snc-global_vs_HMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projections GLOB vs HMA of Snow Cover Extent\n",
    "conda env: `phd_v3` (in `envs/phd`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "3.8.5 | packaged by conda-forge | (default, Jul 24 2020, 01:25:15) \n",
      "[GCC 7.5.0]\n"
     ]
    }
   ],
   "source": [
    "# To reload external files automatically (ex: utils)\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import proplot as plot # New plot library (https://proplot.readthedocs.io/en/latest/)\n",
    "plot.rc['savefig.dpi'] = 300 # 1200 is too big! #https://proplot.readthedocs.io/en/latest/basics.html#Creating-figures\n",
    "from scipy import stats\n",
    "import xesmf as xe # For regridding (https://xesmf.readthedocs.io/en/latest/)\n",
    "\n",
    "import sys\n",
    "sys.path.insert(1, '/home/mlalande/notebooks/utils') # to include my util file in previous directory\n",
    "import utils as u # my personal functions\n",
    "u.check_python_version()\n",
    "# u.check_virtual_memory()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Snow Cover Extent'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "period_future = slice('2015','2100')\n",
    "period_past = slice('1979','2014')\n",
    "latlim, lonlim = u.get_domain_HMA()\n",
    "lonlim_HK, latlim_HK, lonlim_HM, latlim_HM, lonlim_TP, latlim_TP = u.get_zones()\n",
    "seasons = ['Annual', 'DJFMA', 'JJAS']\n",
    "# seasons = ['Annual']\n",
    "\n",
    "# HMA for full domain and the following for the above zones\n",
    "zones = ['GLOB', 'GLOB-land', 'NH-land', 'NH-land-60', 'HMA', 'HK', 'HM', 'TP']\n",
    "zones_df = pd.DataFrame(\n",
    "    [\n",
    "        [slice(None), slice(None)], \n",
    "        [slice(None), slice(None)], \n",
    "        [slice(None), slice(0,90)], \n",
    "        [slice(None), slice(0,60)], \n",
    "        [lonlim, latlim],\n",
    "        [lonlim_HK, latlim_HK],\n",
    "        [lonlim_HM, latlim_HM],\n",
    "        [lonlim_TP, latlim_TP]\n",
    "    ], \n",
    "    columns=pd.Index(['lonlim', 'latlim'], name='Limits'),\n",
    "    index=pd.Index(zones, name='Zones')\n",
    ")\n",
    "\n",
    "var = 'snc'\n",
    "table = u.get_table(var)\n",
    "label, units, levels, cmap, extend, \\\n",
    "    levels_diff, cmap_diff, extend_diff, \\\n",
    "    levels_bias, cmap_bias, extend_bias = u.get_var_infos(var)\n",
    "label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load topography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('GMTED2010_15n240_1000deg.nc').drop_dims('nbounds').swap_dims(\n",
    "    {'nlat': 'latitude', 'nlon': 'longitude'}).drop({'nlat', 'nlon'}).rename(\n",
    "    {'latitude': 'lat', 'longitude': 'lon'})\n",
    "elevation = ds.elevation\n",
    "elevation_std = ds.elevation_stddev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load land/sea mask\n",
    "https://ldas.gsfc.nasa.gov/gldas/vegetation-class-mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = xr.open_dataset('/data/mlalande/GLDASp5_landmask_10d.nc4').GLDAS_mask[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BCC-CSM2-MR (1/10)\n",
      "Load past\n",
      "1.125°x1.112°\n",
      "Annual\n",
      "Reuse existing file: bilinear_160x320_180x360.nc\n",
      "DJFMA\n",
      "Reuse existing file: bilinear_160x320_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_160x320_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "\n",
      "\n",
      "CNRM-CM6-1 (2/10)\n",
      "Load past\n",
      "1.406°x1.389°\n",
      "Annual\n",
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "\n",
      "\n",
      "CNRM-CM6-1-HR (3/10)\n",
      "Load past\n",
      "0.5°x0.495°\n",
      "Annual\n",
      "Reuse existing file: bilinear_360x720_180x360.nc\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuse existing file: bilinear_360x720_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_360x720_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "\n",
      "\n",
      "CNRM-ESM2-1 (4/10)\n",
      "Load past\n",
      "1.406°x1.389°\n",
      "Annual\n",
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "\n",
      "\n",
      "CanESM5 (5/10)\n",
      "Load past\n",
      "2.812°x2.767°\n",
      "Annual\n",
      "Reuse existing file: bilinear_64x128_180x360.nc\n",
      "DJFMA\n",
      "Reuse existing file: bilinear_64x128_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_64x128_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "\n",
      "\n",
      "IPSL-CM6A-LR (6/10)\n",
      "Load past\n",
      "2.5°x1.268°\n",
      "Annual\n",
      "Reuse existing file: bilinear_143x144_180x360.nc\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuse existing file: bilinear_143x144_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_143x144_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "\n",
      "\n",
      "MIROC-ES2L (7/10)\n",
      "Load past\n",
      "2.812°x2.767°\n",
      "Annual\n",
      "Reuse existing file: bilinear_64x128_180x360.nc\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuse existing file: bilinear_64x128_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_64x128_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "\n",
      "\n",
      "MIROC6 (8/10)\n",
      "Load past\n",
      "1.406°x1.389°\n",
      "Annual\n",
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_128x256_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "\n",
      "\n",
      "MRI-ESM2-0 (9/10)\n",
      "Load past\n",
      "1.125°x1.112°\n",
      "Annual\n",
      "Reuse existing file: bilinear_160x320_180x360.nc\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reuse existing file: bilinear_160x320_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_160x320_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data/mlalande/miniconda3/envs/phd_v3/lib/python3.8/site-packages/xarray/core/common.py:1123: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
      "The new arguments that you should use are 'offset' or 'origin'.\n",
      "\n",
      ">>> df.resample(freq=\"3s\", base=2)\n",
      "\n",
      "becomes:\n",
      "\n",
      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
      "\n",
      "  grouper = pd.Grouper(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JJAS\n",
      "\n",
      "\n",
      "UKESM1-0-LL (10/10)\n",
      "Load past\n",
      "1.875°x1.25°\n",
      "Annual\n",
      "Reuse existing file: bilinear_144x192_180x360.nc\n",
      "DJFMA\n",
      "Reuse existing file: bilinear_144x192_180x360.nc\n",
      "JJAS\n",
      "Reuse existing file: bilinear_144x192_180x360.nc\n",
      "Load future\n",
      "ssp126\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp245\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp370\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "ssp585\n",
      "Annual\n",
      "DJFMA\n",
      "JJAS\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "list_models = u.get_model_names_projections()\n",
    "# list_models = list_models[1:2]\n",
    "# list_models = ['IPSL-CM6A-LR']\n",
    "\n",
    "resolution = [None]*len(list_models)\n",
    "\n",
    "da_past_ym_list = [[None] * (len(list_models)) for i in range(len(seasons))]\n",
    "\n",
    "scenarios = ['ssp126', 'ssp245', 'ssp370', 'ssp585']\n",
    "da_ssp126_ym_list = [[None] * (len(list_models)) for i in range(len(seasons))]\n",
    "da_ssp245_ym_list = [[None] * (len(list_models)) for i in range(len(seasons))]\n",
    "da_ssp370_ym_list = [[None] * (len(list_models)) for i in range(len(seasons))]\n",
    "da_ssp585_ym_list = [[None] * (len(list_models)) for i in range(len(seasons))]\n",
    "\n",
    "da_future_ym_list = [da_ssp126_ym_list, da_ssp245_ym_list, da_ssp370_ym_list, da_ssp585_ym_list]\n",
    "\n",
    "\n",
    "for i, model in enumerate(list_models):\n",
    "    print(model + ' ('+str(i+1)+'/'+str(len(list_models))+')')\n",
    "    institute, grid, realization, calendar = u.get_model_infos(model, var)\n",
    "    \n",
    "    #################\n",
    "    ### Load past ###\n",
    "    #################\n",
    "    print('Load past')\n",
    "    da_past = xr.open_mfdataset(\n",
    "        '/bdd/CMIP6/CMIP/'+institute+'/'+model+'/historical/'+realization+'/'+table+'/'+var+'/'+grid+'/latest/*.nc', \n",
    "        combine='by_coords').sel(time=period_past)[var].load()\n",
    "    \n",
    "    np.testing.assert_equal((int(period_past.stop) - int(period_past.start) + 1)*12, da_past.time.size)\n",
    "    \n",
    "    # snc (0-1 to % when needed and take out wrong values)\n",
    "    #################################################################################\n",
    "    \n",
    "    # Some models have values between 0 and 1 even if units in %    \n",
    "    if da_past.max().values <= 1.1:\n",
    "        da_past = da_past*100\n",
    "    \n",
    "    # Some models have wrong values (+-0.1 for numeric precision)\n",
    "    da_past = da_past.where( (da_past >= -0.1) & (da_past <= 100.1) )\n",
    "    \n",
    "    #################################################################################\n",
    "    \n",
    "    # Print resolution and save it\n",
    "    lat = (da_past.lat[1] - da_past.lat[0]).values; lon = (da_past.lon[1] - da_past.lon[0]).values\n",
    "    print(str(lon.round(3))+'°x'+str(lat.round(3))+'°')\n",
    "    resolution[i] = str(lon.round(1))+'°x'+str(lat.round(1))+'°'\n",
    "    \n",
    "    for k, season in enumerate(seasons):\n",
    "        print(season)\n",
    "        # Make year_mean\n",
    "        da_past_ym = u.year_mean(da_past, calendar=calendar, season=season)\n",
    "\n",
    "        # Regrid\n",
    "        regridder = xe.Regridder(da_past, elevation, 'bilinear', periodic=False, reuse_weights=True)\n",
    "        da_past_ym_regrid = regridder(da_past_ym)\n",
    "        \n",
    "        # Remove last year for Annual and summer to fit to winter\n",
    "        if season in ['Annual', 'JJAS']:\n",
    "            da_past_ym_regrid = da_past_ym_regrid.isel(year=slice(0,-1))\n",
    "\n",
    "        # Store result\n",
    "        da_past_ym_list[k][i] = da_past_ym_regrid\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    ###################\n",
    "    ### Load future ###\n",
    "    ###################\n",
    "    print('Load future')\n",
    "    for j, scenario in enumerate(scenarios):\n",
    "        print(scenario)\n",
    "        if model in ['IPSL-CM6A-LR', 'MRI-ESM2-0']:\n",
    "            da_future = xr.open_mfdataset(\n",
    "                '/bdd/CMIP6/ScenarioMIP/'+institute+'/'+model+'/'+scenario+'/'+realization+'/'+table+'/'+var+'/'+grid+'/latest/*210012.nc', \n",
    "                combine='by_coords').sel(time=period_future)[var].load()\n",
    "        else:\n",
    "            da_future = xr.open_mfdataset(\n",
    "                '/bdd/CMIP6/ScenarioMIP/'+institute+'/'+model+'/'+scenario+'/'+realization+'/'+table+'/'+var+'/'+grid+'/latest/*.nc', \n",
    "                combine='by_coords').sel(time=period_future)[var].load()\n",
    "\n",
    "        np.testing.assert_equal((int(period_future.stop) - int(period_future.start) + 1)*12, da_future.time.size)\n",
    "        \n",
    "        # snc (0-1 to % when needed and take out wrong values)\n",
    "        #################################################################################\n",
    "\n",
    "        # Some models have values between 0 and 1 even if units in %    \n",
    "        if da_future.max().values <= 1.1:\n",
    "            da_future = da_future*100\n",
    "\n",
    "        # Some models have wrong values (+-0.1 for numeric precision)\n",
    "        da_future = da_future.where( (da_future >= -0.1) & (da_future <= 100.1) )\n",
    "\n",
    "        #################################################################################\n",
    "        \n",
    "        for k, season in enumerate(seasons):\n",
    "            print(season)\n",
    "            # Make year_mean\n",
    "            da_future_ym = u.year_mean(da_future, calendar=calendar, season=season)\n",
    "\n",
    "            # Regrid\n",
    "            da_future_ym_regrid = regridder(da_future_ym)\n",
    "            \n",
    "            # Remove last year for Annual and summer to fit to winter\n",
    "            if season in ['Annual', 'JJAS']:\n",
    "                da_future_ym_regrid = da_future_ym_regrid.isel(year=slice(0,-1))\n",
    "\n",
    "            # Store result\n",
    "            da_future_ym_list[j][k][i] = da_future_ym_regrid\n",
    "        \n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/bdd/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/LImon/snc/gn/latest'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'/bdd/CMIP6/CMIP/'+institute+'/'+model+'/historical/'+realization+'/'+table+'/'+var+'/'+grid+'/latest'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# '/bdd/CMIP6/ScenarioMIP/'+institute+'/'+model+'/'+scenario+'/'+realization+'/'+table+'/'+var+'/'+grid+'/latest/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenate results by zones and scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model past\n",
    "temp = [None]*len(seasons)\n",
    "for k, season in enumerate(seasons):\n",
    "    temp[k] = xr.concat(\n",
    "        da_past_ym_list[k][:], pd.Index(list_models, name='model'), coords='minimal', compat='override'\n",
    "    )\n",
    "mm_past_ym = xr.concat(temp, pd.Index(seasons, name=\"season\"))\n",
    "\n",
    "temp = [None]*len(zones) \n",
    "for i, zone in enumerate(zones):\n",
    "    if zone in ['HMA', 'HK', 'HM', 'TP']:\n",
    "        temp[i] = u.spatial_average(\n",
    "            mm_past_ym.sel(lat=zones_df.latlim[zone], lon=zones_df.lonlim[zone]).where(elevation > 2500)\n",
    "        )\n",
    "    elif zone == 'GLOB':\n",
    "        temp[i] = u.spatial_average(mm_past_ym)\n",
    "        \n",
    "    elif zone == 'GLOB-land':\n",
    "        temp[i] = u.spatial_average(mm_past_ym.where(mask == 1))\n",
    "        \n",
    "    elif zone in ['NH-land', 'NH-land-60']:\n",
    "        temp[i] = u.spatial_average(mm_past_ym.where(mask == 1).sel(lat=zones_df.latlim[zone], lon=zones_df.lonlim[zone]))\n",
    "        \n",
    "mm_past_ym_zones = xr.concat(temp, pd.Index(zones, name=\"zone\")).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mm_past_ym_zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model future\n",
    "temp_scenarios = [None]*len(scenarios)\n",
    "\n",
    "for j, scenario in enumerate(scenarios):\n",
    "    temp = [None]*len(seasons)\n",
    "    for k, season in enumerate(seasons):\n",
    "        temp[k] = xr.concat(\n",
    "            da_future_ym_list[j][k][:], pd.Index(list_models, name='model'), coords='minimal', compat='override'\n",
    "        )        \n",
    "    mm_future_ym = xr.concat(temp, pd.Index(seasons, name=\"season\")).load()\n",
    "\n",
    "    temp = [None]*len(zones) \n",
    "    for i, zone in enumerate(zones):\n",
    "        if zone in ['HMA', 'HK', 'HM', 'TP']:\n",
    "            temp[i] = u.spatial_average(\n",
    "                mm_future_ym.sel(lat=zones_df.latlim[zone], lon=zones_df.lonlim[zone]).where(elevation > 2500)\n",
    "            )\n",
    "        elif zone == 'GLOB':\n",
    "            temp[i] = u.spatial_average(mm_future_ym)\n",
    "            \n",
    "        elif zone == 'GLOB-land':\n",
    "            temp[i] = u.spatial_average(mm_future_ym.where(mask == 1))\n",
    "        \n",
    "        elif zone in ['NH-land', 'NH-land-60']:\n",
    "            temp[i] = u.spatial_average(mm_future_ym.where(mask == 1).sel(lat=zones_df.latlim[zone], lon=zones_df.lonlim[zone]))\n",
    "        \n",
    "        \n",
    "    temp_scenarios[j] = xr.concat(temp, pd.Index(zones, name=\"zone\")).load()\n",
    "    \n",
    "mm_future_ym_zones_scenarios = xr.concat(temp_scenarios, pd.Index(scenarios, name=\"scenario\")).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mm_future_ym_zones_scenarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'snc'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm_past_ym_zones.to_netcdf('results/projections_GLOB-HMA_'+var+'_'+period_past.start+'-'+period_past.start+'_mm_past_ym_zones.nc')\n",
    "mm_future_ym_zones_scenarios.to_netcdf('results/projections_GLOB-HMA_'+var+'_'+period_future.start+'-'+period_future.start+'_mm_future_ym_zones_scenarios.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_models = u.get_model_names_projections()\n",
    "scenarios = ['ssp126', 'ssp245', 'ssp370', 'ssp585']\n",
    "\n",
    "mm_past_ym_zones = xr.open_dataarray('results/projections_GLOB-HMA_'+var+'_'+period_past.start+'-'+period_past.start+'_mm_past_ym_zones.nc')\n",
    "mm_future_ym_zones_scenarios = xr.open_dataarray('results/projections_GLOB-HMA_'+var+'_'+period_future.start+'-'+period_future.start+'_mm_future_ym_zones_scenarios.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mm_future_ym_zones_scenarios\n",
    "# mm_past_ym_zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'snc'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'%'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 320.833x316.528 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 334,
       "width": 302
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "season='Annual'\n",
    "colors = ['indigo9', 'blue5', 'orange5', 'red6']\n",
    "clim_ref = mm_past_ym_zones.sel(season=season, year=slice('1995','2014')).mean('year')\n",
    "\n",
    "fig, axs = plot.subplots()\n",
    "\n",
    "for i, scenario in enumerate(scenarios):\n",
    "    x = []\n",
    "    y = []\n",
    "    \n",
    "    y_start = 2015\n",
    "    y_end = 0\n",
    "    delta = 10\n",
    "    \n",
    "    while y_end < 2100-delta:\n",
    "        y_end = y_start + 20 - 1\n",
    "        \n",
    "        for model in list_models:\n",
    "            temp = (mm_future_ym_zones_scenarios.sel(season=season, year=slice(str(y_start),str(y_end)), scenario=scenario, model=model).mean('year') - clim_ref.sel(model=model)).squeeze()\n",
    "\n",
    "            x.append(temp.sel(zone='HMA').values)\n",
    "            y.append(temp.sel(zone='GLOB').values)\n",
    "        \n",
    "        y_start += delta\n",
    "    \n",
    "    # Plot\n",
    "    axs[0].scatter(x, y, color=colors[i], label=scenario, zorder=-i, marker='+')\n",
    "\n",
    "# lim = (0,10)\n",
    "axs.format(\n",
    "    xlabel='HMA anomalies ['+units+']',\n",
    "    ylabel='Global anomalies ['+units+']',\n",
    "#     xlim=lim, ylim=lim,\n",
    "    suptitle=label+' anomalies\\n(relative to 1995–2014 average)'\n",
    ")\n",
    "\n",
    "axs.legend(loc='ul', frame=True, ncols=2)\n",
    "\n",
    "filename = 'projections/'+var+'_HMA_global'\n",
    "# fig.save('img/'+filename+'.jpg'); fig.save('img/'+filename+'.png'); fig.save('img/'+filename+'.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:phd_v3] *",
   "language": "python",
   "name": "conda-env-phd_v3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}