2022_BAMS_CORDEX_grand_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Santander Meteorology Group notebooks\n",
    "\n",
    "***\n",
    "\n",
    "> This Work has been submitted to BAMS (Bulletin of the American Meteorological Society). Copyright in this Work may be transferred without further notice.\n",
    "\n",
    "> This notebook reproduces figures 5 and 6 available in the article \"The worldwide C3S CORDEX grand ensemble: A major contribution to assess regional climate change in the IPCC AR6 Atlas\". \n",
    ">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assessing the consistency of the worldwide C3S CORDEX grand ensemble in overlapping regions\n",
    "\n",
    "18/05/2022\n",
    "\n",
    "J. Diez-Sierra (Santander Meteorology Group, Instituto de Física de Cantabria (IFCA), CSIC - Universidad de Cantabria, Santander, Spain)\n",
    "\n",
    "This script works with the dataset aggregated regionally available on the AR6 IPCC-WG1 GitHub repository (Iturbide et al., 2021) for CMIP5 and CORDEX projects (https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally/data/). This dataset contains monthly means of temperature and precipitation spatially averaged over the new subcontinental climatic regions used in the IPCC AR6-WG1. Note that the dataset used here comprises a subset of the CORDEX grand ensemble available at the C3S-CDS, therefore the results are slightly different from those available in the article. Further, in the article we considered only those simulations that overlap more than 90% of the grid cells with the regions analyzed, while here the selection criteria is different (~80%, see the documentations available on the AR6 IPCC-WG1 GitHub: https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading packages\n",
    "\n",
    "This script requires regionmask version 0.6.1 or later and xarray version 0.15.1 or later. Check the documentation of regionmask for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import regionmask\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "import matplotlib.patches as patches\n",
    "from matplotlib.patches import Rectangle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the reference regions\n",
    "\n",
    "The regions are available at regionmask.defined_regions.ar6. The whole set of 58 regions is available under (ar6.all). In addition the land (ar6.land) and ocean (ar6.ocean) regions are given separately. The numbering is kept consistent between the categories. Note that some regions are in the land and in the ocean categories (e.g. the Mediterranean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<regionmask.Regions>\n",
       "Name:     AR6 reference regions\n",
       "Source:   Iturbide et al., 2020 (Earth Syst. Sci. Data)\n",
       "\n",
       "Regions:\n",
       " 0 GIC      Greenland/Iceland\n",
       " 1 NWN      N.W.North-America\n",
       " 2 NEN      N.E.North-America\n",
       " 3 WNA        W.North-America\n",
       " 4 CNA        C.North-America\n",
       "..  ..                    ...\n",
       "53 ARS            Arabian-Sea\n",
       "54 BOB          Bay-of-Bengal\n",
       "55 EIO Equatorial.Indic-Ocean\n",
       "56 SIO          S.Indic-Ocean\n",
       "57 SOO         Southern-Ocean\n",
       "\n",
       "[58 regions]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ar6_all = regionmask.defined_regions.ar6.all\n",
    "ar6_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining some functions which will be used later "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def traverseDir(root):\n",
    "    \"\"\"Function to show all csv files unde a folder \"\"\"\n",
    "    for (dirpath, dirnames, filenames) in os.walk(root):\n",
    "        for file in filenames:\n",
    "            if file.endswith('.csv'):\n",
    "                yield os.path.join(dirpath, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_period_SW(x, y, period_hist, period_fut, summer, winter):\n",
    "    \"\"\"Function to select data for different seasons\"\"\"\n",
    "    #select years \n",
    "    x_period = x.loc[np.in1d(x.index.year, period_hist)]\n",
    "    y_period = y.loc[np.in1d(y.index.year, period_fut)]\n",
    "    # select summer \n",
    "    x_period_s = x_period.loc[np.in1d(x_period.index.month, summer)]\n",
    "    y_period_s = y_period.loc[np.in1d(y_period.index.month, summer)]\n",
    "    # select winter\n",
    "    x_period_w = x_period.loc[np.in1d(x_period.index.month, winter)]\n",
    "    y_period_w = y_period.loc[np.in1d(y_period.index.month, winter)]\n",
    "    return x_period, y_period, x_period_s, y_period_s, x_period_w, y_period_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_sim_CORDEX(file_last, dom, var, mask):\n",
    "    \"\"\"Select CORDEX simulations for a specific: domain, variable and mask\"\"\"\n",
    "    file_dom = [file for file in file_last if 'CORDEX-' + dom + '_' in file]\n",
    "    file_dom_sce = [file for file in file_dom if '_' + var + '_'  in file]\n",
    "    file_dom_sce = [file for file in file_dom_sce if '_' + mask + '/'  in file]\n",
    "    file_dom_sce_res = []\n",
    "    for file in file_dom_sce:\n",
    "        if dom + '-' + '44' in file:\n",
    "            file_22 = file.replace(dom + '-' + '44', dom + '-' + '22')\n",
    "            if not file_22 in file_dom_sce:\n",
    "                file_dom_sce_res.append(file)\n",
    "            else:\n",
    "                a = 1\n",
    "        else:\n",
    "            file_dom_sce_res.append(file)        \n",
    "    return file_dom_sce_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_sim_CMIP5(file_last, var, mask):\n",
    "    \"\"\"Select CMIP5 simulations for a specific: domain, variable and mask\"\"\"\n",
    "    file_dom_sce = [file for file in file_last if 'CMIP5_' + var + '_'  in file]\n",
    "    file_dom_sce = [file for file in file_dom_sce if '_' + mask + '/'  in file]\n",
    "    file_dom_sce_res = []\n",
    "    for file in file_dom_sce:\n",
    "        file_dom_sce_res.append(file)        \n",
    "    return file_dom_sce_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_delta(select_sim_90, reg, period_hist, period_fut, experiment, variable):\n",
    "    \"\"\"Calculate deltas (climate signals) for temperature and precipitation\"\"\"\n",
    "    res_res = pd.DataFrame(index = select_sim_90, columns = ['period', 'summer', 'winter'])\n",
    "    for file in select_sim_90:\n",
    "        if experiment == 'CORDEX':\n",
    "            raw_exp = pd.read_csv(file, index_col = 0, parse_dates = True, comment='#')\n",
    "        elif experiment == 'CMIP5':\n",
    "            raw_exp = pd.read_csv(file, index_col = 0, parse_dates = True, nrows = 100*12, comment='#')\n",
    "        try:\n",
    "            hist_exp = pd.read_csv(file.replace('rcp85', 'historical'), index_col = 0, parse_dates = True, comment='#')\n",
    "        except:\n",
    "            print(\"Historical simulation not available for simulation:\" + file)\n",
    "            continue\n",
    "        \n",
    "        if reg in hist_exp.columns:\n",
    "            if variable == 'pr':\n",
    "                hist_p, raw_p, hist_s, raw_s, hist_w, raw_w = \\\n",
    "                    select_period_SW(hist_exp, raw_exp, period_hist, period_fut, summer, winter)             \n",
    "            elif variable == 'tas':\n",
    "                hist_p, raw_p, hist_s, raw_s, hist_w, raw_w = \\\n",
    "                    select_period_SW(hist_exp, raw_exp, period_hist, period_fut, summer, winter)\n",
    "\n",
    "            if variable == 'pr':\n",
    "                res_res.loc[file]['period'] = (raw_p[reg].mean()-hist_p[reg].mean())/hist_p[reg].mean()\n",
    "                res_res.loc[file]['summer'] = (raw_s[reg].mean()-hist_s[reg].mean())/hist_p[reg].mean()\n",
    "                res_res.loc[file]['winter'] = (raw_w[reg].mean()-hist_w[reg].mean())/hist_p[reg].mean()\n",
    "            elif variable == 'tas':\n",
    "                res_res.loc[file]['period'] = raw_p[reg].mean()-hist_p[reg].mean()\n",
    "                res_res.loc[file]['summer'] = raw_s[reg].mean()-hist_s[reg].mean()\n",
    "                res_res.loc[file]['winter'] = raw_w[reg].mean()-hist_w[reg].mean()\n",
    "\n",
    "    return res_res.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_regions_overlap(tas_mean_dom_AR6_period, tas_mean_dom_AR6_n_sim):\n",
    "    \"\"\"Selecting regions with 2 or more overlapping CORDEX domains\"\"\"\n",
    "    tas_mean_dom_AR6_period_drop = tas_mean_dom_AR6_period.dropna(axis=0, how = 'all')\n",
    "    tas_mean_dom_AR6_period_drop = tas_mean_dom_AR6_period_drop.dropna(axis=1, how = 'all')\n",
    "    tas_mean_dom_AR6_period_drop = tas_mean_dom_AR6_period_drop.dropna(axis=1, thresh = 2)\n",
    "\n",
    "    tas_mean_dom_AR6_n_sim_drop = tas_mean_dom_AR6_n_sim.copy()\n",
    "    tas_mean_dom_AR6_n_sim_drop[tas_mean_dom_AR6_n_sim_drop == 0] = np.nan\n",
    "    tas_mean_dom_AR6_n_sim_drop = tas_mean_dom_AR6_n_sim_drop.dropna(axis=0, how = 'all')\n",
    "    tas_mean_dom_AR6_n_sim_drop = tas_mean_dom_AR6_n_sim_drop.dropna(axis=1, how = 'all')\n",
    "    tas_mean_dom_AR6_n_sim_drop = tas_mean_dom_AR6_n_sim_drop.dropna(axis=1, thresh = 2)\n",
    "\n",
    "    tas_mean_CORDEX = np.mean(tas_mean_dom_AR6_period*tas_mean_dom_AR6_n_sim/tas_mean_dom_AR6_n_sim.mean(axis=0), axis = 0)\n",
    "    tas_mean_CORDEX_drop = tas_mean_CORDEX.loc[tas_mean_dom_AR6_n_sim_drop.columns] \n",
    "    \n",
    "    return tas_mean_CORDEX, tas_mean_CORDEX_drop, tas_mean_dom_AR6_period_drop, tas_mean_dom_AR6_n_sim_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_delta_CMIP5_subensemble(select_sim_90, reg, period_hist, period_fut, files_CMIP5_rcp85, variable):\n",
    "    \"\"\"Calculate delta CMIP5 subensemble (see Boé et al. 2020)\"\"\"\n",
    "    res_res = pd.DataFrame(index = select_sim_90, columns = ['period', 'summer', 'winter'])\n",
    "    for file in select_sim_90:\n",
    "        GCM = file.split('/')[-1].split('_')[0]; run = file.split('/')[-1].split('_')[1]\n",
    "        file_sel = []\n",
    "        for fff in files_CMIP5_rcp85:\n",
    "            if (fff.split('/')[-1].split('_')[1] in GCM) and (run in fff):\n",
    "                file_sel.append(fff)\n",
    "        \n",
    "        if len(file_sel) == 0:\n",
    "    \n",
    "            print(\"CMIP5 model not available \" + reg + ' : ' + GCM  + '_' + run) \n",
    "            res_res.loc[file]['period'] = np.nan; res_res.loc[file]['summer'] = np.nan; res_res.loc[file]['winter'] = np.nan\n",
    "            \n",
    "        else:\n",
    "        \n",
    "            raw_exp = pd.read_csv(file_sel[0], index_col = 0, parse_dates = True, nrows = 100*12, comment='#')\n",
    "            hist_exp = pd.read_csv(file_sel[0].replace('rcp85', 'historical'), index_col = 0, parse_dates = True, comment='#')\n",
    "            \n",
    "            if variable == 'pr':\n",
    "                hist_p, raw_p, hist_s, raw_s, hist_w, raw_w = \\\n",
    "                    select_period_SW(hist_exp, raw_exp, period_hist, period_fut, summer, winter)\n",
    "                res_res.loc[file]['period'] = (raw_p[reg].mean()-hist_p[reg].mean())/hist_p[reg].mean()\n",
    "                res_res.loc[file]['summer'] = (raw_s[reg].mean()-hist_s[reg].mean())/hist_p[reg].mean()\n",
    "                res_res.loc[file]['winter'] = (raw_w[reg].mean()-hist_w[reg].mean())/hist_p[reg].mean()\n",
    "                            \n",
    "            elif variable == 'tas':\n",
    "                hist_p, raw_p, hist_s, raw_s, hist_w, raw_w = \\\n",
    "                    select_period_SW(hist_exp, raw_exp, period_hist, period_fut, summer, winter)\n",
    "                res_res.loc[file]['period'] = raw_p[reg].mean()-hist_p[reg].mean()\n",
    "                res_res.loc[file]['summer'] = raw_s[reg].mean()-hist_s[reg].mean()\n",
    "                res_res.loc[file]['winter'] = raw_w[reg].mean()-hist_w[reg].mean()\n",
    "        \n",
    "    return res_res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining input parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "domains = ['EUR', 'NAM', 'CAM', 'SAM', 'ARC', 'AFR', 'SEA', 'EAS', 'WAS', 'ANT', 'AUS']\n",
    "scenarios = ['historical', 'rcp85']\n",
    "period_hist = np.arange(1986,2006)\n",
    "period_fut = np.arange(2080,2100)\n",
    "mask = 'landsea'\n",
    "variables = ['pr', 'tas']\n",
    "summer = np.array([6,7,8])\n",
    "winter = np.array([12,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we use the data available at: https://github.com/IPCC-WG1/Atlas/tree/main/datasets-aggregated-regionally/data/\n",
    "path_IPCC_WG1 = \".../Atlas/datasets-aggregated-regionally/data/\"\n",
    "path_CORDEX = path_IPCC_WG1 + 'CORDEX/'\n",
    "path_CMIP5 = path_IPCC_WG1 + 'CMIP5/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CORDEX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## select CORDEX files from the Atlas\n",
    "files_CORDEX = list(traverseDir(path_CORDEX))\n",
    "files_CORDEX_rcp85 = [file for file in files_CORDEX if 'rcp85' in file]\n",
    "files_CORDEX_historical = [file for file in files_CORDEX if 'historical' in file]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## define dataframes to save the results\n",
    "pr_mean_dom_AR6_period = pd.DataFrame(index = domains, columns = ar6_all.abbrevs)\n",
    "pr_mean_dom_AR6_n_sim = pd.DataFrame(index = domains, columns = ar6_all.abbrevs)\n",
    "\n",
    "tas_mean_dom_AR6_period = pd.DataFrame(index = domains, columns = ar6_all.abbrevs)\n",
    "tas_mean_dom_AR6_n_sim = pd.DataFrame(index = domains, columns = ar6_all.abbrevs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate CORDEX mosaic ensembles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "# CORDEX: calculate delta changes for pr and tas\n",
    "tas_dic = {}; pr_dic = {}\n",
    "for dom in domains:\n",
    "    for variable in variables:\n",
    "        f_CORDEX = select_sim_CORDEX(files_CORDEX_rcp85, dom, variable, mask)\n",
    "        for reg in ar6_all.abbrevs:\n",
    "\n",
    "            if variable == 'tas':\n",
    "                res_del = calculate_delta(f_CORDEX, reg, period_hist, period_fut, 'CORDEX', variable)\n",
    "                if not len(res_del)==0:\n",
    "                    tas_dic[dom + '_' + reg] = res_del\n",
    "                    tas_mean_dom_AR6_n_sim.loc[dom][reg] = np.sum(~pd.isnull(res_del['period'].values))\n",
    "                    tas_mean_dom_AR6_period.loc[dom][reg] = res_del['period'].mean()\n",
    "\n",
    "            elif variable == 'pr':\n",
    "                res_del = calculate_delta(f_CORDEX, reg, period_hist, period_fut, 'CORDEX', variable)\n",
    "                if not len(res_del)==0:\n",
    "                    pr_dic[dom + '_' + reg] = res_del\n",
    "                    pr_mean_dom_AR6_n_sim.loc[dom][reg] = np.sum(~pd.isnull(res_del['period'].values))\n",
    "                    pr_mean_dom_AR6_period.loc[dom][reg] = res_del['period'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate CORDEX grand ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Calculate Grand Ensemble weighting with equal values those simulations with equal GCM_r_RCM_v for different domains\n",
    "\n",
    "##pr\n",
    "grand_ensemble_pr = pd.DataFrame(index = ar6_all.abbrevs, columns = ['Grand_ensemble'])\n",
    "for reg in ar6_all.abbrevs:\n",
    "    aux_reg = []\n",
    "    n = 0\n",
    "    for ky in pr_dic.keys():\n",
    "        if ky.split('_')[1] == reg:\n",
    "            if n == 0:\n",
    "                aux_reg = pr_dic[ky]['period']\n",
    "            else:\n",
    "                aux_reg = pd.concat([aux_reg, pr_dic[ky]['period']])\n",
    "            n = n +1\n",
    "    if not len(aux_reg)==0:\n",
    "        GCM_r_RCM_v = []\n",
    "        for sim in aux_reg.index:\n",
    "            GCM_r_RCM_v.append('_'.join([sim.split('/')[-1].split('_')[0], sim.split('/')[-1].split('_')[1],  \n",
    "                                    sim.split('/')[-1].split('_')[3], sim.split('/')[-1].split('_')[4]]))\n",
    "        reg_res = []\n",
    "        for sim_st in np.unique(GCM_r_RCM_v):\n",
    "            pos = [ns for ns, sim in enumerate(GCM_r_RCM_v) if sim_st == sim]\n",
    "            if len(pos) == 1:\n",
    "                reg_res.append(aux_reg[pos[0]])\n",
    "            else:\n",
    "                reg_res.append(np.mean([aux_reg[p] for p in pos]))\n",
    "        grand_ensemble_pr['Grand_ensemble'].loc[reg] = np.mean(reg_res)\n",
    "\n",
    "##tas\n",
    "grand_ensemble_tas = pd.DataFrame(index = ar6_all.abbrevs, columns = ['Grand_ensemble'])\n",
    "for reg in ar6_all.abbrevs:\n",
    "    aux_reg = []\n",
    "    n = 0\n",
    "    for ky in tas_dic.keys():\n",
    "        if ky.split('_')[1] == reg:\n",
    "            if n == 0:\n",
    "                aux_reg = tas_dic[ky]['period']\n",
    "            else:\n",
    "                aux_reg = pd.concat([aux_reg, tas_dic[ky]['period']])\n",
    "            n = n +1\n",
    "    if not len(aux_reg)==0:\n",
    "        GCM_r_RCM_v = []\n",
    "        for sim in aux_reg.index:\n",
    "            GCM_r_RCM_v.append('_'.join([sim.split('/')[-1].split('_')[0], sim.split('/')[-1].split('_')[1],  \n",
    "                                    sim.split('/')[-1].split('_')[3], sim.split('/')[-1].split('_')[4]]))\n",
    "        reg_res = []\n",
    "        for sim_st in np.unique(GCM_r_RCM_v):\n",
    "            pos = [ns for ns, sim in enumerate(GCM_r_RCM_v) if sim_st == sim]\n",
    "            if len(pos) == 1:\n",
    "                reg_res.append(aux_reg[pos[0]])\n",
    "            else:\n",
    "                reg_res.append(np.mean([aux_reg[p] for p in pos]))\n",
    "        grand_ensemble_tas['Grand_ensemble'].loc[reg] = np.mean(reg_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_mean_CORDEX, tas_mean_CORDEX_drop, tas_mean_dom_AR6_period_drop, tas_mean_dom_AR6_n_sim_drop = \\\n",
    "                                    select_regions_overlap(tas_mean_dom_AR6_period, tas_mean_dom_AR6_n_sim)\n",
    "\n",
    "pr_mean_CORDEX, pr_mean_CORDEX_drop, pr_mean_dom_AR6_period_drop, pr_mean_dom_AR6_n_sim_drop = \\\n",
    "                                    select_regions_overlap(pr_mean_dom_AR6_period, pr_mean_dom_AR6_n_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_mean_CORDEX_drop_new = grand_ensemble_pr.loc[pr_mean_CORDEX_drop.index]\n",
    "tas_mean_CORDEX_drop_new = grand_ensemble_tas.loc[pr_mean_CORDEX_drop.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CMIP5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## select CMIP5 files from the Atlas\n",
    "files_CMIP5 = list(traverseDir(path_CMIP5))\n",
    "files_CMIP5_rcp85 = [file for file in files_CMIP5 if 'rcp85' in file]\n",
    "files_CMIP5_historical = [file for file in files_CMIP5 if 'historical' in file]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_mean_dom_AR6_CMIP5 = pd.DataFrame(index = ['period', 'n_sim'], columns = ar6_all.abbrevs)\n",
    "tas_mean_dom_AR6_CMIP5 = pd.DataFrame(index = ['period', 'n_sim'], columns = ar6_all.abbrevs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CMIP5: calculate delta changes for pr and tas\n",
    "tas_dic_CMIP5 = {}; pr_dic_CMIP5 = {}\n",
    "for variable in variables:\n",
    "    f_CMIP5 = select_sim_CMIP5(files_CMIP5_rcp85, variable, mask)\n",
    "    for reg in ar6_all.abbrevs:\n",
    "        if variable == 'tas':\n",
    "            res_del = calculate_delta(f_CMIP5, reg, period_hist, period_fut, 'CMIP5', variable)\n",
    "            if not len(res_del)==0:\n",
    "                tas_dic_CMIP5[reg] = res_del\n",
    "                tas_mean_dom_AR6_CMIP5.loc['n_sim'][reg] = len(f_CMIP5)\n",
    "                tas_mean_dom_AR6_CMIP5.loc['period'][reg] = res_del['period'].mean()\n",
    "\n",
    "        elif variable == 'pr':\n",
    "            res_del = calculate_delta(f_CMIP5, reg, period_hist, period_fut, 'CMIP5', variable)\n",
    "            if not len(res_del)==0:\n",
    "                pr_dic_CMIP5[reg] = res_del\n",
    "                pr_mean_dom_AR6_CMIP5.loc['n_sim'][reg] = len(f_CMIP5)\n",
    "                pr_mean_dom_AR6_CMIP5.loc['period'][reg] = res_del['period'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_mean_dom_AR6_CMIP5 =  tas_mean_dom_AR6_CMIP5[tas_mean_dom_AR6_period_drop.columns]\n",
    "pr_mean_dom_AR6_CMIP5 =  pr_mean_dom_AR6_CMIP5[pr_mean_dom_AR6_period_drop.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate subensemble CMIP5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_mean_dom_AR6_period_CMIP5 = pd.DataFrame(index = domains, columns = ar6_all.abbrevs)\n",
    "tas_mean_dom_AR6_period_CMIP5 = pd.DataFrame(index = domains, columns = ar6_all.abbrevs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "##sub ensemble CMIP5\n",
    "# CMIP5 - CORDEX\n",
    "tas_dic_CMIP5_sub = {}; pr_dic_CMIP5_sub = {}\n",
    "for dom in domains:\n",
    "    for variable in variables:\n",
    "        f_CORDEX = select_sim_CORDEX(files_CORDEX_rcp85, dom, variable, mask)\n",
    "        for reg in ar6_all.abbrevs:\n",
    "            \n",
    "            if variable == 'tas':\n",
    "                files_CMIP5_rcp85_tas = [f for f in files_CMIP5_rcp85 if '_tas_' in f]\n",
    "                files_CMIP5_rcp85_tas_landsea = [f for f in files_CMIP5_rcp85_tas if '_' + mask +'/' in f]\n",
    "                res_del = calculate_delta_CMIP5_subensemble(f_CORDEX, reg, period_hist, period_fut,\n",
    "                                                           files_CMIP5_rcp85_tas_landsea, variable)\n",
    "                if not len(res_del)==0:\n",
    "                    tas_dic_CMIP5_sub[dom + '_' + reg] = res_del\n",
    "                    #tas_mean_dom_AR6_n_sim.loc[dom][reg] = len(select_sim_90)\n",
    "                    tas_mean_dom_AR6_period_CMIP5.loc[dom][reg] = res_del['period'].mean()\n",
    "\n",
    "            elif variable == 'pr':\n",
    "                files_CMIP5_rcp85_pr = [f for f in files_CMIP5_rcp85 if '_pr_' in f]\n",
    "                files_CMIP5_rcp85_pr_landsea = [f for f in files_CMIP5_rcp85_pr if  '_' + mask  +'/' in f]\n",
    "                res_del = calculate_delta_CMIP5_subensemble(f_CORDEX, reg, period_hist, period_fut,\n",
    "                                                           files_CMIP5_rcp85_pr_landsea, variable)\n",
    "                if not len(res_del)==0:\n",
    "                    pr_dic_CMIP5_sub[dom + '_' + reg] = res_del\n",
    "                    #pr_mean_dom_AR6_n_sim.loc[dom][reg] = len(select_sim_90)\n",
    "                    pr_mean_dom_AR6_period_CMIP5.loc[dom][reg] = res_del['period'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_mean_dom_AR6_period_drop_CMIP5 = tas_mean_dom_AR6_period_CMIP5.dropna(axis=0, how = 'all')\n",
    "tas_mean_dom_AR6_period_drop_CMIP5 = tas_mean_dom_AR6_period_drop_CMIP5.dropna(axis=1, how = 'all')\n",
    "tas_mean_dom_AR6_period_drop_CMIP5 = tas_mean_dom_AR6_period_drop_CMIP5.dropna(axis=1, thresh = 2)\n",
    "tas_mean_CMIP5_CORDEX = np.mean(tas_mean_dom_AR6_period_CMIP5*tas_mean_dom_AR6_n_sim/tas_mean_dom_AR6_n_sim.mean(axis=0), axis = 0)\n",
    "tas_mean_CMIP5_CORDEX_drop = tas_mean_CMIP5_CORDEX.loc[tas_mean_dom_AR6_n_sim_drop.columns] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_mean_dom_AR6_period_drop_CMIP5 = pr_mean_dom_AR6_period_CMIP5.dropna(axis=0, how = 'all')\n",
    "pr_mean_dom_AR6_period_drop_CMIP5 = pr_mean_dom_AR6_period_drop_CMIP5.dropna(axis=1, how = 'all')\n",
    "pr_mean_dom_AR6_period_drop_CMIP5 = pr_mean_dom_AR6_period_drop_CMIP5.dropna(axis=1, thresh = 2)\n",
    "pr_mean_CMIP5_CORDEX = np.mean(pr_mean_dom_AR6_period_CMIP5*pr_mean_dom_AR6_n_sim/pr_mean_dom_AR6_n_sim.mean(axis=0), axis = 0)\n",
    "pr_mean_CMIP5_CORDEX_drop = pr_mean_CMIP5_CORDEX.loc[pr_mean_dom_AR6_n_sim_drop.columns] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "legend_elements = [Patch(facecolor='w', edgecolor='k',label='CMIP5 (ensemble)'),\n",
    "                   Patch(facecolor='r', edgecolor='r',label='CORDEX (grand ensemble)', alpha = 0.5),\n",
    "                   Patch(facecolor='g', edgecolor='g',label='CORDEX (mosaic ensemble)', alpha = 0.5),\n",
    "                   Line2D([0], [0], marker='o', color='k', label='CMIP5 simulation', \n",
    "                          markerfacecolor='w', markersize=5, linestyle = 'None'),\n",
    "                   Line2D([0], [0], marker='o', color='r', label='CORDEX simulation', \n",
    "                          markerfacecolor='r', markersize=2, linestyle = 'None')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precipitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/javi/anaconda3/envs/AR6_regions/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "    \n",
    "    fig = plt.figure(figsize = (10, 15))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.add_patch(Rectangle((-75, 0), 175+75, 19.2, fill=None, alpha=1))\n",
    "    y_means = []\n",
    "    doms_string = []\n",
    "    posi_y = 0.3\n",
    "    sep_reg = []\n",
    "\n",
    "    for n_reg, reg in enumerate(pr_mean_dom_AR6_period_drop.columns):\n",
    "\n",
    "        doms_COR = pr_mean_dom_AR6_period_drop[reg][pr_mean_dom_AR6_period_drop[reg].notnull()].index\n",
    "        yvals = 100*pr_mean_dom_AR6_period_drop[reg][pr_mean_dom_AR6_period_drop[reg].notnull()]\n",
    "        yvals_CMIP5 = 100*pr_mean_dom_AR6_period_drop_CMIP5[reg][pr_mean_dom_AR6_period_drop_CMIP5[reg].notnull()]\n",
    "        nsim_mosaic = pr_mean_dom_AR6_n_sim_drop[reg].values[~pd.isnull(pr_mean_dom_AR6_n_sim_drop[reg].values)]\n",
    "\n",
    "        if len(yvals) == 2: \n",
    "            for xval_n, xval in enumerate(doms_COR):\n",
    "                if xval_n == 0: sep_reg.append(posi_y)\n",
    "                ax.add_patch(Rectangle((0, posi_y), yvals[xval_n], 0.2, color = 'green', alpha=0.3))\n",
    "                ax.add_patch(Rectangle((0, posi_y), yvals_CMIP5[xval_n], 0.2, fill=None, alpha=1))\n",
    "\n",
    "                delta_sim_CORDEX = 100*pr_dic[doms_COR[xval_n] + '_' + reg]['period'].values  \n",
    "                delta_sim_CMIP5_subensemble = 100*pr_dic_CMIP5_sub[doms_COR[xval_n] + '_' + reg]['period'].values            \n",
    "                ax.plot(delta_sim_CORDEX, np.ones_like(delta_sim_CORDEX)*(posi_y+0.1), '.r', markersize = 3, alpha = 0.5)\n",
    "                ax.scatter(delta_sim_CMIP5_subensemble, np.ones_like(delta_sim_CMIP5_subensemble)*(posi_y+0.1), s=15, \n",
    "                           facecolors='none', edgecolors='k', linewidths=0.5, alpha = 0.5)\n",
    "\n",
    "                posi_y = posi_y + 0.2 \n",
    "\n",
    "            ##join sims\n",
    "            uno = pr_dic[doms_COR[0] + '_' + reg]['period']\n",
    "            uno_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in uno.index]\n",
    "            dos = pr_dic[doms_COR[1] + '_' + reg]['period']\n",
    "            dos_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in dos.index]\n",
    "            for n_sim, sim in enumerate(uno_sims):\n",
    "                if sim in dos_sims:\n",
    "                    posi_posi = np.where(np.array(dos_sims) == np.array(sim))[0]\n",
    "                    for pos in posi_posi:\n",
    "                        a=1\n",
    "                        ax.plot([100*uno.iloc[n_sim], 100*dos.iloc[pos]], [posi_y-0.3, posi_y-0.1], '-r', linewidth=1, alpha = 1)\n",
    "\n",
    "            RCM_ensemble = 100*pr_mean_CORDEX_drop_new.loc[reg]\n",
    "            CMIP5_RCM_ensemble = 100*pr_mean_CMIP5_CORDEX_drop[reg]\n",
    "            GCM_ensemble = 100*pr_mean_dom_AR6_CMIP5.loc['period'][reg]\n",
    "            delta_sim_CMIP5 = 100*pr_dic_CMIP5[reg]['period'].values            \n",
    "\n",
    "            ax.add_patch(Rectangle((0, posi_y), RCM_ensemble.values, 0.2, color = 'red', alpha=0.5))\n",
    "            ax.add_patch(Rectangle((0, posi_y), CMIP5_RCM_ensemble, 0.2, fill=None, alpha=1))\n",
    "\n",
    "            y_means.append(posi_y)\n",
    "            aux_sss = [iicc + '(' + str(nsim_mosaic[nnii]) + ')' for nnii, iicc in enumerate(yvals.index)]    \n",
    "            doms_string.append('-'.join(aux_sss))\n",
    "\n",
    "            posi_y = posi_y + 0.4\n",
    "\n",
    "\n",
    "        elif len(yvals) == 3:\n",
    "\n",
    "            for xval_n, xval in enumerate(doms_COR):\n",
    "                if xval_n == 0: sep_reg.append(posi_y)\n",
    "                ax.add_patch(Rectangle((0, posi_y), yvals[xval_n], 0.2, color = 'green', alpha=0.3))\n",
    "                ax.add_patch(Rectangle((0, posi_y), yvals_CMIP5[xval_n], 0.2, fill=None, alpha=1)) \n",
    "                delta_sim_CORDEX = 100*pr_dic[doms_COR[xval_n] + '_' + reg]['period'].values  \n",
    "                delta_sim_CMIP5_subensemble = 100*pr_dic_CMIP5_sub[doms_COR[xval_n] + '_' + reg]['period'].values            \n",
    "                ax.plot(delta_sim_CORDEX, np.ones_like(delta_sim_CORDEX)*(posi_y+0.1), '.r', markersize = 3, alpha = 0.5)\n",
    "                ax.scatter(delta_sim_CMIP5_subensemble, np.ones_like(delta_sim_CMIP5_subensemble)*(posi_y+0.1), s=15, \n",
    "                           facecolors='none', edgecolors='k', linewidths=0.5, alpha = 0.5)\n",
    "\n",
    "                posi_y = posi_y + 0.2 \n",
    "\n",
    "            ##join sims\n",
    "            uno = pr_dic[doms_COR[0] + '_' + reg]['period']\n",
    "            uno_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in uno.index]\n",
    "            dos = pr_dic[doms_COR[1] + '_' + reg]['period']\n",
    "            dos_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in dos.index]\n",
    "            tres = pr_dic[doms_COR[2] + '_' + reg]['period']\n",
    "            tres_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in tres.index]\n",
    "            for n_sim, sim in enumerate(uno_sims):\n",
    "                if sim in dos_sims:\n",
    "                    posi_posi = np.where(np.array(dos_sims) == np.array(sim))[0]\n",
    "                    for pos in posi_posi:\n",
    "                        a=1\n",
    "                        ax.plot([100*uno.iloc[n_sim], 100*dos.iloc[pos]], [posi_y-0.5, posi_y-0.3], '-r', linewidth=1, alpha = 1)\n",
    "            for n_sim, sim in enumerate(dos_sims):\n",
    "                if sim in tres_sims:\n",
    "                    posi_posi = np.where(np.array(tres_sims) == np.array(sim))[0]\n",
    "                    for pos in posi_posi:\n",
    "                        a=1\n",
    "                        ax.plot([100*dos.iloc[n_sim], 100*tres.iloc[pos]], [posi_y-0.3, posi_y -0.1], '-r', linewidth=1, alpha = 1)\n",
    "\n",
    "            RCM_ensemble = 100*pr_mean_CORDEX_drop_new.loc[reg]\n",
    "            CMIP5_RCM_ensemble = 100*pr_mean_CMIP5_CORDEX_drop[reg]\n",
    "            GCM_ensemble = 100*pr_mean_dom_AR6_CMIP5.loc['period'][reg]\n",
    "            delta_sim_CMIP5 = 100*pr_dic_CMIP5[reg]['period'].values            \n",
    "\n",
    "            ax.add_patch(Rectangle((0, posi_y), RCM_ensemble.values, 0.2, color = 'red', alpha=0.5))\n",
    "            ax.add_patch(Rectangle((0, posi_y), CMIP5_RCM_ensemble, 0.2, fill=None, alpha=1))\n",
    "\n",
    "            y_means.append(posi_y-0.1)\n",
    "            aux_sss = [iicc + '(' + str(nsim_mosaic[nnii]) + ')' for nnii, iicc in enumerate(yvals.index)]    \n",
    "            doms_string.append('-'.join(aux_sss))\n",
    "\n",
    "            posi_y = posi_y + 0.4 \n",
    "\n",
    "        else:\n",
    "            sys.exit()\n",
    "\n",
    "    ax.tick_params(labelbottom=True, labeltop=True, labelleft=True, labelright=False,\n",
    "                         bottom=True, top=True, left=True, right=False)\n",
    "    ax.set_yticks(y_means)\n",
    "    ax.set_yticklabels(pr_mean_dom_AR6_period_drop.columns, fontsize = 15, rotation='horizontal')\n",
    "    ax.set_ylabel('AR6 Regions', fontsize = 15)\n",
    "    ax.set_xlim([-75, 175])\n",
    "    ax.set_xticks(np.arange(-75, 175+25, 25))\n",
    "    ax.set_xticklabels(np.arange(-75, 175+25, 25), fontsize = 12, rotation='horizontal')\n",
    "\n",
    "    ax.legend(handles=legend_elements, loc='lower right', fontsize = 9, facecolor = 'w', framealpha=1)\n",
    "    for ndnd, dom_s in enumerate(doms_string):\n",
    "        n_rr = 0\n",
    "        for nrr, dom_r in enumerate(dom_s.split('-')):\n",
    "            ax.annotate(dom_r, xy=(175+1,sep_reg[ndnd]+n_rr), xytext=(175+1,sep_reg[ndnd]+n_rr), xycoords='data', annotation_clip=False, fontsize = 8)\n",
    "            n_rr = n_rr + 0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/javi/anaconda3/envs/AR6_regions/lib/python3.8/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "\n",
    "    fig = plt.figure(figsize = (10, 15))\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.add_patch(Rectangle((-0.5, 0), 10+0.5, 19.2, fill=None, alpha=1))\n",
    "    y_means = []\n",
    "    doms_string = []\n",
    "    posi_y = 0.3\n",
    "    sep_reg = []\n",
    "\n",
    "    for n_reg, reg in enumerate(pr_mean_dom_AR6_period_drop.columns):\n",
    "\n",
    "        doms_COR = tas_mean_dom_AR6_period_drop[reg][tas_mean_dom_AR6_period_drop[reg].notnull()].index\n",
    "        yvals = tas_mean_dom_AR6_period_drop[reg][tas_mean_dom_AR6_period_drop[reg].notnull()]\n",
    "        yvals_CMIP5 = tas_mean_dom_AR6_period_drop_CMIP5[reg][tas_mean_dom_AR6_period_drop_CMIP5[reg].notnull()]\n",
    "        nsim_mosaic = tas_mean_dom_AR6_n_sim_drop[reg].values[~pd.isnull(tas_mean_dom_AR6_n_sim_drop[reg].values)]\n",
    "\n",
    "        if len(yvals) == 2: \n",
    "            for xval_n, xval in enumerate(doms_COR):\n",
    "                if xval_n == 0: sep_reg.append(posi_y)\n",
    "                ax.add_patch(Rectangle((0, posi_y), yvals[xval_n], 0.2, color = 'green', alpha=0.3))\n",
    "                ax.add_patch(Rectangle((0, posi_y), yvals_CMIP5[xval_n], 0.2, fill=None, alpha=1))           \n",
    "\n",
    "                delta_sim_CORDEX = tas_dic[doms_COR[xval_n] + '_' + reg]['period'].values  \n",
    "                delta_sim_CMIP5_subensemble = tas_dic_CMIP5_sub[doms_COR[xval_n] + '_' + reg]['period'].values            \n",
    "                ax.plot(delta_sim_CORDEX, np.ones_like(delta_sim_CORDEX)*(posi_y+0.1), '.r', markersize = 3, alpha = 0.5)\n",
    "                ax.scatter(delta_sim_CMIP5_subensemble, np.ones_like(delta_sim_CMIP5_subensemble)*(posi_y+0.1), s=15, \n",
    "                           facecolors='none', edgecolors='k', linewidths=0.5, alpha = 0.5)\n",
    "\n",
    "                posi_y = posi_y + 0.2 \n",
    "\n",
    "            ##join sims\n",
    "            uno = tas_dic[doms_COR[0] + '_' + reg]['period']\n",
    "            uno_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in uno.index]\n",
    "            dos = tas_dic[doms_COR[1] + '_' + reg]['period']\n",
    "            dos_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in dos.index]\n",
    "            for n_sim, sim in enumerate(uno_sims):\n",
    "                if sim in dos_sims:\n",
    "                    posi_posi = np.where(np.array(dos_sims) == np.array(sim))[0]\n",
    "                    for pos in posi_posi:\n",
    "                        ax.plot([uno.iloc[n_sim], dos.iloc[pos]], [posi_y-0.3, posi_y-0.1], '-r', linewidth=1, alpha = 1)\n",
    "\n",
    "            RCM_ensemble = tas_mean_CORDEX_drop_new.loc[reg]\n",
    "            CMIP5_RCM_ensemble = tas_mean_CMIP5_CORDEX_drop[reg]\n",
    "            GCM_ensemble = tas_mean_dom_AR6_CMIP5.loc['period'][reg]\n",
    "            delta_sim_CMIP5 = tas_dic_CMIP5[reg]['period'].values            \n",
    "\n",
    "            ax.add_patch(Rectangle((0, posi_y), RCM_ensemble.values, 0.2, color = 'red', alpha=0.5))\n",
    "            ax.add_patch(Rectangle((0, posi_y), CMIP5_RCM_ensemble, 0.2, fill=None, alpha=1))\n",
    "            y_means.append(posi_y)\n",
    "            aux_sss = [iicc + '(' + str(nsim_mosaic[nnii]) + ')' for nnii, iicc in enumerate(yvals.index)]    \n",
    "            doms_string.append('-'.join(aux_sss))\n",
    "\n",
    "            posi_y = posi_y + 0.4\n",
    "\n",
    "        elif len(yvals) == 3:\n",
    "\n",
    "            for xval_n, xval in enumerate(doms_COR):\n",
    "                if xval_n == 0: sep_reg.append(posi_y)\n",
    "                ax.add_patch(Rectangle((0, posi_y), yvals[xval_n], 0.2, color = 'green', alpha=0.3))\n",
    "                ax.add_patch(Rectangle((0, posi_y), yvals_CMIP5[xval_n], 0.2, fill=None, alpha=1)) \n",
    "                delta_sim_CORDEX = tas_dic[doms_COR[xval_n] + '_' + reg]['period'].values  \n",
    "                delta_sim_CMIP5_subensemble = tas_dic_CMIP5_sub[doms_COR[xval_n] + '_' + reg]['period'].values            \n",
    "                ax.plot(delta_sim_CORDEX, np.ones_like(delta_sim_CORDEX)*(posi_y+0.1), '.r', markersize = 3, alpha = 0.5)\n",
    "                ax.scatter(delta_sim_CMIP5_subensemble, np.ones_like(delta_sim_CMIP5_subensemble)*(posi_y+0.1), s=15, \n",
    "                           facecolors='none', edgecolors='k', linewidths=0.5, alpha = 0.5)\n",
    "\n",
    "                posi_y = posi_y + 0.2 \n",
    "\n",
    "            ##join sims\n",
    "            uno = tas_dic[doms_COR[0] + '_' + reg]['period']\n",
    "            uno_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in uno.index]\n",
    "            dos = tas_dic[doms_COR[1] + '_' + reg]['period']\n",
    "            dos_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in dos.index]\n",
    "            tres = tas_dic[doms_COR[2] + '_' + reg]['period']\n",
    "            tres_sims = ['_'.join(sim.split('/')[-1].split('_')[:]) for sim in tres.index]\n",
    "            for n_sim, sim in enumerate(uno_sims):\n",
    "                if sim in dos_sims:\n",
    "                    posi_posi = np.where(np.array(dos_sims) == np.array(sim))[0]\n",
    "                    for pos in posi_posi:\n",
    "                        ax.plot([uno.iloc[n_sim], dos.iloc[pos]], [posi_y-0.5, posi_y-0.3], '-r', linewidth=1, alpha = 1)\n",
    "            for n_sim, sim in enumerate(dos_sims):\n",
    "                if sim in tres_sims:\n",
    "                    posi_posi = np.where(np.array(tres_sims) == np.array(sim))[0]\n",
    "                    for pos in posi_posi:\n",
    "                        ax.plot([dos.iloc[n_sim], tres.iloc[pos]], [posi_y-0.3, posi_y -0.1], '-r', linewidth=1, alpha = 1)\n",
    "\n",
    "            RCM_ensemble = tas_mean_CORDEX_drop_new.loc[reg]\n",
    "            CMIP5_RCM_ensemble = tas_mean_CMIP5_CORDEX_drop[reg]\n",
    "            GCM_ensemble = tas_mean_dom_AR6_CMIP5.loc['period'][reg]\n",
    "            delta_sim_CMIP5 = tas_dic_CMIP5[reg]['period'].values            \n",
    "\n",
    "            ax.add_patch(Rectangle((0, posi_y), RCM_ensemble.values, 0.2, color = 'red', alpha=0.5))\n",
    "            ax.add_patch(Rectangle((0, posi_y), CMIP5_RCM_ensemble, 0.2, fill=None, alpha=1))        \n",
    "            y_means.append(posi_y-0.1)\n",
    "            aux_sss = [iicc + '(' + str(nsim_mosaic[nnii]) + ')' for nnii, iicc in enumerate(yvals.index)]    \n",
    "            doms_string.append('-'.join(aux_sss))\n",
    "\n",
    "            posi_y = posi_y + 0.4 \n",
    "\n",
    "        else:\n",
    "            sys.exit()\n",
    "\n",
    "    ax.tick_params(labelbottom=True, labeltop=True, labelleft=True, labelright=False,\n",
    "                         bottom=True, top=True, left=True, right=False)\n",
    "    ax.set_yticks(y_means)\n",
    "    ax.set_yticklabels(pr_mean_dom_AR6_period_drop.columns, fontsize = 15, rotation='horizontal')\n",
    "    ax.set_ylabel('AR6 Regions', fontsize = 15)\n",
    "    ax.set_xlim([-0.5, 10])\n",
    "    ax.set_xticks(np.arange(0, 11, 1))\n",
    "    ax.set_xticklabels(np.arange(0, 11, 1), fontsize = 12, rotation='horizontal')\n",
    "    ax.legend(handles=legend_elements, loc='upper right', fontsize = 9, facecolor = 'w', framealpha=1)\n",
    "\n",
    "    for ndnd, dom_s in enumerate(doms_string):\n",
    "        n_rr = 0\n",
    "        for nrr, dom_r in enumerate(dom_s.split('-')):\n",
    "            ax.annotate(dom_r, xy=(10+0.05,sep_reg[ndnd]+n_rr), xytext=(10+0.05,sep_reg[ndnd]+n_rr), xycoords='data', annotation_clip=False, fontsize = 8)\n",
    "            n_rr = n_rr + 0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boé, J., Somot, S., Corre, L., & Nabat, P. (2020). Large discrepancies in summer climate change over Europe as projected by global and regional climate models: causes and consequences. Climate Dynamics, 54(5), 2981-3002.\n",
    "\n",
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