AccessingCMIP6dataPython.ipynb

{
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   "source": [
    "# Accessing CMIP6 models from Pangeo cloud and basic plotting\n",
    "This Jupyter notebook shows you how to access CMIP6 model outputs using the Pangeo Cloud Collection through the `intake` and `xmip` libraries. The second part of this notebook shows you how to do basic data manipulation using the `xarray` library. The last part shows you how to create maps and basic timeseries plots using the `matplotlib` and `cartopy` libraries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#These libraries allow you to access the CMIP6 data from Pangeo Cloud\n",
    "import intake\n",
    "from xmip.preprocessing import rename_cmip6, replace_x_y_nominal_lat_lon\n",
    "#These libraries are used to manipulate data\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "#This library is handy to deal with filepaths in your computer\n",
    "import os\n",
    "#These libraries allow you to create plots, including maps\n",
    "import matplotlib.pyplot as plt\n",
    "import cmocean as cm\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cft\n",
    "import dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This line will show plots you create inside this notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing CMIP6 data with `intake` and `xmip`\n",
    "The `intake` library makes it easy to load data that is hosted online with ease into Python. For more information about the function available in this package, you can check its documentation [here](https://intake.readthedocs.io/en/latest/start.html). The `xmip` library helps you standardised data from different CMIP6 models so you can focus on analysing data faster. Their [website](https://cmip6-preprocessing.readthedocs.io/en/latest/tutorial.html) provides some great tutorials to get you started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First we establish a connection to the Pangeo Cloud. The URL below is where CMIP6 data is hosted.\n",
    "pangeo_collection = intake.open_esm_datastore(\"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>activity_id</th>\n",
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       "  </thead>\n",
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       "      <td>pr</td>\n",
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       "      <td>CMIP</td>\n",
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       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>psl</td>\n",
       "      <td>gr</td>\n",
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       "      <td>EC-Earth3-Veg</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>ta</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...</td>\n",
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       "    <tr>\n",
       "      <th>514813</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>EC-Earth-Consortium</td>\n",
       "      <td>EC-Earth3-Veg</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>tas</td>\n",
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       "      <td>historical</td>\n",
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       "      <td>tauu</td>\n",
       "      <td>gr</td>\n",
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       "      <td>CMIP</td>\n",
       "      <td>EC-Earth-Consortium</td>\n",
       "      <td>EC-Earth3-Veg</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Amon</td>\n",
       "      <td>hur</td>\n",
       "      <td>gr</td>\n",
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       "       activity_id       institution_id      source_id experiment_id  \\\n",
       "514808        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514809        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514810        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514811        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514812        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514813        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514814        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514815        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514816        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "514817        CMIP  EC-Earth-Consortium  EC-Earth3-Veg    historical   \n",
       "\n",
       "       member_id table_id variable_id grid_label  \\\n",
       "514808  r1i1p1f1     Amon       clivi         gr   \n",
       "514809  r1i1p1f1     Amon        rlut         gr   \n",
       "514810  r1i1p1f1     Amon          pr         gr   \n",
       "514811  r1i1p1f1     Amon         psl         gr   \n",
       "514812  r1i1p1f1     Amon          ta         gr   \n",
       "514813  r1i1p1f1     Amon         tas         gr   \n",
       "514814  r1i1p1f1     Amon        tauu         gr   \n",
       "514815  r1i1p1f1     Amon         hur         gr   \n",
       "514816  r1i1p1f1     Amon         hus         gr   \n",
       "514817  r1i1p1f1     Amon        tauv         gr   \n",
       "\n",
       "                                                   zstore  dcpp_init_year  \\\n",
       "514808  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514809  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514810  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514811  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514812  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514813  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514814  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514815  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514816  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "514817  gs://cmip6/CMIP6/CMIP/EC-Earth-Consortium/EC-E...             NaN   \n",
       "\n",
       "         version  \n",
       "514808  20211207  \n",
       "514809  20211207  \n",
       "514810  20211207  \n",
       "514811  20211207  \n",
       "514812  20211207  \n",
       "514813  20211207  \n",
       "514814  20211207  \n",
       "514815  20211207  \n",
       "514816  20211207  \n",
       "514817  20211207  "
      ]
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   "source": [
    "#We can now print some of the datasets available through Pangeo. Note that we are printing only the last 10 lines.\n",
    "#You can remove tail() if you would like to print all entries.\n",
    "pangeo_collection.df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first column shows the row number, as you can see there is almost 515,000 CMIP6 datasets available through Pangeo. For this notebook, we will focus on just one model: ACCESS-ESM 1.5. Pangeo Cloud has a number of other models available, you can check the [World Climate Research Programme (WCRP)](https://www.wcrp-climate.org/modelling-wgcm-mip-catalogue/modelling-wgcm-cmip6-endorsed-mips) website for a list of models and variables available.\n",
    "\n",
    "It is worth noting that variable names in all CMIP6 models are standarised. You can search for the name of the variable of your interest in this [website](https://clipc-services.ceda.ac.uk/dreq/mipVars.html). For this notebook, we have chosen to access sea surface temperature (SST).\n",
    "\n",
    "The example below shows a few of the search function parameters available. It is not necessary to declare them all, they are only used here to show these options are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since we have hundreds of thousands of datasets available, we will use the search function to find two models (IPSL AND GFDL) \n",
    "# and variable (SST) we are interested in\n",
    "query = pangeo_collection.search(variable_id = 'tos', #this is the standardised CMIP6 variable name for SST\n",
    "                                 experiment_id = 'historical', #refers to historical data, which covers the years between 1850 and 2014\n",
    "                                 source_id = ['IPSL-CM6A-LR', 'GFDL-ESM4'], #models of interest\n",
    "                                 table_id = 'Omon', #refers to monthly data for variables related to the ocean\n",
    "                                 member_id = 'r1i1p1f1') #model run we are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <th>0</th>\n",
       "      <td>CMIP</td>\n",
       "      <td>IPSL</td>\n",
       "      <td>IPSL-CM6A-LR</td>\n",
       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Omon</td>\n",
       "      <td>tos</td>\n",
       "      <td>gn</td>\n",
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       "      <td>historical</td>\n",
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       "      <td>tos</td>\n",
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       "      <td>historical</td>\n",
       "      <td>r1i1p1f1</td>\n",
       "      <td>Omon</td>\n",
       "      <td>tos</td>\n",
       "      <td>gr</td>\n",
       "      <td>gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20190726</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  activity_id institution_id     source_id experiment_id member_id table_id  \\\n",
       "0        CMIP           IPSL  IPSL-CM6A-LR    historical  r1i1p1f1     Omon   \n",
       "1        CMIP      NOAA-GFDL     GFDL-ESM4    historical  r1i1p1f1     Omon   \n",
       "2        CMIP      NOAA-GFDL     GFDL-ESM4    historical  r1i1p1f1     Omon   \n",
       "\n",
       "  variable_id grid_label                                             zstore  \\\n",
       "0         tos         gn  gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/histor...   \n",
       "1         tos         gn  gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...   \n",
       "2         tos         gr  gs://cmip6/CMIP6/CMIP/NOAA-GFDL/GFDL-ESM4/hist...   \n",
       "\n",
       "   dcpp_init_year   version  \n",
       "0             NaN  20180803  \n",
       "1             NaN  20190726  \n",
       "2             NaN  20190726  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "#We can check the results of our query\n",
    "query.df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we identified the datasets of our interest, we will save this information into our memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
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   "source": [
    "data_dict = query.to_dataset_dict(z_kwargs={'consolidated': True, #Standarises data from all models\n",
    "                                            'decode_times': False}, #Read time as it is, do not decode it\n",
    "                                            aggregate = True, #Data will be aggregated\n",
    "                                            preprocess = rename_cmip6) #This option will standarise the variable names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the results of our search, and we can also check the name of the variables, which will be standarised."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omon.gr ['y', 'x', 'time', 'member_id', 'dcpp_init_year', 'bnds']\n",
      "CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn ['y', 'x', 'time', 'member_id', 'dcpp_init_year', 'vertex', 'bnds']\n",
      "CMIP.NOAA-GFDL.GFDL-ESM4.historical.Omon.gn ['y', 'x', 'time', 'member_id', 'dcpp_init_year', 'bnds', 'vertex']\n"
     ]
    }
   ],
   "source": [
    "for model, ds in data_dict.items():\n",
    "    print(model, list(ds.dims))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data manipulation and visualisation\n",
    "In this section we will rely on the `xarray` library to manipulate the CMIP6 data that we have loaded into memory. We will focus on just one dataset to avoid overloading the local memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (y: 332, x: 362, time: 1980, member_id: 1,\n",
       "                     dcpp_init_year: 1, vertex: 4, bnds: 2)\n",
       "Coordinates:\n",
       "    lat             (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    lon             (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "  * time            (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T...\n",
       "    lon_bounds      (y, x, vertex) float32 dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;\n",
       "    lat_bounds      (y, x, vertex) float32 dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;\n",
       "  * member_id       (member_id) object &#x27;r1i1p1f1&#x27;\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
       "  * x               (x) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * y               (y) float32 -78.08 -78.05 -78.01 -77.98 ... 89.37 89.74 90.0\n",
       "Dimensions without coordinates: vertex, bnds\n",
       "Data variables:\n",
       "    area            (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    tos             (member_id, dcpp_init_year, time, y, x) float32 dask.array&lt;chunksize=(1, 1, 252, 332, 362), meta=np.ndarray&gt;\n",
       "    time_bounds     (time, bnds) datetime64[ns] dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "Attributes: (12/67)\n",
       "    CMIP6_CV_version:                 cv=6.2.3.5-2-g63b123e\n",
       "    Conventions:                      CF-1.7 CMIP-6.2\n",
       "    EXPID:                            historical\n",
       "    NCO:                              &quot;4.6.0&quot;\n",
       "    activity_id:                      CMIP\n",
       "    branch_method:                    standard\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:variable_id:     tos\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR...\n",
       "    intake_esm_attrs:version:         20180803\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    intake_esm_dataset_key:           CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn</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-3bfa24c6-8911-4406-9cfc-59f2aa96b0c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3bfa24c6-8911-4406-9cfc-59f2aa96b0c6' 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'>y</span>: 332</li><li><span class='xr-has-index'>x</span>: 362</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>dcpp_init_year</span>: 1</li><li><span>vertex</span>: 4</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7f911044-4a4d-4c97-b78d-ac2fe7cb0959' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f911044-4a4d-4c97-b78d-ac2fe7cb0959' class='xr-section-summary' >Coordinates: <span>(9)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-1b18974a-15dc-4ef2-a986-09f2289eeee8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b18974a-15dc-4ef2-a986-09f2289eeee8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3bff45e-b7c5-43a8-9ce9-e55b3fd5f259' class='xr-var-data-in' type='checkbox'><label for='data-f3bff45e-b7c5-43a8-9ce9-e55b3fd5f259' 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>bounds_nav_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362) </td>\n",
       "                        <td> (332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-6218e438-7847-495e-9b73-a16068a5d4f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6218e438-7847-495e-9b73-a16068a5d4f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e60d53d8-70fa-4057-88b3-d9c74bd271fe' class='xr-var-data-in' type='checkbox'><label for='data-e60d53d8-70fa-4057-88b3-d9c74bd271fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;</div><input id='attrs-77d98a14-0788-469d-a4b9-f242c18931e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77d98a14-0788-469d-a4b9-f242c18931e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b09eb2f6-d067-4bf9-a879-2d18031b258d' class='xr-var-data-in' type='checkbox'><label for='data-b09eb2f6-d067-4bf9-a879-2d18031b258d' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-b5092bc3-08e0-469b-ad7f-f639ef4e2b34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b5092bc3-08e0-469b-ad7f-f639ef4e2b34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171a321c-2097-4d00-9223-16f41a88bce5' class='xr-var-data-in' type='checkbox'><label for='data-171a321c-2097-4d00-9223-16f41a88bce5' 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;r1i1p1f1&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dcpp_init_year</span></div><div class='xr-var-dims'>(dcpp_init_year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-19a17585-7eae-4dd7-b79e-880460b69bc1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19a17585-7eae-4dd7-b79e-880460b69bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fa268dd-80d7-4992-9d02-d0d33e461728' class='xr-var-data-in' type='checkbox'><label for='data-3fa268dd-80d7-4992-9d02-d0d33e461728' 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([nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-8fea418b-46f3-4cf5-a9e3-225952ae672e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8fea418b-46f3-4cf5-a9e3-225952ae672e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fc1851f-05e0-475f-9245-095afa54755e' class='xr-var-data-in' type='checkbox'><label for='data-1fc1851f-05e0-475f-9245-095afa54755e' 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([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-78.08 -78.05 -78.01 ... 89.74 90.0</div><input id='attrs-7a1ed038-88a3-4b9b-94b5-83088fe62f89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a1ed038-88a3-4b9b-94b5-83088fe62f89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-681d1909-ab1e-4fbe-895c-49b4b8f6471e' class='xr-var-data-in' type='checkbox'><label for='data-681d1909-ab1e-4fbe-895c-49b4b8f6471e' 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([-78.08088 , -78.045616, -78.01033 , ...,  89.36653 ,  89.74177 ,\n",
       "        90.      ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8a1cea5-c968-43d4-9116-1cfa7fd9ba0e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8a1cea5-c968-43d4-9116-1cfa7fd9ba0e' 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>area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-00ede18e-1891-4498-81a1-44c9a3a4659a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00ede18e-1891-4498-81a1-44c9a3a4659a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7d96a46-4035-4cd0-a17b-fe3d63abc245' class='xr-var-data-in' type='checkbox'><label for='data-e7d96a46-4035-4cd0-a17b-fe3d63abc245' 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>cell_area</dd><dt><span>units :</span></dt><dd>m2</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362) </td>\n",
       "                        <td> (332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tos</span></div><div class='xr-var-dims'>(member_id, dcpp_init_year, time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 252, 332, 362), meta=np.ndarray&gt;</div><input id='attrs-36b6e003-1fa8-4e42-90dd-07f257ba5921' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36b6e003-1fa8-4e42-90dd-07f257ba5921' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-957f6009-f820-4883-b360-27daf09cff67' class='xr-var-data-in' type='checkbox'><label for='data-957f6009-f820-4883-b360-27daf09cff67' 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>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>description :</span></dt><dd>This may differ from &quot;surface temperature&quot; in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>interval_operation :</span></dt><dd>2700 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 907.76 MiB </td>\n",
       "                        <td> 115.53 MiB </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 1, 252, 332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-1ba16266-af58-4b76-9300-faf37084cd29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ba16266-af58-4b76-9300-faf37084cd29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d295a50c-f25a-4d89-be23-246ed5a23911' class='xr-var-data-in' type='checkbox'><label for='data-d295a50c-f25a-4d89-be23-246ed5a23911' 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'><table>\n",
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       "                        <td> 30.94 kiB </td>\n",
       "                        <td> 30.94 kiB </td>\n",
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       "                        <td> (1980, 2) </td>\n",
       "                        <td> (1980, 2) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "               &#x27;1850-03-16 12:00:00&#x27;, &#x27;1850-04-16 00:00:00&#x27;,\n",
       "               &#x27;1850-05-16 12:00:00&#x27;, &#x27;1850-06-16 00:00:00&#x27;,\n",
       "               &#x27;1850-07-16 12:00:00&#x27;, &#x27;1850-08-16 12:00:00&#x27;,\n",
       "               &#x27;1850-09-16 00:00:00&#x27;, &#x27;1850-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2014-03-16 12:00:00&#x27;, &#x27;2014-04-16 00:00:00&#x27;,\n",
       "               &#x27;2014-05-16 12:00:00&#x27;, &#x27;2014-06-16 00:00:00&#x27;,\n",
       "               &#x27;2014-07-16 12:00:00&#x27;, &#x27;2014-08-16 12:00:00&#x27;,\n",
       "               &#x27;2014-09-16 00:00:00&#x27;, &#x27;2014-10-16 12:00:00&#x27;,\n",
       "               &#x27;2014-11-16 00:00:00&#x27;, &#x27;2014-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1980, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9f2f27a-4dcd-4b7c-a55b-527d0e583f80' class='xr-index-data-in' type='checkbox'/><label for='index-f9f2f27a-4dcd-4b7c-a55b-527d0e583f80' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r1i1p1f1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dcpp_init_year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2b9d9d28-e46c-4cf6-b66e-3fbd5fa3f877' class='xr-index-data-in' type='checkbox'/><label for='index-2b9d9d28-e46c-4cf6-b66e-3fbd5fa3f877' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([nan], dtype=&#x27;float64&#x27;, name=&#x27;dcpp_init_year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61fdcbe4-7023-4f78-a27c-b060f28f90b5' class='xr-index-data-in' type='checkbox'/><label for='index-61fdcbe4-7023-4f78-a27c-b060f28f90b5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5,\n",
       "              -171.5, -170.5,\n",
       "              ...\n",
       "               170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,\n",
       "               178.5,  179.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=362))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4942ecd2-379d-44ac-ae55-084f130eff47' class='xr-index-data-in' type='checkbox'/><label for='index-4942ecd2-379d-44ac-ae55-084f130eff47' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-78.08087921142578, -78.04561614990234, -78.01033020019531,\n",
       "              -77.97502899169922, -77.93968200683594,  -77.9042739868164,\n",
       "               -77.8687744140625, -77.83313751220703, -77.79731750488281,\n",
       "              -77.76126098632812,\n",
       "              ...\n",
       "               86.31648254394531,  86.77348327636719,  87.22425842285156,\n",
       "               87.66850280761719,  88.10582733154297,  88.53568267822266,\n",
       "               88.95700073242188,  89.36653137207031,  89.74176788330078,\n",
       "                            90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=332))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8feaced-5d58-4917-8b97-a893f14487bb' class='xr-section-summary-in' type='checkbox'  ><label for='section-a8feaced-5d58-4917-8b97-a893f14487bb' class='xr-section-summary' >Attributes: <span>(67)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CMIP6_CV_version :</span></dt><dd>cv=6.2.3.5-2-g63b123e</dd><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>EXPID :</span></dt><dd>historical</dd><dt><span>NCO :</span></dt><dd>&quot;4.6.0&quot;</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>21914.0</dd><dt><span>contact :</span></dt><dd>ipsl-cmip6@listes.ipsl.fr</dd><dt><span>creation_date :</span></dt><dd>2018-07-11T07:36:14Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.21</dd><dt><span>description :</span></dt><dd>CMIP6 historical</dd><dt><span>dr2xml_md5sum :</span></dt><dd>f1e40c1fc5d8281f865f72fbf4e38f9d</dd><dt><span>dr2xml_version :</span></dt><dd>1.11</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>areacello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-CM6A-LR.historical.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>native ocean tri-polar grid with 105 k ocean cells</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>Sat Dec  1 12:15:54 2018: ncatted -O -a realization_index,global,m,i,1 /ccc/work/cont003/cmip6/cmip6/onhold/CM61-LR-histEXT-03.1910/files+ext/tos_Omon_IPSL-CM6A-LR_historical_r1i1p1f1_gn_185001-201412.nc\n",
       "Sat Dec  1 12:09:05 2018: ncatted -O -a realization_index,global,m,i,1 /ccc/work/cont003/cmip6/cmip6/onhold/CM61-LR-hist-03.1910/files/tos_Omon_IPSL-CM6A-LR_historical_r1i1p1f1_gn_185001-201412.nc\n",
       "Sat Dec  1 10:58:36 2018: ncatted -O -a realization_index,global,m,i,1 /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/OCE/tos_Omon_IPSL-CM6A-LR_historical_r1i1p1f1_gn_185001-201412.nc\n",
       "Fri Nov 30 16:47:56 2018: ncatted -O -a realization_index,global,m,s,1 /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/OCE/tos_Omon_IPSL-CM6A-LR_historical_r1i1p1f1_gn_185001-201412.nc\n",
       "Thu Nov 29 16:47:45 2018: ncatted -O -a variant_label,global,m,c,r1i1p1f1 /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/OCE/tos_Omon_IPSL-CM6A-LR_historical_r3i1p1f1_gn_185001-201412.nc\n",
       "Thu Nov 29 16:47:45 2018: ncatted -O -a further_info_url,global,m,c,https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-CM6A-LR.historical.none.r1i1p1f1 /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/OCE/tos_Omon_IPSL-CM6A-LR_historical_r3i1p1f1_gn_185001-201412.nc\n",
       "Thu Nov 29 16:47:45 2018: ncatted -O -a name,global,m,c,/ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/OCE/tos_Omon_IPSL-CM6A-LR_historical_r1i1p1f1_gn_%start_date%-%end_date% /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/OCE/tos_Omon_IPSL-CM6A-LR_historical_r3i1p1f1_gn_185001-201412.nc\n",
       "Mon Sep  3 14:53:26 2018: ncatted -O -a parent_variant_label,global,m,c,r1i1p1f1 tos_Omon_IPSL-CM6A-LR_historical_r3i1p1f1_gn_185001-201412.nc\n",
       "Mon Aug  6 17:58:17 2018: ncatted -O -a coordinates,area,o,c,nav_lon nav_lat /ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/OCE/tos_Omon_IPSL-CM6A-LR_historical_r3i1p1f1_gn_185001-201412.nc\n",
       "none</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Institut Pierre Simon Laplace, Paris 75252, France</dd><dt><span>institution_id :</span></dt><dd>IPSL</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by IPSL is licensed under a Creative Commons Attribution-NonCommercial-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://cmc.ipsl.fr/. 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>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_version :</span></dt><dd>6.1.5</dd><dt><span>name :</span></dt><dd>/ccc/work/cont003/gencmip6/p86caub/IGCM_OUT/IPSLCM6/PROD/historical/CM61-LR-hist-03.1910/CMIP6/OCE/tos_Omon_IPSL-CM6A-LR_historical_r1i1p1f1_gn_%start_date%-%end_date%</dd><dt><span>nominal_resolution :</span></dt><dd>100 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>IPSL-CM6A-LR</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>ocean</dd><dt><span>source :</span></dt><dd>IPSL-CM6A-LR (2017):  atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 40000 m) land: ORCHIDEE (v2.0, Water/Carbon/Energy mode) ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m) ocnBgchem: NEMO-PISCES seaIce: NEMO-LIM3</dd><dt><span>source_id :</span></dt><dd>IPSL-CM6A-LR</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC</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>Omon</dd><dt><span>title :</span></dt><dd>IPSL-CM6A-LR model output prepared for CMIP6 / CMIP historical</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/01f4d96a-9054-4974-b8dd-4f91e73989d2</dd><dt><span>variable_id :</span></dt><dd>tos</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>status :</span></dt><dd>2019-11-10;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/01f4d96a-9054-4974-b8dd-4f91e73989d2</dd><dt><span>version_id :</span></dt><dd>v20180803</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;tos&#x27;]</dd><dt><span>intake_esm_attrs:activity_id :</span></dt><dd>CMIP</dd><dt><span>intake_esm_attrs:institution_id :</span></dt><dd>IPSL</dd><dt><span>intake_esm_attrs:source_id :</span></dt><dd>IPSL-CM6A-LR</dd><dt><span>intake_esm_attrs:experiment_id :</span></dt><dd>historical</dd><dt><span>intake_esm_attrs:member_id :</span></dt><dd>r1i1p1f1</dd><dt><span>intake_esm_attrs:table_id :</span></dt><dd>Omon</dd><dt><span>intake_esm_attrs:variable_id :</span></dt><dd>tos</dd><dt><span>intake_esm_attrs:grid_label :</span></dt><dd>gn</dd><dt><span>intake_esm_attrs:zstore :</span></dt><dd>gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR/historical/r1i1p1f1/Omon/tos/gn/v20180803/</dd><dt><span>intake_esm_attrs:version :</span></dt><dd>20180803</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (y: 332, x: 362, time: 1980, member_id: 1,\n",
       "                     dcpp_init_year: 1, vertex: 4, bnds: 2)\n",
       "Coordinates:\n",
       "    lat             (y, x) float32 dask.array<chunksize=(332, 362), meta=np.ndarray>\n",
       "    lon             (y, x) float32 dask.array<chunksize=(332, 362), meta=np.ndarray>\n",
       "  * time            (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T...\n",
       "    lon_bounds      (y, x, vertex) float32 dask.array<chunksize=(332, 362, 4), meta=np.ndarray>\n",
       "    lat_bounds      (y, x, vertex) float32 dask.array<chunksize=(332, 362, 4), meta=np.ndarray>\n",
       "  * member_id       (member_id) object 'r1i1p1f1'\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
       "  * x               (x) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * y               (y) float32 -78.08 -78.05 -78.01 -77.98 ... 89.37 89.74 90.0\n",
       "Dimensions without coordinates: vertex, bnds\n",
       "Data variables:\n",
       "    area            (y, x) float32 dask.array<chunksize=(332, 362), meta=np.ndarray>\n",
       "    tos             (member_id, dcpp_init_year, time, y, x) float32 dask.array<chunksize=(1, 1, 252, 332, 362), meta=np.ndarray>\n",
       "    time_bounds     (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "Attributes: (12/67)\n",
       "    CMIP6_CV_version:                 cv=6.2.3.5-2-g63b123e\n",
       "    Conventions:                      CF-1.7 CMIP-6.2\n",
       "    EXPID:                            historical\n",
       "    NCO:                              \"4.6.0\"\n",
       "    activity_id:                      CMIP\n",
       "    branch_method:                    standard\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:variable_id:     tos\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:zstore:          gs://cmip6/CMIP6/CMIP/IPSL/IPSL-CM6A-LR...\n",
       "    intake_esm_attrs:version:         20180803\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    intake_esm_dataset_key:           CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First, we make sure that the data set has lat and lon coordinates instead of only indeces\n",
    "sst = replace_x_y_nominal_lat_lon(data_dict['CMIP.IPSL.IPSL-CM6A-LR.historical.Omon.gn'])\n",
    "sst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see above that there is SST data from 1850 to 2014. Let's assume that we are only interested in the last 50 years (1964-2014). We can subset the dataset using the function `sel` as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       ".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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\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;tos&#x27; (time: 612, y: 332, x: 362)&gt;\n",
       "dask.array&lt;getitem, shape=(612, 332, 362), dtype=float32, chunksize=(252, 332, 362), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    lat             (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    lon             (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "  * time            (time) datetime64[ns] 1964-01-16T12:00:00 ... 2014-12-16T...\n",
       "    member_id       &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "    dcpp_init_year  float64 nan\n",
       "  * x               (x) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * y               (y) float32 -78.08 -78.05 -78.01 -77.98 ... 89.37 89.74 90.0\n",
       "Attributes:\n",
       "    cell_measures:       area: areacello\n",
       "    cell_methods:        area: mean where sea time: mean\n",
       "    description:         This may differ from &quot;surface temperature&quot; in region...\n",
       "    history:             none\n",
       "    interval_operation:  2700 s\n",
       "    interval_write:      1 month\n",
       "    long_name:           Sea Surface Temperature\n",
       "    online_operation:    average\n",
       "    standard_name:       sea_surface_temperature\n",
       "    units:               degC</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'>'tos'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 612</li><li><span class='xr-has-index'>y</span>: 332</li><li><span class='xr-has-index'>x</span>: 362</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-550d7315-0b7d-46ae-b77d-141333871ee2' class='xr-array-in' type='checkbox' checked><label for='section-550d7315-0b7d-46ae-b77d-141333871ee2' 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>dask.array&lt;chunksize=(144, 332, 362), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 280.58 MiB </td>\n",
       "                        <td> 115.53 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (612, 332, 362) </td>\n",
       "                        <td> (252, 332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3 chunks in 6 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-edab9461-80ff-4a27-b844-03997b9edb94' class='xr-section-summary-in' type='checkbox'  checked><label for='section-edab9461-80ff-4a27-b844-03997b9edb94' class='xr-section-summary' >Coordinates: <span>(7)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-ba43296b-53d6-4cc4-9ad5-7f90e49b586a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba43296b-53d6-4cc4-9ad5-7f90e49b586a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5baa4fd8-3de8-4354-be84-6d07ebeb3309' class='xr-var-data-in' type='checkbox'><label for='data-5baa4fd8-3de8-4354-be84-6d07ebeb3309' 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>bounds_nav_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362) </td>\n",
       "                        <td> (332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "        </td>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-fd2e676c-8989-410b-9e8b-b2f874392eee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd2e676c-8989-410b-9e8b-b2f874392eee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86dcd0f4-8aaa-4ada-a192-d38d00237b6a' class='xr-var-data-in' type='checkbox'><label for='data-86dcd0f4-8aaa-4ada-a192-d38d00237b6a' 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>bounds_nav_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                        <td> 469.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (332, 362) </td>\n",
       "                        <td> (332, 362) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"160\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"110\" x2=\"120\" y2=\"110\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"110\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"110\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,110.05524861878452 0.0,110.05524861878452\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"130.055249\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >362</text>\n",
       "  <text x=\"140.000000\" y=\"55.027624\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,55.027624)\">332</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1964-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-f362039f-f47d-4d9c-ae0b-0708df7431df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f362039f-f47d-4d9c-ae0b-0708df7431df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9639ae9f-6839-450a-b2ce-f2150e1f05d2' class='xr-var-data-in' type='checkbox'><label for='data-9639ae9f-6839-450a-b2ce-f2150e1f05d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1964-01-16T12:00:00.000000000&#x27;, &#x27;1964-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1964-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-7d213368-c01f-4c27-8865-3dff4e180a65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d213368-c01f-4c27-8865-3dff4e180a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cd70cd7-f89b-46d6-bbf3-a21eafb2aef0' class='xr-var-data-in' type='checkbox'><label for='data-7cd70cd7-f89b-46d6-bbf3-a21eafb2aef0' 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;r1i1p1f1&#x27;, dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dcpp_init_year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-21ebaf19-3b79-487d-baaa-8fac2bafcadb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21ebaf19-3b79-487d-baaa-8fac2bafcadb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8685f4a3-1040-4ea0-ac09-b8ad500c0f29' class='xr-var-data-in' type='checkbox'><label for='data-8685f4a3-1040-4ea0-ac09-b8ad500c0f29' 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(nan)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-abc2e3c5-63d7-47d2-9a7f-85c5e86b0748' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abc2e3c5-63d7-47d2-9a7f-85c5e86b0748' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6192a4c2-b294-45d4-8cdb-dd364713e513' class='xr-var-data-in' type='checkbox'><label for='data-6192a4c2-b294-45d4-8cdb-dd364713e513' 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([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-78.08 -78.05 -78.01 ... 89.74 90.0</div><input id='attrs-a20aaf5f-fdc9-4d36-91c9-cbcfa5c8c4a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a20aaf5f-fdc9-4d36-91c9-cbcfa5c8c4a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-613df568-5366-4ca7-ac0b-4530f4d50beb' class='xr-var-data-in' type='checkbox'><label for='data-613df568-5366-4ca7-ac0b-4530f4d50beb' 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([-78.08088 , -78.045616, -78.01033 , ...,  89.36653 ,  89.74177 ,\n",
       "        90.      ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5951bf2e-113b-4838-97a3-e4e7b1e72af5' class='xr-section-summary-in' type='checkbox'  ><label for='section-5951bf2e-113b-4838-97a3-e4e7b1e72af5' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0815baa7-ca6d-44e5-925c-6bbd4c75b6f5' class='xr-index-data-in' type='checkbox'/><label for='index-0815baa7-ca6d-44e5-925c-6bbd4c75b6f5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1964-01-16 12:00:00&#x27;, &#x27;1964-02-15 12:00:00&#x27;,\n",
       "               &#x27;1964-03-16 12:00:00&#x27;, &#x27;1964-04-16 00:00:00&#x27;,\n",
       "               &#x27;1964-05-16 12:00:00&#x27;, &#x27;1964-06-16 00:00:00&#x27;,\n",
       "               &#x27;1964-07-16 12:00:00&#x27;, &#x27;1964-08-16 12:00:00&#x27;,\n",
       "               &#x27;1964-09-16 00:00:00&#x27;, &#x27;1964-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2014-03-16 12:00:00&#x27;, &#x27;2014-04-16 00:00:00&#x27;,\n",
       "               &#x27;2014-05-16 12:00:00&#x27;, &#x27;2014-06-16 00:00:00&#x27;,\n",
       "               &#x27;2014-07-16 12:00:00&#x27;, &#x27;2014-08-16 12:00:00&#x27;,\n",
       "               &#x27;2014-09-16 00:00:00&#x27;, &#x27;2014-10-16 12:00:00&#x27;,\n",
       "               &#x27;2014-11-16 00:00:00&#x27;, &#x27;2014-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=612, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9ba05bad-bf01-4b41-b2da-a7f6566e7d89' class='xr-index-data-in' type='checkbox'/><label for='index-9ba05bad-bf01-4b41-b2da-a7f6566e7d89' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5,\n",
       "              -171.5, -170.5,\n",
       "              ...\n",
       "               170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,\n",
       "               178.5,  179.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=362))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b4d557d5-c5b5-4213-bf18-1079c2c0e7f9' class='xr-index-data-in' type='checkbox'/><label for='index-b4d557d5-c5b5-4213-bf18-1079c2c0e7f9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-78.08087921142578, -78.04561614990234, -78.01033020019531,\n",
       "              -77.97502899169922, -77.93968200683594,  -77.9042739868164,\n",
       "               -77.8687744140625, -77.83313751220703, -77.79731750488281,\n",
       "              -77.76126098632812,\n",
       "              ...\n",
       "               86.31648254394531,  86.77348327636719,  87.22425842285156,\n",
       "               87.66850280761719,  88.10582733154297,  88.53568267822266,\n",
       "               88.95700073242188,  89.36653137207031,  89.74176788330078,\n",
       "                            90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=332))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e4868ca-5fd1-4700-9649-45f1d1e6bde5' class='xr-section-summary-in' type='checkbox'  ><label for='section-4e4868ca-5fd1-4700-9649-45f1d1e6bde5' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>description :</span></dt><dd>This may differ from &quot;surface temperature&quot; in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>interval_operation :</span></dt><dd>2700 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tos' (time: 612, y: 332, x: 362)>\n",
       "dask.array<getitem, shape=(612, 332, 362), dtype=float32, chunksize=(252, 332, 362), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat             (y, x) float32 dask.array<chunksize=(332, 362), meta=np.ndarray>\n",
       "    lon             (y, x) float32 dask.array<chunksize=(332, 362), meta=np.ndarray>\n",
       "  * time            (time) datetime64[ns] 1964-01-16T12:00:00 ... 2014-12-16T...\n",
       "    member_id       <U8 'r1i1p1f1'\n",
       "    dcpp_init_year  float64 nan\n",
       "  * x               (x) float64 -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * y               (y) float32 -78.08 -78.05 -78.01 -77.98 ... 89.37 89.74 90.0\n",
       "Attributes:\n",
       "    cell_measures:       area: areacello\n",
       "    cell_methods:        area: mean where sea time: mean\n",
       "    description:         This may differ from \"surface temperature\" in region...\n",
       "    history:             none\n",
       "    interval_operation:  2700 s\n",
       "    interval_write:      1 month\n",
       "    long_name:           Sea Surface Temperature\n",
       "    online_operation:    average\n",
       "    standard_name:       sea_surface_temperature\n",
       "    units:               degC"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note that \"time\" is the coordinate with dates. We use \"slice\" to provide a range of values.\n",
    "sst_50yrs = sst.tos.sel(time = slice('1964', '2014'))\n",
    "#We will drop two dimensions that we are not interested in, but we will keep them in the metadata.\n",
    "sst_50yrs = sst_50yrs.squeeze(('member_id', 'dcpp_init_year'))\n",
    "sst_50yrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x22408b5c910>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#If we want to create a map of the mean SST during this period, we just need one line.\n",
    "sst_50yrs[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that we would like to focus on a specific part of the world, such as Australia. We use the `sel` function again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tos&#x27; (time: 612, y: 58, x: 50)&gt;\n",
       "dask.array&lt;getitem, shape=(612, 58, 50), dtype=float32, chunksize=(252, 58, 50), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    lat             (y, x) float32 dask.array&lt;chunksize=(58, 50), meta=np.ndarray&gt;\n",
       "    lon             (y, x) float32 dask.array&lt;chunksize=(58, 50), meta=np.ndarray&gt;\n",
       "  * time            (time) datetime64[ns] 1964-01-16T12:00:00 ... 2014-12-16T...\n",
       "    member_id       &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "    dcpp_init_year  float64 nan\n",
       "  * x               (x) float64 110.5 111.5 112.5 113.5 ... 157.5 158.5 159.5\n",
       "  * y               (y) float32 -49.41 -48.76 -48.09 ... -5.959 -5.545 -5.149\n",
       "Attributes:\n",
       "    cell_measures:       area: areacello\n",
       "    cell_methods:        area: mean where sea time: mean\n",
       "    description:         This may differ from &quot;surface temperature&quot; in region...\n",
       "    history:             none\n",
       "    interval_operation:  2700 s\n",
       "    interval_write:      1 month\n",
       "    long_name:           Sea Surface Temperature\n",
       "    online_operation:    average\n",
       "    standard_name:       sea_surface_temperature\n",
       "    units:               degC</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'>'tos'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 612</li><li><span class='xr-has-index'>y</span>: 58</li><li><span class='xr-has-index'>x</span>: 50</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a63f31a6-953e-4129-b691-1f5717929599' class='xr-array-in' type='checkbox' checked><label for='section-a63f31a6-953e-4129-b691-1f5717929599' 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>dask.array&lt;chunksize=(144, 58, 50), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.77 MiB </td>\n",
       "                        <td> 2.79 MiB </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-39651a16-9fe7-4afb-90ef-dd57ed8b189d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39651a16-9fe7-4afb-90ef-dd57ed8b189d' class='xr-section-summary' >Coordinates: <span>(7)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(58, 50), meta=np.ndarray&gt;</div><input id='attrs-e1454f46-ae38-4211-9c64-d61bcfc3a78d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1454f46-ae38-4211-9c64-d61bcfc3a78d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-985cc315-235f-4c3c-8dfb-9b44259bcd3a' class='xr-var-data-in' type='checkbox'><label for='data-985cc315-235f-4c3c-8dfb-9b44259bcd3a' 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>bounds_nav_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.33 kiB </td>\n",
       "                        <td> 11.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (58, 50) </td>\n",
       "                        <td> (58, 50) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"153\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"103\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"103\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"103\" y1=\"0\" x2=\"103\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 103.44827586206897,0.0 103.44827586206897,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"51.724138\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"123.448276\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,123.448276,60.000000)\">58</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(58, 50), meta=np.ndarray&gt;</div><input id='attrs-49e6d3b2-e5b2-4fb2-940f-24f4d2f20ff7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49e6d3b2-e5b2-4fb2-940f-24f4d2f20ff7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4053c90-5bd6-4d9c-b4df-8f1a87f116cf' class='xr-var-data-in' type='checkbox'><label for='data-f4053c90-5bd6-4d9c-b4df-8f1a87f116cf' 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>bounds_nav_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.33 kiB </td>\n",
       "                        <td> 11.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (58, 50) </td>\n",
       "                        <td> (58, 50) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"153\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"103\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"103\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"103\" y1=\"0\" x2=\"103\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 103.44827586206897,0.0 103.44827586206897,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"51.724138\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"123.448276\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,123.448276,60.000000)\">58</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1964-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-c987dbda-3599-4f25-ac66-3ec68b40ae26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c987dbda-3599-4f25-ac66-3ec68b40ae26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2a68edf-763a-4860-aec8-51ac7014a28e' class='xr-var-data-in' type='checkbox'><label for='data-c2a68edf-763a-4860-aec8-51ac7014a28e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1964-01-16T12:00:00.000000000&#x27;, &#x27;1964-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1964-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-7b78f5db-c735-4b52-8df3-ca0f704d8876' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b78f5db-c735-4b52-8df3-ca0f704d8876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a73ac91-e29c-4dd6-91ce-53dec4905372' class='xr-var-data-in' type='checkbox'><label for='data-6a73ac91-e29c-4dd6-91ce-53dec4905372' 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;r1i1p1f1&#x27;, dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dcpp_init_year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-e4705316-91ef-4a0e-a651-a729820cdf71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4705316-91ef-4a0e-a651-a729820cdf71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9efd02ce-e293-49b2-8ca1-5a618f7e976f' class='xr-var-data-in' type='checkbox'><label for='data-9efd02ce-e293-49b2-8ca1-5a618f7e976f' 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(nan)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>110.5 111.5 112.5 ... 158.5 159.5</div><input id='attrs-1d7d39e1-907f-433d-b3fe-8445c880676b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d7d39e1-907f-433d-b3fe-8445c880676b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf2b20d9-c610-474f-ba82-d2339af131e1' class='xr-var-data-in' type='checkbox'><label for='data-cf2b20d9-c610-474f-ba82-d2339af131e1' 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([110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5, 117.5, 118.5, 119.5,\n",
       "       120.5, 121.5, 122.5, 123.5, 124.5, 125.5, 126.5, 127.5, 128.5, 129.5,\n",
       "       130.5, 131.5, 132.5, 133.5, 134.5, 135.5, 136.5, 137.5, 138.5, 139.5,\n",
       "       140.5, 141.5, 142.5, 143.5, 144.5, 145.5, 146.5, 147.5, 148.5, 149.5,\n",
       "       150.5, 151.5, 152.5, 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-49.41 -48.76 ... -5.545 -5.149</div><input id='attrs-c61307b1-a952-458e-8a57-4f579e54be6c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c61307b1-a952-458e-8a57-4f579e54be6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c65c866a-a5e8-4184-b9c7-772b2ff3644a' class='xr-var-data-in' type='checkbox'><label for='data-c65c866a-a5e8-4184-b9c7-772b2ff3644a' 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([-49.41288 , -48.757957, -48.094387, -47.422142, -46.7412  , -46.051548,\n",
       "       -45.353176, -44.646076, -43.930252, -43.20571 , -42.472458, -41.73051 ,\n",
       "       -40.979897, -40.220642, -39.452785, -38.67636 , -37.891426, -37.09803 ,\n",
       "       -36.296238, -35.48612 , -34.66775 , -33.84122 , -33.006615, -32.16404 ,\n",
       "       -31.313599, -30.455408, -29.589594, -28.716284, -27.835623, -26.947754,\n",
       "       -26.052835, -25.151031, -24.242512, -23.327461, -22.406063, -21.478516,\n",
       "       -20.545023, -19.605793, -18.66211 , -17.715097, -16.767471, -15.823628,\n",
       "       -14.88963 , -13.972492, -13.079   , -12.214899, -11.385137, -10.594805,\n",
       "        -9.849413,  -9.153708,  -8.509959,  -7.917143,  -7.371412,  -6.867243,\n",
       "        -6.398548,  -5.959416,  -5.544503,  -5.14917 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-08704e5f-d26a-4c3f-b3a4-33165b8e2ef5' class='xr-section-summary-in' type='checkbox'  ><label for='section-08704e5f-d26a-4c3f-b3a4-33165b8e2ef5' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1233ba99-1a0b-4967-8383-e77b9272b3b9' class='xr-index-data-in' type='checkbox'/><label for='index-1233ba99-1a0b-4967-8383-e77b9272b3b9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1964-01-16 12:00:00&#x27;, &#x27;1964-02-15 12:00:00&#x27;,\n",
       "               &#x27;1964-03-16 12:00:00&#x27;, &#x27;1964-04-16 00:00:00&#x27;,\n",
       "               &#x27;1964-05-16 12:00:00&#x27;, &#x27;1964-06-16 00:00:00&#x27;,\n",
       "               &#x27;1964-07-16 12:00:00&#x27;, &#x27;1964-08-16 12:00:00&#x27;,\n",
       "               &#x27;1964-09-16 00:00:00&#x27;, &#x27;1964-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2014-03-16 12:00:00&#x27;, &#x27;2014-04-16 00:00:00&#x27;,\n",
       "               &#x27;2014-05-16 12:00:00&#x27;, &#x27;2014-06-16 00:00:00&#x27;,\n",
       "               &#x27;2014-07-16 12:00:00&#x27;, &#x27;2014-08-16 12:00:00&#x27;,\n",
       "               &#x27;2014-09-16 00:00:00&#x27;, &#x27;2014-10-16 12:00:00&#x27;,\n",
       "               &#x27;2014-11-16 00:00:00&#x27;, &#x27;2014-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=612, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2cb36ce2-2bdb-4bdd-b67c-1e986204b381' class='xr-index-data-in' type='checkbox'/><label for='index-2cb36ce2-2bdb-4bdd-b67c-1e986204b381' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5, 117.5, 118.5,\n",
       "              119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5, 126.5, 127.5,\n",
       "              128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5, 135.5, 136.5,\n",
       "              137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5, 144.5, 145.5,\n",
       "              146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5, 153.5, 154.5,\n",
       "              155.5, 156.5, 157.5, 158.5, 159.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f736ef8d-1744-480c-90fa-86a90291f87c' class='xr-index-data-in' type='checkbox'/><label for='index-f736ef8d-1744-480c-90fa-86a90291f87c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-49.412879943847656, -48.757957458496094,  -48.09438705444336,\n",
       "              -47.422142028808594,   -46.7411994934082,  -46.05154800415039,\n",
       "               -45.35317611694336,  -44.64607620239258,  -43.93025207519531,\n",
       "              -43.205711364746094,  -42.47245788574219,  -41.73051071166992,\n",
       "              -40.979896545410156,  -40.22064208984375,  -39.45278549194336,\n",
       "              -38.676361083984375,  -37.89142608642578,  -37.09803009033203,\n",
       "               -36.29623794555664,  -35.48611831665039,  -34.66775131225586,\n",
       "               -33.84122085571289, -33.006614685058594, -32.164039611816406,\n",
       "                -31.3135986328125, -30.455408096313477,   -29.5895938873291,\n",
       "              -28.716283798217773, -27.835622787475586,     -26.94775390625,\n",
       "               -26.05283546447754, -25.151031494140625, -24.242511749267578,\n",
       "               -23.32746124267578, -22.406063079833984,       -21.478515625,\n",
       "               -20.54502296447754, -19.605792999267578,       -18.662109375,\n",
       "              -17.715097427368164, -16.767471313476562, -15.823628425598145,\n",
       "              -14.889630317687988, -13.972492218017578, -13.079000473022461,\n",
       "              -12.214899063110352, -11.385136604309082, -10.594804763793945,\n",
       "                -9.84941291809082,  -9.153707504272461,   -8.50995922088623,\n",
       "               -7.917142868041992, -7.3714118003845215,  -6.867242813110352,\n",
       "               -6.398547649383545,  -5.959415912628174,  -5.544503211975098,\n",
       "               -5.149170398712158],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f538bb1c-0fd1-45a2-b113-08df0085d743' class='xr-section-summary-in' type='checkbox'  ><label for='section-f538bb1c-0fd1-45a2-b113-08df0085d743' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>description :</span></dt><dd>This may differ from &quot;surface temperature&quot; in regions of sea ice or floating ice shelves. For models using conservative temperature as the prognostic field, they should report the top ocean layer as surface potential temperature, which is the same as surface in situ temperature.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>interval_operation :</span></dt><dd>2700 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tos' (time: 612, y: 58, x: 50)>\n",
       "dask.array<getitem, shape=(612, 58, 50), dtype=float32, chunksize=(252, 58, 50), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat             (y, x) float32 dask.array<chunksize=(58, 50), meta=np.ndarray>\n",
       "    lon             (y, x) float32 dask.array<chunksize=(58, 50), meta=np.ndarray>\n",
       "  * time            (time) datetime64[ns] 1964-01-16T12:00:00 ... 2014-12-16T...\n",
       "    member_id       <U8 'r1i1p1f1'\n",
       "    dcpp_init_year  float64 nan\n",
       "  * x               (x) float64 110.5 111.5 112.5 113.5 ... 157.5 158.5 159.5\n",
       "  * y               (y) float32 -49.41 -48.76 -48.09 ... -5.959 -5.545 -5.149\n",
       "Attributes:\n",
       "    cell_measures:       area: areacello\n",
       "    cell_methods:        area: mean where sea time: mean\n",
       "    description:         This may differ from \"surface temperature\" in region...\n",
       "    history:             none\n",
       "    interval_operation:  2700 s\n",
       "    interval_write:      1 month\n",
       "    long_name:           Sea Surface Temperature\n",
       "    online_operation:    average\n",
       "    standard_name:       sea_surface_temperature\n",
       "    units:               degC"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can use more than one variable with \"sel\"\n",
    "sst_aus = sst_50yrs.sel(x = slice(110, 160), y = slice(-50, -5))\n",
    "sst_aus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a map with the climatological SST mean. This map will use a Mercator projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.geocollection.GeoQuadMesh at 0x22408a34a50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's initialise a figure. We can even set the size of the figure.\n",
    "fig = plt.figure(figsize = (12, 10))\n",
    "\n",
    "#We define our projection to be Mercator\n",
    "proj = ccrs.Mercator()\n",
    "\n",
    "#We will add the map to the figure\n",
    "ax = fig.add_subplot(1, 1, 1, projection = proj)\n",
    "\n",
    "#We calculate the climatological mean as seen in the previous step, and plot results\n",
    "sst_aus.mean('time').plot.pcolormesh(ax = ax, \n",
    "#We will use a colour map designed for temperature plots\n",
    "transform = ccrs.PlateCarree(), cmap = cm.cm.thermal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This map is looking better than our first attempt, but we can still make some improvements. We can add a layer for land and change the location and labels of the color bar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Climatological sea surface temperature mean (1964-2014)')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We use the same base code as the previous box\n",
    "#Let's initialise a figure\n",
    "fig = plt.figure(figsize = (12, 10))\n",
    "\n",
    "#We define our projection to be Mercator\n",
    "proj = ccrs.Mercator()\n",
    "\n",
    "#We will add the map to the figure\n",
    "ax = fig.add_subplot(1, 1, 1, projection = proj)\n",
    "\n",
    "#Let's add a land layer from the cartopy library\n",
    "land = cft.NaturalEarthFeature('physical', 'land', '50m',\n",
    "#We can change the border color and width as well as the colour of the polygon\n",
    "edgecolor = 'black', linewidth = 0.5, facecolor = 'gray')\n",
    "\n",
    "#We calculate the climatological mean as seen in the previous step, and plot results\n",
    "map = sst_aus.mean('time').plot.pcolormesh(ax = ax, \n",
    "#We will use a colour map designed for temperature plots\n",
    "transform = ccrs.PlateCarree(), cmap = cm.cm.thermal,\n",
    "#We will remove the colourbar so we can personalise it\n",
    "add_colorbar = False)\n",
    "\n",
    "\n",
    "#Changing the colourbar - let's plot it horizontally\n",
    "cb = fig.colorbar(map, ax = ax, orientation = 'horizontal',\n",
    "#we will also reduce its size and decrease the space between the map and the bar\n",
    "shrink = 0.6, pad = 0.01)\n",
    "\n",
    "#Now we can change the color bar label\n",
    "cb.ax.set_xlabel('Sea surface temperature ($^\\circ$C)')\n",
    "\n",
    "#Now we add coastlines and the land layer\n",
    "ax.coastlines(resolution = '50m')\n",
    "ax.add_feature(land)\n",
    "\n",
    "#Finally we add a title\n",
    "ax.set_title('Climatological sea surface temperature mean (1964-2014)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we are interested in calculating yearly means or seasonal means, we can use the `groupby` function to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x2240c9be610>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculating annual SST means\n",
    "annual_sst = sst_aus.groupby('time.year').mean('time')\n",
    "\n",
    "#Let's check the results. We will plot only the first 10 years\n",
    "annual_sst[0:10].plot(col = 'year', col_wrap = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also summarise data to plot time series. Let's plot SST anomalies as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We will first calculate and save the climatological mean\n",
    "aus_clim = sst_aus.mean('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "\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",
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       "\n",
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       "\n",
       ".xr-var-dims {\n",
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       "}\n",
       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  width: auto;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\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",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tos&#x27; (year: 51, y: 58, x: 50)&gt;\n",
       "dask.array&lt;sub, shape=(51, 58, 50), dtype=float32, chunksize=(1, 58, 50), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    lat             (y, x) float32 dask.array&lt;chunksize=(58, 50), meta=np.ndarray&gt;\n",
       "    lon             (y, x) float32 dask.array&lt;chunksize=(58, 50), meta=np.ndarray&gt;\n",
       "    member_id       &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "    dcpp_init_year  float64 nan\n",
       "  * x               (x) float64 110.5 111.5 112.5 113.5 ... 157.5 158.5 159.5\n",
       "  * y               (y) float32 -49.41 -48.76 -48.09 ... -5.959 -5.545 -5.149\n",
       "  * year            (year) int64 1964 1965 1966 1967 ... 2011 2012 2013 2014</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'>'tos'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>year</span>: 51</li><li><span class='xr-has-index'>y</span>: 58</li><li><span class='xr-has-index'>x</span>: 50</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5b2ad427-5e83-44ff-96d4-8bfacd9f6c55' class='xr-array-in' type='checkbox' checked><label for='section-5b2ad427-5e83-44ff-96d4-8bfacd9f6c55' 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>dask.array&lt;chunksize=(1, 58, 50), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 577.73 kiB </td>\n",
       "                        <td> 11.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (51, 58, 50) </td>\n",
       "                        <td> (1, 58, 50) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 51 chunks in 165 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-ef022b25-8960-472e-8f60-8b08f96f102e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef022b25-8960-472e-8f60-8b08f96f102e' class='xr-section-summary' >Coordinates: <span>(7)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(58, 50), meta=np.ndarray&gt;</div><input id='attrs-df1ca186-76f7-482f-94cd-1fa0ea60cf16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df1ca186-76f7-482f-94cd-1fa0ea60cf16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b564d100-76ed-494f-9edc-d9ed66a7720c' class='xr-var-data-in' type='checkbox'><label for='data-b564d100-76ed-494f-9edc-d9ed66a7720c' 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>bounds_nav_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 11.33 kiB </td>\n",
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       "                        <td> (58, 50) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(58, 50), meta=np.ndarray&gt;</div><input id='attrs-b34a5a55-e755-44ac-a394-c9c1cb112b79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b34a5a55-e755-44ac-a394-c9c1cb112b79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2802d1b1-88bb-4fcd-9210-034363fda642' class='xr-var-data-in' type='checkbox'><label for='data-2802d1b1-88bb-4fcd-9210-034363fda642' 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>bounds_nav_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 11.33 kiB </td>\n",
       "                        <td> 11.33 kiB </td>\n",
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       "  <line x1=\"0\" y1=\"120\" x2=\"103\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"103\" y1=\"0\" x2=\"103\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 103.44827586206897,0.0 103.44827586206897,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"51.724138\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"123.448276\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,123.448276,60.000000)\">58</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-4a1b5bab-d00e-41f4-81d4-534a858c24dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a1b5bab-d00e-41f4-81d4-534a858c24dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b94f7cc-0d17-4c2c-8b8e-3e464fdfdf73' class='xr-var-data-in' type='checkbox'><label for='data-2b94f7cc-0d17-4c2c-8b8e-3e464fdfdf73' 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;r1i1p1f1&#x27;, dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dcpp_init_year</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan</div><input id='attrs-c1193596-3fec-48e1-a556-6471b6175000' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1193596-3fec-48e1-a556-6471b6175000' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b26d58-8642-4aaf-970a-fcb1c0697b52' class='xr-var-data-in' type='checkbox'><label for='data-70b26d58-8642-4aaf-970a-fcb1c0697b52' 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(nan)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>110.5 111.5 112.5 ... 158.5 159.5</div><input id='attrs-5afa8e0d-98f6-4e61-9ca2-ea8b541c781f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5afa8e0d-98f6-4e61-9ca2-ea8b541c781f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db17c0f3-2ece-4443-a99c-22f59c1d5891' class='xr-var-data-in' type='checkbox'><label for='data-db17c0f3-2ece-4443-a99c-22f59c1d5891' 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([110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5, 117.5, 118.5, 119.5,\n",
       "       120.5, 121.5, 122.5, 123.5, 124.5, 125.5, 126.5, 127.5, 128.5, 129.5,\n",
       "       130.5, 131.5, 132.5, 133.5, 134.5, 135.5, 136.5, 137.5, 138.5, 139.5,\n",
       "       140.5, 141.5, 142.5, 143.5, 144.5, 145.5, 146.5, 147.5, 148.5, 149.5,\n",
       "       150.5, 151.5, 152.5, 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-49.41 -48.76 ... -5.545 -5.149</div><input id='attrs-cd1c31ae-ddf3-4b10-a8bf-bb6c3bfeca88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd1c31ae-ddf3-4b10-a8bf-bb6c3bfeca88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94bbf9d4-eb7c-4616-88d5-69d0597519b6' class='xr-var-data-in' type='checkbox'><label for='data-94bbf9d4-eb7c-4616-88d5-69d0597519b6' 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([-49.41288 , -48.757957, -48.094387, -47.422142, -46.7412  , -46.051548,\n",
       "       -45.353176, -44.646076, -43.930252, -43.20571 , -42.472458, -41.73051 ,\n",
       "       -40.979897, -40.220642, -39.452785, -38.67636 , -37.891426, -37.09803 ,\n",
       "       -36.296238, -35.48612 , -34.66775 , -33.84122 , -33.006615, -32.16404 ,\n",
       "       -31.313599, -30.455408, -29.589594, -28.716284, -27.835623, -26.947754,\n",
       "       -26.052835, -25.151031, -24.242512, -23.327461, -22.406063, -21.478516,\n",
       "       -20.545023, -19.605793, -18.66211 , -17.715097, -16.767471, -15.823628,\n",
       "       -14.88963 , -13.972492, -13.079   , -12.214899, -11.385137, -10.594805,\n",
       "        -9.849413,  -9.153708,  -8.509959,  -7.917143,  -7.371412,  -6.867243,\n",
       "        -6.398548,  -5.959416,  -5.544503,  -5.14917 ], dtype=float32)</pre></div></li><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'>1964 1965 1966 ... 2012 2013 2014</div><input id='attrs-87ac8fb2-30b7-46c7-b600-29b22382344c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87ac8fb2-30b7-46c7-b600-29b22382344c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-908e6b8a-8ec9-4ecd-b854-5fa7a1591aed' class='xr-var-data-in' type='checkbox'><label for='data-908e6b8a-8ec9-4ecd-b854-5fa7a1591aed' 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([1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975,\n",
       "       1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,\n",
       "       1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,\n",
       "       2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,\n",
       "       2012, 2013, 2014], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e2b4491-abbc-4bc0-819e-17bdc4cbd3ec' class='xr-section-summary-in' type='checkbox'  ><label for='section-1e2b4491-abbc-4bc0-819e-17bdc4cbd3ec' class='xr-section-summary' >Indexes: <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-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b15e804f-ea0b-4de7-8b08-e9d19b7870ab' class='xr-index-data-in' type='checkbox'/><label for='index-b15e804f-ea0b-4de7-8b08-e9d19b7870ab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5, 117.5, 118.5,\n",
       "              119.5, 120.5, 121.5, 122.5, 123.5, 124.5, 125.5, 126.5, 127.5,\n",
       "              128.5, 129.5, 130.5, 131.5, 132.5, 133.5, 134.5, 135.5, 136.5,\n",
       "              137.5, 138.5, 139.5, 140.5, 141.5, 142.5, 143.5, 144.5, 145.5,\n",
       "              146.5, 147.5, 148.5, 149.5, 150.5, 151.5, 152.5, 153.5, 154.5,\n",
       "              155.5, 156.5, 157.5, 158.5, 159.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-79c43064-e546-4de2-9269-ed58127543d6' class='xr-index-data-in' type='checkbox'/><label for='index-79c43064-e546-4de2-9269-ed58127543d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-49.412879943847656, -48.757957458496094,  -48.09438705444336,\n",
       "              -47.422142028808594,   -46.7411994934082,  -46.05154800415039,\n",
       "               -45.35317611694336,  -44.64607620239258,  -43.93025207519531,\n",
       "              -43.205711364746094,  -42.47245788574219,  -41.73051071166992,\n",
       "              -40.979896545410156,  -40.22064208984375,  -39.45278549194336,\n",
       "              -38.676361083984375,  -37.89142608642578,  -37.09803009033203,\n",
       "               -36.29623794555664,  -35.48611831665039,  -34.66775131225586,\n",
       "               -33.84122085571289, -33.006614685058594, -32.164039611816406,\n",
       "                -31.3135986328125, -30.455408096313477,   -29.5895938873291,\n",
       "              -28.716283798217773, -27.835622787475586,     -26.94775390625,\n",
       "               -26.05283546447754, -25.151031494140625, -24.242511749267578,\n",
       "               -23.32746124267578, -22.406063079833984,       -21.478515625,\n",
       "               -20.54502296447754, -19.605792999267578,       -18.662109375,\n",
       "              -17.715097427368164, -16.767471313476562, -15.823628425598145,\n",
       "              -14.889630317687988, -13.972492218017578, -13.079000473022461,\n",
       "              -12.214899063110352, -11.385136604309082, -10.594804763793945,\n",
       "                -9.84941291809082,  -9.153707504272461,   -8.50995922088623,\n",
       "               -7.917142868041992, -7.3714118003845215,  -6.867242813110352,\n",
       "               -6.398547649383545,  -5.959415912628174,  -5.544503211975098,\n",
       "               -5.149170398712158],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c135681-0077-42ac-ab44-0dbe2c2f36ad' class='xr-index-data-in' type='checkbox'/><label for='index-3c135681-0077-42ac-ab44-0dbe2c2f36ad' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974,\n",
       "            1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "            1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "            1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
       "            2008, 2009, 2010, 2011, 2012, 2013, 2014],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5aa74688-d86a-4bc3-ba38-ddabee98aaf1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5aa74688-d86a-4bc3-ba38-ddabee98aaf1' 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.DataArray 'tos' (year: 51, y: 58, x: 50)>\n",
       "dask.array<sub, shape=(51, 58, 50), dtype=float32, chunksize=(1, 58, 50), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat             (y, x) float32 dask.array<chunksize=(58, 50), meta=np.ndarray>\n",
       "    lon             (y, x) float32 dask.array<chunksize=(58, 50), meta=np.ndarray>\n",
       "    member_id       <U8 'r1i1p1f1'\n",
       "    dcpp_init_year  float64 nan\n",
       "  * x               (x) float64 110.5 111.5 112.5 113.5 ... 157.5 158.5 159.5\n",
       "  * y               (y) float32 -49.41 -48.76 -48.09 ... -5.959 -5.545 -5.149\n",
       "  * year            (year) int64 1964 1965 1966 1967 ... 2011 2012 2013 2014"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can calculate the anomalies using the year means\n",
    "anom_yrs = annual_sst-aus_clim\n",
    "anom_yrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We initiliase our figure\n",
    "fig = plt.figure(figsize = (10, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#Before plotting, we calculate the mean over space \n",
    "anom_yrs.mean(('x', 'y')).plot()\n",
    "#Let's include a horizontal line along zero, so it is easier to see if anomalies are positive or negative\n",
    "plt.hlines(0, colors = 'red', xmin = anom_yrs.year.values.min(), \n",
    "xmax =  anom_yrs.year.values.max())\n",
    "\n",
    "#We add label names to axes\n",
    "ax.set_xlabel('Years')\n",
    "ax.set_ylabel('Annual average anomaly - SST ($^\\circ$C)')\n",
    "\n",
    "#We add a title to the figure\n",
    "ax.set_title('Annual average SST anomaly in Australian waters (1964-2014)')\n",
    "\n",
    "#If we like our plot, we can save the result\n",
    "folder_out = 'Figure'\n",
    "#We can check if a folder exists with the following line. If no folder exists, it will create one.\n",
    "os.makedirs(folder_out, exist_ok = True)\n",
    "\n",
    "fig.savefig(os.path.join(folder_out, 'AustraliaSSTanomYr_1964-2014.png'), dpi = 300)"
   ]
  }
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