projections-cmip6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![logo](./img/LogoLine_horizon_C3S.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot an Ensemble of CMIP6 Climate Projections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### About"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides a practical introduction on how to access and process [CMIP6 global climate projections](https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=overview) data available in the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). The workflow shows how to compute and visualize the output of an ensemble of models for the annual global average temperature between 1850 to 2100. You will use the `historical` experiment for the temporal period 1850 to 2014 and the three scenarios `SSP1-2.6`, `SSP2-4.5` and `SSP5-8.5` for the period from 2015 to 2100.\n",
    "\n",
    "For the sake of simplicity, and to facilitate data download, the tutorial will make use of some of the coarser resolution models that have a smaller data size. It is nevertheless only a choice for this exercise and not a recommendation (since ideally all models, including those with highest resolution, should be used). Many more models are available on the CDS, and when calculating an ensemble of models, it is best practice to use as many as possible for a more reliable output. See [here](https://confluence.ecmwf.int/display/CKB/CMIP6%3A+Global+climate+projections#CMIP6:Globalclimateprojections-Models,gridsandpressurelevels) a full list of models included in the CDS-CMIP6 dataset.\n",
    "\n",
    "Learn [here](https://confluence.ecmwf.int/display/CKB/CMIP6%3A+Global+climate+projections#CMIP6:Globalclimateprojections) more about CMIP6 global climate projections and the CMIP6 experiments in the CDS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The notebook has the following outline:\n",
    "\n",
    "1. Request data from the CDS programmatically with the CDS API\n",
    "2. Unzip the downloaded data files\n",
    "3. Load and prepare CMIP6 data for one model and one experiment\n",
    "4. Load and prepare CMIP6 data for all models and experiments\n",
    "5. Visualize CMIP6 annual global average temperature between 1850 to 2100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook introduces you to [CMIP6 Global climate projections](https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=overview). The datasets used in the notebook have the following specifications:\n",
    "\n",
    "> **Data**: CMIP6 global climate projections of near-surface air temperature <br>\n",
    "> **Experiments**: Historical, SSP1-2.6, SSP2-4.5, SSP5-8.5 <br>\n",
    "> **Models**: 7 models from Germany, France, UK, Japan and Russia <br>\n",
    "> **Temporal range**: Historical: 1850 - 2014. Scenarios: 2015 - 2100 <br>\n",
    "> **Spatial coverage**: Global <br>\n",
    "> **Format**: NetCDF, compressed into zip files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style>\n",
    "td, th {\n",
    "   border: 1px solid white;\n",
    "   border-collapse: collapse;\n",
    "}\n",
    "</style>\n",
    "<table align=\"left\">\n",
    "  <tr>\n",
    "    <th>Run the tutorial via free cloud platforms: </th>\n",
    "    <th><a href=\"https://mybinder.org/v2/gh/ecmwf-projects/copernicus-training-c3s/main?labpath=projections-cmip6.ipynb\">\n",
    "        <img src = \"https://mybinder.org/badge.svg\" alt = \"Binder\"></th>\n",
    "    <th><a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ecmwf-projects/copernicus-training-c3s/blob/main/projections-cmip6.ipynb\">\n",
    "        <img src = \"https://kaggle.com/static/images/open-in-kaggle.svg\" alt = \"Kaggle\"></th>\n",
    "    <th><a href=\"https://colab.research.google.com/github/ecmwf-projects/copernicus-training-c3s/blob/main/projections-cmip6.ipynb\">\n",
    "        <img src = \"https://colab.research.google.com/assets/colab-badge.svg\" alt = \"Colab\"></th>\n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General libs for file paths, data extraction, etc\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "from os.path import basename\n",
    "import zipfile # To extract zipfiles\n",
    "import urllib3 \n",
    "urllib3.disable_warnings() # Disable warnings for data download via API\n",
    "\n",
    "# CDS API\n",
    "import cdsapi\n",
    "\n",
    "# Libraries for working with multi-dimensional arrays\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "# Libraries for plotting and visualising data\n",
    "import matplotlib.path as mpath\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "import cartopy.feature as cfeature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Request data from the CDS programmatically with the CDS API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will request data from the Climate Data Store (CDS) 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. The string of characters that make up your KEY include your personal User ID and CDS API key. To obtain these, first register or login to the CDS (http://cds.climate.copernicus.eu), then visit https://cds.climate.copernicus.eu/api-how-to and copy the string of characters listed after \"key:\". Replace the `#########` below with this string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "URL = 'https://cds.climate.copernicus.eu/api/v2'\n",
    "KEY = '##################################'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we specify a data directory in which we will download our data and all output files that we will generate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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, we loop through multiple data requests. These include data for different models and scenarios. It is not possible to specify multiple models in one data request as their spatial resolution varies.\n",
    "\n",
    "We will download monthly aggregated data. These are disseminated as netcdf files within a zip archive.\n",
    "\n",
    "In order to loop through the various experiments and models in our data requests, we will specify them as Python 'lists' here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments = ['historical', 'ssp126', 'ssp245', 'ssp585']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = ['hadgem3_gc31_ll', 'inm_cm5_0', 'inm_cm4_8', 'ipsl_cm6a_lr', \n",
    "          'miroc_es2l', 'mpi_esm1_2_lr', 'ukesm1_0_ll']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** Note that these are a selection of the lightest models (in terms of data volume), to facilitate download for the sake of this exercise. There are many [more models available on the CDS](https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=overview)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can download the data for each model and experiment sequentially. We will do this separately for the historical experiments and for the various future scenarios, given that they refer to two different time periods.\n",
    "\n",
    "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": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** For more information about data access through the Climate Data Store, please see the dedicated tutorial [here](./climate_data_store_intro.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DOWNLOAD DATA FOR HISTORICAL PERIOD\n",
    "\n",
    "c = cdsapi.Client(url=URL, key=KEY)\n",
    "\n",
    "for j in models:\n",
    "    c.retrieve(\n",
    "        'projections-cmip6',\n",
    "        {\n",
    "            'format': 'zip',\n",
    "            'temporal_resolution': 'monthly',\n",
    "            'experiment': 'historical',\n",
    "            'level': 'single_levels',\n",
    "            'variable': 'near_surface_air_temperature',\n",
    "            'model': f'{j}',\n",
    "            'date': '1850-01-01/2014-12-31',\n",
    "        },\n",
    "        f'{DATADIR}cmip6_monthly_1850-2014_historical_{j}.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DOWNLOAD DATA FOR FUTURE SCENARIOS\n",
    "\n",
    "c = cdsapi.Client(url=URL, key=KEY)\n",
    "\n",
    "for i in experiments[1:]:\n",
    "    for j in models:\n",
    "        c.retrieve(\n",
    "            'projections-cmip6',\n",
    "            {\n",
    "                'format': 'zip',\n",
    "                'temporal_resolution': 'monthly',\n",
    "                'experiment': f'{i}',\n",
    "                'level': 'single_levels',\n",
    "                'variable': 'near_surface_air_temperature',\n",
    "                'model': f'{j}',\n",
    "                'date': '2015-01-01/2100-12-31',\n",
    "            },\n",
    "            f'{DATADIR}cmip6_monthly_2015-2100_{i}_{j}.zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unzip the downloaded data files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the CDS, CMIP6 data are available as `NetCDF` files compressed into `zip` archives. For this reason, before we can load any data, we have to extract the files. Having downloaded the four experiments `historical`, `SSP1-2.6`, `SSP2-4.5` and `SSP5-8.5` as seperate zip files, we can use the functions from the `zipfile` Python package to extract their contents. For each zip file we first construct a `ZipFile()` object, then we apply the function `extractall()` to extract its content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmip6_zip_paths = glob(f'{DATADIR}*.zip')\n",
    "for j in cmip6_zip_paths:\n",
    "    with zipfile.ZipFile(j, 'r') as zip_ref:\n",
    "        zip_ref.extractall(f'{DATADIR}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a list of the extracted files\n",
    "\n",
    "To facilitate batch processing later in the tutorial, here we create a list of the extracted NetCDF files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmip6_nc = list()\n",
    "cmip6_nc_rel = glob(f'{DATADIR}tas*.nc')\n",
    "for i in cmip6_nc_rel:\n",
    "    cmip6_nc.append(os.path.basename(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will briefly inspect this list by printing the first five elements, corresponding to the filenames of a sample of the extracted NetCDF files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tas_Amon_HadGEM3-GC31-LL_historical_r1i1p1f3_gn_18500216-20141216_v20190624.nc',\n",
       " 'tas_Amon_HadGEM3-GC31-LL_ssp126_r1i1p1f3_gn_201501-204912_v20200114.nc',\n",
       " 'tas_Amon_HadGEM3-GC31-LL_ssp126_r1i1p1f3_gn_205001-210012_v20200114.nc',\n",
       " 'tas_Amon_HadGEM3-GC31-LL_ssp245_r1i1p1f3_gn_201501-204912_v20190908.nc',\n",
       " 'tas_Amon_HadGEM3-GC31-LL_ssp245_r1i1p1f3_gn_205001-210012_v20190908.nc']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmip6_nc[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and prepare CMIP6 data for one model and one experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have downloaded and extracted the data, we can prepare it in order to view a time series of the spread of annual global temperature for the model ensemble. These preparation steps include the following:\n",
    "\n",
    "1. **Spatial aggregation**: to have a single global temperature value for each model/experiment dataset, and for each time step\n",
    "2. **Temporal aggregation**: from monthly to yearly\n",
    "3. **Conversion of temperature units** from degrees Kelvin to Celsius\n",
    "4. **Addition of data dimensions** in preparation for the merging of datasets from different models and experiments\n",
    "\n",
    "In this section we apply these steps to a single dataset from one model and one experiment. In the next section we merge data from all models/experiments in preparation for the final processing and plotting of the temperature time series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load and inspect data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by loading the first of the NetCDF files in our list. We will use the Python library [xarray](http://xarray.pydata.org/en/stable/) and its function `open_dataset` to read NetCDF files.\n",
    "\n",
    "The result is a `xarray.Dataset` object with four dimensions: `bnds`, `lat`, `lon`, `time`, of which the dimension `bnds` is not callable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  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.Dataset&gt;\n",
       "Dimensions:    (time: 1979, bnds: 2, lat: 144, lon: 192)\n",
       "Coordinates:\n",
       "  * time       (time) object 1850-02-16 00:00:00 ... 2014-12-16 00:00:00\n",
       "  * lat        (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
       "  * lon        (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
       "    height     float64 1.5\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object 1850-02-01 00:00:00 ... 2015-01-01 00:00:00\n",
       "    lat_bnds   (time, lat, bnds) float64 ...\n",
       "    lon_bnds   (time, lon, bnds) float64 ...\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "Attributes: (12/46)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  0.0\n",
       "    creation_date:          2019-06-19T11:21:17Z\n",
       "    ...                     ...\n",
       "    title:                  HadGEM3-GC31-LL output prepared for CMIP6\n",
       "    variable_id:            tas\n",
       "    variant_label:          r1i1p1f3\n",
       "    license:                CMIP6 model data produced by the Met Office Hadle...\n",
       "    cmor_version:           3.4.0\n",
       "    tracking_id:            hdl:21.14100/b6959414-d5ed-4cd9-a627-59238e52132d</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-5681388b-94ef-4c6c-be68-98adaff1cb75' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5681388b-94ef-4c6c-be68-98adaff1cb75' 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>: 1979</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 144</li><li><span class='xr-has-index'>lon</span>: 192</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39dd8668-2684-4987-98b9-4f10095a77a4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39dd8668-2684-4987-98b9-4f10095a77a4' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>1850-02-16 00:00:00 ... 2014-12-...</div><input id='attrs-c76133e6-226e-46cb-bae2-896cee42c07b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c76133e6-226e-46cb-bae2-896cee42c07b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da6d06da-c426-48eb-94d9-c5b7c23e4225' class='xr-var-data-in' type='checkbox'><label for='data-da6d06da-c426-48eb-94d9-c5b7c23e4225' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.Datetime360Day(1850, 2, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1850, 3, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(1850, 4, 16, 0, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.Datetime360Day(2014, 10, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.Datetime360Day(2014, 12, 16, 0, 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.38 -88.12 ... 88.12 89.38</div><input id='attrs-cc8cb79f-ecef-4d58-a0a0-f5359ef5ca22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc8cb79f-ecef-4d58-a0a0-f5359ef5ca22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-350d789a-fc2c-4527-b2c6-9a33ee5e309f' class='xr-var-data-in' type='checkbox'><label for='data-350d789a-fc2c-4527-b2c6-9a33ee5e309f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.375, -88.125, -86.875, -85.625, -84.375, -83.125, -81.875, -80.625,\n",
       "       -79.375, -78.125, -76.875, -75.625, -74.375, -73.125, -71.875, -70.625,\n",
       "       -69.375, -68.125, -66.875, -65.625, -64.375, -63.125, -61.875, -60.625,\n",
       "       -59.375, -58.125, -56.875, -55.625, -54.375, -53.125, -51.875, -50.625,\n",
       "       -49.375, -48.125, -46.875, -45.625, -44.375, -43.125, -41.875, -40.625,\n",
       "       -39.375, -38.125, -36.875, -35.625, -34.375, -33.125, -31.875, -30.625,\n",
       "       -29.375, -28.125, -26.875, -25.625, -24.375, -23.125, -21.875, -20.625,\n",
       "       -19.375, -18.125, -16.875, -15.625, -14.375, -13.125, -11.875, -10.625,\n",
       "        -9.375,  -8.125,  -6.875,  -5.625,  -4.375,  -3.125,  -1.875,  -0.625,\n",
       "         0.625,   1.875,   3.125,   4.375,   5.625,   6.875,   8.125,   9.375,\n",
       "        10.625,  11.875,  13.125,  14.375,  15.625,  16.875,  18.125,  19.375,\n",
       "        20.625,  21.875,  23.125,  24.375,  25.625,  26.875,  28.125,  29.375,\n",
       "        30.625,  31.875,  33.125,  34.375,  35.625,  36.875,  38.125,  39.375,\n",
       "        40.625,  41.875,  43.125,  44.375,  45.625,  46.875,  48.125,  49.375,\n",
       "        50.625,  51.875,  53.125,  54.375,  55.625,  56.875,  58.125,  59.375,\n",
       "        60.625,  61.875,  63.125,  64.375,  65.625,  66.875,  68.125,  69.375,\n",
       "        70.625,  71.875,  73.125,  74.375,  75.625,  76.875,  78.125,  79.375,\n",
       "        80.625,  81.875,  83.125,  84.375,  85.625,  86.875,  88.125,  89.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9375 2.812 4.688 ... 357.2 359.1</div><input id='attrs-b528edbe-4daa-4d25-824a-2e8488a4c447' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b528edbe-4daa-4d25-824a-2e8488a4c447' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dfaa9d2-2a46-436c-aec9-d4e310b1a985' class='xr-var-data-in' type='checkbox'><label for='data-1dfaa9d2-2a46-436c-aec9-d4e310b1a985' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.9375,   2.8125,   4.6875,   6.5625,   8.4375,  10.3125,  12.1875,\n",
       "        14.0625,  15.9375,  17.8125,  19.6875,  21.5625,  23.4375,  25.3125,\n",
       "        27.1875,  29.0625,  30.9375,  32.8125,  34.6875,  36.5625,  38.4375,\n",
       "        40.3125,  42.1875,  44.0625,  45.9375,  47.8125,  49.6875,  51.5625,\n",
       "        53.4375,  55.3125,  57.1875,  59.0625,  60.9375,  62.8125,  64.6875,\n",
       "        66.5625,  68.4375,  70.3125,  72.1875,  74.0625,  75.9375,  77.8125,\n",
       "        79.6875,  81.5625,  83.4375,  85.3125,  87.1875,  89.0625,  90.9375,\n",
       "        92.8125,  94.6875,  96.5625,  98.4375, 100.3125, 102.1875, 104.0625,\n",
       "       105.9375, 107.8125, 109.6875, 111.5625, 113.4375, 115.3125, 117.1875,\n",
       "       119.0625, 120.9375, 122.8125, 124.6875, 126.5625, 128.4375, 130.3125,\n",
       "       132.1875, 134.0625, 135.9375, 137.8125, 139.6875, 141.5625, 143.4375,\n",
       "       145.3125, 147.1875, 149.0625, 150.9375, 152.8125, 154.6875, 156.5625,\n",
       "       158.4375, 160.3125, 162.1875, 164.0625, 165.9375, 167.8125, 169.6875,\n",
       "       171.5625, 173.4375, 175.3125, 177.1875, 179.0625, 180.9375, 182.8125,\n",
       "       184.6875, 186.5625, 188.4375, 190.3125, 192.1875, 194.0625, 195.9375,\n",
       "       197.8125, 199.6875, 201.5625, 203.4375, 205.3125, 207.1875, 209.0625,\n",
       "       210.9375, 212.8125, 214.6875, 216.5625, 218.4375, 220.3125, 222.1875,\n",
       "       224.0625, 225.9375, 227.8125, 229.6875, 231.5625, 233.4375, 235.3125,\n",
       "       237.1875, 239.0625, 240.9375, 242.8125, 244.6875, 246.5625, 248.4375,\n",
       "       250.3125, 252.1875, 254.0625, 255.9375, 257.8125, 259.6875, 261.5625,\n",
       "       263.4375, 265.3125, 267.1875, 269.0625, 270.9375, 272.8125, 274.6875,\n",
       "       276.5625, 278.4375, 280.3125, 282.1875, 284.0625, 285.9375, 287.8125,\n",
       "       289.6875, 291.5625, 293.4375, 295.3125, 297.1875, 299.0625, 300.9375,\n",
       "       302.8125, 304.6875, 306.5625, 308.4375, 310.3125, 312.1875, 314.0625,\n",
       "       315.9375, 317.8125, 319.6875, 321.5625, 323.4375, 325.3125, 327.1875,\n",
       "       329.0625, 330.9375, 332.8125, 334.6875, 336.5625, 338.4375, 340.3125,\n",
       "       342.1875, 344.0625, 345.9375, 347.8125, 349.6875, 351.5625, 353.4375,\n",
       "       355.3125, 357.1875, 359.0625])</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'>...</div><input id='attrs-79720cd7-5de5-41e2-b0f1-ac0e1ead9091' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79720cd7-5de5-41e2-b0f1-ac0e1ead9091' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3d8fc4c-4ef4-43f2-8e12-e5ecd79daba8' class='xr-var-data-in' type='checkbox'><label for='data-e3d8fc4c-4ef4-43f2-8e12-e5ecd79daba8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(1.5)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-82b1784d-1492-44f8-8cb4-0cbf81108681' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82b1784d-1492-44f8-8cb4-0cbf81108681' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>...</div><input id='attrs-f0380d82-1de6-42f8-8577-7572147a5b20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0380d82-1de6-42f8-8577-7572147a5b20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-831480e9-462a-46af-a1c5-a54e170fa247' class='xr-var-data-in' type='checkbox'><label for='data-831480e9-462a-46af-a1c5-a54e170fa247' 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.Datetime360Day(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.Datetime360Day(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.Datetime360Day(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.Datetime360Day(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.Datetime360Day(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.Datetime360Day(1850, 5, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       ...,\n",
       "       [cftime.Datetime360Day(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.Datetime360Day(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.Datetime360Day(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.Datetime360Day(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True)],\n",
       "       [cftime.Datetime360Day(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "        cftime.Datetime360Day(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eca48b47-dcbc-47d7-86bd-dab7f68dca29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eca48b47-dcbc-47d7-86bd-dab7f68dca29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb356ef7-1a02-4a29-be63-3c7610f86f6a' class='xr-var-data-in' type='checkbox'><label for='data-fb356ef7-1a02-4a29-be63-3c7610f86f6a' 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>[569952 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f894d3f-d91d-430b-917d-3afa0fe9a2eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f894d3f-d91d-430b-917d-3afa0fe9a2eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25f1fe65-5c26-4d4e-a414-a53d28357153' class='xr-var-data-in' type='checkbox'><label for='data-25f1fe65-5c26-4d4e-a414-a53d28357153' 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>[759936 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f239ec91-d88a-46f7-b6a8-ee3a6dacb479' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f239ec91-d88a-46f7-b6a8-ee3a6dacb479' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dce1f1e-df81-4ddd-878d-1c2698f094c8' class='xr-var-data-in' type='checkbox'><label for='data-3dce1f1e-df81-4ddd-878d-1c2698f094c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>original_name :</span></dt><dd>mo: (stash: m01s03i236, lbproc: 128)</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-06-19T11:21:17Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-06-19T11:21:17Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd></dl></div><div class='xr-var-data'><pre>[54715392 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa099ab3-8b89-4967-b9ad-b3caa1e2a23b' class='xr-section-summary-in' type='checkbox'  ><label for='section-fa099ab3-8b89-4967-b9ad-b3caa1e2a23b' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>0.0</dd><dt><span>creation_date :</span></dt><dd>2019-06-19T11:21:17Z</dd><dt><span>cv_version :</span></dt><dd>6.2.20.1</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>3</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.MOHC.HadGEM3-GC31-LL.historical.none.r1i1p1f3</dd><dt><span>grid :</span></dt><dd>Native N96 grid; 192 x 144 longitude/latitude</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-06-19T11:11:07Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.;\n",
       "2019-06-19T11:10:54Z MIP Convert v1.1.0, Python v2.7.12, Iris v1.13.0, Numpy v1.13.3, netcdftime v1.4.1.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK</dd><dt><span>institution_id :</span></dt><dd>MOHC</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>mo_runid :</span></dt><dd>u-bg466</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>HadGEM3-GC31-LL</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-01-01-00-00-00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>source :</span></dt><dd>HadGEM3-GC31-LL (2016): \n",
       "aerosol: UKCA-GLOMAP-mode\n",
       "atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km)\n",
       "atmosChem: none\n",
       "land: JULES-HadGEM3-GL7.1\n",
       "landIce: none\n",
       "ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m)\n",
       "ocnBgchem: none\n",
       "seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude)</dd><dt><span>source_id :</span></dt><dd>HadGEM3-GC31-LL</dd><dt><span>source_type :</span></dt><dd>AOGCM AER</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(13 December 2018) MD5:2b12b5db6db112aa8b8b0d6c1645b121</dd><dt><span>title :</span></dt><dd>HadGEM3-GC31-LL output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f3</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by the Met Office Hadley Centre is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at https://ukesm.ac.uk/cmip6. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/b6959414-d5ed-4cd9-a627-59238e52132d</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 1979, bnds: 2, lat: 144, lon: 192)\n",
       "Coordinates:\n",
       "  * time       (time) object 1850-02-16 00:00:00 ... 2014-12-16 00:00:00\n",
       "  * lat        (lat) float64 -89.38 -88.12 -86.88 -85.62 ... 86.88 88.12 89.38\n",
       "  * lon        (lon) float64 0.9375 2.812 4.688 6.562 ... 355.3 357.2 359.1\n",
       "    height     float64 ...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object ...\n",
       "    lat_bnds   (time, lat, bnds) float64 ...\n",
       "    lon_bnds   (time, lon, bnds) float64 ...\n",
       "    tas        (time, lat, lon) float32 ...\n",
       "Attributes: (12/46)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    branch_method:          standard\n",
       "    branch_time_in_child:   0.0\n",
       "    branch_time_in_parent:  0.0\n",
       "    creation_date:          2019-06-19T11:21:17Z\n",
       "    ...                     ...\n",
       "    title:                  HadGEM3-GC31-LL output prepared for CMIP6\n",
       "    variable_id:            tas\n",
       "    variant_label:          r1i1p1f3\n",
       "    license:                CMIP6 model data produced by the Met Office Hadle...\n",
       "    cmor_version:           3.4.0\n",
       "    tracking_id:            hdl:21.14100/b6959414-d5ed-4cd9-a627-59238e52132d"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(f'{DATADIR}{cmip6_nc[0]}')\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By examining the data above, we can see from the temporal range (1850 to 2014) that it is from the `historical` experiment.\n",
    "\n",
    "We see that the data dimensions have been given labelled coordinates of time, latitude and longitude. We can find more about the dataset from the `Attributes`, such information includes the model name, description of the variable (`long_name`), units, etc.\n",
    "\n",
    "Some of this information we will need later, this includes the experiment and model IDs. We will save these into variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = ds.attrs['experiment_id']\n",
    "mod = ds.attrs['source_id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An `xarray.Dataset()` may contain arrays of multiple variables. We only have one variable in the dataset, which is near-surface air temperature, `tas`. Below we create an `xarray.DataArray()` object, which takes only one variable, but gives us more flexibility in processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "da = ds['tas']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spatial aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to aggregate the temperature values spatially (i.e. average over the latitude and longitude dimensions) and compute the global monthly near-surface temperature.\n",
    "\n",
    "A very important consideration however is that the gridded data cells do not all correspond to the same areas. The size covered by each data point varies as a function of latitude. We need to take this into account when averaging. One way to do this is to use the cosine of the latitude as a proxy for the varying sizes. \n",
    "\n",
    "This can be implemented by first calculating weights as a function of the cosine of the latitude, then applying these weights to the data array with the xarray function `weighted()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = np.cos(np.deg2rad(da.lat))\n",
    "weights.name = \"weights\"\n",
    "da_weighted = da.weighted(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is then to compute the mean across the latitude and longitude dimensions of the weighted data array with the function `mean()`. The result is a DataArray with one dimension (`time`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_agg = da_weighted.mean(['lat', 'lon'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temporal aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now aggregate the monthly global near-surface air temperature values to annual global near-surface air temperature values. This operation can be done in two steps: first, all the values for one specific year 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 one-dimensional DataArray. Please note that this operation changes the name of the dimension from `time` to `year`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_yr = da_agg.groupby('time.year').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conversion from Kelvin to Celsius"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metadata of the original data (before it was stripped during the subsequent processing steps) tells us that the near-surface air temperature data values are in units of Kelvin. We will convert them to degrees Celsius by subtracting 273.15 from the data values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_yr = da_yr - 273.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create additional data dimensions (to later combine data from multiple models & experiments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will create additional dimensions for the model and for the experiment. These we will label with the model and experiment name as taken from the metadata of the original data (see above). These will be useful when we repeat the processes above for all models and experiments, and combine them into one array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_yr = da_yr.assign_coords(model=mod)\n",
    "da_yr = da_yr.expand_dims('model')\n",
    "da_yr = da_yr.assign_coords(experiment=exp)\n",
    "da_yr = da_yr.expand_dims('experiment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and prepare CMIP6 data for all models and experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To repeat the steps above for all models and all experiments, we will collect all of the commands we have used so far into a function, which we can then apply to a batch of files corresponding to the data from all models and experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to aggregate in geographical lat lon dimensions\n",
    "\n",
    "def geog_agg(fn):\n",
    "    ds = xr.open_dataset(f'{DATADIR}{fn}')\n",
    "    exp = ds.attrs['experiment_id']\n",
    "    mod = ds.attrs['source_id']\n",
    "    da = ds['tas']\n",
    "    weights = np.cos(np.deg2rad(da.lat))\n",
    "    weights.name = \"weights\"\n",
    "    da_weighted = da.weighted(weights)\n",
    "    da_agg = da_weighted.mean(['lat', 'lon'])\n",
    "    da_yr = da_agg.groupby('time.year').mean()\n",
    "    da_yr = da_yr - 273.15\n",
    "    da_yr = da_yr.assign_coords(model=mod)\n",
    "    da_yr = da_yr.expand_dims('model')\n",
    "    da_yr = da_yr.assign_coords(experiment=exp)\n",
    "    da_yr = da_yr.expand_dims('experiment')\n",
    "    da_yr.to_netcdf(path=f'{DATADIR}cmip6_agg_{exp}_{mod}_{str(da_yr.year[0].values)}.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can apply this function to all the extracted NetCDF files. The `try` and `except` clauses ensure that all NetCDF files are attempted, even if some fail to be processed. One reason why some may fail is if the data are labelled differently, e.g. the model *MCM-UA-1-0* has coordinates labelled as \"*latitude*\" and *longitude*\". This differs from the suggested standard, and more commonly applied labels of \"*lat*\" and \"*lon*\". Any that fail will be recorded in a print statement, and these can be processed separately. See [here](https://confluence.ecmwf.int/display/CKB/CMIP6%3A+Global+climate+projections#CMIP6:Globalclimateprojections-QualitycontroloftheCDS-CMIP6subset) more details on the quality control of the CMIP6 datasets on the CDS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cmip6_nc:\n",
    "    try:\n",
    "        geog_agg(i)\n",
    "    except: print(f'{i} failed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the absence of any print statements, we see that all files were successfully processed. \n",
    "\n",
    "We will now combine these processed files into one dataset for the final steps to create a visualisation of near-surface air temperature from the model ensemble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If all files have the same coordinates, the function `xarray.open_mfdataset` will merge the data according to the same coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_ds = xr.open_mfdataset(f'{DATADIR}cmip6_agg*.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset created by `xarray.open_mfdataset` is by default in the form of \"lazy Dask arrays\". \n",
    "\n",
    "Dask divides arrays into many small pieces, called chunks, each of which is presumed to be small enough to fit into memory. As opposed to eager evaluation, operations on Dask arrays are lazy, i.e. operations queue up a series of tasks mapped over blocks, and no computation is performed until you request values to be computed. For more details, see https://xarray.pydata.org/en/stable/user-guide/dask.html. \n",
    "\n",
    "To facilitate further processing we would need to convert these Dask arrays into in-memory \"eager\" arrays, which we can do by using the `load()` method: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  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.Dataset&gt;\n",
       "Dimensions:     (year: 251, model: 7, experiment: 4)\n",
       "Coordinates:\n",
       "  * year        (year) int64 1850 1851 1852 1853 1854 ... 2097 2098 2099 2100\n",
       "    height      (model) float64 1.5 2.0 2.0 2.0 2.0 2.0 1.5\n",
       "  * model       (model) object &#x27;HadGEM3-GC31-LL&#x27; &#x27;INM-CM4-8&#x27; ... &#x27;UKESM1-0-LL&#x27;\n",
       "  * experiment  (experiment) object &#x27;historical&#x27; &#x27;ssp126&#x27; &#x27;ssp245&#x27; &#x27;ssp585&#x27;\n",
       "Data variables:\n",
       "    tas         (experiment, model, year) float64 13.75 13.62 ... 20.48 20.63</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-7210d041-fb0d-460e-b406-93e78f944f6c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7210d041-fb0d-460e-b406-93e78f944f6c' 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'>year</span>: 251</li><li><span class='xr-has-index'>model</span>: 7</li><li><span class='xr-has-index'>experiment</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fff99b50-a135-4107-a7df-4fc18dea6bf4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fff99b50-a135-4107-a7df-4fc18dea6bf4' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1850 1851 1852 ... 2098 2099 2100</div><input id='attrs-ee15cfa2-53c1-4c0c-8475-c90088d35cbe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee15cfa2-53c1-4c0c-8475-c90088d35cbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b3b0815-4c1b-4107-b154-412ef50263a8' class='xr-var-data-in' type='checkbox'><label for='data-1b3b0815-4c1b-4107-b154-412ef50263a8' 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([1850, 1851, 1852, ..., 2098, 2099, 2100], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 2.0 2.0 2.0 2.0 2.0 1.5</div><input id='attrs-928aadec-2b79-435c-8607-4611800f13da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-928aadec-2b79-435c-8607-4611800f13da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d33ae3c6-7096-4700-82cb-3d53a6288e96' class='xr-var-data-in' type='checkbox'><label for='data-d33ae3c6-7096-4700-82cb-3d53a6288e96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.5, 2. , 2. , 2. , 2. , 2. , 1.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>model</span></div><div class='xr-var-dims'>(model)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;HadGEM3-GC31-LL&#x27; ... &#x27;UKESM1-0-LL&#x27;</div><input id='attrs-16d77baf-03be-45ed-9bbc-ec71951a9a3f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16d77baf-03be-45ed-9bbc-ec71951a9a3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6448dee-636f-4f3e-a66b-1257690066b5' class='xr-var-data-in' type='checkbox'><label for='data-e6448dee-636f-4f3e-a66b-1257690066b5' 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([&#x27;HadGEM3-GC31-LL&#x27;, &#x27;INM-CM4-8&#x27;, &#x27;INM-CM5-0&#x27;, &#x27;IPSL-CM6A-LR&#x27;,\n",
       "       &#x27;MIROC-ES2L&#x27;, &#x27;MPI-ESM1-2-LR&#x27;, &#x27;UKESM1-0-LL&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>experiment</span></div><div class='xr-var-dims'>(experiment)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;historical&#x27; &#x27;ssp126&#x27; ... &#x27;ssp585&#x27;</div><input id='attrs-dd51790c-3656-4a48-8636-3c36d521fb1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd51790c-3656-4a48-8636-3c36d521fb1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6857c78-b83a-40d6-b42a-fae79b92fbef' class='xr-var-data-in' type='checkbox'><label for='data-d6857c78-b83a-40d6-b42a-fae79b92fbef' 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([&#x27;historical&#x27;, &#x27;ssp126&#x27;, &#x27;ssp245&#x27;, &#x27;ssp585&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6fa65b73-5a2e-419f-a7ed-ed2e22bba1e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6fa65b73-5a2e-419f-a7ed-ed2e22bba1e5' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(experiment, model, year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>13.75 13.62 13.7 ... 20.48 20.63</div><input id='attrs-801b61d2-cefc-4b7a-8beb-f979f63f087e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-801b61d2-cefc-4b7a-8beb-f979f63f087e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1eb5b28-4e01-4e40-913a-e508e349b762' class='xr-var-data-in' type='checkbox'><label for='data-a1eb5b28-4e01-4e40-913a-e508e349b762' 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([[[13.75399058, 13.61972281, 13.69983418, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [13.58175664, 13.36504047, 13.38635866, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [13.42228756, 13.19990532, 13.16567923, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [15.15067915, 15.10892567, 15.03257159, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [13.65744156, 13.45913109, 13.46609431, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [13.54177956, 13.33535187, 13.43955638, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ..., 16.29245683,\n",
       "         16.36082827, 16.33036985],\n",
       "        [        nan,         nan,         nan, ..., 14.76511278,\n",
       "         14.71776501, 14.78597346],\n",
       "        [        nan,         nan,         nan, ..., 14.57112824,\n",
       "         14.57076709, 14.51991082],\n",
       "...\n",
       "        [        nan,         nan,         nan, ..., 17.47279885,\n",
       "         17.79239638, 17.58084508],\n",
       "        [        nan,         nan,         nan, ..., 16.0461109 ,\n",
       "         16.01744213, 16.02138782],\n",
       "        [        nan,         nan,         nan, ..., 17.62745672,\n",
       "         17.5299521 , 17.59425743]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ..., 20.39295861,\n",
       "         20.52182487, 20.57955248],\n",
       "        [        nan,         nan,         nan, ..., 17.39707069,\n",
       "         17.3183903 , 17.38916422],\n",
       "        [        nan,         nan,         nan, ..., 17.16603799,\n",
       "         17.24878839, 17.27704339],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ..., 19.3107107 ,\n",
       "         19.39395087, 19.69432393],\n",
       "        [        nan,         nan,         nan, ..., 18.06633781,\n",
       "         17.98003727, 17.73531158],\n",
       "        [        nan,         nan,         nan, ..., 20.65340704,\n",
       "         20.48425935, 20.63318635]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-82084eef-2c61-4462-b7a4-affd3165e974' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-82084eef-2c61-4462-b7a4-affd3165e974' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (year: 251, model: 7, experiment: 4)\n",
       "Coordinates:\n",
       "  * year        (year) int64 1850 1851 1852 1853 1854 ... 2097 2098 2099 2100\n",
       "    height      (model) float64 1.5 2.0 2.0 2.0 2.0 2.0 1.5\n",
       "  * model       (model) object 'HadGEM3-GC31-LL' 'INM-CM4-8' ... 'UKESM1-0-LL'\n",
       "  * experiment  (experiment) object 'historical' 'ssp126' 'ssp245' 'ssp585'\n",
       "Data variables:\n",
       "    tas         (experiment, model, year) float64 13.75 13.62 ... 20.48 20.63"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ds.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we create an Xarray DataArray object for the near-surface air temperature variable, 'tas':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_ds['tas']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the CMIP6 annual global average temperature between 1850 to 2100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now create a plot of the model ensemble of near-surface air temperature for the historical and future periods, according to the three selected scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate quantiles for model ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than plotting the data from all models, we will instead view the range of values as given by quantiles, including the 10th (near to lower limit), the 50th (mid-range) and the 90th (near to upper limit) quantiles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_90 = data.quantile(0.9, dim='model')\n",
    "data_10 = data.quantile(0.1, dim='model')\n",
    "data_50 = data.quantile(0.5, dim='model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note:** The warning message is due to the presence of NaN (Not a Number) data given that the historical and scenario datasets represent only parts (historical and future respectively) of the entire time series. As these two datasets have been merged, NaN values will exist (e.g. there will be no data for the historical experiment for the future period)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### View time-series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we will visualise this data in one time-series plot. We will use the matplotlib function `plot()`. The dimension `year` will be the x-axis and the near-surface air temperature values in degrees Celsius will be the y-axis. \n",
    "\n",
    "The plotting function below has four main parts:\n",
    "* **Initiate the plot**: initiate a matplotlib plot with `plt.subplots()`\n",
    "* **Plot the time-series**: plot the data for each experiment, including the historical experiment and three scenarios with the `plot()` function\n",
    "* **Set axes limits, labels, title and legend**: Define title and axes labels, and add additional items to the plot, such as legend or gridlines\n",
    "* **Save the figure**: Save the figure as a PNG file with the `matplotlib.pyplot.savefig()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (16, 8))\n",
    "\n",
    "colours = ['black','red','green','blue']\n",
    "for i in np.arange(len(experiments)):\n",
    "    ax.plot(data_50.year, data_50[i,:], color=f'{colours[i]}', \n",
    "            label=f'{data_50.experiment[i].values} 50th quantile')\n",
    "    ax.fill_between(data_50.year, data_90[i,:], data_10[i,:], alpha=0.1, color=f'{colours[i]}', \n",
    "            label=f'{data_50.experiment[i].values} 10th and 90th quantile range')\n",
    "\n",
    "ax.set_xlim(1850,2100)\n",
    "ax.set_title('CMIP6 annual global average temperature (1850 to 2100)')\n",
    "ax.set_ylabel('tam (Celsius)')\n",
    "ax.set_xlabel('year')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels)\n",
    "ax.grid(linestyle='--')\n",
    "\n",
    "fig.savefig(f'{DATADIR}CMIP6_annual_global_tas.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualization of the `CMIP6 annual global average temperature (1850 to 2100)` above shows that the global average temperature was more or less stable in the pre-industrial phase, but steadily increases since the 1990s. It shows further that, depending on the SSP scenario, the course and increase of the global annual temperature differs. While for the best case `SSP1-2.6` scenario, the global annual temperature could stabilize around 15 degC, in the worst case `SSP5-8.5` scenario, the global annual temperature could increase to above 20 degC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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
}