xarray_08_model_uncertainty_CMIP6.ipynb

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    "# Understand the Uncertainty in CMIP6 Dataset\n",
    "\n",
    "In this notebook we demonstrate how to calculate model uncertainty to see the different temperature trend shown among the 6 members of the ssp126 experiment of CNRM-CM6-1 model in CMIP6 archive:\n",
    "\n",
    "* access data that include multiple ensemble members  \n",
    "* make plots with multiple lines\n",
    "\n",
    "This example uses Coupled Model Intercomparison Project (CMIP6) collections. For more information, please visit [data catalogue]( https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f6600_2266_8675_3563) and [terms of use]( https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html).\n",
    "\n",
    "---"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "- Authors: NCI Virtual Research Environment Team\n",
    "- Keywords: CMIP, xarray, uncertainty\n",
    "- Create Date: 2020-Apr\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import dask\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use xarray to open ensemble data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dir='/g/data/oi10/replicas/CMIP6/ScenarioMIP/CNRM-CERFACS/CNRM-CM6-1'\n",
    "Files=[Dir+'/ssp126/r1i1p1f2/Amon/tas/gr/v20190219/tas_Amon_CNRM-CM6-1_ssp126_r1i1p1f2_gr_201501-210012.nc',\n",
    "      Dir+'/ssp126/r2i1p1f2/Amon/tas/gr/v20190410/tas_Amon_CNRM-CM6-1_ssp126_r2i1p1f2_gr_201501-210012.nc',\n",
    "      Dir+'/ssp126/r3i1p1f2/Amon/tas/gr/v20190410/tas_Amon_CNRM-CM6-1_ssp126_r3i1p1f2_gr_201501-210012.nc',\n",
    "      Dir+'/ssp126/r4i1p1f2/Amon/tas/gr/v20190410/tas_Amon_CNRM-CM6-1_ssp126_r4i1p1f2_gr_201501-210012.nc',\n",
    "      Dir+'/ssp126/r5i1p1f2/Amon/tas/gr/v20190410/tas_Amon_CNRM-CM6-1_ssp126_r5i1p1f2_gr_201501-210012.nc',\n",
    "      Dir+'/ssp126/r6i1p1f2/Amon/tas/gr/v20190410/tas_Amon_CNRM-CM6-1_ssp126_r6i1p1f2_gr_201501-210012.nc']\n",
    "\n",
    "ds1=xr.open_dataset(Files[0])\n",
    "ds2=xr.open_dataset(Files[1])\n",
    "ds3=xr.open_dataset(Files[2])\n",
    "ds4=xr.open_dataset(Files[3])\n",
    "ds5=xr.open_dataset(Files[4])\n",
    "ds6=xr.open_dataset(Files[5])"
   ]
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   "execution_count": 3,
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       "<pre>&lt;xarray.DataArray &#x27;tas&#x27; (time: 1032, lat: 128, lon: 256)&gt;\n",
       "[33816576 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -88.93 -87.54 -86.14 -84.74 ... 86.14 87.54 88.93\n",
       "  * lon      (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6\n",
       "    height   float64 ...\n",
       "  * time     (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "Attributes:\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: time: mean\n",
       "    interval_operation:  900 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       air_temperature\n",
       "    description:         Near-Surface Air Temperature\n",
       "    long_name:           Near-Surface Air Temperature\n",
       "    history:             none\n",
       "    units:               K\n",
       "    cell_measures:       area: areacella</pre>"
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       "<xarray.DataArray 'tas' (time: 1032, lat: 128, lon: 256)>\n",
       "[33816576 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -88.93 -87.54 -86.14 -84.74 ... 86.14 87.54 88.93\n",
       "  * lon      (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6\n",
       "    height   float64 ...\n",
       "  * time     (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "Attributes:\n",
       "    online_operation:    average\n",
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       "    interval_operation:  900 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       air_temperature\n",
       "    description:         Near-Surface Air Temperature\n",
       "    long_name:           Near-Surface Air Temperature\n",
       "    history:             none\n",
       "    units:               K\n",
       "    cell_measures:       area: areacella"
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    "ds1.tas"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate ensemble files into one dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_new=xr.concat([ds1.tas, ds2.tas, ds3.tas, ds4.tas, ds5.tas, ds6.tas], 'new_dim')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of reading each individual file and concatenating them, you can real them all in one dataset using an open multiple datasets function. The procedure above aims to demonstrate the concatenate function in Xarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "</style><div class='xr-wrap'><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-96618169-9ebb-4fee-96a7-2be5f0482d56' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-96618169-9ebb-4fee-96a7-2be5f0482d56' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>axis_nbounds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 128</li><li><span class='xr-has-index'>lon</span>: 256</li><li><span>member_id</span>: 6</li><li><span class='xr-has-index'>time</span>: 1032</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6c664a6e-945e-4f1f-a325-5cb9a17ccc0e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c664a6e-945e-4f1f-a325-5cb9a17ccc0e' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-fe307e04-6833-4f7f-b7ff-50d62c1d3f98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe307e04-6833-4f7f-b7ff-50d62c1d3f98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bf1bf15-c702-4c10-8bc3-f053397fe468' class='xr-var-data-in' type='checkbox'><label for='data-9bf1bf15-c702-4c10-8bc3-f053397fe468' 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>name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></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'>2015-01-16T12:00:00 ... 2100-12-16T12:00:00</div><input id='attrs-4d7c3cff-49dc-42b4-b34b-f1a47efd8195' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d7c3cff-49dc-42b4-b34b-f1a47efd8195' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af130b24-686d-4ebb-b758-2e8c8bf30c04' class='xr-var-data-in' type='checkbox'><label for='data-af130b24-686d-4ebb-b758-2e8c8bf30c04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><pre class='xr-var-data'>array([&#x27;2015-01-16T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, ..., &#x27;2100-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2100-11-16T00:00:00.000000000&#x27;, &#x27;2100-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-88.93 -87.54 ... 87.54 88.93</div><input id='attrs-f4191709-3943-4730-8c1e-97b6602113a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4191709-3943-4730-8c1e-97b6602113a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34433936-c726-479c-9890-9ec3aa9541b1' class='xr-var-data-in' type='checkbox'><label for='data-34433936-c726-479c-9890-9ec3aa9541b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><pre class='xr-var-data'>array([-88.927735, -87.538705, -86.141472, -84.742386, -83.342596, -81.942466,\n",
       "       -80.542146, -79.14171 , -77.741196, -76.340629, -74.940023, -73.539389,\n",
       "       -72.138732, -70.738059, -69.337372, -67.936673, -66.535966, -65.135251,\n",
       "       -63.73453 , -62.333803, -60.933072, -59.532337, -58.131598, -56.730857,\n",
       "       -55.330112, -53.929366, -52.528617, -51.127867, -49.727115, -48.326361,\n",
       "       -46.925606, -45.52485 , -44.124093, -42.723335, -41.322576, -39.921816,\n",
       "       -38.521056, -37.120294, -35.719532, -34.31877 , -32.918007, -31.517244,\n",
       "       -30.11648 , -28.715716, -27.314951, -25.914186, -24.513421, -23.112655,\n",
       "       -21.71189 , -20.311124, -18.910357, -17.509591, -16.108824, -14.708057,\n",
       "       -13.30729 , -11.906523, -10.505756,  -9.104989,  -7.704221,  -6.303454,\n",
       "        -4.902687,  -3.501919,  -2.101151,  -0.700384,   0.700384,   2.101151,\n",
       "         3.501919,   4.902687,   6.303454,   7.704221,   9.104989,  10.505756,\n",
       "        11.906523,  13.30729 ,  14.708057,  16.108824,  17.509591,  18.910357,\n",
       "        20.311124,  21.71189 ,  23.112655,  24.513421,  25.914186,  27.314951,\n",
       "        28.715716,  30.11648 ,  31.517244,  32.918007,  34.31877 ,  35.719532,\n",
       "        37.120294,  38.521056,  39.921816,  41.322576,  42.723335,  44.124093,\n",
       "        45.52485 ,  46.925606,  48.326361,  49.727115,  51.127867,  52.528617,\n",
       "        53.929366,  55.330112,  56.730857,  58.131598,  59.532337,  60.933072,\n",
       "        62.333803,  63.73453 ,  65.135251,  66.535966,  67.936673,  69.337372,\n",
       "        70.738059,  72.138732,  73.539389,  74.940023,  76.340629,  77.741196,\n",
       "        79.14171 ,  80.542146,  81.942466,  83.342596,  84.742386,  86.141472,\n",
       "        87.538705,  88.927735])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.406 2.812 ... 357.2 358.6</div><input id='attrs-4724a82c-fa40-40c3-8136-01d8f2dacb6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4724a82c-fa40-40c3-8136-01d8f2dacb6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77453f86-d437-41d9-80c9-520b9fea9c95' class='xr-var-data-in' type='checkbox'><label for='data-77453f86-d437-41d9-80c9-520b9fea9c95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><pre class='xr-var-data'>array([  0.     ,   1.40625,   2.8125 , ..., 355.78125, 357.1875 , 358.59375])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-a81c6f0d-fd78-48ca-bc60-e667bf1dac83' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a81c6f0d-fd78-48ca-bc60-e667bf1dac83' class='xr-section-summary' >Data variables: <span>(2)</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_bounds</span></div><div class='xr-var-dims'>(member_id, time, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1032, 2), meta=np.ndarray&gt;</div><input id='attrs-ee0fbd3f-8276-45f0-8015-f4fdda11e09c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee0fbd3f-8276-45f0-8015-f4fdda11e09c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d933e58-bedb-4391-b78d-992c9fd05337' class='xr-var-data-in' type='checkbox'><label for='data-6d933e58-bedb-4391-b78d-992c9fd05337' 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><pre class='xr-var-data'><table>\n",
       "<tr>\n",
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As a tier 2 option, this simulation should be extended to year 2300</dd><dt><span>title :</span></dt><dd>CNRM-CM6-1 model output prepared for CMIP6 / ScenarioMIP ssp126</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP</dd><dt><span>contact :</span></dt><dd>contact.cmip@meteo.fr</dd><dt><span>data_specs_version :</span></dt><dd>01.00.21</dd><dt><span>dr2xml_version :</span></dt><dd>1.16</dd><dt><span>experiment_id :</span></dt><dd>ssp126</dd><dt><span>experiment :</span></dt><dd>update of RCP2.6 based on SSP1</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>2</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS.CNRM-CM6-1.ssp126.none.r1i1p1f2</dd><dt><span>grid :</span></dt><dd>data regridded to a T127 gaussian grid (128x256 latlon) from a native atmosphere T127l reduced gaussian grid</dd><dt><span>grid_label :</span></dt><dd>gr</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution_id :</span></dt><dd>CNRM-CERFACS</dd><dt><span>institution :</span></dt><dd>CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France)</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CNRM-CERFACS is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and at http://www.umr-cnrm.fr/cmip6/. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_source_id :</span></dt><dd>CNRM-CM6-1</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f2</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>http://www.umr-cnrm.fr/cmip6/references</dd><dt><span>source :</span></dt><dd>CNRM-CM6-1 (2017):  aerosol: prescribed monthly fields computed by TACTIC_v2 scheme atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km) atmosChem: OZL_v2 land: Surfex 8.0c ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m) seaIce: Gelato 6.1</dd><dt><span>source_id :</span></dt><dd>CNRM-CM6-1</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f2</dd><dt><span>EXPID :</span></dt><dd>CNRM-CM6-1_ssp126_r1i1p1f2</dd><dt><span>CMIP6_CV_version :</span></dt><dd>cv=6.2.3.0-7-g2019642</dd><dt><span>dr2xml_md5sum :</span></dt><dd>c2dce418e78ca835be1e2ff817c2c403</dd><dt><span>xios_commit :</span></dt><dd>1442-shuffle</dd><dt><span>nemo_gelato_commit :</span></dt><dd>49095b3accd5d4c_6524fe19b00467a</dd><dt><span>arpege_minor_version :</span></dt><dd>6.3.2</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/05131cf0-ec69-4bfc-92f8-e1d40aae26f5</dd></dl></div></li></ul></div></div>"
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       "  * time         (time) datetime64[ns] 2015-01-16T12:00:00 ... 2100-12-16T12:00:00\n",
       "  * lat          (lat) float64 -88.93 -87.54 -86.14 -84.74 ... 86.14 87.54 88.93\n",
       "  * lon          (lon) float64 0.0 1.406 2.812 4.219 ... 354.4 355.8 357.2 358.6\n",
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       "    creation_date:          2018-11-15T10:36:39Z\n",
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       "    experiment:             update of RCP2.6 based on SSP1\n",
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       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.CNRM-CERFACS...\n",
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       "    nominal_resolution:     250 km\n",
       "    initialization_index:   1\n",
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       "    institution:            CNRM (Centre National de Recherches Meteorologiqu...\n",
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       "    mip_era:                CMIP6\n",
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       "    branch_time_in_parent:  60265.0\n",
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       "    physics_index:          1\n",
       "    product:                model-output\n",
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       "    realm:                  atmos\n",
       "    references:             http://www.umr-cnrm.fr/cmip6/references\n",
       "    source:                 CNRM-CM6-1 (2017):  aerosol: prescribed monthly f...\n",
       "    source_id:              CNRM-CM6-1\n",
       "    source_type:            AOGCM\n",
       "    sub_experiment_id:      none\n",
       "    sub_experiment:         none\n",
       "    table_id:               Amon\n",
       "    variable_id:            tas\n",
       "    variant_label:          r1i1p1f2\n",
       "    EXPID:                  CNRM-CM6-1_ssp126_r1i1p1f2\n",
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_all=xr.open_mfdataset(''+Dir+'/ssp126/r*i1p1f2/Amon/tas/gr/*/tas_Amon_CNRM-CM6-1_ssp126_r*i1p1f2_gr_201501-210012.nc', concat_dim='member_id',combine='nested')\n",
    "ds_all"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data analysis and plotting\n",
    "\n",
    "There exists uncertainty in model simulations, which is the reason that we need multiple models and multiple ensembles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "\n",
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       "\n",
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       "  display: inline-block;\n",
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       "\n",
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       "</style><div class='xr-wrap'><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-999c87c2-5f7b-4300-9346-a621362c43a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-999c87c2-5f7b-4300-9346-a621362c43a6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>member_id</span>: 6</li><li><span class='xr-has-index'>time</span>: 86</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c6a0e1b9-9270-4fe4-a0ac-f5bcef521820' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6a0e1b9-9270-4fe4-a0ac-f5bcef521820' class='xr-section-summary' >Coordinates: <span>(2)</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'>2015-12-31 ... 2100-12-31</div><input id='attrs-18d6482a-9e3c-4834-bdcc-54cfe0e585f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18d6482a-9e3c-4834-bdcc-54cfe0e585f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40273bcc-0fa2-46cb-8c16-774a0994ac24' class='xr-var-data-in' type='checkbox'><label for='data-40273bcc-0fa2-46cb-8c16-774a0994ac24' 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><pre class='xr-var-data'>array([&#x27;2015-12-31T00:00:00.000000000&#x27;, &#x27;2016-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-12-31T00:00:00.000000000&#x27;, &#x27;2018-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-12-31T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-12-31T00:00:00.000000000&#x27;, &#x27;2022-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-12-31T00:00:00.000000000&#x27;, &#x27;2024-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2025-12-31T00:00:00.000000000&#x27;, &#x27;2026-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2027-12-31T00:00:00.000000000&#x27;, &#x27;2028-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2029-12-31T00:00:00.000000000&#x27;, &#x27;2030-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2031-12-31T00:00:00.000000000&#x27;, &#x27;2032-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2033-12-31T00:00:00.000000000&#x27;, &#x27;2034-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2035-12-31T00:00:00.000000000&#x27;, &#x27;2036-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2037-12-31T00:00:00.000000000&#x27;, &#x27;2038-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2039-12-31T00:00:00.000000000&#x27;, &#x27;2040-12-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2049-12-31T00:00:00.000000000&#x27;, &#x27;2050-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2051-12-31T00:00:00.000000000&#x27;, &#x27;2052-12-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2065-12-31T00:00:00.000000000&#x27;, &#x27;2066-12-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2069-12-31T00:00:00.000000000&#x27;, &#x27;2070-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2071-12-31T00:00:00.000000000&#x27;, &#x27;2072-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2073-12-31T00:00:00.000000000&#x27;, &#x27;2074-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2075-12-31T00:00:00.000000000&#x27;, &#x27;2076-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2077-12-31T00:00:00.000000000&#x27;, &#x27;2078-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2079-12-31T00:00:00.000000000&#x27;, &#x27;2080-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2081-12-31T00:00:00.000000000&#x27;, &#x27;2082-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2083-12-31T00:00:00.000000000&#x27;, &#x27;2084-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2085-12-31T00:00:00.000000000&#x27;, &#x27;2086-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2087-12-31T00:00:00.000000000&#x27;, &#x27;2088-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2089-12-31T00:00:00.000000000&#x27;, &#x27;2090-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2091-12-31T00:00:00.000000000&#x27;, &#x27;2092-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2093-12-31T00:00:00.000000000&#x27;, &#x27;2094-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2095-12-31T00:00:00.000000000&#x27;, &#x27;2096-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2097-12-31T00:00:00.000000000&#x27;, &#x27;2098-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2099-12-31T00:00:00.000000000&#x27;, &#x27;2100-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-1de62a09-852c-4222-a6f3-f0c4d5e74ebd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1de62a09-852c-4222-a6f3-f0c4d5e74ebd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47bbfb20-e1d6-402d-a66b-0e6658997208' class='xr-var-data-in' type='checkbox'><label for='data-47bbfb20-e1d6-402d-a66b-0e6658997208' 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>name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><pre class='xr-var-data'>array(2.)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-8f95091d-40b7-4012-8749-0f256e088222' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f95091d-40b7-4012-8749-0f256e088222' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, member_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</div><input id='attrs-486a52d8-f172-4e6e-abe9-2a4cc04be1fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-486a52d8-f172-4e6e-abe9-2a4cc04be1fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf0df978-b925-411d-a7d7-72b2d7bc0124' class='xr-var-data-in' type='checkbox'><label for='data-cf0df978-b925-411d-a7d7-72b2d7bc0124' 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><pre class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
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       "    <tr><th> Shape </th><td> (86, 6) </td> <td> (1, 1) </td></tr>\n",
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       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (member_id: 6, time: 86)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2015-12-31 2016-12-31 ... 2100-12-31\n",
       "    height   float64 2.0\n",
       "Dimensions without coordinates: member_id\n",
       "Data variables:\n",
       "    tas      (time, member_id) float32 dask.array<chunksize=(1, 1), meta=np.ndarray>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_yr=ds_all.mean(dim=('lat','lon')).resample(time='Y').mean(dim='time') #annual average data\n",
    "ds_yr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add ensemble mean to dataset as member_id: mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U21</div><div class='xr-var-preview xr-preview'>&#x27;1&#x27; &#x27;2&#x27; &#x27;3&#x27; &#x27;4&#x27; &#x27;5&#x27; &#x27;6&#x27; &#x27;mean&#x27;</div><input id='attrs-032ba19b-0f3c-470c-8dc1-e2085b87864d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-032ba19b-0f3c-470c-8dc1-e2085b87864d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2ecfeae-a5a5-4dcb-97af-0f8944ded1c1' class='xr-var-data-in' type='checkbox'><label for='data-e2ecfeae-a5a5-4dcb-97af-0f8944ded1c1' 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><pre class='xr-var-data'>array([&#x27;1&#x27;, &#x27;2&#x27;, &#x27;3&#x27;, &#x27;4&#x27;, &#x27;5&#x27;, &#x27;6&#x27;, &#x27;mean&#x27;], dtype=&#x27;&lt;U21&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-9c749b57-99a6-4405-b12c-83c62359556b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9c749b57-99a6-4405-b12c-83c62359556b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, member_id)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1), meta=np.ndarray&gt;</div><input id='attrs-1f774530-6b01-48e4-8fd1-f008cc0affd8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1f774530-6b01-48e4-8fd1-f008cc0affd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6bf8f45-5ffa-4d28-92a7-9f9990909bde' class='xr-var-data-in' type='checkbox'><label for='data-d6bf8f45-5ffa-4d28-92a7-9f9990909bde' 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><pre class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (member_id: 7, time: 86)\n",
       "Coordinates:\n",
       "    height     float64 2.0\n",
       "  * time       (time) datetime64[ns] 2015-12-31 2016-12-31 ... 2100-12-31\n",
       "  * member_id  (member_id) <U21 '1' '2' '3' '4' '5' '6' 'mean'\n",
       "Data variables:\n",
       "    tas        (time, member_id) float32 dask.array<chunksize=(1, 1), meta=np.ndarray>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_yr_ens_mean=ds_yr.mean(dim='member_id')\n",
    "ds_yr_addMean=xr.concat([ds_yr, ds_yr_ens_mean],'member_id')\n",
    "ds_yr_addMean=ds_yr_addMean.assign_coords({\"member_id\": [1,2,3,4,5,6,'mean'] }) #change coordinates of member_id\n",
    "ds_yr_addMean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1549ac240d50>,\n",
       " <matplotlib.lines.Line2D at 0x1549a8116ad0>,\n",
       " <matplotlib.lines.Line2D at 0x1549aa8936d0>,\n",
       " <matplotlib.lines.Line2D at 0x1549a80fca10>,\n",
       " <matplotlib.lines.Line2D at 0x1549a80fc650>,\n",
       " <matplotlib.lines.Line2D at 0x1549a8091f90>,\n",
       " <matplotlib.lines.Line2D at 0x1549a8091150>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_yr_addMean=xr.Dataset.to_array(ds_yr_addMean)[0,:,:]\n",
    "ds_yr_addMean.plot.line(x='time', hue='member_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we measure the average distance of individual ensemble members to the ensemble mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Mapping.values of <xarray.Dataset>\n",
       "Dimensions:  (member_id: 6)\n",
       "Coordinates:\n",
       "    height   float64 2.0\n",
       "Dimensions without coordinates: member_id\n",
       "Data variables:\n",
       "    tas      (member_id) float32 dask.array<chunksize=(1,), meta=np.ndarray>>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "dis=np.sqrt((np.square(ds_yr-ds_yr.mean(dim='member_id'))).mean(dim='time'))\n",
    "dis.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see that the uncertainty is around 0.15 degree Celsius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "This example shows how to concatenate multiple ensemble files and plot them all together to get the sense of model uncertainty. We can see different simulation members show different results regarding the future temperature projection under scenario ssp126. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}