05 Climate Projections CORDEX.ipynb

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    "![logo](./img/LogoLine_horizon_C3S.png)"
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   "source": [
    "<br>"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Climate Projections (CORDEX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is a practical introduction to [CORDEX regional climate model data on single levels](https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview). CORDEX data are available for 14 regional domains and variable spatial resolutions, from 0.11 x 0.11 degrees up to 0.44 x 0.44 degrees. This workflow will demonstrate how to compute the difference in the air temperature climatology for 2071-2100 (according to a projected scenario) relative to the reference period 1971-2000 in Africa, with a spatial resolution of 0.44 x 0.44 degrees. \n",
    "\n",
    "We will use the historical experiment to compute the past climatology and repeat the steps to compute the projected climatology according to the scenario RCP4.5. Finally, we will take the difference between the two in order to assess the extent of change. This is called the \"delta method\" which takes the climate change signal as the difference between the future and past considering that the model biases are the same and can therefore be removed with the subtraction.\n",
    "\n",
    "This particular example uses data from the Canadian Regional Climate Model `CCCma_CanRCM4`, with lateral boundary conditions from the Global Climate Model (GCM) `CanESM2`, also Canadian. This coupled model is referred to as `CanRCM4 - CanESM2`. See [here](https://confluence.ecmwf.int/display/CKB/CORDEX%3A+Regional+climate+projections#CORDEX:Regionalclimateprojections-DrivingGlobalClimateModelsandRegionalClimateModels) a full list of models included in the CDS-CORDEX subset.\n",
    "\n",
    "**Note:** The workflow uses a specific model, scenario and geographical area, but you can use any model, scenario or domain, and indeed any period or variable. To have a more accurate idea of projected climate change, it is typical to calculate an ensemble of multiple models. Only for the sake of simplicity do we focus on one model.\n",
    "\n",
    "<br>\n",
    "\n",
    "In general, CORDEX experiments can be divided into three main categories:\n",
    "\n",
    "* **Evaluation**: model simulations for the past with imposed \"perfect\" lateral boundary conditions following ERA-Interim reanalysis (1979-2015).\n",
    "\n",
    "* **Historical**: Historical experiments cover the period where modern climate observations exist. These experiements show how the RCMs perform for the past climate when forced by the Global Circulation Model (GCM) and can be used as a reference period for comparison with scenario runs for the future. \n",
    "\n",
    "* **Scenario experiments RCP2.6, RCP4.5, RCP8.5**: ensemble of CORDEX climate projection experiments driven by boundary conditions from GCMs using RCP (Representative Concentration Pathways) forcing scenarios. The scenarios used here are RCP 2.6, 4.5 and 8.5, which provide different pathways of the future climate forcing.\n",
    "\n",
    "Learn [here](https://confluence.ecmwf.int/display/CKB/CORDEX%3A+Regional+climate+projections#CORDEX:Regionalclimateprojections-Experiments) more about CORDEX experiments in the Copernicus Climate Data Store (CDS).\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook has the following outline:\n",
    "1. Request data from the CDS programmatically with the CDS API\n",
    "2. Unzip the downloaded data files\n",
    "3. Compute CORDEX historical climatology for reference period 1971-2000\n",
    "4. Compute CORDEX projected climatology for target period 2071-2100 based on scenario RCP4.5\n",
    "5. Compute and visualize the climate change signal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook introduces you to [CORDEX regional climate model data on single levels](https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview). The data used in the notebook has the following specifications:\n",
    "\n",
    "> **Data**: `CORDEX regional climate model data on single levels - Experiment: Historical` <br>\n",
    "> **Temporal coverage**: `1 Jan 1971 to 31 Dec 2000` <br>\n",
    "> **Spatial coverage**: `Domain: Africa` <br>\n",
    "> **Format**: `NetCDF in zip archives`\n",
    "\n",
    "\n",
    "> **Data**: `CORDEX regional climate model data on single levels - Experiment: RCP4.5` <br>\n",
    "> **Temporal coverage**: `1 Jan 2071 to 31 Dec 2100` <br>\n",
    "> **Spatial coverage**: `Domain: Africa` <br>\n",
    "> **Format**: `NetCDF in zip archives`\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to access the notebook\n",
    "\n",
    "This tutorial is in the form of a [Jupyter notebook](https://jupyter.org/). You will not need to install any software for the training as there are a number of free cloud-based services to create, edit, run and export Jupyter notebooks such as this. Here are some suggestions (simply click on one of the links below to run the notebook):"
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   "source": [
    "|Binder|Kaggle|NBViewer|\n",
    "|:-:|:-:|:-:|\n",
    "|[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/ecmwf-projects/copernicus-training/HEAD?urlpath=lab/tree/C3S_CORDEX-regional-climate-projections.ipynb)|[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/ecmwf-projects/copernicus-training/blob/master/C3S_CORDEX-regional-climate-projections.ipynb)|[![NBViewer](https://raw.githubusercontent.com/ecmwf-projects/copernicus-training/master/img/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/ecmwf-projects/copernicus-training/blob/master/C3S_CORDEX-regional-climate-projections.ipynb)|\n",
    "|(Binder may take some time to load, so please be patient!)|(will need to login/register, and switch on the internet via *settings*)|(this will not run the notebook, only render it)|"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like to run this notebook in your own environment, we suggest you install [Anaconda](https://docs.anaconda.com/anaconda/install/), which contains most of the libraries you will need. You will also need to install [Xarray](http://xarray.pydata.org/en/stable/) for working with multidimensional data in netcdf files, and the CDS API (`pip install cdsapi`) for downloading data programatically from the CDS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install CDS API via pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install cdsapi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CDS API\n",
    "import cdsapi\n",
    "\n",
    "# Libraries for working with multidimensional arrays\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "# Libraries for plotting and visualising data\n",
    "import matplotlib.path as mpath\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Other libraries (e.g. paths, filenames, zipfile extraction)\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "from os.path import basename\n",
    "import zipfile\n",
    "import urllib3 \n",
    "urllib3.disable_warnings() # Disable \"InsecureRequestWarning\" \n",
    "                           # for data download via API"
   ]
  },
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   "metadata": {},
   "source": [
    "<hr>"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Request data from the CDS programmatically with the CDS API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to request data from the Climate Data Store programmatically with the help of the CDS API. Let us make use of the option to manually set the CDS API credentials. First, you have to define two variables: `URL` and `KEY` which build together your CDS API key. Below, you have to replace the `#########` with your personal CDS key. Please find [here](https://cds.climate.copernicus.eu/api-how-to) your personal CDS key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'https://cds.climate.copernicus.eu/api/v2'\n",
    "KEY = '##################################'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = './'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is then to request the data with the help of the CDS API. Below are two blocks of code for the following data requests:\n",
    "* **Historical experiment**: Daily aggregated historical 2m air temperature (from the CanRCM4 - CanESM2 model) from 1971 to 2000 for Africa.\n",
    "* **RCP4.5 experiment**: Daily aggregated RCP4.5 projections of 2m air temperature (from the CanRCM4 - CanESM2 model) from 2071 to 2100 for Africa\n",
    "\n",
    "> **Note:** Before you run the cells below, the terms and conditions on the use of the data need to have been accepted in the CDS. You can view and accept these conditions by logging into the [CDS](http://cds.climate.copernicus.eu), searching for the dataset, then scrolling to the end of the *Download data* section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-03 22:13:47,034 INFO Welcome to the CDS\n",
      "2022-03-03 22:13:47,049 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/projections-cordex-domains-single-levels\n",
      "2022-03-03 22:13:47,142 INFO Request is queued\n",
      "2022-03-03 22:13:48,199 INFO Request is running\n",
      "2022-03-03 22:18:05,397 INFO Request is completed\n",
      "2022-03-03 22:18:05,400 INFO Downloading https://download-0012.copernicus-climate.eu/cache-compute-0012/cache/data6/dataset-projections-cordex-domains-single-levels-19a7525a-dc2e-4e68-bba7-609414960047.zip to ./cordex/1971-2000_cordex_historical_africa.zip (908.2M)\n",
      "2022-03-03 22:32:25,835 INFO Download rate 1.1M/s                                                                      \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=952308195,content_type=application/zip,location=https://download-0012.copernicus-climate.eu/cache-compute-0012/cache/data6/dataset-projections-cordex-domains-single-levels-19a7525a-dc2e-4e68-bba7-609414960047.zip)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = cdsapi.Client(url=URL, key=KEY)\n",
    "\n",
    "c.retrieve(\n",
    "    'projections-cordex-domains-single-levels',\n",
    "    {\n",
    "        'format': 'zip',\n",
    "        'domain': 'africa',\n",
    "        'experiment': 'historical',\n",
    "        'horizontal_resolution': '0_44_degree_x_0_44_degree',\n",
    "        'temporal_resolution': 'daily_mean',\n",
    "        'variable': '2m_air_temperature',\n",
    "        'gcm_model': 'cccma_canesm2',\n",
    "        'rcm_model': 'cccma_canrcm4',\n",
    "        'ensemble_member': 'r1i1p1',\n",
    "        'start_year': ['1971', '1976', '1981', '1986', '1991', '1996'],\n",
    "        'end_year': ['1975', '1980', '1985', '1990', '1995', '2000'],\n",
    "    },\n",
    "    f'{DATADIR}1971-2000_cordex_historical_africa.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-03 22:03:00,724 INFO Welcome to the CDS\n",
      "2022-03-03 22:03:00,730 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/projections-cordex-domains-single-levels\n",
      "2022-03-03 22:03:00,863 INFO Request is queued\n",
      "2022-03-03 22:03:01,925 INFO Request is running\n",
      "2022-03-03 22:09:19,471 INFO Request is completed\n",
      "2022-03-03 22:09:19,478 INFO Downloading https://download-0000.copernicus-climate.eu/cache-compute-0000/cache/data3/dataset-projections-cordex-domains-single-levels-b19f6b3d-9012-41cd-98fa-c4820a3daeae.zip to ./cordex/2071-2100_cordex_rcp_4_5_africa.zip (908.1M)\n",
      "2022-03-03 22:12:09,215 INFO Download rate 5.4M/s                                                                      \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Result(content_length=952229444,content_type=application/zip,location=https://download-0000.copernicus-climate.eu/cache-compute-0000/cache/data3/dataset-projections-cordex-domains-single-levels-b19f6b3d-9012-41cd-98fa-c4820a3daeae.zip)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.retrieve(\n",
    "    'projections-cordex-domains-single-levels',\n",
    "    {\n",
    "        'format': 'zip',\n",
    "        'domain': 'africa',\n",
    "        'experiment': 'rcp_4_5',\n",
    "        'horizontal_resolution': '0_44_degree_x_0_44_degree',\n",
    "        'temporal_resolution': 'daily_mean',\n",
    "        'variable': '2m_air_temperature',\n",
    "        'gcm_model': 'cccma_canesm2',\n",
    "        'rcm_model': 'cccma_canrcm4',\n",
    "        'ensemble_member': 'r1i1p1',\n",
    "        'start_year': ['2071', '2076', '2081', '2086', '2091', '2096'],\n",
    "        'end_year': ['2075', '2080', '2085', '2090', '2095', '2100'],\n",
    "    },\n",
    "    f'{DATADIR}2071-2100_cordex_rcp_4_5_africa.zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unzip the downloaded data files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Copernicus Climate Data Store, CORDEX data are available in `NetCDF` format in compressed archives, either `zip` or `tar.gz`. For this reason, before we can load any data, we have to unzip or untar the files. Having downloaded the two experiments `historical` and `RCP4.5` as seperate zip files, we can use the functions from the `zipfile` Python package to extract the content of a zip file. First, we construct a `ZipFile()` object and second, we apply the function `extractall()` to extract the content of the zip file.\n",
    "\n",
    "Below, the same process is repeated for the two zip files downloaded. We see that the actual data files are disseminated in `NetCDF`. CORDEX files are generally organised in five year periods. For this reason, for each zip file, six `NetCDF` files in five year periods are extracted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cordex_zip_paths = glob(f'{DATADIR}*.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in cordex_zip_paths:\n",
    "    with zipfile.ZipFile(j, 'r') as zip_ref:\n",
    "        zip_ref.extractall(f'{DATADIR}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute CORDEX historical climatology for reference period 1971-2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to load the data of the `historical` experiment.\n",
    "\n",
    "We can use the Python library [xarray](http://xarray.pydata.org/en/stable/) and its function `open_mfdataset` to read and concatenate multiple NetCDF files at once.\n",
    "\n",
    "The result is a `xarray.Dataset` object with four dimensions: `bnds`, `rlat`, `rlon`, `time`, of which the dimension `bnds` is not callable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  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:       (time: 10950, rlon: 194, rlat: 201, bnds: 2)\n",
       "Coordinates:\n",
       "  * time          (time) object 1971-01-01 12:00:00 ... 2000-12-31 12:00:00\n",
       "  * rlon          (rlon) float64 -24.64 -24.2 -23.76 -23.32 ... 59.4 59.84 60.28\n",
       "  * rlat          (rlat) float64 -45.76 -45.32 -44.88 ... 41.36 41.8 42.24\n",
       "    lon           (rlat, rlon) float64 335.4 335.8 336.2 ... 59.4 59.84 60.28\n",
       "    lat           (rlat, rlon) float64 -45.76 -45.76 -45.76 ... 42.24 42.24\n",
       "    height        float64 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    rotated_pole  (time) |S1 b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; ... b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27;\n",
       "    tas           (time, rlat, rlon) float32 279.3 279.3 279.3 ... 269.3 268.8\n",
       "    time_bnds     (time, bnds) object 1971-01-01 00:00:00 ... 2001-01-01 00:0...\n",
       "Attributes: (12/26)\n",
       "    title:                          CanRCM4 model output prepared for CORDEX ...\n",
       "    institution:                    CCCma (Canadian Centre for Climate Modell...\n",
       "    institute_id:                   CCCma\n",
       "    contact:                        cccma_info@ec.gc.ca\n",
       "    Conventions:                    CF-1.4\n",
       "    experiment:                     Historical run driven by CCCma-CanESM2\n",
       "    ...                             ...\n",
       "    history:                        created: 2012-06-29 23:52:08 by rcm2nc\n",
       "    data_licence:                   1) GRANT OF LICENCE - The Government of C...\n",
       "    creation_date:                  2012-06-29-T23:47:03Z\n",
       "    c3s_comment:                    This data has been published at ESGF with...\n",
       "    tracking_id:                    hdl:21.14103/3d35a8d3-105d-451c-88fa-4d46...\n",
       "    c3s_disclaimer:                 This data has been curated and prepared i...</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-02d07935-22b2-45e6-ad44-929968ff93d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02d07935-22b2-45e6-ad44-929968ff93d9' 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'>time</span>: 10950</li><li><span class='xr-has-index'>rlon</span>: 194</li><li><span class='xr-has-index'>rlat</span>: 201</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d7fa1cd3-06be-49bc-b685-9a7ad459d103' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7fa1cd3-06be-49bc-b685-9a7ad459d103' class='xr-section-summary' >Coordinates: <span>(6)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1971-01-01 12:00:00 ... 2000-12-...</div><input id='attrs-406e4a9c-a110-4a84-8ea6-db78320ac363' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-406e4a9c-a110-4a84-8ea6-db78320ac363' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd97492e-c8d3-4432-b2c4-a00c87192abc' class='xr-var-data-in' type='checkbox'><label for='data-dd97492e-c8d3-4432-b2c4-a00c87192abc' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1971, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1971, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1971, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2000, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlon</span></div><div class='xr-var-dims'>(rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-24.64 -24.2 -23.76 ... 59.84 60.28</div><input id='attrs-57cd4f3b-41e1-483d-b297-aa4f33f19e5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57cd4f3b-41e1-483d-b297-aa4f33f19e5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ae18e94-0ce8-4245-9122-4f56af28c645' class='xr-var-data-in' type='checkbox'><label for='data-7ae18e94-0ce8-4245-9122-4f56af28c645' 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>long_name :</span></dt><dd>longitude in rotated pole grid</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-24.639999, -24.200012, -23.76001 , -23.320023, -22.880005, -22.440002,\n",
       "       -22.      , -21.560013, -21.120026, -20.680008, -20.240021, -19.800003,\n",
       "       -19.360016, -18.919998, -18.480011, -18.040024, -17.600006, -17.160019,\n",
       "       -16.720001, -16.280014, -15.840012, -15.400009, -14.960007, -14.520004,\n",
       "       -14.080002, -13.639999, -13.200012, -12.76001 , -12.320023, -11.880005,\n",
       "       -11.440002, -11.      , -10.560013, -10.120026,  -9.680008,  -9.240021,\n",
       "        -8.800003,  -8.360016,  -7.920029,  -7.480011,  -7.040024,  -6.600006,\n",
       "        -6.160019,  -5.720001,  -5.280014,  -4.840012,  -4.400009,  -3.960007,\n",
       "        -3.520004,  -3.080002,  -2.639999,  -2.200012,  -1.76001 ,  -1.320023,\n",
       "        -0.880005,  -0.440002,   0.      ,   0.439987,   0.879974,   1.319992,\n",
       "         1.759979,   2.199997,   2.639984,   3.079971,   3.519989,   3.959976,\n",
       "         4.399994,   4.839981,   5.279999,   5.719986,   6.159988,   6.599991,\n",
       "         7.039993,   7.479996,   7.919998,   8.359985,   8.799988,   9.23999 ,\n",
       "         9.679977,  10.119995,  10.559998,  11.      ,  11.439987,  11.879974,\n",
       "        12.319992,  12.759979,  13.199997,  13.639984,  14.079971,  14.519989,\n",
       "        14.959976,  15.399994,  15.839981,  16.279999,  16.719986,  17.159988,\n",
       "        17.599991,  18.039993,  18.479996,  18.919998,  19.359985,  19.799988,\n",
       "        20.23999 ,  20.679977,  21.119995,  21.559998,  21.999969,  22.439987,\n",
       "        22.879974,  23.319992,  23.759979,  24.199997,  24.639984,  25.079971,\n",
       "        25.519989,  25.959976,  26.399994,  26.839981,  27.279999,  27.719986,\n",
       "        28.159988,  28.599991,  29.039993,  29.479996,  29.919998,  30.359985,\n",
       "        30.799988,  31.23999 ,  31.679977,  32.119995,  32.559998,  32.999969,\n",
       "        33.439987,  33.879974,  34.319992,  34.759979,  35.199997,  35.639984,\n",
       "        36.079971,  36.519989,  36.959976,  37.399994,  37.839981,  38.279984,\n",
       "        38.719986,  39.159988,  39.599991,  40.039993,  40.479996,  40.919998,\n",
       "        41.359985,  41.799988,  42.23999 ,  42.679977,  43.119995,  43.559998,\n",
       "        43.999969,  44.439987,  44.879974,  45.319992,  45.759979,  46.199997,\n",
       "        46.639984,  47.079971,  47.519989,  47.959976,  48.399994,  48.839981,\n",
       "        49.279984,  49.719986,  50.159988,  50.599991,  51.039993,  51.479996,\n",
       "        51.919968,  52.359985,  52.799988,  53.23999 ,  53.679977,  54.119995,\n",
       "        54.559982,  54.999969,  55.439987,  55.879974,  56.319992,  56.759979,\n",
       "        57.199997,  57.639984,  58.079971,  58.519989,  58.959976,  59.399994,\n",
       "        59.839981,  60.279984])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlat</span></div><div class='xr-var-dims'>(rlat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-45.76 -45.32 -44.88 ... 41.8 42.24</div><input id='attrs-10eaac56-63b0-4b85-84b5-415e9145b1f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10eaac56-63b0-4b85-84b5-415e9145b1f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db34e24e-db2f-40cb-95d9-c48762828239' class='xr-var-data-in' type='checkbox'><label for='data-db34e24e-db2f-40cb-95d9-c48762828239' 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>long_name :</span></dt><dd>latitude in rotated pole grid</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>grid_latitude</dd></dl></div><div class='xr-var-data'><pre>array([-45.759998, -45.32    , -44.879997, ...,  41.360001,  41.800003,\n",
       "        42.240002])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(rlat, rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>335.4 335.8 336.2 ... 59.84 60.28</div><input id='attrs-2af31ae0-4d6b-4036-a9ba-70a98b10ae09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2af31ae0-4d6b-4036-a9ba-70a98b10ae09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6f269b5-7a04-475b-b565-dafa00150878' class='xr-var-data-in' type='checkbox'><label for='data-f6f269b5-7a04-475b-b565-dafa00150878' 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>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([[335.36001587, 335.79998779, 336.23999023, ...,  59.39997864,\n",
       "         59.83998489,  60.27998734],\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39997864,\n",
       "         59.83998489,  60.27998734],\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39997864,\n",
       "         59.83998489,  60.27998734],\n",
       "       ...,\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39998245,\n",
       "         59.83998489,  60.27998734],\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39998245,\n",
       "         59.83998489,  60.27998734],\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39998245,\n",
       "         59.83998489,  60.27998734]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(rlat, rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-45.76 -45.76 ... 42.24 42.24</div><input id='attrs-4b126440-8dbe-417f-9931-6d8b550993e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b126440-8dbe-417f-9931-6d8b550993e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91f90c54-4067-4575-8f2c-f62bceaa3b9b' class='xr-var-data-in' type='checkbox'><label for='data-91f90c54-4067-4575-8f2c-f62bceaa3b9b' 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>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[-45.75999451, -45.75999451, -45.75999451, ..., -45.75999451,\n",
       "        -45.75999451, -45.75999451],\n",
       "       [-45.31999588, -45.31999588, -45.31999588, ..., -45.31999588,\n",
       "        -45.31999588, -45.31999588],\n",
       "       [-44.87999344, -44.87999344, -44.87999344, ..., -44.87999344,\n",
       "        -44.87999344, -44.87999344],\n",
       "       ...,\n",
       "       [ 41.3599968 ,  41.3599968 ,  41.3599968 , ...,  41.3599968 ,\n",
       "         41.3599968 ,  41.3599968 ],\n",
       "       [ 41.79999924,  41.79999924,  41.79999924, ...,  41.79999924,\n",
       "         41.79999924,  41.79999924],\n",
       "       [ 42.24000168,  42.24000168,  42.24000168, ...,  42.24000168,\n",
       "         42.24000168,  42.24000168]])</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-47013f49-33ab-4fde-927a-32f49b1eee68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47013f49-33ab-4fde-927a-32f49b1eee68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd225706-4c59-422b-95c5-3256d6820833' class='xr-var-data-in' type='checkbox'><label for='data-bd225706-4c59-422b-95c5-3256d6820833' 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>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e350877a-820a-4120-b77a-a2979dbe5691' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e350877a-820a-4120-b77a-a2979dbe5691' class='xr-section-summary' >Data variables: <span>(3)</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>rotated_pole</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; ... b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27; b&#x27;&#x27;</div><input id='attrs-e55f35cb-16e2-4497-88e4-05f1bf6233ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e55f35cb-16e2-4497-88e4-05f1bf6233ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66a5a988-9ecd-4e19-810c-8711367f9fcd' class='xr-var-data-in' type='checkbox'><label for='data-66a5a988-9ecd-4e19-810c-8711367f9fcd' 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>grid_mapping_name :</span></dt><dd>rotated_latitude_longitude</dd><dt><span>grid_north_pole_longitude :</span></dt><dd>180.0</dd><dt><span>grid_north_pole_latitude :</span></dt><dd>90.0</dd></dl></div><div class='xr-var-data'><pre>array([b&#x27;&#x27;, b&#x27;&#x27;, b&#x27;&#x27;, ..., b&#x27;&#x27;, b&#x27;&#x27;, b&#x27;&#x27;], dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>279.3 279.3 279.3 ... 269.3 268.8</div><input id='attrs-7a00fc52-6966-4b1e-99a8-65a3a24b4d25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a00fc52-6966-4b1e-99a8-65a3a24b4d25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b38e7a53-c000-4f12-a852-f985b9f3e935' class='xr-var-data-in' type='checkbox'><label for='data-b38e7a53-c000-4f12-a852-f985b9f3e935' 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>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>grid_mapping :</span></dt><dd>rotated_pole</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><pre>array([[[279.2568 , 279.26056, 279.26654, ..., 277.77548, 277.74847,\n",
       "         277.72147],\n",
       "        [279.64325, 279.64853, 279.6545 , ..., 278.1537 , 278.12518,\n",
       "         278.10565],\n",
       "        [280.0147 , 280.02823, 280.03497, ..., 278.54614, 278.5334 ,\n",
       "         278.52063],\n",
       "        ...,\n",
       "        [286.1471 , 286.06378, 286.12534, ..., 265.70358, 265.35767,\n",
       "         265.00046],\n",
       "        [285.68558, 285.6721 , 285.67133, ..., 263.67523, 263.27975,\n",
       "         263.11618],\n",
       "        [285.18805, 285.2241 , 285.2526 , ..., 261.59732, 261.50504,\n",
       "         261.1576 ]],\n",
       "\n",
       "       [[278.82474, 278.78436, 278.7532 , ..., 277.96725, 277.92688,\n",
       "         277.89856],\n",
       "        [279.1356 , 279.1129 , 279.0931 , ..., 278.36023, 278.34042,\n",
       "         278.31915],\n",
       "        [279.44855, 279.4174 , 279.39758, ..., 278.7688 , 278.7674 ,\n",
       "         278.75464],\n",
       "...\n",
       "        [288.48715, 288.50494, 288.51172, ..., 272.33804, 271.80722,\n",
       "         271.60403],\n",
       "        [288.1138 , 288.12567, 288.1392 , ..., 272.37784, 272.03494,\n",
       "         271.43893],\n",
       "        [287.74554, 287.77008, 287.80225, ..., 272.35327, 271.90204,\n",
       "         270.8226 ]],\n",
       "\n",
       "       [[281.28778, 281.2156 , 281.14505, ..., 278.70392, 278.6908 ,\n",
       "         278.6793 ],\n",
       "        [281.68234, 281.60852, 281.54044, ..., 279.0599 , 279.0583 ,\n",
       "         279.05008],\n",
       "        [282.05804, 281.99896, 281.93417, ..., 279.44708, 279.4233 ,\n",
       "         279.40854],\n",
       "        ...,\n",
       "        [289.0427 , 289.0788 , 289.12308, ..., 271.27466, 270.85794,\n",
       "         270.69144],\n",
       "        [288.75888, 288.79825, 288.85077, ..., 269.81375, 269.76205,\n",
       "         269.11487],\n",
       "        [288.44717, 288.49805, 288.5538 , ..., 269.28876, 269.27728,\n",
       "         268.813  ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1971-01-01 00:00:00 ... 2001-01-...</div><input id='attrs-71d8ec9e-2dc8-4945-9db4-493318d3b8af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71d8ec9e-2dc8-4945-9db4-493318d3b8af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adef5a8c-ff27-48e5-b23a-bcee992d7245' class='xr-var-data-in' type='checkbox'><label for='data-adef5a8c-ff27-48e5-b23a-bcee992d7245' 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(1971, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1971, 1, 2, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1971, 1, 2, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1971, 1, 3, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(1971, 1, 3, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(1971, 1, 4, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       ...,\n",
       "       [cftime.DatetimeNoLeap(2000, 12, 29, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 12, 30, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 12, 30, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.DatetimeNoLeap(2000, 12, 31, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.DatetimeNoLeap(2001, 1, 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-f126d18f-4476-42f7-9ea9-73d07db87fbe' class='xr-section-summary-in' type='checkbox'  ><label for='section-f126d18f-4476-42f7-9ea9-73d07db87fbe' class='xr-section-summary' >Attributes: <span>(26)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>CanRCM4 model output prepared for CORDEX Project</dd><dt><span>institution :</span></dt><dd>CCCma (Canadian Centre for Climate Modelling and Analysis, Victoria, BC, Canada)</dd><dt><span>institute_id :</span></dt><dd>CCCma</dd><dt><span>contact :</span></dt><dd>cccma_info@ec.gc.ca</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>experiment :</span></dt><dd>Historical run driven by CCCma-CanESM2</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>driving_experiment :</span></dt><dd>CCCma-CanESM2, historical, r1i1p1</dd><dt><span>driving_model_id :</span></dt><dd>CCCma-CanESM2</dd><dt><span>driving_experiment_name :</span></dt><dd>historical</dd><dt><span>driving_model_ensemble_member :</span></dt><dd>r1i1p1</dd><dt><span>forcing :</span></dt><dd>GHG,Oz,SA,BC,OC,LU,Vl (GHG includes CO2,CH4,N2O,CFC11,effective CFC12)</dd><dt><span>model_id :</span></dt><dd>CCCma-CanRCM4</dd><dt><span>rcm_version_id :</span></dt><dd>r2</dd><dt><span>project_id :</span></dt><dd>CORDEX</dd><dt><span>CORDEX_domain :</span></dt><dd>AFR-44</dd><dt><span>frequency :</span></dt><dd>day</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>CCCma_runid :</span></dt><dd>afr019_igm44</dd><dt><span>references :</span></dt><dd>http://www.cccma.ec.gc.ca/models</dd><dt><span>history :</span></dt><dd>created: 2012-06-29 23:52:08 by rcm2nc</dd><dt><span>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>creation_date :</span></dt><dd>2012-06-29-T23:47:03Z</dd><dt><span>c3s_comment :</span></dt><dd>This data has been published at ESGF with the support of the Copernicus Climate Change Service (C3S - https://climate.copernicus.eu/) in the context of the C3S_34d contract.</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14103/3d35a8d3-105d-451c-88fa-4d462dd9c22d</dd><dt><span>c3s_disclaimer :</span></dt><dd>This data has been curated and prepared in the context of the C3S_34d contract for the Climate Data Store within the Copernicus Climate Change Service (C3S - https://climate.copernicus.eu/). While abiding by the highest scientific and technical standards, ECMWF cannot warrant that any information provided by the C3S will be entirely free from errors or omissions or that such errors or omissions can or will be rectified entirely. This applies to data from projects that continue to be developed, but are made publicly available for the purpose of feedback and testing. Some data or metadata may have been created or structured in files or formats that are not error-free. ECMWF accepts no responsibility with regard to such problems incurred as a result of using this data (see http://climate.copernicus.eu/disclaimer-privacy for the full disclaimer).</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (time: 10950, rlon: 194, rlat: 201, bnds: 2)\n",
       "Coordinates:\n",
       "  * time          (time) object 1971-01-01 12:00:00 ... 2000-12-31 12:00:00\n",
       "  * rlon          (rlon) float64 -24.64 -24.2 -23.76 -23.32 ... 59.4 59.84 60.28\n",
       "  * rlat          (rlat) float64 -45.76 -45.32 -44.88 ... 41.36 41.8 42.24\n",
       "    lon           (rlat, rlon) float64 335.4 335.8 336.2 ... 59.4 59.84 60.28\n",
       "    lat           (rlat, rlon) float64 -45.76 -45.76 -45.76 ... 42.24 42.24\n",
       "    height        float64 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    rotated_pole  (time) |S1 b'' b'' b'' b'' b'' b'' ... b'' b'' b'' b'' b'' b''\n",
       "    tas           (time, rlat, rlon) float32 279.3 279.3 279.3 ... 269.3 268.8\n",
       "    time_bnds     (time, bnds) object 1971-01-01 00:00:00 ... 2001-01-01 00:0...\n",
       "Attributes: (12/26)\n",
       "    title:                          CanRCM4 model output prepared for CORDEX ...\n",
       "    institution:                    CCCma (Canadian Centre for Climate Modell...\n",
       "    institute_id:                   CCCma\n",
       "    contact:                        cccma_info@ec.gc.ca\n",
       "    Conventions:                    CF-1.4\n",
       "    experiment:                     Historical run driven by CCCma-CanESM2\n",
       "    ...                             ...\n",
       "    history:                        created: 2012-06-29 23:52:08 by rcm2nc\n",
       "    data_licence:                   1) GRANT OF LICENCE - The Government of C...\n",
       "    creation_date:                  2012-06-29-T23:47:03Z\n",
       "    c3s_comment:                    This data has been published at ESGF with...\n",
       "    tracking_id:                    hdl:21.14103/3d35a8d3-105d-451c-88fa-4d46...\n",
       "    c3s_disclaimer:                 This data has been curated and prepared i..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_1971_2000_historical = xr.open_mfdataset(f'{DATADIR}*CanESM2_historical*.nc')\n",
    "ds_1971_2000_historical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to load the data variable `tas` from the Dataset above. We can load data variables by adding the name of the variable (`tas`) in square brackets. The resulting object is a `xarray.DataArray`, which offers additional metadata information about the data, e.g. the `unit` or `long_name` of the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  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.DataArray &#x27;tas&#x27; (time: 10950, rlat: 201, rlon: 194)&gt;\n",
       "array([[[279.2568 , 279.26056, 279.26654, ..., 277.77548, 277.74847,\n",
       "         277.72147],\n",
       "        [279.64325, 279.64853, 279.6545 , ..., 278.1537 , 278.12518,\n",
       "         278.10565],\n",
       "        [280.0147 , 280.02823, 280.03497, ..., 278.54614, 278.5334 ,\n",
       "         278.52063],\n",
       "        ...,\n",
       "        [286.1471 , 286.06378, 286.12534, ..., 265.70358, 265.35767,\n",
       "         265.00046],\n",
       "        [285.68558, 285.6721 , 285.67133, ..., 263.67523, 263.27975,\n",
       "         263.11618],\n",
       "        [285.18805, 285.2241 , 285.2526 , ..., 261.59732, 261.50504,\n",
       "         261.1576 ]],\n",
       "\n",
       "       [[278.82474, 278.78436, 278.7532 , ..., 277.96725, 277.92688,\n",
       "         277.89856],\n",
       "        [279.1356 , 279.1129 , 279.0931 , ..., 278.36023, 278.34042,\n",
       "         278.31915],\n",
       "        [279.44855, 279.4174 , 279.39758, ..., 278.7688 , 278.7674 ,\n",
       "         278.75464],\n",
       "...\n",
       "        [288.48715, 288.50494, 288.51172, ..., 272.33804, 271.80722,\n",
       "         271.60403],\n",
       "        [288.1138 , 288.12567, 288.1392 , ..., 272.37784, 272.03494,\n",
       "         271.43893],\n",
       "        [287.74554, 287.77008, 287.80225, ..., 272.35327, 271.90204,\n",
       "         270.8226 ]],\n",
       "\n",
       "       [[281.28778, 281.2156 , 281.14505, ..., 278.70392, 278.6908 ,\n",
       "         278.6793 ],\n",
       "        [281.68234, 281.60852, 281.54044, ..., 279.0599 , 279.0583 ,\n",
       "         279.05008],\n",
       "        [282.05804, 281.99896, 281.93417, ..., 279.44708, 279.4233 ,\n",
       "         279.40854],\n",
       "        ...,\n",
       "        [289.0427 , 289.0788 , 289.12308, ..., 271.27466, 270.85794,\n",
       "         270.69144],\n",
       "        [288.75888, 288.79825, 288.85077, ..., 269.81375, 269.76205,\n",
       "         269.11487],\n",
       "        [288.44717, 288.49805, 288.5538 , ..., 269.28876, 269.27728,\n",
       "         268.813  ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 1971-01-01 12:00:00 ... 2000-12-31 12:00:00\n",
       "  * rlon     (rlon) float64 -24.64 -24.2 -23.76 -23.32 ... 59.4 59.84 60.28\n",
       "  * rlat     (rlat) float64 -45.76 -45.32 -44.88 -44.44 ... 41.36 41.8 42.24\n",
       "    lon      (rlat, rlon) float64 335.4 335.8 336.2 336.7 ... 59.4 59.84 60.28\n",
       "    lat      (rlat, rlon) float64 -45.76 -45.76 -45.76 ... 42.24 42.24 42.24\n",
       "    height   float64 2.0\n",
       "Attributes:\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    standard_name:  air_temperature\n",
       "    units:          K\n",
       "    grid_mapping:   rotated_pole\n",
       "    cell_methods:   time: mean</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10950</li><li><span class='xr-has-index'>rlat</span>: 201</li><li><span class='xr-has-index'>rlon</span>: 194</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d6d8675d-0943-4dd9-90bb-82765e194341' class='xr-array-in' type='checkbox' checked><label for='section-d6d8675d-0943-4dd9-90bb-82765e194341' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>279.3 279.3 279.3 279.3 279.3 279.3 ... 268.5 268.9 269.3 269.3 268.8</span></div><div class='xr-array-data'><pre>array([[[279.2568 , 279.26056, 279.26654, ..., 277.77548, 277.74847,\n",
       "         277.72147],\n",
       "        [279.64325, 279.64853, 279.6545 , ..., 278.1537 , 278.12518,\n",
       "         278.10565],\n",
       "        [280.0147 , 280.02823, 280.03497, ..., 278.54614, 278.5334 ,\n",
       "         278.52063],\n",
       "        ...,\n",
       "        [286.1471 , 286.06378, 286.12534, ..., 265.70358, 265.35767,\n",
       "         265.00046],\n",
       "        [285.68558, 285.6721 , 285.67133, ..., 263.67523, 263.27975,\n",
       "         263.11618],\n",
       "        [285.18805, 285.2241 , 285.2526 , ..., 261.59732, 261.50504,\n",
       "         261.1576 ]],\n",
       "\n",
       "       [[278.82474, 278.78436, 278.7532 , ..., 277.96725, 277.92688,\n",
       "         277.89856],\n",
       "        [279.1356 , 279.1129 , 279.0931 , ..., 278.36023, 278.34042,\n",
       "         278.31915],\n",
       "        [279.44855, 279.4174 , 279.39758, ..., 278.7688 , 278.7674 ,\n",
       "         278.75464],\n",
       "...\n",
       "        [288.48715, 288.50494, 288.51172, ..., 272.33804, 271.80722,\n",
       "         271.60403],\n",
       "        [288.1138 , 288.12567, 288.1392 , ..., 272.37784, 272.03494,\n",
       "         271.43893],\n",
       "        [287.74554, 287.77008, 287.80225, ..., 272.35327, 271.90204,\n",
       "         270.8226 ]],\n",
       "\n",
       "       [[281.28778, 281.2156 , 281.14505, ..., 278.70392, 278.6908 ,\n",
       "         278.6793 ],\n",
       "        [281.68234, 281.60852, 281.54044, ..., 279.0599 , 279.0583 ,\n",
       "         279.05008],\n",
       "        [282.05804, 281.99896, 281.93417, ..., 279.44708, 279.4233 ,\n",
       "         279.40854],\n",
       "        ...,\n",
       "        [289.0427 , 289.0788 , 289.12308, ..., 271.27466, 270.85794,\n",
       "         270.69144],\n",
       "        [288.75888, 288.79825, 288.85077, ..., 269.81375, 269.76205,\n",
       "         269.11487],\n",
       "        [288.44717, 288.49805, 288.5538 , ..., 269.28876, 269.27728,\n",
       "         268.813  ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-aa5e4140-d8d2-4267-904c-eea8f2cdbba7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa5e4140-d8d2-4267-904c-eea8f2cdbba7' class='xr-section-summary' >Coordinates: <span>(6)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1971-01-01 12:00:00 ... 2000-12-...</div><input id='attrs-f081f034-8eab-4992-92a1-fbf622ef5bf3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f081f034-8eab-4992-92a1-fbf622ef5bf3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c3e28c7-9698-47e6-88c0-0cb112c5031c' class='xr-var-data-in' type='checkbox'><label for='data-9c3e28c7-9698-47e6-88c0-0cb112c5031c' 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>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1971, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1971, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1971, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2000, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2000, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlon</span></div><div class='xr-var-dims'>(rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-24.64 -24.2 -23.76 ... 59.84 60.28</div><input id='attrs-f94e57a4-1756-4c2a-9b3e-d0d647ecf5a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f94e57a4-1756-4c2a-9b3e-d0d647ecf5a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82e5cbfa-27aa-4510-9219-374621a21472' class='xr-var-data-in' type='checkbox'><label for='data-82e5cbfa-27aa-4510-9219-374621a21472' 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>long_name :</span></dt><dd>longitude in rotated pole grid</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-24.639999, -24.200012, -23.76001 , -23.320023, -22.880005, -22.440002,\n",
       "       -22.      , -21.560013, -21.120026, -20.680008, -20.240021, -19.800003,\n",
       "       -19.360016, -18.919998, -18.480011, -18.040024, -17.600006, -17.160019,\n",
       "       -16.720001, -16.280014, -15.840012, -15.400009, -14.960007, -14.520004,\n",
       "       -14.080002, -13.639999, -13.200012, -12.76001 , -12.320023, -11.880005,\n",
       "       -11.440002, -11.      , -10.560013, -10.120026,  -9.680008,  -9.240021,\n",
       "        -8.800003,  -8.360016,  -7.920029,  -7.480011,  -7.040024,  -6.600006,\n",
       "        -6.160019,  -5.720001,  -5.280014,  -4.840012,  -4.400009,  -3.960007,\n",
       "        -3.520004,  -3.080002,  -2.639999,  -2.200012,  -1.76001 ,  -1.320023,\n",
       "        -0.880005,  -0.440002,   0.      ,   0.439987,   0.879974,   1.319992,\n",
       "         1.759979,   2.199997,   2.639984,   3.079971,   3.519989,   3.959976,\n",
       "         4.399994,   4.839981,   5.279999,   5.719986,   6.159988,   6.599991,\n",
       "         7.039993,   7.479996,   7.919998,   8.359985,   8.799988,   9.23999 ,\n",
       "         9.679977,  10.119995,  10.559998,  11.      ,  11.439987,  11.879974,\n",
       "        12.319992,  12.759979,  13.199997,  13.639984,  14.079971,  14.519989,\n",
       "        14.959976,  15.399994,  15.839981,  16.279999,  16.719986,  17.159988,\n",
       "        17.599991,  18.039993,  18.479996,  18.919998,  19.359985,  19.799988,\n",
       "        20.23999 ,  20.679977,  21.119995,  21.559998,  21.999969,  22.439987,\n",
       "        22.879974,  23.319992,  23.759979,  24.199997,  24.639984,  25.079971,\n",
       "        25.519989,  25.959976,  26.399994,  26.839981,  27.279999,  27.719986,\n",
       "        28.159988,  28.599991,  29.039993,  29.479996,  29.919998,  30.359985,\n",
       "        30.799988,  31.23999 ,  31.679977,  32.119995,  32.559998,  32.999969,\n",
       "        33.439987,  33.879974,  34.319992,  34.759979,  35.199997,  35.639984,\n",
       "        36.079971,  36.519989,  36.959976,  37.399994,  37.839981,  38.279984,\n",
       "        38.719986,  39.159988,  39.599991,  40.039993,  40.479996,  40.919998,\n",
       "        41.359985,  41.799988,  42.23999 ,  42.679977,  43.119995,  43.559998,\n",
       "        43.999969,  44.439987,  44.879974,  45.319992,  45.759979,  46.199997,\n",
       "        46.639984,  47.079971,  47.519989,  47.959976,  48.399994,  48.839981,\n",
       "        49.279984,  49.719986,  50.159988,  50.599991,  51.039993,  51.479996,\n",
       "        51.919968,  52.359985,  52.799988,  53.23999 ,  53.679977,  54.119995,\n",
       "        54.559982,  54.999969,  55.439987,  55.879974,  56.319992,  56.759979,\n",
       "        57.199997,  57.639984,  58.079971,  58.519989,  58.959976,  59.399994,\n",
       "        59.839981,  60.279984])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlat</span></div><div class='xr-var-dims'>(rlat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-45.76 -45.32 -44.88 ... 41.8 42.24</div><input id='attrs-eb7ad336-e5a3-4947-8ae5-1fea11ae90ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb7ad336-e5a3-4947-8ae5-1fea11ae90ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-338e2f3d-5952-4146-b118-5c456516fda0' class='xr-var-data-in' type='checkbox'><label for='data-338e2f3d-5952-4146-b118-5c456516fda0' 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>long_name :</span></dt><dd>latitude in rotated pole grid</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>grid_latitude</dd></dl></div><div class='xr-var-data'><pre>array([-45.759998, -45.32    , -44.879997, ...,  41.360001,  41.800003,\n",
       "        42.240002])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(rlat, rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>335.4 335.8 336.2 ... 59.84 60.28</div><input id='attrs-dd63d60c-d33f-4a60-803b-60fbc3ce40c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd63d60c-d33f-4a60-803b-60fbc3ce40c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88664ba7-e5f6-45da-aed5-efa71738e39f' class='xr-var-data-in' type='checkbox'><label for='data-88664ba7-e5f6-45da-aed5-efa71738e39f' 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>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([[335.36001587, 335.79998779, 336.23999023, ...,  59.39997864,\n",
       "         59.83998489,  60.27998734],\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39997864,\n",
       "         59.83998489,  60.27998734],\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39997864,\n",
       "         59.83998489,  60.27998734],\n",
       "       ...,\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39998245,\n",
       "         59.83998489,  60.27998734],\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39998245,\n",
       "         59.83998489,  60.27998734],\n",
       "       [335.36001587, 335.79998779, 336.23999023, ...,  59.39998245,\n",
       "         59.83998489,  60.27998734]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(rlat, rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-45.76 -45.76 ... 42.24 42.24</div><input id='attrs-85090908-c9f8-4c58-bb94-4686c1d454de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85090908-c9f8-4c58-bb94-4686c1d454de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32174c52-8743-41c5-90e7-985e4429a82d' class='xr-var-data-in' type='checkbox'><label for='data-32174c52-8743-41c5-90e7-985e4429a82d' 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>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[-45.75999451, -45.75999451, -45.75999451, ..., -45.75999451,\n",
       "        -45.75999451, -45.75999451],\n",
       "       [-45.31999588, -45.31999588, -45.31999588, ..., -45.31999588,\n",
       "        -45.31999588, -45.31999588],\n",
       "       [-44.87999344, -44.87999344, -44.87999344, ..., -44.87999344,\n",
       "        -44.87999344, -44.87999344],\n",
       "       ...,\n",
       "       [ 41.3599968 ,  41.3599968 ,  41.3599968 , ...,  41.3599968 ,\n",
       "         41.3599968 ,  41.3599968 ],\n",
       "       [ 41.79999924,  41.79999924,  41.79999924, ...,  41.79999924,\n",
       "         41.79999924,  41.79999924],\n",
       "       [ 42.24000168,  42.24000168,  42.24000168, ...,  42.24000168,\n",
       "         42.24000168,  42.24000168]])</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-d226f794-9aae-4517-a75b-faf8c0d9021f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d226f794-9aae-4517-a75b-faf8c0d9021f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-297e4de3-d9bb-4880-84da-d3378e6488a4' class='xr-var-data-in' type='checkbox'><label for='data-297e4de3-d9bb-4880-84da-d3378e6488a4' 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>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ba39b01-dcad-431d-b9d7-28f380a516db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ba39b01-dcad-431d-b9d7-28f380a516db' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>grid_mapping :</span></dt><dd>rotated_pole</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tas' (time: 10950, rlat: 201, rlon: 194)>\n",
       "array([[[279.2568 , 279.26056, 279.26654, ..., 277.77548, 277.74847,\n",
       "         277.72147],\n",
       "        [279.64325, 279.64853, 279.6545 , ..., 278.1537 , 278.12518,\n",
       "         278.10565],\n",
       "        [280.0147 , 280.02823, 280.03497, ..., 278.54614, 278.5334 ,\n",
       "         278.52063],\n",
       "        ...,\n",
       "        [286.1471 , 286.06378, 286.12534, ..., 265.70358, 265.35767,\n",
       "         265.00046],\n",
       "        [285.68558, 285.6721 , 285.67133, ..., 263.67523, 263.27975,\n",
       "         263.11618],\n",
       "        [285.18805, 285.2241 , 285.2526 , ..., 261.59732, 261.50504,\n",
       "         261.1576 ]],\n",
       "\n",
       "       [[278.82474, 278.78436, 278.7532 , ..., 277.96725, 277.92688,\n",
       "         277.89856],\n",
       "        [279.1356 , 279.1129 , 279.0931 , ..., 278.36023, 278.34042,\n",
       "         278.31915],\n",
       "        [279.44855, 279.4174 , 279.39758, ..., 278.7688 , 278.7674 ,\n",
       "         278.75464],\n",
       "...\n",
       "        [288.48715, 288.50494, 288.51172, ..., 272.33804, 271.80722,\n",
       "         271.60403],\n",
       "        [288.1138 , 288.12567, 288.1392 , ..., 272.37784, 272.03494,\n",
       "         271.43893],\n",
       "        [287.74554, 287.77008, 287.80225, ..., 272.35327, 271.90204,\n",
       "         270.8226 ]],\n",
       "\n",
       "       [[281.28778, 281.2156 , 281.14505, ..., 278.70392, 278.6908 ,\n",
       "         278.6793 ],\n",
       "        [281.68234, 281.60852, 281.54044, ..., 279.0599 , 279.0583 ,\n",
       "         279.05008],\n",
       "        [282.05804, 281.99896, 281.93417, ..., 279.44708, 279.4233 ,\n",
       "         279.40854],\n",
       "        ...,\n",
       "        [289.0427 , 289.0788 , 289.12308, ..., 271.27466, 270.85794,\n",
       "         270.69144],\n",
       "        [288.75888, 288.79825, 288.85077, ..., 269.81375, 269.76205,\n",
       "         269.11487],\n",
       "        [288.44717, 288.49805, 288.5538 , ..., 269.28876, 269.27728,\n",
       "         268.813  ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 1971-01-01 12:00:00 ... 2000-12-31 12:00:00\n",
       "  * rlon     (rlon) float64 -24.64 -24.2 -23.76 -23.32 ... 59.4 59.84 60.28\n",
       "  * rlat     (rlat) float64 -45.76 -45.32 -44.88 -44.44 ... 41.36 41.8 42.24\n",
       "    lon      (rlat, rlon) float64 335.4 335.8 336.2 336.7 ... 59.4 59.84 60.28\n",
       "    lat      (rlat, rlon) float64 -45.76 -45.76 -45.76 ... 42.24 42.24 42.24\n",
       "    height   float64 2.0\n",
       "Attributes:\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    standard_name:  air_temperature\n",
       "    units:          K\n",
       "    grid_mapping:   rotated_pole\n",
       "    cell_methods:   time: mean"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas_1971_2000_historical = ds_1971_2000_historical['tas']\n",
    "tas_1971_2000_historical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now compute the monthly climatology for the reference period. The monthly climatology represents for each month, and each grid point, the average value over the reference period of 30 years, from 1971 to 2000. We can compute the climatology for each grid point with xarray in two steps: first, all the values for one specific month have to be grouped with the function `groupby()` and second, we can create the average of each group with the function `mean()`.\n",
    "\n",
    "The result is a three-dimensional DataArray with the dimensions `month`, `rlat` and `rlon`. The operation has changed the name of the `time` dimension to `month`, which has the average near-surface air temperature for each month. Please note that this operation also drops all the attributes (metadata) from the DataArray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_1971_2000_climatology = tas_1971_2000_historical.groupby('time.month').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a last step, we want to convert the near-surface air temperature values in Kelvin to degrees Celsisus. We can do this by subtracting 273.15 from air temperature values in Kelvin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_1971_2000_climatology_degC = tas_1971_2000_climatology - 273.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute CORDEX future climatology for target period 2071-2100 based on scenario RCP4.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now repeat the steps executed for the historical experiment data with the `scenario RCP4.5` data. With the projection data for the target period 2071 to 2100, we can compute the projected climatology according to this particular scenario, which can be compared with the historical climatology from the reference period.\n",
    "\n",
    "Again, we begin with the function `open_mfdataset()` to load the multiple `NetCDF` files. The scenario data is also organised into separate files for each 5-year period. These will be concatenated along the `time` dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_2071_2100_projection = xr.open_mfdataset(f'{DATADIR}*CanESM2_rcp45*.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we load the data variable `tas` from the Dataset above into an `xarray.DataArray` object to facilitate further processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_2071_2100_projection = ds_2071_2100_projection['tas']\n",
    "\n",
    "# uncomment the line below before running this cell to view the data characteristics\n",
    "# tas_2071_2100_projection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will compute the monthly climatology for the target period 2071 to 2100, in the same way as we did for the historical period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_2071_2100_climatology = tas_2071_2100_projection.groupby('time.month').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally we convert from degrees Kelvin to Celsius:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_2071_2100_climatology_degC = tas_2071_2100_climatology - 273.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute and visualize the climate change signal between the target (2071-2100) and reference (1971-2000) period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can compute the difference between the two climatologies. Since both DataArrays have the same structure, we can simply subtract the historical climatology from the projected climatology. The resulting data array has the projected averaged temperature difference for each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_difference_climatology = tas_2071_2100_climatology - tas_1971_2000_climatology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us visualize the climate change signal between the target (2071-2100) and reference (1971-2000) period for Africa for June. The plotting function below has five main parts:\n",
    "* **Initiate the plot**: initiate a matplotlib plot with `plt.figure` and `plt.subplot()`\n",
    "* **Plot the 2D map with pcolormesh**: plot the xarray data array as 2D map with the function pcolormesh\n",
    "* **Add coast- and gridlines**: add additional items to the plot, such as coast- and gridlines\n",
    "* **Add title and legend**: Set title and legend of the map\n",
    "* **Save the figure as png file**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the value for the variable month and we see that the near-surface air temperature difference is for some regions more pronounced for some months, e.g. June, compared to others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month=5\n",
    "\n",
    "# Initiate the plot\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = plt.subplot(1,1,1, projection=ccrs.PlateCarree())\n",
    "\n",
    "# Plot the 2D map with pcolormesh\n",
    "im = plt.pcolormesh(tas_difference_climatology.rlon, tas_difference_climatology.rlat,\n",
    "                   tas_difference_climatology[month,:,:], cmap='coolwarm', transform=ccrs.PlateCarree())\n",
    "\n",
    "# Add coast- and gridlines\n",
    "ax.coastlines(color='black')\n",
    "ax.set_extent([-20,60,-40,40], crs=ccrs.PlateCarree())\n",
    "gl = ax.gridlines(draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--')\n",
    "gl.top_labels = False\n",
    "gl.right_labels = False\n",
    "\n",
    "# Add title and legend\n",
    "ax.set_title('CORDEX near surface air temp difference in June: (2071 to 2100 climatology) minus (1971 to 2000 climatology)\\n', fontsize=12)\n",
    "cbar = plt.colorbar(im,fraction=0.046, pad=0.04)\n",
    "cbar.set_label('\\nTemperature difference (Kelvin/Celsius)\\n')\n",
    "\n",
    "# Save the figure\n",
    "fig.savefig(f'{DATADIR}TAS_1971-2000_2071-2100_june.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a last step, we repeat the visualization of climate change signal between the target (2071-2100) and reference (1971-2000) period. But this time, we create the average signal for the whole period, not only one month. For this, we have to compute the average over all months with the function `mean()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = plt.subplot(1,1,1, projection=ccrs.PlateCarree())\n",
    "\n",
    "im = plt.pcolormesh(tas_difference_climatology.rlon, tas_difference_climatology.rlat,\n",
    "                   tas_difference_climatology.mean('month'), cmap='coolwarm', transform=ccrs.PlateCarree())\n",
    "\n",
    "ax.coastlines(color='black')\n",
    "ax.set_extent([-20,60,-40,40], crs=ccrs.PlateCarree())\n",
    "gl = ax.gridlines(draw_labels=True, linewidth=1, color='gray', alpha=0.5, linestyle='--')\n",
    "gl.top_labels = False\n",
    "gl.right_labels = False\n",
    "ax.set_title('CORDEX near surface air temp difference: (2071 to 2100 climatology) minus (1971 to 2000 climatology)\\n', fontsize=12)\n",
    "\n",
    "cbar = plt.colorbar(im,fraction=0.046, pad=0.04)\n",
    "cbar.set_label('\\nTemperature difference (Kelvin/Celsius)\\n')\n",
    "\n",
    "fig.savefig(f'{DATADIR}TAS_1971-2000_2071-2100.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p></p>\n",
    "<span style='float:right'><p style=\\\"text-align:right;\\\">This project is licensed under <a href=\"./LICENSE\">APACHE License 2.0</a>. | <a href=\\\"https://github.com/ecmwf-projects/copernicus-training\">View on GitHub</a></span>"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}