CMIP6_ERA5_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import matplotlib.dates as mdates\n",
    "import datetime\n",
    "import cftime\n",
    "import netCDF4\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.style.use(\"default\")\n",
    "import os\n",
    "from global_land_mask import globe\n",
    "import seaborn as sns\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from shapely.geometry import box, mapping\n",
    "import geopandas as gpd\n",
    "from matplotlib import cm\n",
    "import global_land_mask\n",
    "import logging\n",
    "import matplotlib.dates as mdates\n",
    "import xskillscore as xs\n",
    "import CMIP6_xgrid_utils\n",
    "from CMIP6_ridgeplot import CMIP6_ridgeplot\n",
    "from CMIP6_IO import CMIP6_IO\n",
    "from shapely.geometry import box, mapping\n",
    "sns.set_theme(font_scale=1.5, style=\"whitegrid\")\n",
    "io = CMIP6_IO()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare GHI from ERA5 and RTM for 1979-01-01 to 2023-12-16\n",
    "Script that compares the ERA5 and RTM GHI irradiance for the Barents Sea region. We select the period 1979-01-01 to 2023-12-16 for the comparison and we used script `CMIP6_download_ERA5.ipynb` to download the ERA5 data from Copernicus. ERA5 data can be downloaded from Copernicus: https://climate.copernicus.eu/climate-reanalysis\n",
    "\n",
    "\n",
    "RTM: The GHI data covers the full wavelength range 200-2700 nm.\n",
    "\n",
    "ERA5: The global horizontal irradiation (GHI) is offered as shortwave radiation in Watt per square meter W/m². It is the sum of direct and diffuse radiation on the horizontal plane. These values are not instantaneous, but backwards averaged over the preceding hour. A value at 14:00 is the average irradiation between 13:00 and 14:00. Therefore, these values cannot be compared directly with instantaneous measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_LME_records():\n",
    "    lme_file = \"gs://actea-shared/Shapefiles/LME66/LMEs66.shp\"\n",
    "    return gpd.read_file(lme_file)\n",
    "\n",
    "def get_LME_records_180():\n",
    "    lme_file = \"gs://actea-shared/Shapefiles/LME66_180/LME66_180.shp\"\n",
    "    return gpd.read_file(lme_file)\n",
    "\n",
    "def clip_using_highres_world(ds, LME, var_name):\n",
    "    lme_file = \"gs://actea-shared/Shapefiles/LME66/LMEs66.shp\"\n",
    "    shdf = gpd.read_file(lme_file)\n",
    "    shdf_4326 = shdf.to_crs(epsg=4326)\n",
    "    shdf_sel = shdf_4326[shdf_4326[\"LME_NAME\"] == LME]\n",
    "\n",
    "    ds.coords['lon'] = (ds.coords['lon'] + 180) % 360 - 180\n",
    "    ds = ds.sortby(ds.lon)\n",
    "    \n",
    "    if \"x\" in ds.dims:\n",
    "        ds.rio.set_spatial_dims(y_dim=\"y\", x_dim=\"x\", inplace=True)\n",
    "    else:\n",
    "        ds.rio.set_spatial_dims(y_dim=\"lat\", x_dim=\"lon\", inplace=True)\n",
    "    ds.rio.write_crs(4326,  inplace=True)\n",
    "                \n",
    "    clipped = ds.rio.clip(geometries=shdf_sel.geometry.apply(mapping), crs=ds.rio.crs)\n",
    "    if \"x\" in ds.dims:\n",
    "        clipped = clipped.rename({\"x\": \"lon\", \"y\": \"lat\"}) \n",
    "        \n",
    "    #clip the data\n",
    "    data_clipped = clipped.rio.clip(world.geometry.apply(mapping), world.crs, drop = True)           \n",
    "    data_clipped['mask'] = xr.where(np.isnan(data_clipped[var_name]), 1, np.nan)        \n",
    "    clipped = clipped * data_clipped['mask']\n",
    "                \n",
    "    # Mask out indices where values are always zero across latitude\n",
    "    clipped = clipped.fillna(0.0)\n",
    "    always_zero = ~(clipped[var_name].any(dim='time') != 0)\n",
    "    masked_da = clipped.where(~always_zero)\n",
    "    \n",
    "    return masked_da\n",
    "            \n",
    "def get_data_within_LME(ds,var_name,LME):\n",
    "\n",
    "    # Extract the polygon defining the boundaries of the LME\n",
    "    shdf = get_LME_records()\n",
    "\n",
    "    shdf_sel = shdf[ shdf['LME_NAME']==LME ]\n",
    "    ds.coords['lon'] = (ds.coords['lon'] + 180) % 360 - 180\n",
    "    ds = ds.sortby(ds.lon)\n",
    "\n",
    "    \n",
    "    tos=ds.rename({'lon': 'x','lat': 'y'})\n",
    "    tos=tos.rio.write_crs(4326)\n",
    "\n",
    "   # data_clipped = tos.rio.clip(world.geometry.apply(mapping), world.crs, drop = True)           \n",
    "   # data_clipped['mask'] = xr.where(np.isnan(data_clipped[var_name]), 1, np.nan)        \n",
    "   # tos = tos * data_clipped['mask']\n",
    "    \n",
    "    # Rioxarray requires x and y dimensions - we convert these back to lon and lat later.\n",
    "    # We also add the projection (lat-lon) so that rioxarray can do the clipping of the data according to the\n",
    "    # shapefile.\n",
    "\n",
    "\n",
    "    # Clip the data within the LME. We have to convert the polygon geometry to a geodataframe using\n",
    "    # `shapely.geometry`. The clipping of data within the polygon is done using rioxarray.clip function\n",
    "\n",
    "    clipped = tos.rio.clip(geometries=shdf_sel.geometry.apply(mapping), crs=tos.rio.crs)\n",
    "    clipped=clipped.rename({'x': 'lon','y': 'lat'})\n",
    "    if isinstance(clipped, xr.DataArray):\n",
    "        clipped = clipped.to_dataset()\n",
    "    \n",
    "      \n",
    "    # Mask out indices where values are always zero across latitude\n",
    "    clipped = clipped.fillna(0.0)\n",
    "    always_zero = ~(clipped[var_name].any(dim='time') != 0)\n",
    "    masked_da = clipped.where(~always_zero)\n",
    "    return masked_da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_era5(era_datafile, start_time, end_time):\n",
    "    era = netCDF4.Dataset(era_datafile)\n",
    "    units = 'hours since 1900-01-01 00:00:00.0'\n",
    "    time_gregorian = cftime.num2date(era.variables[\"time\"], units, 'gregorian')\n",
    "    units_to_wm2 =1./3600.\n",
    "\n",
    "    times=[]\n",
    "    for t in time_gregorian:\n",
    "        obj = datetime.datetime(year=t.year, month=t.month, day=t.month, hour=t.hour)\n",
    "        times.append(obj)\n",
    "    times = pd.to_datetime(times,dayfirst=False)\n",
    "\n",
    "    era = xr.open_dataset(era_datafile,\n",
    "                          mask_and_scale=True)*units_to_wm2\n",
    "\n",
    "    era = era.assign_coords({\"time\":times,\"latitude\":era.latitude,\"longitude\":era.longitude})\n",
    "    return era.rename({\"latitude\":\"lat\", \"longitude\":\"lon\"}).sel(time=slice(start_time,end_time))\n",
    "\n",
    "def get_mean_era5(era_datafile, start_time, end_time):\n",
    "    era = netCDF4.Dataset(era_datafile)    \n",
    "    units = 'hours since 1900-01-01 00:00:00.0'\n",
    "    time_gregorian = cftime.num2date(era.variables[\"time\"], units, 'gregorian')\n",
    "    units_to_wm2 =1.\n",
    "\n",
    "    times=[]\n",
    "    for t in time_gregorian:\n",
    "        obj = datetime.datetime(year=t.year, month=t.month, day=t.month, hour=t.hour)\n",
    "        times.append(obj)\n",
    "    times = pd.to_datetime(times,dayfirst=False)\n",
    "\n",
    "    era = xr.open_dataset(era_datafile,\n",
    "                          mask_and_scale=True)*units_to_wm2\n",
    "\n",
    "    era = era.sel(expver=1).drop(\"expver\")\n",
    "    era = era.assign_coords({\"time\":times,\"latitude\":era.latitude,\"longitude\":era.longitude})\n",
    "    return era.rename({\"latitude\":\"lat\", \"longitude\":\"lon\"}).sel(time=slice(start_time,end_time))\n",
    "\n",
    "def convert_to_180(ds):\n",
    "    ds = (ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180))).sortby(\"lon\")\n",
    "    return ds\n",
    "\n",
    "def convert_time(ds):\n",
    "    if not ds.indexes[\"time\"].dtype in [\"datetime64[ns]\"]:\n",
    "        time_objects = ds.indexes[\"time\"].to_datetimeindex()\n",
    "        ds = ds.assign_coords({\"time\": time_objects})\n",
    "        ds = xr.decode_cf(ds)\n",
    "\n",
    "    return ds\n",
    "\n",
    "def get_area_averaged_ds(fname,scenario,model,start_time,end_time):\n",
    "\n",
    "    if isinstance(fname, xr.Dataset):\n",
    "        ds = fname\n",
    "    else:\n",
    "        ds = io.open_dataset_on_gs(fname)\n",
    "  \n",
    "    if isinstance(ds, xr.Dataset):\n",
    "        ds=convert_to_180(ds)\n",
    "  \n",
    "        ds=ds.sel(time=slice(start_time, end_time)).mean({\"lat\",\"lon\"})\n",
    "        df = ds.to_dataframe().dropna()\n",
    "        df = df.reset_index()\n",
    "        df[\"scenario\"]=scenario\n",
    "        df[\"model\"]=model\n",
    "\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 25th Percentile\n",
    "def q25(x):\n",
    "    return x.quantile(0.25)\n",
    "\n",
    "# 75th Percentile\n",
    "def q75(x):\n",
    "    return x.quantile(0.75)\n",
    "\n",
    "# Calculate stats on dataframe\n",
    "def calculate_stats(df, var_name):\n",
    "    # https://stackoverflow.com/questions/53519823/confidence-interval-in-python-dataframe\n",
    "    stats = df[var_name].groupby(df.time.dt.month).agg(['mean', 'count', 'std', q25, q75,])\n",
    "    ci95_hi = []\n",
    "    ci95_lo = []\n",
    "\n",
    "    for i in stats.index:\n",
    "        m, c, s, mi, ma = stats.loc[i]\n",
    "        ci95_hi.append(m + s)\n",
    "        ci95_lo.append(m - s)\n",
    "\n",
    "    stats['ci95_hi'] = ci95_hi\n",
    "    stats['ci95_lo'] = ci95_lo\n",
    "\n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CMIP6_IO] Opening file actea-shared/light/ncfiles_nobias/ssp245/ensemble/CMIP6_ensemble_stats_ssp245_ghi_weighted.nc\n",
      "Correlation for Barents Sea ensemble  -0.230 (0.14200178) : rmse: 2.833\n",
      "Correlation PAR:           mean_x    mean_y\n",
      "mean_x  1.000000  0.997602\n",
      "mean_y  0.997602  1.000000\n",
      "Created figure Figures/CMIP6_lightpaper_sw+uv_compared_ERA_Barents Sea.png\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 960x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scenarios=[\"ssp245\"]\n",
    "member_range=1\n",
    "ensemble_ids = [\"r{}i{}p{}f{}\".format(str(i + 1), str(ii + 1), str(iii + 1), str(iv + 1)) for i in\n",
    "                           range(member_range)\n",
    "                           for ii in range(member_range) for iii in range(member_range) for iv in range(member_range)]\n",
    "\n",
    "period=\"1979-01-01-2099-12-16\"\n",
    "start_time=\"1979-01-01\"\n",
    "end_time=\"2020-12-16\"\n",
    "min_lat=60\n",
    "max_lat=85\n",
    "min_lon=-180\n",
    "max_lon=180\n",
    "\n",
    "models=[\"CanESM5_r3i1p2f1\",\"CanESM5_r7i1p2f1\", \n",
    "        \"MPI-ESM1-2-HR_r1i1p1f1\",\"MPI-ESM1-2-HR_r2i1p1f1\", \n",
    "        \"UKESM1-0-LL_r1i1p1f2\",\"MPI-ESM1-2-LR_r1i1p1f1\",\n",
    "        \"MPI-ESM1-2-LR_r2i1p1f1\",\"CanESM5_r10i1p2f1\"]\n",
    "\n",
    "models=[\"ensemble\"] #\"ensemble\"] #\"\"CanESM5_r10i1p2f1\", \"CanESM5_r7i1p2f1\"]\n",
    "\n",
    "era_var_name1=\"msnswrf\"\n",
    "ds_var_name1=\"ghi\" \n",
    "root = f\"light/ncfiles_nobias\"\n",
    "\n",
    "LMES=['Barents Sea'] #,'Northern Bering - Chukchi Seas']\n",
    "\n",
    "era_datafile=\"/mnt/disks/actea-disk-1/light/era5/ERA5_global_mean_shortwave_uvb.nc\"\n",
    "era = get_mean_era5(era_datafile, start_time, end_time).compute()\n",
    "era = era.reindex(lat=list(reversed(era.lat)))\n",
    "\n",
    "era = era.sel(lat=slice(min_lat,max_lat),lon=slice(min_lon,max_lon))\n",
    "era_ds1=era \n",
    "\n",
    "for LME in LMES:\n",
    "\n",
    "    era5_sw = get_data_within_LME(era_ds1, era_var_name1, LME).resample(time=\"MS\").mean().sel(lat=slice(60,76), lon=slice(20,40))   \n",
    "    sw_dfs=[]\n",
    "\n",
    "    for scenario in scenarios:\n",
    "        for model in models:\n",
    "            if model == \"ensemble\":\n",
    "                fname1 = f\"{root}/{scenario}/ensemble/CMIP6_ensemble_stats_{scenario}_ghi_weighted.nc\"\n",
    "                ds_var_name1=f\"{ds_var_name1}_mean\"\n",
    "                osa=\"osa_mean\"\n",
    "            else:\n",
    "                fname1 = f\"{root}/{scenario}/{ds_var_name1}_{model}_1979-01-01-2099-12-16_scenario_osa_{scenario}.nc\"\n",
    "                osa=\"osa\"\n",
    "                \n",
    "         \n",
    "            # CALCULATE NET RTM SHORTWAVE RADIATION\n",
    "            ds_rtm = io.open_dataset_on_gs(fname1).sel(time=slice(start_time, end_time)).resample(time=\"MS\").mean().sel(lat=slice(71,76), lon=slice(20,40))  \n",
    "            \n",
    "            era1 = era5_sw.mean({\"lat\",\"lon\"})\n",
    "            era_df1=era1.to_dataframe().reset_index()\n",
    "            ds_rtm_absorbed=ds_rtm\n",
    "            ds_rtm_absorbed = get_data_within_LME(ds_rtm_absorbed, ds_var_name1, LME)\n",
    "            \n",
    "            frequency=\"A\"    \n",
    "            ds_rtm_absorbed_corr = ds_rtm_absorbed.mean({\"lat\", \"lon\"}).sel(time=slice(start_time,end_time))\n",
    "            ds_rtm_absorbed_corr= ds_rtm_absorbed_corr[ds_var_name1].resample(time=frequency).mean(skipna=True)\n",
    "            era1_corr = era1.sel(time=slice(start_time,end_time))\n",
    "            era1_corr=era1_corr[era_var_name1]\n",
    "            \n",
    "            r = xs.pearson_r(ds_rtm_absorbed_corr, \n",
    "                                era1_corr.resample(time=frequency).mean(skipna=True), \n",
    "                                dim='time')\n",
    "            rmse = xs.rmse(ds_rtm_absorbed_corr, \n",
    "                                era1_corr.resample(time=frequency).mean(skipna=True), \n",
    "                                dim='time')\n",
    "            p = xs.pearson_r_p_value(ds_rtm_absorbed_corr, \n",
    "                                era1_corr.resample(time=frequency).mean(skipna=True), \n",
    "                                dim='time')\n",
    "            \n",
    "            print(f\"Correlation for {LME} {model}  {r:2.3f} ({p:2.8f}) : rmse: {rmse:3.3f}\")\n",
    "            \n",
    "            df1 = get_area_averaged_ds(ds_rtm_absorbed,scenario,model,start_time,end_time)\n",
    "           \n",
    "            if df1 is not None and isinstance(df1, pd.DataFrame):\n",
    "                sw_dfs.append(df1)\n",
    "           \n",
    "    sw_df=pd.concat(sw_dfs)\n",
    "    \n",
    "    # Shortwave radiation as function of month (mean + 0.95 CI)\n",
    "    era_df_mean1 = calculate_stats(era_df1, era_var_name1)\n",
    "    sw_mean = calculate_stats(sw_df, ds_var_name1)\n",
    "\n",
    "    fig1=plt.figure(figsize=(12,12))\n",
    "    axes1 = fig1.add_subplot(111)\n",
    "    \n",
    "    axes1.plot(era_df_mean1.index, era_df_mean1[\"mean\"], linewidth=3, alpha=1.0, color=\"tab:red\", label=\"ERA5 GHI\")\n",
    "    axes1.fill_between(era_df_mean1.index, era_df_mean1[\"ci95_lo\"], era_df_mean1[\"ci95_hi\"], alpha=0.1, color=\"tab:red\")\n",
    "\n",
    "    axes1.plot(sw_mean.index, sw_mean[\"mean\"], linewidth=3, alpha=1.0, color=\"tab:blue\", label=f\"RTM GHI (200-2700 nm)\")\n",
    "    axes1.fill_between(sw_mean.index, sw_mean[\"ci95_lo\"], sw_mean[\"ci95_hi\"], alpha=0.1, color=\"tab:blue\")\n",
    "    axes1.legend(loc=\"lower right\")\n",
    "\n",
    "    my_xticks = ['January','March','May','July','September','November']\n",
    "    axes1.set_xticklabels(my_xticks)\n",
    "\n",
    "\n",
    "    axes1.set_ylabel(\"Downward shortwave radiation (Wm$^{-2}$)\")\n",
    "    axes1.set_xlabel(\"Month\")\n",
    "\n",
    "    merged = pd.merge_asof(era_df_mean1[\"mean\"], sw_mean[\"mean\"], left_index=True, right_index=True, direction='nearest')\n",
    "   \n",
    "    print(\"Correlation PAR: {}\".format(merged.corr()))\n",
    "\n",
    "    plotfile=f\"Figures/CMIP6_lightpaper_sw+uv_compared_ERA_{LME}.png\"\n",
    "    print(f\"Created figure {plotfile}\")\n",
    "    plt.savefig(plotfile, dpi=300, bbox_inches = 'tight')\n",
    "    plt.show()\n"
   ]
  },
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