4_supplimentary_information/4.3_regression/Export.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# export heat flux\n",
    "- This script is used to export heat flux variables;\n",
    "- Simulations: CNTL, ROOF_TV, IMPROAD_TV, WALL_TV, ROOF_IMPROAD_TV, ROOF_IMPROAD_WALL_TV;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cftime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/work/n02/n02/yuansun/cesm/'\n",
    "# surface\n",
    "sfile = path + 'cesm_inputdata/lnd/clm2/surfdata_map/release-clm5.0.18/surfdata_0.9x1.25_hist_16pfts_Irrig_CMIP6_simyr1850_c190214.nc'\n",
    "sf = xr.open_dataset(sfile)\n",
    "#case2\n",
    "path_2 = path + 'archive/case2/lnd/hist/'\n",
    "#case3\n",
    "path_3 = path + 'archive/case3/lnd/hist/'\n",
    "#case4\n",
    "path_4 = path + 'archive/case4/lnd/hist/'\n",
    "#case5\n",
    "path_5 = path + 'archive/case5/lnd/hist/'\n",
    "#case6\n",
    "path_6 = path + 'archive/case6/lnd/hist/'\n",
    "\n",
    "# dynamic roof\n",
    "sfile_dyn_rf = path + 'cesm_inputdata/lnd/clm2/urbandata/CLM50_DynUrbanAlbedoRoof_YuanSun_2023_0.9x1.25_simyr1849-2106_c20231005.nc'\n",
    "dyn_rf = xr.open_dataset(sfile_dyn_rf).sel(time = slice('2015-01-01 00:00:00', '2100-01-01 00:00:00'))\n",
    "\n",
    "start_year = 2015\n",
    "end_year = 2100\n",
    "\n",
    "dyn_rf['time'] = dyn_rf['time'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsmmask = np.any(sf['PCT_URBAN'] != 0, axis=0)\n",
    "mask = lsmmask.rename({'lsmlat': 'lat', 'lsmlon': 'lon'})\n",
    "\n",
    "roof = (sf['WTLUNIT_ROOF']).where(lsmmask)\n",
    "perroad = ((1 - sf['WTLUNIT_ROOF']) * sf['WTROAD_PERV']).where(lsmmask)\n",
    "improad = ((1 - sf['WTLUNIT_ROOF']) * (1-sf['WTROAD_PERV'])).where(lsmmask)\n",
    "\n",
    "alb_perroad = (sf['ALB_PERROAD_DIF'][0,:,:,:]).where(lsmmask)\n",
    "alb_improad = (sf['ALB_IMPROAD_DIF'][0,:,:,:]).where(lsmmask)\n",
    "alb_rf_tbd = (dyn_rf['dyn_alb_roof_TBD']).where(mask)\n",
    "alb_rf_hd = (dyn_rf['dyn_alb_roof_HD']).where(mask)\n",
    "alb_rf_md = (dyn_rf['dyn_alb_roof_MD']).where(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'WTLUNIT_ROOF' ()>\n",
      "array(0.54381721) <xarray.DataArray ()>\n",
      "array(0.23361668) <xarray.DataArray ()>\n",
      "array(0.22256611)\n"
     ]
    }
   ],
   "source": [
    "arearoof = roof.mean()\n",
    "areaperroad = perroad.mean()\n",
    "areaimproad = improad.mean() \n",
    "print(arearoof, areaperroad, areaimproad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_tbd = (roof[0,:,:].rename({'lsmlat': 'lat', 'lsmlon': 'lon'})) * alb_rf_tbd \n",
    "rf_hd  = (roof[1,:,:].rename({'lsmlat': 'lat', 'lsmlon': 'lon'})) * alb_rf_hd \n",
    "rf_md  = (roof[2,:,:].rename({'lsmlat': 'lat', 'lsmlon': 'lon'})) * alb_rf_md\n",
    "rf = xr.concat([rf_tbd, rf_hd, rf_md], dim='numurbl')\n",
    "#rf # numurbl: 3lat: 192lon: 288time: 86"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\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.DataArray (numurbl: 3, lat: 192, lon: 288, time: 86)&gt;\n",
       "array([[[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.7\n",
       "  * time     (time) int64 2015 2016 2017 2018 2019 ... 2096 2097 2098 2099 2100\n",
       "Dimensions without coordinates: numurbl</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span>numurbl</span>: 3</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>time</span>: 86</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0b63ac35-1be3-4d21-9bda-db5eb4fd4dd3' class='xr-array-in' type='checkbox' checked><label for='section-0b63ac35-1be3-4d21-9bda-db5eb4fd4dd3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-e216ce7b-ccd3-4ed0-9752-2069174e0cd0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e216ce7b-ccd3-4ed0-9752-2069174e0cd0' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-75813625-625d-448f-b3a1-fec9348d5f2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75813625-625d-448f-b3a1-fec9348d5f2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-740ef286-688c-4b4a-92b8-851da19ee9ba' class='xr-var-data-in' type='checkbox'><label for='data-740ef286-688c-4b4a-92b8-851da19ee9ba' 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>degree_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</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'>0.0 1.25 2.5 ... 356.2 357.5 358.7</div><input id='attrs-9284fa1d-888f-42ed-b4e2-78b4e25f8d1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9284fa1d-888f-42ed-b4e2-78b4e25f8d1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1648f5bb-fc58-49e3-af15-62cfb62d22c5' class='xr-var-data-in' type='checkbox'><label for='data-1648f5bb-fc58-49e3-af15-62cfb62d22c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2015 2016 2017 ... 2098 2099 2100</div><input id='attrs-0f3e96d3-3981-4fb0-99d0-6d01f0f434d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f3e96d3-3981-4fb0-99d0-6d01f0f434d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52dd6b44-ae60-4701-b850-0a141ec28636' class='xr-var-data-in' type='checkbox'><label for='data-52dd6b44-ae60-4701-b850-0a141ec28636' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026,\n",
       "       2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038,\n",
       "       2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050,\n",
       "       2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062,\n",
       "       2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074,\n",
       "       2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086,\n",
       "       2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098,\n",
       "       2099, 2100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-65ac1f53-5a14-4dda-9249-2fa0d68ac527' class='xr-section-summary-in' type='checkbox'  ><label for='section-65ac1f53-5a14-4dda-9249-2fa0d68ac527' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7f2c161-cf04-45ea-a4bc-d32acbfc37ea' class='xr-index-data-in' type='checkbox'/><label for='index-b7f2c161-cf04-45ea-a4bc-d32acbfc37ea' 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([             -90.0, -89.05759162303677, -88.11518324607341,\n",
       "              -87.17277486911004,  -86.2303664921467, -85.28795811518334,\n",
       "              -84.34554973821999, -83.40314136125662, -82.46073298429327,\n",
       "              -81.51832460732992,\n",
       "              ...\n",
       "               81.51832460732992,  82.46073298429327,  83.40314136125662,\n",
       "               84.34554973821999,  85.28795811518334,   86.2303664921467,\n",
       "               87.17277486911004,  88.11518324607341,  89.05759162303677,\n",
       "                            90.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</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-50bcc8ea-87bb-4d39-a318-eda58a35b6d0' class='xr-index-data-in' type='checkbox'/><label for='index-50bcc8ea-87bb-4d39-a318-eda58a35b6d0' 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([               0.0,               1.25,                2.5,\n",
       "                            3.75,                5.0,               6.25,\n",
       "                             7.5,  8.749999999999998,               10.0,\n",
       "                           11.25,\n",
       "              ...\n",
       "                           347.5, 348.74999999999994, 349.99999999999994,\n",
       "                          351.25,              352.5,             353.75,\n",
       "                           355.0,             356.25, 357.49999999999994,\n",
       "              358.74999999999994],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc8bfec0-2e2e-47db-95c0-b127f2b3abb4' class='xr-index-data-in' type='checkbox'/><label for='index-fc8bfec0-2e2e-47db-95c0-b127f2b3abb4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025,\n",
       "            2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036,\n",
       "            2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047,\n",
       "            2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058,\n",
       "            2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069,\n",
       "            2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080,\n",
       "            2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091,\n",
       "            2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1906f4b1-b2a9-460e-8fad-c77dfe4b31ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1906f4b1-b2a9-460e-8fad-c77dfe4b31ed' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (numurbl: 3, lat: 192, lon: 288, time: 86)>\n",
       "array([[[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "        [[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.7\n",
       "  * time     (time) int64 2015 2016 2017 2018 2019 ... 2096 2097 2098 2099 2100\n",
       "Dimensions without coordinates: numurbl"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipd = (perroad * alb_perroad).rename({'lsmlat': 'lat', 'lsmlon': 'lon'})\n",
    "id = (improad * alb_improad).rename({'lsmlat': 'lat', 'lsmlon': 'lon'})\n",
    "#alb = (rf + ipd + id)/(roof +perroad + improad).rename({'lsmlat': 'lat', 'lsmlon': 'lon'})\n",
    "alb = (rf/roof.rename({'lsmlat': 'lat', 'lsmlon': 'lon'}))\n",
    "alb # numurbl: 3lat: 192lon: 288time: 86"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>time</th>\n",
       "      <th>alb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755451</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2096</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755452</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755453</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2098</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755454</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2099</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755455</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4755456 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          lat     lon  time  alb\n",
       "0       -90.0    0.00  2015  NaN\n",
       "1       -90.0    0.00  2016  NaN\n",
       "2       -90.0    0.00  2017  NaN\n",
       "3       -90.0    0.00  2018  NaN\n",
       "4       -90.0    0.00  2019  NaN\n",
       "...       ...     ...   ...  ...\n",
       "4755451  90.0  358.75  2096  NaN\n",
       "4755452  90.0  358.75  2097  NaN\n",
       "4755453  90.0  358.75  2098  NaN\n",
       "4755454  90.0  358.75  2099  NaN\n",
       "4755455  90.0  358.75  2100  NaN\n",
       "\n",
       "[4755456 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alb = alb.mean(axis = 0).to_dataframe(name='alb').reset_index()\n",
    "df_alb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lat       90.00\n",
       "lon      358.75\n",
       "time    2100.00\n",
       "alb        0.90\n",
       "dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alb.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>year</th>\n",
       "      <th>alb</th>\n",
       "      <th>lsmlon</th>\n",
       "      <th>lsmlat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-90.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755451</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755452</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755453</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755454</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2099</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4755455</th>\n",
       "      <td>90.0</td>\n",
       "      <td>358.75</td>\n",
       "      <td>2100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4755456 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          lat     lon  year  alb  lsmlon  lsmlat\n",
       "0       -90.0    0.00  2015  NaN       0       0\n",
       "1       -90.0    0.00  2016  NaN       0       0\n",
       "2       -90.0    0.00  2017  NaN       0       0\n",
       "3       -90.0    0.00  2018  NaN       0       0\n",
       "4       -90.0    0.00  2019  NaN       0       0\n",
       "...       ...     ...   ...  ...     ...     ...\n",
       "4755451  90.0  358.75  2096  NaN     286     191\n",
       "4755452  90.0  358.75  2097  NaN     286     191\n",
       "4755453  90.0  358.75  2098  NaN     286     191\n",
       "4755454  90.0  358.75  2099  NaN     286     191\n",
       "4755455  90.0  358.75  2100  NaN     286     191\n",
       "\n",
       "[4755456 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alb.rename(columns={'time': 'year'}, inplace=True)\n",
    "df_alb['lsmlon'] = (df_alb['lon']/1.25).astype(int)\n",
    "unique_lat_values = df_alb['lat'].unique()\n",
    "lat_mapping = {lat: index for index, lat in enumerate(unique_lat_values)}\n",
    "df_alb['lsmlat'] = df_alb['lat'].map(lat_mapping)\n",
    "df_alb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>year</th>\n",
       "      <th>alb</th>\n",
       "      <th>lsmlon</th>\n",
       "      <th>lsmlat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>936454</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.394333</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936455</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.404333</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936456</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.414333</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936457</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.424333</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936458</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.434333</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212791</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2096</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212792</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2097</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212793</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2098</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212794</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2099</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212795</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2100</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>394568 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               lat     lon  year       alb  lsmlon  lsmlat\n",
       "936454  -55.130890  291.25  2015  0.394333     233      37\n",
       "936455  -55.130890  291.25  2016  0.404333     233      37\n",
       "936456  -55.130890  291.25  2017  0.414333     233      37\n",
       "936457  -55.130890  291.25  2018  0.424333     233      37\n",
       "936458  -55.130890  291.25  2019  0.434333     233      37\n",
       "...            ...     ...   ...       ...     ...     ...\n",
       "4212791  70.209424   31.25  2096  0.900000      25     170\n",
       "4212792  70.209424   31.25  2097  0.900000      25     170\n",
       "4212793  70.209424   31.25  2098  0.900000      25     170\n",
       "4212794  70.209424   31.25  2099  0.900000      25     170\n",
       "4212795  70.209424   31.25  2100  0.900000      25     170\n",
       "\n",
       "[394568 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort = df_alb.dropna()\n",
    "sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALB = sort.groupby('year').mean().reset_index()['alb']\n",
    "ALB.to_csv('../UHI/gridcell_alb_dynroof.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>year</th>\n",
       "      <th>alb</th>\n",
       "      <th>lsmlon</th>\n",
       "      <th>lsmlat</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>936454</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.309167</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936455</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.315833</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936456</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.322500</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936457</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.329167</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936458</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.335833</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "      <td>0.006667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212791</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2096</td>\n",
       "      <td>0.562167</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212792</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2097</td>\n",
       "      <td>0.562167</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212793</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2098</td>\n",
       "      <td>0.562167</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212794</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2099</td>\n",
       "      <td>0.562167</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4212795</th>\n",
       "      <td>70.209424</td>\n",
       "      <td>31.25</td>\n",
       "      <td>2100</td>\n",
       "      <td>0.562167</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>394568 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               lat     lon  year       alb  lsmlon  lsmlat     delta\n",
       "936454  -55.130890  291.25  2015  0.309167     233      37       NaN\n",
       "936455  -55.130890  291.25  2016  0.315833     233      37  0.006667\n",
       "936456  -55.130890  291.25  2017  0.322500     233      37  0.006667\n",
       "936457  -55.130890  291.25  2018  0.329167     233      37  0.006667\n",
       "936458  -55.130890  291.25  2019  0.335833     233      37  0.006667\n",
       "...            ...     ...   ...       ...     ...     ...       ...\n",
       "4212791  70.209424   31.25  2096  0.562167      25     170  0.000000\n",
       "4212792  70.209424   31.25  2097  0.562167      25     170  0.000000\n",
       "4212793  70.209424   31.25  2098  0.562167      25     170  0.000000\n",
       "4212794  70.209424   31.25  2099  0.562167      25     170  0.000000\n",
       "4212795  70.209424   31.25  2100  0.562167      25     170  0.000000\n",
       "\n",
       "[394568 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check delta_albedo\n",
    "df_sorted = sort.sort_values(by=['lat', 'lon', 'year'])\n",
    "df_sorted['delta'] = df_sorted.groupby(['lat', 'lon'])['alb'].diff()\n",
    "df_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "case2_flux = pd.DataFrame()\n",
    "for year in range(start_year,end_year):\n",
    "    fn = 'case2.clm2.h1.' + '%04.0f' % year + '-02-01-00000.nc'\n",
    "    if(os.path.exists(path_2+fn)):\n",
    "        ds = xr.open_dataset(path_2+fn)\n",
    "        df = (ds['FIRA_U']).mean('time').to_dataframe(name='FIRA_U').reset_index()\n",
    "        df['year'] = year\n",
    "        df['lsmlon'] = (df['lon']/1.25).astype(int)\n",
    "        unique_lat_values = df['lat'].unique()\n",
    "        lat_mapping = {lat: index for index, lat in enumerate(unique_lat_values)}\n",
    "        df['lsmlat'] = df['lat'].map(lat_mapping)\n",
    "        df = df.dropna()\n",
    "        df['FSA_U'] = (ds['FSA_U']).mean('time').to_dataframe(name='FSA_U').reset_index()['FSA_U']\n",
    "        df['FSDS'] = (ds['FSDS']).where(mask).mean('time').to_dataframe(name='FSDS').reset_index()['FSDS']\n",
    "        case2_flux = pd.concat([case2_flux, df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>FIRA_U</th>\n",
       "      <th>year</th>\n",
       "      <th>lsmlon</th>\n",
       "      <th>lsmlat</th>\n",
       "      <th>FSA_U</th>\n",
       "      <th>FSH_U</th>\n",
       "      <th>EFLX_LH_TOT_U</th>\n",
       "      <th>FGR_U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-55.130890</td>\n",
       "      <td>291.25</td>\n",
       "      <td>21.685835</td>\n",
       "      <td>2015</td>\n",
       "      <td>233</td>\n",
       "      <td>37</td>\n",
       "      <td>62.550735</td>\n",
       "      <td>2.463449</td>\n",
       "      <td>39.729191</td>\n",
       "      <td>-0.133372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-54.188480</td>\n",
       "      <td>292.50</td>\n",
       "      <td>31.818651</td>\n",
       "      <td>2015</td>\n",
       "      <td>234</td>\n",
       "      <td>38</td>\n",
       "      <td>80.481018</td>\n",
       "      <td>18.697359</td>\n",
       "      <td>32.053322</td>\n",
       "      <td>-1.362421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-53.246075</td>\n",
       "      <td>288.75</td>\n",
       "      <td>23.530861</td>\n",
       "      <td>2015</td>\n",
       "      <td>231</td>\n",
       "      <td>39</td>\n",
       "      <td>64.388817</td>\n",
       "      <td>3.854504</td>\n",
       "      <td>38.917187</td>\n",
       "      <td>-0.712562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-53.246075</td>\n",
       "      <td>291.25</td>\n",
       "      <td>33.501308</td>\n",
       "      <td>2015</td>\n",
       "      <td>233</td>\n",
       "      <td>39</td>\n",
       "      <td>81.254112</td>\n",
       "      <td>20.069777</td>\n",
       "      <td>29.788179</td>\n",
       "      <td>-1.453790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-51.361256</td>\n",
       "      <td>291.25</td>\n",
       "      <td>45.561325</td>\n",
       "      <td>2015</td>\n",
       "      <td>233</td>\n",
       "      <td>41</td>\n",
       "      <td>101.318092</td>\n",
       "      <td>32.175941</td>\n",
       "      <td>23.949677</td>\n",
       "      <td>-0.139984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360480</th>\n",
       "      <td>69.267014</td>\n",
       "      <td>32.50</td>\n",
       "      <td>31.259878</td>\n",
       "      <td>2099</td>\n",
       "      <td>26</td>\n",
       "      <td>169</td>\n",
       "      <td>44.533813</td>\n",
       "      <td>16.100861</td>\n",
       "      <td>15.078340</td>\n",
       "      <td>-13.352802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360481</th>\n",
       "      <td>69.267014</td>\n",
       "      <td>33.75</td>\n",
       "      <td>29.729136</td>\n",
       "      <td>2099</td>\n",
       "      <td>27</td>\n",
       "      <td>169</td>\n",
       "      <td>43.593094</td>\n",
       "      <td>11.749278</td>\n",
       "      <td>17.140741</td>\n",
       "      <td>-11.311886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360482</th>\n",
       "      <td>69.267014</td>\n",
       "      <td>87.50</td>\n",
       "      <td>29.782660</td>\n",
       "      <td>2099</td>\n",
       "      <td>70</td>\n",
       "      <td>169</td>\n",
       "      <td>53.420910</td>\n",
       "      <td>31.040018</td>\n",
       "      <td>13.927350</td>\n",
       "      <td>-16.305197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360483</th>\n",
       "      <td>69.267014</td>\n",
       "      <td>88.75</td>\n",
       "      <td>30.175827</td>\n",
       "      <td>2099</td>\n",
       "      <td>71</td>\n",
       "      <td>169</td>\n",
       "      <td>51.676563</td>\n",
       "      <td>40.280163</td>\n",
       "      <td>10.795192</td>\n",
       "      <td>-21.738813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360484</th>\n",
       "      <td>70.209427</td>\n",
       "      <td>31.25</td>\n",
       "      <td>29.393560</td>\n",
       "      <td>2099</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>43.801010</td>\n",
       "      <td>23.977777</td>\n",
       "      <td>15.340923</td>\n",
       "      <td>-20.425804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360485 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              lat     lon     FIRA_U  year  lsmlon  lsmlat       FSA_U  \\\n",
       "0      -55.130890  291.25  21.685835  2015     233      37   62.550735   \n",
       "1      -54.188480  292.50  31.818651  2015     234      38   80.481018   \n",
       "2      -53.246075  288.75  23.530861  2015     231      39   64.388817   \n",
       "3      -53.246075  291.25  33.501308  2015     233      39   81.254112   \n",
       "4      -51.361256  291.25  45.561325  2015     233      41  101.318092   \n",
       "...           ...     ...        ...   ...     ...     ...         ...   \n",
       "360480  69.267014   32.50  31.259878  2099      26     169   44.533813   \n",
       "360481  69.267014   33.75  29.729136  2099      27     169   43.593094   \n",
       "360482  69.267014   87.50  29.782660  2099      70     169   53.420910   \n",
       "360483  69.267014   88.75  30.175827  2099      71     169   51.676563   \n",
       "360484  70.209427   31.25  29.393560  2099      25     170   43.801010   \n",
       "\n",
       "            FSH_U  EFLX_LH_TOT_U      FGR_U  \n",
       "0        2.463449      39.729191  -0.133372  \n",
       "1       18.697359      32.053322  -1.362421  \n",
       "2        3.854504      38.917187  -0.712562  \n",
       "3       20.069777      29.788179  -1.453790  \n",
       "4       32.175941      23.949677  -0.139984  \n",
       "...           ...            ...        ...  \n",
       "360480  16.100861      15.078340 -13.352802  \n",
       "360481  11.749278      17.140741 -11.311886  \n",
       "360482  31.040018      13.927350 -16.305197  \n",
       "360483  40.280163      10.795192 -21.738813  \n",
       "360484  23.977777      15.340923 -20.425804  \n",
       "\n",
       "[360485 rows x 10 columns]"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case2_flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.merge(case2_flux, sort, on=['lsmlat', 'lsmlon', 'year'], how='inner').dropna()\n",
    "merged_df.to_csv('case2_export_annual.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# jja\n",
    "case2_flux_jja = pd.DataFrame()\n",
    "for year in range(start_year,end_year):\n",
    "    fn = 'case2.clm2.h1.' + '%04.0f' % year + '-02-01-00000.nc'\n",
    "    if(os.path.exists(path_2+fn)):\n",
    "        ds = xr.open_dataset(path_2+fn)\n",
    "        df = ds['FIRA_U'][6:9,:,:].mean('time').to_dataframe(name='FIRA_U').reset_index()\n",
    "        df['year'] = year\n",
    "        df['lsmlon'] = (df['lon']/1.25).astype(int)\n",
    "        unique_lat_values = df['lat'].unique()\n",
    "        lat_mapping = {lat: index for index, lat in enumerate(unique_lat_values)}\n",
    "        df['lsmlat'] = df['lat'].map(lat_mapping)\n",
    "        df = df.dropna()\n",
    "        df['FSA_U'] = ds['FSA_U'][6:9,:,:].mean('time').to_dataframe(name='FSA_U').reset_index()['FSA_U']\n",
    "        df['FSDS'] = (ds['FSDS'][6:9,:,:]).where(mask).mean('time').to_dataframe(name='FSDS').reset_index()['FSDS']\n",
    "        case2_flux_jja = pd.concat([case2_flux_jja, df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_jja = pd.merge(case2_flux_jja, sort, on=['lsmlat', 'lsmlon', 'year'], how='inner').dropna()\n",
    "merged_df_jja.to_csv('case2_export_jja.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# djf\n",
    "case2_flux_djf = pd.DataFrame()\n",
    "for year in range(start_year,end_year):\n",
    "    fn = 'case2.clm2.h1.' + '%04.0f' % year + '-02-01-00000.nc'\n",
    "    fn_1 = 'case2.clm2.h1.' + '%04.0f' % year + '-02-01-00000.nc'\n",
    "    if(os.path.exists(path_2+fn)):\n",
    "        ds = xr.open_dataset(path_2+fn)\n",
    "        ds_1 = xr.open_dataset(path_2+fn)\n",
    "        df = xr.concat([ds['FIRA_U'].isel(time=11),ds_1['FIRA_U'].isel(time=slice(0, 2))], dim='time').mean('time').to_dataframe(name='FIRA_U').reset_index()\n",
    "        df['year'] = year\n",
    "        df['lsmlon'] = (df['lon']/1.25).astype(int)\n",
    "        unique_lat_values = df['lat'].unique()\n",
    "        lat_mapping = {lat: index for index, lat in enumerate(unique_lat_values)}\n",
    "        df['lsmlat'] = df['lat'].map(lat_mapping)\n",
    "        df = df.dropna()\n",
    "        df['FSA_U'] = xr.concat([ds['FSA_U'].isel(time=11),ds_1['FSA_U'].isel(time=slice(0, 2))], dim='time').mean('time').to_dataframe(name='FSA_U').reset_index()['FSA_U']\n",
    "        df['FSDS'] = xr.concat([ds['FSDS'].isel(time=11),ds_1['FSDS'].isel(time=slice(0, 2))], dim='time').where(mask).mean('time').to_dataframe(name='FSDS').reset_index()['FSDS']\n",
    "        case2_flux_djf = pd.concat([case2_flux_djf, df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_djf = pd.merge(case2_flux_djf, sort, on=['lsmlat', 'lsmlon', 'year'], how='inner').dropna()\n",
    "merged_df_djf.to_csv('case2_export_djf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# case3\n",
    "case3_flux = pd.DataFrame()\n",
    "for year in range(start_year,end_year):\n",
    "    fn = 'case3.clm2.h1.' + '%04.0f' % year + '-02-01-00000.nc'\n",
    "    if(os.path.exists(path_3+fn)):\n",
    "        ds = xr.open_dataset(path_3+fn)\n",
    "        df = (ds['FIRA_U']).mean('time').to_dataframe(name='FIRA_U').reset_index()\n",
    "        df['year'] = year\n",
    "        df['lsmlon'] = (df['lon']/1.25).astype(int)\n",
    "        unique_lat_values = df['lat'].unique()\n",
    "        lat_mapping = {lat: index for index, lat in enumerate(unique_lat_values)}\n",
    "        df['lsmlat'] = df['lat'].map(lat_mapping)\n",
    "        df = df.dropna()\n",
    "        df['FSA_U'] = (ds['FSA_U']).mean('time').to_dataframe(name='FSA_U').reset_index()['FSA_U']\n",
    "        df['FSDS'] = (ds['FSDS']).where(mask).mean('time').to_dataframe(name='FSDS').reset_index()['FSDS']\n",
    "        case3_flux = pd.concat([case3_flux, df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.merge(case3_flux, sort, on=['lsmlat', 'lsmlon', 'year'], how='inner').dropna()\n",
    "merged_df.to_csv('case3_export_annual.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# case4\n",
    "case4_flux = pd.DataFrame()\n",
    "for year in range(start_year,end_year):\n",
    "    fn = 'case4.clm2.h1.' + '%04.0f' % year + '-02-01-00000.nc'\n",
    "    if(os.path.exists(path_4+fn)):\n",
    "        ds = xr.open_dataset(path_4+fn)\n",
    "        df = (ds['FIRA_U']).mean('time').to_dataframe(name='FIRA_U').reset_index()\n",
    "        df['year'] = year\n",
    "        df['lsmlon'] = (df['lon']/1.25).astype(int)\n",
    "        unique_lat_values = df['lat'].unique()\n",
    "        lat_mapping = {lat: index for index, lat in enumerate(unique_lat_values)}\n",
    "        df['lsmlat'] = df['lat'].map(lat_mapping)\n",
    "        df = df.dropna()\n",
    "        df['FSA_U'] = (ds['FSA_U']).mean('time').to_dataframe(name='FSA_U').reset_index()['FSA_U']\n",
    "        df['FSDS'] = (ds['FSDS']).where(mask).mean('time').to_dataframe(name='FSDS').reset_index()['FSDS']\n",
    "        case4_flux = pd.concat([case4_flux, df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.merge(case4_flux, sort, on=['lsmlat', 'lsmlon', 'year'], how='inner').dropna()\n",
    "merged_df.to_csv('case4_export_annual.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# case5\n",
    "case5_flux = pd.DataFrame()\n",
    "for year in range(start_year,end_year):\n",
    "    fn = 'case5.clm2.h1.' + '%04.0f' % year + '-02-01-00000.nc'\n",
    "    if(os.path.exists(path_5+fn)):\n",
    "        ds = xr.open_dataset(path_5+fn)\n",
    "        df = (ds['FIRA_U']).mean('time').to_dataframe(name='FIRA_U').reset_index()\n",
    "        df['year'] = year\n",
    "        df['lsmlon'] = (df['lon']/1.25).astype(int)\n",
    "        unique_lat_values = df['lat'].unique()\n",
    "        lat_mapping = {lat: index for index, lat in enumerate(unique_lat_values)}\n",
    "        df['lsmlat'] = df['lat'].map(lat_mapping)\n",
    "        df = df.dropna()\n",
    "        df['FSA_U'] = (ds['FSA_U']).mean('time').to_dataframe(name='FSA_U').reset_index()['FSA_U']\n",
    "        df['FSDS'] = (ds['FSDS']).where(mask).mean('time').to_dataframe(name='FSDS').reset_index()['FSDS']\n",
    "        case5_flux = pd.concat([case5_flux, df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.merge(case5_flux, sort, on=['lsmlat', 'lsmlon', 'year'], how='inner').dropna()\n",
    "merged_df.to_csv('case5_export_annual.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# case6\n",
    "case6_flux = pd.DataFrame()\n",
    "for year in range(start_year,end_year):\n",
    "    fn = 'case6.clm2.h1.' + '%04.0f' % year + '-02-01-00000.nc'\n",
    "    if(os.path.exists(path_6+fn)):\n",
    "        ds = xr.open_dataset(path_6+fn)\n",
    "        df = (ds['FIRA_U']).mean('time').to_dataframe(name='FIRA_U').reset_index()\n",
    "        df['year'] = year\n",
    "        df['lsmlon'] = (df['lon']/1.25).astype(int)\n",
    "        unique_lat_values = df['lat'].unique()\n",
    "        lat_mapping = {lat: index for index, lat in enumerate(unique_lat_values)}\n",
    "        df['lsmlat'] = df['lat'].map(lat_mapping)\n",
    "        df = df.dropna()\n",
    "        df['FSA_U'] = (ds['FSA_U']).mean('time').to_dataframe(name='FSA_U').reset_index()['FSA_U']\n",
    "        df['FSDS'] = (ds['FSDS']).where(mask).mean('time').to_dataframe(name='FSDS').reset_index()['FSDS']\n",
    "        case6_flux = pd.concat([case6_flux, df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = pd.merge(case6_flux, sort, on=['lsmlat', 'lsmlon', 'year'], how='inner').dropna()\n",
    "merged_df.to_csv('case6_export_annual.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}