extract_miroc5-iso_data_xarray.ipynb

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   "cell_type": "markdown",
   "id": "3fdb8e01-7d89-4c5c-99bb-205b0aff0942",
   "metadata": {},
   "source": [
    "# Extract data from MIROC5-iso"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa08674-87cc-4ef2-a4d6-7a649d82918e",
   "metadata": {},
   "source": [
    "This notebook allows to plot and extract data at a specific location and time range from MIROC5-iso simulations. Simulations nudged to ERA5 and JRA-55 are available. \n",
    "- The mean tritium in precipitation map can be plotted for a specified area and time period. The map can be downloaded as a pdf file.\n",
    "- Time series of tritium and $\\delta$$^{18}$O in precipitation, precipitation rate and air temperature at 2 m can be plotted for a specified location and time interval. The data can be downloaded as a CSV file, and the figure can be downloaded as a pdf file.\n",
    "- The map of vertically integrated water vapor transport can be plotted for a given area and month. The figure can be downloaded as a pdf file. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d0e474-bcba-4972-b45d-aa5388d0dd21",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Packages and path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5810a7a6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import wget\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import solara\n",
    "import datetime\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "55a2ce97",
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    "tags": []
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   "source": [
    "# For the size of the figures\n",
    "plt.rcParams['figure.dpi'] = 200\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "from matplotlib_inline.backend_inline import set_matplotlib_formats\n",
    "set_matplotlib_formats('pdf', 'png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffae5025",
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    "tags": []
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   "source": [
    "# some shortcuts to download and read the miroc5-iso netcdf files\n",
    "work =\"data_tritium_bomb_miroc/\"\n",
    "exp_tritium = \"bomb_tritium_\"\n",
    "exp_tritium_era5  = \"bomb_tritium_era5\"\n",
    "exp_tritium_jra55 = \"bomb_tritium_jra55\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee9fc56-1e2f-4656-bc1a-1b535c0e0c1a",
   "metadata": {},
   "source": [
    "## Download MIROC5-iso outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9d240d3-3a11-4a8a-8c4b-c5b990b26deb",
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     "text": [
      "bomb_tritium_era5_miroc5_monmean.prcpTU.nc was already downloaded\n",
      "bomb_tritium_jra55_miroc5_monmean.prcpTU.nc was already downloaded\n",
      "bomb_tritium_era5_miroc5_monmean.prcp.nc was already downloaded\n",
      "bomb_tritium_jra55_miroc5_monmean.prcp.nc was already downloaded\n",
      "bomb_tritium_era5_miroc5_monmean.temp2.nc was already downloaded\n",
      "bomb_tritium_jra55_miroc5_monmean.temp2.nc was already downloaded\n",
      "bomb_tritium_era5_miroc5_monmean.d18Op.nc was already downloaded\n",
      "bomb_tritium_jra55_miroc5_monmean.d18Op.nc was already downloaded\n",
      "bomb_tritium_era5_miroc5_monmean.dexp.nc was already downloaded\n",
      "bomb_tritium_jra55_miroc5_monmean.dexp.nc was already downloaded\n",
      "bomb_tritium_era5_miroc5_monmean.evap.nc was already downloaded\n",
      "bomb_tritium_jra55_miroc5_monmean.evap.nc was already downloaded\n",
      "bomb_tritium_era5_miroc5_monmean.qtot_u.nc was already downloaded\n",
      "bomb_tritium_jra55_miroc5_monmean.qtot_u.nc was already downloaded\n",
      "bomb_tritium_era5_miroc5_monmean.qtot_v.nc was already downloaded\n",
      "bomb_tritium_jra55_miroc5_monmean.qtot_v.nc was already downloaded\n"
     ]
    }
   ],
   "source": [
    "# download from isotope server\n",
    "for var in [\"prcpTU\", \"prcp\", \"temp2\", \"d18Op\", \"dexp\", \"evap\", \"qtot_u\", \"qtot_v\"]:\n",
    "    if os.path.isfile(exp_tritium_era5+\"_miroc5_monmean.\"+var+\".nc\"):\n",
    "        print(exp_tritium_era5+\"_miroc5_monmean.\"+var+\".nc was already downloaded\")\n",
    "        print(exp_tritium_jra55+\"_miroc5_monmean.\"+var+\".nc was already downloaded\")\n",
    "    else:\n",
    "        print(\"\")\n",
    "        print(\"downloading \"+exp_tritium_era5+\"_miroc5_monmean.\"+var+\".nc file\")\n",
    "        wget.download(\"http://isotope.iis.u-tokyo.ac.jp/~acauquoin/\"+work+\"/\"+exp_tritium_era5+\"_miroc5_monmean.\"+var+\".nc\")\n",
    "        print(\"\")\n",
    "        print(\"downloading \"+exp_tritium_jra55+\"_miroc5_monmean.\"+var+\".nc file\")\n",
    "        wget.download(\"http://isotope.iis.u-tokyo.ac.jp/~acauquoin/\"+work+\"/\"+exp_tritium_jra55+\"_miroc5_monmean.\"+var+\".nc\")       "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "625ba12c-9532-446c-91a4-5df393d380ea",
   "metadata": {},
   "source": [
    "## Read and extract MIROC5-iso outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b208224b-ffc1-42fe-aa37-2d22022789c0",
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   "source": [
    "# read netcdf files\n",
    "#\n",
    "ifile_prcpTU_era5  = exp_tritium_era5  + \"_miroc5_monmean.prcpTU.nc\"\n",
    "ifile_prcp_era5    = exp_tritium_era5  + \"_miroc5_monmean.prcp.nc\"\n",
    "ifile_temp2_era5   = exp_tritium_era5  + \"_miroc5_monmean.temp2.nc\"\n",
    "ifile_d18Op_era5   = exp_tritium_era5  + \"_miroc5_monmean.d18Op.nc\"\n",
    "ifile_dexp_era5    = exp_tritium_era5  + \"_miroc5_monmean.dexp.nc\"\n",
    "ifile_evap_era5    = exp_tritium_era5  + \"_miroc5_monmean.evap.nc\"\n",
    "ifile_uqint_era5   = exp_tritium_era5  + \"_miroc5_monmean.qtot_u.nc\"\n",
    "ifile_vqint_era5   = exp_tritium_era5  + \"_miroc5_monmean.qtot_v.nc\"\n",
    "ifile_prcpTU_jra55 = exp_tritium_jra55 + \"_miroc5_monmean.prcpTU.nc\"\n",
    "ifile_prcp_jra55   = exp_tritium_jra55 + \"_miroc5_monmean.prcp.nc\"\n",
    "ifile_temp2_jra55  = exp_tritium_jra55 + \"_miroc5_monmean.temp2.nc\"\n",
    "ifile_d18Op_jra55  = exp_tritium_jra55 + \"_miroc5_monmean.d18Op.nc\"\n",
    "ifile_dexp_jra55   = exp_tritium_jra55 + \"_miroc5_monmean.dexp.nc\"\n",
    "ifile_evap_jra55   = exp_tritium_jra55 + \"_miroc5_monmean.evap.nc\"\n",
    "ifile_uqint_jra55  = exp_tritium_jra55 + \"_miroc5_monmean.qtot_u.nc\"\n",
    "ifile_vqint_jra55  = exp_tritium_jra55 + \"_miroc5_monmean.qtot_v.nc\"\n",
    "#\n",
    "ds_prcpTU_era5  = xr.open_dataset(ifile_prcpTU_era5)\n",
    "ds_prcp_era5    = xr.open_dataset(ifile_prcp_era5)\n",
    "ds_temp2_era5   = xr.open_dataset(ifile_temp2_era5)\n",
    "ds_d18Op_era5   = xr.open_dataset(ifile_d18Op_era5)\n",
    "ds_dexp_era5    = xr.open_dataset(ifile_dexp_era5)\n",
    "ds_evap_era5    = xr.open_dataset(ifile_evap_era5)\n",
    "ds_uqint_era5   = xr.open_dataset(ifile_uqint_era5)\n",
    "ds_vqint_era5   = xr.open_dataset(ifile_vqint_era5)\n",
    "ds_prcpTU_jra55 = xr.open_dataset(ifile_prcpTU_jra55)\n",
    "ds_prcp_jra55   = xr.open_dataset(ifile_prcp_jra55)\n",
    "ds_temp2_jra55  = xr.open_dataset(ifile_temp2_jra55)\n",
    "ds_d18Op_jra55  = xr.open_dataset(ifile_d18Op_jra55)\n",
    "ds_dexp_jra55   = xr.open_dataset(ifile_dexp_jra55)\n",
    "ds_evap_jra55   = xr.open_dataset(ifile_evap_jra55)\n",
    "ds_uqint_jra55  = xr.open_dataset(ifile_uqint_jra55)\n",
    "ds_vqint_jra55  = xr.open_dataset(ifile_vqint_jra55)\n",
    "#\n",
    "ds_prcpTU_era5  = ds_prcpTU_era5.isel(bnds=0)\n",
    "ds_prcp_era5    = ds_prcp_era5.isel(bnds=0)\n",
    "ds_temp2_era5   = ds_temp2_era5.isel(bnds=0)\n",
    "ds_d18Op_era5   = ds_d18Op_era5.isel(bnds=0)\n",
    "ds_dexp_era5    = ds_dexp_era5.isel(bnds=0)\n",
    "ds_evap_era5    = ds_evap_era5.isel(bnds=0)\n",
    "ds_uqint_era5   = xr.open_dataset(ifile_uqint_era5)\n",
    "ds_uqint_era5   = ds_uqint_era5.isel(bnds=0)\n",
    "ds_vqint_era5   = ds_vqint_era5.isel(bnds=0)\n",
    "ds_prcpTU_jra55 = ds_prcpTU_jra55.isel(bnds=0)\n",
    "ds_prcp_jra55   = ds_prcp_jra55.isel(bnds=0)\n",
    "ds_temp2_jra55  = ds_temp2_jra55.isel(bnds=0)\n",
    "ds_d18Op_jra55  = ds_d18Op_jra55.isel(bnds=0)\n",
    "ds_dexp_jra55   = ds_dexp_jra55.isel(bnds=0)\n",
    "ds_evap_jra55   = ds_evap_jra55.isel(bnds=0)\n",
    "ds_uqint_jra55  = ds_uqint_jra55.isel(bnds=0)\n",
    "ds_vqint_jra55  = ds_vqint_jra55.isel(bnds=0)\n",
    "#\n",
    "ds_prcp_era5   = ds_prcp_era5.rename({\"pr\": \"precip\"})\n",
    "ds_temp2_era5  = ds_temp2_era5.rename({\"tas\": \"temp2\"})\n",
    "ds_evap_era5   = ds_evap_era5.rename({\"hfls\": \"evap\"})\n",
    "ds_prcp_jra55  = ds_prcp_jra55.rename({\"pr\": \"precip\"})\n",
    "ds_temp2_jra55 = ds_temp2_jra55.rename({\"tas\": \"temp2\"})\n",
    "ds_evap_jra55  = ds_evap_jra55.rename({\"hfls\": \"evap\"})\n",
    "#\n",
    "ds_prcpTU_era5.TU_precip.attrs['units'] = \"TU\"\n",
    "ds_prcpTU_era5.TU_precip.attrs['standard_name'] = \"tritium in precipitation\"\n",
    "ds_prcp_era5.precip.attrs['units'] = \"mm/month\"\n",
    "ds_prcp_era5.precip.attrs['standard_name'] = \"precipitation\"\n",
    "ds_temp2_era5.temp2.attrs['units'] = \"°C\"\n",
    "ds_temp2_era5.temp2.attrs['standard_name'] = \"2m air temperature\"\n",
    "ds_d18Op_era5.d18O_precip.attrs['units'] = \"‰\"\n",
    "ds_d18Op_era5.d18O_precip.attrs['standard_name'] = \"d18O in precipitation\"\n",
    "ds_dexp_era5.dex_precip.attrs['units'] = \"‰\"\n",
    "ds_dexp_era5.dex_precip.attrs['standard_name'] = \"d-excess in precipitation\"\n",
    "ds_evap_era5.evap.attrs['standard_name'] = \"surface upward latent heat flux\"\n",
    "ds_evap_era5.evap.attrs['units'] = \"W m-2\"\n",
    "ds_uqint_era5.UQ.attrs['units'] = \"kg m-1 s-1\"\n",
    "ds_uqint_era5.UQ.attrs['standard_name'] = \"vert. int. water vapor transport - u component\"\n",
    "ds_vqint_era5.VQ.attrs['units'] = \"kg m-1 s-1\"\n",
    "ds_vqint_era5.VQ.attrs['standard_name'] = \"vert. int. water vapor transport - v component\"\n",
    "ds_prcpTU_jra55.TU_precip.attrs['units'] = \"TU\"\n",
    "ds_prcpTU_jra55.TU_precip.attrs['standard_name'] = \"tritium in precipitation\"\n",
    "ds_prcp_jra55.precip.attrs['units']      = \"mm/month\"\n",
    "ds_prcp_jra55.precip.attrs['standard_name'] = \"precipitation\"\n",
    "ds_temp2_jra55.temp2.attrs['units']      = \"°C\"\n",
    "ds_temp2_jra55.temp2.attrs['standard_name'] = \"2m air temperature\"\n",
    "ds_d18Op_jra55.d18O_precip.attrs['units'] = \"‰\"\n",
    "ds_d18Op_jra55.d18O_precip.attrs['standard_name'] = \"d18O in precipitation\"\n",
    "ds_dexp_jra55.dex_precip.attrs['units'] = \"‰\"\n",
    "ds_dexp_jra55.dex_precip.attrs['standard_name'] = \"d-excess in precipitation\"\n",
    "ds_evap_jra55.evap.attrs['standard_name'] = \"surface upward latent heat flux\"\n",
    "ds_evap_jra55.evap.attrs['units'] = \"W m-2\"\n",
    "ds_uqint_jra55.UQ.attrs['units'] = \"kg m-1 s-1\"\n",
    "ds_uqint_jra55.UQ.attrs['standard_name'] = \"vert. int. water vapor transport - u component\"\n",
    "ds_vqint_jra55.VQ.attrs['units'] = \"kg m-1 s-1\"\n",
    "ds_vqint_jra55.VQ.attrs['standard_name'] = \"vert. int. water vapor transport - v component\"\n",
    "#\n",
    "variables = ['TU_precip', 'precip', 'temp2', 'd18O_precip', 'dex_precip', 'evap', 'UQ', 'VQ']\n",
    "ds_era5  = xr.merge([ds_prcpTU_era5,  ds_prcp_era5,  ds_temp2_era5, ds_d18Op_era5, ds_dexp_era5, ds_evap_era5, ds_uqint_era5, ds_vqint_era5])\n",
    "ds_era5 = ds_era5[variables]\n",
    "ds_jra55 = xr.merge([ds_prcpTU_jra55, ds_prcp_jra55, ds_temp2_jra55, ds_d18Op_jra55, ds_dexp_jra55, ds_evap_jra55, ds_uqint_jra55, ds_vqint_jra55])\n",
    "ds_jra55 = ds_jra55[variables]"
   ]
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   "source": [
    "def convert360_180(_ds):\n",
    "    \"\"\"\n",
    "    convert longitude from 0-360 to -180 -- 180 deg\n",
    "    \"\"\"\n",
    "    # check if already \n",
    "    attrs = _ds['lon'].attrs\n",
    "    if _ds['lon'].min() >= 0:\n",
    "        with xr.set_options(keep_attrs=True): \n",
    "            _ds.coords['lon'] = (_ds['lon'] + 180) % 360 - 180\n",
    "        _ds = _ds.sortby('lon')\n",
    "    return _ds"
   ]
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   "id": "255b7d21-f894-4979-beb2-b09bc2dda0a9",
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   "source": [
    "ds_era5  = convert360_180(ds_era5)\n",
    "ds_jra55 = convert360_180(ds_jra55)"
   ]
  },
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
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       "  margin-bottom: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
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       "\n",
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       "\n",
       ".xr-sections {\n",
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       "  display: grid;\n",
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       ".xr-section-item {\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
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       "\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
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       "  font-size: 11px;\n",
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       "  text-align: center;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
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       "\n",
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       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\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",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\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",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (time: 840, lat: 64, lon: 128)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1952-01-16T12:00:00 ... 2021-12-16T12:...\n",
       "  * lon          (lon) float64 -180.0 -177.2 -174.4 -171.6 ... 171.6 174.4 177.2\n",
       "  * lat          (lat) float64 87.86 85.1 82.31 79.53 ... -82.31 -85.1 -87.86\n",
       "Data variables:\n",
       "    TU_precip    (time, lat, lon) float32 ...\n",
       "    precip       (time, lat, lon) float32 ...\n",
       "    temp2        (time, lat, lon) float32 ...\n",
       "    d18O_precip  (time, lat, lon) float32 ...\n",
       "    dex_precip   (time, lat, lon) float32 ...\n",
       "    evap         (time, lat, lon) float32 ...\n",
       "    UQ           (time, lat, lon) float32 ...\n",
       "    VQ           (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.4 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.0\n",
       "    history:      Thu Sep 07 17:47:18 2023: cdo -f nc -setreftime,1952-01-01 ...\n",
       "    CDO:          Climate Data Operators version 2.0.4 (https://mpimet.mpg.de...</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-40286c3f-2c7a-4bdd-8a3d-264ba805f447' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-40286c3f-2c7a-4bdd-8a3d-264ba805f447' 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>: 840</li><li><span class='xr-has-index'>lat</span>: 64</li><li><span class='xr-has-index'>lon</span>: 128</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-83275c85-469b-4367-8478-5724bf7ac376' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83275c85-469b-4367-8478-5724bf7ac376' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>1952-01-16T12:00:00 ... 2021-12-...</div><input id='attrs-910ed674-5a57-41a1-9e4b-1d4c7dfb4a02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-910ed674-5a57-41a1-9e4b-1d4c7dfb4a02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6fe6556-a74c-4c85-afc2-c092bf90f5fa' class='xr-var-data-in' type='checkbox'><label for='data-e6fe6556-a74c-4c85-afc2-c092bf90f5fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1952-01-16T12:00:00.000000000&#x27;, &#x27;1952-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1952-03-16T12:00:00.000000000&#x27;, ..., &#x27;2021-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-16T00:00:00.000000000&#x27;, &#x27;2021-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span 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'>-180.0 -177.2 ... 174.4 177.2</div><input id='attrs-9cf3efd0-f5cc-4093-95c5-59a5a95e5a6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cf3efd0-f5cc-4093-95c5-59a5a95e5a6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b93078ed-6508-404c-a7b2-3b7877287626' class='xr-var-data-in' type='checkbox'><label for='data-b93078ed-6508-404c-a7b2-3b7877287626' 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</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd></dl></div><div class='xr-var-data'><pre>array([-180.    , -177.1875, -174.375 , -171.5625, -168.75  , -165.9375,\n",
       "       -163.125 , -160.3125, -157.5   , -154.6875, -151.875 , -149.0625,\n",
       "       -146.25  , -143.4375, -140.625 , -137.8125, -135.    , -132.1875,\n",
       "       -129.375 , -126.5625, -123.75  , -120.9375, -118.125 , -115.3125,\n",
       "       -112.5   , -109.6875, -106.875 , -104.0625, -101.25  ,  -98.4375,\n",
       "        -95.625 ,  -92.8125,  -90.    ,  -87.1875,  -84.375 ,  -81.5625,\n",
       "        -78.75  ,  -75.9375,  -73.125 ,  -70.3125,  -67.5   ,  -64.6875,\n",
       "        -61.875 ,  -59.0625,  -56.25  ,  -53.4375,  -50.625 ,  -47.8125,\n",
       "        -45.    ,  -42.1875,  -39.375 ,  -36.5625,  -33.75  ,  -30.9375,\n",
       "        -28.125 ,  -25.3125,  -22.5   ,  -19.6875,  -16.875 ,  -14.0625,\n",
       "        -11.25  ,   -8.4375,   -5.625 ,   -2.8125,    0.    ,    2.8125,\n",
       "          5.625 ,    8.4375,   11.25  ,   14.0625,   16.875 ,   19.6875,\n",
       "         22.5   ,   25.3125,   28.125 ,   30.9375,   33.75  ,   36.5625,\n",
       "         39.375 ,   42.1875,   45.    ,   47.8125,   50.625 ,   53.4375,\n",
       "         56.25  ,   59.0625,   61.875 ,   64.6875,   67.5   ,   70.3125,\n",
       "         73.125 ,   75.9375,   78.75  ,   81.5625,   84.375 ,   87.1875,\n",
       "         90.    ,   92.8125,   95.625 ,   98.4375,  101.25  ,  104.0625,\n",
       "        106.875 ,  109.6875,  112.5   ,  115.3125,  118.125 ,  120.9375,\n",
       "        123.75  ,  126.5625,  129.375 ,  132.1875,  135.    ,  137.8125,\n",
       "        140.625 ,  143.4375,  146.25  ,  149.0625,  151.875 ,  154.6875,\n",
       "        157.5   ,  160.3125,  163.125 ,  165.9375,  168.75  ,  171.5625,\n",
       "        174.375 ,  177.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>87.86 85.1 82.31 ... -85.1 -87.86</div><input id='attrs-34adcef5-fe2d-4b32-aa97-5818e4a7a2ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34adcef5-fe2d-4b32-aa97-5818e4a7a2ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34bbc78f-8486-4916-9b16-98b1b270408b' class='xr-var-data-in' type='checkbox'><label for='data-34bbc78f-8486-4916-9b16-98b1b270408b' 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</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd></dl></div><div class='xr-var-data'><pre>array([ 87.863799,  85.096527,  82.312913,  79.525607,  76.7369  ,  73.947515,\n",
       "        71.157752,  68.367756,  65.577607,  62.787352,  59.99702 ,  57.206632,\n",
       "        54.4162  ,  51.625734,  48.835241,  46.044727,  43.254195,  40.463648,\n",
       "        37.67309 ,  34.882521,  32.091944,  29.30136 ,  26.510769,  23.720174,\n",
       "        20.929574,  18.138971,  15.348365,  12.557756,   9.767146,   6.976534,\n",
       "         4.185921,   1.395307,  -1.395307,  -4.185921,  -6.976534,  -9.767146,\n",
       "       -12.557756, -15.348365, -18.138971, -20.929574, -23.720174, -26.510769,\n",
       "       -29.30136 , -32.091944, -34.882521, -37.67309 , -40.463648, -43.254195,\n",
       "       -46.044727, -48.835241, -51.625734, -54.4162  , -57.206632, -59.99702 ,\n",
       "       -62.787352, -65.577607, -68.367756, -71.157752, -73.947515, -76.7369  ,\n",
       "       -79.525607, -82.312913, -85.096527, -87.863799])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9aa233ee-7196-462e-bb73-87dbd2db7b31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9aa233ee-7196-462e-bb73-87dbd2db7b31' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TU_precip</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4e64f813-f036-40ad-b05e-0c4962e68d1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e64f813-f036-40ad-b05e-0c4962e68d1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee1a4155-f9f6-4763-baa1-5ee66d219b8e' class='xr-var-data-in' type='checkbox'><label for='data-ee1a4155-f9f6-4763-baa1-5ee66d219b8e' 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>TU</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>32</dd><dt><span>orignal_name :</span></dt><dd>PRCP03</dd><dt><span>standard_name :</span></dt><dd>tritium in precipitation</dd></dl></div><div class='xr-var-data'><pre>[6881280 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>precip</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe826816-c907-4764-9df6-64da43cd35fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe826816-c907-4764-9df6-64da43cd35fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c9ec179-6edb-4ec0-94b5-7839d6e8eae0' class='xr-var-data-in' type='checkbox'><label for='data-2c9ec179-6edb-4ec0-94b5-7839d6e8eae0' 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>precipitation</dd><dt><span>units :</span></dt><dd>mm/month</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>32</dd><dt><span>orignal_name :</span></dt><dd>PRCP</dd></dl></div><div class='xr-var-data'><pre>[6881280 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp2</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba099aa6-381d-4350-af45-40c39dbd8c3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba099aa6-381d-4350-af45-40c39dbd8c3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cee8fa18-146d-4bbe-b914-e50433157750' class='xr-var-data-in' type='checkbox'><label for='data-cee8fa18-146d-4bbe-b914-e50433157750' 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>2m air temperature</dd><dt><span>units :</span></dt><dd>°C</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>32</dd><dt><span>orignal_name :</span></dt><dd>T2</dd></dl></div><div class='xr-var-data'><pre>[6881280 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>d18O_precip</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7e0c0c3-df97-4d56-b961-3c70d5fa12c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7e0c0c3-df97-4d56-b961-3c70d5fa12c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-255cb251-36d7-478c-920e-f43f89f19348' class='xr-var-data-in' type='checkbox'><label for='data-255cb251-36d7-478c-920e-f43f89f19348' 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>‰</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>32</dd><dt><span>orignal_name :</span></dt><dd>PRCP01</dd><dt><span>standard_name :</span></dt><dd>d18O in precipitation</dd></dl></div><div class='xr-var-data'><pre>[6881280 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dex_precip</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e164e7a3-f268-47b6-9d1c-01dc3a893f2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e164e7a3-f268-47b6-9d1c-01dc3a893f2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dd3df51-956a-460a-bcc9-1157866dad73' class='xr-var-data-in' type='checkbox'><label for='data-8dd3df51-956a-460a-bcc9-1157866dad73' 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>‰</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>32</dd><dt><span>orignal_name :</span></dt><dd>PRCP02</dd><dt><span>standard_name :</span></dt><dd>d-excess in precipitation</dd></dl></div><div class='xr-var-data'><pre>[6881280 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>evap</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3461610-d840-42f2-ba96-4fa930e1f364' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3461610-d840-42f2-ba96-4fa930e1f364' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18efd0de-f8e2-4ee8-b450-426e2b863587' class='xr-var-data-in' type='checkbox'><label for='data-18efd0de-f8e2-4ee8-b450-426e2b863587' 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>surface upward latent heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>32</dd><dt><span>orignal_name :</span></dt><dd>EVAP</dd></dl></div><div class='xr-var-data'><pre>[6881280 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UQ</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c850805-cc1e-4725-b5c9-0aae42322304' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c850805-cc1e-4725-b5c9-0aae42322304' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d63fbcf8-ebdc-4d05-a8da-d8963c19ea40' class='xr-var-data-in' type='checkbox'><label for='data-d63fbcf8-ebdc-4d05-a8da-d8963c19ea40' 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>kg m-1 s-1</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>32</dd><dt><span>orignal_name :</span></dt><dd>UQ</dd><dt><span>standard_name :</span></dt><dd>vert. int. water vapor transport - u component</dd></dl></div><div class='xr-var-data'><pre>[6881280 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VQ</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5392ea9-28c6-489c-899a-e6235a1b2b82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5392ea9-28c6-489c-899a-e6235a1b2b82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-934a487c-ce33-4e3e-8c2d-dd512e82e2d1' class='xr-var-data-in' type='checkbox'><label for='data-934a487c-ce33-4e3e-8c2d-dd512e82e2d1' 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>kg m-1 s-1</dd><dt><span>CDI_grid_type :</span></dt><dd>gaussian</dd><dt><span>CDI_grid_num_LPE :</span></dt><dd>32</dd><dt><span>orignal_name :</span></dt><dd>VQ</dd><dt><span>standard_name :</span></dt><dd>vert. int. water vapor transport - v component</dd></dl></div><div class='xr-var-data'><pre>[6881280 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d4cd9ed-e6a2-41fb-9110-02f5da825738' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d4cd9ed-e6a2-41fb-9110-02f5da825738' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-33c51fd1-aa30-4616-a9bd-2680c92f5816' class='xr-index-data-in' type='checkbox'/><label for='index-33c51fd1-aa30-4616-a9bd-2680c92f5816' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1952-01-16 12:00:00&#x27;, &#x27;1952-02-15 12:00:00&#x27;,\n",
       "               &#x27;1952-03-16 12:00:00&#x27;, &#x27;1952-04-16 00:00:00&#x27;,\n",
       "               &#x27;1952-05-16 12:00:00&#x27;, &#x27;1952-06-16 00:00:00&#x27;,\n",
       "               &#x27;1952-07-16 12:00:00&#x27;, &#x27;1952-08-16 12:00:00&#x27;,\n",
       "               &#x27;1952-09-16 00:00:00&#x27;, &#x27;1952-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2021-03-16 12:00:00&#x27;, &#x27;2021-04-16 00:00:00&#x27;,\n",
       "               &#x27;2021-05-16 12:00:00&#x27;, &#x27;2021-06-16 00:00:00&#x27;,\n",
       "               &#x27;2021-07-16 12:00:00&#x27;, &#x27;2021-08-16 12:00:00&#x27;,\n",
       "               &#x27;2021-09-16 00:00:00&#x27;, &#x27;2021-10-16 12:00:00&#x27;,\n",
       "               &#x27;2021-11-16 00:00:00&#x27;, &#x27;2021-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=840, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7061d46f-1569-4f01-b62d-72f06af1744f' class='xr-index-data-in' type='checkbox'/><label for='index-7061d46f-1569-4f01-b62d-72f06af1744f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([   -180.0, -177.1875,  -174.375, -171.5625,   -168.75, -165.9375,\n",
       "               -163.125, -160.3125,    -157.5, -154.6875,\n",
       "              ...\n",
       "                151.875,  154.6875,     157.5,  160.3125,   163.125,  165.9375,\n",
       "                 168.75,  171.5625,   174.375,  177.1875],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=128))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8205431c-2b4b-45f0-bea6-f8e60051ae55' class='xr-index-data-in' type='checkbox'/><label for='index-8205431c-2b4b-45f0-bea6-f8e60051ae55' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([  87.86379883923263,   85.09652698831736,   82.31291294788628,\n",
       "                79.52560657265944,   76.73689968036831,   73.94751515398967,\n",
       "                71.15775201158732,   68.36775610831317,   65.57760701082782,\n",
       "               62.787351798963066,  59.997020108491306,  57.206631527643246,\n",
       "                54.41619952608621,   51.62573367493825,   48.83524096625059,\n",
       "                46.04472663110168,   43.25419466535094,   40.46364817811504,\n",
       "               37.673089629045336,   34.88252099377346,   32.09194388174401,\n",
       "               29.301359621762735,  26.510769325210994,   23.72017393353475,\n",
       "               20.929574254489513,  18.138970990239354,  15.348364759491494,\n",
       "               12.557756115230678,   9.767145559195567,   6.976533553948636,\n",
       "               4.1859205331891545,  1.3953069108194962, -1.3953069108194962,\n",
       "              -4.1859205331891545,  -6.976533553948636,  -9.767145559195567,\n",
       "              -12.557756115230678, -15.348364759491494, -18.138970990239354,\n",
       "              -20.929574254489513,  -23.72017393353475, -26.510769325210994,\n",
       "              -29.301359621762735,  -32.09194388174401,  -34.88252099377346,\n",
       "              -37.673089629045336,  -40.46364817811504,  -43.25419466535094,\n",
       "               -46.04472663110168,  -48.83524096625059,  -51.62573367493825,\n",
       "               -54.41619952608621, -57.206631527643246, -59.997020108491306,\n",
       "              -62.787351798963066,  -65.57760701082782,  -68.36775610831317,\n",
       "               -71.15775201158732,  -73.94751515398967,  -76.73689968036831,\n",
       "               -79.52560657265944,  -82.31291294788628,  -85.09652698831736,\n",
       "               -87.86379883923263],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a589efa2-920c-4a70-b33f-5e3a3ec39400' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a589efa2-920c-4a70-b33f-5e3a3ec39400' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.0.4 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>history :</span></dt><dd>Thu Sep 07 17:47:18 2023: cdo -f nc -setreftime,1952-01-01 -chvar,PRCP03,TU_precip -divc,40 -mulc,18 -div /data44/acauquoin/miroc5-iso/out/bomb_tritium_era5/post/prcpHTO_miroc5_catted_mm.nc /data44/acauquoin/miroc5-iso/out/bomb_tritium_era5/post/prcp_miroc5_catted_mm.nc /data44/acauquoin/miroc5-iso/out/bomb_tritium_era5/post/bomb_tritium_era5_miroc5_monmean.prcpTU.nc\n",
       "Thu Sep 07 17:47:07 2023: cdo -f nc -setmisstoc,0. -ifthen -nec,0. prcp_miroc5_catted_mm.nc prcpHTO_miroc5_catted_mm.nc dummy\n",
       "Thu Sep 07 17:47:04 2023: cdo -f nc -mulc,2.628e6 prcpHTO_miroc5_catted_mm.nc dummy\n",
       "Thu Sep 07 17:46:57 2023: cdo mergetime prcpHTO_1952 prcpHTO_1953 prcpHTO_1954 prcpHTO_1955 prcpHTO_1956 prcpHTO_1957 prcpHTO_1958 prcpHTO_1959 prcpHTO_1960 prcpHTO_1961 prcpHTO_1962 prcpHTO_1963 prcpHTO_1964 prcpHTO_1965 prcpHTO_1966 prcpHTO_1967 prcpHTO_1968 prcpHTO_1969 prcpHTO_1970 prcpHTO_1971 prcpHTO_1972 prcpHTO_1973 prcpHTO_1974 prcpHTO_1975 prcpHTO_1976 prcpHTO_1977 prcpHTO_1978 prcpHTO_1979 prcpHTO_1980 prcpHTO_1981 prcpHTO_1982 prcpHTO_1983 prcpHTO_1984 prcpHTO_1985 prcpHTO_1986 prcpHTO_1987 prcpHTO_1988 prcpHTO_1989 prcpHTO_1990 prcpHTO_1991 prcpHTO_1992 prcpHTO_1993 prcpHTO_1994 prcpHTO_1995 prcpHTO_1996 prcpHTO_1997 prcpHTO_1998 prcpHTO_1999 prcpHTO_2000 prcpHTO_2001 prcpHTO_2002 prcpHTO_2003 prcpHTO_2004 prcpHTO_2005 prcpHTO_2006 prcpHTO_2007 prcpHTO_2008 prcpHTO_2009 prcpHTO_2010 prcpHTO_2011 prcpHTO_2012 prcpHTO_2013 prcpHTO_2014 prcpHTO_2015 prcpHTO_2016 prcpHTO_2017 prcpHTO_2018 prcpHTO_2019 prcpHTO_2020 prcpHTO_2021 prcpHTO_miroc5_catted_mm.nc</dd><dt><span>CDO :</span></dt><dd>Climate Data Operators version 2.0.4 (https://mpimet.mpg.de/cdo)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (time: 840, lat: 64, lon: 128)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 1952-01-16T12:00:00 ... 2021-12-16T12:...\n",
       "  * lon          (lon) float64 -180.0 -177.2 -174.4 -171.6 ... 171.6 174.4 177.2\n",
       "  * lat          (lat) float64 87.86 85.1 82.31 79.53 ... -82.31 -85.1 -87.86\n",
       "Data variables:\n",
       "    TU_precip    (time, lat, lon) float32 ...\n",
       "    precip       (time, lat, lon) float32 ...\n",
       "    temp2        (time, lat, lon) float32 ...\n",
       "    d18O_precip  (time, lat, lon) float32 ...\n",
       "    dex_precip   (time, lat, lon) float32 ...\n",
       "    evap         (time, lat, lon) float32 ...\n",
       "    UQ           (time, lat, lon) float32 ...\n",
       "    VQ           (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 2.0.4 (https://mpimet.mpg.de...\n",
       "    Conventions:  CF-1.0\n",
       "    history:      Thu Sep 07 17:47:18 2023: cdo -f nc -setreftime,1952-01-01 ...\n",
       "    CDO:          Climate Data Operators version 2.0.4 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_era5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6eb0c6f-7d6a-4b50-b933-327d5a71f85d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Map of tritium in precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "852f4eb9-9f3e-4234-883b-4e83a6fd14df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def map_tritium(ndg, time_min, time_max, lon_min, lon_max, lat_min, lat_max):\n",
    "    \n",
    "    global f\n",
    "    \n",
    "    # ERA5 or JRA55\n",
    "    if ndg == 'era5':\n",
    "        ds = ds_era5\n",
    "    elif ndg == 'jra55':\n",
    "        ds = ds_jra55\n",
    "    \n",
    "    # conversion of some variables\n",
    "    time_min = time_min.strftime(\"%Y-%m\")\n",
    "    time_max = time_max.strftime(\"%Y-%m\")\n",
    "    lon_min = float(lon_min)\n",
    "    lon_max = float(lon_max)\n",
    "    lat_min = float(lat_min)\n",
    "    lat_max = float(lat_max)\n",
    "    if time_min == time_max:\n",
    "        title = 'Mean values for ' + ds.sel(time=time_min).time.dt.strftime('%Y-%m').values[0]\n",
    "    else:\n",
    "        title = 'Mean values over the period ' + ds.sel(time=time_min).time.dt.strftime('%Y-%m').values[0] + ' to ' + ds.sel(time=time_max).time.dt.strftime('%Y-%m').values[0]\n",
    "    \n",
    "    f,ax = plt.subplots(subplot_kw={'projection':ccrs.PlateCarree()})\n",
    "    \n",
    "    TU_weight = ds['TU_precip'] * ds['precip']\n",
    "    num = TU_weight.sel(time=slice(time_min, time_max), lon=slice(lon_min, lon_max), lat=slice(lat_max, lat_min)).sum(axis=0)\n",
    "    denom = ds['precip'].sel(time=slice(time_min, time_max), lon=slice(lon_min, lon_max), lat=slice(lat_max, lat_min)).sum(axis=0)\n",
    "    TU_mean = num / denom\n",
    "    \n",
    "    TU_mean.plot.pcolormesh(\n",
    "        cmap = plt.get_cmap('RdYlBu_r'),ax=ax,\n",
    "        cbar_kwargs={\n",
    "            'label':'Tritium in precipitation (TU)', \n",
    "            'orientation':'horizontal',\n",
    "            'pad':0.03, \n",
    "            'aspect':30\n",
    "        }, \n",
    "        transform=ccrs.PlateCarree(), \n",
    "        x='lon',y='lat', levels=30, \n",
    "        edgecolors='face', shading='nearest')\n",
    "    \n",
    "    ax.coastlines()\n",
    "    ax.set_title(title)\n",
    "    \n",
    "def save_image_tritium(b):\n",
    "    f.savefig('tritium_map.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1afcc9b9-20f3-4fa8-883f-de6e634f0d71",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def build_tritium_map():\n",
    "    \n",
    "    # create widget to select the simulation nudged to ERA5 or JRA-55\n",
    "    ndg = widgets.Dropdown(options=[('ERA5', 'era5'), ('JRA-55', 'jra55')], value='era5', \n",
    "                           description='nudging:', \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    \n",
    "    # create widget for coordinates selection\n",
    "    lon_min = widgets.Text(value=\"-180\", \n",
    "                           placeholder='between -180 and 180', \n",
    "                           description='lon min:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    lon_max = widgets.Text(value=\"180\", \n",
    "                           placeholder='between -180 and 180', \n",
    "                           description='lon max:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    lat_min = widgets.Text(value=\"-90\", \n",
    "                           placeholder='between -90 and 90', \n",
    "                           description='lat min:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    lat_max = widgets.Text(value=\"90\", \n",
    "                           placeholder='between -90 and 90', \n",
    "                           description='lat max:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    coord_lon = lon_min, lon_max\n",
    "    coord_lat = lat_min, lat_max\n",
    "    \n",
    "    # create widget for the date range\n",
    "    time_min = widgets.DatePicker(description='date min:', disabled=False, continuous_update=False, value = datetime.date(2021,1,1))\n",
    "    time_max = widgets.DatePicker(description='date max:', disabled=False, continuous_update=False, value = datetime.date(2021,12,1))\n",
    "    time_input = time_min, time_max\n",
    "    \n",
    "    # Arrange the control inputs:\n",
    "    controls = widgets.HBox([\n",
    "        ndg,\n",
    "        widgets.VBox(coord_lon),\n",
    "        widgets.VBox(coord_lat),\n",
    "        widgets.VBox(time_input)\n",
    "    ])\n",
    "    \n",
    "    # link the map_tritium function with the text input\n",
    "    plot = widgets.interactive_output(map_tritium, \n",
    "                                      {'ndg': ndg, \n",
    "                                       'time_min': time_min,\n",
    "                                       'time_max': time_max,\n",
    "                                       'lon_min': lon_min,\n",
    "                                       'lon_max': lon_max,\n",
    "                                       'lat_min': lat_min,\n",
    "                                       'lat_max': lat_max\n",
    "                                      })\n",
    "    \n",
    "    # button to save the map\n",
    "    btn_save = widgets.Button(description=\"save figure\", icon='file-pdf', layout=widgets.Layout(margin='1em 0 0 0'))\n",
    "    btn_save.on_click(save_image_tritium)\n",
    "\n",
    "    # Build the UI panel, the controls at the top, the cdo output at the bottom\n",
    "    comment1 = widgets.HTML('Note 1: the day in the date pickers is not considered.', layout=widgets.Layout(margin='1em 1em 0 0'))\n",
    "    comment2 = widgets.HTML('Note 2: The average value of each grid cell for the period between \\'date min\\' and \\'date max\\' is calculated.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment3 = widgets.HTML('Note 3: the colorbar scale is updated according to the mean tritium values in the considered area.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment4 = widgets.HTML('Note 4: for the coordinate boxes, type \\'Enter\\' or click on another box to update your change.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment5 = widgets.HTML('Note 5: the figure is saved in the current repository as tritium_map.pdf.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    ui = widgets.VBox([plot, controls, btn_save, comment1, comment2, comment3, comment4, comment5])\n",
    "    \n",
    "    return ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a52dafbb-b2a3-43fe-9a2e-096d2eb11e5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fea043b3fa640b4bd20f2e97790974e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Output(), HBox(children=(Dropdown(description='nudging:', layout=Layout(width='200px'), options…"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_tritium_map()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f52128-208b-49e9-bbef-7ef8fe775ef5",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Plot MIROC5-iso data at selected location and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa2af30a-89ce-437a-a470-1e1574644355",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_and_save_data_site(lon_station, lat_station, \n",
    "                            ndg, var, date_min, date_max, \n",
    "                            logscale, \n",
    "                            save_tu, save_precip, save_evap, save_temp2, save_d18Op, save_dexp):\n",
    "    \n",
    "    global fig\n",
    "    \n",
    "    # nudging choice\n",
    "    if ndg == 'era5':\n",
    "        ds = ds_era5\n",
    "    elif ndg == 'jra55':\n",
    "        ds = ds_jra55\n",
    "    save_var = [save_tu, save_precip, save_evap, save_temp2, save_d18Op, save_dexp]\n",
    "    \n",
    "    # log scale\n",
    "    if logscale == True:\n",
    "        yscale = 'log'\n",
    "    else:\n",
    "        yscale = 'linear'\n",
    "    \n",
    "    # conversion of some variables\n",
    "    date_min = date_min.strftime(\"%Y-%m\")\n",
    "    date_max = date_max.strftime(\"%Y-%m\")\n",
    "    lon_station = float(lon_station)\n",
    "    lat_station = float(lat_station)\n",
    "    \n",
    "    # selection of date\n",
    "    ds_sel_date = ds.sel(time=slice(date_min, date_max))\n",
    "    \n",
    "    # plotting the data on the nearest grid cell of the chosen location\n",
    "    fig, ax = plt.subplots(figsize=(8,3), constrained_layout=True)\n",
    "    ax = ds_sel_date[var].sel(lon=lon_station, lat=lat_station, \n",
    "                              method='nearest').plot(x='time', \n",
    "                                                     ax=ax, yscale=yscale)\n",
    "    \n",
    "    # download button\n",
    "    cols = []\n",
    "    var_plot = ['TU_precip', 'precip', 'evap', 'temp2', 'd18O_precip', 'dex_precip']\n",
    "    for i in range(len(var_plot)):\n",
    "        if save_var[i] == True:\n",
    "            cols.append(var_plot[i])\n",
    "    ds_sel_date = ds_sel_date.sel(lon=lon_station, lat=lat_station, method='nearest')\n",
    "    df = ds_sel_date.to_dataframe()\n",
    "    df = df[cols]\n",
    "    df.index = df.index.strftime('%Y-%m')\n",
    "    w = widgets.VBox([solara.FileDownload.widget(data=df.to_csv(sep=';'), filename=\"data_miroc5-iso.csv\", layout=widgets.Layout(margin='1em 0 0 0'))])\n",
    "    display(w)\n",
    "    \n",
    "    # print message\n",
    "    print(\"\")\n",
    "    print(\"Information of the plot:\")\n",
    "    print(\"Variable:\", var)\n",
    "    print(\"Longitude:\", lon_station, \"; Latitude:\", lat_station)\n",
    "    print(\"Nudging:\", ndg)\n",
    "    print(\"Date range:\", date_min, \"to\", date_max)\n",
    "\n",
    "    \n",
    "def save_monitoring_plot(b):\n",
    "    fig.savefig('monitoring_plot.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6176de5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# update the tritium time series based on coordinates values\n",
    "def build_monitoring_plot():\n",
    "    \n",
    "    # create widget for coordinates selection\n",
    "    lon_station = widgets.Text(value=\"0\", \n",
    "                               placeholder='between -180 and 180', description='Longitude:', \n",
    "                               disabled=False, continuous_update=False, layout=widgets.Layout(width='250px'))\n",
    "    lat_station = widgets.Text(value=\"0\", \n",
    "                               placeholder='between -90 and 90', description='Latitude:', \n",
    "                               disabled=False, continuous_update=False, layout=widgets.Layout(width='250px'))\n",
    "    coord_input = lon_station, lat_station\n",
    "    \n",
    "    # create widget to select the simulation nudged to ERA5 or JRA-55\n",
    "    ndg = widgets.Dropdown(options=[('ERA5', 'era5'), ('JRA-55', 'jra55')], value='era5', \n",
    "                         description='Nudging:', continuous_update=False, layout=widgets.Layout(width='275px'))\n",
    "    \n",
    "    # create widget to select the variable\n",
    "    selvar = widgets.Dropdown(options=[('TU in precipitation', 'TU_precip'), \n",
    "                                       ('2m air temperature', 'temp2'), \n",
    "                                       ('precipitation', 'precip'),\n",
    "                                       ('evapotranspiration', 'evap'),\n",
    "                                       ('d18O in precipitation', 'd18O_precip'),\n",
    "                                       ('d-excess in precipitation', 'dex_precip')], \n",
    "                              value='TU_precip', description='Variable:',\n",
    "                              continuous_update=False,\n",
    "                              layout=widgets.Layout(width='275px'))\n",
    "    \n",
    "    # create a widget to set the y-scale on log or linear scale\n",
    "    check_logscale = widgets.Checkbox(value=False, description=\"log scale\", continuous_update=False,\n",
    "                                      indent=False, layout=widgets.Layout(margin='0 0 0 2.5em'))\n",
    "    \n",
    "    # create widget to select the variables to save in the csv file\n",
    "    data = [\"TU in precipitation\", \"Precipitation\", \"Evapotranspiration\", \"2m air temperature\", \"d18O in precipitation\", \"d-excess in precipitation\"]\n",
    "    checkboxes = [widgets.Checkbox(value=False, description=label, indent=False, continuous_update=False,\n",
    "                                   layout=widgets.Layout(width='175px', margin='0 0 0 0')) for label in data]\n",
    "    \n",
    "    # create widget for the date range\n",
    "    date_min = widgets.DatePicker(description='Date min:', disabled=False, continuous_update=False, value = datetime.date(1952,1,1))\n",
    "    date_max = widgets.DatePicker(description='Date max:', disabled=False, continuous_update=False, value = datetime.date(2021,12,1))\n",
    "    date_input = date_min, date_max\n",
    "    \n",
    "    # button to save the time series\n",
    "    btn_save = widgets.Button(description=\"save figure\", icon='file-pdf')\n",
    "    btn_save.on_click(save_monitoring_plot)\n",
    "    \n",
    "    # Arrange the control inputs:\n",
    "    controls = widgets.HBox([\n",
    "        widgets.VBox(coord_input),\n",
    "        widgets.VBox(date_input),\n",
    "        widgets.VBox([ndg, selvar]),\n",
    "        check_logscale,\n",
    "        btn_save\n",
    "    ])\n",
    "    \n",
    "    # link the data extraction command with the text input\n",
    "    plot = widgets.interactive_output(plot_and_save_data_site, \n",
    "                                      {'lon_station': lon_station, 'lat_station': lat_station,\n",
    "                                       'ndg': ndg, 'var': selvar,\n",
    "                                       'date_min': date_min, 'date_max': date_max,\n",
    "                                       'logscale': check_logscale,\n",
    "                                       'save_tu': checkboxes[0], \n",
    "                                       'save_precip': checkboxes[1],\n",
    "                                       'save_evap': checkboxes[2],\n",
    "                                       'save_temp2': checkboxes[3],\n",
    "                                       'save_d18Op': checkboxes[4],\n",
    "                                       'save_dexp': checkboxes[5]})\n",
    "    \n",
    "    # Build the UI panel, the controls at the top, the cdo output at the bottom\n",
    "    label = widgets.HTML('<b>Select the variables to download:</b>', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment1 = widgets.HTML('Note 1: the day in the date pickers is not considered.', layout=widgets.Layout(margin='1em 1em 0 0'))\n",
    "    comment2 = widgets.HTML('Note 2: for the coordinate boxes, type \\'Enter\\' or click on another box to update your change.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment3 = widgets.HTML('Note 3: the evapotranspiration is expressed as latent heat flux in W m<sup>-2</sup>.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment4 = widgets.HTML('Note 4: the figure is saved in the current repository as monitoring_plot.pdf.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    ui = widgets.VBox([widgets.HBox([label, widgets.HBox(children=checkboxes)]), plot, controls, comment1, comment2, comment3, comment4])\n",
    "    \n",
    "    return ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4c4398ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f89cff63ac042c09b7597f9b1f58fdb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(HTML(value='<b>Select the variables to download:</b>', layout=Layout(margin='0 1…"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_monitoring_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af807a2-1c19-430a-b7eb-4379fb072f8c",
   "metadata": {},
   "source": [
    "## Map of water vapor transport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b7624ac-f7c7-4373-a6f6-27cd2c43c864",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def map_water_vapor_transport(ndg, date, lon_min, lon_max, lat_min, lat_max, spacing):\n",
    "    \n",
    "    global f_wt\n",
    "    \n",
    "    # ERA5 or JRA55\n",
    "    if ndg == 'era5':\n",
    "        ds = ds_era5\n",
    "    elif ndg == 'jra55':\n",
    "        ds = ds_jra55\n",
    "    \n",
    "    # conversion of some variables\n",
    "    date    = date.strftime(\"%Y-%m\")\n",
    "    lon_min = float(lon_min)\n",
    "    lon_max = float(lon_max)\n",
    "    lat_min = float(lat_min)\n",
    "    lat_max = float(lat_max)\n",
    "    spacing = int(spacing)\n",
    "    \n",
    "    title = 'Vertically integrated water vapor transport for ' + ds.sel(time=date).time.dt.strftime('%Y-%m').values[0]\n",
    "    \n",
    "    # Select month, coordinates, and calculate wind speed\n",
    "    ds_quiver = ds[['UQ', 'VQ']].sel(time=date, lon=slice(lon_min, lon_max, spacing), lat=slice(lat_max, lat_min, spacing)).squeeze()\n",
    "    ds_u = ds['UQ'].sel(time=date, lon=slice(lon_min, lon_max), lat=slice(lat_max, lat_min)).squeeze()\n",
    "    ds_v = ds['VQ'].sel(time=date, lon=slice(lon_min, lon_max), lat=slice(lat_max, lat_min)).squeeze()\n",
    "    ds_speed = np.sqrt(ds_v**2 + ds_u**2)\n",
    "    \n",
    "    # figure\n",
    "    f_wt,ax = plt.subplots(subplot_kw={'projection':ccrs.PlateCarree()})\n",
    "    \n",
    "    ds_speed.plot.pcolormesh(\n",
    "        cmap = plt.get_cmap('BrBG'), ax=ax,\n",
    "        cbar_kwargs={\n",
    "            'label':'Vertically integrated water vapor transport (kg.m$^{-1}$.s$^{-1}$)', \n",
    "            'orientation':'horizontal',\n",
    "            'pad':0.03, \n",
    "            'aspect':30\n",
    "        },\n",
    "        transform=ccrs.PlateCarree(), \n",
    "        x='lon',y='lat', levels=30, \n",
    "        edgecolors='face', shading='nearest')\n",
    "    \n",
    "    ax.coastlines()\n",
    "    ax.set_title(title)\n",
    "    Q = ax.quiver(ds_quiver['lon'], ds_quiver['lat'], ds_quiver['UQ'], ds_quiver['VQ'], scale=None, transform=ccrs.PlateCarree())\n",
    "    Q._init()\n",
    "    assert isinstance(Q.scale, float)\n",
    "    ax.quiver(ds_quiver['lon'], ds_quiver['lat'], ds_quiver['UQ'], ds_quiver['VQ'], scale_units='inches', scale=Q.scale, transform=ccrs.PlateCarree())\n",
    "    \n",
    "def save_image_transport(b):\n",
    "    f_wt.savefig('water_vapor_transport_map.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ebbffc2d-0afb-4968-82e8-2273acc44383",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def build_water_vapor_trans_map():\n",
    "    \n",
    "    # create widget to select the simulation nudged to ERA5 or JRA-55\n",
    "    ndg = widgets.Dropdown(options=[('ERA5', 'era5'), ('JRA-55', 'jra55')], value='era5', \n",
    "                           description='nudging:', \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='250px'))\n",
    "    \n",
    "    # create widget for coordinates selection\n",
    "    lon_min = widgets.Text(value=\"-180\", \n",
    "                           placeholder='between -180 and 180', \n",
    "                           description='lon min:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    lon_max = widgets.Text(value=\"180\", \n",
    "                           placeholder='between -180 and 180', \n",
    "                           description='lon max:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    lat_min = widgets.Text(value=\"-90\", \n",
    "                           placeholder='between -90 and 90', \n",
    "                           description='lat min:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    lat_max = widgets.Text(value=\"90\", \n",
    "                           placeholder='between -90 and 90', \n",
    "                           description='lat max:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='200px'))\n",
    "    coord_lon = lon_min, lon_max\n",
    "    coord_lat = lat_min, lat_max\n",
    "    \n",
    "    # create widget for coordinates spacing (for the vectors)\n",
    "    spacing = widgets.Text(value=\"1\", \n",
    "                           placeholder='higher or equal to 1', \n",
    "                           description='vector spacing:', \n",
    "                           disabled=False, \n",
    "                           continuous_update=False,\n",
    "                           layout=widgets.Layout(width='250px'),\n",
    "                           style= {'description_width': 'initial'})\n",
    "    \n",
    "    # create widget for the date selection\n",
    "    date = widgets.DatePicker(description='date:', disabled=False, continuous_update=False, value = datetime.date(2021,1,1))\n",
    "    \n",
    "    # Arrange the control inputs:\n",
    "    controls = widgets.HBox([\n",
    "        widgets.VBox([ndg, spacing]),\n",
    "        date,\n",
    "        widgets.VBox(coord_lon),\n",
    "        widgets.VBox(coord_lat)\n",
    "    ])\n",
    "    \n",
    "    # link the map_tritium function with the text input\n",
    "    plot = widgets.interactive_output(map_water_vapor_transport, \n",
    "                                      {'ndg': ndg, \n",
    "                                       'date': date,\n",
    "                                       'lon_min': lon_min,\n",
    "                                       'lon_max': lon_max,\n",
    "                                       'lat_min': lat_min,\n",
    "                                       'lat_max': lat_max,\n",
    "                                       'spacing': spacing\n",
    "                                      })\n",
    "    \n",
    "    # button to save the map\n",
    "    btn_save = widgets.Button(description=\"save figure\", icon='file-pdf', layout=widgets.Layout(margin='1em 0 0 0'))\n",
    "    btn_save.on_click(save_image_transport)\n",
    "\n",
    "    # Build the UI panel, the controls at the top, the cdo output at the bottom\n",
    "    comment1 = widgets.HTML('Note 1: the day in the date pickers is not considered.', layout=widgets.Layout(margin='1em 1em 0 0'))\n",
    "    comment2 = widgets.HTML('Note 2: the colorbar scale is updated according to the mean tritium values in the considered area.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment3 = widgets.HTML('Note 3: for the coordinate boxes, type \\'Enter\\' or click on another box to update your change.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment4 = widgets.HTML('Note 4: spacing parameter controls the space between vectors (1: every grid cell, 2: every two grid cells, ...).', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    comment5 = widgets.HTML('Note 5: the figure is saved in the current repository as water_vapor_transport_map.pdf.', layout=widgets.Layout(margin='0 1em 0 0'))\n",
    "    ui = widgets.VBox([plot, controls, btn_save, comment1, comment2, comment3, comment4, comment5])\n",
    "    \n",
    "    return ui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e1172c6d-3efe-437a-8cbd-f8993f29e8d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40c607f87b394483b0db66ba11283346",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Output(), HBox(children=(VBox(children=(Dropdown(description='nudging:', layout=Layout(width='2…"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_water_vapor_trans_map()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1d3cb9-d4b6-4b0e-95fa-f7d425b91b4b",
   "metadata": {},
   "source": [
    "## Packages version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43d623eb-dbb1-4e27-b46e-a4383c48cdb4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.11.2\n",
      "IPython version      : 8.12.0\n",
      "\n",
      "wget      : 3.2\n",
      "numpy     : 1.24.2\n",
      "xarray    : 2023.3.0\n",
      "solara    : 1.19.0\n",
      "ipywidgets: 8.0.6\n",
      "matplotlib: 3.6.3\n",
      "cartopy   : 0.21.1\n",
      "watermark : 2.4.3\n",
      "\n",
      "Compiler    : Clang 14.0.6 \n",
      "OS          : Darwin\n",
      "Release     : 22.6.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 10\n",
      "Architecture: 64bit\n",
      "\n",
      " \n",
      "Last updated: Tue Oct 24 2023 14:17:30JST\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "\n",
    "# python, ipython, packages, and machine characteristics\n",
    "%watermark -v -m -p wget,numpy,xarray,solara,ipywidgets,matplotlib,cartopy,watermark\n",
    "\n",
    "# date\n",
    "print (\" \")\n",
    "%watermark -u -n -t -z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59498e27-e6e1-47bf-a880-44fc5af2c146",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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