{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate the Averaged Temperature Anomaly \n",
    "\n",
    "## Averaged Temperature Anomaly 2081-2100 vs 1971-2000 SSP585\n",
    "\n",
    "This example calculates the averaged temperature anomaly (using the **TG** indicator) for the period 2081-2100 compared to the reference 1971-2000 for SSP585 and several climate models.\n",
    "\n",
    "We assume to have the **tas** variable in netCDF files in a `./data` folder.\n",
    "The data can be dowloaded using the [metalink](data/cmcc_gfdl_tas.metalink) provided with this notebook.\n",
    "The data described in a `.metalink` file can be dowloaded with tools such as [aria2](https://aria2.github.io/) or a browser plugin such as [DownThemAll!](https://addons.mozilla.org/en-US/firefox/addon/downthemall/)\n",
    "If you wish to use a different dataset, you can use the [climate 4 impact portal](https://www.climate4impact.eu/c4i-frontend/) to search and select the data you wish to use and a metalink file to the [ESGF](https://esgf.llnl.gov/) data will be provided.\n",
    "\n",
    "\n",
    "The data is read using xarray and a plot of the time series averaged over Europe is generated, as well as an average spatial map. Several output types examples are shown.\n",
    "\n",
    "The datasets that are expected for this notebook are tas parameter (needed to calculate the TG indicator) for several climate models, for the historical (1971-2000) and ssp585 (2081-2100) experiments and for one member. Daily data is used. In [C4I](https://www.climate4impact.eu/c4i-frontend/search), you can find all of the data needed in the CMIP6 project, at the **esgf-data3.ceda.ac.uk** and **esgf3.dkrz.de** mirrors.\n",
    "\n",
    "## Preparation of the needed modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting nc_time_axis\n",
      "  Downloading nc_time_axis-1.4.1-py3-none-any.whl (17 kB)\n",
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      "Installing collected packages: nc_time_axis\n",
      "Successfully installed nc_time_axis-1.4.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install icclim matplotlib nc_time_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python:  3.11.7 | packaged by conda-forge | (main, Dec 15 2023, 08:38:37) [GCC 12.3.0]\n",
      "numpy:  1.26.2\n",
      "xarray:  2023.10.1\n",
      "pandas:  2.1.4\n",
      "icclim:  6.6.0\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "import icclim\n",
    "\n",
    "# provides cftime axis in matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "print(\"python: \", sys.version)\n",
    "print(\"numpy: \", np.__version__)\n",
    "print(\"xarray: \", xr.__version__)\n",
    "print(\"pandas: \", pd.__version__)\n",
    "print(\"icclim: \", icclim.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datasets\n",
    "\n",
    "The metafile associated with this notebook describes how to download the dataset we use here.\\\n",
    "We reccomend to use [aria2](https://github.com/aria2/aria2) to parse the metafile and download the dataset it describes.\\\n",
    "On ubuntu, aria2 can be installed with `sudo apt install -y aria2`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specification of the parameters and period of interest\n",
    "\n",
    "The time period of interest as well as the reference period are defined here.\n",
    "A list of models is listed here as an example.\n",
    "Here we used Monthly data (Amon) but daily data could also be used.\n",
    "The corresponding datafiles must have been selected by the user, containing both the studied and referenced periods.\n",
    "\n",
    "icclim is then executed for both periods for each climate model separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-19 09:56:34,358 --- icclim 6.6.0\n",
      "2024-01-19 09:56:34,359 --- BEGIN EXECUTION\n",
      "2024-01-19 09:56:34,360 Processing: 0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing model: CMCC-ESM2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:41: UserWarning: Variable does not have a `cell_methods` attribute.\n",
      "  _check_cell_methods(\n",
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:45: UserWarning: Variable does not have a `standard_name` attribute.\n",
      "  check_valid(vardata, \"standard_name\", data[\"standard_name\"])\n",
      "2024-01-19 09:56:50,956 Processing: 100%\n",
      "2024-01-19 09:56:50,957 --- icclim 6.6.0\n",
      "2024-01-19 09:56:50,958 --- CPU SECS = 28.970    \n",
      "2024-01-19 09:56:50,958 --- END EXECUTION\n",
      "2024-01-19 09:56:50,971 --- icclim 6.6.0\n",
      "2024-01-19 09:56:50,972 --- BEGIN EXECUTION\n",
      "2024-01-19 09:56:50,973 Processing: 0%\n",
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:41: UserWarning: Variable does not have a `cell_methods` attribute.\n",
      "  _check_cell_methods(\n",
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:45: UserWarning: Variable does not have a `standard_name` attribute.\n",
      "  check_valid(vardata, \"standard_name\", data[\"standard_name\"])\n",
      "2024-01-19 09:57:14,403 Processing: 100%\n",
      "2024-01-19 09:57:14,404 --- icclim 6.6.0\n",
      "2024-01-19 09:57:14,405 --- CPU SECS = 61.512    \n",
      "2024-01-19 09:57:14,406 --- END EXECUTION\n",
      "2024-01-19 09:57:14,424 --- icclim 6.6.0\n",
      "2024-01-19 09:57:14,425 --- BEGIN EXECUTION\n",
      "2024-01-19 09:57:14,426 Processing: 0%\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing model: GFDL-ESM4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:41: UserWarning: Variable does not have a `cell_methods` attribute.\n",
      "  _check_cell_methods(\n",
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:45: UserWarning: Variable does not have a `standard_name` attribute.\n",
      "  check_valid(vardata, \"standard_name\", data[\"standard_name\"])\n",
      "2024-01-19 09:57:29,710 Processing: 100%\n",
      "2024-01-19 09:57:29,711 --- icclim 6.6.0\n",
      "2024-01-19 09:57:29,712 --- CPU SECS = 82.298    \n",
      "2024-01-19 09:57:29,713 --- END EXECUTION\n",
      "2024-01-19 09:57:29,726 --- icclim 6.6.0\n",
      "2024-01-19 09:57:29,727 --- BEGIN EXECUTION\n",
      "2024-01-19 09:57:29,727 Processing: 0%\n",
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:41: UserWarning: Variable does not have a `cell_methods` attribute.\n",
      "  _check_cell_methods(\n",
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xclim/core/cfchecks.py:45: UserWarning: Variable does not have a `standard_name` attribute.\n",
      "  check_valid(vardata, \"standard_name\", data[\"standard_name\"])\n",
      "2024-01-19 09:57:53,531 Processing: 100%\n",
      "2024-01-19 09:57:53,531 --- icclim 6.6.0\n",
      "2024-01-19 09:57:53,532 --- CPU SECS = 114.398   \n",
      "2024-01-19 09:57:53,533 --- END EXECUTION\n"
     ]
    }
   ],
   "source": [
    "# studied period\n",
    "dt1 = datetime.datetime(2081, 1, 1, tzinfo=datetime.timezone.utc)\n",
    "dt2 = datetime.datetime(2100, 12, 31, tzinfo=datetime.timezone.utc)\n",
    "\n",
    "# reference period\n",
    "dtr1 = datetime.datetime(1971, 1, 1, tzinfo=datetime.timezone.utc)\n",
    "dtr2 = datetime.datetime(2000, 12, 31, tzinfo=datetime.timezone.utc)\n",
    "\n",
    "models = [\"CMCC-ESM2\", \"GFDL-ESM4\"]\n",
    "out_f = {}\n",
    "out_hist_f = {}\n",
    "for model in models:\n",
    "    print(f\"Processing model: {model}\")\n",
    "    out_f[model] = f\"data/tg_icclim_{model}.nc\"\n",
    "    out_hist_f[model] = f\"data/tg_icclim_{model}_hist.nc\"\n",
    "    filenames_hist = Path(\"data\").glob(f\"tas_day_{model}_historical_*.nc\")\n",
    "    filenames = Path(\"data\").glob(f\"tas_day_{model}_ssp585_*.nc\")\n",
    "    icclim.index(\n",
    "        index_name=\"TG\",\n",
    "        in_files=[str(f) for f in filenames],\n",
    "        var_name=\"tas\",\n",
    "        slice_mode=\"year\",\n",
    "        time_range=[dt1, dt2],\n",
    "        out_file=out_f[model],\n",
    "        logs_verbosity=\"LOW\",\n",
    "    )\n",
    "    icclim.index(\n",
    "        index_name=\"TG\",\n",
    "        in_files=[str(f) for f in filenames_hist],\n",
    "        var_name=\"tas\",\n",
    "        slice_mode=\"year\",\n",
    "        time_range=[dtr1, dtr2],\n",
    "        out_file=out_hist_f[model],\n",
    "        logs_verbosity=\"LOW\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation\n",
    "\n",
    "Here all data is loaded in 2 separate variables, one containing all the historical periods for all the models, and the same for the future time period.\n",
    "\n",
    "An example is shown on how to select a specific data location. But this is not used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Long, Lat values: 3.75 43.8219895287958\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bzah/micromamba/envs/icclim-dev/lib/python3.11/site-packages/xarray/core/options.py:117: FutureWarning: The enable_cftimeindex option is now a no-op and will be removed in a future version of xarray.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Open datasets\n",
    "tg = []\n",
    "tg_hist = []\n",
    "ds = []\n",
    "ds_hist = []\n",
    "xr.set_options(enable_cftimeindex=False)\n",
    "for model in models:\n",
    "    dsl = xr.open_dataset(out_f[model], decode_times=False)\n",
    "    dsl[\"time\"] = xr.decode_cf(dsl).time\n",
    "    dsl = dsl.assign_coords({\"model_id\": model})\n",
    "    tg.append(dsl[\"TG\"])\n",
    "\n",
    "    dshl = xr.open_dataset(out_hist_f[model], decode_times=False)\n",
    "    dshl[\"time\"] = xr.decode_cf(dshl).time\n",
    "    dshl = dshl.assign_coords({\"model_id\": model})\n",
    "    tg_hist.append(dshl[\"TG\"])\n",
    "\n",
    "# Select a single x,y combination from the data\n",
    "longitude = tg[0][\"lon\"].sel(lon=3.5, method=\"nearest\").values\n",
    "latitude = tg[0][\"lat\"].sel(lat=44.2, method=\"nearest\").values\n",
    "\n",
    "print(\"Long, Lat values:\", longitude, latitude)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform spacial average on all the geographical domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average different grids\n",
    "for ii in range(len(tg)):\n",
    "    tg[ii] = tg[ii].sel(lat=[0.0, 90.0], method=\"nearest\").mean(dim=[\"lon\", \"lat\"])\n",
    "for ii in range(len(tg_hist)):\n",
    "    tg_hist[ii] = (\n",
    "        tg_hist[ii].sel(lat=[0.0, 90.0], method=\"nearest\").mean(dim=[\"lon\", \"lat\"])\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a function to align all different calendar types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to align different calendars using annual data\n",
    "def to_pandas_dt(da):\n",
    "    \"\"\"Takes an annual DataArray. Change the calendar to a pandas datetime.\"\"\"\n",
    "    val = da.copy()\n",
    "    # val.resample(time='Y').mean('time')\n",
    "    timev = []\n",
    "    years = [int(val) for val in da.time.dt.strftime(\"%Y\")]\n",
    "    for itime in range(val.sizes[\"time\"]):\n",
    "        timev.append(years[itime])\n",
    "\n",
    "    timevp = pd.to_datetime(timev, format=\"%Y\")\n",
    "    time1 = xr.DataArray(data=timevp, dims=[\"time\"])\n",
    "    time1.name = \"time\"\n",
    "    # We rename the time dimension and coordinate to time360 to make it clear it isn't\n",
    "    # the original time coordinate.\n",
    "    val = val.rename({\"time\": \"timepd\"})\n",
    "    time1 = time1.rename({\"time\": \"timepd\"})\n",
    "    return val.assign_coords({\"timepd\": time1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Align calendars of all input data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert all calendars to annual precision (we have configured icclim to output yearly\n",
    "# data)\n",
    "\n",
    "ll = [to_pandas_dt(da) for da in tg]\n",
    "ll_hist = [to_pandas_dt(da) for da in tg_hist]\n",
    "\n",
    "# Concatenate all models into one\n",
    "full_tg = xr.concat(ll, \"model_id\", join=\"outer\")\n",
    "full_tg_hist = xr.concat(ll_hist, \"model_id\", join=\"outer\")\n",
    "full_tg_anomaly = full_tg - full_tg_hist.mean(dim=\"timepd\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot a multi-model time series for the future time period\n",
    "\n",
    "Temperature for SSP585 of the period 2080-2100.\n",
    "\n",
    "The multi-model average is shown in bold black line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4969ec6b50>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot future time period and superimpose multi-model average in bold black line\n",
    "full_tg.plot(hue=\"model_id\")\n",
    "full_tg.mean(dim=\"model_id\").plot(color=\"black\", linewidth=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot a time series of the anomaly of temperature\n",
    "Anomaly of temperature for SSP585 of the period 2080-2100 compared to 1971-2000.\n",
    "\n",
    "The multi-model average is shown in bold black line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f492256d350>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot temperature anomaly compared to historical period and superimpose multi-model\n",
    "# average in bold black line\n",
    "full_tg_anomaly.plot(hue=\"model_id\")\n",
    "full_tg_anomaly.mean(dim=\"model_id\").plot(color=\"black\", linewidth=4)"
   ]
  }
 ],
 "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}