notebooks/gsat_scenarios/gsat_scenarios_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "846d833e-4d2c-4927-8c58-cbb696b1aca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import xarray\n",
    "import dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ecd1cbc-4875-45ae-842a-34a1e9d8ef61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dask.config.set at 0x7fd0c591f990>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask.config.set(scheduler=\"processes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a21ebfd-598a-4c10-b5b1-967df914ff47",
   "metadata": {},
   "source": [
    "## Climate change indicators\n",
    "\n",
    "**E. Cimadevilla** (Santander Meteorology Group. Instituto de Física de Cantabria, CSIC-UC, Santander, Spain).\n",
    "\n",
    "This notebook computes Global Surface Air Temperature (GSAT) changes relative to the 1995–2014 and 1850–1900 averages, according to CMIP6 climate models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "304cb06e-00ba-4a6a-a560-16e75fee9dd9",
   "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>location</th>\n",
       "      <th>type</th>\n",
       "      <th>variable</th>\n",
       "      <th>project</th>\n",
       "      <th>experiment</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>https://hub.climate4r.ifca.es/thredds/dodsC/ip...</td>\n",
       "      <td>opendap</td>\n",
       "      <td>t</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>ssp245</td>\n",
       "      <td>mon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>https://hub.climate4r.ifca.es/thredds/dodsC/ip...</td>\n",
       "      <td>opendap</td>\n",
       "      <td>t</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>historical</td>\n",
       "      <td>mon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>https://hub.climate4r.ifca.es/thredds/dodsC/ip...</td>\n",
       "      <td>opendap</td>\n",
       "      <td>t</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>ssp585</td>\n",
       "      <td>mon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>https://hub.climate4r.ifca.es/thredds/dodsC/ip...</td>\n",
       "      <td>opendap</td>\n",
       "      <td>t</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>ssp370</td>\n",
       "      <td>mon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>https://hub.climate4r.ifca.es/thredds/dodsC/ip...</td>\n",
       "      <td>opendap</td>\n",
       "      <td>t</td>\n",
       "      <td>CMIP6</td>\n",
       "      <td>ssp126</td>\n",
       "      <td>mon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              location     type variable  \\\n",
       "41   https://hub.climate4r.ifca.es/thredds/dodsC/ip...  opendap        t   \n",
       "67   https://hub.climate4r.ifca.es/thredds/dodsC/ip...  opendap        t   \n",
       "87   https://hub.climate4r.ifca.es/thredds/dodsC/ip...  opendap        t   \n",
       "111  https://hub.climate4r.ifca.es/thredds/dodsC/ip...  opendap        t   \n",
       "117  https://hub.climate4r.ifca.es/thredds/dodsC/ip...  opendap        t   \n",
       "\n",
       "    project  experiment frequency  \n",
       "41    CMIP6      ssp245       mon  \n",
       "67    CMIP6  historical       mon  \n",
       "87    CMIP6      ssp585       mon  \n",
       "111   CMIP6      ssp370       mon  \n",
       "117   CMIP6      ssp126       mon  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../../inventory.csv\")\n",
    "subset = df.query('type == \"opendap\" & variable == \"t\" & project == \"CMIP6\" & frequency == \"mon\"')\n",
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e421cf6f-797b-43f9-9805-3006cab1e3e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = xarray.open_dataset(subset[subset[\"experiment\"] == \"historical\"][\"location\"].iloc[0]).chunk(member=1, time=100)\n",
    "ssp126 = xarray.open_dataset(subset[subset[\"experiment\"] == \"ssp126\"][\"location\"].iloc[0]).chunk(member=1, time=100)\n",
    "ssp245 = xarray.open_dataset(subset[subset[\"experiment\"] == \"ssp245\"][\"location\"].iloc[0]).chunk(member=1, time=100)\n",
    "ssp370 = xarray.open_dataset(subset[subset[\"experiment\"] == \"ssp370\"][\"location\"].iloc[0]).chunk(member=1, time=100)\n",
    "ssp585 = xarray.open_dataset(subset[subset[\"experiment\"] == \"ssp585\"][\"location\"].iloc[0]).chunk(member=1, time=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "264922de-7571-46ae-b987-54a3edbb9955",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 35s, sys: 9min 45s, total: 15min 21s\n",
      "Wall time: 35min 13s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "weights = np.cos(np.deg2rad(hist[\"lat\"]))\n",
    "mean_hist_1995_2014 = hist[\"t\"].sel(time=slice(\"19950101\", \"20141231\")).weighted(weights).mean([\"time\", \"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_hist_1850_1900 = hist[\"t\"].sel(time=slice(\"18500101\", \"19001231\")).weighted(weights).mean([\"time\", \"lat\", \"lon\"]).compute(num_workers=4)\n",
    "\n",
    "mean_hist = hist[\"t\"].sel(time=slice(\"19500101\", None)).weighted(weights).mean([\"member\", \"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_ssp126 = ssp126[\"t\"].weighted(weights).mean([\"member\", \"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_ssp245 = ssp245[\"t\"].weighted(weights).mean([\"member\", \"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_ssp370 = ssp370[\"t\"].weighted(weights).mean([\"member\", \"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_ssp585 = ssp585[\"t\"].weighted(weights).mean([\"member\", \"lat\", \"lon\"]).compute(num_workers=4)\n",
    "\n",
    "mean_spatial_hist = hist[\"t\"].sel(time=slice(\"19500101\", None)).weighted(weights).mean([\"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_spatial_ssp126 = ssp126[\"t\"].weighted(weights).mean([\"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_spatial_ssp245 = ssp245[\"t\"].weighted(weights).mean([\"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_spatial_ssp370 = ssp370[\"t\"].weighted(weights).mean([\"lat\", \"lon\"]).compute(num_workers=4)\n",
    "mean_spatial_ssp585 = ssp585[\"t\"].weighted(weights).mean([\"lat\", \"lon\"]).compute(num_workers=4)\n",
    "\n",
    "mean_hist_1995_2014_YE = (mean_hist - mean_hist_1995_2014).resample(time=\"YE\").mean()\n",
    "mean_ssp126_1995_2014_YE = (mean_ssp126 - mean_hist_1995_2014).resample(time=\"YE\").mean()\n",
    "mean_ssp245_1995_2014_YE = (mean_ssp245 - mean_hist_1995_2014).resample(time=\"YE\").mean()\n",
    "mean_ssp370_1995_2014_YE = (mean_ssp370 - mean_hist_1995_2014).resample(time=\"YE\").mean()\n",
    "mean_ssp585_1995_2014_YE = (mean_ssp585 - mean_hist_1995_2014).resample(time=\"YE\").mean()\n",
    "\n",
    "mean_hist_1850_1900_YE = (mean_hist - mean_hist_1850_1900).resample(time=\"YE\").mean()\n",
    "mean_ssp126_1850_1900_YE = (mean_ssp126 - mean_hist_1850_1900).resample(time=\"YE\").mean()\n",
    "mean_ssp245_1850_1900_YE = (mean_ssp245 - mean_hist_1850_1900).resample(time=\"YE\").mean()\n",
    "mean_ssp370_1850_1900_YE = (mean_ssp370 - mean_hist_1850_1900).resample(time=\"YE\").mean()\n",
    "mean_ssp585_1850_1900_YE = (mean_ssp585 - mean_hist_1850_1900).resample(time=\"YE\").mean()\n",
    "\n",
    "q_hist_1995_2014_YE = (mean_spatial_hist - mean_hist_1995_2014).resample(time=\"YE\").mean().quantile([.05, .95], dim=[\"member\"])\n",
    "q_ssp126_1995_2014_YE = (mean_spatial_ssp126 - mean_hist_1995_2014).resample(time=\"YE\").mean().quantile([.05, .95], dim=[\"member\"])\n",
    "q_ssp245_1995_2014_YE = (mean_spatial_ssp245 - mean_hist_1995_2014).resample(time=\"YE\").mean().quantile([.05, .95], dim=[\"member\"])\n",
    "q_ssp370_1995_2014_YE = (mean_spatial_ssp370 - mean_hist_1995_2014).resample(time=\"YE\").mean().quantile([.05, .95], dim=[\"member\"])\n",
    "q_ssp585_1995_2014_YE = (mean_spatial_ssp585 - mean_hist_1995_2014).resample(time=\"YE\").mean().quantile([.05, .95], dim=[\"member\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f174187-acbb-439f-ad45-b95ccde0cd3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "\n",
    "ytop = 6\n",
    "\n",
    "ax.fill_between(q_hist_1995_2014_YE.time,\n",
    "                q_hist_1995_2014_YE.sel(quantile=.05),\n",
    "                q_hist_1995_2014_YE.sel(quantile=.95),\n",
    "                color=\"#616161\", alpha=0.25)\n",
    "ax.fill_between(q_ssp126_1995_2014_YE.time,\n",
    "                q_ssp126_1995_2014_YE.sel(quantile=.05),\n",
    "                q_ssp126_1995_2014_YE.sel(quantile=.95),\n",
    "                color=\"#0e57cc\", alpha=0.25)\n",
    "ax.fill_between(q_ssp370_1995_2014_YE.time,\n",
    "                q_ssp370_1995_2014_YE.sel(quantile=.05),\n",
    "                q_ssp370_1995_2014_YE.sel(quantile=.95),\n",
    "                color=\"#e81515\", alpha=0.25)\n",
    "ax.set_ylim([-2, ytop])\n",
    "ax.set_ylabel(\"Relative to 1995-2014 (°C)\")\n",
    "ax.axhline(y=0, color=\"black\")\n",
    "ax.text(np.datetime64('2024-12-31T00:00:00.000000000'), -1, \"Near\\nterm\")\n",
    "ax.text(np.datetime64('2044-12-31T00:00:00.000000000'), -1, \"Mid\\nterm\")\n",
    "ax.text(np.datetime64('2084-12-31T00:00:00.000000000'), -1, \"Long\\nterm\")\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(mean_hist_1850_1900_YE.time, mean_hist_1850_1900_YE.mean(\"member\"), color=\"#010101\", label=f\"Historical  {len(hist['member'])}\")\n",
    "ax2.plot(mean_ssp126_1850_1900_YE.time, mean_ssp126_1850_1900_YE.mean(\"member\"), color=\"#0e57cc\", label=f\"SSP1-2.6  {len(ssp126['member'])}\")\n",
    "ax2.plot(mean_ssp245_1850_1900_YE.time, mean_ssp245_1850_1900_YE.mean(\"member\"), color=\"#e68d26\", label=f\"SSP2-4.5  {len(ssp245['member'])}\")\n",
    "ax2.plot(mean_ssp370_1850_1900_YE.time, mean_ssp370_1850_1900_YE.mean(\"member\"), color=\"#e81515\", label=f\"SSP3-7.0  {len(ssp370['member'])}\")\n",
    "ax2.plot(mean_ssp585_1850_1900_YE.time, mean_ssp585_1850_1900_YE.mean(\"member\"), color=\"#9b1a22\", label=f\"SSP5-8.5  {len(ssp585['member'])}\")\n",
    "ax2.set_ylabel(\"Relative to 1850-1900 (°C)\")\n",
    "ax2.set_ylim(bottom=-2+.82, top=ytop+.82)\n",
    "\n",
    "ax2.fill_between(\n",
    "    mean_ssp126_1995_2014_YE[\"time\"].sel(time=slice(\"20200101\", \"20400101\")),\n",
    "    -2+.82, ytop+.82, alpha=.33, color=\"grey\")\n",
    "ax2.fill_between(\n",
    "    mean_ssp126_1995_2014_YE[\"time\"].sel(time=slice(\"20400101\", \"20600101\")),\n",
    "    -2+.82, ytop+.82, alpha=.33, color=\"grey\")\n",
    "ax2.fill_between(\n",
    "    mean_ssp126_1995_2014_YE[\"time\"].sel(time=slice(\"20800101\", None)),\n",
    "    -2+.82, ytop+.82, alpha=.33, color=\"grey\")\n",
    "\n",
    "ax.margins(0)\n",
    "ax2.margins(0)\n",
    "ax2.legend()\n",
    "\n",
    "plt.savefig(\"t_CMIP6_scenarios.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1523b8a-d521-4f6e-972f-1145961a557e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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