EN422l09_Lee.ipynb

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    "import matplotlib.pyplot as plt  # plotting library\n",
    "import numpy as np  # numerical library\n",
    "import xarray as xr  # netCDF library\n",
    "import cartopy  # Map projections libary\n",
    "import cartopy.crs as ccrs  # Projections list\n",
    "import cftime\n",
    "# Some defaults:\n",
    "plt.rcParams['figure.figsize'] = (12, 5)  # Default plot size"
   ]
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    "data_dir='/Volumes/HD/Climatologias/EN.4.2.2.l09/NC/'"
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    "files = []\n",
    "for iy in range(1950,2024):\n",
    "    for im in range(1,13):\n",
    "        files.append(data_dir+\"EN.4.2.2.f.analysis.l09.%04d%02d.nc\"%(iy,im))"
   ]
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   "source": [
    "data = xr.open_mfdataset(files)"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 13GB\n",
       "Dimensions:                          (depth: 42, lat: 173, lon: 360, time: 120,\n",
       "                                      bnds: 2)\n",
       "Coordinates:\n",
       "  * depth                            (depth) float32 168B 5.022 ... 5.35e+03\n",
       "  * lat                              (lat) float32 692B -83.0 -82.0 ... 89.0\n",
       "  * lon                              (lon) float32 1kB 1.0 2.0 ... 359.0 360.0\n",
       "  * time                             (time) datetime64[ns] 960B 1950-01-16T12...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    temperature                      (time, depth, lat, lon) float64 3GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity                         (time, depth, lat, lon) float64 3GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    temperature_uncertainty          (time, depth, lat, lon) float64 3GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity_uncertainty             (time, depth, lat, lon) float64 3GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    temperature_observation_weights  (time, depth, lat, lon) float32 1GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity_observation_weights     (time, depth, lat, lon) float32 1GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    time_bnds                        (time, bnds) datetime64[ns] 2kB dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    depth_bnds                       (time, depth, bnds) float32 40kB dask.array&lt;chunksize=(1, 42, 2), meta=np.ndarray&gt;\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover...</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-1f2cc17a-afca-404d-880e-f92f847bd584' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f2cc17a-afca-404d-880e-f92f847bd584' 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'>depth</span>: 42</li><li><span class='xr-has-index'>lat</span>: 173</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>time</span>: 120</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7f35ab49-0bb3-43f4-994f-4c46c51989e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f35ab49-0bb3-43f4-994f-4c46c51989e8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.022 15.08 ... 5.051e+03 5.35e+03</div><input id='attrs-43f78d8d-3650-48a8-b9f9-a777584df03a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43f78d8d-3650-48a8-b9f9-a777584df03a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-755e6507-fd85-4939-b0b5-087f63ac8c39' class='xr-var-data-in' type='checkbox'><label for='data-755e6507-fd85-4939-b0b5-087f63ac8c39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd></dl></div><div class='xr-var-data'><pre>array([5.021590e+00, 1.507854e+01, 2.516046e+01, 3.527829e+01, 4.544776e+01,\n",
       "       5.569149e+01, 6.604198e+01, 7.654591e+01, 8.727029e+01, 9.831118e+01,\n",
       "       1.098062e+02, 1.219519e+02, 1.350285e+02, 1.494337e+02, 1.657285e+02,\n",
       "       1.846975e+02, 2.074254e+02, 2.353862e+02, 2.705341e+02, 3.153741e+02,\n",
       "       3.729655e+02, 4.468009e+02, 5.405022e+02, 6.573229e+02, 7.995496e+02,\n",
       "       9.679958e+02, 1.161806e+03, 1.378661e+03, 1.615291e+03, 1.868071e+03,\n",
       "       2.133517e+03, 2.408583e+03, 2.690780e+03, 2.978166e+03, 3.269278e+03,\n",
       "       3.563041e+03, 3.858676e+03, 4.155628e+03, 4.453502e+03, 4.752021e+03,\n",
       "       5.050990e+03, 5.350272e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-83.0 -82.0 -81.0 ... 88.0 89.0</div><input id='attrs-050077a4-23b7-40c5-b853-7f44619fc120' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-050077a4-23b7-40c5-b853-7f44619fc120' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d4e6492-66c2-4480-8dbe-d40e09ec2474' class='xr-var-data-in' type='checkbox'><label for='data-0d4e6492-66c2-4480-8dbe-d40e09ec2474' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-83., -82., -81., -80., -79., -78., -77., -76., -75., -74., -73., -72.,\n",
       "       -71., -70., -69., -68., -67., -66., -65., -64., -63., -62., -61., -60.,\n",
       "       -59., -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,\n",
       "       -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36.,\n",
       "       -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24.,\n",
       "       -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12.,\n",
       "       -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
       "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,\n",
       "        13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,\n",
       "        25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,\n",
       "        37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,\n",
       "        49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,\n",
       "        61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,\n",
       "        73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,\n",
       "        85.,  86.,  87.,  88.,  89.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-0cd41c4b-b0b1-4bb5-abc9-530f11895bca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cd41c4b-b0b1-4bb5-abc9-530f11895bca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbda908b-2860-4471-9803-cb4b7c0199b3' class='xr-var-data-in' type='checkbox'><label for='data-bbda908b-2860-4471-9803-cb4b7c0199b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></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'>1950-01-16T12:00:00 ... 1959-12-...</div><input id='attrs-eb4d0108-3af5-4c78-b204-020133949817' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb4d0108-3af5-4c78-b204-020133949817' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf950f16-392f-43e7-9c37-45e2bb5f142e' class='xr-var-data-in' type='checkbox'><label for='data-cf950f16-392f-43e7-9c37-45e2bb5f142e' 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>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-16T12:00:00.000000000&#x27;, &#x27;1950-02-15T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1950-11-16T00:00:00.000000000&#x27;, &#x27;1950-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1951-01-16T12:00:00.000000000&#x27;, &#x27;1951-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1951-03-16T12:00:00.000000000&#x27;, &#x27;1951-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1951-05-16T12:00:00.000000000&#x27;, &#x27;1951-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1951-07-16T12:00:00.000000000&#x27;, &#x27;1951-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1951-09-16T00:00:00.000000000&#x27;, &#x27;1951-10-16T12:00:00.000000000&#x27;,\n",
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       "       &#x27;1954-03-16T12:00:00.000000000&#x27;, &#x27;1954-04-16T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1954-07-16T12:00:00.000000000&#x27;, &#x27;1954-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1954-09-16T00:00:00.000000000&#x27;, &#x27;1954-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1954-11-16T00:00:00.000000000&#x27;, &#x27;1954-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1955-01-16T12:00:00.000000000&#x27;, &#x27;1955-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1955-03-16T12:00:00.000000000&#x27;, &#x27;1955-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1955-05-16T12:00:00.000000000&#x27;, &#x27;1955-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1955-07-16T12:00:00.000000000&#x27;, &#x27;1955-08-16T12:00:00.000000000&#x27;,\n",
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       "       &#x27;1956-01-16T12:00:00.000000000&#x27;, &#x27;1956-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1956-03-16T12:00:00.000000000&#x27;, &#x27;1956-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1956-05-16T12:00:00.000000000&#x27;, &#x27;1956-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1956-07-16T12:00:00.000000000&#x27;, &#x27;1956-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1956-09-16T00:00:00.000000000&#x27;, &#x27;1956-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1956-11-16T00:00:00.000000000&#x27;, &#x27;1956-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1957-01-16T12:00:00.000000000&#x27;, &#x27;1957-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1957-03-16T12:00:00.000000000&#x27;, &#x27;1957-04-16T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1958-07-16T12:00:00.000000000&#x27;, &#x27;1958-08-16T12:00:00.000000000&#x27;,\n",
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       "       &#x27;1959-01-16T12:00:00.000000000&#x27;, &#x27;1959-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1959-03-16T12:00:00.000000000&#x27;, &#x27;1959-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1959-05-16T12:00:00.000000000&#x27;, &#x27;1959-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1959-07-16T12:00:00.000000000&#x27;, &#x27;1959-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1959-09-16T00:00:00.000000000&#x27;, &#x27;1959-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1959-11-16T00:00:00.000000000&#x27;, &#x27;1959-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b97e98ba-aaf4-4e1c-b6e6-566072c89cd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b97e98ba-aaf4-4e1c-b6e6-566072c89cd8' class='xr-section-summary' >Data variables: <span>(8)</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>temperature</span></div><div class='xr-var-dims'>(time, depth, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;</div><input id='attrs-dfd19318-4892-4327-8888-593c7bdf20a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfd19318-4892-4327-8888-593c7bdf20a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-327c14df-eeb9-4183-9977-f2b316281826' class='xr-var-data-in' type='checkbox'><label for='data-327c14df-eeb9-4183-9977-f2b316281826' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-5.0</dd><dt><span>valid_max :</span></dt><dd>45.0</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> 2.34 GiB </td>\n",
       "                        <td> 2.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (120, 42, 173, 360) </td>\n",
       "                        <td> (1, 21, 87, 180) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 960 chunks in 241 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                        <td> 1.25 MiB </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0362e2f5-2978-4d96-a228-1b2fe6b522a8' class='xr-section-summary-in' type='checkbox'  ><label for='section-0362e2f5-2978-4d96-a228-1b2fe6b522a8' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b680f9f-d30a-4f4e-8d1e-56b0b43c8b57' class='xr-index-data-in' type='checkbox'/><label for='index-9b680f9f-d30a-4f4e-8d1e-56b0b43c8b57' 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([ 5.021589756011963, 15.078539848327637, 25.160459518432617,\n",
       "       35.278289794921875,  45.44776153564453,  55.69149398803711,\n",
       "        66.04198455810547,  76.54591369628906,   87.2702865600586,\n",
       "         98.3111801147461,  109.8061752319336, 121.95189666748047,\n",
       "       135.02854919433594, 149.43373107910156, 165.72845458984375,\n",
       "        184.6974639892578, 207.42544555664062, 235.38616943359375,\n",
       "       270.53411865234375, 315.37408447265625,  372.9654541015625,\n",
       "        446.8009338378906,   540.502197265625,  657.3229370117188,\n",
       "        799.5496215820312,   967.995849609375,  1161.805908203125,\n",
       "       1378.6610107421875,   1615.29052734375, 1868.0706787109375,\n",
       "         2133.51708984375,   2408.58349609375,    2690.7802734375,\n",
       "           2978.166015625,  3269.278076171875,  3563.040771484375,\n",
       "         3858.67626953125,    4155.6279296875,     4453.501953125,\n",
       "         4752.02099609375,   5050.98974609375,   5350.27197265625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d1cf46b3-8108-4e56-a0ff-a3c650593625' class='xr-index-data-in' type='checkbox'/><label for='index-d1cf46b3-8108-4e56-a0ff-a3c650593625' 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([-83.0, -82.0, -81.0, -80.0, -79.0, -78.0, -77.0, -76.0, -75.0, -74.0,\n",
       "       ...\n",
       "        80.0,  81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=173))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-46a6709d-58fd-4da2-b628-e0662b0b4b1a' class='xr-index-data-in' type='checkbox'/><label for='index-46a6709d-58fd-4da2-b628-e0662b0b4b1a' 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([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,  10.0,\n",
       "       ...\n",
       "       351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0, 360.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><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-9f08e6db-71f0-4217-b4d3-d8fb9a5634e0' class='xr-index-data-in' type='checkbox'/><label for='index-9f08e6db-71f0-4217-b4d3-d8fb9a5634e0' 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;1950-01-16 12:00:00&#x27;, &#x27;1950-02-15 00:00:00&#x27;,\n",
       "               &#x27;1950-03-16 12:00:00&#x27;, &#x27;1950-04-16 00:00:00&#x27;,\n",
       "               &#x27;1950-05-16 12:00:00&#x27;, &#x27;1950-06-16 00:00:00&#x27;,\n",
       "               &#x27;1950-07-16 12:00:00&#x27;, &#x27;1950-08-16 12:00:00&#x27;,\n",
       "               &#x27;1950-09-16 00:00:00&#x27;, &#x27;1950-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;1959-03-16 12:00:00&#x27;, &#x27;1959-04-16 00:00:00&#x27;,\n",
       "               &#x27;1959-05-16 12:00:00&#x27;, &#x27;1959-06-16 00:00:00&#x27;,\n",
       "               &#x27;1959-07-16 12:00:00&#x27;, &#x27;1959-08-16 12:00:00&#x27;,\n",
       "               &#x27;1959-09-16 00:00:00&#x27;, &#x27;1959-10-16 12:00:00&#x27;,\n",
       "               &#x27;1959-11-16 00:00:00&#x27;, &#x27;1959-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=120, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1d6e606-6a9a-42d8-8692-1ebd643d0a4d' class='xr-section-summary-in' type='checkbox'  ><label for='section-d1d6e606-6a9a-42d8-8692-1ebd643d0a4d' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>title :</span></dt><dd>Temperature and salinity analysis</dd><dt><span>DSD_entry_id :</span></dt><dd>UKMO-L4UHFnd-GLOB-v01</dd><dt><span>references :</span></dt><dd>Website and paper: https://www.metoffice.gov.uk/hadobs/en4/; Good, S. A., M. J. Martin and N. A. Rayner, 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Journal of Geophysical Research: Oceans, 118, 6704-6716, doi:10.1002/2013JC009067</dd><dt><span>institution :</span></dt><dd>UK Met Office</dd><dt><span>contact :</span></dt><dd>Rachel Killick - rachel.killick@metoffice.gov.uk</dd><dt><span>GDS_version_id :</span></dt><dd>v1.7</dd><dt><span>netcdf_version_id :</span></dt><dd>3.5</dd><dt><span>creation_date :</span></dt><dd>2021-03-16 15:08:41.466 -00:00</dd><dt><span>product_version :</span></dt><dd>1.0</dd><dt><span>history :</span></dt><dd></dd><dt><span>grid_resolution :</span></dt><dd>   1.00000 degree</dd><dt><span>start_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>start_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>stop_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>stop_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.5</dd><dt><span>northernmost_latitude :</span></dt><dd>89.5</dd><dt><span>westernmost_longitude :</span></dt><dd>0.5</dd><dt><span>easternmost_longitude :</span></dt><dd>362.5</dd><dt><span>file_quality_index :</span></dt><dd>0</dd><dt><span>licence :</span></dt><dd>EN4 is distributed under the Non Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/. The data are available for non-commercial use with attribution to the data providers, please see the product website (see global attribute: references) for terms and conditions.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 13GB\n",
       "Dimensions:                          (depth: 42, lat: 173, lon: 360, time: 120,\n",
       "                                      bnds: 2)\n",
       "Coordinates:\n",
       "  * depth                            (depth) float32 168B 5.022 ... 5.35e+03\n",
       "  * lat                              (lat) float32 692B -83.0 -82.0 ... 89.0\n",
       "  * lon                              (lon) float32 1kB 1.0 2.0 ... 359.0 360.0\n",
       "  * time                             (time) datetime64[ns] 960B 1950-01-16T12...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    temperature                      (time, depth, lat, lon) float64 3GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity                         (time, depth, lat, lon) float64 3GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    temperature_uncertainty          (time, depth, lat, lon) float64 3GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity_uncertainty             (time, depth, lat, lon) float64 3GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    temperature_observation_weights  (time, depth, lat, lon) float32 1GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity_observation_weights     (time, depth, lat, lon) float32 1GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    time_bnds                        (time, bnds) datetime64[ns] 2kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    depth_bnds                       (time, depth, bnds) float32 40kB dask.array<chunksize=(1, 42, 2), meta=np.ndarray>\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3a7c54c-f42b-42c1-afbe-4acd4387b213",
   "metadata": {},
   "outputs": [],
   "source": [
    "EN = data.drop_vars(\"temperature_uncertainty\").drop_vars(\"salinity_uncertainty\")\n",
    "EN = EN.drop_vars(\"time_bnds\").drop_vars(\"depth_bnds\")\n",
    "EN = EN.drop_vars(\"temperature_observation_weights\").drop_vars(\"salinity_observation_weights\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8ed626f-6a35-4ce0-9e5b-67f2621951d3",
   "metadata": {},
   "outputs": [
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       "  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",
       "}\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",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5GB\n",
       "Dimensions:      (depth: 42, lat: 173, lon: 360, time: 120)\n",
       "Coordinates:\n",
       "  * depth        (depth) float32 168B 5.022 15.08 25.16 ... 5.051e+03 5.35e+03\n",
       "  * lat          (lat) float32 692B -83.0 -82.0 -81.0 -80.0 ... 87.0 88.0 89.0\n",
       "  * lon          (lon) float32 1kB 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time         (time) datetime64[ns] 960B 1950-01-16T12:00:00 ... 1959-12-1...\n",
       "Data variables:\n",
       "    temperature  (time, depth, lat, lon) float64 3GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity     (time, depth, lat, lon) float64 3GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover...</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-e06cf354-3d66-4be4-af38-0d988a4ea8bb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e06cf354-3d66-4be4-af38-0d988a4ea8bb' 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'>depth</span>: 42</li><li><span class='xr-has-index'>lat</span>: 173</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>time</span>: 120</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-75b00050-2a48-4b16-8773-88ac24d6171a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75b00050-2a48-4b16-8773-88ac24d6171a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.022 15.08 ... 5.051e+03 5.35e+03</div><input id='attrs-fc49130a-2453-481a-b055-983d9248d01a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc49130a-2453-481a-b055-983d9248d01a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4552d0c0-3e98-4bf7-a860-66acf5073e89' class='xr-var-data-in' type='checkbox'><label for='data-4552d0c0-3e98-4bf7-a860-66acf5073e89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd></dl></div><div class='xr-var-data'><pre>array([5.021590e+00, 1.507854e+01, 2.516046e+01, 3.527829e+01, 4.544776e+01,\n",
       "       5.569149e+01, 6.604198e+01, 7.654591e+01, 8.727029e+01, 9.831118e+01,\n",
       "       1.098062e+02, 1.219519e+02, 1.350285e+02, 1.494337e+02, 1.657285e+02,\n",
       "       1.846975e+02, 2.074254e+02, 2.353862e+02, 2.705341e+02, 3.153741e+02,\n",
       "       3.729655e+02, 4.468009e+02, 5.405022e+02, 6.573229e+02, 7.995496e+02,\n",
       "       9.679958e+02, 1.161806e+03, 1.378661e+03, 1.615291e+03, 1.868071e+03,\n",
       "       2.133517e+03, 2.408583e+03, 2.690780e+03, 2.978166e+03, 3.269278e+03,\n",
       "       3.563041e+03, 3.858676e+03, 4.155628e+03, 4.453502e+03, 4.752021e+03,\n",
       "       5.050990e+03, 5.350272e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-83.0 -82.0 -81.0 ... 88.0 89.0</div><input id='attrs-a9aba040-bd58-4dff-9070-abc2337d9f03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9aba040-bd58-4dff-9070-abc2337d9f03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77123de5-136a-4e6c-a1da-fb3f3b123563' class='xr-var-data-in' type='checkbox'><label for='data-77123de5-136a-4e6c-a1da-fb3f3b123563' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-83., -82., -81., -80., -79., -78., -77., -76., -75., -74., -73., -72.,\n",
       "       -71., -70., -69., -68., -67., -66., -65., -64., -63., -62., -61., -60.,\n",
       "       -59., -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,\n",
       "       -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36.,\n",
       "       -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24.,\n",
       "       -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12.,\n",
       "       -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
       "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,\n",
       "        13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,\n",
       "        25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,\n",
       "        37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,\n",
       "        49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,\n",
       "        61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,\n",
       "        73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,\n",
       "        85.,  86.,  87.,  88.,  89.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-120030fa-ed63-4ba1-9086-aa44114a85f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-120030fa-ed63-4ba1-9086-aa44114a85f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e8b8f45-7748-4ec3-85f9-49952cb6cc70' class='xr-var-data-in' type='checkbox'><label for='data-5e8b8f45-7748-4ec3-85f9-49952cb6cc70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></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'>1950-01-16T12:00:00 ... 1959-12-...</div><input id='attrs-8c6fe424-deb6-47d7-bba5-dc2197761c45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c6fe424-deb6-47d7-bba5-dc2197761c45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa24f6b2-1355-4b33-878d-6afae07cd5f4' class='xr-var-data-in' type='checkbox'><label for='data-fa24f6b2-1355-4b33-878d-6afae07cd5f4' 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>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-16T12:00:00.000000000&#x27;, &#x27;1950-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1950-03-16T12:00:00.000000000&#x27;, &#x27;1950-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1950-05-16T12:00:00.000000000&#x27;, &#x27;1950-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1950-07-16T12:00:00.000000000&#x27;, &#x27;1950-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1950-09-16T00:00:00.000000000&#x27;, &#x27;1950-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1950-11-16T00:00:00.000000000&#x27;, &#x27;1950-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1951-01-16T12:00:00.000000000&#x27;, &#x27;1951-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1951-03-16T12:00:00.000000000&#x27;, &#x27;1951-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1951-05-16T12:00:00.000000000&#x27;, &#x27;1951-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1951-07-16T12:00:00.000000000&#x27;, &#x27;1951-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1951-09-16T00:00:00.000000000&#x27;, &#x27;1951-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1951-11-16T00:00:00.000000000&#x27;, &#x27;1951-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1952-01-16T12:00:00.000000000&#x27;, &#x27;1952-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1952-03-16T12:00:00.000000000&#x27;, &#x27;1952-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1952-05-16T12:00:00.000000000&#x27;, &#x27;1952-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1952-07-16T12:00:00.000000000&#x27;, &#x27;1952-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1952-09-16T00:00:00.000000000&#x27;, &#x27;1952-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1952-11-16T00:00:00.000000000&#x27;, &#x27;1952-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1953-01-16T12:00:00.000000000&#x27;, &#x27;1953-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1953-03-16T12:00:00.000000000&#x27;, &#x27;1953-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1953-05-16T12:00:00.000000000&#x27;, &#x27;1953-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1953-07-16T12:00:00.000000000&#x27;, &#x27;1953-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1953-09-16T00:00:00.000000000&#x27;, &#x27;1953-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1953-11-16T00:00:00.000000000&#x27;, &#x27;1953-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1954-01-16T12:00:00.000000000&#x27;, &#x27;1954-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1954-03-16T12:00:00.000000000&#x27;, &#x27;1954-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1954-05-16T12:00:00.000000000&#x27;, &#x27;1954-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1954-07-16T12:00:00.000000000&#x27;, &#x27;1954-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1954-09-16T00:00:00.000000000&#x27;, &#x27;1954-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1954-11-16T00:00:00.000000000&#x27;, &#x27;1954-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1955-01-16T12:00:00.000000000&#x27;, &#x27;1955-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1955-03-16T12:00:00.000000000&#x27;, &#x27;1955-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1955-05-16T12:00:00.000000000&#x27;, &#x27;1955-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1955-07-16T12:00:00.000000000&#x27;, &#x27;1955-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1955-09-16T00:00:00.000000000&#x27;, &#x27;1955-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1955-11-16T00:00:00.000000000&#x27;, &#x27;1955-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1956-01-16T12:00:00.000000000&#x27;, &#x27;1956-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1956-03-16T12:00:00.000000000&#x27;, &#x27;1956-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1956-05-16T12:00:00.000000000&#x27;, &#x27;1956-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1956-07-16T12:00:00.000000000&#x27;, &#x27;1956-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1956-09-16T00:00:00.000000000&#x27;, &#x27;1956-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1956-11-16T00:00:00.000000000&#x27;, &#x27;1956-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1957-01-16T12:00:00.000000000&#x27;, &#x27;1957-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1957-03-16T12:00:00.000000000&#x27;, &#x27;1957-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1957-05-16T12:00:00.000000000&#x27;, &#x27;1957-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1957-07-16T12:00:00.000000000&#x27;, &#x27;1957-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1957-09-16T00:00:00.000000000&#x27;, &#x27;1957-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1957-11-16T00:00:00.000000000&#x27;, &#x27;1957-12-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1958-01-16T12:00:00.000000000&#x27;, &#x27;1958-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-03-16T12:00:00.000000000&#x27;, &#x27;1958-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-05-16T12:00:00.000000000&#x27;, &#x27;1958-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-07-16T12:00:00.000000000&#x27;, &#x27;1958-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1958-09-16T00:00:00.000000000&#x27;, &#x27;1958-10-16T12:00:00.000000000&#x27;,\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-084d897c-b823-416a-9308-f6b8d5803b34' class='xr-section-summary-in' type='checkbox'  ><label for='section-084d897c-b823-416a-9308-f6b8d5803b34' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-18cdd97a-ac6b-43aa-b16a-9b44c3d6f0f2' class='xr-index-data-in' type='checkbox'/><label for='index-18cdd97a-ac6b-43aa-b16a-9b44c3d6f0f2' 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([ 5.021589756011963, 15.078539848327637, 25.160459518432617,\n",
       "       35.278289794921875,  45.44776153564453,  55.69149398803711,\n",
       "        66.04198455810547,  76.54591369628906,   87.2702865600586,\n",
       "         98.3111801147461,  109.8061752319336, 121.95189666748047,\n",
       "       135.02854919433594, 149.43373107910156, 165.72845458984375,\n",
       "        184.6974639892578, 207.42544555664062, 235.38616943359375,\n",
       "       270.53411865234375, 315.37408447265625,  372.9654541015625,\n",
       "        446.8009338378906,   540.502197265625,  657.3229370117188,\n",
       "        799.5496215820312,   967.995849609375,  1161.805908203125,\n",
       "       1378.6610107421875,   1615.29052734375, 1868.0706787109375,\n",
       "         2133.51708984375,   2408.58349609375,    2690.7802734375,\n",
       "           2978.166015625,  3269.278076171875,  3563.040771484375,\n",
       "         3858.67626953125,    4155.6279296875,     4453.501953125,\n",
       "         4752.02099609375,   5050.98974609375,   5350.27197265625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-334a6e0a-422b-4437-a1c0-e25cf4cf6b33' class='xr-index-data-in' type='checkbox'/><label for='index-334a6e0a-422b-4437-a1c0-e25cf4cf6b33' 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([-83.0, -82.0, -81.0, -80.0, -79.0, -78.0, -77.0, -76.0, -75.0, -74.0,\n",
       "       ...\n",
       "        80.0,  81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=173))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fa2f9ebb-adc6-4c48-91ad-8532bef48291' class='xr-index-data-in' type='checkbox'/><label for='index-fa2f9ebb-adc6-4c48-91ad-8532bef48291' 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([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,  10.0,\n",
       "       ...\n",
       "       351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0, 360.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><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-0b814965-bc7a-4ed7-9992-5dbc0d16c7be' class='xr-index-data-in' type='checkbox'/><label for='index-0b814965-bc7a-4ed7-9992-5dbc0d16c7be' 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;1950-01-16 12:00:00&#x27;, &#x27;1950-02-15 00:00:00&#x27;,\n",
       "               &#x27;1950-03-16 12:00:00&#x27;, &#x27;1950-04-16 00:00:00&#x27;,\n",
       "               &#x27;1950-05-16 12:00:00&#x27;, &#x27;1950-06-16 00:00:00&#x27;,\n",
       "               &#x27;1950-07-16 12:00:00&#x27;, &#x27;1950-08-16 12:00:00&#x27;,\n",
       "               &#x27;1950-09-16 00:00:00&#x27;, &#x27;1950-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;1959-03-16 12:00:00&#x27;, &#x27;1959-04-16 00:00:00&#x27;,\n",
       "               &#x27;1959-05-16 12:00:00&#x27;, &#x27;1959-06-16 00:00:00&#x27;,\n",
       "               &#x27;1959-07-16 12:00:00&#x27;, &#x27;1959-08-16 12:00:00&#x27;,\n",
       "               &#x27;1959-09-16 00:00:00&#x27;, &#x27;1959-10-16 12:00:00&#x27;,\n",
       "               &#x27;1959-11-16 00:00:00&#x27;, &#x27;1959-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=120, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ebdb801c-4ecc-4b2d-be57-f75a317770f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-ebdb801c-4ecc-4b2d-be57-f75a317770f3' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>title :</span></dt><dd>Temperature and salinity analysis</dd><dt><span>DSD_entry_id :</span></dt><dd>UKMO-L4UHFnd-GLOB-v01</dd><dt><span>references :</span></dt><dd>Website and paper: https://www.metoffice.gov.uk/hadobs/en4/; Good, S. A., M. J. Martin and N. A. Rayner, 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Journal of Geophysical Research: Oceans, 118, 6704-6716, doi:10.1002/2013JC009067</dd><dt><span>institution :</span></dt><dd>UK Met Office</dd><dt><span>contact :</span></dt><dd>Rachel Killick - rachel.killick@metoffice.gov.uk</dd><dt><span>GDS_version_id :</span></dt><dd>v1.7</dd><dt><span>netcdf_version_id :</span></dt><dd>3.5</dd><dt><span>creation_date :</span></dt><dd>2021-03-16 15:08:41.466 -00:00</dd><dt><span>product_version :</span></dt><dd>1.0</dd><dt><span>history :</span></dt><dd></dd><dt><span>grid_resolution :</span></dt><dd>   1.00000 degree</dd><dt><span>start_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>start_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>stop_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>stop_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.5</dd><dt><span>northernmost_latitude :</span></dt><dd>89.5</dd><dt><span>westernmost_longitude :</span></dt><dd>0.5</dd><dt><span>easternmost_longitude :</span></dt><dd>362.5</dd><dt><span>file_quality_index :</span></dt><dd>0</dd><dt><span>licence :</span></dt><dd>EN4 is distributed under the Non Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/. The data are available for non-commercial use with attribution to the data providers, please see the product website (see global attribute: references) for terms and conditions.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 5GB\n",
       "Dimensions:      (depth: 42, lat: 173, lon: 360, time: 120)\n",
       "Coordinates:\n",
       "  * depth        (depth) float32 168B 5.022 15.08 25.16 ... 5.051e+03 5.35e+03\n",
       "  * lat          (lat) float32 692B -83.0 -82.0 -81.0 -80.0 ... 87.0 88.0 89.0\n",
       "  * lon          (lon) float32 1kB 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time         (time) datetime64[ns] 960B 1950-01-16T12:00:00 ... 1959-12-1...\n",
       "Data variables:\n",
       "    temperature  (time, depth, lat, lon) float64 3GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity     (time, depth, lat, lon) float64 3GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb61952d-c3e6-4db2-8441-ae597c9dcd16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1 , 1 , figsize = (14,8))\n",
    "ax.plot(EN.time,\n",
    "        EN.temperature.sel(lat=slice( -60, 60)).mean(dim='lat').mean(dim='lon').sel(depth=200,method=\"nearest\"),\n",
    "        label = '200')\n",
    "ax.grid(linestyle='-', linewidth=.9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb98ffa5-2a44-49a0-a32d-f76201d8205e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mEN\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEN_Global_Jan1950_Dic2023.nc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/xarray/core/dataset.py:2298\u001b[0m, in \u001b[0;36mDataset.to_netcdf\u001b[0;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)\u001b[0m\n\u001b[1;32m   2295\u001b[0m     encoding \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m   2296\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_netcdf\n\u001b[0;32m-> 2298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore  # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[1;32m   2299\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2302\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2303\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2304\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2306\u001b[0m \u001b[43m    \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2307\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2308\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmultifile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   2309\u001b[0m \u001b[43m    \u001b[49m\u001b[43minvalid_netcdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minvalid_netcdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2310\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/xarray/backends/api.py:1348\u001b[0m, in \u001b[0;36mto_netcdf\u001b[0;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)\u001b[0m\n\u001b[1;32m   1345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m multifile:\n\u001b[1;32m   1346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m writer, store\n\u001b[0;32m-> 1348\u001b[0m writes \u001b[38;5;241m=\u001b[39m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(target, BytesIO):\n\u001b[1;32m   1351\u001b[0m     store\u001b[38;5;241m.\u001b[39msync()\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/xarray/backends/common.py:297\u001b[0m, in \u001b[0;36mArrayWriter.sync\u001b[0;34m(self, compute, chunkmanager_store_kwargs)\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunkmanager_store_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    295\u001b[0m     chunkmanager_store_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 297\u001b[0m delayed_store \u001b[38;5;241m=\u001b[39m \u001b[43mchunkmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    302\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflush\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    303\u001b[0m \u001b[43m    \u001b[49m\u001b[43mregions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mchunkmanager_store_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    306\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msources \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    307\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtargets \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/xarray/namedarray/daskmanager.py:249\u001b[0m, in \u001b[0;36mDaskManager.store\u001b[0;34m(self, sources, targets, **kwargs)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstore\u001b[39m(\n\u001b[1;32m    242\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    243\u001b[0m     sources: Any \u001b[38;5;241m|\u001b[39m Sequence[Any],\n\u001b[1;32m    244\u001b[0m     targets: Any,\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    246\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    247\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m store\n\u001b[0;32m--> 249\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43msources\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtargets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/array/core.py:1236\u001b[0m, in \u001b[0;36mstore\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   1234\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m compute:\n\u001b[1;32m   1235\u001b[0m     store_dsk \u001b[38;5;241m=\u001b[39m HighLevelGraph(layers, dependencies)\n\u001b[0;32m-> 1236\u001b[0m     \u001b[43mcompute_as_if_collection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mArray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore_dsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1237\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/base.py:402\u001b[0m, in \u001b[0;36mcompute_as_if_collection\u001b[0;34m(cls, dsk, keys, scheduler, get, **kwargs)\u001b[0m\n\u001b[1;32m    400\u001b[0m schedule \u001b[38;5;241m=\u001b[39m get_scheduler(scheduler\u001b[38;5;241m=\u001b[39mscheduler, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mcls\u001b[39m, get\u001b[38;5;241m=\u001b[39mget)\n\u001b[1;32m    401\u001b[0m dsk2 \u001b[38;5;241m=\u001b[39m optimization_function(\u001b[38;5;28mcls\u001b[39m)(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/threaded.py:90\u001b[0m, in \u001b[0;36mget\u001b[0;34m(dsk, keys, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pool, multiprocessing\u001b[38;5;241m.\u001b[39mpool\u001b[38;5;241m.\u001b[39mPool):\n\u001b[1;32m     88\u001b[0m         pool \u001b[38;5;241m=\u001b[39m MultiprocessingPoolExecutor(pool)\n\u001b[0;32m---> 90\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mget_async\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_max_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[43m    \u001b[49m\u001b[43mget_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_thread_get_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpack_exception\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpack_exception\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     98\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     99\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;66;03m# Cleanup pools associated to dead threads\u001b[39;00m\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pools_lock:\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/local.py:501\u001b[0m, in \u001b[0;36mget_async\u001b[0;34m(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwaiting\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mor\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mready\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mor\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrunning\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    500\u001b[0m     fire_tasks(chunksize)\n\u001b[0;32m--> 501\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m key, res_info, failed \u001b[38;5;129;01min\u001b[39;00m \u001b[43mqueue_get\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqueue\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mresult():\n\u001b[1;32m    502\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m failed:\n\u001b[1;32m    503\u001b[0m             exc, tb \u001b[38;5;241m=\u001b[39m loads(res_info)\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/local.py:138\u001b[0m, in \u001b[0;36mqueue_get\u001b[0;34m(q)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mqueue_get\u001b[39m(q):\n\u001b[0;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/queue.py:171\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    170\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qsize():\n\u001b[0;32m--> 171\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnot_empty\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be a non-negative number\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/threading.py:327\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:    \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    326\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 327\u001b[0m         \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    328\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    329\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "EN.to_netcdf('EN_Global_Jan1950_Dic2023.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38183c7e-2e8b-4f00-b81b-428646711294",
   "metadata": {},
   "outputs": [],
   "source": [
    "EN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4594277d-f81f-4162-bd64-36a871472b5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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