{ "cells": [ { "cell_type": "markdown", "id": "b33d27cb-878c-4f98-93e5-3eee28e71c9b", "metadata": {}, "source": [ "# Loading, processing, and visualizing data\n", "\n", "This notebook shows how to load a dataset, use the preprocessor functions, and visualize the result. In this notebook we will plot the annual mean temperature from 1850 till 2100 from one model." ] }, { "cell_type": "code", "execution_count": 1, "id": "13fef9db-3809-4d18-9097-f5f2ff43f3de", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import iris.quickplot\n", "\n", "from esmvalcore.config import CFG\n", "from esmvalcore.dataset import Dataset\n", "from esmvalcore.esgf import download, ESGFFile\n", "from esmvalcore.preprocessor import area_statistics, annual_statistics" ] }, { "cell_type": "markdown", "id": "5ae60756-f808-4972-9baf-b9827dd6e874", "metadata": {}, "source": [ "Configure ESMValCore so it searches the ESGF for data" ] }, { "cell_type": "code", "execution_count": 2, "id": "c53e31ac-7cf4-4397-b9aa-d860b7f4909e", "metadata": {}, "outputs": [], "source": [ "CFG['search_esgf'] = 'when_missing'" ] }, { "cell_type": "markdown", "id": "90ac9a7b-2683-41c9-bd8f-9f828ac7efac", "metadata": {}, "source": [ "Define the dataset we are going to use. In this case surface air temperature (`tas`)." ] }, { "cell_type": "code", "execution_count": 3, "id": "1c540dff-6fcb-4b74-8690-8893d9f19b94", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset:\n", "{'dataset': 'MPI-ESM-MR',\n", " 'project': 'CMIP5',\n", " 'mip': 'Amon',\n", " 'short_name': 'tas',\n", " 'ensemble': 'r1i1p1',\n", " 'exp': 'historical',\n", " 'timerange': '1850/2000'}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tas = Dataset(\n", " short_name='tas',\n", " mip='Amon',\n", " project='CMIP5',\n", " dataset='MPI-ESM-MR',\n", " ensemble='r1i1p1',\n", " exp='historical',\n", " timerange='1850/2000',\n", ")\n", "tas" ] }, { "cell_type": "markdown", "id": "23ba6512-9769-44ca-a2a4-b9f79555a9a5", "metadata": {}, "source": [ "In order to compute the area average later on, we add the cell areas (`areacella`) as a supplementary dataset. This will append a new dataset to the list of supplementary datasets. Its facets are copied from the `tas` dataset and updated with the provided facets:" ] }, { "cell_type": "code", "execution_count": 4, "id": "c30418f2-d725-4708-8d28-7ac48c6a9863", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Dataset:\n", " {'dataset': 'MPI-ESM-MR',\n", " 'project': 'CMIP5',\n", " 'mip': 'fx',\n", " 'short_name': 'areacella',\n", " 'ensemble': 'r0i0p0',\n", " 'exp': 'historical',\n", " 'timerange': '1850/2000'}]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tas.add_supplementary(short_name='areacella', mip='fx', ensemble='r0i0p0')\n", "tas.supplementaries" ] }, { "cell_type": "markdown", "id": "a33ae420-326c-4570-8741-e447b160c9e2", "metadata": {}, "source": [ "ESMValCore can automatically add extra facets based on the `project`, `mip`, `short_name`, and `dataset`. These extra facets are automatically added and used when searching for input files." ] }, { "cell_type": "code", "execution_count": 5, "id": "e761c044-a695-4bb3-8fdd-0041f83a4ff9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Dataset:\n", "{'dataset': 'MPI-ESM-MR',\n", " 'project': 'CMIP5',\n", " 'mip': 'Amon',\n", " 'short_name': 'tas',\n", " 'ensemble': 'r1i1p1',\n", " 'exp': 'historical',\n", " 'frequency': 'mon',\n", " 'institute': ['MPI-M'],\n", " 'long_name': 'Near-Surface Air Temperature',\n", " 'modeling_realm': ['atmos'],\n", " 'original_short_name': 'tas',\n", " 'product': ['output1', 'output2'],\n", " 'standard_name': 'air_temperature',\n", " 'timerange': '1850/2000',\n", " 'units': 'K'}\n", "supplementaries:\n", " {'dataset': 'MPI-ESM-MR',\n", " 'project': 'CMIP5',\n", " 'mip': 'fx',\n", " 'short_name': 'areacella',\n", " 'ensemble': 'r0i0p0',\n", " 'exp': 'historical',\n", " 'frequency': 'fx',\n", " 'institute': ['MPI-M'],\n", " 'long_name': 'Atmosphere Grid-Cell Area',\n", " 'modeling_realm': ['atmos', 'land'],\n", " 'original_short_name': 'areacella',\n", " 'product': ['output1', 'output2'],\n", " 'standard_name': 'cell_area',\n", " 'units': 'm2'}\n", "session: 'session-686367c0-001c-4864-839d-c20887cf7415_20230301_160531'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tas.augment_facets()\n", "tas" ] }, { "cell_type": "markdown", "id": "a1bc53a1-3977-4a22-801c-647cc7087c69", "metadata": {}, "source": [ "Use the `find_files` method to find the files corresponding to the dataset." ] }, { "cell_type": "code", "execution_count": 6, "id": "6235e9f6-8ab6-4a30-b521-cb85c04daf36", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ESGFFile:cmip5/output1/MPI-M/MPI-ESM-MR/historical/mon/atmos/Amon/r1i1p1/v20120503/tas_Amon_MPI-ESM-MR_historical_r1i1p1_185001-200512.nc on hosts ['aims3.llnl.gov', 'esgf.ceda.ac.uk', 'esgf.nci.org.au', 'esgf1.dkrz.de']]\n", "[ESGFFile:cmip5/output1/MPI-M/MPI-ESM-MR/historical/fx/atmos/fx/r0i0p0/v20120503/areacella_fx_MPI-ESM-MR_historical_r0i0p0.nc on hosts ['aims3.llnl.gov', 'esgf.ceda.ac.uk', 'esgf.nci.org.au', 'esgf1.dkrz.de']]\n" ] } ], "source": [ "tas.find_files()\n", "print(tas.files)\n", "for supplementary_ds in tas.supplementaries:\n", " print(supplementary_ds.files)" ] }, { "cell_type": "markdown", "id": "60272c2a-c58d-464f-808d-96d95a9a150e", "metadata": {}, "source": [ "If the files are not available locally, ESMValCore can download them for us." ] }, { "cell_type": "code", "execution_count": 7, "id": "886a38f4-9e37-438e-af75-e8735ee7481d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LocalFile('~/climate_data/cmip5/output1/MPI-M/MPI-ESM-MR/historical/mon/atmos/Amon/r1i1p1/v20120503/tas_Amon_MPI-ESM-MR_historical_r1i1p1_185001-200512.nc')]\n", "[LocalFile('~/climate_data/cmip5/output1/MPI-M/MPI-ESM-MR/historical/fx/atmos/fx/r0i0p0/v20120503/areacella_fx_MPI-ESM-MR_historical_r0i0p0.nc')]\n" ] } ], "source": [ "files = list(tas.files)\n", "for supplementary_ds in tas.supplementaries:\n", " files.extend(supplementary_ds.files)\n", "files = [file for file in files if isinstance(file, ESGFFile)]\n", "download(files, CFG['download_dir'])\n", "tas.find_files()\n", "print(tas.files)\n", "for supplementary_ds in tas.supplementaries:\n", " print(supplementary_ds.files)" ] }, { "cell_type": "markdown", "id": "e6916630-00d5-4043-b594-5df0f4142a9e", "metadata": {}, "source": [ "The data in the files can be loaded into an [iris.cube.Cube](https://scitools-iris.readthedocs.io/en/latest/generated/api/iris/cube.html#iris.cube.Cube). ESMValCore will automatically check for and fix problems with the data formatting and attach the cell area." ] }, { "cell_type": "code", "execution_count": 8, "id": "a5dfd5dd-e3ff-4443-a745-ad130a1bb52c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\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", " \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", " \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", "\n", " \n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", " \n", " \n", "\n", "
Air Temperature (K)timelatitudelongitude
Shape181296192
Dimension coordinates
\ttimex--
\tlatitude-x-
\tlongitude--x
Cell measures
\tcell_area-xx
Scalar coordinates
\theight2.0 m
Cell methods
\tmeantime
Attributes
\tConventions'CF-1.4'
\tassociated_files'baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_MPI-ESM-MR_historical_r0i0p0.nc ...'
\tbranch_time0.0
\tcmor_version'2.6.0'
\tcontact'cmip5-mpi-esm@dkrz.de'
\texperiment'historical'
\texperiment_id'historical'
\tforcing'GHG,Oz,SD,Sl,Vl,LU'
\tfrequency'mon'
\tinitialization_method1
\tinstitute_id'MPI-M'
\tinstitution'Max Planck Institute for Meteorology'
\tmodel_id'MPI-ESM-MR'
\tmodeling_realm'atmos'
\tparent_experiment'N/A'
\tparent_experiment_id'N/A'
\tparent_experiment_rip'N/A'
\tphysics_version1
\tproduct'output'
\tproject_id'CMIP5'
\trealization1
\treferences'ECHAM6: n/a; JSBACH: Raddatz et al., 2007. Will the tropical land biosphere ...'
\tsource'MPI-ESM-MR 2011; URL: http://svn.zmaw.de/svn/cosmos/branches/releases/mpi-esm-cmip5/src/mod; ...'
\ttable_id'Table Amon (27 April 2011) a5a1c518f52ae340313ba0aada03f862'
\ttitle'MPI-ESM-MR model output prepared for CMIP5 historical'
\n", " " ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cube = tas.load()\n", "cube" ] }, { "cell_type": "code", "execution_count": 9, "id": "3999db7a-a5b3-4a54-a16f-a38937528f51", "metadata": {}, "outputs": [ { "data": { "text/plain": [ " shape(96, 192)>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cell_area = cube.cell_measures()[0]\n", "cell_area" ] }, { "cell_type": "markdown", "id": "1a0ba10c-78a0-4eec-a9c4-85f823b29485", "metadata": {}, "source": [ "This code shows how to use some [esmvalcore.preprocessor](https://docs.esmvaltool.org/projects/esmvalcore/en/latest/api/esmvalcore.preprocessor.html) functions to compute the global annual mean temperature in degrees Celsius:" ] }, { "cell_type": "code", "execution_count": 10, "id": "bba8cc35-ffe7-42a4-b538-dad2ccb4d96c", "metadata": {}, "outputs": [], "source": [ "cube = area_statistics(cube, operator='mean')\n", "cube = annual_statistics(cube, operator='mean')\n", "cube.convert_units('degrees_C')" ] }, { "cell_type": "markdown", "id": "50f61460-194a-40ba-a0d0-cb9e0d4d5762", "metadata": {}, "source": [ "The [iris.quickplot](https://scitools-iris.readthedocs.io/en/latest/generated/api/iris/quickplot.html) module has useful functions for quickly plotting the results:" ] }, { "cell_type": "code", "execution_count": 11, "id": "a835bf2b-a959-48e0-9d85-7601101e4cbb", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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