CMIP6_compare_individial_with_observations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import rioxarray\n",
    "from pathlib import Path\n",
    "from typing import List, Dict\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import mapping\n",
    "import numpy as np\n",
    "import gcsfs\n",
    "import seaborn as sns\n",
    "from CMIP6_IO import CMIP6_IO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "lme_file = \"gs://actea-shared/Shapefiles/LME66/LMEs66.shp\"\n",
    "shdf = gpd.read_file(lme_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Create plot of models and their realizations and compare with ensemble and observations\n",
    "This script will loop over all CMIP6 models and the realization combinations to create plots showing the various variables as to how each model and realization is able to capture the inter-annual dynamics and each compare with ensemble. \n",
    "For example, we plot 10 realizations of the CanESM5 model and see how the spread is within the same model. The script compares observations of sea surface temperature with the models. The script can plot any model variable including:\n",
    "\n",
    "`var_names = [\"par\",\"uv\",\"uvb\", \"uvi\", \"uva\", \"siconc\",\"sithick\",\"sisnthick\",\"sisnconc\", \"tos\"]`\n",
    "\n",
    "The output figures are stored under `Figures` folder.\n",
    "Used to create `Figure 2` in paper `Climate change impacts  on ocean light in Arctic ecosystems beyond sea ice.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_ersst(var_name: str):\n",
    "    bucketname = \"actea-shared\"\n",
    "    zarr_path = f\"gs://{bucketname}/zarr/ersst.v5\"\n",
    "    fs = gcsfs.GCSFileSystem(project=\"downscale\")\n",
    "    mapper = fs.get_mapper(zarr_path)\n",
    "    fs = gcsfs.GCSFileSystem(project=\"downscale\")\n",
    "    ds = xr.open_zarr(mapper, consolidated=True) #.sel(time=slice(obs_start, obs_end))\n",
    "    if var_name == \"tos\":\n",
    "        if \"latitude\" in list(ds.variables):\n",
    "            ds = ds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n",
    "        if \"analysed_sst\" in list(ds.variables):\n",
    "            ds = ds.rename({\"analysed_sst\": var_name})\n",
    "        if \"TEMP\" in list(ds.variables):\n",
    "            ds = ds.rename({\"TEMP\": var_name})\n",
    "    for item in [\"time_bnds\", \"lev\", \"nv\"]:\n",
    "        if item in list(ds.keys()):\n",
    "            if item == \"lev\":\n",
    "                ds = ds.isel(lev=0)\n",
    "                ds = ds.drop_vars([item])\n",
    "            else:\n",
    "                ds = ds.drop_vars([item])\n",
    "    return ds\n",
    "\n",
    "\n",
    "def get_G10010_SIBT1850(var_name: str):\n",
    "    # Add the G10010_SIBT1850 siconc\n",
    "    bucketname = \"actea-shared\"\n",
    "    zarr_path = f\"gs://{bucketname}/zarr/G10010_SIBT1850/\"\n",
    "    fs = gcsfs.GCSFileSystem(project=\"downscale\")\n",
    "    mapper = fs.get_mapper(zarr_path)\n",
    "    fs = gcsfs.GCSFileSystem(project=\"downscale\")\n",
    "    ds = xr.open_zarr(mapper, consolidated=True) \n",
    "    ds = ds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n",
    "    return (ds.rename({\"seaice_conc\": \"siconc\"})[\"siconc\"]).to_dataset(name=\"siconc\")\n",
    "            \n",
    "def get_hadlsst_dataset(var_name: str):\n",
    "    bucketname = \"actea-shared\"\n",
    "    zarr_path = f\"gs://{bucketname}/zarr/HadlSST_sst\"\n",
    "   \n",
    "    fs = gcsfs.GCSFileSystem(project=\"downscale\")\n",
    "        \n",
    "    mapper = fs.get_mapper(zarr_path)\n",
    "    ds = xr.open_zarr(mapper, consolidated=True) #.sel(time=slice(obs_start, obs_end))\n",
    "    if var_name == \"tos\":\n",
    "        if \"latitude\" in list(ds.variables):\n",
    "            ds = ds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n",
    "        if \"analysed_sst\" in list(ds.variables):\n",
    "            ds = ds.rename({\"analysed_sst\": var_name})\n",
    "        if \"TEMP\" in list(ds.variables):\n",
    "            ds = ds.rename({\"TEMP\": var_name})\n",
    "    for item in [\"time_bnds\", \"lev\", \"nv\"]:\n",
    "        if item in list(ds.keys()):\n",
    "            if item == \"lev\":\n",
    "                ds = ds.isel(lev=0)\n",
    "                ds = ds.drop_vars([item])\n",
    "            else:\n",
    "                ds = ds.drop_vars([item])\n",
    "    # SST has fill_value of -1000 - converting to np.nan\n",
    "    ds = xr.where(ds == -1000, np.nan, ds)\n",
    "    \n",
    "    ds = ds.reindex(lat=list(reversed(ds.lat)))\n",
    "    return ds.transpose(\"time\", \"lat\", \"lon\")\n",
    "\n",
    "def get_cora_dataset(var_name: str):\n",
    "    bucketname = \"actea-shared\"\n",
    "    zarr_path = f\"gs://{bucketname}/zarr/cora5.2/temp\"\n",
    "   \n",
    "    fs = gcsfs.GCSFileSystem(project=\"downscale\")\n",
    "        \n",
    "    mapper = fs.get_mapper(zarr_path)\n",
    "    ds = xr.open_zarr(mapper, consolidated=True) #.sel(time=slice(obs_start, obs_end))\n",
    "   \n",
    "    ds = ds.isel(depth=0) # 0=1m, 2=5m, 3=1.00e+01\n",
    "    if var_name == \"tos\":\n",
    "        if \"latitude\" in list(ds.variables):\n",
    "            ds = ds.rename({\"latitude\": \"lat\", \"longitude\": \"lon\"})\n",
    "        if \"analysed_sst\" in list(ds.variables):\n",
    "            ds = ds.rename({\"analysed_sst\": var_name})\n",
    "        if \"TEMP\" in list(ds.variables):\n",
    "            ds = ds.rename({\"TEMP\": var_name})\n",
    "    for item in [\"time_bnds\", \"lev\", \"nv\"]:\n",
    "        if item in list(ds.keys()):\n",
    "            if item == \"lev\":\n",
    "                ds = ds.isel(lev=0)\n",
    "                ds = ds.drop_vars([item])\n",
    "            else:\n",
    "                ds = ds.drop_vars([item])\n",
    "\n",
    "    return ds.transpose(\"time\", \"lat\", \"lon\")\n",
    "    \n",
    "def collect_ds(scenario: str, var_name: str, ds_list: Dict, source_ids: List):\n",
    "    max_models=300\n",
    "    io = CMIP6_IO()\n",
    "    counter=0\n",
    "\n",
    "    if var_name not in [\"tos\", \"siconc\", \"sithick\", \"tas\", \"sisnthick\", \"sisnconc\", \"uas\", \"vas\"]:\n",
    "        lightpath = \"light/ncfiles_nobias\"\n",
    "    else:\n",
    "        lightpath = f\"light/{scenario}\"\n",
    "        \n",
    "    current = f\"{lightpath}\"\n",
    "    file_on_gcs = io.list_dataset_on_gs(current)\n",
    "  \n",
    "    # Loop over all files and filter on the models defined in source_ids.\n",
    "    for f in file_on_gcs:\n",
    "        \n",
    "        var_name_mod = var_name\n",
    "        #if var_name not in [\"tos\", \"siconc\", \"sithick\", \"tas\", \"sisnthick\", \"sisnconc\", \"uas\", \"vas\"]:\n",
    "        #var_name_mod = f\"{var_name}_\"\n",
    "        # Filter to only use the models we prefer\n",
    "        if any(model in f for model in source_ids) and var_name_mod in f and \"uv_srf\" not in f and scenario in f:\n",
    "            if counter >= max_models:\n",
    "                pass\n",
    "            else:\n",
    "                p = Path(f).name\n",
    "                model = p.split(\"_\")[1] + \"_\" + p.split(\"_\")[2]\n",
    "               # if \"MPI-ESM1-2-HR_r2i1p1f1\" not in f:\n",
    "                ds = io.open_dataset_on_gs(f)\n",
    "                \n",
    "                ds_resampled = ds[var_name]\n",
    "                ds_list[model] = ds_resampled\n",
    "                #print(\"PERIOD\", ds_resampled.time.values[0], ds_resampled.time.values[-1])\n",
    "                counter+=1\n",
    "             #   else:\n",
    "             #       print(f\"Skipping {f}\")\n",
    "            \n",
    "    # Add the ensemble weighted\n",
    "    if var_name in [\"tos\"]:\n",
    "        f = f\"{lightpath}/ensemble/CMIP6_ensemble_stats_{scenario}_{var_name}_weighted.nc\"\n",
    "    else:\n",
    "        f = f\"{lightpath}/ensemble/CMIP6_ensemble_stats_{scenario}_{var_name}_weighted.nc\"\n",
    "    ds = io.open_dataset_on_gs(f)\n",
    "    print(f)\n",
    "    print(ds)\n",
    "    if f\"{var_name}_mean\" in ds.variables:\n",
    "        ds = ds.rename({f\"{var_name}_mean\": f\"{var_name}\"})\n",
    "    ds_list[\"ensemble_weighted\"] = ds[var_name]\n",
    "    if var_name in [\"tos\"]:\n",
    "        f = f\"{lightpath}/{scenario}/ensemble/CMIP6_ensemble_stats_{scenario}_{var_name}.nc\"\n",
    "    else:\n",
    "        f = f\"{lightpath}/{scenario}/ensemble/CMIP6_ensemble_stats_{scenario}_{var_name}.nc\"\n",
    "    ds = io.open_dataset_on_gs(f)\n",
    "   \n",
    "    ds = ds.rename({f\"{var_name}_mean\": f\"{var_name}\"})\n",
    "    ds_list[\"ensemble_unweighted\"] = ds[var_name]\n",
    "    \n",
    "    return ds_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_models(ds_list: Dict, var_name: str, shdf_sel, scenario: str, LME: str):\n",
    "        _, ax = plt.subplots(figsize=(16,8))\n",
    "        \n",
    "        saved_model_name = None\n",
    "        color_index=0      \n",
    "        for model in ds_list.keys():\n",
    "                \n",
    "                ds = ds_list[model]\n",
    "                print(\"test\", ds, \"x\" in ds.dims)\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",
    "                 \n",
    "                if \"x\" in ds.dims:\n",
    "                        clipped = clipped.rename({\"x\": \"lon\", \"y\": \"lat\"}) \n",
    "                \n",
    "                clipped = clipped.resample(time=\"YE\").mean(skipna=True)\n",
    "               # clipped =  clipped.sel(time=slice(\"1990-01-01\",\"2000-01-01\"))\n",
    "                clipped = clipped.mean({\"lat\",\"lon\"})\n",
    "                print(f\"Mean value {model} {clipped.mean().values}  - max {clipped.min().values} max {clipped.max().values}\")\n",
    "                colors =  plt.cm.tab20( (4./3*np.arange(20*3/4)).astype(int) )\n",
    "                model_name = model.split(\"_\")[0]\n",
    "\n",
    "                if saved_model_name is None or saved_model_name != model_name:\n",
    "                        saved_model_name = model_name\n",
    "                        color=colors[color_index]\n",
    "                        color_index+=1\n",
    "                linewidth=1.0\n",
    "                alpha=0.8\n",
    "                print(f\"Running calculations and plotting {model}\")        \n",
    "                \n",
    "                if model in [\"ensemble_weighted\"]:\n",
    "                        color=\"tab:orange\"\n",
    "                        linewidth=2.5\n",
    "                        alpha=1.0\n",
    "                if model in [\"ensemble_unweighted\"]:\n",
    "                        color=\"tab:blue\"\n",
    "                        linewidth=2.5\n",
    "                        alpha=1.0\n",
    "                \n",
    "                ax.plot(clipped.time.values,\n",
    "                        clipped,\n",
    "                        linewidth=linewidth,\n",
    "                        color=color,\n",
    "                        alpha=alpha,\n",
    "                        label=f\"{model}\")\n",
    "             \n",
    "        if var_name in [\"siconc\"]:\n",
    "                use_obs = False\n",
    "                if use_obs:\n",
    "                        obs_an = get_G10010_SIBT1850(var_name)\n",
    "                        \n",
    "                        obs_an.rio.set_spatial_dims(y_dim=\"lat\", x_dim=\"lon\", inplace=True)\n",
    "                        obs_an.rio.write_crs(4326,  inplace=True)\n",
    "                                \n",
    "                        clipped = obs_an.rio.clip(geometries=shdf_sel.geometry.apply(mapping), crs=obs_an.rio.crs)\n",
    "                        clipped=clipped.sel(time=slice(\"1950-01-01\",\"2021-01-01\"))\n",
    "\n",
    "                        print(f\"Running calculations and plotting G10010_SIBT1850\")       \n",
    "                        clipped[\"siconc\"] = clipped[\"siconc\"] * 100.0\n",
    "                        clipped[\"siconc\"] = xr.where(clipped[\"siconc\"] > 100, 100, clipped[\"siconc\"])\n",
    "                        clipped[\"siconc\"]= xr.where(clipped[\"siconc\"] < 0, 0, clipped[\"siconc\"])\n",
    "        \n",
    "                        clipped = clipped.resample(time=\"A\").mean() \n",
    "                        clipped = clipped.mean({\"lat\",\"lon\"}) \n",
    "                \n",
    "                        ax.plot(clipped.time.values,\n",
    "                                        clipped[var_name],\n",
    "                                        linewidth=2.5,\n",
    "                                        color=\"black\",\n",
    "                                        alpha=1.0,\n",
    "                                        label=\"G10010_SIBT1850\",\n",
    "                                        linestyle='dashed')  \n",
    "                         \n",
    "        if var_name in [\"tos\"]:\n",
    "                # Finally add the observations\n",
    "        \n",
    "                use_cora = False\n",
    "                if use_cora:\n",
    "                        # CORA5.2 -----------------\n",
    "                        obs_an = get_cora_dataset(var_name)\n",
    "                        obs_an.rio.set_spatial_dims(y_dim=\"lat\", x_dim=\"lon\", inplace=True)\n",
    "                        obs_an.rio.write_crs(4326,  inplace=True)\n",
    "                        \n",
    "                        clipped = obs_an.rio.clip(geometries=shdf_sel.geometry.apply(mapping), crs=obs_an.rio.crs)\n",
    "                        clipped=clipped.sel(time=slice(\"1950-01-01\",\"2021-01-01\"))\n",
    "                \n",
    "                        print(f\"Running calculations and plotting CORA\")       \n",
    "                        \n",
    "                        clipped = clipped.resample(time=\"A\").mean() \n",
    "                        clipped = clipped.mean({\"lat\",\"lon\"}) \n",
    "                        \n",
    "                        ax.plot(clipped.time.values,\n",
    "                                clipped[var_name],\n",
    "                                linewidth=2.5,\n",
    "                                color=\"black\",\n",
    "                                alpha=1.0,\n",
    "                                label=\"CORA5.2\",\n",
    "                                linestyle='dashed')\n",
    "                        \n",
    "                # ERSSTv5 --------------------\n",
    "                obs_an = get_ersst(var_name)\n",
    "                obs_an.rio.set_spatial_dims(y_dim=\"lat\", x_dim=\"lon\", inplace=True)\n",
    "                obs_an.rio.write_crs(4326,  inplace=True)\n",
    "\n",
    "                clipped = obs_an.rio.clip(geometries=shdf_sel.geometry.apply(mapping), crs=obs_an.rio.crs)  \n",
    "                clipped = clipped.sel(time=slice(\"1950-01-01\",\"2021-01-01\")).mean({\"lat\",\"lon\"}) \n",
    "                clipped = clipped.resample(time=\"A\").mean() \n",
    "                print(f\"Running calculations and plotting ERSSTv5\")       \n",
    "        \n",
    "                ax.plot(clipped.time.values,\n",
    "                        clipped[\"sst\"],\n",
    "                        linewidth=2.5,\n",
    "                        color=\"tab:red\",\n",
    "                        alpha=1.0,\n",
    "                        label=\"NOAA ERSSTv5\",\n",
    "                        linestyle='dashed')\n",
    "                \n",
    "        if var_name == \"par\":\n",
    "                plt.ylabel(r\"Irradiance ($W m^{-2}$)\", fontsize=14)\n",
    "                loc = 'upper left'\n",
    "        elif var_name == \"uv\":\n",
    "                plt.ylabel(r\"UV ($W m^{-2}$)\", fontsize=14)\n",
    "                loc = 'upper left'\n",
    "        elif var_name == \"uvb\":\n",
    "                plt.ylabel(r\"Irradiance ($W m^{-2}$)\", fontsize=14)\n",
    "                loc = 'upper right'\n",
    "        elif var_name == \"siconc\":\n",
    "                plt.ylabel(r\"Sea ice concentration ($percent$)\", fontsize=14)\n",
    "                loc = 'lower left'\n",
    "        elif var_name == \"sithick\":\n",
    "                plt.ylabel(r\"Sea ice thickness ($m$)\", fontsize=14)\n",
    "                loc = 'upper right'\n",
    "        elif var_name == \"sisnthick\":\n",
    "                plt.ylabel(r\"Sea ice snow thickness ($m$)\", fontsize=14)\n",
    "                loc = 'upper right'\n",
    "        elif var_name == \"sisnconc\":\n",
    "             #   plt.ylabel(r\"Sea ice snow concentration ($percent$)\", fontsize=14)\n",
    "                loc = 'upper right'\n",
    "        elif var_name == \"tos\":\n",
    "             #   plt.ylabel(r\"Ocean temperature ($^{o}C$)\", fontsize=14)\n",
    "                loc='upper left'\n",
    "        else:\n",
    "                loc = 'upper left'\n",
    "        if var_name == \"tos\" and LME=='Northern Bering - Chukchi Seas':\n",
    "                ax.legend(title=\"Model-scenario\",fontsize=15, title_fontsize=16, loc=loc, ncol=2,  labelspacing=0.05)\n",
    "                \n",
    "     #   plt.xlabel(r\"Year\", fontsize=14)\n",
    "        plt.yticks(fontsize=20)\n",
    "        plt.xticks(fontsize=20)\n",
    "        plotfile=f\"Figures/{var_name}_comparison_members_vs_ensemble_{scenario}_{LME}.png\"\n",
    "        if Path(plotfile).exists():\n",
    "                Path(plotfile).unlink()\n",
    "        plt.savefig(plotfile, dpi=300, bbox_inches='tight')\n",
    "        print(f\"Saved to plotfile {plotfile}\")\n",
    "\n",
    "def get_lme_polygon(LME: str):\n",
    "        \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",
    "    return shdf_4326[shdf_4326[\"LME_NAME\"] == LME]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r10i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r10i1p2f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r1i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r1i1p2f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r2i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r2i1p2f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r3i1p2f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r4i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r7i1p2f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/CanESM5/CMIP6_CanESM5_r9i1p2f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/MPI-ESM1-2-HR/CMIP6_MPI-ESM1-2-HR_r1i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/MPI-ESM1-2-HR/CMIP6_MPI-ESM1-2-HR_r2i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/MPI-ESM1-2-LR/CMIP6_MPI-ESM1-2-LR_r10i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/MPI-ESM1-2-LR/CMIP6_MPI-ESM1-2-LR_r1i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/MPI-ESM1-2-LR/CMIP6_MPI-ESM1-2-LR_r2i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/MPI-ESM1-2-LR/CMIP6_MPI-ESM1-2-LR_r4i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/MPI-ESM1-2-LR/CMIP6_MPI-ESM1-2-LR_r5i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/MPI-ESM1-2-LR/CMIP6_MPI-ESM1-2-LR_r6i1p1f1_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/UKESM1-0-LL/CMIP6_UKESM1-0-LL_r1i1p1f2_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/UKESM1-0-LL/CMIP6_UKESM1-0-LL_r2i1p1f2_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/UKESM1-0-LL/CMIP6_UKESM1-0-LL_r3i1p1f2_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/UKESM1-0-LL/CMIP6_UKESM1-0-LL_r4i1p1f2_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/UKESM1-0-LL/CMIP6_UKESM1-0-LL_r8i1p1f2_ssp245_tos.nc\n",
      "[CMIP6_IO] Opening file actea-shared/light/ssp245/ensemble/CMIP6_ensemble_stats_ssp245_tos_weighted.nc\n",
      "light/ssp245/ensemble/CMIP6_ensemble_stats_ssp245_tos_weighted.nc\n",
      "<xarray.Dataset> Size: 1GB\n",
      "Dimensions:    (time: 1800, y: 90, x: 360)\n",
      "Coordinates:\n",
      "  * time       (time) datetime64[ns] 14kB 1950-01-01 1950-02-01 ... 2099-12-01\n",
      "    lon        (y, x) float64 259kB ...\n",
      "    lat        (y, x) float64 259kB ...\n",
      "Dimensions without coordinates: y, x\n",
      "Data variables:\n",
      "    tos_mean   (time, y, x) float64 467MB ...\n",
      "    tos_stdev  (time, y, x) float64 467MB ...\n",
      "    tos_max    (time, y, x) float32 233MB ...\n",
      "    tos_min    (time, y, x) float32 233MB ...\n",
      "Attributes:\n",
      "    history:  [2023-09-30 16:34:01] : Computation of statistics on 23 ensembl...\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'rename'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[39], line 19\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var_name \u001b[38;5;129;01min\u001b[39;00m var_names:\n\u001b[1;32m     18\u001b[0m     ds_list \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m---> 19\u001b[0m     ds_list \u001b[38;5;241m=\u001b[39m \u001b[43mcollect_ds\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscenario\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvar_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mds_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m    \u001b[38;5;66;03m# print(ds_list.keys())\u001b[39;00m\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of model-scenario combinations: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(ds_list\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[37], line 144\u001b[0m, in \u001b[0;36mcollect_ds\u001b[0;34m(scenario, var_name, ds_list, source_ids)\u001b[0m\n\u001b[1;32m    141\u001b[0m     f \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlightpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscenario\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/ensemble/CMIP6_ensemble_stats_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscenario\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.nc\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    142\u001b[0m ds \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mopen_dataset_on_gs(f)\n\u001b[0;32m--> 144\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrename\u001b[49m({\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_mean\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m})\n\u001b[1;32m    145\u001b[0m ds_list[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensemble_unweighted\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m ds[var_name]\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds_list\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'rename'"
     ]
    }
   ],
   "source": [
    "sns.set_theme(style=\"white\", rc={\"axes.facecolor\": (0, 0, 0, 0)}, font_scale=2.5)\n",
    "\n",
    "LMES = [\"Northern Bering - Chukchi Seas\",\"Barents Sea\"]\n",
    "# Define what models, scenarios, and variables to calculate ensemble files from\n",
    "\n",
    "source_ids = [\"MPI-ESM1-2-HR\",\"MPI-ESM1-2-LR\", \"CanESM5\", \"UKESM1-0-LL\"]\n",
    "scenarios=[\"ssp245\"]#, \"ssp585\"]\n",
    "var_names = [\"par\",\"uv\",\"uvb\", \"uvi_\", \"uva_\", \"uv_srf_\"]  # ,\"_tos.nc\",\"_siconc.nc\"]\n",
    "var_names = [\"siconc\",\"sithick\",\"sisnthick\",\"sisnconc\"] #, \"_tos.nc\",\"_siconc.nc\"]\n",
    "var_names = [\"tos\",\"siconc\"]\n",
    "\n",
    "for LME in LMES:\n",
    "    shdf_sel = get_lme_polygon(LME)\n",
    "\n",
    "    for scenario in scenarios:\n",
    "        for var_name in var_names:\n",
    "        \n",
    "            ds_list = {}\n",
    "            ds_list = collect_ds(scenario, var_name, ds_list, source_ids)\n",
    "           # print(ds_list.keys())\n",
    "            print(f\"Number of model-scenario combinations: {len(ds_list.keys())}\")\n",
    "\n",
    "            plot_models(ds_list, var_name, shdf_sel, scenario, LME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1300x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset> Size: 36MB\n",
      "Dimensions:      (lat: 77, lon: 272, time: 216)\n",
      "Coordinates:\n",
      "  * lat          (lat) float32 308B 82.88 82.62 82.38 ... 64.38 64.12 63.88\n",
      "  * lon          (lon) float32 1kB 0.125 0.375 0.625 0.875 ... 67.38 67.62 67.88\n",
      "  * time         (time) datetime64[ns] 2kB 2000-01-16 2000-02-16 ... 2017-12-16\n",
      "    spatial_ref  int64 8B 0\n",
      "Data variables:\n",
      "    siconc       (time, lat, lon) float64 36MB dask.array<chunksize=(1, 77, 272), meta=np.ndarray>\n",
      "<xarray.Dataset> Size: 169kB\n",
      "Dimensions:      (lat: 77, lon: 272)\n",
      "Coordinates:\n",
      "  * lat          (lat) float32 308B 82.88 82.62 82.38 ... 64.38 64.12 63.88\n",
      "  * lon          (lon) float32 1kB 0.125 0.375 0.625 0.875 ... 67.38 67.62 67.88\n",
      "    spatial_ref  int64 8B 0\n",
      "Data variables:\n",
      "    siconc       (lat, lon) float64 168kB dask.array<chunksize=(77, 272), meta=np.ndarray>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tk/.conda/envs/opendrift/envs/actea/lib/python3.11/site-packages/dask/array/chunk.py:278: RuntimeWarning: invalid value encountered in cast\n",
      "  return x.astype(astype_dtype, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1300x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import xarray as xr\n",
    "fig = plt.figure(figsize=(13, 8))\n",
    "\n",
    "obs_an = get_G10010_SIBT1850(var_name)\n",
    "          \n",
    "#obs_an = get_hadlsst_dataset()\n",
    "obs_an.rio.set_spatial_dims(y_dim=\"lat\", x_dim=\"lon\", inplace=True)\n",
    "obs_an.rio.write_crs(4326,  inplace=True)\n",
    "shdf_sel.plot()\n",
    "plt.show()\n",
    "ds = obs_an.rio.clip(geometries=shdf_sel.geometry.apply(mapping), crs=obs_an.rio.crs)  \n",
    "ds = ds.sel(time=slice(\"2000-01-01\",\"2018-01-01\"))\n",
    "\n",
    "ds[\"siconc\"] = ds[\"siconc\"] * 100.0\n",
    "ds[\"siconc\"] = xr.where(ds[\"siconc\"] > 100, 100, ds[\"siconc\"])\n",
    "ds[\"siconc\"]= xr.where(ds[\"siconc\"] < 0, 0, ds[\"siconc\"])\n",
    "print(ds)\n",
    "ds = ds.mean(\"time\")\n",
    "print(ds)\n",
    "\n",
    "fig = plt.figure(figsize=(13, 8))\n",
    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree(central_longitude=0))\n",
    "ds.siconc.plot(ax=ax)\n",
    "ax.add_feature(cfeature.COASTLINE, linewidth=0.5, zorder=21)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "actea",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}