notebooks/Introduction_to_ESMValTool_with_output.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width=200 src=\"https://docs.esmvaltool.org/en/v2.5.0/_static/ESMValTool-logo-2.png\"> <img width=200 src=\"https://jupyter.org/assets/homepage/hublogo.svg\"> <img width=200 src=\"https://zenodo.org/api/files/00000000-0000-0000-0000-000000000000/is-enes3/logo.png\"> <img width=200 src=\"https://www.smhi.se/polopoly_fs/1.135796.1527766089!/image/LoggaEUCP.png_gen/derivatives/Original_366px/image/LoggaEUCP.png\"> <img width=200 src=\"https://www.dkrz.de/@@site-logo/dkrz.svg\"> <img width=200 src=\"https://upload.wikimedia.org/wikipedia/commons/8/85/SMHI_Logo.svg\"> <img width=200 src=\"https://www.dtls.nl/wp-content/uploads/2015/03/NleSc.png\">\\"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to ESMValTool\n",
    "\n",
    "[ESMValTool](https://github.com/ESMValGroup/ESMValTool) is a library of climate data analysis workflows (\"recipes\") and [ESMValCore](https://github.com/ESMValGroup/ESMValCore) is the tool that can run those recipes. With ESMValTool, it is easy to reproduce already published analyses as well as develop new ones. A useful feature is that you can directly access all the output (data, images, etc) and further process them in the notebook. ESMValTool has specifically been designed to analyse data produced as part of the [Climate Model Intercomparison Project](https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6), though it also [supports many observational and reanalysis datasets](https://docs.esmvaltool.org/en/latest/input.html#supported-datasets-for-which-a-cmorizer-script-is-available). It is used extensively to (re)produce the analyses in the [IPCC Assessment Reports](https://www.ipcc.ch/assessment-report/ar6/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/k/k206100/.conda/envs/esmvaltool/lib/python3.10/site-packages/esmvalcore/experimental/_warnings.py:13: UserWarning: \n",
      "  Thank you for trying out the new ESMValCore API.\n",
      "  Note that this API is experimental and may be subject to change.\n",
      "  More info: https://github.com/ESMValGroup/ESMValCore/issues/498\n",
      "ERROR 1: PROJ: proj_create_from_database: Open of /home/k/k206100/.conda/envs/esmvaltool/share/proj failed\n"
     ]
    }
   ],
   "source": [
    "# Import the tool\n",
    "import esmvalcore.experimental as esmvaltool\n",
    "\n",
    "# Import tools for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import iris.quickplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with ESMValTool recipes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see a list of [all the available ESMValTool recipes](https://docs.esmvaltool.org/en/latest/recipes/index.html) (only the first 10 are shown here):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Recipe('Recipe eady growth rate'),\n",
       " Recipe('Recipe autoassess radiation rms cfmon all'),\n",
       " Recipe('Recipe ecs scatter'),\n",
       " Recipe('Recipe ocean bgc'),\n",
       " Recipe('Recipe miles block'),\n",
       " Recipe('Recipe tcr'),\n",
       " Recipe('Recipe impact'),\n",
       " Recipe('Recipe extreme index'),\n",
       " Recipe('Recipe seaice feedback'),\n",
       " Recipe('Recipe wenzel16nat')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_recipes = esmvaltool.get_all_recipes()\n",
    "all_recipes[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we'll just run the following recipe. Documentation for this recipe is available [here](https://docs.esmvaltool.org/en/latest/recipes/recipe_examples.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>Recipe that runs an example diagnostic written in Python.</h1>\n",
       "\n",
       "<p>\n",
       "  Example recipe that plots a map and timeseries of temperature.\n",
       "</p>\n",
       "\n",
       "<h3>Authors</h3>\n",
       "\n",
       "<ul>\n",
       "  \n",
       "    <li>Bouwe Andela (NLeSC, Netherlands; <a href='https://orcid.org/0000-0001-9005-8940'>https://orcid.org/0000-0001-9005-8940</a>)</li>\n",
       "  \n",
       "    <li>Mattia Righi (DLR, Germany; <a href='https://orcid.org/0000-0003-3827-5950'>https://orcid.org/0000-0003-3827-5950</a>)</li>\n",
       "  \n",
       "</ul>\n",
       "\n",
       "<h3>Maintainers</h3>\n",
       "\n",
       "<ul>\n",
       "  \n",
       "    <li>Manuel Schlund (DLR, Germany; <a href='https://orcid.org/0000-0001-5251-0158'>https://orcid.org/0000-0001-5251-0158</a>)</li>\n",
       "  \n",
       "</ul>\n",
       "\n",
       "<h3>Projects</h3>\n",
       "\n",
       "<ul>\n",
       "  \n",
       "    <li>DLR project ESMVal</li>\n",
       "  \n",
       "    <li>Copernicus Climate Change Service 34a Lot 2 (MAGIC) project</li>\n",
       "  \n",
       "</ul>\n",
       "\n",
       "<h3>References</h3>\n",
       "\n",
       "<ul>\n",
       "  \n",
       "    <li>Please acknowledge the project(s).</li>\n",
       "  \n",
       "</ul>"
      ],
      "text/plain": [
       "Recipe('Recipe python')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_recipe = esmvaltool.get_recipe(\"examples/recipe_python.yml\")\n",
    "example_recipe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The recipe contains a [specification](https://docs.esmvaltool.org/projects/esmvalcore/en/latest/recipe/overview.html) that describes the analysis to run. It is written in YAML format. The most important elements are:\n",
    "- which variables to use\n",
    "- from which datasets\n",
    "- how to preprocess (e.g. regrid, compute statistics, etc) those\n",
    "- and finally, which analysis scripts to run\n",
    "\n",
    "This recipe looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ESMValTool\n",
      "# recipe_python.yml\n",
      "---\n",
      "documentation:\n",
      "  description: |\n",
      "    Example recipe that plots a map and timeseries of temperature.\n",
      "\n",
      "  title: Recipe that runs an example diagnostic written in Python.\n",
      "\n",
      "  authors:\n",
      "    - andela_bouwe\n",
      "    - righi_mattia\n",
      "\n",
      "  maintainer:\n",
      "    - schlund_manuel\n",
      "\n",
      "  references:\n",
      "    - acknow_project\n",
      "\n",
      "  projects:\n",
      "    - esmval\n",
      "    - c3s-magic\n",
      "\n",
      "datasets:\n",
      "  - {dataset: BCC-ESM1, project: CMIP6, exp: historical, ensemble: r1i1p1f1, grid: gn}\n",
      "  - {dataset: CanESM2, project: CMIP5, exp: historical, ensemble: r1i1p1}\n",
      "\n",
      "preprocessors:\n",
      "\n",
      "  select_january:\n",
      "    extract_month:\n",
      "      month: 1\n",
      "\n",
      "  annual_mean_amsterdam:\n",
      "    extract_point:\n",
      "      latitude: 52.379189\n",
      "      longitude: 4.899431\n",
      "      scheme: linear\n",
      "    annual_statistics:\n",
      "      operator: mean\n",
      "    multi_model_statistics:\n",
      "      statistics:\n",
      "        - mean\n",
      "      span: overlap\n",
      "\n",
      "  annual_mean_global:\n",
      "    area_statistics:\n",
      "      operator: mean\n",
      "      fx_variables:\n",
      "        areacella:\n",
      "    annual_statistics:\n",
      "      operator: mean\n",
      "\n",
      "diagnostics:\n",
      "\n",
      "  map:\n",
      "    description: Global map of temperature in January 2000.\n",
      "    themes:\n",
      "      - phys\n",
      "    realms:\n",
      "      - atmos\n",
      "    variables:\n",
      "      tas:\n",
      "        mip: Amon\n",
      "        preprocessor: select_january\n",
      "        start_year: 2000\n",
      "        end_year: 2000\n",
      "    scripts:\n",
      "      script1:\n",
      "        script: examples/diagnostic.py\n",
      "        quickplot:\n",
      "          plot_type: pcolormesh\n",
      "          cmap: Reds\n",
      "\n",
      "  timeseries:\n",
      "    description: Annual mean temperature in Amsterdam and global mean since 1850.\n",
      "    themes:\n",
      "      - phys\n",
      "    realms:\n",
      "      - atmos\n",
      "    variables:\n",
      "      tas_amsterdam:\n",
      "        short_name: tas\n",
      "        mip: Amon\n",
      "        preprocessor: annual_mean_amsterdam\n",
      "        start_year: 1850\n",
      "        end_year: 2000\n",
      "      tas_global:\n",
      "        short_name: tas\n",
      "        mip: Amon\n",
      "        preprocessor: annual_mean_global\n",
      "        start_year: 1850\n",
      "        end_year: 2000\n",
      "    scripts:\n",
      "      script1:\n",
      "        script: examples/diagnostic.py\n",
      "        quickplot:\n",
      "          plot_type: plot\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(example_recipe.path.read_text())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring the tool\n",
    "\n",
    "The [user configuration file](https://docs.esmvaltool.org/projects/esmvalcore/en/latest/quickstart/configure.html) is where you configure the tool. For example, the `rootpath` setting tells the tool where your data lives, if you have any.  Typically this file is stored in `~/.esmvaltool/config-user.yml`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Config({'auxiliary_data_dir': PosixPath('/work/bd0854/DATA/ESMValTool2/AUX'),\n",
       "        'compress_netcdf': False,\n",
       "        'config_developer_file': None,\n",
       "        'config_file': PosixPath('/home/k/k206100/.esmvaltool/config-user.yml'),\n",
       "        'download_dir': PosixPath('/home/k/k206100/climate_data'),\n",
       "        'drs': {'CMIP3': 'DKRZ',\n",
       "                'CMIP5': 'DKRZ',\n",
       "                'CMIP6': 'DKRZ',\n",
       "                'CORDEX': 'BADC',\n",
       "                'OBS': 'default',\n",
       "                'OBS6': 'default',\n",
       "                'ana4mips': 'default',\n",
       "                'native6': 'default',\n",
       "                'obs4MIPs': 'default'},\n",
       "        'exit_on_warning': False,\n",
       "        'extra_facets_dir': (),\n",
       "        'log_level': 'info',\n",
       "        'max_parallel_tasks': None,\n",
       "        'offline': False,\n",
       "        'output_dir': PosixPath('/home/k/k206100/esmvaltool_output'),\n",
       "        'output_file_type': 'png',\n",
       "        'profile_diagnostic': False,\n",
       "        'remove_preproc_dir': True,\n",
       "        'resume_from': [],\n",
       "        'rootpath': {'CMIP3': [PosixPath('/work/bd0854/DATA/ESMValTool2/CMIP3')],\n",
       "                     'CMIP5': [PosixPath('/work/bd0854/DATA/ESMValTool2/CMIP5_DKRZ')],\n",
       "                     'CMIP6': [PosixPath('/work/bd0854/DATA/ESMValTool2/CMIP6_DKRZ')],\n",
       "                     'CORDEX': [PosixPath('/work/ik1017/C3SCORDEX/data/c3s-cordex/output')],\n",
       "                     'OBS': [PosixPath('/work/bd0854/DATA/ESMValTool2/OBS')],\n",
       "                     'OBS6': [PosixPath('/work/bd0854/DATA/ESMValTool2/OBS')],\n",
       "                     'RAWOBS': [PosixPath('/work/bd0854/DATA/ESMValTool2/RAWOBS')],\n",
       "                     'ana4mips': [PosixPath('/work/bd0854/DATA/ESMValTool2/OBS')],\n",
       "                     'native6': [PosixPath('/work/bd0854/DATA/ESMValTool2/RAWOBS')],\n",
       "                     'obs4MIPs': [PosixPath('/work/bd0854/DATA/ESMValTool2/OBS')]},\n",
       "        'save_intermediary_cubes': False})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esmvaltool.CFG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since version 2.4, the tool can automatically download the climate data files required to run a recipe for you. This can be enabled by setting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "esmvaltool.CFG['offline'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure that the automatic download works, we create the directory beforehand (e.g. needed when running on [Binder](https://mybinder.org/)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "esmvaltool.CFG['download_dir'].mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A major advantage of working on a machine like [Levante](https://docs.dkrz.de/doc/levante/index.html), is that a lot of [CMIP data is already available](https://docs.dkrz.de/doc/dataservices/finding_and_accessing_data/index.html#cmip-data-pool), so typically only a few files will need to be downloaded to run a recipe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running a recipe\n",
    "\n",
    "Let's run our first recipe and inspect the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:esmvalcore._config._esgf_pyclient:\n",
      "ESGF credentials missing, only data that is accessible without\n",
      "logging in will be available.\n",
      "\n",
      "See https://esgf.github.io/esgf-user-support/user_guide.html\n",
      "for instructions on how to create an account if you do not have\n",
      "one yet.\n",
      "\n",
      "Next, configure your system so esmvaltool can use your\n",
      "credentials. This can be done using the keyring package, or\n",
      "you can just enter them in /home/k/k206100/.esmvaltool/esgf-pyclient.yml.\n",
      "\n",
      "keyring\n",
      "=======\n",
      "First install the keyring package (requires a supported\n",
      "backend, see https://pypi.org/project/keyring/):\n",
      "$ pip install keyring\n",
      "\n",
      "Next, set your username and password by running the commands:\n",
      "$ keyring set ESGF hostname\n",
      "$ keyring set ESGF username\n",
      "$ keyring set ESGF password\n",
      "\n",
      "To check that you entered your credentials correctly, run:\n",
      "$ keyring get ESGF hostname\n",
      "$ keyring get ESGF username\n",
      "$ keyring get ESGF password\n",
      "\n",
      "configuration file\n",
      "==================\n",
      "You can store the hostname, username, and password or your OpenID\n",
      "account in a plain text in the file /home/k/k206100/.esmvaltool/esgf-pyclient.yml like this:\n",
      "\n",
      "logon:\n",
      "  hostname: \"your-hostname\"\n",
      "  username: \"your-username\"\n",
      "  password: \"your-password\"\n",
      "\n",
      "or your can configure an interactive log in:\n",
      "\n",
      "logon:\n",
      "  interactive: true\n",
      "\n",
      "Note that storing your password in plain text in the configuration\n",
      "file is less secure. On shared systems, make sure the permissions\n",
      "of the file are set so only you can read it, i.e.\n",
      "\n",
      "$ ls -l /home/k/k206100/.esmvaltool/esgf-pyclient.yml\n",
      "\n",
      "shows permissions -rw-------.\n",
      "\n",
      "\n",
      "WARNING:esmvalcore._recipe:Missing data for fx variable 'areacella' of dataset CMIP6\n"
     ]
    }
   ],
   "source": [
    "output = example_recipe.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots and further analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the recipe consists of images and data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timeseries/script1:\n",
       "  DataFile('tas_amsterdam_CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_1850-2000.nc')\n",
       "  ImageFile('tas_global_CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_1850-2000.png')\n",
       "  ImageFile('tas_amsterdam_MultiModelMean_historical_Amon_tas_1850-2000.png')\n",
       "  DataFile('tas_global_CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_1850-2000.nc')\n",
       "  ImageFile('tas_amsterdam_CMIP5_CanESM2_Amon_historical_r1i1p1_tas_1850-2000.png')\n",
       "  DataFile('tas_amsterdam_CMIP5_CanESM2_Amon_historical_r1i1p1_tas_1850-2000.nc')\n",
       "  DataFile('tas_global_CMIP5_CanESM2_Amon_historical_r1i1p1_tas_1850-2000.nc')\n",
       "  ImageFile('tas_amsterdam_CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_1850-2000.png')\n",
       "  DataFile('tas_amsterdam_MultiModelMean_historical_Amon_tas_1850-2000.nc')\n",
       "  ImageFile('tas_global_CMIP5_CanESM2_Amon_historical_r1i1p1_tas_1850-2000.png')\n",
       "\n",
       "map/script1:\n",
       "  DataFile('CMIP5_CanESM2_Amon_historical_r1i1p1_tas_2000-2000.nc')\n",
       "  ImageFile('CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_2000-2000.png')\n",
       "  ImageFile('CMIP5_CanESM2_Amon_historical_r1i1p1_tas_2000-2000.png')\n",
       "  DataFile('CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_2000-2000.nc')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is also available as files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/k/k206100/esmvaltool_output/recipe_python_20220520_122757/work/map/script1/CMIP5_CanESM2_Amon_historical_r1i1p1_tas_2000-2000.nc\n",
      "/home/k/k206100/esmvaltool_output/recipe_python_20220520_122757/plots/map/script1/png/CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_2000-2000.png\n",
      "/home/k/k206100/esmvaltool_output/recipe_python_20220520_122757/plots/map/script1/png/CMIP5_CanESM2_Amon_historical_r1i1p1_tas_2000-2000.png\n",
      "/home/k/k206100/esmvaltool_output/recipe_python_20220520_122757/work/map/script1/CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_2000-2000.nc\n"
     ]
    }
   ],
   "source": [
    "for result in output['map/script1']:\n",
    "    print(result.path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at one of the plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Average Near-Surface Air Temperature between 2000 and 2000 according to BCC-ESM1.<img 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      ],
      "text/plain": [
       "ImageFile('CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_2000-2000.png')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plots = [f for f in output['map/script1'] if isinstance(f, esmvaltool.recipe_output.ImageFile)]\n",
    "plots[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data used to create the plots is also available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DataFile('CMIP5_CanESM2_Amon_historical_r1i1p1_tas_2000-2000.nc'),\n",
       " DataFile('CMIP6_BCC-ESM1_Amon_historical_r1i1p1f1_tas_gn_2000-2000.nc')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_files = [f for f in output['map/script1'] if isinstance(f, esmvaltool.recipe_output.DataFile)]\n",
    "data_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the output is a dataset, you can load it with `xarray` or `iris`. In this way, you can immediately continue to work with the (pre-)processed data in your notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (lat: 64, lon: 128, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat           (lat) float64 -87.86 -85.1 -82.31 -79.53 ... 82.31 85.1 87.86\n",
       "  * lon           (lon) float64 0.0 2.812 5.625 8.438 ... 351.6 354.4 357.2\n",
       "    height        float64 2.0\n",
       "    month_number  int64 1\n",
       "    time          object 2000-01-16 12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas           (lat, lon) float32 242.2 242.0 241.8 ... 243.8 244.0 244.1\n",
       "    lat_bnds      (lat, bnds) float64 -90.0 -86.48 -86.48 ... 86.48 86.48 90.0\n",
       "    lon_bnds      (lon, bnds) float64 -1.406 1.406 1.406 ... 355.8 355.8 358.6\n",
       "    time_bnds     (bnds) object 2000-01-01 00:00:00 2000-02-01 00:00:00\n",
       "Attributes: (12/32)\n",
       "    CCCma_data_licence:     1) GRANT OF LICENCE - The Government of Canada (E...\n",
       "    CCCma_parent_runid:     IGA\n",
       "    CCCma_runid:            IGM\n",
       "    associated_files:       baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLoc...\n",
       "    branch_time:            171915.0\n",
       "    branch_time_YMDH:       2321:01:01:00\n",
       "    ...                     ...\n",
       "    software:               Created with ESMValTool v2.5.0\n",
       "    source:                 CanESM2 2010 atmosphere: CanAM4 (AGCM15i, T63L35)...\n",
       "    table_id:               Table Amon (31 January 2011) 53b766a395ac41696af4...\n",
       "    title:                  CanESM2 model output prepared for CMIP5 historical\n",
       "    Conventions:            CF-1.7\n",
       "    caption:                Average Near-Surface Air Temperature between 2000...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-64177b15-1829-4659-be00-9b1c9bf88744' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-64177b15-1829-4659-be00-9b1c9bf88744' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 64</li><li><span class='xr-has-index'>lon</span>: 128</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-03b9cbb2-86ac-4481-b047-8992d319e01e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03b9cbb2-86ac-4481-b047-8992d319e01e' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.86 -85.1 -82.31 ... 85.1 87.86</div><input id='attrs-f9aaa1a2-c1ff-4d56-b208-4aa1d1cbd918' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9aaa1a2-c1ff-4d56-b208-4aa1d1cbd918' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9781c18f-f1b3-46df-a34c-7d19fc286133' class='xr-var-data-in' type='checkbox'><label for='data-9781c18f-f1b3-46df-a34c-7d19fc286133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.863801, -85.096529, -82.312915, -79.525609, -76.736902, -73.947518,\n",
       "       -71.157755, -68.367759, -65.57761 , -62.787354, -59.997023, -57.206634,\n",
       "       -54.416202, -51.625736, -48.835243, -46.044729, -43.254197, -40.463651,\n",
       "       -37.673092, -34.882523, -32.091946, -29.301362, -26.510772, -23.720176,\n",
       "       -20.929577, -18.138973, -15.348367, -12.557759,  -9.767148,  -6.976536,\n",
       "        -4.185923,  -1.395309,   1.395309,   4.185923,   6.976536,   9.767148,\n",
       "        12.557759,  15.348367,  18.138973,  20.929577,  23.720176,  26.510772,\n",
       "        29.301362,  32.091946,  34.882523,  37.673092,  40.463651,  43.254197,\n",
       "        46.044729,  48.835243,  51.625736,  54.416202,  57.206634,  59.997023,\n",
       "        62.787354,  65.57761 ,  68.367759,  71.157755,  73.947518,  76.736902,\n",
       "        79.525609,  82.312915,  85.096529,  87.863801])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.812 5.625 ... 354.4 357.2</div><input id='attrs-6f9795ab-8f9a-48ce-8379-3a46c1aee165' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f9795ab-8f9a-48ce-8379-3a46c1aee165' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1929c4d3-c6de-433c-8ea7-bbb6546e0284' class='xr-var-data-in' type='checkbox'><label for='data-1929c4d3-c6de-433c-8ea7-bbb6546e0284' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.    ,   2.8125,   5.625 ,   8.4375,  11.25  ,  14.0625,  16.875 ,\n",
       "        19.6875,  22.5   ,  25.3125,  28.125 ,  30.9375,  33.75  ,  36.5625,\n",
       "        39.375 ,  42.1875,  45.    ,  47.8125,  50.625 ,  53.4375,  56.25  ,\n",
       "        59.0625,  61.875 ,  64.6875,  67.5   ,  70.3125,  73.125 ,  75.9375,\n",
       "        78.75  ,  81.5625,  84.375 ,  87.1875,  90.    ,  92.8125,  95.625 ,\n",
       "        98.4375, 101.25  , 104.0625, 106.875 , 109.6875, 112.5   , 115.3125,\n",
       "       118.125 , 120.9375, 123.75  , 126.5625, 129.375 , 132.1875, 135.    ,\n",
       "       137.8125, 140.625 , 143.4375, 146.25  , 149.0625, 151.875 , 154.6875,\n",
       "       157.5   , 160.3125, 163.125 , 165.9375, 168.75  , 171.5625, 174.375 ,\n",
       "       177.1875, 180.    , 182.8125, 185.625 , 188.4375, 191.25  , 194.0625,\n",
       "       196.875 , 199.6875, 202.5   , 205.3125, 208.125 , 210.9375, 213.75  ,\n",
       "       216.5625, 219.375 , 222.1875, 225.    , 227.8125, 230.625 , 233.4375,\n",
       "       236.25  , 239.0625, 241.875 , 244.6875, 247.5   , 250.3125, 253.125 ,\n",
       "       255.9375, 258.75  , 261.5625, 264.375 , 267.1875, 270.    , 272.8125,\n",
       "       275.625 , 278.4375, 281.25  , 284.0625, 286.875 , 289.6875, 292.5   ,\n",
       "       295.3125, 298.125 , 300.9375, 303.75  , 306.5625, 309.375 , 312.1875,\n",
       "       315.    , 317.8125, 320.625 , 323.4375, 326.25  , 329.0625, 331.875 ,\n",
       "       334.6875, 337.5   , 340.3125, 343.125 , 345.9375, 348.75  , 351.5625,\n",
       "       354.375 , 357.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-b51e41b6-812b-420d-8041-34ac18d78070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b51e41b6-812b-420d-8041-34ac18d78070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec084f34-509e-4a32-999d-4600e4035380' class='xr-var-data-in' type='checkbox'><label for='data-ec084f34-509e-4a32-999d-4600e4035380' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-c0e621f1-f206-49cd-ae9d-53fc3a5db911' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0e621f1-f206-49cd-ae9d-53fc3a5db911' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-670bf38b-ada4-4ebc-ade8-ddb87e6ae02c' class='xr-var-data-in' type='checkbox'><label for='data-670bf38b-ada4-4ebc-ade8-ddb87e6ae02c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>month_number</dd></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2000-01-16 12:00:00</div><input id='attrs-568f7013-c6c3-4a3b-8e0e-9cc0b4322959' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-568f7013-c6c3-4a3b-8e0e-9cc0b4322959' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8380e3a7-549f-4665-8b1e-da380a44d0a9' class='xr-var-data-in' type='checkbox'><label for='data-8380e3a7-549f-4665-8b1e-da380a44d0a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(2000, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b563a4ae-0c11-478d-967e-3e38ce2f0c08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b563a4ae-0c11-478d-967e-3e38ce2f0c08' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>242.2 242.0 241.8 ... 244.0 244.1</div><input id='attrs-1ed77e0d-7156-418c-8e85-8b954f0821c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ed77e0d-7156-418c-8e85-8b954f0821c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29299cb1-8409-4946-a303-fc1e69492414' class='xr-var-data-in' type='checkbox'><label for='data-29299cb1-8409-4946-a303-fc1e69492414' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 15 minutes)</dd></dl></div><div class='xr-var-data'><pre>array([[242.20467, 242.0047 , 241.83128, ..., 242.83815, 242.63023,\n",
       "        242.40685],\n",
       "       [241.11821, 240.70073, 240.37233, ..., 242.8626 , 242.18985,\n",
       "        241.62096],\n",
       "       [240.19365, 239.75677, 239.49928, ..., 242.65651, 241.67825,\n",
       "        240.85217],\n",
       "       ...,\n",
       "       [248.83395, 248.76094, 248.6048 , ..., 247.33266, 248.19754,\n",
       "        248.69681],\n",
       "       [244.43646, 244.29176, 244.3909 , ..., 243.75423, 244.08028,\n",
       "        244.4064 ],\n",
       "       [244.17209, 243.02011, 243.11401, ..., 243.83354, 243.96913,\n",
       "        244.07257]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -86.48 -86.48 ... 86.48 90.0</div><input id='attrs-2d6df69c-152a-4380-83ec-791091986075' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d6df69c-152a-4380-83ec-791091986075' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57a9bd51-74a0-41bf-af6a-113e6d0a3daf' class='xr-var-data-in' type='checkbox'><label for='data-57a9bd51-74a0-41bf-af6a-113e6d0a3daf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-90.        , -86.48016542],\n",
       "       [-86.48016542, -83.70472247],\n",
       "       [-83.70472247, -80.91926226],\n",
       "       [-80.91926226, -78.13125563],\n",
       "       [-78.13125563, -75.34220992],\n",
       "       [-75.34220992, -72.55263609],\n",
       "       [-72.55263609, -69.76275656],\n",
       "       [-69.76275656, -66.97268406],\n",
       "       [-66.97268406, -64.18248191],\n",
       "       [-64.18248191, -61.39218846],\n",
       "       [-61.39218846, -58.60182832],\n",
       "       [-58.60182832, -55.81141803],\n",
       "       [-55.81141803, -53.0209691 ],\n",
       "       [-53.0209691 , -50.23048983],\n",
       "       [-50.23048983, -47.4399863 ],\n",
       "       [-47.4399863 , -44.64946315],\n",
       "       [-44.64946315, -41.85892393],\n",
       "       [-41.85892393, -39.06837141],\n",
       "       [-39.06837141, -36.27780782],\n",
       "       [-36.27780782, -33.48723494],\n",
       "...\n",
       "       [ 33.48723494,  36.27780782],\n",
       "       [ 36.27780782,  39.06837141],\n",
       "       [ 39.06837141,  41.85892393],\n",
       "       [ 41.85892393,  44.64946315],\n",
       "       [ 44.64946315,  47.4399863 ],\n",
       "       [ 47.4399863 ,  50.23048983],\n",
       "       [ 50.23048983,  53.0209691 ],\n",
       "       [ 53.0209691 ,  55.81141803],\n",
       "       [ 55.81141803,  58.60182832],\n",
       "       [ 58.60182832,  61.39218846],\n",
       "       [ 61.39218846,  64.18248191],\n",
       "       [ 64.18248191,  66.97268406],\n",
       "       [ 66.97268406,  69.76275656],\n",
       "       [ 69.76275656,  72.55263609],\n",
       "       [ 72.55263609,  75.34220992],\n",
       "       [ 75.34220992,  78.13125563],\n",
       "       [ 78.13125563,  80.91926226],\n",
       "       [ 80.91926226,  83.70472247],\n",
       "       [ 83.70472247,  86.48016542],\n",
       "       [ 86.48016542,  90.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.406 1.406 1.406 ... 355.8 358.6</div><input id='attrs-ea049cf4-5943-4e05-9c1c-575c95a4a4c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea049cf4-5943-4e05-9c1c-575c95a4a4c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb82f74d-d2f3-4cc8-a326-122771447c81' class='xr-var-data-in' type='checkbox'><label for='data-fb82f74d-d2f3-4cc8-a326-122771447c81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -1.40625,   1.40625],\n",
       "       [  1.40625,   4.21875],\n",
       "       [  4.21875,   7.03125],\n",
       "       [  7.03125,   9.84375],\n",
       "       [  9.84375,  12.65625],\n",
       "       [ 12.65625,  15.46875],\n",
       "       [ 15.46875,  18.28125],\n",
       "       [ 18.28125,  21.09375],\n",
       "       [ 21.09375,  23.90625],\n",
       "       [ 23.90625,  26.71875],\n",
       "       [ 26.71875,  29.53125],\n",
       "       [ 29.53125,  32.34375],\n",
       "       [ 32.34375,  35.15625],\n",
       "       [ 35.15625,  37.96875],\n",
       "       [ 37.96875,  40.78125],\n",
       "       [ 40.78125,  43.59375],\n",
       "       [ 43.59375,  46.40625],\n",
       "       [ 46.40625,  49.21875],\n",
       "       [ 49.21875,  52.03125],\n",
       "       [ 52.03125,  54.84375],\n",
       "...\n",
       "       [302.34375, 305.15625],\n",
       "       [305.15625, 307.96875],\n",
       "       [307.96875, 310.78125],\n",
       "       [310.78125, 313.59375],\n",
       "       [313.59375, 316.40625],\n",
       "       [316.40625, 319.21875],\n",
       "       [319.21875, 322.03125],\n",
       "       [322.03125, 324.84375],\n",
       "       [324.84375, 327.65625],\n",
       "       [327.65625, 330.46875],\n",
       "       [330.46875, 333.28125],\n",
       "       [333.28125, 336.09375],\n",
       "       [336.09375, 338.90625],\n",
       "       [338.90625, 341.71875],\n",
       "       [341.71875, 344.53125],\n",
       "       [344.53125, 347.34375],\n",
       "       [347.34375, 350.15625],\n",
       "       [350.15625, 352.96875],\n",
       "       [352.96875, 355.78125],\n",
       "       [355.78125, 358.59375]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2000-01-01 00:00:00 2000-02-01 0...</div><input id='attrs-781dc49e-950f-4984-a939-dc7b1142a6fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-781dc49e-950f-4984-a939-dc7b1142a6fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f12c8e01-c354-40e0-882b-ed76b6c7eb7c' class='xr-var-data-in' type='checkbox'><label for='data-f12c8e01-c354-40e0-882b-ed76b6c7eb7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(2000, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 2, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a36a85a4-b372-453f-b614-f09dc92aafc5' class='xr-section-summary-in' type='checkbox'  ><label for='section-a36a85a4-b372-453f-b614-f09dc92aafc5' class='xr-section-summary' >Attributes: <span>(32)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CCCma_data_licence :</span></dt><dd>1) GRANT OF LICENCE - The Government of Canada (Environment Canada) is the \n",
       "owner of all intellectual property rights (including copyright) that may exist in this Data \n",
       "product. You (as &quot;The Licensee&quot;) are hereby granted a non-exclusive, non-assignable, \n",
       "non-transferable unrestricted licence to use this data product for any purpose including \n",
       "the right to share these data with others and to make value-added and derivative \n",
       "products from it. This licence is not a sale of any or all of the owner&#x27;s rights.\n",
       "2) NO WARRANTY - This Data product is provided &quot;as-is&quot;; it has not been designed or \n",
       "prepared to meet the Licensee&#x27;s particular requirements. Environment Canada makes no \n",
       "warranty, either express or implied, including but not limited to, warranties of \n",
       "merchantability and fitness for a particular purpose. In no event will Environment Canada \n",
       "be liable for any indirect, special, consequential or other damages attributed to the \n",
       "Licensee&#x27;s use of the Data product.</dd><dt><span>CCCma_parent_runid :</span></dt><dd>IGA</dd><dt><span>CCCma_runid :</span></dt><dd>IGM</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CanESM2_historical_r0i0p0.nc areacella: areacella_fx_CanESM2_historical_r0i0p0.nc</dd><dt><span>branch_time :</span></dt><dd>171915.0</dd><dt><span>branch_time_YMDH :</span></dt><dd>2321:01:01:00</dd><dt><span>cmor_version :</span></dt><dd>2.5.4</dd><dt><span>contact :</span></dt><dd>cccma_info@ec.gc.ca</dd><dt><span>experiment :</span></dt><dd>historical</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>forcing :</span></dt><dd>GHG,Oz,SA,BC,OC,LU,Sl,Vl (GHG includes CO2,CH4,N2O,CFC11,effective CFC12)</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>initialization_method :</span></dt><dd>1</dd><dt><span>institute_id :</span></dt><dd>CCCma</dd><dt><span>institution :</span></dt><dd>CCCma (Canadian Centre for Climate Modelling and Analysis, Victoria, BC, Canada)</dd><dt><span>model_id :</span></dt><dd>CanESM2</dd><dt><span>modeling_realm :</span></dt><dd>atmos</dd><dt><span>original_name :</span></dt><dd>ST</dd><dt><span>parent_experiment :</span></dt><dd>pre-industrial control</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_experiment_rip :</span></dt><dd>r1i1p1</dd><dt><span>physics_version :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>project_id :</span></dt><dd>CMIP5</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>references :</span></dt><dd>http://www.cccma.ec.gc.ca/models</dd><dt><span>software :</span></dt><dd>Created with ESMValTool v2.5.0</dd><dt><span>source :</span></dt><dd>CanESM2 2010 atmosphere: CanAM4 (AGCM15i, T63L35) ocean: CanOM4 (OGCM4.0, 256x192L40) and CMOC1.2 sea ice: CanSIM1 (Cavitating Fluid, T63 Gaussian Grid) land: CLASS2.7 and CTEM1</dd><dt><span>table_id :</span></dt><dd>Table Amon (31 January 2011) 53b766a395ac41696af40aab76a49ae5</dd><dt><span>title :</span></dt><dd>CanESM2 model output prepared for CMIP5 historical</dd><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>caption :</span></dt><dd>Average Near-Surface Air Temperature between 2000 and 2000 according to CanESM2.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (lat: 64, lon: 128, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat           (lat) float64 -87.86 -85.1 -82.31 -79.53 ... 82.31 85.1 87.86\n",
       "  * lon           (lon) float64 0.0 2.812 5.625 8.438 ... 351.6 354.4 357.2\n",
       "    height        float64 2.0\n",
       "    month_number  int64 1\n",
       "    time          object 2000-01-16 12:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    tas           (lat, lon) float32 242.2 242.0 241.8 ... 243.8 244.0 244.1\n",
       "    lat_bnds      (lat, bnds) float64 -90.0 -86.48 -86.48 ... 86.48 86.48 90.0\n",
       "    lon_bnds      (lon, bnds) float64 -1.406 1.406 1.406 ... 355.8 355.8 358.6\n",
       "    time_bnds     (bnds) object 2000-01-01 00:00:00 2000-02-01 00:00:00\n",
       "Attributes: (12/32)\n",
       "    CCCma_data_licence:     1) GRANT OF LICENCE - The Government of Canada (E...\n",
       "    CCCma_parent_runid:     IGA\n",
       "    CCCma_runid:            IGM\n",
       "    associated_files:       baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLoc...\n",
       "    branch_time:            171915.0\n",
       "    branch_time_YMDH:       2321:01:01:00\n",
       "    ...                     ...\n",
       "    software:               Created with ESMValTool v2.5.0\n",
       "    source:                 CanESM2 2010 atmosphere: CanAM4 (AGCM15i, T63L35)...\n",
       "    table_id:               Table Amon (31 January 2011) 53b766a395ac41696af4...\n",
       "    title:                  CanESM2 model output prepared for CMIP5 historical\n",
       "    Conventions:            CF-1.7\n",
       "    caption:                Average Near-Surface Air Temperature between 2000..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xrds = data_files[0].load_xarray()\n",
    "xrds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "</style>\n",
       "<table class=\"iris\" id=\"140735759227712\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Air Temperature (K)</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">64</td>\n",
       "<td class=\"iris iris-inclusion-cell\">128</td>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Scalar coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"><br>height                      2.0 m<br>month_number                1</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime                        2000-01-16 12</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">0:00, bound=(2000-01-01 00:00:00, 2000-02-01 00:00:00)</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Cell methods</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tmean                        time (15 minutes)</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"><br>CCCma_data_licence          &#x27;1) GRANT OF LICENCE - The Government of Canada (Environment Canada) is...<br>CCCma_parent_runid          IGA<br>CCCma_runid                 IGM<br>Conventions                 CF-1.7</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tassociated_files            baseURL</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CanESM2_historical_r0i0p0.nc...<br>branch_time                 171915.0</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tbranch_time_YMDH            2321</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">1:01:00<br>caption                     Average Near-Surface Air Temperature between 2000 and 2000 according to...<br>cmor_version                2.5.4<br>contact                     cccma_info@ec.gc.ca<br>experiment                  historical<br>experiment_id               historical<br>forcing                     GHG,Oz,SA,BC,OC,LU,Sl,Vl (GHG includes CO2,CH4,N2O,CFC11,effective CFC...<br>frequency                   mon<br>initialization_method       1<br>institute_id                CCCma<br>institution                 CCCma (Canadian Centre for Climate Modelling and Analysis, Victoria, BC,...<br>model_id                    CanESM2<br>modeling_realm              atmos<br>original_name               ST<br>parent_experiment           pre-industrial control<br>parent_experiment_id        piControl<br>parent_experiment_rip       r1i1p1<br>physics_version             1<br>product                     output<br>project_id                  CMIP5<br>realization                 1</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\treferences                  http</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">/www.cccma.ec.gc.ca/models<br>software                    Created with ESMValTool v2.5.0</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource                      CanESM2 2010 atmosphere</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"2\">CanAM4 (AGCM15i, T63L35) ocean: CanOM4 (OGCM4.0,...<br>table_id                    Table Amon (31 January 2011) 53b766a395ac41696af40aab76a49ae5<br>title                       CanESM2 model output prepared for CMIP5 historical</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of air_temperature / (K) (latitude: 64; longitude: 128)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube = data_files[0].load_iris()[0]\n",
    "cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This allows us to create our own plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plot\n",
    "iris.quickplot.contourf(cube)\n",
    "\n",
    "# Set the size of the figure\n",
    "plt.gcf().set_size_inches(12, 10)\n",
    "\n",
    "# Draw coastlines\n",
    "plt.gca().coastlines()\n",
    "\n",
    "# Show the resulting figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More information and contact details\n",
    "\n",
    "For more information on all available recipes, visit the [ESMValTool documentation](https://docs.esmvaltool.org/en/latest/recipes/index.html). If this presentation has sparked your interest, you are welcome to join one of our [online monthly meetings](https://docs.esmvaltool.org/en/latest/introduction.html#monthly-meetings) (open to anyone with an interest in ESMValTool), [mailing list](https://docs.esmvaltool.org/en/latest/introduction.html#user-mailing-list), or join us on [GitHub](https://github.com/esmvalgroup/esmvaltool/discussions).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ESMValTool",
   "language": "python",
   "name": "esmvaltool"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}