ax.set_title('Snow Depth (m) Category ' + str(i+1))\n", - "\n", - "# gridcell mean snow volume in the final subplot\n", - "ax = axs[-1]\n", - "ax.set_boundary(circle, transform=ax.transAxes)\n", - "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - "this=ax.pcolormesh(ds['TLONG'],\n", - " ds['TLAT'],\n", - " ds['hs'].sel({'time':'1850-02-01 00:00:00'}).squeeze(),\n", - " cmap=cmap,vmax=hs_max,vmin=0,\n", - " transform=ccrs.PlateCarree())\n", - "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - "ax.set_title('Average Snow Depth (m)')" - ] - }, - { - "cell_type": "markdown", - "id": "5d21d343-ee9f-41ef-8672-5f10fe96b6e0", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution to final two panels
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/advanced_plot_2.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "d3e1e661-f909-4c25-bf61-96581e697b86", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "In addition to the per-category snow thickness values we plotted the grid cell mean snow thickness. How do these values compare?" - ] - }, - { - "cell_type": "markdown", - "id": "24792b73-508f-4501-976d-3bfef0508050", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The per-category snow thickness can be higher than the grid cell mean (see Category 4 and the grid cell mean). This means the thicker ice tends to have thicker snow, but if the overall concentration of the thick ice categories is small then the mean over the grid call can be lower.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e85aa3e0-6a08-4592-b616-2dc711b28230", - "metadata": {}, - "source": [ - "_______________\n", - "## Example 3: Ice area related tracer\n", - "\n", - "The default CICE outputs are averaged over the entire grid cell, including the open water. Thus if a grid cell happened to be half covered in 1-m-thick ice and half open water then `hi` would be 0.5 m. Some tracers are written out just for the ice-covered area of the grid cell. These are indicated by have `_ai` appended to the variable name.\n", - "\n", - "Below is an example of this for the sensible heat flux." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "41e55cf3-f620-41f9-8b10-2f9d38c03fe4", - "metadata": {}, - "outputs": [], - "source": [ - "ds['fsens_diff'] = ds['fsens_ai'] - ds['fsens']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5055a66c-9826-4e9e-8429-bbd43ced1ab5", - "metadata": {}, - "outputs": [], - "source": [ - "# Min and max\n", - "mins = [-60, -60, -30, 0]\n", - "maxs = [60, 60, 30, 1]\n", - "\n", - "# make circular boundary for polar stereographic circular plots\n", - "theta = np.linspace(0, 2*np.pi, 100)\n", - "center, radius = [0.5, 0.5], 0.5\n", - "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", - "circle = mpath.Path(verts * radius + center)\n", - "\n", - "cmap = plt.cm.get_cmap('RdBu') \n", - "\n", - "vars_to_plt = ['fsens_ai', 'fsens', 'fsens_diff', 'aice']\n", - "\n", - "fig, axs = plt.subplots(2, 2, figsize=(20,20),\n", - " subplot_kw={'projection':ccrs.NorthPolarStereo()})\n", - "axs = np.ravel(axs)\n", - "\n", - "# set up the plots for sensible heat flux over the sea ice, gridcell mean sensible heat flux,\n", - "# difference, and the gridcell mean ice concentration aice\n", - "\n", - "for i in np.arange(4):\n", - " if i == 3:\n", - " cmap = plt.cm.get_cmap('Blues_r')\n", - " ax = axs[i]\n", - " ax.set_boundary(circle, transform=ax.transAxes)\n", - " ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - " ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - " this=ax.pcolormesh(ds['TLONG'],\n", - " ds['TLAT'],\n", - " ds[vars_to_plt[i]].sel({'time':'1850-02-01 00:00:00'}\n", - " ).squeeze(),\n", - " cmap=cmap,vmax=maxs[i],vmin=mins[i],\n", - " transform=ccrs.PlateCarree())\n", - " plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - " ax.set_title(vars_to_plt[i])\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "71e4bcb8-ffa5-4c11-9e9f-de0bb2bc6ae2", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution to final two panels
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/advanced_plot_3.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "af18fd70-9b61-46d7-9512-65da395e6496", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Where are the differences in the fluxes most pronounced and why?" - ] - }, - { - "cell_type": "markdown", - "id": "4ae871ba-9383-4d6d-9c41-1b9c8bedadcf", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The differences are most pronounced near the ice edge where ice concentrations are lowest. This is because these areas have a lot of open water so the grid cell mean flux will be very different than that over just the ice covered portion. In the central Arctic most cells are nearly all ice covered, so the fluxes are not substantially different.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "097273a5-204d-438f-8f68-19d7f4b75e04", - "metadata": {}, - "source": [ - "_______________\n", - "## Example 4: Remapping a sea ice gridded field\n", - "\n", - "The sea ice and ocean grids usually have a transformation where the grid North Pole is moved into land to avoid instability problems with converging meridians (See [Orthogonal Grids - Murray 1996](https://doi.org/10.1006/jcph.1996.0136)). In the CESM we use the POP dipole and tripole grids and will soon be using the MOM6 tripole grids. While matplotlib can actually use the 2D latitude and longitude fields to make plots, it is sometimes necessary to remap or regrid the data onto a standard latitude-longitude grid. This can be done using the ESMF remapping tools [xESMF](https://xesmf.readthedocs.io/en/latest/) for python." - ] - }, - { - "cell_type": "markdown", - "id": "e8d2c0c2-b13b-41f1-9ff4-6324f8018f4b", - "metadata": {}, - "source": [ - "Let's first plot the raw field so the dipole grid can be visualized." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "50a62138-19cc-4a3b-8a6e-011bc508dbb8", - "metadata": {}, - "outputs": [], - "source": [ - "aice = ds['aice']\n", - "\n", - "aice" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9b3429a1-e2b6-419b-bd13-7ef651b5a5a0", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.set_facecolor('0.5') # Fill in model land by making the axis background whatever color you want\n", - "im1 = ax.pcolormesh(ds.coords['ni'],ds.coords['nj'],aice[0],cmap='Blues_r',vmin=0,vmax=1)\n", - "cbar = plt.colorbar(im1)\n", - "cbar.set_label('Sea Ice Concentration',fontsize=18)\n", - "plt.xticks([]);\n", - "plt.yticks([]);" - ] - }, - { - "cell_type": "markdown", - "id": "f83bb11b-dc76-468b-91dc-e751ac0ddbd5", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/advanced_plot_4.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "126ece37-f3be-45f4-90a8-030d9eb25921", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "What do you notice about the Northern Hemisphere? What is going on with Greenland? Do you notice anything odd in the Southern Hemisphere?" - ] - }, - { - "cell_type": "markdown", - "id": "e242c44d-3e3d-4704-aaa3-02c70eb77cec", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "See how the Northern Hemisphere looks weird? Greenland is stretched completely across the top edge of the plot. In contrast, the Southern Hemisphere looks normal.\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "fa913808-0583-480b-a087-23593d036389", - "metadata": {}, - "source": [ - "Next we will import the `xesmf` library and create a \"destination\" grid. This is just a simple one-degree by one-degree latitude longitude grid." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9ca157eb-230d-45c4-8116-654bd9bc2919", - "metadata": {}, - "outputs": [], - "source": [ - "import xesmf as xe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1392cd38-64bd-4139-a957-ad38e030dc2c", - "metadata": {}, - "outputs": [], - "source": [ - "# Setting up a target grid to only regrid the sea ice data\n", - "lat=np.arange(-90,90,1.0) \n", - "lon=np.arange(0,361,1.0)\n", - "#create a meshgrid (2D fields of lats and lons)\n", - "lon2d,lat2d=np.meshgrid(lon,lat) \n", - "#set up the target grid as an xarray Dataset\n", - "target_grid=xr.Dataset({'lat': (['y', 'x'], lat2d),'lon': (['y', 'x'], lon2d)})\n", - "\n", - "target_grid" - ] - }, - { - "cell_type": "markdown", - "id": "098c750b-9a23-43b6-a879-b3a0a6908b54", - "metadata": {}, - "source": [ - "Now we need to create a Dataset. For some reason the regridder does not accept a DataArray." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9048bc8e-5df6-468e-a38b-763e158d9aad", - "metadata": {}, - "outputs": [], - "source": [ - "# Define the dataset and a variable `aice` with dimensions time, nj, and ni. Also, fill the\n", - "# lat and lon arrays from the POPgrid dataset.\n", - "\n", - "ds_siconc_ocean = xr.Dataset()\n", - "ds_siconc_ocean['aice'] = (('time','nj','ni'),ds['aice'].values)\n", - "ds_siconc_ocean.coords['lat'] = (('nj','ni'),grid['TLAT'].values)\n", - "ds_siconc_ocean.coords['lon'] = (('nj','ni'),grid['TLONG'].values)\n", - "sic_ocean = ds_siconc_ocean['aice']" - ] - }, - { - "cell_type": "markdown", - "id": "e7684c0a-19d4-45cc-95f0-045a9355290b", - "metadata": {}, - "source": [ - "Next we will use xesmf to regrid from the POP gx1v7 grid to the one degree by one degree regular grid." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7dbe677b-bdde-464a-8e31-c3f8e651dedd", - "metadata": {}, - "outputs": [], - "source": [ - "#input grid, output grid, method, keyword arguments\n", - "#NOTE: This may throw a \"FutureWarning\", but feel free to ignore it.\n", - "regridder=xe.Regridder(sic_ocean[0,:,:], target_grid, 'nearest_s2d',periodic=True,reuse_weights=False)" - ] - }, - { - "cell_type": "markdown", - "id": "dba1a8bd-ac35-4bcf-b3ae-201575ec4f56", - "metadata": {}, - "source": [ - "Now that we have the mapping or regridder we can translate the Xarray Dataset from gx1v7 to a one-degree regular grid." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f66e319-147a-44d4-8d07-6db65e6295b8", - "metadata": {}, - "outputs": [], - "source": [ - "sic_rg = regridder(sic_ocean)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a523793f-1e06-47b1-b960-98c20125c3ea", - "metadata": {}, - "outputs": [], - "source": [ - "# Plot the first slice of the new dataset.\n", - "fig, ax = plt.subplots(figsize=(12,8))\n", - "ax.set_facecolor('0.5') # Fill in model land by making the axis background whatever color you want\n", - "im4 = ax.pcolormesh(lon,lat,sic_rg[0,:,:],cmap='Blues_r',vmin=0,vmax=1)\n", - "cbar = plt.colorbar(im4)\n", - "cbar.set_label('Sea Ice Concentration [%]',fontsize=18)\n", - "plt.xticks([]);\n", - "plt.yticks([]);" - ] - }, - { - "cell_type": "markdown", - "id": "ecbf2b91-8b62-4fc1-9783-7818018e9095", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/advanced_plot_5.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e24f8ca8-53b3-4389-bf3b-cbb0a05d5920", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "What do you notice about the Northern Hemisphere now?" - ] - }, - { - "cell_type": "markdown", - "id": "f33b34d8-12d3-4029-8978-70e1125f0807", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "See how the Northern Hemisphere looks weird? Greenland is stretched completely across the top edge of the plot. In contrast, the Southern Hemisphere looks normal.\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "0e470b63-5cb9-4a26-8ed1-e83b78786a79", - "metadata": {}, - "source": [ - "This data could now be differenced against an observational dataset on a one-degree grid. Additionally, if you wanted to plot the model data using `contourf` instead of `pcolormesh` (which is what we used in the rest of these exercises)." - ] - }, - { - "cell_type": "markdown", - "id": "8653d261-e3c2-42f7-8782-e2d94e7f7358", - "metadata": {}, - "source": [ - "Now let's plot the original data with `pcolormesh`, the regridded data with `pcolormesh`, and the regridded data with `contourf` to see how this looks different." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "26499fe9-e5dd-4446-a862-4d41c96adeba", - "metadata": {}, - "outputs": [], - "source": [ - "# make circular boundary for polar stereographic circular plots\n", - "theta = np.linspace(0, 2*np.pi, 100)\n", - "center, radius = [0.5, 0.5], 0.5\n", - "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", - "circle = mpath.Path(verts * radius + center)\n", - "\n", - "# define the colormap\n", - "cmap = plt.cm.get_cmap('Blues_r') \n", - "\n", - "# create the figure\n", - "fig = plt.figure(figsize=(20,20))\n", - "\n", - "### make first plot - native grid - using pcolormesh\n", - "ax = fig.add_subplot(1,3,1, projection=ccrs.NorthPolarStereo())\n", - "ax.set_boundary(circle, transform=ax.transAxes)\n", - "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - "\n", - "# sets the latitude / longitude boundaries of the plot\n", - "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - "\n", - "#Plot the first timeslice of aice\n", - "this=ax.pcolormesh(sic_ocean['lon'],\n", - " sic_ocean['lat'],\n", - " sic_ocean.isel(time=0).squeeze(),\n", - " cmap=cmap,vmax=1,vmin=0,\n", - " transform=ccrs.PlateCarree())\n", - "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - "plt.title('Native grid and pcolormesh',fontsize=20)\n", - "\n", - "### make second plot - regridded - using pcolormesh\n", - "ax = fig.add_subplot(1,3,2, projection=ccrs.NorthPolarStereo())\n", - "ax.set_boundary(circle, transform=ax.transAxes)\n", - "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - "\n", - "# sets the latitude / longitude boundaries of the plot\n", - "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - "\n", - "#Plot the first timeslice of aice\n", - "this=ax.pcolormesh(lon,\n", - " lat,\n", - " sic_rg.isel(time=0).squeeze(),\n", - " cmap=cmap,vmax=1,vmin=0,\n", - " transform=ccrs.PlateCarree())\n", - "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - "plt.title('Regridded and pcolormesh',fontsize=20)\n", - "\n", - "### make third plot - regridded - using contourf\n", - "ax = fig.add_subplot(1,3,3, projection=ccrs.NorthPolarStereo())\n", - "ax.set_boundary(circle, transform=ax.transAxes)\n", - "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - "\n", - "# sets the latitude / longitude boundaries of the plot\n", - "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - "\n", - "#Plot the first timeslice of aice\n", - "this=ax.contourf(lon,\n", - " lat,\n", - " sic_rg.isel(time=0).squeeze(),\n", - " cmap=cmap,levels=[0,0.2,0.4,0.6,0.8,1.0],\n", - " transform=ccrs.PlateCarree())\n", - "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - "plt.title('Regridded and pcolormesh',fontsize=20)\n" - ] - }, - { - "cell_type": "markdown", - "id": "f48bde78-24ee-43b0-ae54-5beecc91735c", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/advanced_plot_6.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "21e7ca55-a6bf-4b76-aae8-09a09f71818c", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "How does the ice edge compare in the two `pcolormesh` plots? What's different in the regridded `pcolormesh` and `contourf` plots?" - ] - }, - { - "cell_type": "markdown", - "id": "c4d58cad-56b5-455e-8499-8d6385656d3d", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The regridded data is more coarse and so the filled gridcells using `pcolormesh` in the regridded plot (center) are larger than in the native grid (left). The `contourf` differences are most notable at the ice edge near Russia where you can see clear contour lines. You might have more luck trying the contours on a month in the summer instead of winter.\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02ca1b60-88e2-4898-b593-280c6025c6c8", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023a", - "language": "python", - "name": "npl-2023a" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/cice/basics_cice.ipynb b/notebooks_copy/diagnostics/cice/basics_cice.ipynb deleted file mode 100644 index 15208a071..000000000 --- a/notebooks_copy/diagnostics/cice/basics_cice.ipynb +++ /dev/null @@ -1,816 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "33070195-2d5f-4ee3-b6cb-a658209b1f4d", - "metadata": {}, - "source": [ - "# Basic Plotting" - ] - }, - { - "cell_type": "markdown", - "id": "f837eedc-2e5c-44a7-b4a4-6be632371012", - "metadata": {}, - "source": [ - "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023a`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023a`." - ] - }, - { - "cell_type": "markdown", - "id": "4fc43611-93db-4fa6-9396-362101c0fc33", - "metadata": {}, - "source": [ - "_______________\n", - "This activity was developed primarily by David Bailey and Alice DuVivier." - ] - }, - { - "cell_type": "markdown", - "id": "55f2ff0c-2b84-4c10-bde3-bc23722cb0ab", - "metadata": {}, - "source": [ - "_______________\n", - "These are examples of typical variables and plots that we look at in our sea ice diagnostics package. The most current version of the sea ice diagnostics are in the CESM Postprocessing repository. More information can be found here: [CESM Postprocessing](https://github.com/NCAR/CESM_postprocessing)." - ] - }, - { - "cell_type": "markdown", - "id": "3d56ba02-c652-4a97-a466-d3633e53aeb1", - "metadata": {}, - "source": [ - "The first step is to import the libraries needed to plot the data. Here we will use `xarray` as a tool to read the netCDF file. We will use `numpy` for some basic math calculations. For plotting the data we will need `matplotlib`, `pop_tools`, `geocat` and `cartopy`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e7f10ae-61c8-45f1-9d5c-9be60632b556", - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.path as mpath\n", - "from matplotlib.gridspec import GridSpec\n", - "import pop_tools\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "import nc_time_axis\n", - "import cftime\n", - "import os" - ] - }, - { - "cell_type": "markdown", - "id": "98894413-8550-49ba-b5ff-c027900b253a", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 1: Plot Sea Ice Concentration on a polar projection." - ] - }, - { - "cell_type": "markdown", - "id": "0a9043ca-ec38-4bc9-84e9-54e375c8f4e6", - "metadata": {}, - "source": [ - "Here you will learn about plotting sea ice area and other variables on a polar projection." - ] - }, - { - "cell_type": "markdown", - "id": "4fa4bed0-8207-4cb6-ae96-2507b56f7aef", - "metadata": {}, - "source": [ - "The first step is to grab sea ice (CICE) history files from your CESM model run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da58ab48-2fc9-4bd8-8892-2af0782f1e59", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# Set your username here:\n", - "username = \"PUT_USER_NAME_HERE\"\n", - "\n", - "# Here we point to the archive directory from your b.day2.1 simulation\n", - "monthly_output_path = f\"/glade/derecho/scratch/{username}/archive/b.day2.1/ice/hist\"\n", - "\n", - "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", - "# this provided simulation data instead:\n", - "#monthly_output_path = \"/glade/campaign/cesm/tutorial/tutorial_2023_archive/b.day2.1/ice/hist\"\n", - "\n", - "# Name of CESM run\n", - "run_name = \"b.day2.1\"\n", - "\n", - "# Create path to all files, including unix wild card for all dates\n", - "files = os.path.join(monthly_output_path, run_name + \".cice.h.*\")\n", - "\n", - "# read in files as an xarray dataset:\n", - "ds = xr.open_mfdataset(files)\n", - "\n", - "### For this analysis, choose which variable to keep. Start with `aice` and then later try `hi`\n", - "var_in = 'aice' # sea ice concentration\n", - "#var_in = 'hi' # sea ice thickness\n", - "\n", - "var_to_keep = ds[var_in]\n", - "\n", - "print(var_to_keep)" - ] - }, - { - "cell_type": "markdown", - "id": "ded5a0a7-9e8f-4e00-8f5e-383c68cdb201", - "metadata": {}, - "source": [ - "The next step is to read in some grid information for the `gx1v7` dipole grid used in POP and CICE. We will read in three main variables: `tarea`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Also, we will print the longitude array `TLONG` to see the metadata." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19cf3c69-1a1d-467e-8071-e2be50d33150", - "metadata": {}, - "outputs": [], - "source": [ - "# get pop grid grid cell areas\n", - "grid = pop_tools.get_grid('POP_gx1v7')\n", - "\n", - "# convert tarea to m^2\n", - "with xr.set_options(keep_attrs=True):\n", - " grid['TAREA'] = grid['TAREA']/(1e4)\n", - "grid['TAREA'].attrs['units'] = 'm^2'\n", - "\n", - "grid\n", - "\n", - "grid['TLONG']" - ] - }, - { - "cell_type": "markdown", - "id": "b89ce1e7-cad5-4b14-a95b-58de981e73c7", - "metadata": {}, - "source": [ - "We will merge in three main variables: `TAREA`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Note that this overwrites the dataset object from above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e6d86e3-2876-4c79-9cc8-8c02f935deb9", - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.merge([var_to_keep.drop(['TLAT', 'TLON', 'ULAT', 'ULON']),\n", - " grid[['TLAT', 'TLONG', 'TAREA']].rename_dims({'nlat':'nj','nlon':'ni'})],\n", - " compat='identical', combine_attrs='no_conflicts')\n", - "grid['TLAT']" - ] - }, - { - "cell_type": "markdown", - "id": "101ce439-4fd7-412b-9f1d-1e7f34060747", - "metadata": {}, - "source": [ - "The next step is to set up the northern hemisphere polar stereographic projection for plotting the sea ice variable. We start with sea ice concentration `aice`. We are using a \"rainbow\" colormap here and cutting off the plot with a circular boundary. Note that we are only plotting the first timeslice of the `aice` array here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28a0da20-6bad-45d7-a61a-43d55ea2acd2", - "metadata": {}, - "outputs": [], - "source": [ - "# make circular boundary for polar stereographic circular plots\n", - "theta = np.linspace(0, 2*np.pi, 100)\n", - "center, radius = [0.5, 0.5], 0.5\n", - "verts = np.vstack([np.sin(theta), np.cos(theta)]).T\n", - "circle = mpath.Path(verts * radius + center)\n", - "\n", - "# define the colormap\n", - "cmap = plt.cm.get_cmap('rainbow') \n", - "\n", - "# set up the figure with a North Polar Stereographic projection\n", - "fig = plt.figure(figsize=(20,20))\n", - "ax = fig.add_subplot(1,2,1, projection=ccrs.NorthPolarStereo())\n", - "ax.set_boundary(circle, transform=ax.transAxes)\n", - "ax.add_feature(cfeature.LAND,zorder=100,edgecolor='k')\n", - "\n", - "# sets the latitude / longitude boundaries of the plot\n", - "ax.set_extent([0.005, 360, 90, 55], crs=ccrs.PlateCarree())\n", - "\n", - "#Plot the first timeslice of aice\n", - "this=ax.pcolormesh(ds['TLONG'],\n", - " ds['TLAT'],\n", - " ds[var_in].isel(time=0).squeeze(),\n", - " cmap=cmap,vmax=1,vmin=0,\n", - " transform=ccrs.PlateCarree())\n", - "plt.colorbar(this,orientation='vertical',fraction=0.04,pad=0.01)\n", - "plt.title('Sea Ice Concentration',fontsize=20)" - ] - }, - { - "cell_type": "markdown", - "id": "0ed4273f-6855-40fd-b69f-03d78fcde11f", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/basics_plot_1.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "24071060-19f0-467c-9835-221ce0456333", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Why is the scale from 0-1 for `aice`?" - ] - }, - { - "cell_type": "markdown", - "id": "5c5c4859-c3de-4160-a2e2-cc6d8293173a", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The variable `aice` is actually ice fraction. To convert it to concentration you would need to multipy it by 100. In the rest of these notebooks we use the ice fraction fairly interchangeably with ice concentration. But if you are plotting these you should check your range and see if the maximum value is 1 or 100 because that's important for setting colorbar limits.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "032392b9-ebb9-4fc2-83a7-991d15248f15", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "What month did you plot above? How could you plot the September ice concentration? (Remember that the monthly files are written at the last timestep of the month, so the June files are written out on July 1 at 00Z)." - ] - }, - { - "cell_type": "markdown", - "id": "6090f64c-0763-46af-b345-ae4c6d4979e0", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "You can check the date plotted by using the following command.\n", - " \n", - "```python\n", - "ds['aice'].isel(time=0).time\n", - "\n", - "```\n", - "\n", - "You should see that the date is Feb.1 of year 1 at 00Z. Which means it is the January mean sea ice concentration. To plot the September mean sea ice concentration, use the timeslice `time=8`.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "90114326-683d-4c15-9a60-d3a49545cc7a", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "What do you think of that rainbow colormap? Try other colormaps like `viridis`, `plasma`, `Blues`. Note that if you add the string `_r` to the colormap name it will reverse the colormap. What is a more intutitive map for plotting sea ice concentration?" - ] - }, - { - "cell_type": "markdown", - "id": "bce3bc22-5c2d-4f6b-85c4-b742ff4e6584", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "We think the reversed Blues colormap is more intuitive.\n", - " \n", - "```python\n", - "cmap = plt.cm.get_cmap('Blues_r') \n", - "\n", - "```\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "1d05d033-37b9-439f-8aa9-0d5727612a73", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Can you plot the annual mean over the data? What about computing the September mean over the data and plotting that?" - ] - }, - { - "cell_type": "markdown", - "id": "997afdf3-a145-4ca7-98d0-8e3ca3cdba5e", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "Try entering the following data to plot in the `pcolormesh` function.\n", - " \n", - "```python\n", - "ds['aice'].mean(dim='time').squeeze(),\n", - "\n", - "```\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f99f305c-be5a-44de-8c19-72551c10b51e", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "You have plotted the sea ice concentration (`aice`), now try plotting the thickness (`hi`). Note that the units of thickness are in meters, so think about how you should adjust the colorbar range to be sensible? Note that you will have to change the `var_in` at the top of this section." - ] - }, - { - "cell_type": "markdown", - "id": "58760c0a-ed62-40a0-b315-a018741de8cd", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "After selecting `var_in = hi`, run through the following cells. Then check different ranges for `vmax` that might make more sense for the sea ice thickness (e.g. `vmax=5`)\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "5ed91922-b419-4685-84e2-62c49a57a135", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "All of the previous plots were of the Northern Hemisphere. How would you plot the Southern Hemisphere? How might you want to consider the seasonality if you're looking at Antarctic sea ice?" - ] - }, - { - "cell_type": "markdown", - "id": "32e34c3e-b7b4-4da1-b297-f34cfe0a6a72", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "Use the following to plot the Southern Hemisphere\n", - " \n", - "```\n", - "ax = fig.add_subplot(1,2,1, projection=ccrs.SouthPolarStereo())\n", - "\n", - "ax.set_extent([0.005, 360, -90, -55], crs=ccrs.PlateCarree())\n", - "```\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "42b295a0-4675-4d4e-814f-5aac53b90973", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 2: Plot a time series of total sea ice area." - ] - }, - { - "cell_type": "markdown", - "id": "c0dd3647-5e9e-4d57-8cfb-4e0f073a269c", - "metadata": {}, - "source": [ - "The next few steps read in sea ice concentration or (`aice`) from one of the CESM2 large ensemble historical runs. Note this operation points to multiple files on the campaign file system, so we are using the `xarray` function `open_mfdataset` for a multifile dataset. We will also print the `aice` dataset to get an idea of the metadata and dimensions. Note that we could use the output from the tutorial simulations. However, those runs are very short and thus are not a very interesting timeseries. Instead we will use one of the CESM2 Large Ensemble historical simulations, as described in [Rodgers et al. 2021](https://doi.org/10.5194/esd-12-1393-2021)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3f591cc-dd67-4a3f-abe7-bfc740743db1", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# First remove the old 'ds' variable:\n", - "del ds\n", - "\n", - "### Here we point to the CESM2-LE datasets on campaign disk\n", - "\n", - "monthly_output_path = \"/glade/campaign/cgd/cesm/CESM2-LE/ice/proc/tseries/month_1\"\n", - "run_name = \"b.e21.BHISTcmip6.f09_g17.LE2-1001.001\"\n", - "\n", - "\n", - "\n", - "### For this we will use the sea ice concentration (aice) and ice thickness (hi)\n", - "\n", - "var_names = ['aice',\n", - " 'hi',\n", - " ]\n", - "\n", - "### This piece of code opens the files and combines them into a single xarray dataset\n", - "\n", - "da_list = []\n", - "\n", - "for var_name in var_names:\n", - " files = os.path.join(monthly_output_path, var_name,\n", - " run_name + \".cice.h.\" + var_name + \".*\")\n", - " ds_in = xr.open_mfdataset(files)\n", - " da_list.append(ds_in[var_name])\n", - " del ds_in\n", - "\n", - "ds = xr.merge(da_list)\n", - "\n", - "del da_list\n", - "\n", - "aice = ds['aice']\n", - "\n", - "print(aice)" - ] - }, - { - "cell_type": "markdown", - "id": "d38f2b64-72e7-4021-810b-8124062985b8", - "metadata": {}, - "source": [ - "The next step is to read in some grid information for the `gx1v7` dipole grid used in POP and CICE. We will read in three main variables: `TAREA`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Also, we will print the area array `TAREA` to see the metadata." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "560ec8fb-7200-460e-a240-538dc223509d", - "metadata": {}, - "outputs": [], - "source": [ - "# get pop grid grid cell areas\n", - "grid = pop_tools.get_grid('POP_gx1v7')\n", - "\n", - "# convert tarea to m^2\n", - "with xr.set_options(keep_attrs=True):\n", - " grid['TAREA'] = grid['TAREA']/(1e4)\n", - "grid['TAREA'].attrs['units'] = 'm^2'\n", - "\n", - "grid\n", - "\n", - "grid['TAREA']" - ] - }, - { - "cell_type": "markdown", - "id": "2bb581ad-11b2-4682-be94-98c9eeedd11d", - "metadata": {}, - "source": [ - "We will merge in three main variables: `TAREA`, `TLAT`, and `TLON`. These are the areas of the gridcells along with the latitudes and longitudes of the gridcell centers. Note that this overwrites the dataset object from above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ddcac5a-2e22-470a-8243-a3219d0488be", - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.merge([aice.drop(['TLAT', 'TLON', 'ULAT', 'ULON']),\n", - " grid[['TLAT', 'TLONG', 'TAREA']].rename_dims({'nlat':'nj','nlon':'ni'})],\n", - " compat='identical', combine_attrs='no_conflicts')\n", - "grid['TLAT']" - ] - }, - { - "cell_type": "markdown", - "id": "9aa682c3-d6ed-4188-885f-d34268deac50", - "metadata": {}, - "source": [ - "The next step is to compute the ice area in each grid cell by multiplying the grid cell areas by the ice area fraction. Then we subset to just grid cells in the Northern hemisphere (using `where`). Finally, we sum over all of the grid cells. We can do these operations in a single line:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ab1c8910-677e-4345-8f06-ec3f7a4a9f07", - "metadata": {}, - "outputs": [], - "source": [ - "ds_area = (ds.TAREA*ds.aice).where(ds.TLAT>0).sum(dim=['nj','ni'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d177fa4-70c3-4f58-8b2f-4b9193ee3cbe", - "metadata": {}, - "outputs": [], - "source": [ - "ds_area.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "8d7a8183-ab18-4fbc-a542-43d01a53f75f", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/basics_plot_2.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9efb6f20-f442-42b6-a838-c0af66a98ba6", - "metadata": {}, - "source": [ - "This plot looks very noisy! Why do you think this is? Hint: you have plotted how many years of monthly mean data? Let's try just plotting September instead.\n", - "\n", - "**Question:** Why do we use month 10 (`ds_area.time.dt.month.isin([10])`) to reference September data?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90d62f0a-e43b-4bad-a759-0133bc2860aa", - "metadata": {}, - "outputs": [], - "source": [ - "ds_area.sel(time=ds_area.time.dt.month.isin([10])).plot()" - ] - }, - { - "cell_type": "markdown", - "id": "83148fbe-aa53-41fe-b8fd-aeb7c2a5dc0c", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/basics_plot_3.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "5ea1c8b6-19b9-441d-ae08-82db58ddb90a", - "metadata": {}, - "source": [ - "Now let's plot the observational data on top. The NSIDC Sea Ice Index will be the values we want. These are computed from satellite observations. More information can be found here: [Sea Ice Index](https://nsidc.org/data/seaice_index)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de2232e9-2705-4894-b98a-a93082ae7f79", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "##### Add the data values manually from the datafile.\n", - "##### Create an xarray object with the NSIDC values and the years from 1979 to 2022.\n", - "\n", - "seaice_index = [4.58,4.87,4.44,4.43,4.7,4.11,4.23,4.72,5.64,5.36,4.86,4.55,4.51,5.43,4.58,5.13,4.43,5.62,\\\n", - " 4.89,4.3,4.29,4.35,4.59,4.03,4.05,4.39,4.07,4.01,2.82,3.26,3.76,3.34,3.21,2.41,3.78,3.74,\\\n", - " 3.42,2.91,3.35,3.35,3.17,2.83,3.47,3.47]\n", - "\n", - "# Convert to m^2\n", - "seaice_index = np.array(seaice_index)\n", - "seaice_index *= 1e12\n", - "\n", - "nsidc_time = [cftime.datetime(y, 10, 15) for y in range(1979,2023)]\n", - "\n", - "nsidc_index = xr.DataArray(data=seaice_index,coords={\"time\":nsidc_time})\n", - "\n", - "nsidc_index\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00fc5097-af7d-4cf2-94ab-bb4e0a63960c", - "metadata": {}, - "outputs": [], - "source": [ - "ds_area.sel(time=ds_area.time.dt.month.isin([10])).plot()\n", - "nsidc_index.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "6193d89f-ce8a-48df-aaf1-cb291f360dcc", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/cice/basics_plot_4.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "eea0e9cc-1769-45ad-9461-c9d3d8c51264", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Why do you think the observed (orange) does not perfectly correspond to the model? " - ] - }, - { - "cell_type": "markdown", - "id": "f07628e0-6c6a-4f7d-89f2-e2f115e586a9", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The model is one of 100 ensemble realizations of the 20th century and evolves independently, meaning each simulation has different weather and that weather will almost certainly be different from the observations. It will not, and should not, exactly match the observations because the climate system is chaotic.\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "412e002b-6a7f-4604-93fa-dfa7718ab2e3", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "What do you think you'll see if you plot the March total area timeseries?" - ] - }, - { - "cell_type": "markdown", - "id": "ed26c32a-79e4-4842-8ca7-ca4ec6f68b2a", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "March total area.\n", - "```\n", - "ds_area.sel(time=ds_area.time.dt.month.isin([4])).plot()\n", - "```\n", - "This should be higher mean and have a negative, but smaller, trend than the September trend. \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c4f36db3-ca0f-4f6b-9b60-9e618222d0b8", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Try computing total ice volume and plotting it." - ] - }, - { - "cell_type": "markdown", - "id": "9e96388f-b421-4a3c-b719-c83908e36f30", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "To calculate ice volume, use the variable `hi` in place of `aice`. Note that volume is units of $m^3$. So, you might want to scale the volume by $1.0e-13$.\n", - " \n", - "```\n", - "ds_vol = (ds.TAREA*ds.hi).where(ds.TLAT>0).sum(dim=['nj','ni'])\n", - "``` \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "2ce5c6f9-4272-4a8c-9b7b-3e6a31a5a708", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "What does the Southern Hemisphere sea ice area and volume look like?" - ] - }, - { - "cell_type": "markdown", - "id": "7e921a48-2488-45f9-a7a4-ec860e7e21ec", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "To calculate the Southern Hemisphere total area or volume, set the following values so that you only include points where the latitude is *less than* zero.\n", - "```\n", - "ds_area = (ds.TAREA*ds.aice).where(ds.TLAT<0).sum(dim=['nj','ni'])\n", - "``` \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25cf9352-2ee8-4b2b-ae7f-d7edc764977a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023a", - "language": "python", - "name": "npl-2023a" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/cice/cice.ipynb b/notebooks_copy/diagnostics/cice/cice.ipynb deleted file mode 100644 index f793a813b..000000000 --- a/notebooks_copy/diagnostics/cice/cice.ipynb +++ /dev/null @@ -1,160 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Sea Ice" - ] - }, - { - "cell_type": "markdown", - "id": "30ca3a26-9666-433a-b9d6-852999d9455e", - "metadata": {}, - "source": [ - "## Basic Plotting" - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "### Learning Goals\n", - "\n", - "- Making polar projection plots\n", - "- Understanding sea ice variables such as concentration and thickness\n", - "- Masking and area averaging\n", - "- Computing total sea ice area or volume" - ] - }, - { - "cell_type": "markdown", - "id": "e73ce5d6-d2b1-4f32-b64f-337a1b02e2d0", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 1: Making polar projection plots of sea ice concentration.\n", - "\n", - "Here we will use some custom python tools to produce a polar projection plot of sea ice concentration. Some things to try include changing the colormap, plotting sea ice thickness instead of concentration, and changing hemispheres." - ] - }, - { - "cell_type": "markdown", - "id": "815e0869-0518-4cf9-9417-cd9b08965ca1", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 2: Compute total sea ice area and plot versus time.\n", - "\n", - "Here will we learn how to compute the total sea ice area for the northern hemisphere. Then plot this versus time. Some exercises will be plotting the monthly mean sea ice area, plotting just September or March mean sea ice area, or computing sea ice volume and then plotting it.\n" - ] - }, - { - "cell_type": "markdown", - "id": "27dafcc2-beec-4d75-920f-a60f46fff27b", - "metadata": {}, - "source": [ - "_______________\n", - "## Advanced Plotting" - ] - }, - { - "cell_type": "markdown", - "id": "c494a32c-cd81-4a0a-b290-eb0835919532", - "metadata": { - "tags": [] - }, - "source": [ - "### Learning Goals\n", - "\n", - "- Using sea ice thickness distribution (ITD) category information\n", - "- Using sea ice tracers\n", - "- Remapping sea ice fields." - ] - }, - { - "cell_type": "markdown", - "id": "850641b7-2a63-4a20-8a26-d18a400c6160", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 1: Plot per-category ice area\n", - "\n", - "Here you will plot a field that uses the subgridscale ice thickness distribution (ITD) categories." - ] - }, - { - "cell_type": "markdown", - "id": "9fc458d8-6ea2-461f-b4d3-6808accfca41", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 2: Plot per-category snow thickness\n", - "\n", - "Here is another example of an ITD related field, the snow thickness. Actual snow thickness is derived from the snow volume (per unit area)." - ] - }, - { - "cell_type": "markdown", - "id": "4a75149d-ef7b-4b89-91b5-4daec351d95c", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 3: Plot ice area related tracer\n", - "\n", - "In this exercise you will learn about a variable related to the ice covered area of a grid cell instead of the grid cell mean.\n" - ] - }, - { - "cell_type": "markdown", - "id": "82beac53-81f6-494f-ba15-b1718e14421b", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 4: Remapping or regridding sea ice fields\n", - "\n", - "The ocean and sea ice grids are different in that the North Pole is moved into land. In this exercise\n", - "you will learn how to \"remap\" or \"regrid\" a sea ice field onto a standard one degree by one degree grid." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5184b86-b584-451c-b37a-15d6f27be115", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/clm_ctsm/basics_clm.ipynb b/notebooks_copy/diagnostics/clm_ctsm/basics_clm.ipynb deleted file mode 100644 index 7acf2078c..000000000 --- a/notebooks_copy/diagnostics/clm_ctsm/basics_clm.ipynb +++ /dev/null @@ -1,926 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Basic Plotting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023a`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023a`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_______________\n", - "This activity was developed primarily by Will Wieder and Peter Lawrence." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_______________\n", - "## Global Visualizations\n", - "\n", - "These are examples of typical variables and plots that we look at in our land model diagnostics package. You can see examples of output from the [land model dagnostics package here](https://www2.cesm.ucar.edu/experiments/cesm2.0/land/diagnostics/clm_diag_PCKG.html). The most current version of the land model diagnostics are in the CESM Postprocessing. More information here: [CESM Postprocessing](https://github.com/NCAR/CESM_postprocessing).\n", - "\n", - "\n", - "## Notebook Objectives\n", - "1. Become familiar with Jupyter Notebooks\n", - "2. Begin getting acquainted with python packages and their utilities\n", - "3. Plot a map and timeseries of global results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "****\n", - "History files from CESM are saved in netcdf format (denoted with the `.nc` file extension), a file format commonly used for storing large, multi-dimensional scientific variables.\n", - "Netcdf files are platform independent and self-describing; each file includes metadata that describes the data, including: **variables**, **dimensions**, and **attributes**.\n", - "\n", - "The figure below provides a generic example of the data structure in a netcdf file. The dataset illustrated has two variables (temperature and pressure) that have three dimensions. Coordinate data (e.g., latitude, longitude, time) that describe the data are also included. \n", - "\n", - "*Note that each simulation file has multiple variables. Within each `h0` file, most of the variables have three dimensions (lat x lon x time), while a few soil variables (e.g., moisture, temperature) have 4 dimensions (lat x lon x time x depth).*\n", - "\n", - "![Netcdf](https://xarray.pydata.org/en/stable/_images/dataset-diagram.png)\n", - "****\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*We'll start by loading some packages*\n", - "\n", - "The first step is to import the libraries needed to plot the data. Here we will use `xarray` as a tool to read the netCDF file. We will use `numpy` for some basic math calculations. For plotting the data we will need `matplotlib` and `cartopy`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# python packages\n", - "import os\n", - "import xarray as xr\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "# resources for plotting\n", - "import matplotlib.pyplot as plt\n", - "import cartopy\n", - "import cartopy.crs as ccrs\n", - "import cftime\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "NOTE: This example largely uses features of xarray and \n", - " matplotlib packages. We won't go into the basics \n", - " of python or features included in these packages, but there are lots of \n", - " resources to help get you started. Some of these are listed below. \n", - "\n", - "
\n", - "\n", - "- [NCAR python tutorial](https://ncar.github.io/python-tutorial/tutorials/yourfirst.html), which introduces python, the conda package manager, and more on github.\n", - "- [NCAR ESDS tutorial series](https://ncar.github.io/esds/blog/tag/python-tutorial-series/), features several recorded tutorials on a wide variety of topics.\n", - "- [Project Pythia](https://projectpythia.org/) links to lots of great resources!\n", - "- [GeoCAT examples](https://geocat-examples.readthedocs.io/en/latest/), with some nice plotting examples\n", - "\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Reading and formatting data\n", - "\n", - "**Note**: the drop-down solutions, below, assume you used b.day2.1 output for plotting for this section" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.1 Point to the data\n", - "The first step is to grab land (CTSM) history files from your CESM model run\n", - "\n", - "For this example we will use:\n", - "- reflected solar radiation (FSR), \n", - "- incident solar radiation (FSDS), and\n", - "- exposed leaf area index (ELAI)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set your username here:\n", - "username = \"PUT_USER_NAME_HERE\"\n", - "\n", - "# Here we point to the archive directory from your b.day2.1 simulation\n", - "monthly_output_path = f\"/glade/derecho/scratch/{username}/archive/b.day2.1/lnd/hist\"\n", - "\n", - "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", - "# this provided simulation data instead:\n", - "#monthly_output_path = \"/glade/campaign/cesm/tutorial/tutorial_2023_archive/b.day2.1/lnd/hist\"\n", - "\n", - "# Name of CESM run\n", - "run_name = \"b.day2.1\"\n", - "\n", - "# Create path to all files, including unix wild card for all dates\n", - "files = os.path.join(monthly_output_path, run_name + \".clm2.h0.*\")\n", - "\n", - "# read in files as an xarray dataset:\n", - "ds = xr.open_mfdataset(files)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "NOTE: These are the raw history files that CTSM writes out. \n", - "\n", - "By default, they include grid cell averaged monthly means for different state and flux variables.\n", - "\n", - "
\n", - " TIP: If you want to look at other variables, the fields variable in the cell below is where you can modify what we're reading off of the CLM history files.\n", - "
\n", - "\n", - "#### Printing information about the dataset is helpful for understanding your data. \n", - "- *What dimensions do your data have?*\n", - "- *What are the coordinate variables?*\n", - "- *What variables are we looking at?*\n", - "- *Is there other helpful information, or are there attributes in the dataset we should be aware of?*\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print information about the dataset\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also print information about the variables in your dataset. The example below prints information about one of the data variables we read in. You can modify this cell to look at some of the other variables in the dataset.\n", - "\n", - "*What are the units, long name, and dimensions of your data?*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds.FSDS" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.2 Simple Calculations\n", - "To begin with we'll look at albedo that's simulated by the model. \n", - "\n", - "Albedo can be calculated in several different ways, but the calculations use solar radiation terms that are handled within CTSM.\n", - "\n", - "Here we'll look at 'all sky albedo', which is the ratio of reflected to incoming solar radiation (**FSR/FSDS**).\n", - "Other intereresting variables might include latent heat flux or gross primary productivity. \n", - "\n", - "We will add this as a new variable in the dataset and add appropriate metadata.\n", - "\n", - "*When doing calculations, it is important to avoid dividing by zero. Use the `.where` function for this purpose*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds['ASA'] = ds.FSR/ds.FSDS.where(ds.FSDS>0)\n", - "ds['ASA'].attrs['units'] = 'unitless'\n", - "ds['ASA'].attrs['long_name'] = 'All sky albedo'\n", - "ds['ASA']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "## 2. Plotting\n", - "### 2.1 Easy plots using Xarray\n", - "To get a first look at the data, we can plot a month of data from the simulation, selecting the month using the `.isel` function.\n", - "\n", - "
\n", - " NOTE: The plotting function only works with 1D or 2D data. Our data are 3D (time, lat, lon), so we need to specify a specific value for the other variables. Below, we select a specific time using the isel, leaving the lat and lon dimensions to plot on the x and y axes.\n", - "
\n", - "\n", - "- We will plot all sky albedo (variable = `ASA`). Note that we select the variable by specifying our dataset, `ds`, and the variable. \n", - "- The plot is for the first year of data (`time=slice(0,12)`)\n", - "- This plotting function will plot `ASA` for each simulation in our dataset\n", - "\n", - "*More plotting examples are on the [xarray web site](https://docs.xarray.dev/en/latest/user-guide/plotting.html)*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds.ASA.isel(time=slice(0,12)).plot(x='lon',y='lat',col=\"time\", col_wrap=6) ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_1.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Question:** \n", - "\n", - "Why don't you see the whole globe in some months?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.2 Calculating differences\n", - "\n", - "We can calculate the differences between the beginning and end of the simulation to see the differences in albedo over the simulation period. The below code:\n", - "- Calculates a monthly climatology for the first and last full year of the simulation\n", - "- Defines the difference as a new variable, `dsDiff`\n", - "\n", - "We'll first plot maps of the difference in all sky albedo for each month" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dsIni = ds.isel(time=slice(0,12)).groupby('time.month').mean()\n", - "dsFin = ds.isel(time=slice(-16,-4)).groupby('time.month').mean()\n", - "dsDiff = dsFin-dsIni\n", - "dsDiff.ASA.plot(x='lon',y='lat',col=\"month\", col_wrap=6, robust=True) ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_2.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions:** \n", - "- How is albedo different at the end of the simulation, relative to the begining of simulation?\n", - "- Where are the differences the largest? \n", - "- Are the differences consistent throughout the year?\n", - "- **What's causing these albedo changes** in different regions?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Are the differences coming from incoming or reflected radiation? \n", - "To find out, we can plot each variable. First we will plot incoming radiation (the denominator in all-sky albedo)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dsDiff.FSDS.plot(x='lon',y='lat',col=\"month\", col_wrap=6, vmax=30.0,vmin=-30.0) ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_3.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions:**\n", - "- Do you see any dfferences? \n", - "- Try plotting reflected radiation (the numerator, FSR). What differences do you see? \n", - "\n", - "*Note that you might want to change the minimun (`vmin`) and maximum (`vmax`) colorbar values for the plot when you switch between variables*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Is exposed leaf area index (ELAI) contributing to the differences in albedo?\n", - "Plot the differences in ELAI below. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dsDiff.ELAI.plot(x='lon',y='lat',col=\"month\", col_wrap=6, robust=True) ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_4.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions:** \n", - "- What regions are LAI differences the greatest?\n", - "- What times of year is this true?\n", - "- Are the regions and times of largest differences the same as the differences in albedo? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.3 Calculating Time Series\n", - "\n", - "**Note**: the drop-down solutions, below, assume you used CESM2 Large Ensemble output for plotting for this section\n", - "\n", - "\n", - "As above, the plotting function we use here requires data to be 1D or 2D. Therefore, to plot a time series we either need to select a single point or average over an area.\n", - "\n", - "#### 2.3.1 Time series at a single point\n", - "This example uses `.sel`, which functions similarly to the `.isel` function above, to select a single point in the Amazon. \n", - "\n", - "*What's the difference between `.sel` and `.isel`?*\n", - "- *`.sel` selects a value of a variable (e.g., latitude of -5)*\n", - "- *`.isel` selects an indexed point of a variable (e.g., the 6th point in the data vector)*\n", - "\n", - "In the below examples, we'll also use subplots to see multiple variables in several panels\n", - "\n", - "Also, the next few steps read in land model data from one of the CESM2 large ensemble historical runs. Note this operation points to multiple files on the campaign file system, so we are using the `xarray` function `open_mfdataset` for a multifile dataset. We will also print the dataset to get an idea of the metadata and dimensions. Note that we could use the output from the tutorial simulation. However, those runs are very short and thus are not a very interesting timeseries. Instead we can use one of the CESM2 Large Ensemble historical simulations (see [Rodgers et al. 2021](https://doi.org/10.5194/esd-12-1393-2021))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#First remove the old datasets\n", - "del ds\n", - "del dsDiff\n", - "\n", - "### Here we point to the CESM2-LE datasets on campaign disk and \n", - "### Look at results from a single ensemble member\n", - "\n", - "monthly_output_path = \"/glade/campaign/cgd/cesm/CESM2-LE/lnd/proc/tseries/month_1\"\n", - "run_name = \"b.e21.BHISTcmip6.f09_g17.LE2-1001.001\"\n", - "\n", - "var_names = ['FSR','FSDS','ELAI']\n", - "\n", - "### This piece of code opens the files and combines them into a single xarray dataset\n", - "\n", - "da_list = []\n", - "\n", - "for var_name in var_names:\n", - " files = os.path.join(monthly_output_path, var_name,\n", - " run_name + \".clm2.h0.\" + var_name + \".*\")\n", - " ds_in = xr.open_mfdataset(files)\n", - " # keep history file attributes: This only needs to be done once\n", - " if var_name == 'FSR':\n", - " da_list.append(ds_in)\n", - " else: \n", - " da_list.append(ds_in[var_name])\n", - " del ds_in\n", - "\n", - "''' quick fix to adjust time vector for monthly data'''\n", - "def fix_time(ds): \n", - " nmonths = len(ds.time)\n", - " yr0 = ds['time.year'][0].values\n", - " ds['time'] =xr.cftime_range(str(yr0),periods=nmonths,freq='MS') \n", - "\n", - " return ds\n", - "\n", - "ds = fix_time(xr.merge(da_list))\n", - "\n", - "print('-- Your dataset is been opened --')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print information about the dataset\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Calculate Albedo and add to dataset\n", - "ds['ASA'] = ds.FSR/ds.FSDS.where(ds.FSDS>0)\n", - "ds['ASA'].attrs['units'] = 'unitless'\n", - "ds['ASA'].attrs['long_name'] = 'All sky albedo'\n", - "ds['ASA']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "point = ds.sel(lon=300, lat=-5, method='nearest')\n", - "point.ASA.plot(x='time') ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_5.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar to the maps above, there are variations in albedo in the simulation at this location. Let's add other variables to explore why we see differences at this location. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(10,6))\n", - "'''this first plot is the same as the one above'''\n", - "plt.subplot(221)\n", - "point.ASA.plot()\n", - "plt.xlabel(None)\n", - "\n", - "'''now we'll look for potential sources of the difference'''\n", - "plt.subplot(222)\n", - "point.ELAI.plot() \n", - "plt.xlabel(None)\n", - "\n", - "plt.subplot(223)\n", - "point.FSDS.plot() \n", - "plt.title(None)\n", - "\n", - "plt.subplot(224)\n", - "point.FSR.plot() \n", - "plt.title(None) ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_6.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Questions:** \n", - "- What variables show differences?\n", - "- What variables are similar?\n", - "- How do the differences and similarities help to explain the differences in albedo?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.3.2 Global time series\n", - "There are many reasons why we may want to calculate globally integrated time series for particular variables.\n", - "This requires weighting the values from each grid cell by the total land area of individual grid cells. The example below does this for our dataset. \n", - "\n", - "#### First calculate the land weights:\n", - "- land area `la` that is the product of land fraction (fraction of land area in each grid cell) and the total area of the grid cell (which changes by latitude). Units are the same as area.\n", - "- land weights `lw`, the fractional weight that each grid makes to the global total, is calculated as the land area of each grid cell divided by the global sum of the land area.\n", - "\n", - "The land weights are shown in the plot below. Note that these are larger near the equator, and smaller at the poles and along the coastline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "la = (ds.landfrac*ds.area).isel(time=0).drop(['time']) \n", - "la = la * 1e6 #converts from land area from km2 to m2 \n", - "la.attrs['units'] = 'm^2'\n", - "lw = la/la.sum()\n", - "lw.plot() ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_7.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Next, calculate and plot a global weighted sum\n", - "
\n", - "\n", - "NOTE: You will likely want to calculate global weighted sum for a variety of different variables. For variables that have area-based units (e.g. GPP, gC/m^2/s), you need to use the land area variable when calculating a global sum. Remember to pay attention to the units and apply any necessary conversions! Keep in mind that grid cell area is reported in km^2. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dsGlobalWgt = (ds * lw).sum(['lat','lon'])\n", - "\n", - "plt.figure(figsize=(12,5))\n", - "plotVars = ['ASA','FSDS','ELAI','FSR']\n", - "for i in range(len(plotVars)):\n", - " # First add metadata for plotting\n", - " dsGlobalWgt[plotVars[i]].attrs['long_name'] = ds[plotVars[i]].attrs['long_name']\n", - " dsGlobalWgt[plotVars[i]].attrs['units'] = ds[plotVars[i]].attrs['units']\n", - "\n", - " # then make plots\n", - " plt.subplot(2,2,(i+1))\n", - " dsGlobalWgt[plotVars[i]].plot()\n", - " \n", - " if i == 0:\n", - " plt.title('Weighted global sum',loc='left', fontsize='large', fontweight='bold')\n", - " \n", - " if i<2:\n", - " plt.xlabel(None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_8.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "### 2.4 Calculate an annual weighted mean and create customized plots\n", - "Annual averages require a different kind of weighting: the number of days per month.\n", - "This example creates python functions that allow you to easily calculate annual averages and create customized plots. \n", - "\n", - "
\n", - "\n", - "Python functions: In python, creating a function allows us to use the same calculation numerous times instead of writing the same code repeatedly.\n", - "
\n", - "\n", - "\n", - "#### 2.4.1 Calculate monthly weights\n", - "The below code creates a function `weighted_annual_mean` to calculate monthly weights. Use this function any time you want to calculate weighted annual means." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create a function that will calculate an annual mean weighted by days per month\n", - "def weighted_annual_mean(array):\n", - " mon_day = xr.DataArray(np.array([31,28,31,30,31,30,31,31,30,31,30,31]), dims=['month'])\n", - " mon_wgt = mon_day/mon_day.sum()\n", - " return (array.rolling(time=12, center=False) # rolling\n", - " .construct(\"month\") # construct the array\n", - " .isel(time=slice(11, None, 12)) # slice so that the first element is [1..12], second is [13..24]\n", - " .dot(mon_wgt, dims=[\"month\"]))\n", - "\n", - "# generate annual means\n", - "for i in range(len(plotVars)):\n", - " temp = weighted_annual_mean(\n", - " ds[plotVars[i]].chunk({\"time\": 12}))\n", - " \n", - " if i ==0:\n", - " dsAnn = temp.to_dataset(name=plotVars[i])\n", - " else:\n", - " dsAnn[plotVars[i]] = temp\n", - "\n", - "# Make a simple plot\n", - "dsAnn.isel(time=0).ELAI.plot(x='lon',y='lat',robust=True) ;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_9.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dsAnn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2.4.2 Customized maps\n", - "Creating a function isn't necessary to plot maps, but this function, which uses python's `cartopy`, allows you to make several pretty maps in one figure.\n", - "\n", - "Additional examples and information are available on the [cartopy website](https://scitools.org.uk/cartopy/docs/v0.15/index.html)\n", - "\n", - "There are two code blocks below. The first block of code defines the function. The second code block creates the plot. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cartopy.feature as cfeature\n", - "from cartopy.util import add_cyclic_point\n", - "import copy\n", - "\n", - "# Generate a function for making panel plots of maps\n", - "## many of these features are not required, but provide additional control over plotting\n", - "def map_function(da, cb=0, cmap='viridis', panel=None, ax=None, \n", - " title=None, vmax=None, vmin=None, units=None,nbins=200):\n", - " '''a function to make one subplot'''\n", - " wrap_data, wrap_lon = add_cyclic_point(da.values, coord=da.lon)\n", - "\n", - " if ax is None: ax = plt.gca()\n", - "\n", - " # define the colormap, including the number of bins\n", - " cmap = copy.copy(plt.get_cmap(cmap,nbins))\n", - " im = ax.pcolormesh(wrap_lon,da.lat,wrap_data,\n", - " transform=ccrs.PlateCarree(),\n", - " vmax=vmax,vmin=vmin,cmap=cmap)\n", - "\n", - " # set the bounds of your plot\n", - " ax.set_extent([-180,180,-56,85], crs=ccrs.PlateCarree())\n", - "\n", - " # add title & panel labels\n", - " ax.set_title(title,loc='left', fontsize='large', fontweight='bold')\n", - " ax.annotate(panel, xy=(0.05, 0.90), xycoords=ax.transAxes,\n", - " ha='center', va='center',fontsize=16) \n", - "\n", - " # add plotting features\n", - " ax.coastlines()\n", - " ocean = ax.add_feature(\n", - " cfeature.NaturalEarthFeature('physical','ocean','110m', facecolor='white'))\n", - " \n", - " # control colorbars on each plot & their location\n", - " if cb == 1:\n", - " cbar = fig.colorbar(im, ax=ax,pad=0.02, fraction = 0.03, orientation='horizontal')\n", - " cbar.set_label(units,size=12,fontweight='bold')\n", - " if cb == 2:\n", - " cbar = fig.colorbar(im, ax=ax,pad=0.02, fraction = 0.05, orientation='vertical') \n", - " cbar.set_label(units,size=12)#,weight='bold')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Now make the plot!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = 0\n", - "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(13,6), constrained_layout=True,\n", - " subplot_kw=dict(projection=ccrs.Robinson()))\n", - "for index, ax in np.ndenumerate(axes):\n", - " if i == 0:\n", - " plotData = dsAnn.ELAI.isel(time=slice(-10,None)).mean('time')\n", - " map_function(plotData, ax=ax,cb=2,\n", - " panel='(a)', nbins=10,\n", - " vmax=5,vmin=0,\n", - " units='Final Annual ELAI')\n", - " if i == 1:\n", - " plotData = (dsAnn.ELAI.isel(time=slice(-10,None)).mean('time')- \\\n", - " dsAnn.ELAI.isel(time=slice(0,10)).mean('time'))\n", - " map_function(plotData, ax=ax,cb=2,panel='(b)',\n", - " units='Annual ELAI Change, Final-Initial',\n", - " cmap='bwr',nbins=7,\n", - " vmax=0.75,vmin=-0.75) \n", - " \n", - " i = i+1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/clm_ctsm/basics_plot_10.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extra credit challenge\n", - "If you have extra time & energy, try running through this notebook with other variables. Interesting options could include: \n", - "- Latent heat flux (the sum of `FCTR`+`FCEV`+`FGEV`) or \n", - "- Gross Primary Production (`GPP`) \n", - "\n", - "
\n", - "\n", - "HINT: pay attention to units for these challenges. \n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023a", - "language": "python", - "name": "npl-2023a" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks_copy/diagnostics/clm_ctsm/clm_ctsm.ipynb b/notebooks_copy/diagnostics/clm_ctsm/clm_ctsm.ipynb deleted file mode 100644 index 4774ffe0d..000000000 --- a/notebooks_copy/diagnostics/clm_ctsm/clm_ctsm.ipynb +++ /dev/null @@ -1,63 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Land" - ] - }, - { - "cell_type": "markdown", - "id": "6a0eda7f-8348-4a1e-8ada-86d9a70c3af6", - "metadata": {}, - "source": [ - "This notebook is an introduction to analyzing CLM land model results from a global simulation. It uses results from the case you ran in the 1a Tutorial. We've prestaged model results from this simulation and another simulation using a different model configuration in a shared directory. This way, you can get started on analyzing simulations results before your simulations finish running and compare differences caused by model structure. " - ] - }, - { - "cell_type": "markdown", - "id": "b6f4905b-cd2a-454e-89cf-ccc585c90247", - "metadata": { - "tags": [] - }, - "source": [ - "## Learning Goals\n", - "\n", - "- Read in global CLM data\n", - "- Make basic spatial plots\n", - "- Make basic timeseries plots" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e961b1bd-a1c8-4e54-bafc-46dcf78454f1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/diagnostics.ipynb b/notebooks_copy/diagnostics/diagnostics.ipynb deleted file mode 100644 index 3b9511709..000000000 --- a/notebooks_copy/diagnostics/diagnostics.ipynb +++ /dev/null @@ -1,366 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0959db1d-1790-4519-a34e-7026444af537", - "metadata": {}, - "source": [ - "# Diagnostics" - ] - }, - { - "cell_type": "markdown", - "id": "9fdd8253-f6b6-4969-b862-b7a8545ee46e", - "metadata": {}, - "source": [ - "_______________\n", - "These activities have been tested and updated by Jesse Nusbaumer and Alice DuVivier" - ] - }, - { - "cell_type": "markdown", - "id": "a7a70c8a-be92-4a7a-8d67-1b3941013b4f", - "metadata": { - "tags": [] - }, - "source": [ - "_______________\n", - "Once the CESM model has been run and the output data has been transfered to the short term archive directory the real job of understanding how the simulation ran and what it means from a scientific perspective begins. \n", - "\n", - "By this point you have run a number of simulations and have looked at model output using `ncview`. In this lab you will go beyond `ncview` by using Python plotting and analysis methods in Jupyterhub to produce additional diagnostic results. There is also a complete CESM diagnostics system currently under active development called [CUPiD](https://github.com/NCAR/CUPiD) that you will get to try out as well, although please note that it is still in its very early stages. Finally, please note that there are many other [CESM analysis tools](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html) and projects available as well, but not all will be covered here.\n", - "\n", - "To start running the Jupyter Notebooks provided for this tutorial, follow the steps below." - ] - }, - { - "cell_type": "markdown", - "id": "90a99498-75c0-46ce-8aa2-9c33ce5d7aed", - "metadata": {}, - "source": [ - "## Step 1. Download CESM Tutorial notebooks with Git Clone" - ] - }, - { - "cell_type": "markdown", - "id": "08427f89-a2c6-40d9-af61-45815753cd92", - "metadata": {}, - "source": [ - "
\n", - "We will use the main branch of the tutorial materials for use in this tutorial.

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f28bf659-f7cb-46e9-8594-c6da04977d68", - "metadata": {}, - "source": [ - "First we will change into the home directory and then we will use the `git clone` command to download the CESM Tutorial diagnostics notebooks.\n", - "\n", - "
\n", - "Change the current directory to the home directory:
\n", - "\n", - "```\n", - "cd \n", - "```\n", - "
\n", - " \n", - "Download the cesm code to your code workspace directory as `CESM-Tutorial`:
\n", - "```\n", - "git clone https://github.com/NCAR/CESM-Tutorial.git CESM-Tutorial\n", - "```\n", - "\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c582acd5-84df-4cf1-8d26-90bfd9aa6ebc", - "metadata": {}, - "source": [ - "## Step 2. Login to JupyterHub" - ] - }, - { - "cell_type": "markdown", - "id": "187dcf75-3527-44ac-9ecd-86f47a3d524e", - "metadata": {}, - "source": [ - "Go to the JupyterHub website ([https://jupyterhub.hpc.ucar.edu/](https://jupyterhub.hpc.ucar.edu/)) and click on the \"Production\" button." - ] - }, - { - "cell_type": "markdown", - "id": "823cbbdc-649f-47dd-8977-a525554e15cf", - "metadata": {}, - "source": [ - "![JupyterHub](../../images/diagnostics/login/Diagnostics_1.png)" - ] - }, - { - "cell_type": "markdown", - "id": "98b5aeea-43d8-4112-a1c5-ab6ebb00dc39", - "metadata": {}, - "source": [ - "This will take you to a page where you enter your username and password. Enter your username as you would for Derecho, but your password will be the following: \n", - "\n", - "**Derecho password,Duo Pin**\n", - "\n", - "Where \"Duo Pin\" is the six numbers you can find in your Duo app under the \"UCAR\" Account listing. **Please note that the comma ( , ) must also be there separating the password from the Duo Pin.**" - ] - }, - { - "cell_type": "markdown", - "id": "de2094b5-9ac8-428d-aec7-d9f16baea936", - "metadata": {}, - "source": [ - "![JupyterHub Login](../../images/diagnostics/login/Diagnostics_2.png)" - ] - }, - { - "cell_type": "markdown", - "id": "d226699a-6342-4887-a190-72b495d8dcc5", - "metadata": {}, - "source": [ - "![JupyterHub Login](../../images/diagnostics/login/Diagnostics_duo_pcode.png)" - ] - }, - { - "cell_type": "markdown", - "id": "8c34bbd4-c129-4481-9df9-b5839b582a21", - "metadata": {}, - "source": [ - "After you do this and click \"Sign In\" you will be taken to a Server landing page where you will need to start your sever on Jupyterhub." - ] - }, - { - "cell_type": "markdown", - "id": "99ec89f3-d470-469e-a7b0-a2bc4497678e", - "metadata": {}, - "source": [ - "![JupyterHub Server](../../images/diagnostics/login/Diagnostics_3.png)" - ] - }, - { - "cell_type": "markdown", - "id": "bc60d1e8-44bd-4cac-b0e1-53eac15eea26", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "bb66a956-7f58-4613-b1a6-7597549e25a4", - "metadata": {}, - "source": [ - " Select the \"Casper PBS Batch\" option in the drop down menu." - ] - }, - { - "cell_type": "markdown", - "id": "9c758769-6b83-4dc1-a03a-2958102a2d52", - "metadata": {}, - "source": [ - "![JupyterHub Casper PBS](../../images/diagnostics/login/Diagnostics_4.png)" - ] - }, - { - "cell_type": "markdown", - "id": "6d9f61c8-9f91-4f9b-b3f9-fb474534d73d", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "17233631-43b6-4dc1-8904-a4adaff7c47b", - "metadata": {}, - "source": [ - "After selecting \"Casper PBS Batch\" you need to make other choices to select the right resources. You should use the queue and project account keys specified below. You may also want to request `03:00:00` for your Wall Time so that your server is active for the entire hands-on activity session. Do not change any other selection for this tutorial. If you are doing further analysis of model experiments you may need to change these settings and can find more information in the [CISL Documentation about JupyterHub](https://arc.ucar.edu/knowledge_base/70549913).\n" - ] - }, - { - "cell_type": "markdown", - "id": "1a3a65c2-65ce-4927-87b2-d9472ce676ed", - "metadata": {}, - "source": [ - "
\n", - "For this tutorial you should use the Queue `casper` and Project Account `UESM0013`.

\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b2b9d327-8f9e-486e-9ae3-94a17c3008c9", - "metadata": {}, - "source": [ - "![JupyterHub Selections](../../images/diagnostics/login/Diagnostics_5.png)" - ] - }, - { - "cell_type": "markdown", - "id": "4a3ffff9-689d-4329-9ebe-0d93c1ba9e8a", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "3fe7c826-800e-4718-9440-a224b97dec44", - "metadata": {}, - "source": [ - "You may have to wait a moment or two for your server to start up." - ] - }, - { - "cell_type": "markdown", - "id": "088f619d-9152-45c4-aa0c-73bfd0e33ec8", - "metadata": {}, - "source": [ - "![JupyterHub Casper PBS](../../images/diagnostics/login/Diagnostics_6.png)" - ] - }, - { - "cell_type": "markdown", - "id": "a08772d2-643c-40fa-9503-f1490c29de80", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "a66777ce-4356-4b95-b513-b33a604cbfdc", - "metadata": {}, - "source": [ - "Your JupyterHub session is now active and ready to run." - ] - }, - { - "cell_type": "markdown", - "id": "850d57a4-eab0-4da2-9f26-d1e36d1d6204", - "metadata": {}, - "source": [ - "## Step 3. Open a Diagnostics Notebook" - ] - }, - { - "cell_type": "markdown", - "id": "70792221-dc94-41ee-a9e2-e632b9eef1c1", - "metadata": {}, - "source": [ - "When your JupyterHub session opens you should be in your home directory on the NCAR HPC. In Step 1 you cloned the \"CESM-Tutorial\" repository, which has the notebooks you will run in this activity. " - ] - }, - { - "cell_type": "markdown", - "id": "b06d1cdb-4f85-4958-9bfe-9af1901c6539", - "metadata": {}, - "source": [ - "![JupyterHub Main Page](../../images/diagnostics/login/Diagnostics_7.png)" - ] - }, - { - "cell_type": "markdown", - "id": "8749817d-6a92-4c33-84f2-f09507c3fea9", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "id": "995b7014-2dd2-4f05-bc63-a22affe3ff9c", - "metadata": {}, - "source": [ - "To get to the Diagnostics notebooks, double click the following sequence of folders:\n", - "1) CESM-Tutorial\n", - "2) notebooks\n", - "3) diagnostics\n", - "4) Click on the folder for the model component that you are interested in running (e.g. cam) \n", - "5) Click on the `basics.ipynb` notebook (see arrow on left, below)\n", - "\n", - "The final path in your browser url line for the following example should be:\n", - "`$USER/CESM-Tutorial/notebooks/diagnostics/cam/basics.ipynb`" - ] - }, - { - "cell_type": "markdown", - "id": "e4cb4eb8-eb39-4a5b-a94a-538e09ab637a", - "metadata": {}, - "source": [ - "![JupyterHub Open Notebook](../../images/diagnostics/login/Diagnostics_8.png)" - ] - }, - { - "cell_type": "markdown", - "id": "1077e225-5d39-4e54-8288-5ef69bd9b90b", - "metadata": {}, - "source": [ - "## Step 4. Check Your Notebook Kernel" - ] - }, - { - "cell_type": "markdown", - "id": "aef068ce-e240-4e9f-b008-5270534b6295", - "metadata": {}, - "source": [ - "Check your kernel (see arrow in upper right corner, above). It should be either `NPL 2023a` or `NPL 2023b`. You should use the specified kernel for any diagnostics you do during the tutorial as it is a default environment available on NCAR HPC and the notebooks here have been tested so that they work with that particular kernel for the analysis environment. We have set the default kernels and specifiy what they should be in each component notebook. However, if you need to change it click on the kernel button and select the correct kernel. " - ] - }, - { - "cell_type": "markdown", - "id": "784c789a-db86-4c80-8c92-d69dbeab7b48", - "metadata": {}, - "source": [ - "## Step 5. Run Jupyter Notebook Cells" - ] - }, - { - "cell_type": "markdown", - "id": "69174e22-0a81-47d6-b122-5b514154b31d", - "metadata": {}, - "source": [ - "To run a Jupyter cell\n", - "- Type your command into the cell\n", - "- To execute the command:\n", - " - Press **shift+return**\n", - "
\n", - " OR\n", - "
\n", - " - Select the cell then click the 'play' button at the top of the window (see red arrow, above)" - ] - }, - { - "cell_type": "markdown", - "id": "beaa3bc1-0ed5-4c73-901a-6fea974076e2", - "metadata": {}, - "source": [ - "All figures will be rendered in the Jupyter Notebook, so there is no need to open any other window for this portion of the lab activities." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae3f94f8-c116-468f-b15f-66b090a6b39f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/pop/advanced_pop.ipynb b/notebooks_copy/diagnostics/pop/advanced_pop.ipynb deleted file mode 100644 index fafddfde6..000000000 --- a/notebooks_copy/diagnostics/pop/advanced_pop.ipynb +++ /dev/null @@ -1,334 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2634ab1d-3022-4b34-b573-cb74e0f31088", - "metadata": {}, - "source": [ - "# Advanced Plotting" - ] - }, - { - "cell_type": "markdown", - "id": "1522591e-1007-41e1-8599-8be707db5d84", - "metadata": {}, - "source": [ - "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023b`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023b`." - ] - }, - { - "cell_type": "markdown", - "id": "ff1b63b8-a5e8-445b-891e-7819f775dfd8", - "metadata": {}, - "source": [ - "_______________\n", - "This activity was developed primarily by Mauricio Rocha and Gustavo Marques." - ] - }, - { - "cell_type": "markdown", - "id": "677407b7-4f55-45c7-9193-123791b854da", - "metadata": {}, - "source": [ - "_______________\n", - "## Setting up the notebook\n", - "Here we load modules needed for our analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36365adf-9a62-4b34-ba16-73c6d89a8d32", - "metadata": {}, - "outputs": [], - "source": [ - "# loading modules\n", - "\n", - "# %load_ext watermark # this is so that in the end we can check which module versions we used\n", - "%load_ext autoreload\n", - "\n", - "import warnings \n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature\n", - "import matplotlib.pyplot as plt\n", - "import xarray as xr\n", - "import numpy as np \n", - "import pop_tools\n", - "import glob" - ] - }, - { - "cell_type": "markdown", - "id": "40c71af7-bb2d-47ec-8c5a-8a2724fab5c1", - "metadata": {}, - "source": [ - "### Get the data " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e1f41c1-1b64-429a-acd7-ffa4f5c50234", - "metadata": {}, - "outputs": [], - "source": [ - "# Set your username here:\n", - "username = \"PUT_USER_NAME_HERE\"\n", - "casename = 'b.day2.1'\n", - "\n", - "# Here we point to the archive directory from your b.day2.1 simulation\n", - "pth = f\"/glade/derecho/scratch/{username}/archive/{casename}/ocn/hist/\"\n", - "\n", - "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", - "# this provided simulation data instead:\n", - "#pth = f'/glade/campaign/cesm/tutorial/tutorial_2023_archive/{casename}/ocn/hist/'\n", - "\n", - "# Print path to screen\n", - "pth\n", - "flist = glob.glob(pth + casename + '.pop.h.00??-??.nc') \n", - "ds = xr.open_mfdataset(flist, compat='override', coords='minimal')" - ] - }, - { - "cell_type": "markdown", - "id": "8a27f988-20f9-457d-8947-371f6f2df74c", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 1" - ] - }, - { - "cell_type": "markdown", - "id": "655e97da-85b0-45ac-acda-f139e524f319", - "metadata": {}, - "source": [ - "Maximum mixed-layer depth for the winter months in the northern hemisphere (January, February, and March) and in the southern hemisphere (July, August, and September)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a4a1832c-10f8-4ebe-b33d-4c28a22657fc", - "metadata": {}, - "outputs": [], - "source": [ - "# POP grid\n", - "pop_grid = pop_tools.get_grid('POP_gx1v7')\n", - "ds['TLONG'] = pop_grid.TLONG; ds['TLAT'] = pop_grid.TLAT\n", - "ds['ULONG'] = pop_grid.ULONG; ds['ULAT'] = pop_grid.ULAT" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b5ee9b5-e146-4655-af65-91e10d80d189", - "metadata": {}, - "outputs": [], - "source": [ - "# July, August, and Septemper (JAS)\n", - "def is_jas(month):\n", - " return (month >= 7) & (month <= 9)\n", - "JAS = ds['XMXL'].sel(time=is_jas(ds['XMXL']['time.month'])).mean('time')\n", - "\n", - "# January, February, and March (JFM)\n", - "def is_jfm(month):\n", - " return (month >= 1) & (month <= 3)\n", - "JFM = ds['XMXL'].sel(time=is_jfm(ds['XMXL']['time.month'])).mean('time')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc5802e9-6cf5-4209-bbe3-a4c6461d932e", - "metadata": {}, - "outputs": [], - "source": [ - "# Find the latitude value closest to the equator\n", - "def find_nearest(array, value):\n", - " array = np.asarray(array)\n", - " idx = (np.abs(array - value)).argmin()\n", - " return array[idx]\n", - "eq=find_nearest(JAS['TLAT']['TLAT'][:,0], value=0)\n", - "print(eq)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a6d3ee5-8bba-4158-a209-b2b2e5105cd1", - "metadata": {}, - "outputs": [], - "source": [ - "# Find the index of this latitude\n", - "idx=np.where(JAS['TLAT']['TLAT'][:,0] == eq)[0]\n", - "print(idx)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1626984-8646-41b6-a5df-943562d8ccfa", - "metadata": {}, - "outputs": [], - "source": [ - "# Create a new array\n", - "winter=JFM.copy()\n", - "\n", - "# Since the variable winter already contains the data for the Northern Hemisphere, we will now add the data for the Southern Hemisphere\n", - "winter.loc[0:187,:]=JAS.loc[0:187,:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7d3bc83-0643-4cf6-9367-678f2e7823d5", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(8,6));\n", - "ax = plt.axes(projection=ccrs.Robinson());\n", - "orig_map=plt.cm.get_cmap('turbo')\n", - "scale_color=orig_map.reversed()\n", - "cf=(winter*0.01).plot.pcolormesh(ax=ax, # Multiply by 0.01 to transform centimeters to meters\n", - " vmax=800,vmin=0,\n", - " transform=ccrs.PlateCarree(),\n", - " x='TLONG',\n", - " y='TLAT',\n", - " cmap=scale_color,\n", - " add_colorbar=False,\n", - " ) \n", - "ax.coastlines()\n", - "ax.add_feature(cartopy.feature.LAND)\n", - "ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n", - " linewidth=2, color='gray', alpha=0.5, linestyle='-')\n", - "cbar = plt.colorbar(cf, ax=ax, shrink=0.5, pad=0.1, ticks=np.arange(0,800,100), label='XMXL [m]')\n", - "plt.title('Maximum Mixed-Layer Depth for the Winter', fontsize=14)\n", - "#plt.savefig('advanced_plot_1.png', bbox_inches='tight')# uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "0b35d911-bd15-4825-8a6f-12c583f9c05e", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/advanced_plot_1.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "db016402-9b77-4145-b92e-a4503c03dcc3", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 2" - ] - }, - { - "cell_type": "markdown", - "id": "d326ce71-ed14-47b1-9095-a55f45e969cd", - "metadata": {}, - "source": [ - "Calculate the heat storage (HS) per area from the temperature tendency for the upper 2000m.\n", - "Equation: $$\\rm{HF = \\uprho_\\uptheta~C_p~\\int_{z_2}^{z_1}\\uptheta_{(z)}'~dz},$$\n", - "where:\n", - "* HF is heat storage ($\\rm{W~m^{-2}}$),\n", - "* $\\uprho_\\uptheta$ is the sea water density ($\\rm{kg~m^{-3}}$),\n", - "* $\\rm{C_p}$ is the sea water specific heat ($\\rm{J~kg^{-1}~^{\\circ}C^{-1}}$),\n", - "* $\\rm{dz}$ is the cell thickness (m),\n", - "* and $\\uptheta$' is the temperature tendency ($\\rm{^{\\circ}C^{-1}~s^{-1}}$). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "354537e8-6e2f-4cbd-87a3-c807545828ed", - "metadata": {}, - "outputs": [], - "source": [ - "ds_HS=ds['TEND_TEMP'].sel(z_t=slice(0,200000))*ds['dz'].sel(z_t=slice(0,200000))*0.01 # Select the depth and multiply by dz. Unit: oC.s-1.m \n", - "ds_HS=ds_HS.sum('z_t') # Sum in depth\n", - "ds_HS=ds_HS*1026 # Multiply it by the sea water density. Unit: oC.s-1.kg.m-2\n", - "ds_HS=ds_HS*3996 # Multiply it by the sea water heat specific. Unit: W.m-2 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "258d8431-27bb-4ce1-9afa-a7f9221e2d1c", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(8,6))\n", - "ax = plt.axes(projection=ccrs.Robinson())\n", - "orig_map=plt.cm.get_cmap('RdBu')\n", - "scale_color=orig_map.reversed()\n", - "cf=ds_HS.mean('time').plot.pcolormesh(ax=ax,\n", - " transform=ccrs.PlateCarree(),\n", - " vmin=-50,\n", - " vmax=50,\n", - " x='TLONG',\n", - " y='TLAT',\n", - " cmap=scale_color,\n", - " add_colorbar=False,\n", - " ) \n", - "ax.coastlines()\n", - "ax.add_feature(cartopy.feature.LAND)\n", - "ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n", - " linewidth=2, color='gray', alpha=0.5, linestyle='-')\n", - "cbar = plt.colorbar(cf, ax=ax, shrink=0.5, pad=0.1, label='HS [W m$^{-2}$]')\n", - "plt.title('Heat Storage per area for the upper 2000 m', fontsize=14)\n", - "#plt.savefig('advanced_plot_2.png', bbox_inches='tight')# uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "a5eebe10-e058-4475-a69e-18a8faafaf7f", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/advanced_plot_2.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023b", - "language": "python", - "name": "npl-2023b" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/pop/basics_pop.ipynb b/notebooks_copy/diagnostics/pop/basics_pop.ipynb deleted file mode 100644 index 4b6c8e8e6..000000000 --- a/notebooks_copy/diagnostics/pop/basics_pop.ipynb +++ /dev/null @@ -1,1236 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "989fe00b-ac80-497c-a432-36d0ae227636", - "metadata": {}, - "source": [ - "# Basic Plotting" - ] - }, - { - "cell_type": "markdown", - "id": "e97a3589-e319-4477-9123-f2f7e4189dc9", - "metadata": {}, - "source": [ - "**BEFORE BEGINNING THIS EXERCISE** - Check that your kernel (upper right corner, above) is `NPL 2023a`. This should be the default kernel, but if it is not, click on that button and select `NPL 2023a`." - ] - }, - { - "cell_type": "markdown", - "id": "ed83e6c7-6801-4b43-af1c-32f6e3577f30", - "metadata": {}, - "source": [ - "_______________\n", - "This activity was developed primarily by Anna-Lena Deppenmeier and Gustavo Marques.\n" - ] - }, - { - "cell_type": "markdown", - "id": "cb535d76-8352-4736-b74f-4577b32a3aa9", - "metadata": { - "tags": [] - }, - "source": [ - "_______________\n", - "## Setting up the notebook\n", - "Here we load modules needed for our analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe59e1e9-a503-4c89-803e-01dee0fadd75", - "metadata": {}, - "outputs": [], - "source": [ - "# loading modules\n", - "\n", - "# %load_ext watermark # this is so that in the end we can check which module versions we used\n", - "%load_ext autoreload\n", - "\n", - "import warnings \n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "import datetime\n", - "import glob\n", - "import os\n", - "import warnings\n", - "import dask\n", - "import dask_jobqueue\n", - "import distributed\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import xgcm\n", - "from matplotlib import ticker, cm\n", - "import pop_tools\n", - "from cartopy import crs as ccrs, feature as cfeature\n", - "import cartopy" - ] - }, - { - "cell_type": "markdown", - "id": "7bb804be-f371-40a6-915d-803d3cc67151", - "metadata": {}, - "source": [ - "### Define some functions\n", - "These functions will be used more than once to read data and add a cyclic point. These could go in a package if you like." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5cc3a601-4a26-4a26-9761-f5ba830e353d", - "metadata": {}, - "outputs": [], - "source": [ - "# define function to get you the data you want relatively quickly \n", - "\n", - "def read_dat(files, variables, pop=False):\n", - " def preprocess(ds):\n", - " return ds[variables].reset_coords(drop=True) # reset coords means they are reset as variables\n", - "\n", - " ds = xr.open_mfdataset(files, parallel=True, preprocess=preprocess,\n", - " chunks={'time':1, 'nlon': -1, 'nlat':-1},\n", - " combine='by_coords')\n", - " if pop==True:\n", - " file0 = xr.open_dataset(files[0])\n", - " ds.update(file0[['ULONG', 'ULAT', 'TLONG', 'TLAT']])\n", - " file0.close()\n", - "\n", - " ds\n", - " return ds\n", - "\n", - "# define function to be able to plot POP output properly on cartopy projections\n", - "def pop_add_cyclic(ds):\n", - " \n", - " nj = ds.TLAT.shape[0]\n", - " ni = ds.TLONG.shape[1]\n", - "\n", - " xL = int(ni/2 - 1)\n", - " xR = int(xL + ni)\n", - "\n", - " tlon = ds.TLONG.data\n", - " tlat = ds.TLAT.data\n", - " \n", - " tlon = np.where(np.greater_equal(tlon, min(tlon[:,0])), tlon-360., tlon) \n", - " lon = np.concatenate((tlon, tlon + 360.), 1)\n", - " lon = lon[:, xL:xR]\n", - "\n", - " if ni == 320:\n", - " lon[367:-3, 0] = lon[367:-3, 0] + 360. \n", - " lon = lon - 360.\n", - " \n", - " lon = np.hstack((lon, lon[:, 0:1] + 360.))\n", - " if ni == 320:\n", - " lon[367:, -1] = lon[367:, -1] - 360.\n", - "\n", - " #-- trick cartopy into doing the right thing:\n", - " # it gets confused when the cyclic coords are identical\n", - " lon[:, 0] = lon[:, 0] - 1e-8\n", - "\n", - " #-- periodicity\n", - " lat = np.concatenate((tlat, tlat), 1)\n", - " lat = lat[:, xL:xR]\n", - " lat = np.hstack((lat, lat[:,0:1]))\n", - "\n", - " TLAT = xr.DataArray(lat, dims=('nlat', 'nlon'))\n", - " TLONG = xr.DataArray(lon, dims=('nlat', 'nlon'))\n", - " \n", - " dso = xr.Dataset({'TLAT': TLAT, 'TLONG': TLONG})\n", - "\n", - " # copy vars\n", - " varlist = [v for v in ds.data_vars if v not in ['TLAT', 'TLONG']]\n", - " for v in varlist:\n", - " v_dims = ds[v].dims\n", - " if not ('nlat' in v_dims and 'nlon' in v_dims):\n", - " dso[v] = ds[v]\n", - " else:\n", - " # determine and sort other dimensions\n", - " other_dims = set(v_dims) - {'nlat', 'nlon'}\n", - " other_dims = tuple([d for d in v_dims if d in other_dims])\n", - " lon_dim = ds[v].dims.index('nlon')\n", - " field = ds[v].data\n", - " field = np.concatenate((field, field), lon_dim)\n", - " field = field[..., :, xL:xR]\n", - " field = np.concatenate((field, field[..., :, 0:1]), lon_dim) \n", - " dso[v] = xr.DataArray(field, dims=other_dims+('nlat', 'nlon'), \n", - " attrs=ds[v].attrs)\n", - "\n", - "\n", - " # copy coords\n", - " for v, da in ds.coords.items():\n", - " if not ('nlat' in da.dims and 'nlon' in da.dims):\n", - " dso = dso.assign_coords(**{v: da})\n", - " \n", - " \n", - " return dso" - ] - }, - { - "cell_type": "markdown", - "id": "657b093a-e719-4f68-92a4-907a3ecf89bb", - "metadata": {}, - "source": [ - "### Setting up the Dask cluster\n", - "Remember to: \n", - "- change the project number if doing this outside the tutorial\n", - "- potentially change the walltime depending on what you want to do\n", - "- check the memory if you are loading a different dataset with different needs\n", - "- check the number of cores if you are loading a different dataset with different needs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ca107e9-941f-4398-8df5-d6587064492f", - "metadata": {}, - "outputs": [], - "source": [ - "# Set your username here:\n", - "username = \"PUT_USER_NAME_HERE\"\n", - "\n", - "if \"client\" in locals():\n", - " client.close()\n", - " del client\n", - "if \"cluster\" in locals():\n", - " cluster.close()\n", - "\n", - "cluster = dask_jobqueue.PBSCluster(\n", - " cores=2, # The number of cores you want\n", - " memory=\"8GB\", # Amount of memory\n", - " processes=1, # How many processes\n", - " queue=\"casper\", # The type of queue to utilize (/glade/u/apps/dav/opt/usr/bin/execcasper)\n", - " log_directory=f\"/glade/derecho/scratch/{username}/dask/\", # Use your local directory\n", - " resource_spec=\"select=1:ncpus=1:mem=8GB\", # Specify resources\n", - " project=\"UESM0013\", # Input your project ID here\n", - " walltime=\"02:00:00\", # Amount of wall time\n", - ")\n", - "# cluster.adapt(maximum_jobs=24, minimum_jobs=2) # If you want to force everything to be quicker, increase the number of minimum jobs, \n", - "# # but sometimes then it will take a while until you get them assigned, so it's a trade-off\n", - "cluster.scale(12) # I changed this because currently dask is flaky, this might have to be adjusted during the tutorial\n", - "client = distributed.Client(cluster)\n", - "client" - ] - }, - { - "cell_type": "markdown", - "id": "884e8473-6d10-46e9-9a43-2175ae8631a0", - "metadata": {}, - "source": [ - "### Get the data \n", - "\n", - "**Note**: the drop-down solutions, below, assume you used b.day2.1 output for plotting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1a789cc-95ed-44f6-a566-215e6ed0668d", - "metadata": {}, - "outputs": [], - "source": [ - "# Set your casename here:\n", - "casename = 'b.day2.1'\n", - "\n", - "# Here we point to the archive directory from your b.day2.1 simulation\n", - "pth = f\"/glade/derecho/scratch/{username}/archive/{casename}/ocn/hist/\"\n", - "\n", - "# If you were unable to successfully run the b.day2.1 simulation, then feel free to use\n", - "# this provided simulation data instead:\n", - "#pth = f'/glade/campaign/cesm/tutorial/tutorial_2023_archive/{casename}/ocn/hist/'\n", - "\n", - "# Print path to screen\n", - "pth" - ] - }, - { - "cell_type": "markdown", - "id": "ad58f537-556f-406b-a223-a2474ae87361", - "metadata": {}, - "source": [ - "##### Details on files \n", - "- b.day2.1.pop.h.0001-01.nc : one timestep year ???? and month -?? for a number of 2D and 3D variables and constants\n", - "- b.day2.1.pop.h.nday1.0001-01-01.nc : daily timestep output for one month for SST, SST variance, SSS and (max) mixed layer depth\n", - "- b.day2.1.pop.h.once.nc : (background) mixing values\n", - "- b.day2.1.pop.hv.nc: viscosities" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a0dd260-76ab-4de7-931d-1cc343d82384", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# how quick this is depends among other things on the availability of workers on casper\n", - "# you can check progress by clicking on the link for the cluster above which will show you the dask dashboard \n", - "flist = glob.glob(pth + casename + '.pop.h.00??-??.nc') #also might want to use just some years not all \n", - "ds_pop = read_dat(flist, ['TEMP', 'SHF'], pop=True)\n", - "ds_pop = ds_pop.sortby(ds_pop.time)\n", - "tlist = np.asarray([time.replace(year=time.year+1957) for time in ds_pop.time.values]) # this makes sure the time axis is useful\n", - "ds_pop['time'] = tlist\n", - "ds_pop[\"time\"] = ds_pop.indexes[\"time\"].to_datetimeindex()\n", - "ds_pop #print some meta-data to screen" - ] - }, - { - "cell_type": "markdown", - "id": "ec0ade1b-407c-4bae-b4a7-62fdee617961", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 1\n", - "Means of global Surface Heat Flux and Sea Surface Temperature" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5d20c79-d1d1-4c62-8300-fade14d8e588", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%%time\n", - "fig, ax = plt.subplots(1, 2, figsize=(12,3), sharex=True, sharey=True)\n", - "\n", - "ds_pop.SHF.mean('time').plot(robust=True, ax=ax[0])\n", - "ax[0].set_title(r'Surface Heat Flux [W/m$^2$]')\n", - "\n", - "ds_pop.TEMP.sel(z_t=0, method='nearest').mean('time').plot(robust=True, ax=ax[1], levels=np.arange(0,32,1))\n", - "ax[1].set_title(r'Sea Surface Temperature [$^{\\circ}$C]')\n", - "#plt.savefig('basics_plot_1.png', bbox_inches='tight') # uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "9b3d3721-a43b-4bde-9d15-7392173cc52b", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_1.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f8da7c30-27d9-4b43-b05e-afeeb94a2422", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Can you plot 50m ocean temperature instead of surface heat flux (SHF)?" - ] - }, - { - "cell_type": "markdown", - "id": "df7c4390-a6d0-4247-9ebc-10998086182d", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "First, try using Xarray's ```sel``` function to select temperature values at the POP z-axis (z_t) index closest to 50m (note that the values in z_t are in centimeters):\n", - " \n", - "```\n", - "ds_pop.TEMP.sel(z_t=5000, method='nearest').mean('time').plot(robust=True, ax=ax[0], levels=np.arange(0,32,1))\n", - "```\n", - "\n", - "What was the depth selected? \n", - " \n", - "```\n", - "ds_pop.TEMP.sel(z_t=5000, method='nearest').mean('time').z_t.values\n", - "```\n", - " \n", - "There is not a layer with the midpoint at 50m. There are layers with the midpoint at 45m and 55m. \n", - "\n", - "```\n", - "(ds_pop.z_t)/100\n", - "```\n", - "\n", - "To estimate the temperature at 50m, we can use Xarray's ```interp``` to interpolate the values along the z-axis. By default, this function uses a linear interpolation method:\n", - " \n", - "```\n", - "ds_pop.TEMP.interp(z_t=5000).mean('time').plot(robust=True, ax=ax[0], levels=np.arange(0,32,1))\n", - "``` \n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "0ff3c68f-99b3-4e51-afb9-d987bd918d22", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Can you plot sea surface height (SSH) instead of surface heat flux (SHF)?" - ] - }, - { - "cell_type": "markdown", - "id": "e307984c-ce16-4c4e-bda3-ba6d940f36a8", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "We didn't initially load SSH as a variable we kept, so you will need to do that above.\n", - " \n", - "```\n", - "ds_pop = read_dat(flist, ['TEMP', 'SHF','SSH'], pop=True)\n", - "```\n", - "\n", - "Always be aware of which variables might be in a file.\n", - " \n", - "Once you've loaded SSH in the dataset, then plot it instead of SHF as follows:\n", - "\n", - "```\n", - "ds_pop.SSH.mean('time').plot(robust=True, ax=ax[0])\n", - "ax[0].set_title(r'Sea Surface Height (cm)')\n", - "```\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b4652dc4-1383-4023-ad2e-ca28b2510687", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Can you plot standard deviations instead of means?" - ] - }, - { - "cell_type": "markdown", - "id": "36d0ea5a-ee50-4ca7-9d2b-8c9c3fff8c3f", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "Replace the `.mean` function with `.std` in the plotting call.\n", - " \n", - "```\n", - "ds_pop.SHF.std('time').plot(robust=True, ax=ax[0])\n", - "\n", - "ds_pop.TEMP.sel(z_t=0, method='nearest').std('time').plot(robust=True, ax=ax[1], levels=np.arange(0,32,1))\n", - "```\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "829160aa-36f6-46b1-8cad-860bd3735dba", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 2\n", - "\n", - "Let's make some nicer plots! Have you noticed the x and y axes of the plots above? They are indices rather than longitudes and latitudes. POP output is on a curvilinear grid which means that the grid is not regularly (evenly) spaced. ```TLAT``` and ```TLONG``` are 2D variables depending on these indices, let's have a look at how to make maps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8847b2d5-9290-422c-8772-40b0764937c5", - "metadata": {}, - "outputs": [], - "source": [ - "# learn what TLAT and TLONG look like \n", - "fig, ax = plt.subplots(1, 2, figsize=(12,3), sharex=True, sharey=True)\n", - "\n", - "ds_pop.TLAT.plot(ax=ax[0], levels=np.arange(-90,95,5))\n", - "ax[0].set_title('TLAT')\n", - "ds_pop.TLONG.plot(ax=ax[1], levels=np.arange(0,370,10))\n", - "ax[1].set_title('TLONG')" - ] - }, - { - "cell_type": "markdown", - "id": "88135e00-bfc9-4b81-88e6-712679ec3b29", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_2.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e1517969-1f19-4909-bf9a-f1b10e917700", - "metadata": {}, - "source": [ - "**Question**\n", - "\n", - "Can you see the irregularity in TLAT? What does the discontinuity in TLONG mean? " - ] - }, - { - "cell_type": "markdown", - "id": "b93cd75a-97ce-4d82-85d4-5b0c223d2ee9", - "metadata": {}, - "source": [ - "### 1. Make global maps" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76c3a229-16cb-4001-833f-5f6ae18317c6", - "metadata": {}, - "outputs": [], - "source": [ - "# Add cyclic point\n", - "ds_pop_cyc = pop_add_cyclic(ds_pop)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "527f3cbe-1724-49ba-a539-f333af917d0d", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "\n", - "# initiate the figure\n", - "fig = plt.figure(dpi=150, figsize=(12,3))\n", - "\n", - "# add the first subplot\n", - "ax_shf = plt.subplot(1, 2, 1, projection=ccrs.Robinson(central_longitude=300.0))\n", - "\n", - "pc = ax_shf.contourf(ds_pop_cyc.TLONG, ds_pop_cyc.TLAT, ds_pop_cyc.SHF.mean('time'),\n", - " transform=ccrs.PlateCarree(), cmap='RdYlBu_r', extend='both', levels=np.arange(-120,130,10))\n", - "\n", - "ax_shf.set_global() \n", - "\n", - "land = ax_shf.add_feature(\n", - " cartopy.feature.NaturalEarthFeature('physical', 'land', '110m',\n", - " linewidth=0.5,\n", - " edgecolor='black',\n", - " facecolor='darkgray'))\n", - "\n", - "shf_cbar = plt.colorbar(pc, shrink=0.55, ax=ax_shf);\n", - "shf_cbar.set_label(r'[W/m$^{2}$]')\n", - "\n", - "ax_shf.set_title('Surface Heat Flux')\n", - "\n", - "# add the second subplot\n", - "ax_sst = plt.subplot(1, 2, 2, projection=ccrs.Robinson(central_longitude=300.0))\n", - "\n", - "pc = ax_sst.contourf(ds_pop_cyc.TLONG, ds_pop_cyc.TLAT, ds_pop_cyc.TEMP.isel(z_t=0).mean('time'),\n", - " transform=ccrs.PlateCarree(), cmap='RdYlBu_r', extend='both', levels=np.arange(0,32,1))\n", - "\n", - "ax_sst.set_global() \n", - "\n", - "land = ax_sst.add_feature(\n", - " cartopy.feature.NaturalEarthFeature('physical', 'land', '110m',\n", - " linewidth=0.5,\n", - " edgecolor='black',\n", - " facecolor='darkgray'))\n", - "\n", - "sst_cbar = plt.colorbar(pc, shrink=0.55, ax=ax_sst);\n", - "sst_cbar.set_label(r'[$^{\\circ}$C]')\n", - "ax_sst.set_title('Sea Surface Temperature')\n", - "\n", - "#plt.savefig('basics_plot_3.png', bbox_inches='tight') # uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "97823b3e-49d7-4c8e-97e7-b957f93b533a", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_3.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9a562d62-1efb-45e0-ab7b-5408c893ddbb", - "metadata": {}, - "source": [ - "### 2. Make regional map over continental US" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a419e0b-bb58-4740-9877-35a358acbd0b", - "metadata": {}, - "outputs": [], - "source": [ - "# define the extent of the map\n", - "lonW = -140\n", - "lonE = -40\n", - "latS = 15\n", - "latN = 65\n", - "cLat = (latN + latS) / 2\n", - "cLon = (lonW + lonE) / 2\n", - "res = '110m'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1dacb156-9942-43a3-9b57-1bd4fe6491aa", - "metadata": {}, - "outputs": [], - "source": [ - "# what does sea surface temperature around the US look like? (i.e. where would you like to go swimming..)\n", - "fig = plt.figure(figsize=(11, 8.5))\n", - "ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", - "ax.set_title('')\n", - "gl = ax.gridlines(\n", - " draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n", - ")\n", - "ax.set_extent([lonW, lonE, latS, latN], crs=ccrs.PlateCarree())\n", - "ax.coastlines(resolution=res, color='black')\n", - "ax.add_feature(cfeature.STATES, linewidth=0.3, edgecolor='brown')\n", - "ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='blue');\n", - "tdat = ax.pcolormesh(ds_pop.TLONG, ds_pop.TLAT, ds_pop.TEMP.isel(z_t=0, time=10), cmap='RdYlBu_r')\n", - "plt.colorbar(tdat, ax=ax, shrink=0.5, pad=0.1)\n", - "#plt.savefig('basics_plot_4.png', bbox_inches='tight')# uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "72f9a1c9-7bf1-4dd7-b193-dcb5baf8bf77", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_4.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "cbda596b-5483-4221-b228-f87fbb0278cd", - "metadata": {}, - "source": [ - "### 3. Make regional map over the Pacific\n", - "\n", - "There's an awful lot of not-ocean over the continental US. Let's look at the Pacific instead." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "39a879e8-1dc0-4c9f-afae-c98a33fa7778", - "metadata": {}, - "outputs": [], - "source": [ - "# define the extent of the map\n", - "lonW = -180\n", - "lonE = -60\n", - "latS = -30\n", - "latN = 30\n", - "cLat = (latN + latS) / 2\n", - "cLon = (lonW + lonE) / 2\n", - "res = '110m'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fcce69d0-c184-4f2f-9b20-775c05489bdd", - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(11, 8.5))\n", - "ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", - "ax.set_title('SST')\n", - "gl = ax.gridlines(\n", - " draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n", - ")\n", - "ax.set_extent([lonW, lonE, latS, latN], crs=ccrs.PlateCarree())\n", - "ax.coastlines(resolution=res, color='black')\n", - "ax.add_feature(cfeature.STATES, linewidth=0.3, edgecolor='brown')\n", - "ax.add_feature(cfeature.BORDERS, linewidth=0.5, edgecolor='blue');\n", - "tdat = ax.pcolormesh(ds_pop.TLONG, ds_pop.TLAT, ds_pop.TEMP.isel(z_t=0, time=10), cmap='RdYlBu_r', vmin=15, vmax=31)\n", - "cbar = plt.colorbar(tdat, ax=ax, shrink=0.5, pad=0.1, ticks=np.arange(15,35,5))\n", - "cbar.set_label(r'[$^{\\circ}$C]')\n", - "#plt.savefig('basics_plot_5.png', bbox_inches='tight')# uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "843c70eb-926e-4e86-92f9-3fd98aab069a", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_5.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6fd00b8a-9894-4745-a4ca-21e9d9ade96a", - "metadata": {}, - "source": [ - "### 4. Plotting contours\n", - "\n", - "The figures above use `pcolormesh` to plot, but if you want to make filled contours using `contourf` you will need to make your dataset cyclical." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efe1c49f-f602-4dfb-bc10-12da1dc9300b", - "metadata": {}, - "outputs": [], - "source": [ - "#ds_pop_cyc = pop_add_cyclic(ds_pop)# uncomment this if you have not run this line before" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71d64e38-7de2-4db4-9759-5e850b0a3aca", - "metadata": {}, - "outputs": [], - "source": [ - "# define the extent of the map\n", - "lonW = -180\n", - "lonE = -60\n", - "latS = -30\n", - "latN = 30\n", - "cLat = (latN + latS) / 2\n", - "cLon = (lonW + lonE) / 2\n", - "res = '110m'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc6fd1db-1e65-4a23-984d-00e7406317cc", - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(11, 8.5))\n", - "ax = plt.subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", - "ax.set_title('SST')\n", - "gl = ax.gridlines(\n", - " draw_labels=True, linewidth=2, color='gray', alpha=0.5, linestyle='--'\n", - ")\n", - "ax.set_extent([lonW, lonE, latS, latN], crs=ccrs.PlateCarree())\n", - "ax.coastlines(resolution=res, color='black')\n", - "ax.stock_img() # something else than the boarders for a change\n", - "tdat = ax.contourf(ds_pop_cyc.TLONG, ds_pop_cyc.TLAT, ds_pop_cyc.TEMP.isel(z_t=0, time=10), cmap='RdYlBu_r', levels=np.arange(10,31,1))\n", - "cbar = plt.colorbar(tdat, ax=ax, shrink=0.5, pad=0.1, ticks=np.arange(15,35,5))\n", - "cbar.set_label(r'[$^{\\circ}$C]')\n", - "#plt.savefig('basics_plot_6.png', bbox_inches='tight')# uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "aa52cb62-eec6-4390-8a12-76d34723d704", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_6.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c8b0288e-e6b4-4ef1-8624-b31e4eebff57", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Try looking at the Equatorial Atlantic Ocean or other region that interests you (Gulf of Mexico, Gulf of Maine, California Coast)." - ] - }, - { - "cell_type": "markdown", - "id": "c9d29a3b-d7c7-4d4c-aeaf-7eeedf48caa3", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "Before plotting the region, you'll need to modify the latitude/longitude bounds. Here are the bounds for the Equatorial Atlantic Ocean: \n", - "```\n", - "# define the extent of the map\n", - "lonW = -60\n", - "lonE = 20\n", - "latS = -30\n", - "latN = 30\n", - "cLat = (latN + latS) / 2\n", - "cLon = (lonW + lonE) / 2\n", - "res = '110m'\n", - "```\n", - "\n", - "You can play with these to look at other regions of interest to you.\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9f98a8bf-9012-4edd-8288-149579c13252", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Try plotting other variables like sea surface height (SSH) or 50m temperature." - ] - }, - { - "cell_type": "markdown", - "id": "12ba7997-bf17-48cb-8eec-5ea7c467b319", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "See hints in exercise 1. \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f5e01632-7866-4aa3-bdca-c292c5d66389", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 3\n" - ] - }, - { - "cell_type": "markdown", - "id": "8088c31c-46a0-44f9-b5c1-f435a0a712aa", - "metadata": {}, - "source": [ - "So far we've just looked at 2D ocean properties, primarily at the surface. But the ocean is deep and you might want to look at how a variable changes with depth. Here we'll plot a cross section of an ocean variable with depth and how it changes with time.\n", - "\n", - "The difficulty here is that you can't easily select your lat and lon location, you need to find the nlon and nlat index first. As you could see from the ```TLAT``` and ```TLONG``` plots above, they don't behave regularly, so this is a bit of a challenge. Let's start with the equator (which is a bit easier than high up north)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45ef4912-1b64-4dad-bc2d-e7ae0a92fda2", - "metadata": {}, - "outputs": [], - "source": [ - "# find the latitude that is the smallest, i.e. closest to the equator:\n", - "abs(ds_pop.TLAT).argmin(dim='nlat')" - ] - }, - { - "cell_type": "markdown", - "id": "53e0a032-1b39-4f76-88a1-e67b15bc4d7c", - "metadata": {}, - "source": [ - "**This shows you that the equator is not the same everywhere** \n", - "but it is within one index and so might be just on the south or north of the equator, you can choose either. (there is no latitude where lat=0, can you imagine why?)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f3fb6625-488b-4681-a336-485eee29523d", - "metadata": {}, - "outputs": [], - "source": [ - "# so let's say \n", - "ind_eq = 180" - ] - }, - { - "cell_type": "markdown", - "id": "f0953a2a-ee77-4c2d-b5d4-e9dcd21c72c4", - "metadata": {}, - "source": [ - "Let's now find some location we might be interested in, say 140$^{\\circ}$W" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "402d8417-55d7-4cd4-af1a-728f0352a0b6", - "metadata": {}, - "outputs": [], - "source": [ - "ds_pop.TLONG.isel(nlat=ind_eq).plot()" - ] - }, - { - "cell_type": "markdown", - "id": "574c504b-5c2b-49d4-8118-5a9cb6ddf57a", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_7.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "442fc568-4af4-49c3-98f0-83f4c32e3ef9", - "metadata": {}, - "outputs": [], - "source": [ - "# the longitude goes from 0-360, so if we want 140W which is -140 we would need to select 220\n", - "ind_140w = abs(ds_pop.TLONG.isel(nlat=ind_eq)-220).argmin()" - ] - }, - { - "cell_type": "markdown", - "id": "2d30bf12-3bbb-4be5-a138-30dc2692f30d", - "metadata": {}, - "source": [ - "### 1. First Plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "949a410b-c188-4bfa-ba3b-2be1b8839069", - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(2, 1, figsize=(9,4.5))\n", - "\n", - "ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).plot(y='z_t', ax=ax[0], \n", - " ylim=(250e2,0), levels=np.arange(10,32,2), cmap='RdYlBu_r')\n", - "\n", - "ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).plot(y='z_t', ax=ax[1], \n", - " ylim=(5000e2,0), levels=np.arange(0,10.2,0.2), cmap='Blues')\n", - "\n", - "#plt.savefig('basics_plot_8.png', bbox_inches='tight')# uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "a0aaf85d-6350-410d-b158-e1c276ad6889", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_8.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "417f27d7-c58a-4c8f-980a-a531b0079aa9", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "What is the vertical dimension? Which side of the plot is the ocean surface vs. the ocean floor?" - ] - }, - { - "cell_type": "markdown", - "id": "4c2090a8-349a-4587-ba1d-525b20ec71ca", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "These plots have the surface of the ocean at the top of the plot. So it's oriented physically with how we percieve the world. You should be aware of how the y axis changes when plotting figures like this to make them more easily interpretable.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "d53404ea-6116-49a1-8173-71c57540f934", - "metadata": {}, - "source": [ - "### 2. Nicer axes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74211b70-e318-46b7-855c-4f7333e5fa97", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax_upper, ax_lower) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [1, 3]}, \n", - " figsize=(10,6), sharex=True)\n", - "\n", - "dat_upper = ax_upper.contourf(ds_pop.time, ds_pop.z_t/100, ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).T, \n", - " levels=np.arange(10,32,1), cmap='RdYlBu_r', extend='both')\n", - "ax_upper.set_ylim(300,0)\n", - "plt.colorbar(dat_upper, ax=ax_upper)\n", - "\n", - "dat_lower = ax_lower.contourf(ds_pop.time, ds_pop.z_t/100, ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).T, \n", - " levels=np.arange(0,10.5,0.5), cmap='Blues', \n", - " extend='both')\n", - "ax_lower.set_ylim(4000,300)\n", - "plt.colorbar(dat_lower, ax=ax_lower, shrink=0.7)\n", - "#plt.savefig('basics_plot_9.png', bbox_inches='tight')# uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "efe789d3-a585-4fb6-bd6e-0de87c9c86a1", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_9.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "15f7e5e0-a9f5-4f73-a334-8c9f39557fab", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "What happened to the vertical axis? Why does this make the plot easier to read?" - ] - }, - { - "cell_type": "markdown", - "id": "6a712296-7198-4a1e-82cc-37d679342bbd", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "The ocean is deep and there is often different rates of change in a variable over the the upper ocean compared to the deep ocean. So using different scales and plotting them separately can be useful for analysis. \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e6feb3cc-5731-42b4-8bdf-4b57f083c3e6", - "metadata": {}, - "source": [ - "_______________\n", - "## Exercise 4" - ] - }, - { - "cell_type": "markdown", - "id": "7ea55dc5-b495-49d1-9c48-d4cd54f2c792", - "metadata": {}, - "source": [ - "The previous exercise showed how to plot a vertical cross section of the ocean over time. But it can also be valuable to plot a profile of a variable with depth at a particular point either at one time, averaged over time, or a profile averaged over a set of points again at one time or averaged over time. \n", - "\n", - "Here we will plot an average profile of temperature with depth. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff0ca0b5-e110-4875-9860-05f841aabfb5", - "metadata": {}, - "outputs": [], - "source": [ - "ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "64360723-834c-4d44-82b7-2f813c61c53c", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# let's load these calculated quantities so that we don't have to calculate them time and time again \n", - "t_0n140w_mean = ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).mean('time').load()\n", - "t_0n140w_std = ds_pop.TEMP.isel(nlon=ind_140w, nlat=ind_eq).std('time').load()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b2438a1-8422-466c-809c-c7053c907473", - "metadata": {}, - "outputs": [], - "source": [ - "# plot the mean profile\n", - "t_0n140w_mean.plot(y='z_t', ylim=(300e2,0), label='mean')\n", - "plt.xlim(10,28)\n", - "plt.title('T at 0$^{\\circ}$N, 140$^{\\circ}$W')\n", - "\n", - "# let's add some error bars --> standard deviation \n", - "\n", - "plt.fill_betweenx(ds_pop.z_t, t_0n140w_mean-t_0n140w_std, t_0n140w_mean+t_0n140w_std, color='black', alpha=0.2, edgecolor=None, label='std')\n", - "\n", - "plt.legend()\n", - "#plt.savefig('basics_plot_10.png', bbox_inches='tight')# uncomment this to save your figure" - ] - }, - { - "cell_type": "markdown", - "id": "bddc4521-24ea-488d-9606-928c56429cc2", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "![plot example](../../../images/diagnostics/pop/basics_plot_10.png)\n", - "\n", - "*

Figure: Plotting solution.

*\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c6d5cf0f-bd2d-4ed9-9f87-305190c2b324", - "metadata": {}, - "source": [ - "**Question:**\n", - "\n", - "Why is there grey shading on the plot above? i.e. How many ensembles have we included? What else could be causing a spread in the output?" - ] - }, - { - "cell_type": "markdown", - "id": "5b8e9dd0-c17c-45b5-aeda-73e0da8b0c28", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - " Click here for hints \n", - "\n", - "Here we take an average of the profiles in one location over time. But you could average over ensembles or over a region at one time, and both would also provide information about the variability in this quantity over depth. \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4bb05d3e-2324-4705-a387-71715a454cd1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "NPL 2023a", - "language": "python", - "name": "npl-2023a" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/diagnostics/pop/pop.ipynb b/notebooks_copy/diagnostics/pop/pop.ipynb deleted file mode 100644 index b029792f8..000000000 --- a/notebooks_copy/diagnostics/pop/pop.ipynb +++ /dev/null @@ -1,136 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Ocean" - ] - }, - { - "cell_type": "markdown", - "id": "18a0c6f1-355a-4f2a-99ac-1fead4de8626", - "metadata": {}, - "source": [ - "## Basic Plotting" - ] - }, - { - "cell_type": "markdown", - "id": "ce7f7f35-19aa-406c-9330-e973dbb576d0", - "metadata": { - "tags": [] - }, - "source": [ - "### Learning Goals\n", - "\n", - "- How to load and plot POP ocean model output\n", - "- How to deal with the curvilinear grid\n", - "- How to select a point for making profiles\n", - "- How to define useful functions" - ] - }, - { - "cell_type": "markdown", - "id": "ae93c512-ccde-43cf-9d73-822cddde7ef2", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 1: Making map plots\n", - "\n", - "Here we will produce a map plot of surface heat flux and sea surface temperature. Some things to try include plotting standard deviations instead of means, plotting another surface variable, or plotting temperature at a particular depth." - ] - }, - { - "cell_type": "markdown", - "id": "a0b48b29-16d6-4474-9d81-152f9849acd3", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 2: Making global and regional maps\n", - "\n", - "Here will we learn how to use the TLAT and TLONG coordinates in POP to plot nicer global or regional maps. Some things to try include plotting different regions or different variables.\n" - ] - }, - { - "cell_type": "markdown", - "id": "589f85ae-bbe7-4e57-8b4b-1eec5d1e326c", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 3: Making timeseries\n", - "\n", - "Here will we learn how to make a timeseries of temperature at a single point throughout the column.\n" - ] - }, - { - "cell_type": "markdown", - "id": "5676b088-c81c-4a4c-8fc7-bce6f09dd32d", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 4: Making a profile\n", - "\n", - "Here we learn how to plot a temperature profile at a single point on the ocean grid." - ] - }, - { - "cell_type": "markdown", - "id": "782bebb4-f00e-4362-8738-736ab39a311d", - "metadata": {}, - "source": [ - "## Advanced Plotting" - ] - }, - { - "cell_type": "markdown", - "id": "c9434bfe-c2c3-4e6a-ba83-de538a6f29ed", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 1: Plot winter time maximum mixed-layer depth\n", - "\n", - "Here we learn how to select winter months and plot the maximum mixed-layer depth for both hemispheres on a single map. " - ] - }, - { - "cell_type": "markdown", - "id": "714dba27-4bcf-40db-9779-9fe0c3dde5f3", - "metadata": {}, - "source": [ - "_______________\n", - "\n", - "### Exercise 2: Compute and plot ocean heat storage per area for the upper 2000m\n", - "\n", - "Here we learn how to calculate the heat storage per area from the temperature trend and plot the results on a global map." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/cesm_expts/clim_data_gateway.ipynb b/notebooks_copy/intro/cesm_expts/clim_data_gateway.ipynb deleted file mode 100644 index 3dbe209c5..000000000 --- a/notebooks_copy/intro/cesm_expts/clim_data_gateway.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Climate Data Gateway" - ] - }, - { - "cell_type": "markdown", - "id": "9d8e3ee1-e605-4894-afd2-b0a83420711a", - "metadata": {}, - "source": [ - "Publicly released CESM data is available via the Climate Data Gateway. Timeseries data in both CESM standard format and CMIP format are available. Registration is quick and easy, and NCAR accounts are not required for access." - ] - }, - { - "cell_type": "markdown", - "id": "4afe3049-6271-4659-b23d-b4627dc31e93", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Climate Data Gateway webpage](https://www.earthsystemgrid.org/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e33a15df-834d-4108-a342-905d66bce697", - "metadata": {}, - "source": [ - "![CDG](../../../images/intro/clim_data_gateway.png)\n", - "\n", - "*

Figure: Climate Data Gateway Homepage.

*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eeccd871-4162-4b13-a29d-cc9529557043", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/cesm_expts/clim_data_guide.ipynb b/notebooks_copy/intro/cesm_expts/clim_data_guide.ipynb deleted file mode 100644 index e606d33f1..000000000 --- a/notebooks_copy/intro/cesm_expts/clim_data_guide.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Climate Data Guide" - ] - }, - { - "cell_type": "markdown", - "id": "43c2831e-6726-4718-8460-2a042109003b", - "metadata": {}, - "source": [ - "The Climate Data Guide describes observations used for Earth System Model evaluation. To date 150+ data sets have been profiled. Expert-user guidance by 45+ scientists is provided to users, including pros and cons of each dataset. There are also comparisons of common variables (e.g. precipitation, sea surface temperature, sea ice concentration, etc.)." - ] - }, - { - "cell_type": "markdown", - "id": "422d7b55-49d9-4059-90ff-eec47665f874", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Climate Data Guide webpage](http://climatedataguide.ucar.edu/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "16c243bf-a625-417d-929b-67a8171b2fab", - "metadata": {}, - "source": [ - "![CDG](../../../images/intro/clim_data_guide.png)\n", - "\n", - "*

Figure: Climate Data Guide Homepage.

*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7d1fc1d-4dc0-4718-92c9-30f38db2bb77", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/cesm_webpage.ipynb b/notebooks_copy/intro/cesm_webpage.ipynb deleted file mode 100644 index b004a7fb1..000000000 --- a/notebooks_copy/intro/cesm_webpage.ipynb +++ /dev/null @@ -1,81 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0086605a-f330-41bd-a79b-13339e0e5d58", - "metadata": { - "tags": [] - }, - "source": [ - "# CESM Webpage" - ] - }, - { - "cell_type": "markdown", - "id": "9546d840-c247-4952-a8f2-52a377e5e180", - "metadata": {}, - "source": [ - "The **CESM Web Page** is the first point of reference for all things CESM. Here we can explore the larger CESM project, delve deeper into individual working groups, models, research activities, model releases, supporting model data, tools, events and much much more.\n" - ] - }, - { - "cell_type": "markdown", - "id": "71155f04-3f3a-4aa2-9caa-96815767ef1c", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM project webpage](http://www.cesm.ucar.edu)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "59650279-f141-4093-b06d-b788d46ae33c", - "metadata": {}, - "source": [ - "**Some points to pay attention to:** \n", - "- CESM **``Project``** Information\n", - "- **``Working Groups``** Information\n", - "- **``Community Projects``** Information\n", - " - CESM2 Large Ensemble\n", - " - CESM2 Stratospheric Aerosol Injection (ARISE-SAI)\n", - "- What **``Version``** of the model should you use?\n", - "- **``Individual Model``** Pages https://www.cesm.ucar.edu/models/ \n", - "- **``Supported Model``** Releases – 2.1.X vs 2.2.X\n", - "- **``Experiments``**, expand a case for details, diagnostics plots for many experiments\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8bd790d-2ad5-445d-aaff-4bc8d5b23a29", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/community_experiments.ipynb b/notebooks_copy/intro/community_experiments.ipynb deleted file mode 100644 index 7d661a29a..000000000 --- a/notebooks_copy/intro/community_experiments.ipynb +++ /dev/null @@ -1,136 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Community Experiments" - ] - }, - { - "cell_type": "markdown", - "id": "59bb8a3f-74a5-4c7f-aaa7-15d22fe59b4a", - "metadata": {}, - "source": [ - "Information about community experiments performed with CESM version 2 and previous verisons of the model (CSM1, CCSM2, CCSM3, CESM1) are documented on an experiments webpage. " - ] - }, - { - "cell_type": "markdown", - "id": "c6540e1c-d489-401b-b227-2da4f69c50ec", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM experiments webpage](https://www.cesm.ucar.edu/experiments)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "387a41d7-3e1f-4272-9ba6-7940dc3c9f7f", - "metadata": {}, - "source": [ - "Explore this webpage and then click on the CESM2 Experiments, Data, and Diagnostic Output link for experiments from the most recent model version." - ] - }, - { - "cell_type": "markdown", - "id": "501487a2-94b7-4947-a273-208b0106c0fd", - "metadata": {}, - "source": [ - "![CESM Experiments](../../images/intro/CESM_exp_1.png)\n", - "\n", - "*

Figure: CESM experiments webpage.

*" - ] - }, - { - "cell_type": "markdown", - "id": "26b40b47-8dfc-4477-99f2-f54f859ea47c", - "metadata": {}, - "source": [ - "This page provides a searchable list of all the CESM2 experiments available to the public. \n", - "To interpret the casenames, it is helpful to remember CESM naming conventions that provide information about the experiment at a glance. Knowing the CESM case naming conventions will help you navigate CESM community experiments. Information about the CESM naming is available at:" - ] - }, - { - "cell_type": "markdown", - "id": "09e4bfeb-04af-427e-9636-f62f001b567e", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM case naming conventions](https://www.cesm.ucar.edu/models/cesm2/naming-conventions)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "ab79cd61-d4c9-4e40-92f2-9253756b2eda", - "metadata": {}, - "source": [ - "Click on the experiment that corresponds to the CESM2 CMIP6 preindustrial control experiment:\n", - "b.e21.B1850.f09_g17.MIP6-piControl.001" - ] - }, - { - "cell_type": "markdown", - "id": "50f1a8e8-90d0-44e7-8fcf-5dd2f0b09d3f", - "metadata": {}, - "source": [ - "![CESM Experiments](../../images/intro/CESM_exp_2.png)\n", - "\n", - "*

Figure: CESM2 CMIP6 PI control experiment.

*" - ] - }, - { - "cell_type": "markdown", - "id": "42e2aada-04d8-40ba-8ae2-3d501782dadb", - "metadata": {}, - "source": [ - "Information about this experiment drops down below the experiment name. There are three main sections of information:\n", - "\n", - "- The title of the experiment with active components listed.\n", - "- Links to the data and/or the NCAR HPC directory with data. (#1, green arrow)\n", - "- Links to the diagnostic component package output and the Climate Variability Diagnostics Package. (#2, blue arrow)\n", - "- Details about the run, including the resolution, compset, etc. (#3, red arrow)" - ] - }, - { - "cell_type": "markdown", - "id": "0d1ff550-d4c7-45be-a6d0-b1ea789d0d67", - "metadata": {}, - "source": [ - "![CESM Experiments](../../images/intro/CESM_exp_3.png)\n", - "\n", - "*

Figure: CESM2 CMIP6 PI control experiment.

*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "084b0a01-829d-4996-bea5-f0e56e2c6d04", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/components.ipynb b/notebooks_copy/intro/components.ipynb deleted file mode 100644 index 8abe5e901..000000000 --- a/notebooks_copy/intro/components.ipynb +++ /dev/null @@ -1,87 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0086605a-f330-41bd-a79b-13339e0e5d58", - "metadata": { - "tags": [] - }, - "source": [ - "# What is CESM ? \n", - "\n", - "CESM is a coupled Earth system model for simulating Earth’s climate system and connected component systems. The model is composed of **separate models** that simultaneously simulate the Earth’s atmosphere, ocean, land, river run-off, land-ice, and sea-ice. The model components that are coupled through the Common Infrastructure for Modeling the Earth known as **[CIME](https://esmci.github.io/cime/versions/master/html/index.html)**. \n" - ] - }, - { - "cell_type": "markdown", - "id": "bac52cc8-695c-49b9-9018-69503ad75f2f", - "metadata": {}, - "source": [ - "![Discuss Image](../../images/intro/CESM2.png)\n", - "*

Figure: CESM2 Structure

*" - ] - }, - { - "cell_type": "markdown", - "id": "cd1f097c-1b20-4905-9af1-1b7c3901cb79", - "metadata": {}, - "source": [ - "## Model Components\n", - "\n", - "Each model component has a page on the CESM website containing descriptions and documentation for active or prognostic models. \n", - "\n", - "You can explore each of the component models and CIME from the links below." - ] - }, - { - "cell_type": "markdown", - "id": "16519007-3902-4d9e-bdb4-380208c14e33", - "metadata": {}, - "source": [ - "
\n", - "Component Links:\n", - " \n", - "- ``Atmosphere`` \n", - "- ``Land`` \n", - "- ``Land Ice`` \n", - "- ``Ocean`` \n", - "- ``River Runoff`` \n", - "- ``Sea Ice`` \n", - "- ``Wave`` \n", - "- ``CIME`` \n", - " \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e00f9ea-6161-4090-ba36-7f4022e9e467", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/coupling.ipynb b/notebooks_copy/intro/coupling.ipynb deleted file mode 100644 index dabf2fed7..000000000 --- a/notebooks_copy/intro/coupling.ipynb +++ /dev/null @@ -1,135 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "163311c4-af4e-4ac5-bfa1-20fc25016f28", - "metadata": { - "tags": [] - }, - "source": [ - "# CIME (Advanced)\n" - ] - }, - { - "cell_type": "markdown", - "id": "b247f4d7-ca36-4b62-bc4d-007c42ed9892", - "metadata": {}, - "source": [ - "The Combined Model Components form the coupled CESM through a shared common set of tools and infrastructure and coupling framework. Here we describe the Common Infrastructure for Modeling the Earth otherwise known as [CIME](https://esmci.github.io/cime/versions/master/html/index.html), as well as the current CESM2 and future coupling frameworks.\n" - ] - }, - { - "cell_type": "markdown", - "id": "e5ddb5ae-4b5f-4f55-8242-5b8e86c831c8", - "metadata": {}, - "source": [ - "## Common Infrastructure for Modeling the Earth (CIME)" - ] - }, - { - "cell_type": "markdown", - "id": "9dca3b1b-e249-4e6c-b34c-46efc6cdc144", - "metadata": {}, - "source": [ - "The Common Infrastructure for Modeling the Earth is a python-based framework that is shared between CESM2 and other modeling center efforts to ensure efficient \n", - "coordination of model development. The schema below shows some of the science benefits and organizations that are using the CIME infrastructure. \n", - "

\n" - ] - }, - { - "cell_type": "markdown", - "id": "d973f124-8754-4bf6-a089-2509ea267671", - "metadata": {}, - "source": [ - "![CESM directories and namelists](../../images/intro/CESM2_CIME_Infrastructure.png)\n", - "*

Figure: Overview of the CESM2 Common Infrastructure for Modeling the Earth (CIME)

*\n" - ] - }, - { - "cell_type": "markdown", - "id": "4c689c94-e8c6-40a6-a2a0-0fa873d1f111", - "metadata": {}, - "source": [ - "## Model Coupling Toolkit (MCT)" - ] - }, - { - "cell_type": "markdown", - "id": "03cb0afa-459e-44f2-948b-452195c8589d", - "metadata": {}, - "source": [ - "The CESM2 coupling is performed through the Model Coupling Toolkit (MCT). Additionally, the MCT coupling framework allows data and stub components to \n", - "replace active or prognostic components providing flexible activation/deactivation of feedbacks. \n", - "The schematic below shows all of the components and their connections.\n", - "

\n" - ] - }, - { - "cell_type": "markdown", - "id": "9bc59881-d61d-49a0-8178-f5ffba630a41", - "metadata": {}, - "source": [ - "![CESM directories and namelists](../../images/intro/CESM2_MCT_Coupling.png)\n", - "*

Figure: Overview of the CESM2 Components and Model Coupling Toolkit (MCT)

*\n" - ] - }, - { - "cell_type": "markdown", - "id": "9c213a95-b901-4db3-b263-4bf2d3e54196", - "metadata": {}, - "source": [ - "## Earth System Modeling Framework (ESMF) Coupling" - ] - }, - { - "cell_type": "markdown", - "id": "70a62a59-021b-4798-9e77-7ff786f54e7e", - "metadata": {}, - "source": [ - "Coupling development beyond CESM2 will be changing to the Earth System Modeling Framework (ESMF) National Unified Operational Prediction Capability (NUOPC) framework. More details on NUOPC can be found at \n", - " https://earthsystemmodeling.org/nuopc/ . The schema below shows the new CESM configuration and coupling with NUOPC.\n", - "

" - ] - }, - { - "cell_type": "markdown", - "id": "d9569358-6f6b-4fcc-9909-11e295957bca", - "metadata": {}, - "source": [ - "\n", - "![CESM directories and namelists](../../images/intro/CESM_NUOPC_Coupling.png)\n", - "*

Figure: Overview of the new CESM Components and National Unified Operational Prediction Capability (NUOPC) framework

*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40abbd6b-70cb-427b-a468-9311191be2a7", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/getting_help.ipynb b/notebooks_copy/intro/getting_help.ipynb deleted file mode 100644 index 4c99fcfa7..000000000 --- a/notebooks_copy/intro/getting_help.ipynb +++ /dev/null @@ -1,88 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Getting Help\n" - ] - }, - { - "cell_type": "markdown", - "id": "de6ee277-ec79-4c5f-9cbf-d2901dd20ba3", - "metadata": {}, - "source": [ - "As you gain experience using and running CESM, you may want additional sources of information to help with problem solving, designing modeling experiments, runtime issues, etc." - ] - }, - { - "cell_type": "markdown", - "id": "aa8da793-cfa0-472f-980e-6ec0c5478791", - "metadata": {}, - "source": [ - "## DiscussCESM Forums" - ] - }, - { - "cell_type": "markdown", - "id": "3dfb2ac3-aeb3-421a-a8ea-636ffe0a61b4", - "metadata": {}, - "source": [ - "The DiscussCESM or Bulletin Board forum is an excellent location to post questions or to search through previously posted and answered questions in regard to problems encounter while using CESM.\n", - "\n", - "Register as a forums user by entering your valid information in the registration form. You can subscribe to forums of interest -- especially the “Announcements” and “Known Problems” -- and this way we can communicate updates to you.\n" - ] - }, - { - "cell_type": "markdown", - "id": "153bdbd3-d587-4125-8f90-a69496f5f264", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[DiscussCESM Forum](https://bb.cgd.ucar.edu/cesm/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f5f4bfa4-9482-4e29-8a9b-b4082ae8f452", - "metadata": {}, - "source": [ - "![Discuss Image](../../images/intro/CESM2_Discuss.png)\n", - "*

Figure: CESM2 Discuss Bulletin Board

*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1dedd34e-f4c7-452d-b31e-b80acc32609a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/getting_involved.ipynb b/notebooks_copy/intro/getting_involved.ipynb deleted file mode 100644 index a0a4a6ffb..000000000 --- a/notebooks_copy/intro/getting_involved.ipynb +++ /dev/null @@ -1,151 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Getting Involved\n" - ] - }, - { - "cell_type": "markdown", - "id": "3d14afe8-0247-4446-a1d4-1a2859156d90", - "metadata": {}, - "source": [ - "## CESM Events" - ] - }, - { - "cell_type": "markdown", - "id": "ae8ef153-ef12-4306-97d7-9d1ce6cda746", - "metadata": {}, - "source": [ - "CESM has a long history of open, Community Events. A schedule of CESM Events is maintained on the CESM Website. CESM events include Community and Working Group Meetings, Tutorials, Seminars, and other related events. The first Community workshop was held in 1996 and continues annually today, and there are winter working-group specific meetings.\n" - ] - }, - { - "cell_type": "markdown", - "id": "d0a9d7fd-4003-4b94-8e42-2342a148909b", - "metadata": {}, - "source": [ - "
\n", - " \n", - "[CESM Events](https://www.cesm.ucar.edu/events)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9347c979-52dc-44f8-9d7f-57a74b722db4", - "metadata": {}, - "source": [ - "![CESM Events Image](../../images/basics/CESM2_Events.png)\n", - "*

Figure: CESM2 Events Page

*" - ] - }, - { - "cell_type": "markdown", - "id": "e231cf65-e68f-4767-a5f1-0a59ad02922c", - "metadata": {}, - "source": [ - "**We hope to see you at a future CESM workshop!**" - ] - }, - { - "cell_type": "markdown", - "id": "2fc2e42c-dcfa-45aa-a16b-c1846590f2f9", - "metadata": {}, - "source": [ - "## CESM Email Lists" - ] - }, - { - "cell_type": "markdown", - "id": "a459419f-4e90-4aac-a7f4-13ce597afd1c", - "metadata": {}, - "source": [ - "There are a number of email lists for the CESM community that you can join to receive updates for the whole CESM community or for particular working groups." - ] - }, - { - "cell_type": "markdown", - "id": "002db32d-d135-446c-9c01-c6807467ae32", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM Email lists](https://www.cesm.ucar.edu/about/faqs)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "47c38994-bf8c-4cf3-8112-9e6b871398de", - "metadata": {}, - "source": [ - "## CESM Github Alerts" - ] - }, - { - "cell_type": "markdown", - "id": "df28fe76-4efe-491c-8784-3c4f0fb2fd04", - "metadata": {}, - "source": [ - "If you create a github account and opt-in, you can watch CESM related repositories." - ] - }, - { - "cell_type": "markdown", - "id": "909d4342-cfab-40ea-90bc-bf9c713939c5", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM Github](https://github.com/ESCOMP/CESM)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "30eb47fb-19c2-4b0a-a0c6-ae412f018ef3", - "metadata": {}, - "source": [ - "![Discuss Image](../../images/intro/CESM_github.png)\n", - "*

Figure: CESM2 Github Page

*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1dedd34e-f4c7-452d-b31e-b80acc32609a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/intro_overview.ipynb b/notebooks_copy/intro/intro_overview.ipynb deleted file mode 100644 index 5e4cc18d5..000000000 --- a/notebooks_copy/intro/intro_overview.ipynb +++ /dev/null @@ -1,87 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0959db1d-1790-4519-a34e-7026444af537", - "metadata": {}, - "source": [ - "# Introduction" - ] - }, - { - "cell_type": "markdown", - "id": "f0015117-d84f-471e-9bc5-ba87dc050baf", - "metadata": {}, - "source": [ - "Welcome to the world of the **Community Earth System Model (CESM)**. This introduction will discuss the structure of the CESM model, provide information about how to become more involved in the CESM community, and how to get help if required.\n" - ] - }, - { - "cell_type": "markdown", - "id": "8a108581-ab10-4779-b8a7-da44ccaf4495", - "metadata": {}, - "source": [ - "## Visualizing this documentation" - ] - }, - { - "cell_type": "markdown", - "id": "864d5c25-d84a-437a-b86f-65a5211db5e8", - "metadata": {}, - "source": [ - "Depending on your computer settings, you may see this documentation with a **white** or **black background**. To toggle between these settings click the sun or moon in the upper right of the page (see red arrow below). \n", - "\n", - "We have developed the documentation with the **white background** and recommend you use these settings. If you choose to use the dark background it is possible some of the visualizations will be more difficult to read." - ] - }, - { - "cell_type": "markdown", - "id": "1a9457c9-8717-4882-b6b2-311ce7ff899c", - "metadata": {}, - "source": [ - "![Light/Dark mode](../../images/intro/light_dark_mode.png)\n", - "\n", - "*

Figure: How to change between light/dark mode.

*" - ] - }, - { - "cell_type": "markdown", - "id": "7f68efa6-c770-4448-aa2a-13882b8fd5f4", - "metadata": {}, - "source": [ - "## Advanced Knowledge\n", - "\n", - "Some of the pages of this tutorial are marked as **Advanced**. Feel free to skip these as needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "acd1bd32-84b7-4d4e-9688-605a832766e6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/intro/project.ipynb b/notebooks_copy/intro/project.ipynb deleted file mode 100644 index b0ce1776a..000000000 --- a/notebooks_copy/intro/project.ipynb +++ /dev/null @@ -1,129 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0086605a-f330-41bd-a79b-13339e0e5d58", - "metadata": { - "tags": [] - }, - "source": [ - "# CESM Working Groups" - ] - }, - { - "cell_type": "markdown", - "id": "cab53fe1-0293-48ad-b0e3-4b9869bc55fb", - "metadata": {}, - "source": [ - "The CESM project has a strong commitment to Community involvment in governance and development of the CESM. The CESM modeling effort is coordinated through **Working Groups** that are responsible for the science and development of individual CESM components or organized to coordinate other across CESM efforts. \n", - "\n", - "Each team takes responsibility for developing and continually improving its component of the CESM consistent with the CESM goal of a fully-coupled model and with the CESM design criteria. There are currently 12 Working Groups." - ] - }, - { - "cell_type": "markdown", - "id": "aa2d188a-e632-43f5-9f52-ea91411d2014", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Atmosphere Model Working Group (AMWG)](https://www.cesm.ucar.edu/working-groups/atmosphere)\n", - " \n", - "[Biogeochemistry Working Group (BGCWG)](https://www.cesm.ucar.edu/working-groups/biogeo)\n", - " \n", - "[Chemistry Climate Working Group (CCWG)](https://https://www.cesm.ucar.edu/working-groups/chemistry)\n", - " \n", - "[Climate Variability and Change Working Group (CVCWG)](https://www.cesm.ucar.edu/working-groups/climate)\n", - " \n", - "[Earth System Prediction Working Group (ESPWG)](https://www.cesm.ucar.edu/working-groups/earth-system)\n", - " \n", - "[Land Ice Working Group (LIWG)](https://www.cesm.ucar.edu/working-groups/land-ice)\n", - " \n", - "[Land Model Working Group (LMWG)](https://www.cesm.ucar.edu/working-groups/land)\n", - " \n", - "[Ocean Model Working Group (OMWG)](https://www.cesm.ucar.edu/working-groups/ocean)\n", - " \n", - "[Paleoclimate Working Group (PWG)](https://www.cesm.ucar.edu/working-groups/paleo)\n", - " \n", - "[Polar Climate Working Group (PCWG)](https://www.cesm.ucar.edu/working-groups/polar)\n", - " \n", - "[Software Engineering Working Group (SEWG)](https://www.cesm.ucar.edu/working-groups/software)\n", - " \n", - "[Whole Atmosphere Model Working Group (WAWG)](https://www.cesm.ucar.edu/working-groups/whole-atmosphere)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e0c8268d-cff5-4ae0-8f65-e5b7017d32fd", - "metadata": {}, - "source": [ - "Each Working Group is coordinated by Co-Chairs. The Working Groups decide their own development priorities and work schedules, consistent with the overall goals of CESM, and are subject to oversight by the CESM Scientific Steering \n", - "Committee (SSC)." - ] - }, - { - "cell_type": "markdown", - "id": "426af3d0-3705-4b5c-a265-3d60fac884d5", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Working Group Co-Chairs and Terms](https://www.cesm.ucar.edu/working-groups/co-chairs)\n", - " \n", - "[Scientific Steering Committee (SSC)](https://www.cesm.ucar.edu/management/ssc)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "09ca207c-608c-4933-b640-e8155e100b10", - "metadata": {}, - "source": [ - "**We welcome and strongly encourage anyone interested to join the relevant CESM working groups to participate more fully in the CESM project and model development.** \n", - "\n", - "**Check the webpage [\"Getting Involved\"](https://ncar.github.io/CESM-Tutorial/notebooks/intro/getting_involved.html) for more information.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8bd790d-2ad5-445d-aaff-4bc8d5b23a29", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "603ca3b9-3c17-4697-9bb3-02526745b130", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml.ipynb b/notebooks_copy/modifications/xml.ipynb deleted file mode 100644 index ae6bd61bb..000000000 --- a/notebooks_copy/modifications/xml.ipynb +++ /dev/null @@ -1,53 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0959db1d-1790-4519-a34e-7026444af537", - "metadata": {}, - "source": [ - "# Simple XML Modifications" - ] - }, - { - "cell_type": "markdown", - "id": "09b1ddf9-0892-4a46-bfa6-11fa05eac358", - "metadata": {}, - "source": [ - "We use XML files to control case settings. This is what we cover in this chapter:\n", - "- The section *Overview* describes what XML files are and the primary files used in CESM.\n", - "- You will then learn how to query and change XML variables.\n", - "- Next, we describe some of the main XML variables used to control run length or run type. \n", - "- The section *Exercise Overview* offers opportunities to practice the concepts learned in this chapter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bb5bff3c-1583-4653-8286-f45e4cc86b5f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/copying_cases.ipynb b/notebooks_copy/modifications/xml/copying_cases.ipynb deleted file mode 100644 index 6659aa2e0..000000000 --- a/notebooks_copy/modifications/xml/copying_cases.ipynb +++ /dev/null @@ -1,35 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Copying Cases \n", - "\n", - "This is a placeholder for this lesson." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/exercises.ipynb b/notebooks_copy/modifications/xml/exercises.ipynb deleted file mode 100644 index 9aa2008d9..000000000 --- a/notebooks_copy/modifications/xml/exercises.ipynb +++ /dev/null @@ -1,53 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bfc54be9-aad8-420b-83c1-874ecd9cdc1b", - "metadata": {}, - "source": [ - "# Exercise Overview" - ] - }, - { - "cell_type": "markdown", - "id": "eb20fd6b-c748-428f-a8dc-565b2c43dfe4", - "metadata": {}, - "source": [ - "We will do three exercises to help us better understand xml modifications.\n", - "\n", - "- In Exercise 1, we will practice runtime variable modifications.\n", - "- In Exercise 2, we will create a branch run and modify the ocean coupling frequency. \n", - "- In Exercise 3, we will create a hybrid run and modify the atmosphere physics timestep. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6b57bbfc-2712-460c-9da6-9545fe3e9869", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/exercises/xml_length_exercise.ipynb b/notebooks_copy/modifications/xml/exercises/xml_length_exercise.ipynb deleted file mode 100644 index 99a9dbcf3..000000000 --- a/notebooks_copy/modifications/xml/exercises/xml_length_exercise.ipynb +++ /dev/null @@ -1,195 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "55295ae2-df19-4226-9f54-2c052e4afadc", - "metadata": {}, - "source": [ - "# Exercise 1: Modify run length " - ] - }, - { - "cell_type": "markdown", - "id": "9b8d7632-491e-4851-b6dc-b28e6620c761", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Exercise: Modify the `env_run.xml` file

\n", - "**Part I**: \n", - "Create a new fully coupled **startup** case named \"b.day2.1\" from the 1850 climate conditions with a resolution of f19_g17. Increase the amount of standard output produced by the model for debugging. Run for 1 month.

\n", - " \n", - "**Part II**:\n", - "*Do part II only **after** Part I has finished running*. \n", - "Change the debug level back to 1. Expand Part I to produce a total of 38 months of model run. Remember that you have already run for 1 month. **You should submit this at the end of the day so that it will (hopefully) run overnight and be available the following day.** You can continue with Exercise 2-3 and submit the run later.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "bed2b80a-b00e-46c5-b39c-4d3fd7363c55", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Hint!\n", - " \n", - "\n", - "1. Which variable controls the amount of standard output for debugging? We can use partial query to find out:\n", - " \n", - "```\n", - " ./xmlquery -p DBUG\n", - "```\n", - "\n", - " \n", - "2. Use `xmlchange` to modify `env_run.xml` \n", - "\n", - "3. Make sure to update the walltime to match the run length.\n", - " \n", - "4. For Part II, how to tell the model that this run continues from the previously finished run of 1 month?\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "448ee61e-bbe3-4149-9029-58abf1247f3f", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution to Part I\n", - " \n", - "\n", - " \n", - "From the `SRCROOT` (`/glade/work/$USER/code/my_cesm_code`) directory, create your case:\n", - " \n", - "```\n", - " cd /glade/work/$USER/code/my_cesm_code/cime/scripts\n", - " ./create_newcase --case /glade/work/$USER/cases/b.day2.1 --res f19_g17 --compset B1850\n", - "```\n", - "
\n", - " \n", - "In your case directory, change debugging levels and set runtime variables:\n", - "\n", - "```\n", - " cd /glade/work/$USER/cases/b.day2.1\n", - " ./xmlchange INFO_DBUG=2,STOP_N=3,STOP_OPTION=nmonths\n", - " ./xmlchange --subgroup case.run JOB_WALLCLOCK_TIME=2:00:00\n", - "```\n", - "
\n", - " \n", - "Remember to manually update your README.case file to document your changes.\n", - "\n", - "\n", - "Carry on to setup, build and submit the run:\n", - " \n", - "```\n", - " ./case.setup \n", - " qcmd -- ./case.build\n", - " ./case.submit\n", - "```\n", - "\n", - "Remember that `qcmd` is used on derecho only.\n", - "\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f72d36bf-b3a6-4862-9479-169cbc2b01e7", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution to Part II\n", - " \n", - "\n", - "
\n", - "1. To continue the run, set `CONTINUE_RUN` to `TRUE`\n", - " \n", - "```\n", - " ./xmlchange CONTINUE_RUN=TRUE\n", - "```\n", - "\n", - "
\n", - "2. Keep `STOP_OPTION` as \"nmonths” \n", - " \n", - " \n", - "
\n", - "3. Set `STOP_N` to “37”\n", - " \n", - "```\n", - " ./xmlchange STOP_N=37\n", - "```\n", - "\n", - "
\n", - "4. Set INFO_DBUG to 1\n", - " \n", - "```\n", - " ./xmlchange INFO_DBUG=1\n", - "```\n", - "\n", - "
\n", - "5. Change wallclock time to use the maximum of the allowed wallclock on derecho:\n", - " \n", - "```\n", - " ./xmlchange --subgroup case.run JOB_WALLCLOCK_TIME=12:00:00\n", - " ./xmlchange --subgroup case.st_archive JOB_WALLCLOCK_TIME=6:00:00\n", - "```\n", - "\n", - "
\n", - "6. Submit (./case.submit) from your b.day2.1 case directory:\n", - "\n", - "```\n", - " ./case.submit\n", - "```\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a8dfc72-6b1a-4d20-8730-dd8515a2758c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/exercises/xml_runtype_exercise.ipynb b/notebooks_copy/modifications/xml/exercises/xml_runtype_exercise.ipynb deleted file mode 100644 index 7943d41ab..000000000 --- a/notebooks_copy/modifications/xml/exercises/xml_runtype_exercise.ipynb +++ /dev/null @@ -1,169 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d8c91f63-5ac2-46e2-8051-a0ee087f62a5", - "metadata": {}, - "source": [ - "# Exercise 2: Modify run type " - ] - }, - { - "cell_type": "markdown", - "id": "7ca45013-1ec6-43e6-94e8-3006ac7f9be1", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Exercise: Modify the `env_run.xml` file

\n", - " \n", - "Branch from the end of Exercise 1 Part I to create a new case named \"b.day2.2\". \n", - "Double the ocean coupling frequency (`OCN_NCPL`). Include an initial file as output data. Run for 1 month, then restart for 1 month. \n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c289a358-fb18-4bb2-b087-33ea5d666aa6", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Hint!\n", - " \n", - "\n", - "1. For a branch run, we need to set several variables related to the reference case in `env_run.xml`. To find variables applied to reference case (Branch and Hybrid runs), use ./xmlquery -p REF.\n", - " \n", - "2. Ocean coupling frequency needs to be changed in `env_run.xml`, controlled by the variable `OCN_NCPL`. What is the default value of `OCN_NCPL`? Use `xmlquery` to find out! You may also take a look at the variable description in `env_run.xml` to learn more about the variable.\n", - " \n", - "3. Make sure to update the walltime to match the run length.\n", - " \n", - "4. Don't forget to put restart files into the run directory `$RUNDIR`.\n", - " \n", - "5. To include an initial file as output, we will need to modify the variable `inithist` in the atmospehre namelist `user_nl_cam`. We will learn more about this in the next chapter namelist modification. \n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "a7917422-93d0-473d-9950-68a39c35c41e", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution\n", - " \n", - "\n", - "
\n", - "1. Create your new case with the command:\n", - " \n", - "```\n", - " cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - " ./create_newcase --case /glade/work/$USER/cases/b.day2.2 --res f19_g17 --compset B1850\n", - "```\n", - "
\n", - "\n", - "
\n", - "2. In your case directory, change the run type to branch, and change the associated RUN_* variables:\n", - "\n", - "```\n", - " cd /glade/work/$USER/cases/b.day2.2\n", - " ./xmlchange RUN_TYPE=branch\n", - " ./xmlchange RUN_REFCASE=b.day2.1,RUN_REFDATE=0001-04-01,GET_REFCASE=FALSE\n", - "```\n", - "\n", - "
\n", - "3. Set run time variables: \n", - " \n", - "```\n", - " ./xmlchange STOP_OPTION=nmonths,STOP_N=1,RESUBMIT=1,JOB_WALLCLOCK_TIME=02:00:00\n", - "``` \n", - "\n", - "
\n", - "4. Double ocean coupling frequency in `env_run.xml`:\n", - " \n", - "```\n", - " ./xmlchange OCN_NCPL=48\n", - "```\n", - "\n", - "
\n", - "5. Set up the case, put the initial and restart files in the `$RUNDIR`:\n", - " \n", - "```\n", - " ./case.setup\n", - " cp /glade/derecho/scratch/$USER/archive/b.day2.1/rest/0001-04-01-00000/* /glade/derecho/scratch/$USER/b.day2.2/run/\n", - "```\n", - " \n", - "
\n", - "6. To include an initial file at the end of the run, add the following lines to file `user_nl_cam`:\n", - "\n", - "``` \n", - " inithist='ENDOFRUN'\n", - "```\n", - "\n", - "
\n", - "7. Run ./preview_namelists:\n", - "\n", - "```\n", - " ./preview_namelists\n", - "```\n", - " \n", - "
\n", - "6. Carry on to build and submit the run:\n", - " \n", - "```\n", - " qcmd -- ./case.build\n", - " ./case.submit\n", - "```\n", - "
\n", - "\n", - "Remember that `qcmd` is used on derecho only.\n", - " \n", - "After the job completes, go to the short term archive space and explore.\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30e887f9-f5fb-4cce-8f3b-9fdc7386f199", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/exercises/xml_timestep_exercise.ipynb b/notebooks_copy/modifications/xml/exercises/xml_timestep_exercise.ipynb deleted file mode 100644 index f90017b0a..000000000 --- a/notebooks_copy/modifications/xml/exercises/xml_timestep_exercise.ipynb +++ /dev/null @@ -1,135 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bd28c148-b907-489d-9892-068ed48b11d9", - "metadata": {}, - "source": [ - "# Exercise 3: Change physics timestep" - ] - }, - { - "cell_type": "markdown", - "id": "dddd8ba3-aa00-451c-938f-35b3927a016f", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Exercise: Change physics timestep in CAM and CLM

\n", - "\n", - "**Hybrid** start a fully coupled run under pre-Industrial conditions with a resolution of f19_g17. Use restart and initial files from Exercise 2. Change the physics timestep in the atmosphere and land to 1200 seconds (default is 1800). Run 5 days.\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "f2dacddc-4f8f-4fb5-9664-65ab2618f97f", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Hint!\n", - " \n", - "\n", - "The atmosphere physics timestep is determined by the base period of coupling `NCPL_BASE_PERIOD` and the coupling frequency `ATM_NCPL`. \n", - "Find out their default values using xmlquery.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "cb35e292-fcbb-45a6-9a28-f02a80df83a6", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution\n", - " \n", - "\n", - "
\n", - "1. From the script directory, create your case:\n", - " \n", - "```\n", - " cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - " ./create_newcase --case /glade/work/$USER/cases/b.day2.3 --res f19_g17 --compset B1850\n", - "```\n", - " \n", - "
\n", - "2. In your case directory, change run type and runtime variables:\n", - " \n", - "```\n", - " cd /glade/work/$USER/cases/b.day2.3\n", - " ./xmlchange RUN_TYPE=hybrid,RUN_REFCASE=b.day2.2,RUN_REFDATE=0001-06-01,GET_REFCASE=FALSE,JOB_WALLCLOCK_TIME=2:00:00\n", - "```\n", - " \n", - "
\n", - "3. Change the physics timestep for CAM: \n", - " \n", - "```\n", - " ./xmlchange ATM_NCPL=72\n", - "```\n", - "(Why `ATM_NCPL` = 72? Do the math).\n", - "\n", - "
\n", - "4. Set up the case and position your initial and restart files in the `$RUNDIR`:\n", - " \n", - "```\n", - " ./case.setup\n", - " cp /glade/derecho/scratch/$USER/archive/b.day2.2/rest/0001-06-01-00000/* /glade/derecho/scratch/$USER/b.day2.3/run/\n", - "```\n", - " \n", - "
\n", - "5. Continue to build and submit: \n", - " \n", - "```\n", - " qcmd -- ./case.build\n", - " ./case.submit\n", - "```\n", - " \n", - "Remember that `qcmd` is used on derecho only.\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd58b568-cd97-4a59-bd26-a6c13f3b0ba3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/model_control_files.ipynb b/notebooks_copy/modifications/xml/model_control_files.ipynb deleted file mode 100644 index 6580967b1..000000000 --- a/notebooks_copy/modifications/xml/model_control_files.ipynb +++ /dev/null @@ -1,435 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# How to Modify XML Files (*.xml)\n", - "\n", - "Now let's learn how to edit and search for variables in an `*.xml` file\n", - "\n", - "We will use **`xmlchange`** to modify xml variables and use **`xmlquery`** to search xml variables. Note that you _can_ change XML files manually, but using the `xmlchange` script prevents XML errors and ensures there is a record of your changes in the `CaseStatus` file.\n", - "\n", - "For the examples below, you can follow along by going to your `CASEROOT` directory b1850.basics that you completed in the Basics section." - ] - }, - { - "cell_type": "markdown", - "id": "0f82a5d4-62f5-462f-b162-bf4854a5e8d0", - "metadata": {}, - "source": [ - "***\n" - ] - }, - { - "cell_type": "markdown", - "id": "37497e69-075a-4099-a65d-e1c5cf91086e", - "metadata": {}, - "source": [ - "## 1. Editing with **`xmlchange`**" - ] - }, - { - "cell_type": "markdown", - "id": "c1f5f422-194f-4bc9-85a1-5610a266b6c6", - "metadata": {}, - "source": [ - "When modifying an `*.xml` file, we highly recommend using the **`xmlchange`** tool. This is done using the syntax `./xmlchange VARIABLE=VALUE` in your case directory. " - ] - }, - { - "cell_type": "markdown", - "id": "057cc9f0-14fc-46f7-b3a5-9eca551dbe16", - "metadata": {}, - "source": [ - "The xml files can also be edited manually with your favorate text editor (vi, emacs, etc.), but users should take care not to introduce any formatting errors that could lead to incomplete environment variable settings. You can use your text editor to take a look at the xml files -- you will find descriptions of each variable, which can be very helpful in understanding what they control. " - ] - }, - { - "cell_type": "markdown", - "id": "2c4a7c72-2021-445f-80e4-84539d782e6f", - "metadata": {}, - "source": [ - "Here is an example for using `xmlchange`. If you want to change the length of your run to one month, edit `env_run.xml` via the `xmlchange` tool by typing on the command line: \n", - "\n", - "```\n", - "./xmlchange STOP_OPTION=nmonths,STOP_N=1\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "3221a454-5c83-40c0-ab4e-744a63fa9971", - "metadata": {}, - "source": [ - "
\n", - " \n", - " \n", - " \n", - " Tips!\n", - " \n", - " \n", - "When using xmlchange to modify multiple environment variables, use comma (and no space!) in between variables.
\n", - "You can always find help by typing ``./xmlchange --help``\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "2e2dbbae-0375-49d1-9655-49b2eb5df884", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - "Click here for example output of ``./xmlchange --help``
\n", - "\n", - "```\n", - "./xmlchange --help\n", - "```\n", - " \n", - "
\n", - " \n", - "Output:
\n", - "\n", - "```\n", - "usage: xmlchange [-h] [-d] [-v] [-s] [--caseroot CASEROOT] [--append]\n", - " [--subgroup SUBGROUP] [--id ID] [--val VAL] [--file FILE]\n", - " [--delimiter DELIMITER] [--dryrun] [--noecho] [-f]\n", - " [-loglevel LOGLEVEL]\n", - " [listofsettings]\n", - "\n", - "Allows changing variables in env_*xml files via a command-line interface.\n", - "\n", - "This provides two main benefits over editing the xml files by hand:\n", - " - Settings are checked immediately for validity\n", - " - Settings are echoed to the CaseStatus file, providing a \"paper trail\" of\n", - " changes made by the user.\n", - "\n", - "Examples:\n", - "\n", - " To set a single variable:\n", - " ./xmlchange REST_N=4\n", - "\n", - " To set multiple variables at once:\n", - " ./xmlchange REST_OPTION=ndays,REST_N=4\n", - "\n", - " Alternative syntax (no longer recommended, but supported for backwards\n", - " compatibility; only works for a single variable at a time):\n", - " ./xmlchange --id REST_N --val 4\n", - "\n", - " Several xml variables that have settings for each component have somewhat special treatment.\n", - " The variables that this currently applies to are:\n", - " NTASKS, NTHRDS, ROOTPE, PIO_TYPENAME, PIO_STRIDE, PIO_NUMTASKS\n", - " For example, to set the number of tasks for all components to 16, use:\n", - " ./xmlchange NTASKS=16\n", - " To set just the number of tasks for the atm component, use:\n", - " ./xmlchange NTASKS_ATM=16\n", - "\n", - " The CIME case xml variables are grouped together in xml elements .\n", - " This is done to associate together xml variables with common features.\n", - " Most variables are only associated with one group. However, in env_batch.xml,\n", - " there are also xml variables that are associated with each potential batch job.\n", - " For these variables, the '--subgroup' option may be used to specify a particular\n", - " group for which the variable's value will be adjusted.\n", - " \n", - "...\n", - "\n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "c1ce0178-094a-4f2d-9a8a-d9764b427a0a", - "metadata": {}, - "source": [ - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "bfcc9eca-86f5-4ad0-a481-3f8b481e5a6c", - "metadata": {}, - "source": [ - "
\n", - "Evaluate your understanding\n", - "\n", - "If you want to manually resubmit an initial case that previously had a `CONTINUE_RUN` value of `FALSE`, how do you change it to `TRUE`?\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "bab2a8f3-4cee-4bba-a8fe-570ac306871d", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n", - "Click here for the solution
\n", - "\n", - "Use xmlchange to modify the variable value with the command:\n", - "``` \n", - " ./xmlchange CONTINUE_RUN=TRUE \n", - "```\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "b4cdce13-52a4-4dcc-bd19-93db33339402", - "metadata": {}, - "source": [ - "
\n", - "\n", - "***" - ] - }, - { - "cell_type": "markdown", - "id": "9dcd9f04-8a62-40bb-8901-c709edab0764", - "metadata": {}, - "source": [ - "## 2. Searching xml variables with **`xmlquery`**" - ] - }, - { - "cell_type": "markdown", - "id": "85e1421c-8386-4d7b-aa51-a0d0240d3a8b", - "metadata": {}, - "source": [ - "We recommend using the **`xmlquery`** tool in your case directory to query xml variables." - ] - }, - { - "cell_type": "markdown", - "id": "01d3cdf9-7eb5-4dc4-9745-1efb571f0efb", - "metadata": {}, - "source": [ - "For example, to find out the run type of your job, search for xml variable `RUN_TYPE`: \n", - "\n", - "```\n", - "./xmlquery RUN_TYPE\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "67864bd6-d229-4b28-b976-6e18883b042b", - "metadata": {}, - "source": [ - "
\n", - " \n", - " \n", - " \n", - " Tips!\n", - " \n", - "\n", - "You can always find help by typing `./xmlquery --help` \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b01a2599-923a-4a78-ae97-42904f679e52", - "metadata": {}, - "source": [ - "This will return the default `RUN_TYPE` value:\n", - ">RUN_TYPE: hybrid" - ] - }, - { - "cell_type": "markdown", - "id": "e83f2c2d-5a57-447a-add3-f0d37ec74bb3", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9adc30a9-549b-4a68-a25a-743fab747aa6", - "metadata": {}, - "source": [ - "
\n", - "Evaluate your understanding\n", - "\n", - "Let's check if the variables in the previous exercise are properly modified.\n", - " \n", - "Query for the value of `STOP_N`, `STOP_OPTION`, and `CONTINUE_RUN`.\n", - "\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "2a664275-f370-4438-af52-7ed9c8526517", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution\n", - " \n", - " \n", - "Use `xmlquery` to search the variables with the command:\n", - "
\n", - "```\n", - " ./xmlquery STOP_N,STOP_OPTION,CONTINUE_RUN \n", - "```\n", - " \n", - "
\n", - "If you see: \n", - "\n", - "```\n", - ">STOP_N: 1 \n", - ">STOP_OPTION: nmonths \n", - ">CONTINUE_RUN: TRUE \n", - "```\n", - "
\n", - " Great! You have successfully queried these variables. \n", - "
\n", - "\n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "ba5e3006-7d70-4e3f-bdf9-351f766bd7ca", - "metadata": {}, - "source": [ - "
\n", - "\n", - "***\n" - ] - }, - { - "cell_type": "markdown", - "id": "55f9f962-c8ae-4685-ae6d-9d4a75f1d5b5", - "metadata": {}, - "source": [ - "## 3. Use the subgroup functionality of **`xmlchange`**" - ] - }, - { - "cell_type": "markdown", - "id": "a3e50d72-dac9-42be-95a7-71e1d8759ea1", - "metadata": {}, - "source": [ - "Now let's first try to find out the wallclock time of your job, search for xml variable `JOB_WALLCLOCK_TIME`: \n", - "\n", - "```\n", - "./xmlquery JOB_WALLCLOCK_TIME\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "f190bd81-be55-481a-820a-522d7f77fb08", - "metadata": {}, - "source": [ - "
\n", - " \n", - " \n", - " \n", - " Tips!\n", - " \n", - " \n", - "If you are unsure about the full name of the xml variable, you can query variables with a partial match, using `--partial-match` or `-p`. \n", - " \n", - "For example: `./xmlquery -p WALLCLOCK`\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "1279be11-3868-4aac-9132-042d7efd515b", - "metadata": {}, - "source": [ - "
\n", - "You will find that the query of wallclock time returns multiple instances:" - ] - }, - { - "cell_type": "markdown", - "id": "fea58693-2073-42e9-a219-568119f870f0", - "metadata": {}, - "source": [ - ">Results in group case.run
\n", - ">JOB_WALLCLOCK_TIME: 00:30:00\n", - "\n", - ">Results in group case.st_archive
\n", - ">JOB_WALLCLOCK_TIME: 00:30:00" - ] - }, - { - "cell_type": "markdown", - "id": "84969b59-8664-4104-a292-76c5e6d36d7f", - "metadata": {}, - "source": [ - "The variable `JOB-WALLCLOCK_TIME` has two instances in two different subgroups: `case.run` and `case.st_archive`.
\n", - "\n", - "Then, how do we specify which instance we want to modify?\n", - "\n", - "For variables that have multiple instances, we can use the **\"subgroups\"** functionality in `xmlchange`.\n", - "For example, if we want to change the default `JOB_WALLCLOCK_TIME` from 30 minutes to 1 hour for the short term archiver subgroup, type in the following command:\n", - "\n", - "```\n", - "./xmlchange --subgroup case.st_archive JOB_WALLCLOCK_TIME=01:00:00\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "be11b2ed-b3a5-49ea-a350-bc4fa7f2e609", - "metadata": {}, - "source": [ - "Try it yourself! Type in the command above and use `xmlquery` to check if your modifications are correctly applied. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1ed48db-f145-4e5c-a656-8249053833f7", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/modify_run_type.ipynb b/notebooks_copy/modifications/xml/modify_run_type.ipynb deleted file mode 100644 index 90d3f43e5..000000000 --- a/notebooks_copy/modifications/xml/modify_run_type.ipynb +++ /dev/null @@ -1,57 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Modifying the Type of Run \n", - "\n", - "CESM has three types of initial runs:\n", - "- startup\n", - "- branch\n", - "- hybrid\n" - ] - }, - { - "cell_type": "markdown", - "id": "fb99d6cf-09ee-45da-9af8-4d404709378a", - "metadata": {}, - "source": [ - "\n", - "The xml variable `$RUN_TYPE` determines the initialization type. \n", - "\n", - "Note that the `RUN_TYPE` setting is ***only important for the initial run*** of a production run when the `$CONTINUE_RUN` variable is set to `FALSE`. After the initial run, the `$CONTINUE_RUN` variable is set to `TRUE`, and the model restarts exactly using input files in a case, date, and bit-for-bit continuous fashion. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9447be66-ca1f-4b05-b93c-4f1a0bb303ad", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/modify_run_type/hybrid_branch_restart.ipynb b/notebooks_copy/modifications/xml/modify_run_type/hybrid_branch_restart.ipynb deleted file mode 100644 index 6a1914580..000000000 --- a/notebooks_copy/modifications/xml/modify_run_type/hybrid_branch_restart.ipynb +++ /dev/null @@ -1,311 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Hybrid, Branch and Startup \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "5ffbef19-73bc-43d9-806b-2b522129f838", - "metadata": {}, - "source": [ - "The CESM model can be initialized in one of three ways: startup, hybrid and branch. These are set with the variable **`$RUN_TYPE`** in `env_run.xml`. \n", - "\n", - "
\n", - "\n", - "***\n" - ] - }, - { - "cell_type": "markdown", - "id": "5f390f8b-76ec-4a3a-8273-a1f2828b4ac2", - "metadata": {}, - "source": [ - "## 1. The three run types\n", - "\n", - "\n", - "#### - STARTUP\n", - "\n", - "- In a startup run (the default), all model components are initialized from the basic **default initial conditions**. The coupler does not need an initial file. \n", - "\n", - "\n", - "\n", - "#### - BRANCH\n", - "\n", - "- In a branch run, all components are initialized using **a consistent set of restart files from a previous run** (determined by the [`$RUN_REFCASE`](./run_variables.ipynb) and [`$RUN_REFDATE`](./run_variables.ipynb) variables in env_run.xml). \n", - "- The case name is generally changed for a branch run, although it does not have to be. \n", - "- In a branch run, setting [`$RUN_STARTDATE`](./run_variables.ipynb) is ignored because the model components obtain the start date from their restart datasets. Therefore, **the start date cannot be changed for a branch run**. This is the same mechanism that is used for performing a restart run (where `$CONTINUE_RUN` is set to TRUE in the env_run.xml file).\n", - "- Branch runs are typically used when sensitivity or parameter studies are required, or when settings for history file output streams need to be modified while still maintaining **bit-for-bit reproducibility**.\n", - "\n", - "\n", - "\n", - "\n", - "#### - HYBRID\n", - "\n", - "- A hybrid run indicates that CESM is initialized more like a startup, but uses initialization datasets from a previous case. This is somewhat analogous to a branch run with relaxed restart constraints. \n", - "- A hybrid run allows users to bring together combinations of initial/restart files from a previous case (specified by [`$RUN_REFCASE`](./run_variables.ipynb)) at a given model output date (specified by [`$RUN_REFDATE`](./run_variables.ipynb)). Unlike a branch run, the starting date of a hybrid run (specified by [`$RUN_STARTDATE`](./run_variables.ipynb)) can be modified relative to the reference case. \n", - "- In a hybrid run, the model does not continue in a bit-for-bit fashion with respect to the reference case. The resulting climate, however, should be continuous provided that no model source code or namelists are changed in the hybrid run. \n", - "- The atmosphere is initialized from initial condition files generated by a user-specified CESM simulation. The land, runoff, ocean and ice are initialized from restart files generated by a user-specified CESM simulation. No coupler file is needed\n", - "\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "62696cba-7150-4f3c-866e-d6ac374a96b8", - "metadata": {}, - "source": [ - "It is important to remember that the **`$RUN_TYPE` variable is only relevant when a run is initialized**. Let's again take a look at the [exercises](../run_length/changing_run_length.ipynb) we did in the chapter of [Changing run length](../run_length/changing_run_length.ipynb):\n" - ] - }, - { - "cell_type": "markdown", - "id": "823344b5-44a0-4f0d-a501-6b9ba8f9608e", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "5e0f253a-2f0e-40c3-b97a-661b15608aa2", - "metadata": { - "tags": [] - }, - "source": [ - "
\n", - "Evaluate your understanding\n", - "\n", - "The tutorial version of the CESM model on derecho simulates ~10 model years per wallclock day. The maximum wallclock request is 12 hours.\n", - "If you want to run the model for 100 years, what values should be set for `STOP_OPTION`, `STOP_N` and `RESUBMIT`? \n", - "\n", - "We worked out that for a total of 100 years, we will need 20 submissions. We can set:\n", - "\n", - "```\n", - "STOP_OPTION = 'nyears', STOP_N = 5, RESUBMIT = 19\n", - "```\n", - "
\n", - " \n", - "Now, **if you want this run to be a hybrid run, what variables should you modify, and at which stage?**\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "44222574-7e88-431d-911d-6c96a9bfde2e", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Hint!\n", - " \n", - " \n", - "The `$RUN_TYPE` variable is only important at the initial run when `CONTINUE_RUN` is set to `FALSE`. \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "1e2de2d0-90eb-4c6c-b5df-636dfc79d623", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution\n", - " \n", - "\n", - "Set `RUN_TYPE='hybrid'` in env_run.xml before submitting the initial run. \n", - "\n", - "Bonus question:
\n", - " \n", - "What happens if you set `run_type='branch'` after the 2nd resubmission when the `CONTINUE_RUN=TRUE`?
\n", - " \n", - "Answer: Nothing changes, because `RUN_TYPE` is only relevant for an initial run when `CONTINUE_RUN` is set to `FALSE`! \n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "7ef2ad7b-8e69-40cc-97df-165d0060ae45", - "metadata": {}, - "source": [ - "
\n", - "\n", - "One more!\n" - ] - }, - { - "cell_type": "markdown", - "id": "e38db72e-ab1a-4600-b5c5-bdec1586e645", - "metadata": {}, - "source": [ - "
\n", - "Evaluate your understanding\n", - "\n", - "\n", - "In the exercise above, if you have set `RUN_TYPE='hybrid'` and `RESUBMIT=19` before the initial run, what are the value of `CONTINUE_RUN`, `RESUBMIT`, and `RUN_TYPE` at the time of:\n", - "- the initial submission of 5 years\n", - "- the next submission of 5 years\n", - "- the 3rd run (2nd resubmission) of 5 years?\n", - " \n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "78e21916-454a-48ce-a423-be0cefbe6dae", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution \n", - " \n", - " \n", - "- **the initial submission of 5 years**: \n", - " \n", - " ``` \n", - " CONTINUE_RUN=FALSE, RESUBMIT=19, RUN_TYPE='hybrid' \n", - " ```\n", - " because when the run is first initialized, `CONTINUE_RUN=FALSE`
\n", - "
\n", - " \n", - "- **the next submission of 5 years**: \n", - " ```\n", - " CONTINUE_RUN=TRUE, RESUBMIT=18, RUN_TYPE='hybrid'\n", - " ```\n", - " because `RESUBMIT>0`, `CONTINUE_RUN` will automatically switch to `TRUE` after the initial run, but `RUN_TYPE` won't change.
\n", - "
\n", - " \n", - "- **the 3rd run (2nd resubmission) of 5 years**: \n", - " ```\n", - " CONTINUE_RUN=TRUE, RESUBMIT=17, RUN_TYPE='hybrid' \n", - " ```\n", - " `CONTINUE_RUN` stays to be `TRUE`, `RESUBMIT` decreases by 1, `RUN_TYPE` won't change.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "9ff63e65-0ae0-4d37-828f-cae55f5c1a7a", - "metadata": {}, - "source": [ - "
\n", - "\n", - "***\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "fca3acd9-afcf-4d36-a287-4b5220f366d7", - "metadata": {}, - "source": [ - "## 2. When to use branch vs. hybrid?" - ] - }, - { - "cell_type": "markdown", - "id": "28612407-0b64-4a29-a1eb-9495203ea223", - "metadata": {}, - "source": [ - "Branch and hybrid runs are useful if you have an experiment which only slightly differs from your control, but you want to make a slight modification, add history output, or start your simulation from a CESM spun-up initial state and maintaining an exact restart (which mimics what the model would do if it had kept running in the original setup).\n", - "\n", - "- **Use a hybrid run**: for most applications where you do NOT need bit for bit restart. You CAN specify a new start date for your model run.\n", - "\n", - "- **Use a branch run**: only for applications which require exact restart. It is typically used when sensitivity or parameter studies are required, or when settings for history file output streams need to be modified while still maintaining bit-for-bit reproducibility. You CANNOT specify a new start date for your model run. It will be assigned by the reference case ([`$RUN_REFDATE`](./run_variables.ipynb)). " - ] - }, - { - "cell_type": "markdown", - "id": "253bec5a-bd70-45a1-a63e-1a935a6caaba", - "metadata": {}, - "source": [ - "
\n", - "\n", - "***\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "1cee2835-49f8-4e36-92dc-7ccca26d1354", - "metadata": {}, - "source": [ - "## 3. Restart files for branch and hybrid runs" - ] - }, - { - "cell_type": "markdown", - "id": "656226b3-9959-4e47-b047-725c3ea64969", - "metadata": {}, - "source": [ - "For runs that are initialized as hybrid or branch runs, we will need restart/initial files from previous model runs (as specified by the variables, [`$RUN_REFCASE`](./run_variables.ipynb) and [`$RUN_REFDATE`](./run_variables.ipynb)). See the Chapter [Run variables related to run type](./run_variables.ipynb) for details. \n", - "\n", - "\n", - "Let's take a look back at the [Chapter on restart files](../run_length/restarting.ipynb) to refresh our memory!\n" - ] - }, - { - "cell_type": "markdown", - "id": "1dfd05c3-12d5-4980-b945-9fe7bcae452b", - "metadata": {}, - "source": [ - "It is important to note that these required files must be prestaged by the user to the case run directory (`$RUNDIR`) before the model run starts. This is normally done by just copying the contents of the relevant `$RUN_REFCASE/rest/$RUN_REFDATE.00000` directory.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71ae1b6e-8bf1-4b17-a00f-86f06f4577ff", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/modify_run_type/run_variables.ipynb b/notebooks_copy/modifications/xml/modify_run_type/run_variables.ipynb deleted file mode 100644 index 3ca13720a..000000000 --- a/notebooks_copy/modifications/xml/modify_run_type/run_variables.ipynb +++ /dev/null @@ -1,61 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Variables Related to Run Type\n", - "\n", - "A summary of the xml variables pertaining to run type in `env_run.xml`. \n", - "\n", - "
\n", - "\n", - "***\n" - ] - }, - { - "cell_type": "markdown", - "id": "a0652973-f463-4459-bcd1-0027c3cc2c9e", - "metadata": {}, - "source": [ - "- **`RUN_TYPE`** ==> Determines the model run initialization type. Only important for the initial run of a production run when the `$CONTINUE_RUN` variable is set to `FALSE`. Valid values are: 'startup', 'hybrid', 'branch'. \n", - "\n", - "- **`RUN_STARTDATE`** ==> Run start date (yyyy-mm-dd). Only used for startup or hybrid runs. \n", - "\n", - "- **`RUN_REFCASE`** ==> For hybrid or branch runs, name of the reference case you are starting from. \n", - "\n", - "- **`RUN_REFDATE`** ==> For hybrid or branch runs, date stamp (yyyy-mm-dd) of reference case you are starting from. \n", - "\n", - "- **`GET_REFCASE`** ==> Flag for automatically prestaging the refcase restart dataset. If `TRUE`, then the refcase data is prestaged into the executable directory. If `FALSE`, the user need to manually put the restart files into the `$RUNDIR`. " - ] - }, - { - "cell_type": "markdown", - "id": "26abab24-5108-4006-8f31-bf696f837aa4", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/other_xml_variables.ipynb b/notebooks_copy/modifications/xml/other_xml_variables.ipynb deleted file mode 100644 index 0e27b5636..000000000 --- a/notebooks_copy/modifications/xml/other_xml_variables.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8f61eb16-f182-4b94-9959-5fb517a5bc7e", - "metadata": {}, - "source": [ - "# Other Useful XML Variables " - ] - }, - { - "cell_type": "markdown", - "id": "b616dc75-7fd6-46db-8e96-27278c7ec731", - "metadata": {}, - "source": [ - "Here are some more xml variables in `env_run.xml` that you may encounter:\n", - "\n", - "- `INFO_DBUG` ==> sets the level of stdout (standard out) print statements. This variable is useful for debugging. If debugging, a higher value may be set. \n", - "- `DOUT_S` ==> turns on short-term archiving. `DOUT_S` is set to `TRUE` by default. \n", - "- `HIST_OPTION` ==> sets driver snapshot history file frequency. \n", - "- `CCSM_CO2_PPMV` ==> CO2 value to be propogated to the ocean component POP and the land component CLM, when `OCN_CO2_TYPE` and `CLM_CO2_TYPE` are set to `'constant'`. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d74f86e-c1dc-45a3-845f-2d0200fbedbd", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/overview.ipynb b/notebooks_copy/modifications/xml/overview.ipynb deleted file mode 100644 index 37d127221..000000000 --- a/notebooks_copy/modifications/xml/overview.ipynb +++ /dev/null @@ -1,132 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": { - "tags": [] - }, - "source": [ - "# Overview \n", - "\n", - "What is an XML file, and how do we use it in CESM?" - ] - }, - { - "cell_type": "markdown", - "id": "55a68c69-196d-42c3-ae01-ad55f10daa6d", - "metadata": {}, - "source": [ - "***" - ] - }, - { - "cell_type": "markdown", - "id": "0637e559-0f3c-46b3-bc80-f7fe1081387a", - "metadata": {}, - "source": [ - "## 1. What is an XML file?\n", - "\n", - "XML (Extensible Markup Language) is a markup language that defines a syntax for encoding documents that both humans and machines could read. It is used to describe data in a structured way and defines how the document should be stored and transported." - ] - }, - { - "cell_type": "markdown", - "id": "027b5e63-e318-411e-b854-9109fc0491ce", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Additional information on XML files](https://www.howtogeek.com/357092/what-is-an-xml-file-and-how-do-i-open-one/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "fa35fe32-ea6a-4e3e-9a9a-070697bca22f", - "metadata": {}, - "source": [ - "\n", - "***" - ] - }, - { - "cell_type": "markdown", - "id": "57f433ea-3424-447f-b6e7-68673a40d24d", - "metadata": {}, - "source": [ - "## 2. CESM XML files and CESM environment variables" - ] - }, - { - "cell_type": "markdown", - "id": "0c67affb-108e-4005-8329-ccd19ef87eda", - "metadata": {}, - "source": [ - "CESM cases are customized, built and run largely through setting what CESM calls **\"environment variables\"**. These actually appear to the user as variables defined in xml files. \n", - "\n", - "\n", - "These files appear in the case directory (`$CASEROOT`) once a case is created and are named something like **`env_*.xml`**. We control how we compile and run the model with these files." - ] - }, - { - "cell_type": "markdown", - "id": "e58d2ab9-957f-4078-a7eb-5b7091c28616", - "metadata": {}, - "source": [ - "## XML file locations\n", - "\n", - "![CESM xml files](../../../images/modifications/CESM2_xml_files.png)\n", - "*

Figure: Overview of the XML file locations

*\n", - "For the current tutorial on derecho, the paths are:\n", - "- `$CASEROOT` = `/glade/work/$USER/cases/$CASE`" - ] - }, - { - "cell_type": "markdown", - "id": "cce5ecd7-e613-47bd-8c4b-1e547b2a75ae", - "metadata": {}, - "source": [ - "There are multiple **`env_*.xml`** files in the `$CASEROOT` directory:\n", - "\n", - "- `env_archive.xml`: specifies rules for short term archive script `case.st_archive`\n", - "- `env_batch.xml`: specifies batch specific settings used in `case.submit` script\n", - "- `env_build.xml`: specifies build information used in the `case.build` script\n", - "- `env_case.xml`: set by create_newcase and **cannot** be modified\n", - "- `env_mach_pes.xml`: specifies PE layout on NCAR HPC for components and used by `case.run` script \n", - "- `env_mach_specific.xml`: specifies machine specific information used in `case.build` script\n", - "- `env_run.xml`: sets run time information (such as length of run, number of submissions, …)\n" - ] - }, - { - "cell_type": "markdown", - "id": "58df9e9f-ef86-4a65-91c2-4b829b33a86e", - "metadata": {}, - "source": [ - "In this section, we will learn and practice **modifying CESM run length and run type using `env_run.xml`**!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/physics_timestep.ipynb b/notebooks_copy/modifications/xml/physics_timestep.ipynb deleted file mode 100644 index 3262199d0..000000000 --- a/notebooks_copy/modifications/xml/physics_timestep.ipynb +++ /dev/null @@ -1,69 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Physics Timestep\n", - "\n", - "**Where and When to Change Time Steps**:\n", - "\n", - "When the model crashes due to large, temporary instabilities, one method to overcome the problem is to change the time step.\n", - "This is typically done in either the atmosphere or ocean components. \n", - "\n", - "- For atmosphere and land component (CAM/CLM): change `ATM_NCPL` in `env_run.xml`.
\n", - "- For ocean (POP): change `dt_count` in POP namelist. \n", - "\n", - "
\n", - "\n", - "***\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "dc35cf0e-f3be-4a26-b2f0-0b716ca440f1", - "metadata": {}, - "source": [ - "- **For the atmosphere component (CAM)**, the physics time step is set by `ATM_NCPL` and `NCPL_BASE_PERIOD`in `env_run.xml`. `NCPL_BASE_PERIOD` specifies base period associated with coupling frequency, the default value is \"day\". The `ATM_NCPL` variable specifies the number of coupling intervals per `NCPL_BASE_PERIOD` between the atmosphere/land and the coupled system. Based on these two variables, the scripts will automatically compute the time step for the atmosphere and land and populate the namelist files accordingly. For example, if `NCPL_BASE_PERIOD` is set to \"day\", and `ATM_NCPL` is 48, then the timestep is 1800 seconds. \n", - "\n", - "- **For the land component (CLM)**, the physics ime step is the same as the CAM time step; this is automatically set with the CAM time step via `ATM_NCPL`. You cannot set this separately.\n", - "\n", - "- **For the ocean component (POP2)**, the physics time step is changed in the `user_nl_pop` file and is based on `$OCN_NCPL` (found in `env_run.xml`), `dt_count` , and `dt_option`. The default `dt_option` is `“steps_per_day”`.\n", - "\n", - "- **For the sea ice component (CICE)**, the physics time step is set by the coupling interval variable `$ICE_NCPL` found in env_run.xml. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d227999f-f3c0-4ae7-adbc-558cc7a6f287", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/run_length.ipynb b/notebooks_copy/modifications/xml/run_length.ipynb deleted file mode 100644 index d727e50fc..000000000 --- a/notebooks_copy/modifications/xml/run_length.ipynb +++ /dev/null @@ -1,46 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Controlling Run Length \n", - "\n", - "The length of your model run is controlled by several run time variables in the **`env_run.xml`** file. \n", - "These variables may be modified at the initialization of the model run and during the course of the model run. \n", - "\n", - "These variables comprise coupler namelist settings for the model stop time, model restart frequency, coupler history frequency and a flag to determine if the run should be flagged as a continuation run." - ] - }, - { - "cell_type": "markdown", - "id": "b69bbda8-c906-420f-a782-7da8f223c8bc", - "metadata": {}, - "source": [ - "We will learn about customizing runtime settings to control starting, stopping, restarting and continuing a model run, and practice how to specify the run length of your long simulations. \n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/run_length/changing_run_length.ipynb b/notebooks_copy/modifications/xml/run_length/changing_run_length.ipynb deleted file mode 100644 index 06378c500..000000000 --- a/notebooks_copy/modifications/xml/run_length/changing_run_length.ipynb +++ /dev/null @@ -1,232 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Changing Run Length\n", - "\n", - "How to set run length for long simulations using variables `$STOP_OPTION`, `$STOP_N` and `$RESUBMIT`?" - ] - }, - { - "cell_type": "markdown", - "id": "555d7d60-109c-45ed-94e2-441bf3a7a93a", - "metadata": {}, - "source": [ - "## 1. Number of submissions and run length" - ] - }, - { - "cell_type": "markdown", - "id": "2730b8e5-def6-4ae3-bde0-bb6f0d0f5e02", - "metadata": {}, - "source": [ - "Recall that we can use [STOP_N and STOP_OPTION](../starting_and_stopping.ipynb) to control the run length of each batch job submission. " - ] - }, - { - "cell_type": "markdown", - "id": "9641f6ad-7a95-4cf1-8178-26a33ca1ecfb", - "metadata": {}, - "source": [ - "A typical long model simulation (say you want to run the model for 100 years) is comprised of many job submissions. This is because we have limited batch wallclock time for each job submission. For example, on derecho, the regular queue wallclock limit is 12 hours. " - ] - }, - { - "cell_type": "markdown", - "id": "dbf6d5d2-ad44-4ff9-8d8b-b18ee368cb6f", - "metadata": {}, - "source": [ - "We can specify **the number of times to resubmit the run using the `$RESUBMIT`** variable in **`env_run.xml`** to complete the long run. " - ] - }, - { - "cell_type": "markdown", - "id": "3db7b888-74cf-40dd-9931-e9e312029cf8", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "58076e6d-4ca6-442d-bd7a-6695e00e6b3d", - "metadata": {}, - "source": [ - "
\n", - "Evaluate your understanding\n", - "\n", - "The tutorial version of the CESM model on derecho simulates ~10 model years per wallclock day. The maximum wallclock request is 12 hours.\n", - "If you want to run the model for 100 years, what values should be set for `STOP_OPTION`, `STOP_N` and `RESUBMIT`?\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "2bbcde94-569d-40cd-bece-f98e511a5c3b", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Hint!\n", - " \n", - " \n", - "- How to set `STOP_N` and `STOP_OPTION` for **each submission**, given the wallclock limit?\n", - " \n", - "- How many times to resubmit the job to reach the 100 years?\n", - " \n", - "- The number of total submission = the initial submission + the number of resubmission.\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "caa0ef50-df17-4841-beaa-485c4b5be2c8", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution \n", - " \n", - "Assume we want to use the full 12 hours for each job submission.\n", - " \n", - "The model runs 10 years / wallclock day, which means that 12 hours would give us 5 years per job submission. \n", - " \n", - "For a total of 100 years, we will need 20 submissions. \n", - "\n", - "``` \n", - "STOP_OPTION='nyears', STOP_N=5, RESUBMIT=19\n", - "``` \n", - "so that initial run of 5 years + (19 resubmits x 5 years per job) = 100 years. \n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6149a7df-1f01-477a-b06a-f7d9196c1060", - "metadata": {}, - "source": [ - "***\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "0bfe03b7-bd83-4c43-a586-3692bc728dbb", - "metadata": {}, - "source": [ - "## 2. RESUBMIT and CONTINUE_RUN" - ] - }, - { - "cell_type": "markdown", - "id": "ba756e82-bbb4-46e5-a603-ff17507f3f8b", - "metadata": {}, - "source": [ - "In the exercise above, the first submission is the initial run, where `CONTINUE_RUN` is by default set to `FALSE`. When you want to continue the run after running the first 5 years, you will need to tell the model to continue by setting `CONTINUE_RUN=TRUE`. " - ] - }, - { - "cell_type": "markdown", - "id": "9af5ecdc-778b-49aa-8847-002f10194841", - "metadata": {}, - "source": [ - "If you have set **`RESUBMIT>0`, your script will automatically change `CONTINUE_RUN=TRUE`** after completion of the first submission for all subsequent submissions into the queue. \n" - ] - }, - { - "cell_type": "markdown", - "id": "fd397d89-a0d5-4a1d-859a-d2cf574af152", - "metadata": {}, - "source": [ - "
\n", - "Evaluate your understanding\n", - "\n", - "In the previous exercise, if we have set `RESUBMIT`=19 before the initial run, what are the value of `CONTINUE_RUN` and `RESUBMIT` at the time of:\n", - "- the initial submission of 5 years\n", - "- the next submission of 5 years\n", - "- the 3rd run (2nd resubmission) of 5 years?\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "fb184cde-0045-4b17-8530-5eb0d1ade77b", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution \n", - " \n", - " \n", - "- **the initial submission of 5 years**: \n", - " ```\n", - " CONTINUE_RUN=FALSE, RESUBMIT=19 \n", - " ```\n", - " because when the run is first initialized, `CONTINUE_RUN=FALSE`.\n", - "
\n", - "- **the next submission of 5 years**: \n", - " ```\n", - " CONTINUE_RUN=TRUE, RESUBMIT=18 \n", - " ```\n", - " because `RESUBMIT>0`, `CONTINUE_RUN` will automatically switch to `TRUE` after completing the initial run.\n", - "
\n", - " \n", - "- **the 3rd run (2nd resubmission) of 5 years**: \n", - " ```\n", - " CONTINUE_RUN=TRUE, RESUBMIT=17\n", - " ```\n", - " because `CONTINUE_RUN` stays to be `TRUE`, `RESUBMIT` decreases by 1.\n", - " \n", - " \n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "610c09ea-94e9-41ef-95b9-bfc537d81da1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/run_length/number_of_submissions.ipynb b/notebooks_copy/modifications/xml/run_length/number_of_submissions.ipynb deleted file mode 100644 index 4387df7be..000000000 --- a/notebooks_copy/modifications/xml/run_length/number_of_submissions.ipynb +++ /dev/null @@ -1,43 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Controlling the Number of Submissions \n", - "\n", - "and continue_run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e59cdc0f-21dc-430d-bdb9-1f9c9f2d7bcb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/run_length/restarting.ipynb b/notebooks_copy/modifications/xml/run_length/restarting.ipynb deleted file mode 100644 index 80ea1367e..000000000 --- a/notebooks_copy/modifications/xml/run_length/restarting.ipynb +++ /dev/null @@ -1,221 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Restarting a Run\n", - "\n", - "How to continue a run.\n", - "\n", - "***" - ] - }, - { - "cell_type": "markdown", - "id": "6dfbe426-d5b3-47fd-a4bd-faf6fb107f5f", - "metadata": {}, - "source": [ - "## 1. Restart files" - ] - }, - { - "cell_type": "markdown", - "id": "8746a209-be0f-4c0d-ad1c-168da2a7e4b6", - "metadata": {}, - "source": [ - "Restart files are written by each model component at intervals dictated by the driver via the setting of the `env_run.xml` variables, **`$REST_OPTION`** and **`$REST_N`**. The default values for these two variables are set to be the same as `$STOP_OPTION` and `$STOP_N`. In most cases, we do not modify these two variables.\n" - ] - }, - { - "cell_type": "markdown", - "id": "b514409e-4215-4627-a783-28fafc852211", - "metadata": {}, - "source": [ - "\n", - "Restart files allow the model to stop and then start again with **bit-for-bit exact** capability (i.e. the model output is exactly the same as if it had never been stopped). The driver coordinates the writing of restart files as well as the time evolution of the model. All components receive restart and stop information from the driver and write restarts or stop as specified by the driver.\n" - ] - }, - { - "cell_type": "markdown", - "id": "bfc93f04-70d4-4cb1-8e90-a9878cdca329", - "metadata": {}, - "source": [ - "Whenever a component writes a restart file, it also writes a restart pointer file of the form, **`rpointer.$component`** (i.e. `rpointer.atm`). The restart pointer file contains the restart filename that was just written by the component. Upon a restart, each component reads its restart pointer file to determine the filename(s) to read in order to continue the model run. As examples, the following pointer files will be created for a component set using full active model components.\n", - "\n", - "- rpointer.atm\n", - "- rpointer.drv\n", - "- rpointer.ice\n", - "- rpointer.lnd\n", - "- rpointer.rof\n", - "- rpointer.cism\n", - "- rpointer.ocn.ovf\n", - "- rpointer.ocn.restart\n" - ] - }, - { - "cell_type": "markdown", - "id": "93d66937-95c1-44cc-bbd3-39a798efc378", - "metadata": {}, - "source": [ - "
\n", - " \n", - " \n", - " \n", - " Tips!\n", - " \n", - " \n", - "1. Try using xmlquery to check the values of `REST_OPTION` and `REST_N`.\n", - "What do you find?
\n", - "2. Take a look at the restart files and restart pointer files in your archive directory (`$DOUT_S_ROOT/rest/yyyy-mm-dd-ssss/`) or run directory (`$RUNDIR`). What do they look like?\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "aca8e554-0df7-4de2-9059-9e1b2e33a590", - "metadata": {}, - "source": [ - "***\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6d6522b2-c30c-4b92-b2f4-c89ce0158dcf", - "metadata": {}, - "source": [ - "## 2. Continue a run" - ] - }, - { - "cell_type": "markdown", - "id": "1c0eb950-6238-4f37-a875-19950279ab4a", - "metadata": {}, - "source": [ - "\n", - "Recall that the flag variable `$CONTINUE_RUN` controls whether a model run is **initialized (FALSE)** or **continues a run (TRUE)**." - ] - }, - { - "cell_type": "markdown", - "id": "a3ff7ec3-941e-447f-b027-3f45fb3b8bb1", - "metadata": {}, - "source": [ - "In the case of our 1-month test run, we submited our initial job with `CONTINUE_RUN = FALSE` (because it was just initialized) and your `RUN_TYPE` (to startup, branch or hybrid). If the run has been finished and everything looks good, and we want to continue the run for another month, what do we do?\n", - "\n", - "We will need to use `xmlchange` to change `CONTINUE_RUN = TRUE` and submit the run again to carry on running the model. The model will use the restart files to continue our run with a bit-for-bit match, as if it had never been stopped. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "a1b40ec3-fdd5-45ea-aa09-74620e62b35c", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "id": "65d2fb52-18b5-43ae-87b5-866ec0c730ef", - "metadata": {}, - "source": [ - "
\n", - "Evaluate your understanding\n", - "\n", - "If we do not modify `CONTINUE_RUN=TRUE` and leave it as `FALSE`, what would happen after we submit the run again?\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "12239227-29af-4738-851b-b1eae99932e4", - "metadata": {}, - "source": [ - "
\n", - " \n", - "
\n", - " \n", - " \n", - " Click here for the solution \n", - " \n", - "\n", - "The model will run the previous month once again instead of carrying on to the next month!\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "43a8d53a-d66d-4c4a-a9fd-2313f4c23dfd", - "metadata": {}, - "source": [ - "The `$CONTINUE_RUN` flag will be automatically set to `TRUE` when the variable `RESUBMIT>0`. Learn more in the chapter [Changing Run Length](./changing_run_length.ipynb)." - ] - }, - { - "cell_type": "markdown", - "id": "707b12c1-e970-4097-99c3-f6fe3b1914c8", - "metadata": {}, - "source": [ - "
\n", - "\n", - "***\n" - ] - }, - { - "cell_type": "markdown", - "id": "2a816fcb-9242-4d6b-98b4-b4e62fab08eb", - "metadata": {}, - "source": [ - "## 3. Backing up to a previous restart" - ] - }, - { - "cell_type": "markdown", - "id": "c2e232e3-37ae-42e7-bdf2-953ef4cc86cb", - "metadata": {}, - "source": [ - "If a run encounters problems and crashes, it is extremely useful to back up to a previous restart. \n", - "\n", - "You will need to find the latest restart files in the `$DOUT_S_ROOT/rest/yyyy-mm-dd-ssss/` directory that was created and copy the contents of that directory into your run directory (`$RUNDIR`). You can then continue the run and these restarts will be used. \n", - "\n", - "It is important to make sure the new `rpointer.*` files overwrite the previous `rpointer.*` files that were in `$RUNDIR`, or the job may not restart in the correct place.\n", - "\n", - "Occasionally, when a run has problems restarting, it is because the rpointer files are out of sync with the restart files. The rpointer files are text files and can easily be edited to match the correct dates of the restart and history files. All the restart files should have the same date." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32d9c126-14c2-41d1-8bce-a895fdf407dd", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/run_length/starting_and_stopping.ipynb b/notebooks_copy/modifications/xml/run_length/starting_and_stopping.ipynb deleted file mode 100644 index 8b78f684a..000000000 --- a/notebooks_copy/modifications/xml/run_length/starting_and_stopping.ipynb +++ /dev/null @@ -1,133 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Starting and Stopping \n", - "\n", - "To start a model run, we will need to submit `$CASE.run` to the batch queue. In addition, we will also need to specify how long we want to run the model in `env_run.xml`. \n", - "\n", - "\n", - "We will learn how to modify the runtime variables in `env_run.xml` to initialize, stop and restart a CESM model run." - ] - }, - { - "cell_type": "markdown", - "id": "ec38ef4d-2a68-44e8-a731-c55e1a2d940a", - "metadata": {}, - "source": [ - "
\n", - "\n", - "***\n" - ] - }, - { - "cell_type": "markdown", - "id": "344c4fb8-da46-44c1-8a64-6808e7b3a814", - "metadata": {}, - "source": [ - "## 1. \"Initial run\" and \"continue run\"\n", - "\n", - "When a CESM model run is first initialized, it is called an **initial run**. \n", - "\n", - "The variable **`$CONTINUE_RUN`** is a flag indicating if the current model run is an **initial run** or a **continue run**. \n", - "- For an intial run, `CONTINUE_RUN` must be set to *`FALSE`*, \n", - "- If the model continues a run, `CONTINUE_RUN` is set to *`TRUE`*. \n", - "\n", - "Your initial CESM run can be initialized in one of three run types: startup, branch,or hybrid. We will get into more details about the **run types** in the Chapter [Modifying Run Types](../modify_run_type/hybrid_branch_restart.ipynb). \n", - "\n", - "For now, we need to know that **setting run types is only important for an initial run when `CONTINUE_RUN` is set to `FALSE`**. \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "37195fbd-8a4f-45f8-81b6-7042bcbf5f08", - "metadata": {}, - "source": [ - "
\n", - "\n", - "****\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "fa6734b1-bb5e-4fc3-96b7-df867adbbf04", - "metadata": {}, - "source": [ - "## 2. Set runtime limits with **`STOP_OPTION`** and **`STOP_N`**" - ] - }, - { - "cell_type": "markdown", - "id": "68eef81e-bd50-43e1-9961-15d0712d17d3", - "metadata": {}, - "source": [ - "Run length options can be set using **`$STOP_OPTION`** and **`$STOP_N`** variables in env_run.xml. \n", - "\n", - "- `STOP_OPTION` : sets the run length time interval type, i.e. nmonths, ndays, nyears.\n", - "- `STOP_N` : sets the number of intervals (set by `STOP_OPTION`) to run the model during each submission within the specified wallclock time. " - ] - }, - { - "cell_type": "markdown", - "id": "da97a05c-ef45-4473-8f05-032aed8dfcf9", - "metadata": {}, - "source": [ - "
\n", - "For example, if you want to run a simulation for 6 months during the job submssion, you will need to set:\n", - "\n", - "```\n", - "./xmlchange STOP_N=6\n", - "./xmlchange STOP_OPTION='nmonths'\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "c5947ac5-c8d6-4abb-9ea7-bce711f48ddd", - "metadata": {}, - "source": [ - "
\n", - "\n", - "`STOP_N` and `STOP_OPTION` control the length of the run **per job submission**. They should be set based on the job queue limit and model throughput. You can find more information on this topic in the chapter [Using timing files](./timing_files.ipynb).\n", - "\n", - "
\n", - "\n", - "A typical simulation is comprised of many job submissions. This is because you can only stay in the computer queue for a limited time. We will learn more about completing long simulations in the chapter [Changing run length](./changing_run_length.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c99fbe29-f232-4534-8476-d12bbbc41ffe", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/modifications/xml/run_length/timing_files.ipynb b/notebooks_copy/modifications/xml/run_length/timing_files.ipynb deleted file mode 100644 index 6608d8f37..000000000 --- a/notebooks_copy/modifications/xml/run_length/timing_files.ipynb +++ /dev/null @@ -1,95 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Using Timing Files \n", - "\n", - "Model timing files contain a summary of various timing information for the run. It is helpful to check the timings after the run to verify that the model is running efficiently. \n", - "\n", - "
\n", - "\n", - "***" - ] - }, - { - "cell_type": "markdown", - "id": "78615f64-047d-421d-9436-925533cf6827", - "metadata": {}, - "source": [ - "## 1. What are timing files and where are they located?\n", - "\n", - "A summary timing output file is produced after every CESM run. This file is placed in `$CASEROOT/timing/cesm_timing.$CASE.$date`, where $date is a datestamp set by CESM at runtime, and contains a summary of various timing information.\n", - "\n", - "The first section in the timing output, \"TIMING PROFILE\", summarizes general timing information for the run. The total run time and cost is given in several metrics including pe-hrs per simulated year (**cost**), simulated years per wall day (**thoughput**), seconds, and seconds per model day. This provides general summary information quickly in several units for analysis and comparison with other runs. The total run time for each component is also provided, as is the time for initialization of the model. These times are the aggregate over the total run and do not take into account any temporal or processor load imbalances.\n", - "\n", - "
\n", - "\n", - "***\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "40e1c00d-ab11-427c-aa03-c6e0f366dceb", - "metadata": { - "tags": [] - }, - "source": [ - "## 2. Use timing files to determine runtime variables\n", - "\n", - "Here is an example of cost information shown in a timing file:\n", - "\n", - "```\n", - "Overall Metrics: \n", - "Model Cost: 327.14 pe-hrs/simulated_year (scale= 0.50)\n", - "Model Throughput: 4.70 simulated_years/day\n", - "```\n", - "\n", - "The model throughput is the estimated number of model years that you can run in a wallclock day. Based on this, you can maximize `$CASE.run` queue limit and change `STOP_OPTION` and `STOP_N` in `env_run.xml`.\n", - "\n", - "For example, say a model’s throughput is 4.7 simulated_years/day. On derecho, the maximum runtime limit is 12 hours. 4.7 model years/24 hours * 12 hours = 2.34 years. On the massively parallel computers, there is always some variability in how long it will take a job to run. On some machines, you may need to leave as much as 20% buffer time in your run to guarantee that jobs finish reliably before the time limit. For that reason we will set our model to run only 2 model year/job. \n", - "\n", - "Continuing to assume that the run is on derecho, we can set:\n", - "\n", - "```\n", - "./xmlchange STOP_OPTION='nyears'\n", - "./xmlchange STOP_N=2\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c03243b7-4aca-40d2-bdab-02312b58e016", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/documentation.ipynb b/notebooks_copy/namelist/documentation.ipynb deleted file mode 100644 index 2dd739a60..000000000 --- a/notebooks_copy/namelist/documentation.ipynb +++ /dev/null @@ -1,67 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Namelist Variables Documentation\n", - "\n", - "\n", - "
\n", - "\n", - "Complete documentation about the namelist variables can be found on the [CESM webpage.](https://www2.cesm.ucar.edu/models/cesm2/settings/current/)\n", - "\n", - "
\n", - "\n", - "For instance, if you want to change the **_output frequency_** in CAM, go to the website and type the word ``frequency`` in the search box as illustrated in *Figure 1*. " - ] - }, - { - "cell_type": "markdown", - "id": "017b45ad-dcff-42b2-8623-06033b8e11d8", - "metadata": {}, - "source": [ - "___\n", - "![Search namelist variables](../../images/namelist/namelist_var_search.png)\n", - "\n", - "*

Figure 1: Search for a namelist variable.

*\n", - "___" - ] - }, - { - "cell_type": "markdown", - "id": "42e1dd6f-c704-4f69-965a-a015d3d0baed", - "metadata": {}, - "source": [ - "This will return the attributes of the namelist variable ``nhtfrq``, as illustrated in *Figure 2*. ``nhtfrq`` is the variable you need in order to change the output frequency of a history file. \n", - "\n", - "___\n", - "![Attribute of a namelist variable](../../images/namelist/nhtfrq.png)\n", - "\n", - "*

Figure 2: Attribute of a namelist variable. Here the variable: ``nhtfrq``

*" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/exercises/exercise_cam_output.ipynb b/notebooks_copy/namelist/exercises/exercise_cam_output.ipynb deleted file mode 100644 index fc37494bc..000000000 --- a/notebooks_copy/namelist/exercises/exercise_cam_output.ipynb +++ /dev/null @@ -1,269 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Modify CAM output" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Customize your CAM history files

\n", - " \n", - "Create a case called `b1850_high_freq` using the compset ``B1850`` at ``f19_g17`` resolution. \n", - " \n", - "In addition to the monthly history file ``h0``, output:\n", - "- ``h1`` file with **_instantaneous_** values of T, Q, U and V every **_24 hour_**.\n", - "- ``h2`` file with **_time-average_** values of T, Q, U and V every **_3 hour_**.\n", - "\n", - "Set your namelist so that you output: \n", - "- a **_single_** ``h1`` file with all the daily output for the month. \n", - "- **_multiple_** ``h2`` files, one for every day of the month.\n", - "\n", - "Your goal is to produce:\n", - "- one ``h1`` file with 31 timesteps and \n", - "- thirty-one ``h2`` files with 8 timesteps each.\n", - "\n", - "Set the run length to **_1 month_** and make a 1-month run.\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "\n", - " Click here for hints \n", - " \n", - "**# How do I compile?**\n", - "\n", - "You can compile with the command:\n", - "```\n", - "qcmd -- ./case.build\n", - "```\n", - "\n", - "**# How do I control the output?**\n", - "\n", - "Use namelist variables: ``nhtfrq``, ``mfilt``, ``fincl``. \n", - "\n", - "Look at the online documentation for these variables.\n", - "\n", - "**# How do I check my solution?**\n", - "\n", - "When your run is completed, go to the archive directory and navigate to the subdirectory `atm/hist`\n", - "\n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/b1850_high_freq\n", - "cd atm/hist\n", - "```\n", - "\n", - "(1) Check that your archive directory contains the files:\n", - "\n", - "- ``h0`` files\n", - "```\n", - "b1850_high_freq.cam.h0.0001-01.nc\n", - "```\n", - "- ``h1`` files\n", - "```\n", - "b1850_high_freq.cam.h1.0001-01-01-00000.nc\n", - "b1850_high_freq.cam.h1.0001-02-01-00000.nc\n", - "```\n", - "- ``h2`` files\n", - "```\n", - "b1850_high_freq.cam.h2.0001-01-01-00000.nc\n", - "…\n", - "b1850_high_freq.cam.h2.0001-01-31-00000.nc\n", - "b1850_high_freq.cam.h2.0001-02-01-00000.nc\n", - "```\n", - "\n", - "(2) Compare the contents of the ``h1`` and ``h2`` files using ``ncdump``.\n", - "\n", - "```\n", - "ncdump -h b1850_high_freq.cam.h1.0001-01-01-00000.nc\n", - "ncdump -h b1850_high_freq.cam.h2.0001-01-01-00000.nc\n", - "```\n", - "\n", - "(3) Check the number of timesteps in the ``h1`` and the ``h2`` files.\n", - "Look at the sizes of the files. \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "**# Create a new case**\n", - "\n", - "Create a new case `b1850_high_freq` with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case ~/cases/b1850_high_freq --compset B1850 --res f19_g17 \n", - "```\n", - "\n", - "**# Setup**\n", - " \n", - "Invoke case.setup with the command:\n", - " \n", - "``` \n", - "cd ~/cases/b1850_high_freq \n", - "./case.setup\n", - "```\n", - "\n", - "**# Customize namelists**\n", - " \n", - "Edit the file `user_nl_cam` and add the lines:\n", - "```\n", - " nhtfrq = 0, -24, -3\n", - " mfilt = 1, 31, 8 \n", - " fincl2 = 'T:I','Q:I','U:I','V:I'\n", - " fincl3 = 'T','Q','U','V'\n", - "```\n", - " \n", - "**# Set run length**\n", - " \n", - "Change the `run length`:\n", - "``` \n", - "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change `job queue` and `account number`.
\n", - "For instance, to run in the queue `regular` and the project number ``UESM0013`` (You should use the project number given for this tutorial), use the command:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "**# Build and submit**\n", - " \n", - "Build the model and submit your job:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "\n", - "____\n", - " \n", - "**# Look at your solution**\n", - "\n", - "When the run is completed, look into the archive directory for: \n", - "`b1850_high_freq`. \n", - " \n", - "(1) Check that your archive directory on derecho (The path will be different on other machines): \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/b1850_high_freq/atm/hist\n", - "ls \n", - "```\n", - "\n", - "(2) Compare the contents of the ``h1`` and ``h2`` files using ``ncdump``.\n", - "Look at the variables attributes. What is the difference between the 2 files ? \n", - "\n", - "The file with the instantaneous output ``h1`` have no cell_methods attribute while the average output ``h2`` has a attribute: \n", - "```\n", - "cell_methods = \"time: mean\"\n", - "```\n", - "\n", - "For instance for the field Q.\n", - "\n", - "In the instantaneous file ``b1850_high_freq.cam.h1.0001-01-01-00000.nc``\n", - "```\n", - "float Q(time, lev, lat, lon) ;\n", - " Q:mdims = 1 ;\n", - " Q:units = \"kg/kg\" ;\n", - " Q:mixing_ratio = \"wet\" ;\n", - " Q:long_name = \"Specific humidity\" ;\n", - "```\n", - "In the time-averaged file ``b1850_high_freq.cam.h2.0001-01-01-00000.nc``\n", - "```\n", - "float Q(time, lev, lat, lon) ;\n", - " Q:mdims = 1 ;\n", - " Q:units = \"kg/kg\" ;\n", - " Q:mixing_ratio = \"wet\" ;\n", - " Q:long_name = \"Specific humidity\" ;\n", - " Q:cell_methods = \"time: mean\" ;\n", - "```\n", - "(3) Check the number of timesteps in the h1 and the h2 files. \n", - "\n", - "- ``h1`` contains 31 time samples. \n", - "In the netcdf file, \n", - "```\n", - "time = UNLIMITED ; // (31 currently)\n", - "```\n", - "- ``h2`` contains 8 time samples \n", - "In the netcdf file, \n", - "```\n", - "time = UNLIMITED ; // (8 currently)\n", - "```\n", - "- Check the size of the files\n", - "```\n", - "du –ks –h /glade/derecho/scratch/$USER/archive/b1850_high_freq/atm/hist/*\n", - "```\n", - "```\n", - "234M b1850_high_freq.cam.h0.0001-01.nc\n", - "\n", - "210M b1850_high_freq.cam.h1.0001-01-01-00000.nc\n", - "7.0M b1850_high_freq.cam.h1.0001-02-01-00000.nc\n", - "\n", - "55M b1850_high_freq.cam.h2.0001-01-01-00000.nc\n", - "55M b1850_high_freq.cam.h2.0001-01-02-00000.nc\n", - "...\n", - "55M b1850_high_freq.cam.h2.0001-01-31-00000.nc\n", - "7.0M b1850_high_freq.cam.h2.0001-02-01-00000.nc\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "815be0bc-515a-474b-a3dd-b7ba02831b9a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/exercises/exercise_tuning_parameter.ipynb b/notebooks_copy/namelist/exercises/exercise_tuning_parameter.ipynb deleted file mode 100644 index 0776f4af4..000000000 --- a/notebooks_copy/namelist/exercises/exercise_tuning_parameter.ipynb +++ /dev/null @@ -1,204 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Change a tuning parameter in CAM" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Modify a parameter, dcs

\n", - " \n", - "\n", - "In the tuning lecture, we talked about the parameter dcs:\n", - "http://www.cesm.ucar.edu/events/tutorials/2019/files/Specialized-hannay.pdf\n", - "\n", - "Create a case called `b1850_dcs` using the compset ``B1850`` at ``f19_g17`` resolution. \n", - "\n", - "Locate where to change the parameter **Dcs** and change from the default value:
\n", - "dcs = 200.D-6
\n", - "to
\n", - "dcs = 500.D-6
\n", - "\n", - "Make a **_1-month_** run.\n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "65b2cbda-2d54-48ee-898b-4c391f16ca79", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - " \n", - " Click here for hints\n", - " \n", - "\n", - "- The trick is going to locate where to change `dcs` \n", - " \n", - "- Compare to this run to the first run you did today: `b1850_high_freq`.\n", - "- You can use `ncdiff` and `ncview` to look at the difference between the 2 runs. For instance:\n", - "```\n", - "ncdiff /glade/derecho/scratch/$USER/archive/b1850_dcs/atm/hist/b1850_dcs.cam.h0.0001-01.nc /glade/derecho/scratch/$USER/archive/b1850_high_freq/atm/hist/b1850_high_freq.cam.h0.0001-01.nc diff.nc\n", - "\n", - "ncview diff.nc\n", - "```\n", - "- Analyze how this does this affect the `LWCF` ?\n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "7dd602b7-372d-4f36-b6d1-df8e22ba1646", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - "\n", - "**# Create a new case**\n", - " \n", - "Create a new case `b1850_dcs`with the command:\n", - "```\n", - "cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case ~/cases/b1850_dcs --compset B1850 --res f19_g17 \n", - "```\n", - "\n", - "**# Setup**\n", - "\n", - "Invoke case.setup with the command:\n", - "``` \n", - "cd ~/cases/b1850_dcs\n", - "./case.setup\n", - "```\n", - "\n", - "**# Customize namelists**\n", - " \n", - "Locate the variable to modify dcs. (Locating variables requires a learning curve. A good place to start is to look at the `Variable namelist documentation`. \n", - " \n", - "The variable is: `micro_mg_dcs`.
\n", - "By default this variable is set to:\n", - "`micro_mg_dcs = 200 microns`\n", - "You want to set it to:\n", - "`micro_mg_dcs = 500 microns`\n", - "\n", - " \n", - "Edit the file `user_nl_cam` and add the lines:\n", - "```\n", - "micro_mg_dcs = 500.D-6 \n", - "```\n", - "\n", - "**# Set run length**\n", - " \n", - "Change the `run length`:\n", - "``` \n", - "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change `job queue` and `account number`.
\n", - "For instance, to run in the queue `regular` and the project number `UESM0013`, use the command:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "\n", - "**# Build and submit**\n", - " \n", - "Build the model and submit your job:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "\n", - "____\n", - " \n", - "**# Look at your solution**\n", - " \n", - "- When the run is completed, compare this run to the first run: \n", - "`b1850_high_freq`. \n", - " \n", - "- Create a file with the difference in `LWCF` between `b1850_dcs` and `b1850_high_freq`.\n", - " \n", - "- You can use `ncdiff` and `ncview` to look at the difference between the 2 runs. \n", - "\n", - "- For instance, use `ncdiff` to create a file with the difference between the 2 runs:\n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/b1850_dcs \n", - "ncdiff /glade/derecho/scratch/$USER/archive/b1850_dcs/atm/hist/b1850_dcs.cam.h0.0001-01.nc /glade/derecho/scratch/$USER/archive/b1850_high_freq/atm/hist/b1850_high_freq.cam.h0.0001-01.nc diff.nc\n", - "``` \n", - "\n", - "- Use `ncview` to look at the file you created.\n", - "```\n", - "ncview diff.nc\n", - "```\n", - "\n", - "**#How does this affect the LWCF ?** \n", - "\n", - "Dcs = Threshold diameter to convert cloud ice particles to snow\n", - "\n", - "*

\n", - " ![dcs representation](../../../images/namelist/dcs.png) \n", - " Figure: Representation of Dcs.

*\n", - " \n", - "We increased Dcs from 200 microns to 500 microns. We should have more ice cloud and the LWCF should be larger. \n", - "\n", - " \n", - "The field `LWCF` difference looks like \n", - "*

\n", - " ![ncview diff LWCF](../../../images/namelist/ncview_diff_dcs.png) \n", - " Figure: Difference of LWCF for micro_mg_dcs = 200.D-6->500.D-6

*\n", - " \n", - "We can see that the change in Dcs affects the tropics where LWCF is large. However, 1-month run is too short to look at robust statistics. \n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "850528d8-e923-4a97-b49a-171ad511026f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/exercises_overview.ipynb b/notebooks_copy/namelist/exercises_overview.ipynb deleted file mode 100644 index d4cfde859..000000000 --- a/notebooks_copy/namelist/exercises_overview.ipynb +++ /dev/null @@ -1,53 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Exercise Overview \n", - "\n", - "## Learning Goals\n", - "\n", - "- Student will practice doing namelist changes \n", - "- Student will edit `user_nl_*` files, run the model and check the results\n", - "\n", - "\n", - "## Exercise overview:\n", - "\n", - "Do at last one exercise:\n", - "- Output high frequency data in CAM.\n", - "- Change a tuning parameter in CAM.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84583445-5875-44e3-91a1-14fe6db10093", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/namelist.ipynb b/notebooks_copy/namelist/namelist.ipynb deleted file mode 100644 index 2a891d5cd..000000000 --- a/notebooks_copy/namelist/namelist.ipynb +++ /dev/null @@ -1,53 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0959db1d-1790-4519-a34e-7026444af537", - "metadata": {}, - "source": [ - "# Namelist Modifications" - ] - }, - { - "cell_type": "markdown", - "id": "285a23b4-2906-4422-a8a8-987546de6f0c", - "metadata": {}, - "source": [ - "There are several ways in which the model can be modified using namelist settings. This is what we cover in this chapter:\n", - "- The section **_Overview_** describes the steps to modify the namelists and provides a visual representation of the directories and files that need to be modified.\n", - "- The section **_Documentation_** explains how to find information about the namelist variables on the CESM webpage.\n", - "- The section **_Customizing output_** describes how to customize the output history files, such as changing the output frequency or adding variables to a file.\n", - "- The section **_Exercises_** offers opportunities to practice the concepts learned in this chapter. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7cf2d79-da13-416d-8345-385bc1977b79", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/output.ipynb b/notebooks_copy/namelist/output.ipynb deleted file mode 100644 index cbf82331f..000000000 --- a/notebooks_copy/namelist/output.ipynb +++ /dev/null @@ -1,61 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Customizing Output\n" - ] - }, - { - "cell_type": "markdown", - "id": "34b38707-9fbb-4e2a-bb99-5d3ddac76dc0", - "metadata": {}, - "source": [ - "If you don't add anything to the namelist, CESM will generate its **``default output``**. This is what was done in previous chapters of this tutorial. Typically, the model outputs monthly means (and in some cases, higher frequency outputs) of a default set of variables. Depending on your needs, you may want to output variables at a different frequency, or add more variables, etc ... \n", - "\n", - "In this chapter **``Customizing output``**, we will cover how to output at a different frequency, add history files, or add variables to the history files.\n", - "\n", - "In the sections below, we provide the basics of customizing the output for the different components (atm, lnd, ice, and pop). The **``Customize CAM output``** is the most **_comprehensive_**, so we recommend **_at least reviewing that section_**." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fd23aec3-cb79-432b-96e1-dcad3c071a90", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4115c790-a2b3-41c1-8291-db1a9de90ef3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/output/output_cam.ipynb b/notebooks_copy/namelist/output/output_cam.ipynb deleted file mode 100644 index 48cdc13f2..000000000 --- a/notebooks_copy/namelist/output/output_cam.ipynb +++ /dev/null @@ -1,253 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Customize CAM output\n", - "\n", - "In this section, we will cover:\n", - "- how to change the **output frequency**\n", - "- how to control the **number of time samples** written to a history file\n", - "- how to output **extra variables**\n", - "- how to output **extra history files**\n", - "\n", - "This can be achieved with 3 namelist variables:\n", - "- **``nhtfrq``**: sets the output frequency\n", - "- **``mfilt``**: maximum number of time samples written to a history file\n", - "- **``fincl``**: add variables to the history file\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "3245bdc2-3932-4582-aec1-30991cabd916", - "metadata": {}, - "source": [ - "## Customizing CAM output frequency: **``nhtfrq``**\n", - "The default history file from CAM is a monthly average. \n", - "We can change the **output frequency** with the namelist variable **``nhtfrq``**\n", - "- If **``nhtfrq=0``**, the file will be a **monthly** average\n", - "- If **``nhtfrq>0``**, frequency is input as number of **timesteps**.\n", - "- If **``nhtfrq<0``**, frequency is input as number of **hours**.\n", - "\n", - "For instance to change the history file from monthly average to daily average, we set the namelist variable:\n", - "\n", - "```\n", - " nhtfrq = -24 \n", - "```\n", - "\n", - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "The default history file in CAM is a monthly average. How do you modify the output to output 3-hourly data ? \n", - "\n", - "
\n", - "\n", - "
\n", - " \n", - "
\n", - "Click here for the solution
\n", - "Add the following line to user_nl_cam:
\n", - "\n", - "```\n", - " nhtfrq = -3\n", - "```\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "42e1dd6f-c704-4f69-965a-a015d3d0baed", - "metadata": {}, - "source": [ - "## Customizing CAM history files: **``mfilt``**\n", - "To control the **number of time samples** in the history file, we use the variable **``mfilt``**.
\n", - "This variable specifies is the maximum number of time samples written to a history file.\n", - "\n", - "For example, if we want to have 10 time samples on each history file, we can set the namelist variable as follows:\n", - "```\n", - " mfilt = 10\n", - "```\n", - "\n", - "\n", - "To output daily data for a 1-year run in a single file, we can use the following values:\n", - "```\n", - " nhtfrq = -24 \n", - " mfilt = 365\n", - "```\n", - "If we want to output daily data with only 1 time sample per file, we can set the variables as follows:\n", - "```\n", - " nhtfrq = -24\n", - " mfilt = 1\n", - "```\n", - "\n", - "NB: we cannot change mfilt for monthly frequency. For monthly frequency, we always have: \n", - "```mfilt = 1``` \n", - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "What is the setting to generate 3 hourly data for a month-long simulation that will create a file every day? \n", - "
\n", - "\n", - "
\n", - "
\n", - "\n", - "Click here for the solution
\n", - "\n", - "Modify the length of the run in `env_run.xml` with the command:\n", - "``` \n", - " ./xmlchange STOP_N=nmonths, STOP_OPTION=1\n", - "```\n", - "\n", - "Add the following line to `user_nl_cam` to output 3 hourly data and to create one file per day:\n", - "``` \n", - " nhtfrq = -3\n", - " mfilt = 8\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "b35a3197-e561-4a00-909d-9f3d80808676", - "metadata": {}, - "source": [ - "\n", - "## Add extra variables and history files: **``fincl``**\n", - "\n", - "You can output up to 10 history files: **``h0``**, **``h1``**, …, **``h9``**. \n", - "- The **``h0``** file contains the default variables or fields. (These are the variables you get by default without doing any modification to the namelist or code).\n", - "\n", - "- For the files **``h1``** to **``h9``**, the user must specify the variables to output. \n", - "\n", - "To control the list of fields in the history files, use the namelist variable **``fincl``**\n", - "- **``fincl1``** controls the fields in **``h0``** \n", - "- **``fincl2``** controls the fields in **``h1``** \n", - "- ...\n", - "- **``fincl10``** controls the fields in **``h9``** \n", - "\n", - "For example, to add the field `PRECT` to the `h0` file, use the line:\n", - "```\n", - " fincl1 = 'PRECT' \n", - "```\n", - "\n", - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "What is the namelist setting to add:\n", - "- a hourly history `h1` with the variables U, V, T \n", - "- and a daily history `h2` with the variable PRECT \n", - "- and output 10 time samples in `h1` and `h2` ? \n", - "
\n", - "\n", - "
\n", - "
\n", - "\n", - "Click here for the solution
\n", - "\n", - "Add the following lines to `user_nl_cam`:\n", - "``` \n", - " nhtfrq = 0, -1, -24 \n", - " mfilt = 1, 10, 10 \n", - " fincl2 = 'U', 'V', 'T' \n", - " fincl3 = 'PRECT'\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "55e82e97-06da-48a1-8b71-29324759a2c3", - "metadata": {}, - "source": [ - "\n", - "## Averaging flag for the **``fincl``** variables \n", - "\n", - "Using a `:` following a field gives the averaging flag for the output field. \n", - "\n", - "Valid flags are: \n", - "- **``A``** ==> Average\n", - "- **``B``** ==> GMT 00:00:00 average\n", - "- **``I``** ==> Instantaneous\n", - "- **``M``** ==> Minimum\n", - "- **``X``** ==> Maximum\n", - "- **``L``** ==> Local-time\n", - "- **``S``** ==> Standard deviation\n", - "\n", - "For instance, the line:\n", - "```\n", - " fincl1 = 'PRECT:M' \n", - "```\n", - "is used to add the minimum of ``PRECT`` to the file ``h0``\n", - "\n", - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "What happens if we set in `user_nl_cam`:\n", - "``` \n", - " fincl2 = 'T:I','Q:I','U:I','V:I'\n", - " nhtfrq = 0, -3 \n", - " mfilt = 1, 8\n", - "```\n", - "\n", - "
\n", - "\n", - "
\n", - "
\n", - "\n", - "Click here for the solution
\n", - "\n", - "In addition to the monthly history file `h0`, \n", - "- we will also output the file `h1` with instantaneous values of T, Q, U, and V. \n", - "- These variables will be output every 3 hours, resulting in 8 time samples in each `h1` file. \n", - "- A new file will be created every day.\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8da564c-942e-4bf1-9522-26061784161a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/output/output_cice.ipynb b/notebooks_copy/namelist/output/output_cice.ipynb deleted file mode 100644 index 209f9ac1b..000000000 --- a/notebooks_copy/namelist/output/output_cice.ipynb +++ /dev/null @@ -1,57 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Customize CICE output\n", - "\n", - "The CICE output can changed with the namelist variables\n", - "\n", - "- **``histfreq``**: Frequency of output written to history files ('1', 'm', 'd', 'y', …)\n", - "- **``histfreq_n``**: Frequency history data is written to history files\n", - "- **``hist_avg``**: if false => instantaneous values, if true => time-averages\n", - "\n", - "\n", - "Here is how to set ``user_nl_cice`` to output an extra history file with daily values (leaving the primary history file as monthly): \n", - "```\n", - " histfreq = 'm','d','x','x','x'\n", - " histfreq_n = 1,1,1,1,1 \n", - "```\n", - "\n", - "See: http://www.cesm.ucar.edu/models/cesm2/settings/current/cice_nml.html\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd6df83b-1a5d-4878-96b5-715e12c4c0a9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/output/output_clm.ipynb b/notebooks_copy/namelist/output/output_clm.ipynb deleted file mode 100644 index b630fbc2b..000000000 --- a/notebooks_copy/namelist/output/output_clm.ipynb +++ /dev/null @@ -1,268 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": { - "tags": [] - }, - "source": [ - "# Customize CLM output\n", - "\n", - "In this section, we will cover:\n", - "- how to change the **output frequency**\n", - "- how to control the **number of time samples** written to a history file\n", - "- how to output **extra variables**\n", - "- how to output **extra history files**\n", - "\n", - "This can be achieved with 3 namelist variables:\n", - "- **``hist_nhtfrq:``**: output frequency of the history file \n", - "- **``hist_mfilt``**: number of samples on each history file\n", - "- **``hist_fincl``**: adding variables and auxiliary history files" - ] - }, - { - "cell_type": "markdown", - "id": "3245bdc2-3932-4582-aec1-30991cabd916", - "metadata": {}, - "source": [ - "## Customizing CLM output frequency: **``hist_nhtfrq``**\n", - "We can change the output frequency with the namelist variable **``hist_nhtfrq``**\n", - "- If **``hist_nhtfrq=0``**, the file will be a **monthly** average\n", - "- If **``hist_nhtfrq>0``**, frequency is input as number of **timesteps**.\n", - "- If **``hist_nhtfrq<0``**, frequency is input as number of **hours**.\n", - "\n", - "For instance, \n", - "\n", - "```\n", - " hist_nhtfrq = -24\n", - "```\n", - "means **daily output**\n", - "\n", - "```\n", - " hist_nhtfrq = 24\n", - "```\n", - "means output every **24 timesteps**\n", - "\n", - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "How do you modify the CLM output to output 3-hourly data ? \n", - "\n", - "
\n", - "\n", - "
\n", - " \n", - "
\n", - "Click here for the solution
\n", - "\n", - "Add the following line to user_nl_clm:
\n", - "```\n", - " hist_nhtfrq = -3\n", - "```\n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "b35a3197-e561-4a00-909d-9f3d80808676", - "metadata": {}, - "source": [ - "\n", - "## Add extra variables and history files: **``hist_fincl``**\n", - "\n", - "You can output up to 10 history files:**``h0``**, **``h1``**, …, **``h9``**. \n", - "\n", - "To control the list of fields in the history files, use the namelist variable **``hist_fincl``**\n", - "- **``hist_fincl1``** controls the fields in **``h0``** \n", - "- **``hist_fincl2``** controls the fields in **``h1``** \n", - "- ...\n", - "- **``hist_fincl10``** controls the fields in **``h9``** \n", - "\n", - "For example, to add the field 'TG' to the ``h0`` file, use the line:\n", - "```\n", - " hist_fincl1 = 'TG' \n", - "```\n", - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "What is the namelist setting to add a monthly history h1 with the variables 'TG', 'TV' and a daily history h2 with the variables 'TG', 'TV'. Output 10 time samples in h1 and h2 ? \n", - "
\n", - "\n", - "
\n", - "
\n", - "\n", - "Click here for the solution
\n", - "\n", - "Modify env_run.xml with the command:\n", - "``` \n", - " ./xmlchange STOP_N=nmonths, STOP_OPTION=1\n", - "```\n", - "\n", - "Add the following lines to user_nl_cam:\n", - "``` \n", - " hist_nhtfrq = 0, 0, -24 \n", - " hist_mfilt = 1, 10, 10 \n", - " hist_fincl2 = 'TG', 'TV' \n", - " hist_fincl3 = 'TG', 'TV' \n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "42e1dd6f-c704-4f69-965a-a015d3d0baed", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "## Customizing CLM history files: **``hist_mfilt``**\n", - "To control the number of time samples in the history file, we use the variable **``hist_mfilt``**.
\n", - "This variable specifies is the maximum number of time samples written to a history file.\n", - "\n", - "For example, if we want to have **10 time samples** on each history file, we can set the namelist variable as follows:\n", - "```\n", - " hist_mfilt = 10\n", - "```\n", - "\n", - "\n", - "To output **daily** data in a single file for a 1-year run, we can use the following values:\n", - "```\n", - " hist_nhtfrq = -24 \n", - " hist_mfilt = 365\n", - "```\n", - "If we want to output **daily** data with only **1 time sample** per file, we can set the variables as follows:\n", - "```\n", - " hist_nhtfrq = -24\n", - " hist_mfilt = 1\n", - "```\n", - "\n", - "NB: we cannot change mfilt for monthly frequency. \u000b", - "\tFor monthly frequency, we always have: \n", - "```hist_mfilt = 1``` \n", - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "What is the setting to generate CLM 3-hourly data for a month-long simulation that will create a file every day?\n", - "\n", - "
\n", - "\n", - "
\n", - "
\n", - "\n", - "Click here for the solution
\n", - "\n", - "Modify env_run.xml with the command:\n", - "```\n", - " ./xmlchange STOP_N=nmonths, STOP_OPTION=1\n", - "```\n", - "\n", - "Add the following line to user_nl_cam:\n", - "``` \n", - " hist_nhtfrq = -3\n", - " hist_mfilt = 8\n", - "```\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "55e82e97-06da-48a1-8b71-29324759a2c3", - "metadata": {}, - "source": [ - "\n", - "## Averaging flag for the **``fincl``** variables \n", - "\n", - "Using a \":\" following a field gives the averaging flag for the output field. \n", - "\n", - "Valid flags are: \n", - "- **``A``** ==> Average\n", - "- **``I``** ==> Instantaneous\n", - "- **``M``** ==> Minimum\n", - "- **``X``** ==> Maximum\n", - "- **``SUM``** ==> Sum\n", - "\n", - "For instance, the line:\n", - "```\n", - " hist_fincl1 = 'TLAI:M' \n", - "```\n", - "is used to add the minimum of ``TLAI`` to the file ``h0``\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "2bbd917e-8123-4bb1-a9cb-3c37d80c1fd2", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Evaluate your understanding\n", - "\n", - "What happens if we set in user_nl_clm:\n", - "```\n", - " hist_fincl2 = 'TG', 'TV' \n", - " hist_fincl3 = 'TG', 'TV' \n", - " hist_fincl4 = 'TG', 'TV'\n", - " hist_fincl5 = 'TG', 'TV' \n", - " hist_nhtfrq = 0, -24, -6, -1, 1 \n", - "```\n", - "\n", - "
\n", - "\n", - "
\n", - "
\n", - "\n", - "Click here for the solution
\n", - "\n", - "In addition to the monthly history file h0, we output 4 extra history files with daily, six-hourly, hourly, and every time-step values of TG and TV (leaving the primary history ``h0`` files as monthly). \n", - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02582628-9da7-4271-b330-e11c2e3ceb0f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/output/output_pop.ipynb b/notebooks_copy/namelist/output/output_pop.ipynb deleted file mode 100644 index f2f18b954..000000000 --- a/notebooks_copy/namelist/output/output_pop.ipynb +++ /dev/null @@ -1,65 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Customize POP output\n", - "\n", - "POP output is controlled by POP's tavg_nml namelist, which variables such as\n", - "\n", - "- **``tavg_freq``**: frequency at which the model fields are written\n", - "- **``tavg_freq_opt``**: units of time for 'tavg_freq’ ('nmonth’, 'nhour’, 'once’,…)\n", - "- **``tavg_file_freq``**:frequency at which the model files are written\n", - "- **``tavg_file_freq_opt``**: units of time for 'tavg_file_freq’ ('nmonth’, 'nhour’, …)\n", - "\n", - "These are arrays, with a value for each output stream.\n", - "A convenient way to change the first stream to be daily averages bundled into monthly\n", - "files is to add the following lines to ``user_nl_pop``: \n", - "```\n", - "tavg_freq_opt(1) = 'nday' \n", - "tavg_freq(1) = 1 \n", - "tavg_file_freq_opt(1) = 'nmonth' \n", - "tavg_file_freq(1) = 1 \n", - "```\n", - "\n", - "See: https://www.cesm.ucar.edu/models/cesm2/settings/current/pop2_nml.html\n", - "\n", - "\n", - "CAUTION: Note that changing tavg_nml variables via ``user_nl_pop`` is non-standard.\n", - "While the above is convenient, attempting more complicated changes can lead to unexpected model behavior.\n", - "For full flexibility, use the **workaround** explained in ``user_nl_pop``.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6857dadc-8cbd-4a2e-af21-14b0be4600ee", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/namelist/overview.ipynb b/notebooks_copy/namelist/overview.ipynb deleted file mode 100644 index 392f4e40f..000000000 --- a/notebooks_copy/namelist/overview.ipynb +++ /dev/null @@ -1,104 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f5e23fcc-d36b-4ca2-a596-9fb3c88c82e8", - "metadata": { - "tags": [] - }, - "source": [ - "# Overview \n", - "\n", - "There are a number of ways that the model can be modified via namelist settings. These values control the way the code is run. \n", - "\n", - "The **namelist variables** include settings to control over output, tune the model for various quantities, and many other options. For instance, the output frequency in the different components can be controlled via the namelists. \n", - "\n", - "
\n", - "\n", - "The **steps** to modify the **namelists** are:\n", - "\n", - "In ``$CASEROOT`` \n", - "- edit the ``user_nl_xxx`` files.\n", - "- generate the namelists by running ``./preview_namelists``. \n", - " \n", - " \n", - "This results in the creation of component namelists, the ``*_in`` files (i.e. `atm_in`, `lnd_in`, and so on). \n", - "\n", - "The ``*_in`` files are located in ``$CASEROOT/CaseDocs/`` and in ``$RUNDIR``.\n", - " \n", - "
\n", - "\n", - "\n", - "An overview of the CESM directories and the location of the namelist files is showed in *Figure 1*\n" - ] - }, - { - "cell_type": "markdown", - "id": "301171cd-e6da-4574-ab96-02d82d5fc95f", - "metadata": {}, - "source": [ - "___\n", - "![CESM directories and namelists](../../images/namelist/CESM_directories_and_namelists.png)\n", - "

Figure 1: Overview of the CESM directories and the namelist files.

\n", - "\n", - "___" - ] - }, - { - "cell_type": "markdown", - "id": "f0a5cc0d-f3cf-4508-9751-ff6866fa3f20", - "metadata": {}, - "source": [ - "\n", - "The ``*_in`` files **should not be directly edited**. Any manual changes of the ``*_in`` files will be overwritten when you compile or submit the run. \n", - "\n", - "Note that step ``./preview_namelists`` is **optional** as the script *preview_namelists* is also called when you build the model. \n", - "\n", - "Note that you cannot change the namelist variables: \n", - "- after the run is submitted \n", - "- or when `CONTINUE_RUN=TRUE`. \n" - ] - }, - { - "cell_type": "markdown", - "id": "c55ea30e-7dc5-4694-82d6-90952257c06a", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Complete documentation about the namelist variables can be found on the [CESM webpage.](https://www2.cesm.ucar.edu/models/cesm2/settings/current/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e30b4f4-1e35-4f70-af22-a81489b56018", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/prereqs/exercises.ipynb b/notebooks_copy/prereqs/exercises.ipynb deleted file mode 100644 index 9d071b53d..000000000 --- a/notebooks_copy/prereqs/exercises.ipynb +++ /dev/null @@ -1,139 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "082bb7b7-5657-4601-8615-4dd2058a4625", - "metadata": { - "tags": [] - }, - "source": [ - "# Exercise Overview\n", - "\n", - "This exercise will introduce you to the derecho computing system. Specifically, in this exercise we will:\n", - "\n", - "- Step 1. Login to the derecho supercomputer.\n", - "- Step 2. Examine the derecho glade file system.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "d9ff1113-8a6d-42b5-8ca7-d3bef493d7fb", - "metadata": {}, - "source": [ - "## Step 1: derecho Login" - ] - }, - { - "cell_type": "markdown", - "id": "f4a25131-701e-4d13-aad7-461d1302b84b", - "metadata": {}, - "source": [ - "From your tutorial terminal window prompt get online.
\n", - "\n", - "
\n", - "Login to derecho:

\n", - "\n", - "```\n", - "ssh –XY username@derecho.hpc.ucar.edu\n", - "```\n", - "\n", - "
\n", - "\n", - "Where `username` is replaced with your tutorial username. Your derecho login will be used through out the tutorial. If you have difficulty getting this to work please ask for help. \n", - "- If you are in the in-person tutorial at NCAR, there will be helpers available.\n", - "- If you are doing this outside of the tutorial then you can contact the user support from the NCAR Computational and Information Systems Laboratory (CISL)." - ] - }, - { - "cell_type": "markdown", - "id": "74512322-32da-400c-8aea-ff65658233db", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL User Support](https://ithelp.ucar.edu/plugins/servlet/desk/site/rc)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "6250a6b0-e65e-43c1-b36d-ebec14345076", - "metadata": {}, - "source": [ - "## Step 2: Look Around derecho File Systems" - ] - }, - { - "cell_type": "markdown", - "id": "ff5b81e1-292a-4bc4-8415-57098366cf80", - "metadata": {}, - "source": [ - "Now that you are on the derecho login nodes you can start to explore the file system. The following simple exercises will start that process.\n", - "\n", - "
\n", - "Show your current directory:
\n", - "\n", - "```\n", - "pwd\n", - "```\n", - "
\n", - "\n", - "List all the directories on the glade file system:
\n", - "\n", - "```\n", - "ls /glade\n", - "```\n", - "
\n", - " \n", - "List the contents of the Input Data directory:
\n", - "\n", - "```\n", - "ls /glade/campaign/cesm/cesmdata/cseg/inputdata/\n", - "```\n", - "\n", - "
\n", - "\n", - "\n" - ] - }, - { - "cell_type": "raw", - "id": "b32ec2e7-06f8-4f26-905f-6d495c4cf299", - "metadata": {}, - "source": [ - "Feel free to look around more of the NCAR HPC system. In the Resources chapter, you will find a section about the [NCAR Supercomputer](https://ncar.github.io/CESM-Tutorial/notebooks/resources/ncar_hpc.html) with links that may be particularly relevant to you." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d338e513-cb0b-45fd-b65a-1e7a66d95971", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/prereqs/prereqs_overview.ipynb b/notebooks_copy/prereqs/prereqs_overview.ipynb deleted file mode 100644 index 2b43ba3bf..000000000 --- a/notebooks_copy/prereqs/prereqs_overview.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Prerequisites for Success\n" - ] - }, - { - "cell_type": "markdown", - "id": "4651282a-0c4f-449a-bf36-74af04e7281e", - "metadata": {}, - "source": [ - "NOTE: Although the materials and notebooks in this tutorial are published as a Jupyter Book, **you will need to use a terminal window to actually run the commands presented in this tutorial.**\n", - "\n", - "To successfully complete this tutorial you will:\n", - "- Utilize a **terminal** window\n", - "- Issue commands at the **command line** using **UNIX**\n", - "- Need to be able to access and run on the **NCAR HPC systems** (HPC = High Performance Computing)\n", - "- Use **JupyterHub** for some basic diagnostic plotting\n", - "\n", - "The links below provide more information about these topics. **If you are not familiar with any of the four topics above, we strongly recommend that you read through the following sections.**" - ] - }, - { - "cell_type": "markdown", - "id": "24e22d53-7b0b-4fb8-82ad-360e75e64ac0", - "metadata": {}, - "source": [ - "___\n", - "## Terminal windows" - ] - }, - { - "cell_type": "markdown", - "id": "14f5fe74-cc59-4ccc-af62-e31650e1372c", - "metadata": {}, - "source": [ - "For most of these tutorial exercises, we assume that you will use the online documentation in tandem with an open terminal window. The commands described in these materials will be entered at the command line in your terminal window.\n", - "\n", - "If you are unfamiliar with opening terminal windows, please review the information at the link below. " - ] - }, - { - "cell_type": "markdown", - "id": "01790d14-4535-4e0a-a7da-38ac467717de", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Terminals](https://ncar.github.io/CESM-Tutorial/notebooks/resources/terminals.html#terminal-windows)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "37406e6f-57c2-4493-86a9-52bc2d5eb3f8", - "metadata": {}, - "source": [ - "___\n", - "## UNIX" - ] - }, - { - "cell_type": "markdown", - "id": "6572f442-e01a-4809-a90d-c251ff79f8af", - "metadata": {}, - "source": [ - "The commands described in these materials will be entered in a UNIX environment. If you are unfamiliar with navigating a UNIX environment, we suggest you use the link below to learn more about UNIX. " - ] - }, - { - "cell_type": "markdown", - "id": "e664eb92-e98d-4c3d-afbe-ea517b71439b", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[UNIX](https://ncar.github.io/CESM-Tutorial/notebooks/resources/unix.html#unix)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "16324ba6-5c27-41c2-8e62-dc2ccc817527", - "metadata": {}, - "source": [ - "___\n", - "## Login to NCAR High Performance Computing (HPC)" - ] - }, - { - "cell_type": "markdown", - "id": "3b3e13f6-45e2-4dc8-bd2a-b550ddb11270", - "metadata": {}, - "source": [ - "When logging into the NCAR HPC machines, we recommend using X11 forwarding. X11 forwarding is an SSH protocol that allows users to run graphical applications on a remote server and interact with them using their local display and I/O devices. It is frequently used by CESM developers for securely interacting with NCAR HPC remote machines.\n", - "\n", - "To enable X11 forwarding when logging into the NCAR HPC, simply add the `-XY` option to your `SSH` command. " - ] - }, - { - "cell_type": "markdown", - "id": "ddb0d0fb-1fd9-4814-bb20-af81d2baae5a", - "metadata": {}, - "source": [ - "```\n", - "ssh -XY username@derecho.hpc.ucar.edu \n", - "```\n", - "OR \n", - "```\n", - "ssh -XY username@casper.hpc.ucar.edu\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "510846c7-4018-420e-a899-96e1b9e92902", - "metadata": {}, - "source": [ - "More information about logging into the NCAR HPC follows below.\n", - "\n", - "
\n", - "\n", - "[NCAR High Performance Computing (HPC)](https://ncar.github.io/CESM-Tutorial/notebooks/resources/ncar_hpc.html)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "3c9cc169-489e-427a-865a-539468cb060d", - "metadata": {}, - "source": [ - "____\n", - "## Using JupyterHub" - ] - }, - { - "cell_type": "markdown", - "id": "4a799266-780e-4281-8955-e20e522cdb4d", - "metadata": {}, - "source": [ - "The diagnostics section of this tutorial will use JupyterHub to run Jupyter Notebooks. You will need to login to the NCAR JupyterHub system to run the notebooks successfully.\n", - "\n", - "NOTE: To run a Jupyter cell\n", - "- Type your command into the cell\n", - "- To execute the command:\n", - " - Press **shift+return**\n", - "
\n", - " OR\n", - "
\n", - " - Select the cell then click the 'play' button at the top of the window\n", - "\n", - "To learn more about JupyterHub, please follow the link below." - ] - }, - { - "cell_type": "markdown", - "id": "7db0afc5-0462-45db-ac81-1ee18c34c716", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation about JupyterHub](https://arc.ucar.edu/knowledge_base/70549913)\n", - " \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "088d2dc6-ae4f-4023-93d4-494aeaf8ba44", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/fortran.ipynb b/notebooks_copy/resources/fortran.ipynb deleted file mode 100644 index ac5770092..000000000 --- a/notebooks_copy/resources/fortran.ipynb +++ /dev/null @@ -1,74 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Fortran" - ] - }, - { - "cell_type": "markdown", - "id": "386d6593-bcbf-4c9a-8e64-9c0ee687de47", - "metadata": {}, - "source": [ - "The CESM model is primarily written in Fortran. If you plan to modify the CESM code in any way, we strongly recommend you be able to navigate the Fortran code." - ] - }, - { - "cell_type": "markdown", - "id": "db9ec999-e1b8-43dc-b72c-77223226e1af", - "metadata": {}, - "source": [ - "The 2-unit module listed below, from The COMET Program, introduces users to scientific programming structures and methods and the history, basic syntax and functionality of Fortran. " - ] - }, - { - "cell_type": "markdown", - "id": "d65da730-a8df-47ad-9e9d-68f283637fef", - "metadata": {}, - "source": [ - "Recommendations for Module Use:\n", - "\n", - "- Users who have little to no programming experience should take both units in full.\n", - "- Users that do have some programming experience, such as a web-based language like HTML, but have limited experience in scientific programming, such as a C-based languages, should start with unit 1 and may find they can complete it quickly. While unit 2, covering Fortran and CESM Fortran examples, should be taken in full.\n", - "- Users that have significant scientific programming experience (C-based languages, for example) can safely start in unit 2, which covers Fortran and CESM Fortran examples.\n", - "- Users that have significant programming experience in Fortran can likely begin at the \"Fortran and CESM\" subsection (near bottom of table of contents) in unit 2." - ] - }, - { - "cell_type": "markdown", - "id": "6924d6f2-f210-4704-8084-415c81a2122a", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Fortran COMET module](https://www.meted.ucar.edu/ucar/fortran)\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/github.ipynb b/notebooks_copy/resources/github.ipynb deleted file mode 100644 index 860bf0e73..000000000 --- a/notebooks_copy/resources/github.ipynb +++ /dev/null @@ -1,162 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Github" - ] - }, - { - "cell_type": "markdown", - "id": "ecbb8df1-076e-43a9-a409-73fea1a4475d", - "metadata": {}, - "source": [ - "The CESM project is managed through the GitHub online software development platform. The CESM Project is hosted under the Earth System Community Modeling Portal (ESCOMP) which is managed by the University Corporation for Atmospheric Research (UCAR). The ESCOMP portal and the CESM project are publicly available and do not require a Github account for read and download access. You can find information, track conversations, create issues, and learn lots about what's going on with CESM development through the CESM GitHub repository and the associated wiki.\n", - "\n", - "**Note: In the tutorial, where git commands are needed we explicitly detail what commands to use. You will not need to use Github for other activities in this Tutorial. We do however suggest that in your own time you become familar with Github, register as a Github user, and then become an active member of the CESM user and development community. When you make a free account in GitHub to access numerous repositories for different software code development at NCAR and beyond. Github, ESCOMP, and the CESM project are vast and you can get lost in this material for hours, days, weeks or even years. Don't go too far off the path in this tutorial as we are expecting that you will focus on learning other derecho and CESM code download tasks.**" - ] - }, - { - "cell_type": "markdown", - "id": "ee473b49-9e16-44b2-a432-235d453094f9", - "metadata": {}, - "source": [ - "## What are Git and GitHub?" - ] - }, - { - "cell_type": "markdown", - "id": "4332ee98-964a-4123-8cd1-56a64af8aa58", - "metadata": {}, - "source": [ - "Git is an open-source version control software to track your changes in the source code. \n", - "\n", - "GitHub is an online software development platform. GitHub provides a centralized online service to host the source code and version control using Git. GitHub is used for storing, tracking, and collaborating on software projects. It makes it easy for developers to share code files and collaborate with fellow developers on open-source projects. GitHub also serves as a social networking site where developers can openly network, collaborate, and pitch their work. GitHub allows software developers to create remote, publicly available repositories on the cloud for free. A repository, or \"repo\" for short, is a coding project’s files and the revision history for each file. " - ] - }, - { - "cell_type": "markdown", - "id": "230ba41c-1c66-482b-b57d-3e14cf326dd2", - "metadata": {}, - "source": [ - "![Github Image](../../images/resources/CESM2_github_main.png)\n", - "*

Figure: Top Level Github Header

*" - ] - }, - { - "cell_type": "markdown", - "id": "2db5b08a-8c5f-4a92-8691-fb9581340d82", - "metadata": {}, - "source": [ - "Learning all the tricks and features of Git and GitHub takes some time to figure out. Below are some links to GitHub tutorials that can help you become more familiar with this powerful tool." - ] - }, - { - "cell_type": "markdown", - "id": "6a271fd9-ef98-4883-bcbd-4d8e83d43a8d", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[GitHub tutorial from software carpentry](https://swcarpentry.github.io/git-novice/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "babdf239-9e83-4f00-b2a6-033f4e1f53b3", - "metadata": {}, - "source": [ - "## ESCOMP" - ] - }, - { - "cell_type": "markdown", - "id": "fcde532b-ea6e-49e2-ab45-52ed8b0f486e", - "metadata": {}, - "source": [ - "The ESCOMP github organization is managed by UCAR and NCAR to organize and manage large, community-oriented earth system modeling projects of broad interest. As shown below there are many projects beyond CESM managed under the ESCOMP organization. The component models (see CAM and CTSM in figure below) that make up CESM are also hosted under the ESCOMP organization." - ] - }, - { - "cell_type": "markdown", - "id": "852d2da7-1b6d-4a7f-b4b1-9d07222de7d9", - "metadata": {}, - "source": [ - "![ESCOMP Image](../../images/resources/CESM2_github_ESCOMP.png)\n", - "*

Figure: ESCOMP Github Header

*" - ] - }, - { - "cell_type": "markdown", - "id": "1306dc86-1d29-48f0-9138-9587b7ff7051", - "metadata": {}, - "source": [ - "## CESM and GitHub" - ] - }, - { - "cell_type": "markdown", - "id": "c37b8978-46bd-47ec-bf45-f403fb53b4de", - "metadata": {}, - "source": [ - "The CESM Project has been hosted on Github since CESM2.0 under the ESCOMP organization. The CESM Project contains all development and release code for all subsequent versions of CESM. The CESM GitHub repository has the tools for managing the exeternal components that make up a CESM model tag. Additionally, the documentation at this repository provides information about software requirements to run CESM, developer guidelines, and a Quikstart guide to accessing CESM through GitHub." - ] - }, - { - "cell_type": "markdown", - "id": "c4e62e1f-7ae6-4420-8bfc-445559d188d3", - "metadata": {}, - "source": [ - "![CESM Project Image](../../images/resources/CESM2_github_CESM.png)\n", - "*

Figure: CESM Project Github Header

*\n" - ] - }, - { - "cell_type": "markdown", - "id": "56c7b16d-5875-463c-88a8-36206fe5ec6a", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CESM GitHub repository](https://github.com/ESCOMP/CESM)\n", - " \n", - "[CESM and GitHub Quickstart guide](https://escomp.github.io/CESM/versions/master/html/index.html)\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cdb5e70e-85ca-4e84-9381-4b1b8ab63692", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/ncar_hpc.ipynb b/notebooks_copy/resources/ncar_hpc.ipynb deleted file mode 100644 index d0adfd4ef..000000000 --- a/notebooks_copy/resources/ncar_hpc.ipynb +++ /dev/null @@ -1,199 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "da307274-8069-4cc8-b333-cb156373cda3", - "metadata": {}, - "source": [ - "# NCAR Supercomputer\n", - "\n", - "Once you have an account and the necessary software, you can log in and run jobs on the NCAR supercomputer. The NCAR supercomputer is also referenced to the NCAR High Performance Computing (HPC). " - ] - }, - { - "cell_type": "markdown", - "id": "28823b60-6956-417c-a48c-3b88668b0968", - "metadata": {}, - "source": [ - "___\n", - "## Logging in on an NCAR system" - ] - }, - { - "cell_type": "markdown", - "id": "3b3e13f6-45e2-4dc8-bd2a-b550ddb11270", - "metadata": {}, - "source": [ - "To log in, start your terminal or Secure Shell client and run an `ssh` command as shown here:\n" - ] - }, - { - "cell_type": "markdown", - "id": "ddb0d0fb-1fd9-4814-bb20-af81d2baae5a", - "metadata": {}, - "source": [ - "```\n", - "ssh -XY username@derecho.hpc.ucar.edu \n", - "```\n", - "OR \n", - "```\n", - "ssh -XY username@casper.hpc.ucar.edu\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "311d9679-8944-49c0-936e-8e8ddcc3ba37", - "metadata": {}, - "source": [ - "\n", - "When you log in to the NCAR HPC machines, we recommend you use `X11` forwarding. `X11` forwarding is an SSH protocol that enables users to run graphical applications on a remote server and interact with them using their local display and I/O devices. It is frequently used by CESM developers for securely interacting with NCAR HPC remote machines.\n", - "\n", - "You can do this by adding the `-XY` option when logging into the machines with `SSH`. More information about logging into the NCAR HPC follows below." - ] - }, - { - "cell_type": "markdown", - "id": "510846c7-4018-420e-a899-96e1b9e92902", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation on Using X11](https://arc.ucar.edu/knowledge_base/72581213#QuickstartonDerecho-LogginginonanNCARsystem)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e731c34f-4386-4869-bdb7-a6e479f07a63", - "metadata": {}, - "source": [ - "___\n", - "## Running on derecho" - ] - }, - { - "cell_type": "markdown", - "id": "202de099-dc2d-47d2-99fb-b6f1b87d654c", - "metadata": {}, - "source": [ - "This tutorial material is designed for use with the derecho HPC system. All of your CESM experiment builds and jobs will be run on derecho.\n", - "\n", - "Please see the link below for more information about the derecho system, including a quick start guide with information about logging into derecho from a terminal, setting up your environment, submitting jobs, etc. " - ] - }, - { - "cell_type": "markdown", - "id": "235c1c69-da82-4cff-95bf-02c1bb2db897", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation on Using derecho HPC](https://arc.ucar.edu/knowledge_base/74317833)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c7e2b935-85ea-47f1-b8f0-654d54714484", - "metadata": {}, - "source": [ - "#### Porting" - ] - }, - { - "cell_type": "markdown", - "id": "a47bc7ae-4bd8-4e55-87e8-27019a689099", - "metadata": {}, - "source": [ - "This tutorial assumes that you are using NCAR HPC assets. In order to run the CESM on a **_different_** computing platform, you will first need to port the CESM code to that environment/machine. We provide information about how to port the model code in the [porting section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/porting.html)." - ] - }, - { - "cell_type": "markdown", - "id": "8a831d36-6629-4cd6-a247-2a8293aa2561", - "metadata": {}, - "source": [ - "___\n", - "## Using Casper" - ] - }, - { - "cell_type": "markdown", - "id": "2f8ad62c-89c5-49c3-9751-219303bce4c7", - "metadata": {}, - "source": [ - "The Casper cluster is a system of specialized data analysis and visualization resources. Casper is not used for building or running CESM, but it is used for CESM data analysis. We will use Casper for the diagnostics section of the lab activities. To utilize the **_additional_** analysis tools described in the [analysis tools section](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html) you will also want to use Casper." - ] - }, - { - "cell_type": "markdown", - "id": "aee2a810-f356-4c55-a19a-c0a32dfd504b", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation on Using Casper Cluster](https://arc.ucar.edu/knowledge_base/70549550)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "603c963b-4d65-4cd6-94a3-ea5ae932578e", - "metadata": {}, - "source": [ - "___\n", - "## Using JupyterHub" - ] - }, - { - "cell_type": "markdown", - "id": "a9423bae-be8c-4848-916c-926f86645da6", - "metadata": {}, - "source": [ - "The diagnostics section of this tutorial will use JupyterHub to run Jupyter Notebooks. You will need to login to the NCAR JupyterHub system to run the notebooks successfully.\n", - "\n", - "NOTE: To run a Jupyter cell\n", - "- Type your command into the cell\n", - "- To execute the command:\n", - " - Press **shift+return**\n", - "
\n", - " OR\n", - "
\n", - " - Select the cell then click the 'play' button at the top of the window\n", - "\n", - "To learn more about JupyterHub, please follow the link below." - ] - }, - { - "cell_type": "markdown", - "id": "6ae8710f-8dd2-4ed6-893f-d2de5b863b94", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation about JupyterHub](https://arc.ucar.edu/knowledge_base/70549913)\n", - " \n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/ncar_hpc_login.ipynb b/notebooks_copy/resources/ncar_hpc_login.ipynb deleted file mode 100644 index 2e4e93dab..000000000 --- a/notebooks_copy/resources/ncar_hpc_login.ipynb +++ /dev/null @@ -1,358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "1e8e7890-9733-47bf-9ff6-91613f4738f1", - "metadata": {}, - "source": [ - "# NCAR Supercomputer\n", - "\n", - "Once you have an account and the necessary software, you can log in and run jobs on the NCAR supercomputer. The NCAR supercomputer is also referenced to the NCAR High Performance Computing (HPC). " - ] - }, - { - "cell_type": "markdown", - "id": "28823b60-6956-417c-a48c-3b88668b0968", - "metadata": {}, - "source": [ - "___\n", - "## Logging in on an NCAR system¶" - ] - }, - { - "cell_type": "markdown", - "id": "3b3e13f6-45e2-4dc8-bd2a-b550ddb11270", - "metadata": {}, - "source": [ - "To log in, start your terminal or Secure Shell client and run an `ssh` command as shown here:\n" - ] - }, - { - "cell_type": "markdown", - "id": "ddb0d0fb-1fd9-4814-bb20-af81d2baae5a", - "metadata": {}, - "source": [ - "```\n", - "ssh -XY username@derecho.hpc.ucar.edu \n", - "```\n", - "OR \n", - "```\n", - "ssh -XY username@casper.hpc.ucar.edu\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "311d9679-8944-49c0-936e-8e8ddcc3ba37", - "metadata": {}, - "source": [ - "\n", - "When you log in to the NCAR HPC machines, we recommend you use `X11` forwarding. `X11` forwarding is an SSH protocol that enables users to run graphical applications on a remote server and interact with them using their local display and I/O devices. It is frequently used by CESM developers for securely interacting with NCAR HPC remote machines.\n", - "\n", - "You can do this by adding the `-XY` option when logging into the machines with `SSH`. More information about logging into the NCAR HPC follows below." - ] - }, - { - "cell_type": "markdown", - "id": "510846c7-4018-420e-a899-96e1b9e92902", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation on Using X11](https://arc.ucar.edu/knowledge_base/72581213#QuickstartonDerecho-LogginginonanNCARsystem)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e731c34f-4386-4869-bdb7-a6e479f07a63", - "metadata": {}, - "source": [ - "___\n", - "## Running on derecho" - ] - }, - { - "cell_type": "markdown", - "id": "202de099-dc2d-47d2-99fb-b6f1b87d654c", - "metadata": {}, - "source": [ - "This tutorial material is designed for use with the derecho HPC system. All of your CESM experiment builds and jobs will be run on derecho.\n", - "\n", - "Please see the link below for more information about the derecho system, including a quick start guide with information about logging into derecho from a terminal, setting up your environment, submitting jobs, etc. " - ] - }, - { - "cell_type": "markdown", - "id": "235c1c69-da82-4cff-95bf-02c1bb2db897", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation on Using derecho HPC](https://arc.ucar.edu/knowledge_base/74317833)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "c7e2b935-85ea-47f1-b8f0-654d54714484", - "metadata": {}, - "source": [ - "#### Porting" - ] - }, - { - "cell_type": "markdown", - "id": "a47bc7ae-4bd8-4e55-87e8-27019a689099", - "metadata": {}, - "source": [ - "This tutorial assumes that you are using NCAR HPC assets. In order to run the CESM on a **_different_** computing platform, you will first need to port the CESM code to that environment/machine. We provide information about how to port the model code in the [porting section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/porting.html)." - ] - }, - { - "cell_type": "markdown", - "id": "8a831d36-6629-4cd6-a247-2a8293aa2561", - "metadata": {}, - "source": [ - "___\n", - "## Using Casper" - ] - }, - { - "cell_type": "markdown", - "id": "2f8ad62c-89c5-49c3-9751-219303bce4c7", - "metadata": {}, - "source": [ - "The Casper cluster is a system of specialized data analysis and visualization resources. Casper is not used for building or running CESM, but it is used for CESM data analysis. We will use Casper for the diagnostics section of the lab activities. To utilize the **_additional_** analysis tools described in the [analysis tools section](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html) you will also want to use Casper." - ] - }, - { - "cell_type": "markdown", - "id": "aee2a810-f356-4c55-a19a-c0a32dfd504b", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation on Using Casper Cluster](https://arc.ucar.edu/knowledge_base/70549550)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "603c963b-4d65-4cd6-94a3-ea5ae932578e", - "metadata": {}, - "source": [ - "___\n", - "## Using JupyterHub" - ] - }, - { - "cell_type": "markdown", - "id": "a9423bae-be8c-4848-916c-926f86645da6", - "metadata": {}, - "source": [ - "The diagnostics section of this tutorial will use JupyterHub to run Jupyter Notebooks. You will need to login to the NCAR JupyterHub system to run the notebooks successfully.\n", - "\n", - "NOTE: To run a Jupyter cell\n", - "- Type your command into the cell\n", - "- To execute the command:\n", - " - Press **shift+return**\n", - "
\n", - " OR\n", - "
\n", - " - Select the cell then click the 'play' button at the top of the window\n", - "\n", - "To learn more about JupyterHub, please follow the link below." - ] - }, - { - "cell_type": "markdown", - "id": "6ae8710f-8dd2-4ed6-893f-d2de5b863b94", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL Documentation about JupyterHub](https://arc.ucar.edu/knowledge_base/70549913)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "144caca2-989b-40a5-b6c0-c8a0b7a470da", - "metadata": {}, - "source": [ - "___\n", - "## Modules on NCAR HPC" - ] - }, - { - "cell_type": "markdown", - "id": "52805170-9397-48b3-8bdf-a2c76fb45583", - "metadata": {}, - "source": [ - "NCAR maintains a number of different programs on the HPC systems that can be added to your user environment by executing the ``module load`` command. " - ] - }, - { - "cell_type": "markdown", - "id": "a551ace3-3b1a-4130-8084-3034842e0259", - "metadata": {}, - "source": [ - "We will use a few modules throughout this tutorial to visualize data, so you need to be able to check which modules you have loaded and to load necessary modules." - ] - }, - { - "cell_type": "markdown", - "id": "f6edec76-66ba-42f0-b3b2-00a4d90f15fd", - "metadata": {}, - "source": [ - "We provide basic instructions below on how to check and load Modules, but to learn more about Modules on NCAR HPC, please follow the link below." - ] - }, - { - "cell_type": "markdown", - "id": "b2aef333-82a9-4f3d-8afa-2f5c3df4ef4c", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL documentation on modules](https://arc.ucar.edu/knowledge_base/72581272)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "33d4014c-bc0f-4ab0-aaf3-cf6cf7572f22", - "metadata": {}, - "source": [ - "### Checking what modules are loaded in your environment\n", - "\n", - "To check what modules you have loaded in your environment, use the following at the command line:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fe867e0-f0df-4d87-a9d6-169294ec222c", - "metadata": {}, - "outputs": [], - "source": [ - "module list" - ] - }, - { - "cell_type": "markdown", - "id": "2a5ec941-4f4a-4a99-a3a0-6e514c448805", - "metadata": {}, - "source": [ - "### Checking what modules are available\n", - "\n", - "You can see which modules are available on the HPC resources by using the following at the command line:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b974222c-a270-4075-b9b9-7be7b4550942", - "metadata": {}, - "outputs": [], - "source": [ - "module avail" - ] - }, - { - "cell_type": "markdown", - "id": "0d2cfc8e-2085-4b72-b917-9e98a1586e13", - "metadata": {}, - "source": [ - "The command returns a list to the terminal window of the modules available to load to your environment." - ] - }, - { - "cell_type": "markdown", - "id": "ec74810f-c422-468d-b30e-ddb9c41d878f", - "metadata": {}, - "source": [ - "![Modules](../../images/prereqs/modules.png)\n", - "\n", - "*

Figure: Some modules available on NCAR HPC resources. D: Default Module. L: Module is loaded.

*" - ] - }, - { - "cell_type": "markdown", - "id": "c523021e-9328-4e0b-8c40-15bcf2ebab75", - "metadata": {}, - "source": [ - "The modules highlighted in blue are text editors discussed further in the [text editors section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/text_editors.html).\n", - "\n", - "The modules highlighted in red are netCDF tools that will be discussed further in the [netCDF section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html).\n", - "\n", - "Note that there are many other analysis software tools available to load into your NCAR HPC environment. These include: idl, julia, matlab, R, python, ncl, etc. While some of these tools are discussed in the [analysis tools section](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html), we do not highlight them all here and encourage you to further explore your environment and needs." - ] - }, - { - "cell_type": "markdown", - "id": "65848ac8-1cad-47bf-b680-8b129ce4e25d", - "metadata": {}, - "source": [ - "### Loading a new module to your environment\n", - "\n", - "You can load one of the available modules (here we show loading ncview) using the following at the command line: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad9db6c8-91a9-4c3e-84b4-6e6fb65de213", - "metadata": {}, - "outputs": [], - "source": [ - "module load ncview" - ] - }, - { - "cell_type": "markdown", - "id": "a1e13c85-65d1-4904-a07b-6cba1249ddfd", - "metadata": {}, - "source": [ - "
\n", - "NOTE: We will provide a file that will load necessary modules for those attending the in person tutorial. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "3c9cc169-489e-427a-865a-539468cb060d", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a1cb820-6efd-4eda-9581-a7067ff4f27d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/ncar_hpc_module.ipynb b/notebooks_copy/resources/ncar_hpc_module.ipynb deleted file mode 100644 index 02eac9de8..000000000 --- a/notebooks_copy/resources/ncar_hpc_module.ipynb +++ /dev/null @@ -1,177 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "144caca2-989b-40a5-b6c0-c8a0b7a470da", - "metadata": {}, - "source": [ - "# Modules on NCAR HPC" - ] - }, - { - "cell_type": "markdown", - "id": "52805170-9397-48b3-8bdf-a2c76fb45583", - "metadata": {}, - "source": [ - "NCAR maintains a number of different programs on the HPC systems that can be added to your user environment by executing the ``module load`` command. " - ] - }, - { - "cell_type": "markdown", - "id": "a551ace3-3b1a-4130-8084-3034842e0259", - "metadata": {}, - "source": [ - "We will use a few modules throughout this tutorial to visualize data, so you need to be able to check which modules you have loaded and to load necessary modules." - ] - }, - { - "cell_type": "markdown", - "id": "f6edec76-66ba-42f0-b3b2-00a4d90f15fd", - "metadata": {}, - "source": [ - "We provide basic instructions below on how to check and load Modules, but to learn more about Modules on NCAR HPC, please follow the link below." - ] - }, - { - "cell_type": "markdown", - "id": "b2aef333-82a9-4f3d-8afa-2f5c3df4ef4c", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CISL documentation on modules](https://arc.ucar.edu/knowledge_base/72581272)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "33d4014c-bc0f-4ab0-aaf3-cf6cf7572f22", - "metadata": {}, - "source": [ - "### Checking what modules are loaded in your environment\n", - "\n", - "To check what modules you have loaded in your environment, use the following at the command line:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fe867e0-f0df-4d87-a9d6-169294ec222c", - "metadata": {}, - "outputs": [], - "source": [ - "module list" - ] - }, - { - "cell_type": "markdown", - "id": "2a5ec941-4f4a-4a99-a3a0-6e514c448805", - "metadata": {}, - "source": [ - "### Checking what modules are available\n", - "\n", - "You can see which modules are available on the HPC resources by using the following at the command line:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b974222c-a270-4075-b9b9-7be7b4550942", - "metadata": {}, - "outputs": [], - "source": [ - "module avail" - ] - }, - { - "cell_type": "markdown", - "id": "0d2cfc8e-2085-4b72-b917-9e98a1586e13", - "metadata": {}, - "source": [ - "The command returns a list to the terminal window of the modules available to load to your environment." - ] - }, - { - "cell_type": "markdown", - "id": "ec74810f-c422-468d-b30e-ddb9c41d878f", - "metadata": {}, - "source": [ - "![Modules](../../images/prereqs/modules.png)\n", - "\n", - "*

Figure: Some modules available on NCAR HPC resources. D: Default Module. L: Module is loaded.

*" - ] - }, - { - "cell_type": "markdown", - "id": "c523021e-9328-4e0b-8c40-15bcf2ebab75", - "metadata": {}, - "source": [ - "The modules highlighted in blue are text editors discussed further in the [text editors section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/text_editors.html).\n", - "\n", - "The modules highlighted in red are netCDF tools that will be discussed further in the [netCDF section](https://ncar.github.io/CESM-Tutorial/notebooks/resources/netcdf.html).\n", - "\n", - "Note that there are many other analysis software tools available to load into your NCAR HPC environment. These include: idl, julia, matlab, R, python, ncl, etc. While some of these tools are discussed in the [analysis tools section](https://ncar.github.io/CESM-Tutorial/notebooks/diagnostics/additional/analysis_tools.html), we do not highlight them all here and encourage you to further explore your environment and needs." - ] - }, - { - "cell_type": "markdown", - "id": "65848ac8-1cad-47bf-b680-8b129ce4e25d", - "metadata": {}, - "source": [ - "### Loading a new module to your environment\n", - "\n", - "You can load one of the available modules (here we show loading ncview) using the following at the command line: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad9db6c8-91a9-4c3e-84b4-6e6fb65de213", - "metadata": {}, - "outputs": [], - "source": [ - "module load ncview" - ] - }, - { - "cell_type": "markdown", - "id": "a1e13c85-65d1-4904-a07b-6cba1249ddfd", - "metadata": {}, - "source": [ - "
\n", - "NOTE: We will provide a file that will load necessary modules for those attending the in person tutorial. \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "3c9cc169-489e-427a-865a-539468cb060d", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a1cb820-6efd-4eda-9581-a7067ff4f27d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/netcdf.ipynb b/notebooks_copy/resources/netcdf.ipynb deleted file mode 100644 index 5bbacda9a..000000000 --- a/notebooks_copy/resources/netcdf.ipynb +++ /dev/null @@ -1,349 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# NetCDF files" - ] - }, - { - "cell_type": "markdown", - "id": "e70f8ee6-91c8-4543-830f-4196163a7131", - "metadata": {}, - "source": [ - "All CESM model output is in netCDF (*.nc) data format. Below, you will find some useful information for how to navigate and manipulate CESM netCDF files." - ] - }, - { - "cell_type": "markdown", - "id": "59bb8a3f-74a5-4c7f-aaa7-15d22fe59b4a", - "metadata": {}, - "source": [ - "## NetCDF\n", - "\n", - "NetCDF stands for “network Common Data Form”. NetCDF files are array-oriented and a community standard for sharing scientific model data.\n", - "\n", - "Some benefits of netCDF files include that they:\n", - "- Are self describing and generally include substantial metadata to assist with comprehension of the data.\n", - "- Supported by a range of languages (Fortran, C, Matlab, ferret, GrADS, NCL, IDL, python).\n", - "- Appendable, so data can be added to a file.\n", - "- Viewable with tools like ncview and panopoly." - ] - }, - { - "cell_type": "markdown", - "id": "5b9c0340-90be-4957-82a7-5d911287a35f", - "metadata": {}, - "source": [ - "The files are saved in netcdf format (denoted with the `.nc` file extension), a file format commonly used for storing large, multi-dimensional scientific variables.\n", - "Netcdf files are platform independent and self-describing; each file includes metadata that describes the data, including: **variables**, **dimensions**, and **attributes**.\n", - "\n", - "The figure below provides a generic example of the data structure in a netcdf file. The dataset illustrated has two variables (temperature and pressure) that have three dimensions. Coordinate data (e.g., latitude, longitude, time) that describe the data are also included. \n", - "\n", - "![Netcdf](https://xarray.pydata.org/en/stable/_images/dataset-diagram.png)" - ] - }, - { - "cell_type": "markdown", - "id": "c6540e1c-d489-401b-b227-2da4f69c50ec", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[netCDF information](https://www.unidata.ucar.edu/software/netcdf/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "eeaa2e1e-68fa-4b72-9415-d12448484719", - "metadata": {}, - "source": [ - "## ncdump\n", - "\n", - "Sometimes you want to see what information a netCDF file without fully opening it. `ncdump` is a command line netCDF utility that allows the user to dump the contents of the netCDF file to the terminal screen or to a file in a human-readable format. \n", - "\n", - "**NOTE:** `ncdump` **is installed by default as part of the netCDF libraries.**" - ] - }, - { - "cell_type": "markdown", - "id": "bf4014fd-c02a-485d-a648-a01d7bb6f204", - "metadata": {}, - "source": [ - "To view the header of a netCDF file, use the following command:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78c32209-629f-4d7e-afe2-64cb6ff4b04c", - "metadata": {}, - "outputs": [], - "source": [ - "ncdump -h file.nc" - ] - }, - { - "cell_type": "markdown", - "id": "177838bf-0241-4265-8418-fcfbdcff7dd0", - "metadata": {}, - "source": [ - "At the command line, you get the following information from the `ncdump -h` command. It includes the dimensions in the file, a list of variables, and global attributes that can be very useful." - ] - }, - { - "cell_type": "markdown", - "id": "c7cdea85-fb03-440b-a65a-53ca69483e02", - "metadata": {}, - "source": [ - "![ncdump](../../images/resources/ncdump_example.png)\n", - "\n", - "*

Figure: Example output from the ncdump -h command.

*" - ] - }, - { - "cell_type": "markdown", - "id": "a06d67d2-b5e5-477a-871b-16dcad514357", - "metadata": {}, - "source": [ - "To view the contents of a variable, use the following command:\n", - "* Note that you must verify the variable exists in the file by first doing a the command above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74324a3f-7129-4b6b-96e8-d3d831247d68", - "metadata": {}, - "outputs": [], - "source": [ - "ncdump -v PSL file.nc" - ] - }, - { - "cell_type": "markdown", - "id": "73922a90-4f0e-488a-a1e5-81d07141a216", - "metadata": {}, - "source": [ - "To view the netCDF file type, use the following command:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1aa78eba-bdab-48fe-a563-6dae2747af2f", - "metadata": {}, - "outputs": [], - "source": [ - "ncdump –k file.nc" - ] - }, - { - "cell_type": "markdown", - "id": "86ba17a2-a119-40d7-bda2-1c0b40694596", - "metadata": {}, - "source": [ - "To print readable date-time strings, use the following command:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e52ddeec-7fe3-49fb-ac4c-e243f5263b72", - "metadata": {}, - "outputs": [], - "source": [ - "ncdump –t –v time file.nc" - ] - }, - { - "cell_type": "markdown", - "id": "b9478866-10ee-4b8f-a80f-7e58f840872c", - "metadata": {}, - "source": [ - "## ncview\n", - "\n", - "`ncview` is a graphical interface which allows the user to quickly view the variables inside a NetCDF file. `ncview` also allows the user to interactively visualize a selected variable aross a selected range (time, spatial).\n", - "\n", - "**NOTE:** `ncview` **must be loaded as a module in your HPC environment.** See section (LINK)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "95c41eb1-01cc-4f24-90fb-ca85ed0da51f", - "metadata": {}, - "outputs": [], - "source": [ - "ncview file.nc" - ] - }, - { - "cell_type": "markdown", - "id": "f31e7620-b199-4fd7-8b9c-715b199c0436", - "metadata": {}, - "source": [ - "![ncview](../../images/resources/ncview.png)\n", - "\n", - "*

Figure: Example output from ncview command.

*" - ] - }, - { - "cell_type": "markdown", - "id": "674a8519-2434-4d87-a418-feb10005b83a", - "metadata": {}, - "source": [ - "## netCDF Operators (NCO) \n", - "\n", - "NCO is a suite of programs designed to perform certain “operations” on netCDF files, i.e., things like averaging, concatenating, subsetting, or metadata manipulation.Command-line operations are extremely useful for processing model data given that modelers often work in a UNIX-type environment.\n", - "\n", - "We will describe a few key types of NCO operators below, but there are many other NCO operators beyond these that could be of use. We recommend visiting the NCO page to get full documentation.\n", - "\n", - "**NOTE:** `nco` **must be loaded as a module in your HPC environment.** See section (LINK)." - ] - }, - { - "cell_type": "markdown", - "id": "fab944fa-04c3-4843-af5b-2763970dd0fd", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[NCO github page](https://github.com/nco/nco)\n", - "\n", - "[NCO sourceforge page](http://nco.sourceforge.net)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "de438002-157e-4f2f-8f3f-d0f2c3d01b41", - "metadata": {}, - "source": [ - "`ncra` is a netCDF record averager that averages across the record dimesion (i.e. time) for the files specified. The example below shows the command for how to average across two files from different months." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "604168a3-5268-43f6-a002-3f4906b9caf9", - "metadata": {}, - "outputs": [], - "source": [ - "ncra file1.nc file2.nc avgfile.nc" - ] - }, - { - "cell_type": "markdown", - "id": "575d65ac-82aa-4d13-9702-9c63ce84321e", - "metadata": {}, - "source": [ - "`ncrcat` is a netCDF record concatenator that combines the files specified across the record dimesion (i.e. time). The example below shows the command for how to concatenate two files from different months into a single file that will have two timesteps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "88a35e2d-64e0-4c9a-9764-7a4587d25a28", - "metadata": {}, - "outputs": [], - "source": [ - "ncrcat file1.nc file2.nc out12.nc" - ] - }, - { - "cell_type": "markdown", - "id": "8b756d23-71bb-45f4-ba63-83b8ee50f781", - "metadata": {}, - "source": [ - "`ncdiff` is a netCDF differener that will reveal differences between files. The example below shows the command for how to create a file showing differences between two files from different months." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca1c37fc-ab49-4769-aa39-e4804b01dd83", - "metadata": {}, - "outputs": [], - "source": [ - "ncdiff file1.nc file2.nc diff.nc" - ] - }, - { - "cell_type": "markdown", - "id": "91216ea2-7278-4887-8a6f-f9a2e1e4eedb", - "metadata": {}, - "source": [ - "`ncks` stands for netCDF kitchen sink and has numerous functions that may be useful. The examples below show \n", - "1) how to use NCO operator options to subset only some specified variables from the input file because the `-v` option operates only on the variables listed.\n", - "2) how to use NCO operator options to subset only a given latitude and longitude range because the `-d` option operates on the dimensions. Note that real numbers indicate coordinate values while integers indicate array indexes. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0d9ba792-79e3-4bdd-9823-816110b5d0a1", - "metadata": {}, - "outputs": [], - "source": [ - "ncks –v T,U,PS file_in.nc file_out.nc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c549782-7aba-4de7-816f-5dcea3264e81", - "metadata": {}, - "outputs": [], - "source": [ - "ncks -d lon,0.,180. -d lat,0,63 file_in.nc file_out.nc" - ] - }, - { - "cell_type": "markdown", - "id": "6b81015d-05e5-44e9-9c3f-6ea31e8eb44f", - "metadata": {}, - "source": [ - "## Climate Data Operators (CDO) \n", - "\n", - "CDO are a suite of command line operators to manipulate and analyze climate and NWP model data. CDO are similar to NCO. The CDO library inclues over 600 command line operators that do a variety of tasks including: detrending, EOF analysis, metadata modification, statistical analysis, etc. \n", - "\n", - "**NOTE:** `cdo` **must be loaded as a module in your HPC environment.** See section (LINK)." - ] - }, - { - "cell_type": "markdown", - "id": "cd5527e1-d0c7-4108-81a9-357a82110f82", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CDO github page](https://github.com/AZed/cdo)\n", - "\n", - "[CDO project page](https://code.mpimet.mpg.de/projects/cdo/)\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Bash", - "language": "bash", - "name": "bash" - }, - "language_info": { - "codemirror_mode": "shell", - "file_extension": ".sh", - "mimetype": "text/x-sh", - "name": "bash" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/porting.ipynb b/notebooks_copy/resources/porting.ipynb deleted file mode 100644 index c84e59b20..000000000 --- a/notebooks_copy/resources/porting.ipynb +++ /dev/null @@ -1,84 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Porting" - ] - }, - { - "cell_type": "markdown", - "id": "fe00d177-b2e2-4067-b1fe-95b0a1d45f3d", - "metadata": {}, - "source": [ - "Moving CESM to a new machine that has not been previously setup requires knowledge of both the CESM model and the target machine architecture and software. Porting details can also be found in specialized lecture slides from previous CESM tutorials.
" - ] - }, - { - "cell_type": "markdown", - "id": "52b78297-4829-477b-8018-6d80512389a5", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[CIME documentation (see part 2)](http://esmci.github.io/cime/)\n", - "\n", - "[Porting CESM2](http://www.cesm.ucar.edu/models/cesm2/linux_cluster/)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "080839b4-514f-4072-88a6-bd514424cf6e", - "metadata": {}, - "source": [ - "![Porting Image](../../images/resources/CESM2_Porting.png)\n", - "*

Figure: Porting CESM2 to the CGD Cluster Hobart

*" - ] - }, - { - "cell_type": "markdown", - "id": "f1ec0337-eadf-46e3-80b5-2085854b9742", - "metadata": {}, - "source": [ - "# Take-away Points\n", - "- On supported machines - no porting is necessary\n", - "- On new machines - porting needs to be done\n", - "- Documentation from CIME and CESM Website\n", - "- Thursday afternoon lecture on Porting" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d2de6735-4420-4b9d-bae1-3384a34de010", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/profile.ipynb b/notebooks_copy/resources/profile.ipynb deleted file mode 100644 index 328c5f7c3..000000000 --- a/notebooks_copy/resources/profile.ipynb +++ /dev/null @@ -1,413 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b2cd974a-dfbb-4937-8ed0-ab87ab60f1ef", - "metadata": {}, - "source": [ - "# Tutorial specific instructions" - ] - }, - { - "cell_type": "markdown", - "id": "4a6f76fe-9658-443c-89a7-9ede364803e5", - "metadata": {}, - "source": [ - "### Tutorial Project Account\n", - "\n", - "
\n", - "You should have access to project account UESM0013 and use this for your simulations.\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "9db9d54e-ef78-44ef-a003-4ddd8a416317", - "metadata": {}, - "source": [ - "### Special Queues\n", - "\n", - "
\n", - "We have a special queue every day for running to ensure you get through the derecho queues quickly and get your jobs run. These are only active for portions of each day during our lab sessions and change for each session. So you should be sure you are using the correct reservation queue. \n", - " \n", - "Schedule for Tutorial Reservation Queues\n", - "```\n", - "Mon 14:00 - 17:00 derecho R???????\n", - "Tue 10:30 - 12:00 derecho R???????\n", - " 14:00 - 17:00 derecho R???????\n", - "Wed 10:30 - 12:00 derecho R???????\n", - " 14:00 - 17:00 derecho R???????\n", - "Thu 13:30 - 17:00 casper R???????\n", - "Fri 13:30 - 17:00 derecho R??????? \n", - "```\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e127deda-73f0-42fc-9c96-e49bb1bb0f8b", - "metadata": {}, - "source": [ - "### Using these tutorial-specific codes" - ] - }, - { - "cell_type": "markdown", - "id": "ec3ec832-823f-48ce-b142-05c69dcc72b1", - "metadata": {}, - "source": [ - "In theory, you _should_ be able to automatically use this account and the reservation queues if you correctly set up your NCAR HPC environment using your `.profile` or `.tcshrc` files, as described below. If you need to change the values for a run, you can change the account and reservation queue in your case directory using the following command:\n", - "\n", - "```\n", - "./xmlchange --force JOB_QUEUE=R???????\n", - "./xmlchange PROJECT=UESM0013\n", - "```\n", - "\n", - "For instance, on Wednesday\n", - "\n", - "- If (Wed 10:30 - 12:00) then\n", - "\n", - "```\n", - "./xmlchange JOB_QUEUE=R??????? --force\n", - "``` \n", - "\n", - "- If (Wed 14:00 - 17:00) then\n", - "\n", - "```\n", - "./xmlchange JOB_QUEUE=R??????? --force\n", - "```\n", - "\n", - "\n", - "- If (Wed at another time) then\n", - "```\n", - "./xmlchange JOB_QUEUE=regular\n", - "```\n", - "\n", - "If you do use these commands, make sure for the `JOB_QUEUE` you are using the **correct** reservation code for the relevant day _and_ time of that lab session. You should not have to set the `PROJECT` more than once during the tutorial if your profile is set up, as described below, but we include it for your reference in case you are having trouble." - ] - }, - { - "cell_type": "markdown", - "id": "0c07b51d-f999-4737-9739-3ea989f20802", - "metadata": {}, - "source": [ - "## Setting up your NCAR HPC environment" - ] - }, - { - "cell_type": "markdown", - "id": "9d6954eb-3505-4a58-b6fa-bea7047c63cf", - "metadata": {}, - "source": [ - "Every time you log onto the NCAR HPC you want to ensure you have the correct modules loaded." - ] - }, - { - "cell_type": "markdown", - "id": "7440f1b8-06af-435a-b1db-d444b99bf846", - "metadata": {}, - "source": [ - "
\n", - "NOTE: The next section describes how to use a file that loads the modules necessary for those attending the in-person tutorial. Once you set up your environment you should not have to do it again for the remainder of the tutorial. Every time you login to NCAR HPC these files will automatically be sourced. However, if you intend to use NCAR HPC after the tutorial you may need to modify your `.profile` or `.tcshrc` file to load the correct modules, project numbers, or queues. \n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "8e6da3c0-4cbf-42e3-b599-9ef4d1b4a72c", - "metadata": {}, - "source": [ - "___\n", - "\n", - "### SHELL environment " - ] - }, - { - "cell_type": "markdown", - "id": "c04124a8-5e76-4104-8d62-7bd0a329ce58", - "metadata": {}, - "source": [ - "To determine what shell environment you are using on the NCAR HPC, type the following command:" - ] - }, - { - "cell_type": "markdown", - "id": "9f8488f1-72e7-4594-be66-f875c51cc4e1", - "metadata": {}, - "source": [ - "
\n", - "\n", - "```\n", - "echo $SHELL\n", - "```\n", - "\n", - " \n", - "If the command returns, you are a bash user\n", - "```/bin/bash```\n", - "\n", - " \n", - "If the command returns, you are a cshell user\n", - "```/bin/tcsh```\n", - "\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "91a8addf-1e89-43b9-a364-1e1aa151454d", - "metadata": {}, - "source": [ - "**NOTE:** All new NCAR HPC accounts default to bash. " - ] - }, - { - "cell_type": "markdown", - "id": "8c4f6e24-577b-4736-a251-69b51553d704", - "metadata": {}, - "source": [ - "___\n", - "\n", - "### Bash users" - ] - }, - { - "cell_type": "markdown", - "id": "a82c1d5f-eddc-4504-bd66-febd2f3179f8", - "metadata": {}, - "source": [ - "**NOTE:** You may already have a `.profile` file in your home directory. \n", - "- If you have an existing `.profile` file and do not wish to overwrite its contents, please save your original `.profile` file to a new filename before copying the tutorial .profile file into your home directory. \n", - "- Alternatively, you could copy the contents of the tutorial profile file into your existing `.profile` file using a text editor. (see special instructions below)" - ] - }, - { - "cell_type": "markdown", - "id": "870dc6c7-98f5-4e3a-b226-2f2437d5321a", - "metadata": {}, - "source": [ - "#### One-time setup\n", - "To set up your environment for the tutorial, follow the following three steps:" - ] - }, - { - "cell_type": "markdown", - "id": "9a2baee0-804e-4586-b8fc-a3636c528484", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Copy over the `.profile` file into your home directory: \n", - " \n", - "```\n", - "cp /glade/campaign/cesm/development/cross-wg/profile ~/.profile\n", - "```\n", - "
\n", - "\n", - "Source the file:\n", - "\n", - "```\n", - "source ~/.profile\n", - "```\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "bf95578e-40e5-4c40-8884-0b80e29eb858", - "metadata": {}, - "source": [ - "\n", - "#### Before each practical\n", - "Do the following step before **every practical**. This is because we are getting a different special queue every day. \n", - "\n", - "
\n", - "\n", - "Source the file:
\n", - "\n", - "```\n", - "source ~/.profile\n", - "```\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "4daab2a7-58c1-41f0-b51b-c9e8a573574b", - "metadata": {}, - "source": [ - "___\n", - "\n", - "### Tcsh users" - ] - }, - { - "cell_type": "markdown", - "id": "42432d90-eaef-4205-b4fe-39cf3c7b5881", - "metadata": {}, - "source": [ - "**NOTE:** You should already have a `.tcshrc` file in your home directory. If you do not wish to overwrite the existing `.tcshrc` file, please save this file to a new name OR copy the contents of the `/glade/campaign/cesm/development/cross-wg/tcshrc` file to your `.tcshrc` file using a text editor." - ] - }, - { - "cell_type": "markdown", - "id": "0722df3e-2497-492c-8301-7d54e7485c05", - "metadata": {}, - "source": [ - "#### One-time setup\n", - "\n", - "To set up your environment for the tutorial, follow the following three steps:" - ] - }, - { - "cell_type": "markdown", - "id": "a77636dd-1651-431a-a72e-f862947b37a6", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
\n", - "\n", - "Copy over the `.tcshrc` file to your home directory:\n", - "```\n", - "cp /glade/campaign/cesm/development/cross-wg/tcshrc ~/.tcshrc\n", - "```\n", - " \n", - "
\n", - "\n", - "Source the file:\n", - "\n", - "```\n", - "source ~/.tcshrc\n", - "```\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "637d5219-104b-4e53-ab41-b3969b275527", - "metadata": {}, - "source": [ - "\n", - "#### Before each practical\n", - "\n", - "Do the following step before **every practical**. This is because we are getting a different special queue every day. \n", - "\n", - "
\n", - " \n", - "Source the file:
\n", - "\n", - "```\n", - "source ~/.tcshrc\n", - "``` \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "5edccc8b-0080-4837-aa70-d50360cb2c6c", - "metadata": {}, - "source": [ - "___\n", - "\n", - "### If you have any trouble\n", - "\n", - "**Please ask for help if you are having trouble with this step!**\n", - "\n", - "The modules necessary for this tutorial and loaded through these files are:\n", - "- ncarenv/1.3\n", - "- intel\n", - "- ncl\n", - "- nco\n", - "- ncview\n", - "- netcdf\n" - ] - }, - { - "cell_type": "markdown", - "id": "71cae660-0d1a-4193-8333-909310c49dac", - "metadata": {}, - "source": [ - "____\n", - "\n", - "### Special instructions (if you already had an account on derecho)\n", - "\n", - "If you already have a `.profile` or `.tcsh` and you do not wish to overwrite its contents, you need to add the following into your existing `.profile` or `.tcsh` using a text editor.\n", - "\n", - "#### Bash:\n", - "\n", - "
\n", - "\n", - "Add to your .profile: \n", - "\n", - "```\n", - "export PROJECT=UESM0013 #<- This should never change over the course of the tutorial\n", - "export TUTORIAL_QUEUE=R??????? #<- This will be different for every practical. \n", - " #<- Look at the schedule below:\n", - "```\n", - "\n", - "Source the file:
\n", - "\n", - "```\n", - "source ~/.profile\n", - "```\n", - "
\n", - "\n", - "#### C-shell:\n", - "\n", - "
\n", - "\n", - "Add to your .tcshrc: \n", - "```\n", - "setenv PROJECT UESM0013 #<-This should never change over the course of the tutorial\n", - "setenv TUTORIAL_QUEUE R??????? #<-This will be different for every practical \n", - " #<- Look at the schedule below:\n", - "```\n", - " \n", - "Source the file:
\n", - "\n", - "```\n", - "source ~/.tcshrc\n", - "``` \n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b77ea28-df80-4c20-a123-ec1e09a1475a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/resources_overview.ipynb b/notebooks_copy/resources/resources_overview.ipynb deleted file mode 100644 index f9ad4a385..000000000 --- a/notebooks_copy/resources/resources_overview.ipynb +++ /dev/null @@ -1,49 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0959db1d-1790-4519-a34e-7026444af537", - "metadata": {}, - "source": [ - "# Resources" - ] - }, - { - "cell_type": "markdown", - "id": "43fca83b-54e5-4950-a9b3-bbfbbcb01c77", - "metadata": {}, - "source": [ - "While there is no way for this tutorial to provide every bit of information or resources needed to successfully learn CESM, this section provides some resources that you may find particularly helpful." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6dc4950b-9954-4019-9c0b-46a3e3b0b3aa", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/terminals.ipynb b/notebooks_copy/resources/terminals.ipynb deleted file mode 100644 index ed38fc238..000000000 --- a/notebooks_copy/resources/terminals.ipynb +++ /dev/null @@ -1,204 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Terminal Windows" - ] - }, - { - "cell_type": "markdown", - "id": "93b754e9-2569-4779-890f-f3f069fd9faa", - "metadata": {}, - "source": [ - "For local or remote execution of a program, a terminal is a window in a graphical interface that is used to display a command line. In the UNIX environment, the terminal window is widely used by developers to perform myriad maintenance operations in local and remote computers. \n", - "\n", - "Most CESM users and developers use terminal windows to run the model. Users open a terminal window and access the NCAR HPC resources remotely. This way the user can be located anywhere and access the HPC assets located in Cheyenne, WY." - ] - }, - { - "cell_type": "markdown", - "id": "c3c5cecc-b0ef-4bd2-bb50-5f92efeb12c7", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Logging into NCAR HPC](https://ncar.github.io/CESM-Tutorial/notebooks/resources/ncar_hpc.html#logging-in-on-an-ncar-system)\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "75f18a09-8ff0-4944-b19f-a51d6473e645", - "metadata": {}, - "source": [ - "## Logging into a terminal from a Mac" - ] - }, - { - "cell_type": "markdown", - "id": "fa3a2300-0e3a-4b87-9456-0c741acb9e96", - "metadata": {}, - "source": [ - "Go to the search spotlight (magnifying glass icon) and type in \"terminal\". \n", - "\n", - "- `Terminal` is automatically built into the MacOS and clicking on it will open a terminal window.\n", - "\n", - "- `iTerm` is software that was installed by the user on their laptop and is another option for opening a terminal. (*Recommended*)" - ] - }, - { - "cell_type": "markdown", - "id": "d4ad6ecc-155e-40f8-965f-a223c119b85c", - "metadata": {}, - "source": [ - "![mac terminal](../../images/resources/term_mac.png)\n", - "\n", - "*

Figure: Finding terminal on a mac.

*" - ] - }, - { - "cell_type": "markdown", - "id": "6b6d2dbf-f7e1-40a1-a81a-a147189a133a", - "metadata": {}, - "source": [ - "## Logging into a terminal from a PC" - ] - }, - { - "cell_type": "markdown", - "id": "1a24b174-9a10-4863-91b2-7a0408cc9d03", - "metadata": {}, - "source": [ - "Go to the start menu and type in \"terminal\". \n", - "\n", - "- `Terminal` will open a terminal window.\n", - "\n", - "- `Command Prompt` will open a terminal window." - ] - }, - { - "cell_type": "markdown", - "id": "705e15f9-ae66-45a6-a9e3-c212cbc83ba1", - "metadata": {}, - "source": [ - "![pc terminal](../../images/resources/term_pc.png)\n", - "\n", - "*

Figure: Finding terminal on a PC.

*" - ] - }, - { - "cell_type": "markdown", - "id": "99e35425-03b2-40ed-a1ff-2d19655bbdad", - "metadata": {}, - "source": [ - "## Logging into a terminal from JupyterHub" - ] - }, - { - "cell_type": "markdown", - "id": "8db0641a-0960-4e2e-9596-cb97f37b84ca", - "metadata": {}, - "source": [ - "If you have a JupyterHub session open you can open a terminal window as well. \n", - "\n", - "- Click on the + symbol in the upper left for a New Launcher\n", - "- Click on the Terminal icon\n", - "\n", - "Note: some terminal functions (e.g. X11 forwarding) do not work in a terminal opened through JupyterHub." - ] - }, - { - "cell_type": "markdown", - "id": "750465fe-e05d-4d1c-b33c-2c82db333f67", - "metadata": {}, - "source": [ - "![JH terminal](../../images/resources/term_jupyter_1.png)\n", - "\n", - "*

Figure: Opening a terminal using JupyterHub.

*" - ] - }, - { - "cell_type": "markdown", - "id": "d80a918d-4cbc-47aa-b10c-a37f255e6f0f", - "metadata": {}, - "source": [ - "![JH terminal](../../images/resources/term_jupyter_2.png)\n", - "\n", - "*

Figure: Terminal interface in JupyterHub.

*" - ] - }, - { - "cell_type": "markdown", - "id": "9004d0e1-c472-4e9a-9115-0a77fd5d3ee4", - "metadata": {}, - "source": [ - "## Logging into a terminal via fastX" - ] - }, - { - "cell_type": "markdown", - "id": "ee17bae0-38d6-4556-94d0-5aada3603a77", - "metadata": {}, - "source": [ - "CISL has provided an option to log into casper via the fastX software. Detailed instructions are provided [here](https://arc.ucar.edu/knowledge_base/72581391)\n", - "\n", - "- Note that you will not be able to use the first option \"Using FastX via web browser\" as this requires being on the internal UCAR network or VPN. \n", - "\n", - "- If you are able to install the [FastX desktop client](https://arc.ucar.edu/knowledge_base/72581391#UsingFastXforremotedesktops-UsingtheFastXdesktopclient), we would recommend that option\n", - "\n", - "- Otherwise, you can create an [ssh tunnel via a terminal window and then start the browser client](https://arc.ucar.edu/knowledge_base/72581391#UsingFastXforremotedesktops-UsingFastXviawebbrowserandsshtunnel)\n" - ] - }, - { - "cell_type": "markdown", - "id": "da9e1c05-6abb-41ca-ac87-fb7e8b5f463c", - "metadata": {}, - "source": [ - "## Using PuTTY and Xmg" - ] - }, - { - "cell_type": "markdown", - "id": "6b212673-a637-4073-9be5-817d32696560", - "metadata": {}, - "source": [ - "For older Windows laptops, it may require the use of PuTTY and Xmg. These might be installed on your machine already, but if not you can download them. Here is a [nice guide](https://laptops.eng.uci.edu/engineering-software/using-linux/how-to-configure-putty-xming-on-your-laptop) from UC-Irvine.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c94441cd-66f7-4fe9-8ddf-2123048149b9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/text_editors.ipynb b/notebooks_copy/resources/text_editors.ipynb deleted file mode 100644 index 2f8e95c80..000000000 --- a/notebooks_copy/resources/text_editors.ipynb +++ /dev/null @@ -1,175 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Text Editors " - ] - }, - { - "cell_type": "markdown", - "id": "d0122319-defa-49ca-a794-91cfc36fb332", - "metadata": {}, - "source": [ - "You will need to edit files directly through the command line when using UNIX. There are quite a few text editors available for UNIX. Below we provide some information about some of the favorite text editors from CESM developers. This list is not comprehensive, so if you have another editor you prefer you can use that." - ] - }, - { - "cell_type": "markdown", - "id": "947bea15-69c9-47e9-815c-f07d89327bab", - "metadata": {}, - "source": [ - "## Vi" - ] - }, - { - "cell_type": "markdown", - "id": "2692e830-0863-4bef-98f7-7796a3bece8e", - "metadata": {}, - "source": [ - "Vi is a text editor included with most UNIX systems, even embedded ones. It was designed to be simple yet powerful for text manipulation. It's a modal text editor, which means has both an insert and command mode. Vi doesn't provide a list of keyboard shortcuts on the screen. \n", - "\n", - "**NOTE:** `vi` **does not need to be loaded in your environment to be used.**" - ] - }, - { - "cell_type": "markdown", - "id": "244ecc7f-b49b-4fba-ad36-9e816701fbb9", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Vi documentation](http://linuxfocus.org/~guido/vi/)\n", - " \n", - "[Vi Introduction](https://www.howtogeek.com/102468/a-beginners-guide-to-editing-text-files-with-vi/)\n", - " \n", - "[Vi cheatsheet](https://www.atmos.albany.edu/daes/atmclasses/atm350/vi_cheat_sheet.pdf)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "da6fee1f-f259-4922-a667-bf903d3536dd", - "metadata": {}, - "source": [ - "## Vim" - ] - }, - { - "cell_type": "markdown", - "id": "9460aa4b-741d-4052-a6a7-82730d5837b7", - "metadata": {}, - "source": [ - "Vim (Vi IMproved) is a clone of Vi but offers more features than Vi. It's free and open source and can be used both at the command-line or in a graphical user interface (GUI).\n", - "\n", - "**NOTE:** `vim` **does not need to be loaded in your environment to be used.**" - ] - }, - { - "cell_type": "markdown", - "id": "bcd11fc0-e9e4-4534-b79c-3963b6b98f7f", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Vi documentation](https://www.vim.org/docs.php)\n", - " \n", - "[Vi cheatsheet](https://vim.rtorr.com/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "234894b0-4983-4d0e-aca1-30c466521c50", - "metadata": {}, - "source": [ - "## Nano" - ] - }, - { - "cell_type": "markdown", - "id": "5e62b82c-dbf8-4724-8e82-869f944f0ba4", - "metadata": {}, - "source": [ - "Nano is a text editor that uses a command line interface. It emulates the Pico text editor and in addition to basic text editing, nanco has features such as an interactive search-and-replace, undo/redo, etc.\n", - "\n", - "**NOTE:** `nano` **must be loaded as a module in your HPC environment.** See section (LINK)." - ] - }, - { - "cell_type": "markdown", - "id": "cf16bd44-45f4-4bb4-927b-eafc24d9aeb1", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Nano documentation](https://www.nano-editor.org/docs.php)\n", - "\n", - "[Nano Introduction](https://www.howtogeek.com/42980/the-beginners-guide-to-nano-the-linux-command-line-text-editor/)\n", - " \n", - "[Nano cheatsheet](https://www.nano-editor.org/dist/latest/cheatsheet.html)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "69a56d2b-7508-4df0-9dc1-33f1f0887127", - "metadata": {}, - "source": [ - "## Emacs" - ] - }, - { - "cell_type": "markdown", - "id": "342a8b59-a009-4974-9878-66e78c46effc", - "metadata": {}, - "source": [ - "Emacs is an extensible, customizable, and self-documenting editor. Emacs opens a GUI to your computer and requires X11 forwarding. Once open, it can be used similar to a normal document including scrolling.\n", - "\n", - "**NOTE:** `emacs` **does not need to be loaded in your environment to be used. While it will work from the terminal command line, it does not work in the Jupyter environment.**" - ] - }, - { - "cell_type": "markdown", - "id": "0761f906-a868-491e-a89d-4d4a6f6f7bcf", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[Emacs documentation](https://www.gnu.org/software/emacs/manual/html_node/emacs/index.html)\n", - "\n", - "[Emacs Introduction](https://www.gnu.org/software/emacs/tour/)\n", - " \n", - "[Emacs cheatsheet](https://www.gnu.org/software/emacs/refcards/pdf/refcard.pdf)\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/tutorial_specific.ipynb b/notebooks_copy/resources/tutorial_specific.ipynb deleted file mode 100644 index 328c5f7c3..000000000 --- a/notebooks_copy/resources/tutorial_specific.ipynb +++ /dev/null @@ -1,413 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b2cd974a-dfbb-4937-8ed0-ab87ab60f1ef", - "metadata": {}, - "source": [ - "# Tutorial specific instructions" - ] - }, - { - "cell_type": "markdown", - "id": "4a6f76fe-9658-443c-89a7-9ede364803e5", - "metadata": {}, - "source": [ - "### Tutorial Project Account\n", - "\n", - "
\n", - "You should have access to project account UESM0013 and use this for your simulations.\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "9db9d54e-ef78-44ef-a003-4ddd8a416317", - "metadata": {}, - "source": [ - "### Special Queues\n", - "\n", - "
\n", - "We have a special queue every day for running to ensure you get through the derecho queues quickly and get your jobs run. These are only active for portions of each day during our lab sessions and change for each session. So you should be sure you are using the correct reservation queue. \n", - " \n", - "Schedule for Tutorial Reservation Queues\n", - "```\n", - "Mon 14:00 - 17:00 derecho R???????\n", - "Tue 10:30 - 12:00 derecho R???????\n", - " 14:00 - 17:00 derecho R???????\n", - "Wed 10:30 - 12:00 derecho R???????\n", - " 14:00 - 17:00 derecho R???????\n", - "Thu 13:30 - 17:00 casper R???????\n", - "Fri 13:30 - 17:00 derecho R??????? \n", - "```\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "e127deda-73f0-42fc-9c96-e49bb1bb0f8b", - "metadata": {}, - "source": [ - "### Using these tutorial-specific codes" - ] - }, - { - "cell_type": "markdown", - "id": "ec3ec832-823f-48ce-b142-05c69dcc72b1", - "metadata": {}, - "source": [ - "In theory, you _should_ be able to automatically use this account and the reservation queues if you correctly set up your NCAR HPC environment using your `.profile` or `.tcshrc` files, as described below. If you need to change the values for a run, you can change the account and reservation queue in your case directory using the following command:\n", - "\n", - "```\n", - "./xmlchange --force JOB_QUEUE=R???????\n", - "./xmlchange PROJECT=UESM0013\n", - "```\n", - "\n", - "For instance, on Wednesday\n", - "\n", - "- If (Wed 10:30 - 12:00) then\n", - "\n", - "```\n", - "./xmlchange JOB_QUEUE=R??????? --force\n", - "``` \n", - "\n", - "- If (Wed 14:00 - 17:00) then\n", - "\n", - "```\n", - "./xmlchange JOB_QUEUE=R??????? --force\n", - "```\n", - "\n", - "\n", - "- If (Wed at another time) then\n", - "```\n", - "./xmlchange JOB_QUEUE=regular\n", - "```\n", - "\n", - "If you do use these commands, make sure for the `JOB_QUEUE` you are using the **correct** reservation code for the relevant day _and_ time of that lab session. You should not have to set the `PROJECT` more than once during the tutorial if your profile is set up, as described below, but we include it for your reference in case you are having trouble." - ] - }, - { - "cell_type": "markdown", - "id": "0c07b51d-f999-4737-9739-3ea989f20802", - "metadata": {}, - "source": [ - "## Setting up your NCAR HPC environment" - ] - }, - { - "cell_type": "markdown", - "id": "9d6954eb-3505-4a58-b6fa-bea7047c63cf", - "metadata": {}, - "source": [ - "Every time you log onto the NCAR HPC you want to ensure you have the correct modules loaded." - ] - }, - { - "cell_type": "markdown", - "id": "7440f1b8-06af-435a-b1db-d444b99bf846", - "metadata": {}, - "source": [ - "
\n", - "NOTE: The next section describes how to use a file that loads the modules necessary for those attending the in-person tutorial. Once you set up your environment you should not have to do it again for the remainder of the tutorial. Every time you login to NCAR HPC these files will automatically be sourced. However, if you intend to use NCAR HPC after the tutorial you may need to modify your `.profile` or `.tcshrc` file to load the correct modules, project numbers, or queues. \n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "8e6da3c0-4cbf-42e3-b599-9ef4d1b4a72c", - "metadata": {}, - "source": [ - "___\n", - "\n", - "### SHELL environment " - ] - }, - { - "cell_type": "markdown", - "id": "c04124a8-5e76-4104-8d62-7bd0a329ce58", - "metadata": {}, - "source": [ - "To determine what shell environment you are using on the NCAR HPC, type the following command:" - ] - }, - { - "cell_type": "markdown", - "id": "9f8488f1-72e7-4594-be66-f875c51cc4e1", - "metadata": {}, - "source": [ - "
\n", - "\n", - "```\n", - "echo $SHELL\n", - "```\n", - "\n", - " \n", - "If the command returns, you are a bash user\n", - "```/bin/bash```\n", - "\n", - " \n", - "If the command returns, you are a cshell user\n", - "```/bin/tcsh```\n", - "\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "91a8addf-1e89-43b9-a364-1e1aa151454d", - "metadata": {}, - "source": [ - "**NOTE:** All new NCAR HPC accounts default to bash. " - ] - }, - { - "cell_type": "markdown", - "id": "8c4f6e24-577b-4736-a251-69b51553d704", - "metadata": {}, - "source": [ - "___\n", - "\n", - "### Bash users" - ] - }, - { - "cell_type": "markdown", - "id": "a82c1d5f-eddc-4504-bd66-febd2f3179f8", - "metadata": {}, - "source": [ - "**NOTE:** You may already have a `.profile` file in your home directory. \n", - "- If you have an existing `.profile` file and do not wish to overwrite its contents, please save your original `.profile` file to a new filename before copying the tutorial .profile file into your home directory. \n", - "- Alternatively, you could copy the contents of the tutorial profile file into your existing `.profile` file using a text editor. (see special instructions below)" - ] - }, - { - "cell_type": "markdown", - "id": "870dc6c7-98f5-4e3a-b226-2f2437d5321a", - "metadata": {}, - "source": [ - "#### One-time setup\n", - "To set up your environment for the tutorial, follow the following three steps:" - ] - }, - { - "cell_type": "markdown", - "id": "9a2baee0-804e-4586-b8fc-a3636c528484", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Copy over the `.profile` file into your home directory: \n", - " \n", - "```\n", - "cp /glade/campaign/cesm/development/cross-wg/profile ~/.profile\n", - "```\n", - "
\n", - "\n", - "Source the file:\n", - "\n", - "```\n", - "source ~/.profile\n", - "```\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "bf95578e-40e5-4c40-8884-0b80e29eb858", - "metadata": {}, - "source": [ - "\n", - "#### Before each practical\n", - "Do the following step before **every practical**. This is because we are getting a different special queue every day. \n", - "\n", - "
\n", - "\n", - "Source the file:
\n", - "\n", - "```\n", - "source ~/.profile\n", - "```\n", - " \n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "4daab2a7-58c1-41f0-b51b-c9e8a573574b", - "metadata": {}, - "source": [ - "___\n", - "\n", - "### Tcsh users" - ] - }, - { - "cell_type": "markdown", - "id": "42432d90-eaef-4205-b4fe-39cf3c7b5881", - "metadata": {}, - "source": [ - "**NOTE:** You should already have a `.tcshrc` file in your home directory. If you do not wish to overwrite the existing `.tcshrc` file, please save this file to a new name OR copy the contents of the `/glade/campaign/cesm/development/cross-wg/tcshrc` file to your `.tcshrc` file using a text editor." - ] - }, - { - "cell_type": "markdown", - "id": "0722df3e-2497-492c-8301-7d54e7485c05", - "metadata": {}, - "source": [ - "#### One-time setup\n", - "\n", - "To set up your environment for the tutorial, follow the following three steps:" - ] - }, - { - "cell_type": "markdown", - "id": "a77636dd-1651-431a-a72e-f862947b37a6", - "metadata": {}, - "source": [ - "
\n", - "\n", - "
\n", - "\n", - "Copy over the `.tcshrc` file to your home directory:\n", - "```\n", - "cp /glade/campaign/cesm/development/cross-wg/tcshrc ~/.tcshrc\n", - "```\n", - " \n", - "
\n", - "\n", - "Source the file:\n", - "\n", - "```\n", - "source ~/.tcshrc\n", - "```\n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "637d5219-104b-4e53-ab41-b3969b275527", - "metadata": {}, - "source": [ - "\n", - "#### Before each practical\n", - "\n", - "Do the following step before **every practical**. This is because we are getting a different special queue every day. \n", - "\n", - "
\n", - " \n", - "Source the file:
\n", - "\n", - "```\n", - "source ~/.tcshrc\n", - "``` \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "5edccc8b-0080-4837-aa70-d50360cb2c6c", - "metadata": {}, - "source": [ - "___\n", - "\n", - "### If you have any trouble\n", - "\n", - "**Please ask for help if you are having trouble with this step!**\n", - "\n", - "The modules necessary for this tutorial and loaded through these files are:\n", - "- ncarenv/1.3\n", - "- intel\n", - "- ncl\n", - "- nco\n", - "- ncview\n", - "- netcdf\n" - ] - }, - { - "cell_type": "markdown", - "id": "71cae660-0d1a-4193-8333-909310c49dac", - "metadata": {}, - "source": [ - "____\n", - "\n", - "### Special instructions (if you already had an account on derecho)\n", - "\n", - "If you already have a `.profile` or `.tcsh` and you do not wish to overwrite its contents, you need to add the following into your existing `.profile` or `.tcsh` using a text editor.\n", - "\n", - "#### Bash:\n", - "\n", - "
\n", - "\n", - "Add to your .profile: \n", - "\n", - "```\n", - "export PROJECT=UESM0013 #<- This should never change over the course of the tutorial\n", - "export TUTORIAL_QUEUE=R??????? #<- This will be different for every practical. \n", - " #<- Look at the schedule below:\n", - "```\n", - "\n", - "Source the file:
\n", - "\n", - "```\n", - "source ~/.profile\n", - "```\n", - "
\n", - "\n", - "#### C-shell:\n", - "\n", - "
\n", - "\n", - "Add to your .tcshrc: \n", - "```\n", - "setenv PROJECT UESM0013 #<-This should never change over the course of the tutorial\n", - "setenv TUTORIAL_QUEUE R??????? #<-This will be different for every practical \n", - " #<- Look at the schedule below:\n", - "```\n", - " \n", - "Source the file:
\n", - "\n", - "```\n", - "source ~/.tcshrc\n", - "``` \n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b77ea28-df80-4c20-a123-ec1e09a1475a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "toc-showcode": false - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/unix-cheatsheet.ipynb b/notebooks_copy/resources/unix-cheatsheet.ipynb deleted file mode 100644 index 0ddd8e275..000000000 --- a/notebooks_copy/resources/unix-cheatsheet.ipynb +++ /dev/null @@ -1,114 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# UNIX cheatsheet\n", - "\n", - "This Unix commands cheat sheet summarizes a few basic commands you will need during the tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "76bc874f-270c-4a05-8fe9-eac874cb0db7", - "metadata": {}, - "source": [ - "
CommandDescriptionOptionsExamples
lsList files and directories.-l: Long format listing.
-a: Include hidden files.		ls -l
displays files and directories with detailed information.

ls -a shows all files and directories, including hidden ones.
cdChange directory.N/Acd /path/to/directory
changes the current directory to the specified path.
pwdPrint current working directory.N/Apwd
displays the current working directory.
mkdirCreate a new directory.N/Amkdir my_directory
creates a new directory named \"my_directory\".
rmRemove files and directories.-r: Remove directories recursively.
-f: Force removal without confirmation.		rm file.txt
deletes the file named \"file.txt\".
rm -r my_directory
deletes the directory \"my_directory\" and its contents.
rm -f file.txt
forcefully deletes the file \"file.txt\" without confirmation.
cpCopy files and directories.-r: Copy directories recursively.cp -r directory destination
copies the directory \"directory\" and its contents to the specified destination.
cp file.txt destination
copies the file \"file.txt\" to the specified destination.
mvMove/rename files and directories.N/Amv file.txt new_name.txt
renames the file \"file.txt\" to \"new_name.txt\".
mv file.txt directory
moves the file \"file.txt\" to the specified directory.
findSearch for files and directories.-name: Search by filename.find /path/to/search -name “*.txt”
searches for all files with the extension “.txt” in the specified directory.
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "805c8473-8106-4ba6-9d8e-c27cee7fcd7d", - "metadata": {}, - "source": [ - "You can also find a lot of resources online. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "96f010ce-2005-4a69-9081-7ced219e66ea", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/resources/unix.ipynb b/notebooks_copy/resources/unix.ipynb deleted file mode 100644 index 195769a04..000000000 --- a/notebooks_copy/resources/unix.ipynb +++ /dev/null @@ -1,95 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# UNIX" - ] - }, - { - "cell_type": "markdown", - "id": "011c91f1-75f0-496e-a329-45ed70297e86", - "metadata": {}, - "source": [ - "Most CESM users and developers use the UNIX environment to set up CESM experiments and do preliminary analysis of their CESM model output data. We strongly suggest those using CESM become familiar with working in a command line environment." - ] - }, - { - "cell_type": "markdown", - "id": "31e68033-a4f3-4866-8048-f2f57900f98b", - "metadata": {}, - "source": [ - "## Some Unix Basics\n", - "\n", - "The module listed below, from The COMET Program, will help those unfamiliar with UNIX. You can learn about the basics of UNIX file structures, how to navigate in a UNIX environment, and you'll get to practice creating, storing and searching for files.\n", - "\n", - "The expected length is 15-30 minutes for users with some UNIX experience, and 30-60 minutes for novices.\n" - ] - }, - { - "cell_type": "markdown", - "id": "b343574d-dae5-4653-87fd-db6d0748b338", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[UNIX module from COMET](https://www.meted.ucar.edu/ucar/unix/)\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "ef5eda98-33cc-460d-be37-fc080c09a1a2", - "metadata": {}, - "source": [ - "## Unix cheatsheet\n", - "\n", - "This Unix commands cheat sheet summarizes a few basic commands you will need during the tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "399759d6-00b3-41f2-b483-541b84032ce3", - "metadata": {}, - "source": [ - "
\n", - "\n", - "[UNIX cheatsheet](https://ncar.github.io/CESM-Tutorial/notebooks/resources/unix-cheatsheet.html)\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66239ceb-8b7c-4d1b-9d6d-56a8c2ace41c", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/sourcemods/add_fields_cam.ipynb b/notebooks_copy/sourcemods/add_fields_cam.ipynb deleted file mode 100644 index f1b3eb835..000000000 --- a/notebooks_copy/sourcemods/add_fields_cam.ipynb +++ /dev/null @@ -1,123 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Source Modification Example\n", - "\n", - "A common source code modification that you may want to do is to output a **new variable** that is not defined in CESM. \n", - "\n", - "As an example, we will add a new variable to CAM: the atmopsheric **temperature at 750hPa**.\n", - "\n", - "## Adding a variable to CAM\n", - "CAM has a history field that corresponds to the **temperature at 500hPa** and a number of other pressure levels, but not at **750hPa**. Suppose you wanted to output the **temperature at 750hPa**. The following two\n", - "calls are required to add an output variable:\n", - "```\n", - "call addfld(’T750’,...) (Add a field to the master field list)\n", - "call outfld(’T750’,...) (Collect values for this field and write to history file)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "08800317-afcb-42b5-a181-38003b2323aa", - "metadata": {}, - "source": [ - "Each of these are now described in more detail:\n", - "\n", - "## ``addfld``\n", - "The sub-routine ``addfld`` adds a field to the master list with the following syntax:\n", - "```\n", - "addfld(fname,type,avgflag,units,long name)\n", - "```\n", - "where\n", - "\n", - "- **``fname``** = field name\n", - "- **``type``** = the type of field. The entry for a single level field would be “horiz only” and the entry\n", - "for a 3D field would be “(/ ’lev’ /)”.\n", - "- **``avgflag``** = Averaging flag, A = average, I=instantaneous\n", - "- **``units``** = the units of the field\n", - "- **``long name``** = Field full name\n", - "\n", - "Appropriate values of these parameters for the output of T750 are:\n", - "\n", - "```\n", - "call addfld(’T750’,horiz only, ’A’, ’K’, ’Temperature at 750hPa pressure surface’)\n", - "```\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "5fe6a0f0-e750-4d0c-bd8a-4966286a4764", - "metadata": {}, - "source": [ - "\n", - "## ``outfld``\n", - "The subroutine ``outfld`` accumulates (or takes the minimum or maximum of, as appropriate) the field into the history buffer for the appropriate history tape with the following syntax\n", - "```\n", - " outfld(fname, field, idim, c)\n", - "```\n", - "\n", - "with\n", - "- **``fname``** = Field name\n", - "- **``field``** = array containing field values\n", - "- **``idim``** = longitude dimension of field array\n", - "- **``c``** = chunk (physics) or latitude (dynamics) index.\n", - "\n", - "For example:\n", - "\n", - "```\n", - " call outfld(’T750’,t750, pcols, lchnk)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "c1978926-c9f9-4405-8574-12d39e3f8ed8", - "metadata": {}, - "source": [ - "\n", - "## ``add_default`` \n", - "Another useful subroutine is the subroutine ``add_default``. The subroutine ``add_default`` Add a field to the list of default fields on history file with the following syntax\n", - "```\n", - " subroutine add_default (fname, tindex, flag)\n", - "```\n", - "where\n", - "- **``fname``** = Field name\n", - "- **``tindex``** = history tape index\n", - "- **``flag``** = Averaging flag: A = average (default). I = instantaneous\n", - "\n", - "\n", - "For example:\n", - "\n", - "```\n", - " call add_default ('T500', 1, ' ')\n", - "```" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/sourcemods/exercises/exercise_add_field.ipynb b/notebooks_copy/sourcemods/exercises/exercise_add_field.ipynb deleted file mode 100644 index 56ba26650..000000000 --- a/notebooks_copy/sourcemods/exercises/exercise_add_field.ipynb +++ /dev/null @@ -1,246 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Add output variable in CAM" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Add an additional output variable

\n", - " \n", - "Create a case called `b1850_T750` using the compset `B1850` at `f19_g17` resolution. \n", - "- Add an output field for the temperature at 750 mbar. \n", - "- Output daily values of `T750` and `T500` in the `h1` history file. \n", - "- Set the namelist to output a single `h1` for the run. \n", - "- Make a 1-month run. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e4e74779-4778-446a-be7e-50e0c1ee125e", - "metadata": {}, - "source": [ - "
\n", - "
\n", - " \n", - " Click here for hints\n", - " \n", - " \n", - "**Tip to add `T750`**
\n", - "- Use `T500` as a template for your changes.\n", - "\n", - "- Find the subroutine containing `T750`. \n", - "For instance, in the CESMROOT, use the command: \n", - "\n", - "```\n", - " grep –r T500 * \n", - "```\n", - " \n", - " \n", - "**Tip to check your solution `T750`**
\n", - "- When the run is completed, go to your archive directory:\n", - "- check the fields `T750` and `T500` are in the file `h1`\n", - "- create a file with the difference between `T750-T500`\n", - "- For instance, you can use ``ncap2`` \n", - "```\n", - " ncap2 -s ’T750_minus_T500=T750-T500' b1850_T750.cam.h1.0001-01-01-00000.nc T750-T500.nc\n", - "```\n", - "- Look at the difference with ``ncview``. \n", - "```\n", - " ncview T750-T500.nc\n", - "```\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "fbfacd19-3e46-4ef4-9111-7bb1b4669b2f", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "**# Create a new case**\n", - " \n", - "Create a new case `b1850_T750` with the command:\n", - "```\n", - " cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - "./create_newcase --case ~/cases/b1850_T750 --compset B1850 --res f19_g17 \n", - "```\n", - "\n", - "**# Setup**\n", - "\n", - "Invoke case.setup with the command:\n", - "``` \n", - "cd ~/cases/b1850_T750\n", - "./case.setup\n", - "```\n", - "\n", - "**# Make Source Modifications** \n", - "Use `T500` as a template for your changes. For that purpose, locate the file where `T500` is computed and copy it into\n", - " `SourceMods/src.atm`:\n", - " \n", - "``` \n", - "cp /glade/campaign/cesm/development/cross-wg/cesm2.1_tutorial2022/components/cam/src/physics/cam/cam_diagnostics.F90 SourceMods/src.cam\n", - "```\n", - "\n", - "Now, let's use `T500` as a template for your changes and add the relevant lines for `T750`: edit the file `SourceMods/src.cam/cam_diagnostics.F90` to add the following.\n", - " \n", - "**First change**\n", - " \n", - "Under the lines: \n", - "```\n", - "!++ add a variable for T500 \n", - "call addfld ('T500', horiz_only, 'A', 'K','Temperature at 500 mbar pressure surface') \n", - "```\n", - "add the lines:\n", - "\n", - "```\n", - "!++ add a variable for T750 \n", - "call addfld ('T750', horiz_only, 'A', 'K','Temperature at 750 mbar pressure surface') \n", - "```\n", - " \n", - "**Second change**\n", - "Under the lines: \n", - "```\n", - "!++ add a variable for T500 \n", - "if (hist_fld_active('T500')) then \n", - " call vertinterp(ncol, pcols, pver, state%pmid, 50000._r8, state%t, p_surf, & \n", - " extrapolate='T', ps=state%ps, phis=state%phis)\n", - " call outfld('T500 ', p_surf, pcols, lchnk )\n", - "end if\n", - "```\n", - "add the lines:\n", - " \n", - "``` \n", - "!++ add a variable for T750 \n", - "if (hist_fld_active('T750')) then \n", - " call vertinterp(ncol, pcols, pver, state%pmid, 75000._r8, state%t, p_surf, & \n", - " extrapolate='T', ps=state%ps, phis=state%phis)\n", - " call outfld('T750 ', p_surf, pcols, lchnk )\n", - "end if\n", - "```\n", - "\n", - "**# Customize namelists**\n", - "Edit the file `user_nl_cam` and add the lines:\n", - "```\n", - " nhtfrq = 0, -24\n", - " mfilt = 1, 31\n", - " fincl2 = 'T750', 'T500'\n", - "```\n", - " \n", - " \n", - "**# Set run length**\n", - "\n", - "Set the **run length** to 1 month:\n", - "``` \n", - "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change `job queue` and `account number`.
\n", - "For instance, to run in the queue `regular` and the project number `UESM0013`, use the command:\n", - "\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "**# Build and submit**\n", - " \n", - "Build the model and submit your job:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "____\n", - " \n", - "**# Look at your solution**\n", - " \n", - "When the run is completed, check the fields `T750` and `T500` are in the file `h1`:\n", - "``` \n", - "cd /glade/derecho/scratch/$USER/archive/b1850_T750/atm/hist/ \n", - "ncdump -h b1850_T750.cam.h1.0001-01-01-00000.nc\n", - "```\n", - " \n", - "The file should contain:\n", - "```\n", - "float T500(time, lat, lon) ;\n", - " T500:units = \"K\" ;\n", - " T500:long_name = \"Temperature at 500 mbar pressure surface\" ;\n", - " T500:cell_methods = \"time: mean\" ;\n", - "float T750(time, lat, lon) ;\n", - " T750:units = \"K\" ;\n", - " T750:long_name = \"Temperature at 750 mbar pressure surface\" ;\n", - " T750:cell_methods = \"time: mean\" ;\n", - "```\n", - "If you don't see these variables, check you correctly set the `user_nl_cam`.\n", - " \n", - "Create a file with the difference between `T750-T500`:\n", - "``` \n", - "cd /glade/derecho/scratch/$USER/archive/b1850_T750/atm/hist/ \n", - "ncap2 -s 'T750_minus_T500=T750-T500' b1850_T750.cam.h1.0001-01-01-00000.nc T750-T500.nc \n", - "```\n", - "\n", - "Look at the difference between `T750-T500` with `ncview`: \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/b1850_T750/atm/hist/ \n", - "ncview T750-T500.nc\n", - "```\n", - " \n", - "The field `T750-T500` looks like:\n", - "\n", - "*

\n", - " ![ncview T750-T500](../../../images/sourcemods/ncview_T750-T500.png) \n", - " Figure: Overview of the CESM directories and the SourceMods directories.

*\n", - " \n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99077aae-e99c-479f-b7a9-b1af1fd4e62f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/sourcemods/exercises/exercise_rain_threshold.ipynb b/notebooks_copy/sourcemods/exercises/exercise_rain_threshold.ipynb deleted file mode 100644 index 007546c7c..000000000 --- a/notebooks_copy/sourcemods/exercises/exercise_rain_threshold.ipynb +++ /dev/null @@ -1,203 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "be068a4c-5d8d-442f-aa26-65712f572c65", - "metadata": {}, - "source": [ - "# Modify the `rain_threshold` in CLM\n", - "\n", - "In the below exercise, we will change the rain threshold for stress deciduous vegetation, which includes C3 grasses. The rain threshold is the amount of rain required to initiate leaf onset. Reaching the rain threshold is one of several requirements for stress deciduous vegetation leaf onset. If you are interested, you can find more information about the stress deciduous phenology representation in the CLM Technical Note.\n", - "\n", - "The current value of rain_threshold is 20mm as specified in the code:\n", - "\n", - "```\n", - "rain_threshold = 20._r8 \n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "id": "571a03a6-51cb-425a-b9c2-d674b097f5b8", - "metadata": {}, - "source": [ - "\n", - "\n", - "
\n", - "Exercise: Add an additional output variable

\n", - " \n", - "Create a case called `b1850_rain_threshold` using the compset `B1850` at `f19_g17` resolution. \n", - "- Change the rain_threshold from 20mm to 1mm. \n", - "- Output daily values of ??? in the `h1` history file. \n", - "- Make a 1-month run. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e4e74779-4778-446a-be7e-50e0c1ee125e", - "metadata": {}, - "source": [ - "
\n", - "
\n", - " \n", - " Click here for hints\n", - " \n", - " \n", - "**Tip to add `T750`**
\n", - "- Use `T500`as a template for your changes.\n", - "\n", - "- Find the subroutine containing `T750`. \n", - "For instance, in the CESMROOT, use the command: \n", - "\n", - "```\n", - " grep –r T500 * \n", - "```\n", - " \n", - " \n", - "**Tip to check your solution `T750`**
\n", - "- When the run is completed, go to your archive directory:\n", - "- check the fields `T750` and `T500` are in the file `h1`\n", - "- create a file with the difference between `T750-T500`\n", - "- For instance, you can use ``ncap2`` \n", - "```\n", - " ncap2 -s ’T750_minus_T500=T750-T500' b1850_T750.cam.h1.0001-01-01-00000.nc T750-T500.nc\n", - "```\n", - "- Look at the difference with ``ncview``. \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "fbfacd19-3e46-4ef4-9111-7bb1b4669b2f", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "Create a new case `b1850_rain_threshold` with the command:\n", - "```\n", - "cd /glade/campaign/cesm/development/cross-wg/cesm2.1_tutorial2022/cime/scripts\n", - "./create_newcase --case ~/cases/b1850_rain_threshold --compset B1850 --res f19_g17 \n", - "```\n", - "\n", - "Case `setup`:\n", - "``` \n", - "cd ~/cases/b1850_rain_threshold\n", - "./case.setup\n", - "```\n", - "\n", - " \n", - "Locate the file where `rain_threshold` is defined and copy it into\n", - " `SourceMods/src.lnd`:\n", - "``` \n", - "cp /glade/campaign/cesm/development/cross-wg/cesm2.1_tutorial2022/components/clm/src/biogeochem/CNPhenologyMod.F90 SourceMods/src.clm\n", - "```\n", - " \n", - "Edit the file `SourceMods/src.clm/CNPhenologyMod.F90 ` and add the lines:\n", - "```\n", - " ! specify rain threshold for leaf onset \n", - " rain_threshold = 20._r8\n", - "```\n", - "to \n", - "```\n", - " ! specify rain threshold for leaf onset \n", - " rain_threshold = 1._r8\n", - "```\n", - " \n", - " \n", - "Change the `run length`:\n", - "``` \n", - "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", - "```\n", - "\n", - "If needed, change `job queue` and `account number` \n", - "For instance:\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "Build and submit:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - " \n", - "When the run is completed, check the fields `T750` and `T500` are in the file `h1`:\n", - "``` \n", - "cd /glade/derecho/scratch/$USER/archive/b1850_rain_threshold/atm/hist/ \n", - "ncdump -h bb1850_rain_threshold.cam.h1.0001-01-01-00000.nc\n", - "```\n", - " \n", - "The file should contain:\n", - "```\n", - "float T500(time, lat, lon) ;\n", - " T500:units = \"K\" ;\n", - " T500:long_name = \"Temperature at 500 mbar pressure surface\" ;\n", - " T500:cell_methods = \"time: mean\" ;\n", - "float T750(time, lat, lon) ;\n", - " T750:units = \"K\" ;\n", - " T750:long_name = \"Temperature at 750 mbar pressure surface\" ;\n", - " T750:cell_methods = \"time: mean\" ;\n", - "```\n", - "If you don't see these variables, check you correctly set the `user_nl_cam`.\n", - " \n", - "Create a file with the difference between `T750-T500`:\n", - "``` \n", - "cd /glade/derecho/scratch/$USER/archive/b1850_rain_threshold/atm/hist/ \n", - "ncap2 -s ‘T750_minus_T500=T750-T500' b1850_T750.cam.h1.0001-01-01-00000.nc T750-T500.nc \n", - "```\n", - "\n", - "Look at the difference between `T750-T500` with `ncview`: \n", - "```\n", - "cd /glade/derecho/scratch/$USER/archive/b1850_rain_threshold/atm/hist/ \n", - "ncview T750-T500.nc\n", - "```\n", - " \n", - "The field `T750-T500` looks like:\n", - "\n", - "*

\n", - " ![ncview T750-T500](../../../images/sourcemods/ncview_T750-T500.png) \n", - " Figure: Overview of the CESM directories and the SourceMods directories.

*\n", - " \n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "831a1859-4cd1-48aa-b1d2-616216d2f9cb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/sourcemods/exercises_overview.ipynb b/notebooks_copy/sourcemods/exercises_overview.ipynb deleted file mode 100644 index 537a72a47..000000000 --- a/notebooks_copy/sourcemods/exercises_overview.ipynb +++ /dev/null @@ -1,43 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Exercise Overview \n", - "\n", - "## Learning Goals\n", - "\n", - "- Student will practice how to do a code modification in CESM. \n", - "\n", - "\n", - "## Exercise overview:\n", - "\n", - "Do at least one exercise:\n", - "- Add a new variable in CAM\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/sourcemods/overview.ipynb b/notebooks_copy/sourcemods/overview.ipynb deleted file mode 100644 index f4168b24c..000000000 --- a/notebooks_copy/sourcemods/overview.ipynb +++ /dev/null @@ -1,73 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Overview\n", - "\n", - "If you want to make modifications to the CESM code, the best practise is to copy the relevant sub-routine into the relevant sub-directory within the `SourceMods` directory in your case directory. \n", - "\n", - "For example, if you wanted to modify a CAM subroutine, in the case called case01, you would copy that subroutine to the following location\n", - "\n", - "```\n", - " case01/SourceMods/src.cam\n", - "```\n", - "and modify it there.\n", - "\n", - "*Figure 1* shows the location of the `SourceMods` directory and sub-directories.\n", - "\n", - "
\n", - "\n", - "The **steps** when making a **source code modification** are:\n", - "\n", - "- In the **CESM code**, find the subroutine you want to modify.\n", - "- Copy this subrouting to the relevant **SourceMods** directory within your **case directory**\n", - "- Make your **modifications**\n", - "- **Compile** and **run** the model \n", - " \n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "017b45ad-dcff-42b2-8623-06033b8e11d8", - "metadata": {}, - "source": [ - "![CESM directories and namelists](../../images/sourcemods/CESM_directories_and_SourceMods.png)\n", - "\n", - "*

Figure: Overview of the CESM directories and the SourceMods directories.

*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "14f20e06-e171-4891-96de-759c5f5182a9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/sourcemods/sourcemods.ipynb b/notebooks_copy/sourcemods/sourcemods.ipynb deleted file mode 100644 index d9f0b8b21..000000000 --- a/notebooks_copy/sourcemods/sourcemods.ipynb +++ /dev/null @@ -1,49 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0959db1d-1790-4519-a34e-7026444af537", - "metadata": {}, - "source": [ - "# Source Modifications\n", - "\n", - "The goal of this chapter is to understand how to modify the CESM code. This is referred to as **_source modifications_**.\n", - "\n", - "- The section **_Overview_** describes the steps for making source modifications and provides a visual representation of the directories and files that need to be modified.\n", - "\n", - "- The section **_Source Modification Example_** provides detailed instructions on how to add a new variable to CAM: the atmospheric temperature at 750hPa.\n", - "\n", - "- The section **_Exercises_** offers opportunities to practice the concepts learned in this chapter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35b1a8e6-f30e-4e15-b519-a87d23bfffc6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/troubleshooting/debugging_flag.ipynb b/notebooks_copy/troubleshooting/debugging_flag.ipynb deleted file mode 100644 index 25c651135..000000000 --- a/notebooks_copy/troubleshooting/debugging_flag.ipynb +++ /dev/null @@ -1,52 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "afbbe9ba-517d-4db4-9c7c-0b3daaf0e7ac", - "metadata": {}, - "source": [ - "# Adding debugging info\n", - "\n", - "If you cannot find the reason of the crash in the **log** files, there are two ways to add more debugging information. \n", - "- Increase the value of the run-time xml variable ``INFO_DBUG`` (This **does NOT require rebuilding**): \n", - "```\n", - "./xmlchange INFO_DBUG=2. \n", - "```\n", - "This adds more information to the ``cpl.log`` file that can be useful if you can’t tell what component is aborting the run, or where bad coupling fields are originating.\n", - "\n", - "- Try rebuilding and rerunning with the variable DEBUG set to TRUE (This ** requires rebuilding**): \n", - "```\n", - "./xmlchange DEBUG=TRUE.\n", - "```\n", - "This adds various runtime checks that trap conditions such as out-of-bounds array indexing, divide by 0, and other floating point exceptions.\n", - "Before running, you must rebuild run \n", - "```\n", - "./case.build --clean-all\n", - "qcmd -- ./case.build.\n", - "```\n", - "Note that the model will run **significantly slower** in ``DEBUG mode``, so this may not be feasible if the model has to run a long time before producing the error. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/troubleshooting/exercises/troubleshooting_cam.ipynb b/notebooks_copy/troubleshooting/exercises/troubleshooting_cam.ipynb deleted file mode 100644 index 36df98d3e..000000000 --- a/notebooks_copy/troubleshooting/exercises/troubleshooting_cam.ipynb +++ /dev/null @@ -1,201 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Debugging CAM" - ] - }, - { - "cell_type": "markdown", - "id": "b90d4773-7ca0-4131-ab07-517608a3e976", - "metadata": {}, - "source": [ - "\n", - "
\n", - "Exercise: Add an additional output variable

\n", - " \n", - "Create a case called ``b1850_high_freq_bugfixing`` using the compset ``B1850`` at ``f19_g17`` resolution. \n", - "Set the run length to ``1 month``. \n", - " \n", - "Now in addition to the default monthly output, add the following output:\n", - "- an ``h1`` file containing daily averages of ``T2M`` and set your namelist so that there is one file per day for this daily averaged output.\n", - "\n", - "Set up, build and submit your case. \n", - "\n", - "NB: Your goal is to make the model crashes. And then to troubleshoot why it crashed. \n", - "\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "e4e74779-4778-446a-be7e-50e0c1ee125e", - "metadata": {}, - "source": [ - "
\n", - "
\n", - " \n", - " Click here for hints\n", - " \n", - "\n", - "**Tip to add a ``h1`` file** \n", - "\n", - "For more information about how to add a ``h1`` file, check the section about namelist modifications. \n", - "\n", - "If you don't have time to check the section immediately, the way to add an ``h1`` file with daily averages of ``T2M`` and create one file per day for this daily averaged output is:\n", - "\n", - "add the following lines in ``user nl cam``\n", - " \n", - "```\n", - " fincl2 = ’T2M:A’\n", - " nhtfrq = 0,-24\n", - " mfilt = 1,1\n", - "```\n", - "\n", - "**Tip to for troubleshooting** \n", - "\n", - "Check the derecho queue and wait until your run doesn't show in the queue anymore. \n", - "\n", - "When your run is not in the queue anymore:\n", - "- Go to the ``archive`` directory: can you see the history files in the archive directory? The answer should be no. Why? \n", - "- Go to the ``run`` directory: Is there any evidence of history files or restart files being created by the run? The answer, again, should be no. This is because we have tricked you, with a bug.\n", - "\n", - "Look at the log files in the ``RUNDIR`` to try to understand why the run crashed. \n", - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "id": "fbfacd19-3e46-4ef4-9111-7bb1b4669b2f", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "Click here for the solution
\n", - " \n", - "**# Create a new case**\n", - " \n", - "Create a new case ``b1850_high_freq_bugfixing`` with the command:\n", - "```\n", - " cd /glade/work/$USER/code/my_cesm_code/cime/scripts/\n", - " ./create_newcase --case ~/cases/b1850_high_freq_bugfixing --compset B1850 --res f19_g17 \n", - "```\n", - "\n", - "**# Setup**\n", - "\n", - "Invoke case.setup with the command:\n", - "``` \n", - " cd ~/cases/b1850_high_freq_bugfixing\n", - " ./case.setup\n", - "```\n", - "\n", - "**# Customize namelists**\n", - " \n", - "Add the daily output of ``T2M`` by editing the file ``user_nl_cam`` and adding the lines:\n", - "```\n", - " fincl2 = 'T2M:A'\n", - " nhtfrq = 0,-24\n", - " mfilt = 1,1\n", - "```\n", - "\n", - "**# Set run length**\n", - "\n", - "Set the **run length** to 1 month:\n", - "``` \n", - "./xmlchange STOP_N=1,STOP_OPTION=nmonths\n", - "```\n", - "\n", - "**# Change the job queue and account number**\n", - "\n", - "If needed, change `job queue` and `account number`.
\n", - "For instance, to run in the queue `regular` and the project number ``UESM0013`` (you should use the project number given for this tutorial), use the command:\n", - "\n", - "``` \n", - "./xmlchange JOB_QUEUE=regular,PROJECT=UESM0013\n", - "```\n", - "\n", - "**# Build and submit**\n", - " \n", - "Build the model and submit your job:\n", - "```\n", - "qcmd -- ./case.build\n", - "./case.submit\n", - "```\n", - "\n", - "____\n", - " \n", - "**# Look at what happened**\n", - "\n", - "**Your run should crash !!!**. This is normal. The goal of the exercise is to troubleshooting. \n", - "\n", - "What you should find in your run directory is three log files. \n", - "- One for the coupler ``cpl.log.*``,\n", - "- one for CAM ``atm.log.*`` \n", - "- and one for CESM ``cesm.log.*``.\n", - "\n", - "Somewhere in these log files is information about what has gone wrong, but it is often not entirely\n", - "straightforward to find. \n", - "- Often at the bottom of the log file, there are errors that are not relative to your problem because\n", - "they are just demonstrating that individual processes are exiting. \n", - "- Often the relevant error lies above this and can sometimes be found by searching for the first occurrence of ERROR or ABORT or cesm.exe. \n", - "\n", - "In this case, searching for the first occurrence of ERROR in ``cesm.log.*`` gives us some relevant information. We find\n", - "```\n", - "ERROR: FLDLST: 1 errors found, see log\n", - "```\n", - "This tells us is that something has gone wrong with the list of output variables that we have asked for.\n", - "\n", - "More information can then be found in the CAM log file ``atm.log.*``.\n", - "Look at the very end of that file and you should see\n", - "```\n", - "FLDLST: T2M in fincl(1, 2) not found\n", - "ERROR: FLDLST: 1 errors found, see log\n", - "```\n", - "This tells us that ``T2M`` is not a valid history variable for CAM. That’s because the correct variable\n", - "for near surface temperature is ``TREFHT``. ``T2M`` is not a CAM\n", - "history field and this has caused CESM to crash\n", - " \n", - " \n", - " \n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "848cf254-9146-4f58-ad1b-2d46325e5067", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/troubleshooting/exercises_overview.ipynb b/notebooks_copy/troubleshooting/exercises_overview.ipynb deleted file mode 100644 index eb628ef5b..000000000 --- a/notebooks_copy/troubleshooting/exercises_overview.ipynb +++ /dev/null @@ -1,40 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# Exercise Overview \n", - "\n", - "## Learning Goals\n", - "- The goal of the exercise is to make the model crashes. \n", - "- And then to troubleshoot why it crashed.\n", - "\n", - "## Exercise\n", - "- Do the exercise: Debugging CAM. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/troubleshooting/log_files.ipynb b/notebooks_copy/troubleshooting/log_files.ipynb deleted file mode 100644 index 440851204..000000000 --- a/notebooks_copy/troubleshooting/log_files.ipynb +++ /dev/null @@ -1,62 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f406f992-92bd-4b17-9bd3-b99c5c8abaf3", - "metadata": {}, - "source": [ - "# The log files\n", - "\n", - "The log files are files in the format ``$model.log.*`` \n", - "- When the model is running, it produces the log files in the **run directory**: ``RUNDIR``. \n", - "- When the run completes successfully, the model moves the log files into the **archive** directory: ``DOUT_S_ROOT``\n", - "- When the model fails, the log files remains in the run directory ``RUNDIR``\n", - "\n", - "![CESM directories and namelists](../../images/troubleshooting/CESM_directories_and_log_files.png)\n", - "\n", - "*

Figure: Overview of the CESM directories and the log files.

*\n" - ] - }, - { - "cell_type": "markdown", - "id": "017b45ad-dcff-42b2-8623-06033b8e11d8", - "metadata": {}, - "source": [ - "## What to do when a run fails? \n", - "\n", - "First, check the latest ``cpl.log.*``, which will often tell you when the model failed. If a run completed successfully, the last several lines of the ``cpl.log.*`` file will have a string like ``SUCCESSFUL TERMINATION OF CESM``. \n", - "If you don't see this message, it means the run has failed. \n", - "\n", - "Check these things first when a job fails:\n", - "- Did the model time out?\n", - "- Was a disk quota limit hit?\n", - "- Did a machine go down?\n", - "- Did a file system become full?\n", - "If any of those things happened, take appropriate corrective action and resubmit the job.\n", - "\n", - "If it is not clear that any of the above caused a case to fail, check the rest of the component log files ``$model.log.*`` for error messages. It takes a bit of practice to interpret message errors. We will look at an example in this chapter exercices. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks_copy/troubleshooting/troubleshooting.ipynb b/notebooks_copy/troubleshooting/troubleshooting.ipynb deleted file mode 100644 index 6f0ca89a5..000000000 --- a/notebooks_copy/troubleshooting/troubleshooting.ipynb +++ /dev/null @@ -1,44 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0959db1d-1790-4519-a34e-7026444af537", - "metadata": {}, - "source": [ - "# Troubleshooting runtime errors\n", - "\n", - "There are several places to look for information if a job fails. \n", - "- The logs files will probably give you a hint. \n", - "- It is also possible to run with more debugging info. \n", - "\n", - "\n", - "
\n", - "\n", - "More information about troubleshooting can be found in the [CIME documentation](https://esmci.github.io/cime/versions/master/html/users_guide/troubleshooting.html).\n", - "\n", - "
" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}
CommandDescriptionOptionsExamples
lsList files and directories.-l: Long format listing.
-a: Include hidden files.		ls -l
displays files and directories with detailed information.

ls -a shows all files and directories, including hidden ones.
cdChange directory.N/Acd /path/to/directory
changes the current directory to the specified path.
pwdPrint current working directory.N/Apwd
displays the current working directory.
mkdirCreate a new directory.N/Amkdir my_directory
creates a new directory named \"my_directory\".
rmRemove files and directories.-r: Remove directories recursively.
-f: Force removal without confirmation.		rm file.txt
deletes the file named \"file.txt\".
rm -r my_directory
deletes the directory \"my_directory\" and its contents.
rm -f file.txt
forcefully deletes the file \"file.txt\" without confirmation.
cpCopy files and directories.-r: Copy directories recursively.cp -r directory destination
copies the directory \"directory\" and its contents to the specified destination.
cp file.txt destination
copies the file \"file.txt\" to the specified destination.
mvMove/rename files and directories.N/Amv file.txt new_name.txt
renames the file \"file.txt\" to \"new_name.txt\".
mv file.txt directory
moves the file \"file.txt\" to the specified directory.
findSearch for files and directories.-name: Search by filename.find /path/to/search -name “*.txt”
searches for all files with the extension “.txt” in the specified directory.