bd_pr_remo-regcm-noresm.ipynb

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    "#writing some conda package or module\n",
    "#!pip install netCDF4\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "from netCDF4 import Dataset\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "#!pip install cartopy\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.mpl.ticker as cticker\n",
    "from cartopy.util import add_cyclic_point\n"
   ]
  },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                     (time: 9131, bnds: 2, rlat: 255, rlon: 385,\n",
       "                                 vertices: 4)\n",
       "Coordinates:\n",
       "  * time                        (time) datetime64[ns] 1981-01-01T12:00:00 ......\n",
       "  * rlat                        (rlat) float64 -21.23 -21.01 ... 34.43 34.65\n",
       "  * rlon                        (rlon) float64 -32.23 -32.01 ... 52.03 52.25\n",
       "    lat                         (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;\n",
       "    lon                         (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    time_bnds                   (time, bnds) datetime64[ns] dask.array&lt;chunksize=(1826, 2), meta=np.ndarray&gt;\n",
       "    rotated_latitude_longitude  (time) int32 -2147483647 ... -2147483647\n",
       "    lat_vertices                (time, rlat, rlon, vertices) float32 dask.array&lt;chunksize=(1826, 255, 385, 4), meta=np.ndarray&gt;\n",
       "    lon_vertices                (time, rlat, rlon, vertices) float32 dask.array&lt;chunksize=(1826, 255, 385, 4), meta=np.ndarray&gt;\n",
       "    pr                          (time, rlat, rlon) float32 dask.array&lt;chunksize=(1826, 255, 385), meta=np.ndarray&gt;\n",
       "Attributes: (12/34)\n",
       "    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...\n",
       "    institute_id:                   GERICS\n",
       "    experiment_id:                  historical\n",
       "    source:                         GERICS-REMO2015\n",
       "    model_id:                       GERICS-REMO2015\n",
       "    forcing:                        N/A\n",
       "    ...                             ...\n",
       "    title:                          GERICS-REMO2015 model output prepared for...\n",
       "    parent_experiment:              N/A\n",
       "    modeling_realm:                 atmos\n",
       "    realization:                    1\n",
       "    cmor_version:                   2.9.1\n",
       "    tracking_id:                    hdl:21.14103/a5bf17ab-c247-4be0-a27e-06f8...</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-11381eb4-9afc-45e1-b482-effbd21b032b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-11381eb4-9afc-45e1-b482-effbd21b032b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 9131</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>rlat</span>: 255</li><li><span class='xr-has-index'>rlon</span>: 385</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-770b5654-8f82-4ade-b8f8-870ce2b1bf4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-770b5654-8f82-4ade-b8f8-870ce2b1bf4c' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1981-01-01T12:00:00 ... 2005-12-...</div><input id='attrs-2bde27bc-76be-4816-9317-35c2a221f3b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bde27bc-76be-4816-9317-35c2a221f3b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c6b470d-2b9a-4599-829c-6976954b1e46' class='xr-var-data-in' type='checkbox'><label for='data-1c6b470d-2b9a-4599-829c-6976954b1e46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-01-01T12:00:00.000000000&#x27;, &#x27;1981-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1981-01-03T12:00:00.000000000&#x27;, ..., &#x27;2005-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-12-30T12:00:00.000000000&#x27;, &#x27;2005-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlat</span></div><div class='xr-var-dims'>(rlat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-21.23 -21.01 ... 34.43 34.65</div><input id='attrs-a87304ea-a49b-48ee-9d01-a30ca232ddce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a87304ea-a49b-48ee-9d01-a30ca232ddce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8381f26-1dbf-4fef-ac63-65d83ab6ade8' class='xr-var-data-in' type='checkbox'><label for='data-e8381f26-1dbf-4fef-ac63-65d83ab6ade8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude in rotated pole grid</dd><dt><span>standard_name :</span></dt><dd>grid_latitude</dd></dl></div><div class='xr-var-data'><pre>array([-21.23, -21.01, -20.79, ...,  34.21,  34.43,  34.65])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlon</span></div><div class='xr-var-dims'>(rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.23 -32.01 ... 52.03 52.25</div><input id='attrs-5e277247-9cef-48a9-aa1f-6746c99eb3e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e277247-9cef-48a9-aa1f-6746c99eb3e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36f2ba07-6615-496f-a6d6-37df0c109cb3' class='xr-var-data-in' type='checkbox'><label for='data-36f2ba07-6615-496f-a6d6-37df0c109cb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude in rotated pole grid</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-32.23, -32.01, -31.79, ...,  51.81,  52.03,  52.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</div><input id='attrs-4013720a-4da1-4f21-bb27-87a91e51495b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4013720a-4da1-4f21-bb27-87a91e51495b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72dad490-d51d-4965-aebb-6e41fa5f2d32' class='xr-var-data-in' type='checkbox'><label for='data-72dad490-d51d-4965-aebb-6e41fa5f2d32' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>lat_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 383.50 kiB </td>\n",
       "                        <td> 383.50 kiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (255, 385) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 20 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\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'>(rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</div><input id='attrs-a9e6476f-0278-47d4-91b5-cd772e91e06b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9e6476f-0278-47d4-91b5-cd772e91e06b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c69ebea-ad02-427e-a304-db24ec69ee8b' class='xr-var-data-in' type='checkbox'><label for='data-3c69ebea-ad02-427e-a304-db24ec69ee8b' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>lon_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (255, 385) </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e6a18fc-ca33-4562-bf1e-0097d9c1be40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e6a18fc-ca33-4562-bf1e-0097d9c1be40' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</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=(1826, 2), meta=np.ndarray&gt;</div><input id='attrs-12abe126-87fb-4454-a7fa-d23ea799b3dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12abe126-87fb-4454-a7fa-d23ea799b3dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83c8fbe1-527d-4481-b1e9-7d08921ddfbf' class='xr-var-data-in' type='checkbox'><label for='data-83c8fbe1-527d-4481-b1e9-7d08921ddfbf' 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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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 142.67 kiB </td>\n",
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       "                        <th> Count </th>\n",
       "                        <td> 15 Tasks </td>\n",
       "                        <td> 5 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rotated_latitude_longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647 ... -2147483647</div><input id='attrs-c4666516-0302-4130-b2ce-7e478953cede' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4666516-0302-4130-b2ce-7e478953cede' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0efccef8-c9dc-4ac1-b74b-322c66ec1bf6' class='xr-var-data-in' type='checkbox'><label for='data-0efccef8-c9dc-4ac1-b74b-322c66ec1bf6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>rotated_latitude_longitude</dd><dt><span>grid_north_pole_latitude :</span></dt><dd>79.95</dd><dt><span>grid_north_pole_longitude :</span></dt><dd>-123.34</dd><dt><span>north_pole_grid_longitude :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
       "       -2147483647, -2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_vertices</span></div><div class='xr-var-dims'>(time, rlat, rlon, vertices)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1826, 255, 385, 4), meta=np.ndarray&gt;</div><input id='attrs-1d22fa99-b9ee-4060-8831-f79749516e4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d22fa99-b9ee-4060-8831-f79749516e4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3768061-70aa-4eee-babb-8250f73f5a46' class='xr-var-data-in' type='checkbox'><label for='data-e3768061-70aa-4eee-babb-8250f73f5a46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                     (time: 9131, bnds: 2, rlat: 255, rlon: 385,\n",
       "                                 vertices: 4)\n",
       "Coordinates:\n",
       "  * time                        (time) datetime64[ns] 1981-01-01T12:00:00 ......\n",
       "  * rlat                        (rlat) float64 -21.23 -21.01 ... 34.43 34.65\n",
       "  * rlon                        (rlon) float64 -32.23 -32.01 ... 52.03 52.25\n",
       "    lat                         (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>\n",
       "    lon                         (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    time_bnds                   (time, bnds) datetime64[ns] dask.array<chunksize=(1826, 2), meta=np.ndarray>\n",
       "    rotated_latitude_longitude  (time) int32 -2147483647 ... -2147483647\n",
       "    lat_vertices                (time, rlat, rlon, vertices) float32 dask.array<chunksize=(1826, 255, 385, 4), meta=np.ndarray>\n",
       "    lon_vertices                (time, rlat, rlon, vertices) float32 dask.array<chunksize=(1826, 255, 385, 4), meta=np.ndarray>\n",
       "    pr                          (time, rlat, rlon) float32 dask.array<chunksize=(1826, 255, 385), meta=np.ndarray>\n",
       "Attributes: (12/34)\n",
       "    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...\n",
       "    institute_id:                   GERICS\n",
       "    experiment_id:                  historical\n",
       "    source:                         GERICS-REMO2015\n",
       "    model_id:                       GERICS-REMO2015\n",
       "    forcing:                        N/A\n",
       "    ...                             ...\n",
       "    title:                          GERICS-REMO2015 model output prepared for...\n",
       "    parent_experiment:              N/A\n",
       "    modeling_realm:                 atmos\n",
       "    realization:                    1\n",
       "    cmor_version:                   2.9.1\n",
       "    tracking_id:                    hdl:21.14103/a5bf17ab-c247-4be0-a27e-06f8..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#opening the noresm data\n",
    "\n",
    "#data = xr.open_dataset('/home/mkibria/Academic/pr_of_thesis/regcm driven by gcm/remo/NorESM1/pr_WAS-22_NCC-NorESM1-M_historical_r1i1p1_GERICS-REMO2015_v1_day_1981-2005.nc')\n",
    "\n",
    "\n",
    "data = xr.open_mfdataset('/home/mkibria/Academic/pr_of_thesis/regcm driven by gcm/remo/NorESM1/all/pr_WAS-22_NCC-NorESM1-M_historical_r1i1p1_GERICS-REMO2015_v1_day_*.nc')\n",
    "\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to select the year from 1980 to 2015\n",
    "#data = data.sel(time=slice('1981-01-16T12:00:00.000000000','2005-12-16T00:00:00.000000000'))\n",
    "#data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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      "/home/mkibria/miniconda3/envs/esp-sp/lib/python3.9/site-packages/xarray/core/indexing.py:1228: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                     (time: 3050, bnds: 2, rlat: 255, rlon: 385,\n",
       "                                 vertices: 4)\n",
       "Coordinates:\n",
       "  * time                        (time) datetime64[ns] 1981-06-01T12:00:00 ......\n",
       "  * rlat                        (rlat) float64 -21.23 -21.01 ... 34.43 34.65\n",
       "  * rlon                        (rlon) float64 -32.23 -32.01 ... 52.03 52.25\n",
       "    lat                         (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;\n",
       "    lon                         (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    time_bnds                   (time, bnds) datetime64[ns] dask.array&lt;chunksize=(610, 2), meta=np.ndarray&gt;\n",
       "    rotated_latitude_longitude  (time) int32 -2147483647 ... -2147483647\n",
       "    lat_vertices                (time, rlat, rlon, vertices) float32 dask.array&lt;chunksize=(610, 255, 385, 4), meta=np.ndarray&gt;\n",
       "    lon_vertices                (time, rlat, rlon, vertices) float32 dask.array&lt;chunksize=(610, 255, 385, 4), meta=np.ndarray&gt;\n",
       "    pr                          (time, rlat, rlon) float32 dask.array&lt;chunksize=(610, 255, 385), meta=np.ndarray&gt;\n",
       "Attributes: (12/34)\n",
       "    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...\n",
       "    institute_id:                   GERICS\n",
       "    experiment_id:                  historical\n",
       "    source:                         GERICS-REMO2015\n",
       "    model_id:                       GERICS-REMO2015\n",
       "    forcing:                        N/A\n",
       "    ...                             ...\n",
       "    title:                          GERICS-REMO2015 model output prepared for...\n",
       "    parent_experiment:              N/A\n",
       "    modeling_realm:                 atmos\n",
       "    realization:                    1\n",
       "    cmor_version:                   2.9.1\n",
       "    tracking_id:                    hdl:21.14103/a5bf17ab-c247-4be0-a27e-06f8...</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-59bfafe8-4272-4008-bb81-e88de77f07d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59bfafe8-4272-4008-bb81-e88de77f07d9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 3050</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>rlat</span>: 255</li><li><span class='xr-has-index'>rlon</span>: 385</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-837492c9-fcb8-4797-ae3e-e434d32f1f8f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-837492c9-fcb8-4797-ae3e-e434d32f1f8f' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1981-06-01T12:00:00 ... 2005-09-...</div><input id='attrs-3fece2e3-789c-4f97-ad54-6ec8f1afc23e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fece2e3-789c-4f97-ad54-6ec8f1afc23e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-053181ee-1611-4552-8088-2b1f9e726d03' class='xr-var-data-in' type='checkbox'><label for='data-053181ee-1611-4552-8088-2b1f9e726d03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1981-06-01T12:00:00.000000000&#x27;, &#x27;1981-06-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1981-06-03T12:00:00.000000000&#x27;, ..., &#x27;2005-09-28T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-29T12:00:00.000000000&#x27;, &#x27;2005-09-30T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlat</span></div><div class='xr-var-dims'>(rlat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-21.23 -21.01 ... 34.43 34.65</div><input id='attrs-18e5c8bb-355e-4d98-a935-f8e5696782fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18e5c8bb-355e-4d98-a935-f8e5696782fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-471ad8cf-d3cd-4d33-bcb3-4f6a48a68fce' class='xr-var-data-in' type='checkbox'><label for='data-471ad8cf-d3cd-4d33-bcb3-4f6a48a68fce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude in rotated pole grid</dd><dt><span>standard_name :</span></dt><dd>grid_latitude</dd></dl></div><div class='xr-var-data'><pre>array([-21.23, -21.01, -20.79, ...,  34.21,  34.43,  34.65])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlon</span></div><div class='xr-var-dims'>(rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.23 -32.01 ... 52.03 52.25</div><input id='attrs-8fc5a8c1-43b3-4500-8b11-d99713dace11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fc5a8c1-43b3-4500-8b11-d99713dace11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6ca78be-4f3a-41fb-87a6-c1009707e481' class='xr-var-data-in' type='checkbox'><label for='data-a6ca78be-4f3a-41fb-87a6-c1009707e481' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude in rotated pole grid</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-32.23, -32.01, -31.79, ...,  51.81,  52.03,  52.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</div><input id='attrs-8c8349f2-81c0-478a-a169-f6c3c5469815' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c8349f2-81c0-478a-a169-f6c3c5469815' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4332eb2c-9069-431c-bb03-7a490fbc3bc9' class='xr-var-data-in' type='checkbox'><label for='data-4332eb2c-9069-431c-bb03-7a490fbc3bc9' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>lat_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 383.50 kiB </td>\n",
       "                        <td> 383.50 kiB </td>\n",
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       "                        <td> (255, 385) </td>\n",
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       "                    <tr>\n",
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       "                        <td> 20 Tasks </td>\n",
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       "                    <th> Type </th>\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'>(rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</div><input id='attrs-65d1b8c8-115b-4c57-b0cb-04c12a69d19c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65d1b8c8-115b-4c57-b0cb-04c12a69d19c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c441ee3-ec65-424a-9b07-427d554af9f8' class='xr-var-data-in' type='checkbox'><label for='data-6c441ee3-ec65-424a-9b07-427d554af9f8' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>lon_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-60cd48b8-1649-4b4d-8b5b-87527c5ca756' class='xr-section-summary-in' type='checkbox'  checked><label for='section-60cd48b8-1649-4b4d-8b5b-87527c5ca756' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</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=(610, 2), meta=np.ndarray&gt;</div><input id='attrs-8d372d91-9da9-46e6-880a-be206a23d5c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d372d91-9da9-46e6-880a-be206a23d5c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79f564e7-e074-4187-9d02-db366c7a7b3f' class='xr-var-data-in' type='checkbox'><label for='data-79f564e7-e074-4187-9d02-db366c7a7b3f' 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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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 47.66 kiB </td>\n",
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       "                        <th> Count </th>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rotated_latitude_longitude</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-2147483647 ... -2147483647</div><input id='attrs-decc8fba-5639-472f-8b63-32d0c3327187' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-decc8fba-5639-472f-8b63-32d0c3327187' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d415991-bca2-4878-88f4-68ccfd1d91f5' class='xr-var-data-in' type='checkbox'><label for='data-5d415991-bca2-4878-88f4-68ccfd1d91f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>rotated_latitude_longitude</dd><dt><span>grid_north_pole_latitude :</span></dt><dd>79.95</dd><dt><span>grid_north_pole_longitude :</span></dt><dd>-123.34</dd><dt><span>north_pole_grid_longitude :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>array([-2147483647, -2147483647, -2147483647, ..., -2147483647,\n",
       "       -2147483647, -2147483647], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_vertices</span></div><div class='xr-var-dims'>(time, rlat, rlon, vertices)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(610, 255, 385, 4), meta=np.ndarray&gt;</div><input id='attrs-185704ed-bb4c-498b-ab9f-59f3e5290dad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-185704ed-bb4c-498b-ab9f-59f3e5290dad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a8b9d02-9134-4351-a1c4-381595812a82' class='xr-var-data-in' type='checkbox'><label for='data-2a8b9d02-9134-4351-a1c4-381595812a82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                     (time: 3050, bnds: 2, rlat: 255, rlon: 385,\n",
       "                                 vertices: 4)\n",
       "Coordinates:\n",
       "  * time                        (time) datetime64[ns] 1981-06-01T12:00:00 ......\n",
       "  * rlat                        (rlat) float64 -21.23 -21.01 ... 34.43 34.65\n",
       "  * rlon                        (rlon) float64 -32.23 -32.01 ... 52.03 52.25\n",
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       "    lon                         (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
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       "    rotated_latitude_longitude  (time) int32 -2147483647 ... -2147483647\n",
       "    lat_vertices                (time, rlat, rlon, vertices) float32 dask.array<chunksize=(610, 255, 385, 4), meta=np.ndarray>\n",
       "    lon_vertices                (time, rlat, rlon, vertices) float32 dask.array<chunksize=(610, 255, 385, 4), meta=np.ndarray>\n",
       "    pr                          (time, rlat, rlon) float32 dask.array<chunksize=(610, 255, 385), meta=np.ndarray>\n",
       "Attributes: (12/34)\n",
       "    institution:                    Helmholtz-Zentrum Geesthacht, Climate Ser...\n",
       "    institute_id:                   GERICS\n",
       "    experiment_id:                  historical\n",
       "    source:                         GERICS-REMO2015\n",
       "    model_id:                       GERICS-REMO2015\n",
       "    forcing:                        N/A\n",
       "    ...                             ...\n",
       "    title:                          GERICS-REMO2015 model output prepared for...\n",
       "    parent_experiment:              N/A\n",
       "    modeling_realm:                 atmos\n",
       "    realization:                    1\n",
       "    cmor_version:                   2.9.1\n",
       "    tracking_id:                    hdl:21.14103/a5bf17ab-c247-4be0-a27e-06f8..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting only JJAS months\n",
    "\n",
    "def is_jjas(month):\n",
    "    return (month >= 6) & (month <= 9)\n",
    "\n",
    "jjas_data = data.sel(time=is_jjas(data['time.month']))\n",
    "\n",
    "jjas_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[\"pr\"][\"x\"][379:429].values\n",
    "#data[\"pr\"][\"y\"][237:337].values\n",
    "#data[\"pr\"][\"time\"].values\n",
    "#jjas_data[\"pr\"][\"lat\"][:].values\n",
    "\n",
    "#jjas_data[\"pr\"][\"lon\"][:].values\n",
    "\n",
    "\n",
    "#data[\"pr\"][\"x\"].values\n",
    "\n",
    "#data.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[\"pr\"][\"y\"][287:337].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to select the x and y coordinates\n",
    "#jjas_data = jjas_data.sel(x=slice(4125000.,5350000.),y=slice(2975000.,4200000.))\n",
    "#jjas_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pr&#x27; (rlat: 255, rlon: 385)&gt;\n",
       "dask.array&lt;mean_agg-aggregate, shape=(255, 385), dtype=float32, chunksize=(255, 385), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * rlat     (rlat) float64 -21.23 -21.01 -20.79 -20.57 ... 34.21 34.43 34.65\n",
       "  * rlon     (rlon) float64 -32.23 -32.01 -31.79 -31.57 ... 51.81 52.03 52.25\n",
       "    lat      (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;\n",
       "    lon      (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</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'>'pr'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>rlat</span>: 255</li><li><span class='xr-has-index'>rlon</span>: 385</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5dae4ceb-a13d-4a1f-bd18-c0b6d1f62f4c' class='xr-array-in' type='checkbox' checked><label for='section-5dae4ceb-a13d-4a1f-bd18-c0b6d1f62f4c' 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=(255, 385), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\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> 383.50 kiB </td>\n",
       "                        <td> 383.50 kiB </td>\n",
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       "                    \n",
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       "                        <td> (255, 385) </td>\n",
       "                        <td> (255, 385) </td>\n",
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       "                        <td> 28 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
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       "  <text x=\"60.000000\" y=\"99.480519\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >385</text>\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'pr' (rlat: 255, rlon: 385)>\n",
       "dask.array<mean_agg-aggregate, shape=(255, 385), dtype=float32, chunksize=(255, 385), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * rlat     (rlat) float64 -21.23 -21.01 -20.79 -20.57 ... 34.21 34.43 34.65\n",
       "  * rlon     (rlon) float64 -32.23 -32.01 -31.79 -31.57 ... 51.81 52.03 52.25\n",
       "    lat      (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>\n",
       "    lon      (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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    }
   ],
   "source": [
    "#getting the mean for JJAS data\n",
    "jjas_mean = jjas_data.pr.mean('time')\n",
    " \n",
    "jjas_mean "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pr is given in kg m $^{-2}$  s $^{-1}$ so we have to convert it into mm/day.\n",
    "\n",
    "The formula is 1 kgm$^{-2}$ s$^{-1}$  = 86400 mm/day. \n",
    "\n",
    "so we have to divide pr value by 86400\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pr&#x27; (rlat: 255, rlon: 385)&gt;\n",
       "dask.array&lt;mul, shape=(255, 385), dtype=float64, chunksize=(255, 385), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * rlat     (rlat) float64 -21.23 -21.01 -20.79 -20.57 ... 34.21 34.43 34.65\n",
       "  * rlon     (rlon) float64 -32.23 -32.01 -31.79 -31.57 ... 51.81 52.03 52.25\n",
       "    lat      (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;\n",
       "    lon      (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</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'>'pr'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>rlat</span>: 255</li><li><span class='xr-has-index'>rlon</span>: 385</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ef3d0411-2fa8-4261-8fa7-80274d2f2de0' class='xr-array-in' type='checkbox' checked><label for='section-ef3d0411-2fa8-4261-8fa7-80274d2f2de0' 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=(255, 385), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 766.99 kiB </td>\n",
       "                        <td> 766.99 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (255, 385) </td>\n",
       "                        <td> (255, 385) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 29 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"129\" 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=\"79\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,79.48051948051948 0.0,79.48051948051948\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"99.480519\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >385</text>\n",
       "  <text x=\"140.000000\" y=\"39.740260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,39.740260)\">255</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-ac3ac41b-d60d-49fb-9e33-a0592c8f45b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac3ac41b-d60d-49fb-9e33-a0592c8f45b0' 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'>rlat</span></div><div class='xr-var-dims'>(rlat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-21.23 -21.01 ... 34.43 34.65</div><input id='attrs-4c6c37f6-9d22-4e43-a2ca-19d2544af06b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c6c37f6-9d22-4e43-a2ca-19d2544af06b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99babd16-d3a2-484a-8793-e215e0e6c2e1' class='xr-var-data-in' type='checkbox'><label for='data-99babd16-d3a2-484a-8793-e215e0e6c2e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude in rotated pole grid</dd><dt><span>standard_name :</span></dt><dd>grid_latitude</dd></dl></div><div class='xr-var-data'><pre>array([-21.23, -21.01, -20.79, ...,  34.21,  34.43,  34.65])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlon</span></div><div class='xr-var-dims'>(rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.23 -32.01 ... 52.03 52.25</div><input id='attrs-6b7a85fc-2f63-4763-9bf4-749e39b9338f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b7a85fc-2f63-4763-9bf4-749e39b9338f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21bd8408-c3c3-4dfe-aa62-36b1de31654a' class='xr-var-data-in' type='checkbox'><label for='data-21bd8408-c3c3-4dfe-aa62-36b1de31654a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude in rotated pole grid</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-32.23, -32.01, -31.79, ...,  51.81,  52.03,  52.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</div><input id='attrs-8fac0269-3793-4acf-ad9e-4a73d90b211b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fac0269-3793-4acf-ad9e-4a73d90b211b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f2a1195-4551-44c8-9586-131bf8cbe4b2' class='xr-var-data-in' type='checkbox'><label for='data-0f2a1195-4551-44c8-9586-131bf8cbe4b2' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>lat_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 383.50 kiB </td>\n",
       "                        <td> 383.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (255, 385) </td>\n",
       "                        <td> (255, 385) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 20 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"129\" 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=\"79\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,79.48051948051948 0.0,79.48051948051948\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"99.480519\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >385</text>\n",
       "  <text x=\"140.000000\" y=\"39.740260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,39.740260)\">255</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'>(rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</div><input id='attrs-4a0cb7df-6c0c-4ce5-8bb3-cf08e5a75b8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a0cb7df-6c0c-4ce5-8bb3-cf08e5a75b8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1298f8b9-7caf-4176-a7a8-6fe6d60d38ca' class='xr-var-data-in' type='checkbox'><label for='data-1298f8b9-7caf-4176-a7a8-6fe6d60d38ca' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>lon_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 383.50 kiB </td>\n",
       "                        <td> 383.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (255, 385) </td>\n",
       "                        <td> (255, 385) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 20 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"129\" 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=\"79\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"79\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"99.480519\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >385</text>\n",
       "  <text x=\"140.000000\" y=\"39.740260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,39.740260)\">255</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-03b5809e-a7c9-442a-a3b8-754c867ae68a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-03b5809e-a7c9-442a-a3b8-754c867ae68a' 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 'pr' (rlat: 255, rlon: 385)>\n",
       "dask.array<mul, shape=(255, 385), dtype=float64, chunksize=(255, 385), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * rlat     (rlat) float64 -21.23 -21.01 -20.79 -20.57 ... 34.21 34.43 34.65\n",
       "  * rlon     (rlon) float64 -32.23 -32.01 -31.79 -31.57 ... 51.81 52.03 52.25\n",
       "    lat      (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>\n",
       "    lon      (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_mm = jjas_mean*86400\n",
    "pr_mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting with cartopy jjas_pr_mean\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "plt.rcParams['font.size'] = '16'\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,9.5))\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([88,93,20,27])\n",
    "\n",
    "\n",
    " \n",
    "m1 = ax.contourf(pr_mm['lon'][:], pr_mm['lat'][:],pr_mm,\n",
    "                 np.arange(1,35+1,5),transform = ccrs.PlateCarree(),cmap='YlGnBu',extend='both')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Define the xticks for longitude\n",
    "ax.set_xticks(np.arange(87,94,1), crs=ccrs.PlateCarree())\n",
    "lon_formatter = cticker.LongitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "\n",
    "# Define the yticks for latitude\n",
    "ax.set_yticks(np.arange(20,28,1), crs=ccrs.PlateCarree())\n",
    "lat_formatter = cticker.LatitudeFormatter()\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.colorbar(m1, orientation = 'horizontal',shrink=0.5, fraction = 0.04 , label = 'rain_rate mm/day')\n",
    "ax.coastlines(linewidth=3)\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=3)\n",
    " \n",
    "\n",
    "plt.title('RegCM(NorESM) Mean jjas precip')\n",
    " \n",
    "\n",
    "plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/bd_pr_remo_noresm_jjas_mean.png\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will plot the annual precipitation rate in mm/day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "<xarray.DataArray 'pr' (rlat: 255, rlon: 385)>\n",
       "dask.array<mean_agg-aggregate, shape=(255, 385), dtype=float32, chunksize=(255, 385), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * rlat     (rlat) float64 -21.23 -21.01 -20.79 -20.57 ... 34.21 34.43 34.65\n",
       "  * rlon     (rlon) float64 -32.23 -32.01 -31.79 -31.57 ... 51.81 52.03 52.25\n",
       "    lat      (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>\n",
       "    lon      (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
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    }
   ],
   "source": [
    "annual_mean = data.pr.mean('time')\n",
    "annual_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;pr&#x27; (rlat: 255, rlon: 385)&gt;\n",
       "dask.array&lt;mul, shape=(255, 385), dtype=float64, chunksize=(255, 385), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * rlat     (rlat) float64 -21.23 -21.01 -20.79 -20.57 ... 34.21 34.43 34.65\n",
       "  * rlon     (rlon) float64 -32.23 -32.01 -31.79 -31.57 ... 51.81 52.03 52.25\n",
       "    lat      (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;\n",
       "    lon      (rlat, rlon) float32 dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</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'>'pr'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>rlat</span>: 255</li><li><span class='xr-has-index'>rlon</span>: 385</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-df27f26a-eed8-4f93-bf8b-9acab9ad390b' class='xr-array-in' type='checkbox' checked><label for='section-df27f26a-eed8-4f93-bf8b-9acab9ad390b' 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=(255, 385), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 766.99 kiB </td>\n",
       "                        <td> 766.99 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (255, 385) </td>\n",
       "                        <td> (255, 385) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 24 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"129\" 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=\"79\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,79.48051948051948 0.0,79.48051948051948\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"99.480519\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >385</text>\n",
       "  <text x=\"140.000000\" y=\"39.740260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,39.740260)\">255</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-8890a802-76a6-435a-b1d9-213c6e796eba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8890a802-76a6-435a-b1d9-213c6e796eba' 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'>rlat</span></div><div class='xr-var-dims'>(rlat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-21.23 -21.01 ... 34.43 34.65</div><input id='attrs-b9b1f4bd-8a04-47dc-92fc-bf684efe4146' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9b1f4bd-8a04-47dc-92fc-bf684efe4146' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83e63264-f375-45f0-b0d9-1007d0566a51' class='xr-var-data-in' type='checkbox'><label for='data-83e63264-f375-45f0-b0d9-1007d0566a51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude in rotated pole grid</dd><dt><span>standard_name :</span></dt><dd>grid_latitude</dd></dl></div><div class='xr-var-data'><pre>array([-21.23, -21.01, -20.79, ...,  34.21,  34.43,  34.65])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rlon</span></div><div class='xr-var-dims'>(rlon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-32.23 -32.01 ... 52.03 52.25</div><input id='attrs-11d7033e-d5ab-4362-bfb9-4df34f11a220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11d7033e-d5ab-4362-bfb9-4df34f11a220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f5cad81-32fe-4151-bda3-44a57d9dc823' class='xr-var-data-in' type='checkbox'><label for='data-2f5cad81-32fe-4151-bda3-44a57d9dc823' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude in rotated pole grid</dd><dt><span>standard_name :</span></dt><dd>grid_longitude</dd></dl></div><div class='xr-var-data'><pre>array([-32.23, -32.01, -31.79, ...,  51.81,  52.03,  52.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</div><input id='attrs-af299b8a-a21e-4d1b-9288-4ac2cfee74fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af299b8a-a21e-4d1b-9288-4ac2cfee74fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8bb00ac-53e3-4cad-b612-36819776bd27' class='xr-var-data-in' type='checkbox'><label for='data-c8bb00ac-53e3-4cad-b612-36819776bd27' 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>latitude</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>bounds :</span></dt><dd>lat_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 383.50 kiB </td>\n",
       "                        <td> 383.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (255, 385) </td>\n",
       "                        <td> (255, 385) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 20 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"129\" 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=\"79\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,79.48051948051948 0.0,79.48051948051948\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"99.480519\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >385</text>\n",
       "  <text x=\"140.000000\" y=\"39.740260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,39.740260)\">255</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'>(rlat, rlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(255, 385), meta=np.ndarray&gt;</div><input id='attrs-9f09cc1c-61cb-458e-958c-01954cc176ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f09cc1c-61cb-458e-958c-01954cc176ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46e858ab-2c0a-494b-9bdc-790b6b5e2c12' class='xr-var-data-in' type='checkbox'><label for='data-46e858ab-2c0a-494b-9bdc-790b6b5e2c12' 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>longitude</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>bounds :</span></dt><dd>lon_vertices</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\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> 383.50 kiB </td>\n",
       "                        <td> 383.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (255, 385) </td>\n",
       "                        <td> (255, 385) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 20 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"129\" 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=\"79\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,79.48051948051948 0.0,79.48051948051948\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"99.480519\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >385</text>\n",
       "  <text x=\"140.000000\" y=\"39.740260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,39.740260)\">255</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-20d28b29-4444-4033-bf95-98bbf9f75138' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-20d28b29-4444-4033-bf95-98bbf9f75138' 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 'pr' (rlat: 255, rlon: 385)>\n",
       "dask.array<mul, shape=(255, 385), dtype=float64, chunksize=(255, 385), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * rlat     (rlat) float64 -21.23 -21.01 -20.79 -20.57 ... 34.21 34.43 34.65\n",
       "  * rlon     (rlon) float64 -32.23 -32.01 -31.79 -31.57 ... 51.81 52.03 52.25\n",
       "    lat      (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>\n",
       "    lon      (rlat, rlon) float32 dask.array<chunksize=(255, 385), meta=np.ndarray>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting from kgm-2s-2 to mm/day\n",
    "\n",
    "annual_pr_mm = annual_mean*86400\n",
    "annual_pr_mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x684 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting annual_pr_mean\n",
    "\n",
    "#plotting with cartopy\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,9.5))\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.set_extent([88,93,20,27])\n",
    "\n",
    "\n",
    "m1 = ax.contourf(annual_pr_mm['lon'], annual_pr_mm['lat'],annual_pr_mm,\n",
    "                 np.arange(1,35+1,5),transform = ccrs.PlateCarree(),cmap='YlGnBu',extend='both')\n",
    "\n",
    " \n",
    "# Define the xticks for longitude\n",
    "ax.set_xticks(np.arange(87,94,1), crs=ccrs.PlateCarree())\n",
    "lon_formatter = cticker.LongitudeFormatter()\n",
    "ax.xaxis.set_major_formatter(lon_formatter)\n",
    "\n",
    "# Define the yticks for latitude\n",
    "ax.set_yticks(np.arange(20,28,1), crs=ccrs.PlateCarree())\n",
    "lat_formatter = cticker.LatitudeFormatter()\n",
    "ax.yaxis.set_major_formatter(lat_formatter)\n",
    "\n",
    "\n",
    "plt.colorbar(m1, orientation = 'horizontal',shrink=0.5, fraction = 0.04 , label = 'mm/day')\n",
    "ax.coastlines(linewidth=3)\n",
    "ax.add_feature(cfeature.BORDERS,linewidth=3)\n",
    "\n",
    "#ax.add_feature(cfeature.STATES)\n",
    "#plt.title('RegCM(NorESM1-M) Annual mean precip rate in mm/day',fontweight='bold',fontsize=15)\n",
    "\n",
    "plt.title('RegCM(NorESM) Annual mean precip rate in mm/day')\n",
    "\n",
    "\n",
    "plt.savefig(\"/home/mkibria/Academic/pr_of_thesis/figures/bd_pr_remo_noresm_annual_mean.png\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}