Figure4_extreme_precip_lw_vs_4K.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure 4\n",
    "\n",
    "Compare the change in extreme precipitation between the LWoff and +4K experiments.\n",
    "\n",
    "Fractional changes in extreme precipitation fraction ($\\delta \\varphi$) and tropical ocean mean precipitation ($\\delta P$) for additional experiments. As in Figure 3 but for: (a) changes with uniform 4K SST warming (\\textit{amip-p4K} minus \\textit{amip}), (b) the response to a quadrupling of atmospheric CO$_2$ with unchanged prescribed SST (\\textit{amip-4xCO2} minus \\textit{amip}), (c) changes with uniform warming between the LWoff simulations (\\textit{amip-p4K-lwoff} minus \\textit{amip-lwoff}), (d) changes when LWCRE is removed in a warmer climate (i.e., as in Figure 3, but with a warmer reference climate, \\textit{amip-p4K-lwoff} minus \\textit{amip-p4K}). Italics denote CMIP experiment names, using the AMIP names (aquaplanets follow a similar pattern)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "# from sklearn.neighbors import KernelDensity\n",
    "import colorcet as cc\n",
    "\n",
    "from pathlib import Path\n",
    "import esmlab # [https://esmlab.readthedocs.io/en/latest/]\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_colors = {}\n",
    "for i, m in [(0, \"cesm\"), (1, \"cnrm\"), (2, \"ipsl\"), (3, \"mri\"), (6, \"hadgem\")]:\n",
    "    model_colors[m] = cc.cm.glasbey_hv(i) \n",
    "\n",
    "\n",
    "# hard code locking to other blues, assumes CESM is first in list.\n",
    "model_colors['f-lock'] = cc.cm.glasbey_hv(5)\n",
    "model_colors['f-cntl'] = model_colors['f-lock']\n",
    "model_colors['c-lock'] = cc.cm.glasbey_hv(8)\n",
    "model_colors['c-cntl'] = model_colors['c-lock']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quick_load_var(loc, var, table, model, experiment, member):\n",
    "    fils = sorted(\n",
    "        list(Path(loc).glob(\"_\".join([var, table, model, experiment, member, \"*.nc\"])))\n",
    "    )\n",
    "    if len(fils) == 0:\n",
    "        raise IOError(\"Can not find the files.\")\n",
    "    elif len(fils) == 1:\n",
    "        ds = xr.open_dataset(fils[0])\n",
    "    else:\n",
    "        ds = xr.open_mfdataset(fils, combine=\"by_coords\")\n",
    "    return ds[var].compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DRIVE = Path(\"/Volumes/Jedha\")\n",
    "data_loc = DRIVE / \"CMIP6\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## do these:\n",
    "\n",
    "exp0 = \"amip\"\n",
    "exp1 = \"amip-lwoff\"\n",
    "exp2 = \"amip-p4K\"\n",
    "exp2a = \"amip-p4K-lwoff\"\n",
    "exp3 = \"aqua-control\"\n",
    "exp4 = \"aqua-control-lwoff\"\n",
    "exp5 = \"aqua-p4K\"\n",
    "exp5a = \"aqua-p4K-lwoff\"\n",
    "exp6 = \"amip-4xCO2\"\n",
    "exp7 = \"aqua-4xCO2\"\n",
    "\n",
    "sims = {\"cesm-amip\": (\"CESM2\", exp0, \"r1i1p1f1\"),\n",
    "        \"cesm-amip-off\": (\"CESM2\", exp1, \"r1i1p1f1\"),\n",
    "        \"cesm-amip-4k\": (\"CESM2\", exp2, \"r1i1p1f1\"),\n",
    "        \"cesm-amip-4k-off\": (\"CESM2\", exp2a, \"r1i1p1f1\"),\n",
    "        \"cesm-aqua\": (\"CESM2\", exp3, \"r1i1p1f1\"),\n",
    "        \"cesm-aqua-off\": (\"CESM2\", exp4, \"r1i1p1f1\"),\n",
    "        \"cesm-aqua-4k\": (\"CESM2\", exp5, \"r1i1p1f1\"), \n",
    "        \"cesm-aqua-4k-off\": (\"CESM2\", exp5a, \"r1i1p1f1\"),\n",
    "        \"cesm-aqua-4xCO2\" : (\"CESM2\", exp7, \"r1i1p1f1\"),\n",
    "        \"cesm-amip-4xCO2\" : (\"CESM2\", exp6, \"r1i1p1f1\"),\n",
    "        \"ipsl-amip\": (\"IPSL-CM6A-LR\", exp0, \"r1i1p1f1\"),\n",
    "        \"ipsl-amip-off\": (\"IPSL-CM6A-LR\", exp1, \"r1i1p1f1\"),\n",
    "        \"ipsl-amip-4k\": (\"IPSL-CM6A-LR\", exp2, \"r1i1p1f1\"),\n",
    "        \"ipsl-amip-4k-off\": (\"IPSL-CM6A-LR\", exp2a, \"r1i1p1f1\"),\n",
    "        \"ipsl-aqua\": (\"IPSL-CM6A-LR\", exp3, \"r1i1p1f1\"),\n",
    "        \"ipsl-aqua-off\": (\"IPSL-CM6A-LR\", exp4, \"r1i1p1f1\"),\n",
    "        \"ipsl-aqua-4k\": (\"IPSL-CM6A-LR\", exp5, \"r1i1p1f1\"),\n",
    "        \"ipsl-aqua-4k-off\": (\"IPSL-CM6A-LR\", exp5a, \"r1i1p1f1\"),\n",
    "        \"ipsl-aqua-4xCO2\" : (\"IPSL-CM6A-LR\", exp7, \"r1i1p1f1\"),\n",
    "        \"ipsl-amip-4xCO2\" : (\"IPSL-CM6A-LR\", exp6, \"r1i1p1f1\"),\n",
    "        \"cnrm-amip\": (\"CNRM-CM6-1\", exp0, \"r1i1p1f2\"),\n",
    "        \"cnrm-amip-off\": (\"CNRM-CM6-1\", exp1, \"r1i1p1f2\"),\n",
    "        \"cnrm-amip-4k\": (\"CNRM-CM6-1\", exp2, \"r1i1p1f2\"),\n",
    "        \"cnrm-amip-4k-off\": (\"CNRM-CM6-1\", exp2a, \"r1i1p1f2\"),\n",
    "        \"cnrm-aqua\": (\"CNRM-CM6-1\", exp3, \"r1i1p1f2\"),\n",
    "        \"cnrm-aqua-off\": (\"CNRM-CM6-1\", exp4, \"r1i1p1f2\"),\n",
    "        \"cnrm-aqua-4k\": (\"CNRM-CM6-1\", exp5, \"r1i1p1f2\"),\n",
    "        \"cnrm-aqua-4k-off\": (\"CNRM-CM6-1\", exp5a, \"r1i1p1f2\"),\n",
    "        \"cnrm-aqua-4xCO2\" : (\"CNRM-CM6-1\", exp7, \"r1i1p1f2\"),\n",
    "        \"cnrm-amip-4xCO2\" : (\"CNRM-CM6-1\", exp6, \"r1i1p1f2\"),\n",
    "        \"mri-amip\" : (\"MRI-ESM2-0\", exp0, \"r1i1p1f1\"),\n",
    "        \"mri-amip-off\" : (\"MRI-ESM2-0\", exp1, \"r1i1p1f1\"),\n",
    "        \"mri-amip-4k\" : (\"MRI-ESM2-0\", exp2, \"r1i1p1f1\"),\n",
    "        \"mri-amip-4k-off\" : (\"MRI-ESM2-0\", exp2a, \"r1i1p1f1\"),\n",
    "        \"mri-amip-4xCO2\" : (\"MRI-ESM2-0\", exp6, \"r1i1p1f1\"),\n",
    "        \"hadgem-amip\" :(\"HadGEM3-GC31-LL\", exp0, \"r1i1p1f3\"),\n",
    "        \"hadgem-amip-off\": (\"HadGEM3-GC31-LL\", exp1, \"r5i1p1f2\"),\n",
    "        \"hadgem-amip-4k\":(\"HadGEM3-GC31-LL\", exp2, \"r5i1p1f3\"),\n",
    "        \"hadgem-amip-4xCO2\": (\"HadGEM3-GC31-LL\", exp6, \"r5i1p1f3\"),\n",
    "        \"hadgem-aqua\" :(\"HadGEM3-GC31-LL\", exp3, \"r1i1p1f3\"),\n",
    "        \"hadgem-aqua-off\": (\"HadGEM3-GC31-LL\", exp4, \"r1i1p1f3\"),\n",
    "        \"hadgem-aqua-4k\":(\"HadGEM3-GC31-LL\", exp5, \"r1i1p1f3\"),\n",
    "        \"hadgem-aqua-4xCO2\": (\"HadGEM3-GC31-LL\", exp7, \"r1i1p1f3\")\n",
    "       }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-amip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n",
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'sftlf' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-amip-off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n",
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'sftlf' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-amip-4k\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n",
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'sftlf' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-amip-4k-off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n",
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'sftlf' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-aqua\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-aqua-off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-aqua-4k\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-aqua-4k-off\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-aqua-4xCO2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on cesm-amip-4xCO2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'pr' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n",
      "/Users/brianpm/miniconda3/envs/py39/lib/python3.9/site-packages/xarray/conventions.py:492: SerializationWarning: variable 'sftlf' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  new_vars[k] = decode_cf_variable(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on ipsl-amip\n",
      "working on ipsl-amip-off\n",
      "working on ipsl-amip-4k\n",
      "working on ipsl-amip-4k-off\n",
      "working on ipsl-aqua\n",
      "working on ipsl-aqua-off\n",
      "working on ipsl-aqua-4k\n",
      "working on ipsl-aqua-4k-off\n",
      "working on ipsl-aqua-4xCO2\n",
      "working on ipsl-amip-4xCO2\n",
      "working on cnrm-amip\n",
      "working on cnrm-amip-off\n",
      "working on cnrm-amip-4k\n",
      "working on cnrm-amip-4k-off\n",
      "working on cnrm-aqua\n",
      "working on cnrm-aqua-off\n",
      "working on cnrm-aqua-4k\n",
      "working on cnrm-aqua-4k-off\n",
      "working on cnrm-aqua-4xCO2\n",
      "working on cnrm-amip-4xCO2\n",
      "working on mri-amip\n",
      "working on mri-amip-off\n",
      "working on mri-amip-4k\n",
      "working on mri-amip-4k-off\n",
      "working on mri-amip-4xCO2\n",
      "working on hadgem-amip\n",
      "working on hadgem-amip-off\n",
      "working on hadgem-amip-4k\n",
      "working on hadgem-amip-4xCO2\n",
      "working on hadgem-aqua\n",
      "working on hadgem-aqua-off\n",
      "working on hadgem-aqua-4k\n",
      "working on hadgem-aqua-4xCO2\n"
     ]
    }
   ],
   "source": [
    "# for each simulation, I want to get:\n",
    "# - global mean precip\n",
    "# - global mean precip, ocean only\n",
    "# - tropical mean precip\n",
    "# - tropical mean precip, ocean only\n",
    "# - tropical UNWEIGHTED mean precip, ocean only\n",
    "# - 95th percentile of tropical ocean precip\n",
    "# - sum of tropical ocean precip with p >= p95\n",
    "# - sum of tropical ocean precip\n",
    "# Define a namedtuple:\n",
    "Result = namedtuple('Result', ['case', 'gavg', 'goavg', 'tavg', 'toavg', 'toavg_uw', 'p95', 'tsum', 'xsum'])\n",
    "\n",
    "\n",
    "def get_pr_info(dloc, model, experiment, member):\n",
    "    tropics = slice(-30,30)\n",
    "    pr = quick_load_var(data_loc, \"pr\", \"day\", model, experiment, member)\n",
    "    lat = pr['lat']\n",
    "    wgt = np.cos(np.radians(lat))\n",
    "    gavg = esmlab.weighted_mean(pr.mean(dim=('time','lon')), dim=[\"lat\"], weights=wgt).item()\n",
    "    tavg = esmlab.weighted_mean(pr.sel(lat=tropics).mean(dim=('time','lon')), dim=[\"lat\"], weights=wgt.sel(lat=tropics)).item()\n",
    "    if 'amip' in c:\n",
    "        land = quick_load_var(data_loc, \"sftlf\", \"fx\", model, experiment, member)\n",
    "        land_trop = land.sel(lat=tropics)\n",
    "        msk, _ = xr.broadcast(land, pr)\n",
    "        pr = pr.where(msk <= 0)\n",
    "        goavg = esmlab.weighted_mean(pr.mean(dim=('time','lon')), dim=[\"lat\"], weights=wgt).item()\n",
    "        toavg = esmlab.weighted_mean(pr.sel(lat=tropics).mean(dim=('time','lon')), dim=[\"lat\"], weights=wgt.sel(lat=tropics)).item()\n",
    "    else:\n",
    "        goavg = gavg\n",
    "        toavg = tavg\n",
    "    ptrop = pr.sel(lat=tropics)\n",
    "    toavg_uw = ptrop.mean().item()\n",
    "    p95 = np.nanquantile(ptrop.values.flatten(), 0.95)\n",
    "    tsum = np.nansum(ptrop.values.flatten())\n",
    "    xsum = np.nansum(ptrop.where(ptrop>=p95).values.flatten())\n",
    "    return Result(\"_\".join([model,experiment,member]), gavg, goavg, tavg, toavg, toavg_uw, p95, tsum, xsum)\n",
    "\n",
    "\n",
    "R = {}\n",
    "for c in sims:\n",
    "    print(f\"working on {c}\")\n",
    "    R[c] = get_pr_info(data_loc, *sims[c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frac_diff(acase, bcase):\n",
    "    return (bcase - acase)/acase\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('cesm-amip', 'cesm-amip-4k'), ('cesm-aqua', 'cesm-aqua-4k'), ('cnrm-amip', 'cnrm-amip-4k'), ('cnrm-aqua', 'cnrm-aqua-4k'), ('ipsl-amip', 'ipsl-amip-4k'), ('ipsl-aqua', 'ipsl-aqua-4k'), ('mri-amip', 'mri-amip-4k'), ('hadgem-amip', 'hadgem-amip-4k'), ('hadgem-aqua', 'hadgem-aqua-4k')]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# plot of fractional change in Extreme vs Mean for :\n",
    "# - warming (base-climate vs p4K)\n",
    "# - warming w/o LWCRE (lwoff vs p4K-lwoff)\n",
    "# - CRE in warmer climate (p4K vs p4K-lwoff)\n",
    "\n",
    "pairs1 = []\n",
    "pairs2 = []\n",
    "pairs3 = []\n",
    "pairs4 = []\n",
    "for m in ('cesm', 'cnrm', 'ipsl', 'mri', 'hadgem'):\n",
    "    for a in ('amip', 'aqua'):\n",
    "        if \"-\".join([m,a]) in R:\n",
    "            pairs1.append((\"-\".join([m,a]), \"-\".join([m,a,'4k'])))  # base vs p4K\n",
    "            pairs2.append((\"-\".join([m,a,'off']), \"-\".join([m,a,'4k-off'])))  # warming w/o LWCRE\n",
    "            pairs3.append((\"-\".join([m,a,'4k']), \"-\".join([m,a,'4k-off'])))  # CRE in warmer climate\n",
    "            pairs4.append((\"-\".join([m,a]), \"-\".join([m,a,'4xCO2'])))  # base vs 4xCO2\n",
    "\n",
    "print(pairs1)\n",
    "with plt.style.context('ggplot'):\n",
    "    fig, aa = plt.subplots(figsize=(9,9), ncols=2, nrows=2, sharex=True, sharey=True, constrained_layout=True)\n",
    "    ax = aa.ravel()\n",
    "    [a.axhline(0.0, linestyle='solid', color='darkgray', linewidth=1) for a in ax]\n",
    "    [a.axvline(0.0, linestyle='solid', color='darkgray', linewidth=1) for a in ax]\n",
    "    for i, pairs in enumerate([pairs1, pairs4, pairs2, pairs3]):\n",
    "        # now get the fractional change in extreme rain\n",
    "        # for each experiment and keep in a dict:\n",
    "        # the key should just be the 'experiment' name\n",
    "        delta_xfrac = {}\n",
    "        delta_mean = {}\n",
    "        for c in pairs:\n",
    "            k = c[1]\n",
    "            model = k.split('-')[0]\n",
    "            if (c[0] in R) and (c[1] in R):\n",
    "                xfrac0 = R[c[0]].xsum/R[c[0]].tsum\n",
    "                xfrac1 = R[c[1]].xsum/R[c[1]].tsum\n",
    "                delta_xfrac[k] = frac_diff(xfrac0, xfrac1)\n",
    "                delta_mean[k] = frac_diff(R[c[0]].toavg, R[c[1]].toavg)\n",
    "                if 'aqua' in k:\n",
    "                    m = 'o'\n",
    "                else:\n",
    "                    m = '^'\n",
    "                xval = delta_mean[k]\n",
    "                yval = delta_xfrac[k]\n",
    "                ax[i].plot(xval, yval, marker=m, color=model_colors[model], markersize=10)\n",
    "\n",
    "    #     ax.plot([-1, 1], [-1, 1], linestyle=\":\", color='gray')\n",
    "    [a.set_xlabel(\"$\\u03b4 P$\", loc='center', fontsize=18) for a in ax[2:]]\n",
    "    [a.set_ylabel(\"$\\u03b4 \\u03c6$\", loc='center', fontsize=18) for a in ax[[0,2]]] # characters are just unicode, use mathmode to get good spacing\n",
    "    [a.spines['top'].set_visible(False) for a in ax]\n",
    "    [a.spines['right'].set_visible(False) for a in ax]\n",
    "    [a.set_ylim([-0.5, 0.2]) for a in ax]\n",
    "    [a.set_xlim([-0.12, .2]) for a in ax]\n",
    "\n",
    "    ax[1].set_title(\"Change with 4×CO$_2$\")\n",
    "    ax[0].set_title(\"Change with uniform SST+4K\")\n",
    "    ax[2].set_title(\"Change with SST+4K\\n with no LWCRE in either climate\")\n",
    "    ax[3].set_title(\"Change with LWCRE-off\\n in SST+4K climate\")\n",
    "    \n",
    "        \n",
    "    # FOR FINAL VERSION ADD PANEL LABELS:\n",
    "    axr = aa.ravel()\n",
    "    for i, label in enumerate(('a', 'b', 'c', 'd')):\n",
    "        addto = axr[i]\n",
    "        addto.text(-0.045, 1.09, label, transform=addto.transAxes,\n",
    "          fontsize=16, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    legend_elements = [Line2D([0], [0], marker=\"None\", linestyle=\"None\", color=model_colors['cesm'], label='CESM2'),\n",
    "                       Line2D([0], [0], marker=\"None\", linestyle=\"None\", color=model_colors['cnrm'], label='CNRM'),\n",
    "                       Line2D([0], [0], marker=\"None\", linestyle=\"None\", color=model_colors['hadgem'], label='HadGEM'),                   \n",
    "                       Line2D([0], [0], marker=\"None\", linestyle=\"None\", color=model_colors['ipsl'], label='IPSL'),\n",
    "                       Line2D([0], [0], marker=\"None\", linestyle=\"None\", color=model_colors['mri'], label='MRI'),\n",
    "                       Line2D([0], [0], marker='^', color='gray', linestyle=\"None\", label='AMIP'),\n",
    "                       Line2D([0], [0], marker='o', color='gray', linestyle=\"None\", label='AQUA'),\n",
    "                      ]\n",
    "\n",
    "    ax[1].legend(handles=legend_elements, loc='lower left', labelcolor='linecolor')\n",
    "\n",
    "#     fig.savefig(\"/Users/brianpm/Dropbox/Manuscripts/M_cre_pex/mean_versus_extreme_OtherExperiments_v5.pdf\", bbox_inches='tight')\n",
    "    fig.savefig(\"/Users/brianpm/Dropbox/Apps/Overleaf/cre_pex/Figure_4_final.pdf\", bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional work\n",
    "\n",
    "Exploration of some other visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auto\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "for x in delta_xfrac:\n",
    "    model = x.split('-')[0]    \n",
    "    if 'aqua' in x:\n",
    "        m = 'o'\n",
    "    else:\n",
    "        m = '^'\n",
    "    if 'off' in x:\n",
    "        continue  # skip off -- just doing +4K\n",
    "    xval = delta_mean[x]\n",
    "    yval = delta_xfrac[x]\n",
    "    ax.plot(xval, yval, marker=m, color=model_colors[model], markersize=10)\n",
    "\n",
    "ax.set_xlabel(\"\\u03b4 P\", loc='center', fontsize=18)\n",
    "ax.set_ylabel(\"\\u03b4 \\u03c6\", loc='center', fontsize=18)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_xlim([0, 0.2])\n",
    "ax.set_ylim([-0.1, 0.1])\n",
    "# ax.set_aspect('equal')\n",
    "print(ax.get_aspect())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Extreme Precipitation Fraction')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfrac = {}\n",
    "for s in R:\n",
    "    xfrac[s] = R[s].xsum/R[s].tsum\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(np.arange(1, len(xfrac)+1), xfrac.values())\n",
    "ax.set_xticks(np.arange(1, len(xfrac)+1))\n",
    "ax.set_xticklabels(list(xfrac.keys()), rotation=90)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_title(\"Extreme Precipitation Fraction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '95th Percentile')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OQpmStG04OCKeAjwrb6+qE4qIUyNifkTMnzNnzhQuZ4wxpkqJ0bgV2KayvzVwW6HMsLR35C4s8m/j+ERE/Dz/3gt8ltT9ZYwxZg1R4kbkMmCupB2AnwMHAn/bJ7MUOErSGcDTgXsi4nZJK4akXQocCpyQf88ZpoSk9YFNIuIuSRsA+wHfLNDf1GAX5saYydBoNCLiAUlHAecBs4BPRMS1ko7I5xcD5wL7AsuB+4DDhqXNWZ8AnCnpcOBm4IDeNSXdBGwMzJa0P7A38L/AedlgzCIZjI9O6e6NMca0oshhYUScSzIM1WOLK38HcGRp2nz8buD5A9JsP0CV3Uv0NcYY0w1eEW6MMaYYGw1jjDHF2GgYY4wpxkbDGGNMMTYaxhhjirHRMMYYU4yNhjHGmGKK1mkYszbStCoevDLemH7c0jDGGFOMjYYxxphibDSMMcYUY6NhjDGmGA+Em7WKmTa4PdP0NcYtDWOMMcXYaBhjjCnG3VPGFOBuJGMSbmkYY4wpxi0NM204Tnk73Nox44BbGsYYY4pxS8OMPf7CNmZ8cEvDGGNMMTYaxhhjirHRMMYYU4zHNIwZMR6DMWszbmkYY4wpxkbDGGNMMUVGQ9ICSddJWi7pmJrzknRSPn+1pN2a0kraTNL5kq7Pv5vm44+WdKGk30r6777r7C7pmpzXSZI0+Vs3xhjTlkajIWkWcDKwDzAPOEjSvD6xfYC5eVsEnFKQ9hjggoiYC1yQ9wH+ABwHvKVGnVNy/r1rLSi6S2OMMSOhpKWxB7A8Im6IiD8CZwAL+2QWAqdH4hJgE0lbNqRdCCzJfy8B9geIiN9FxHdIxmMlOb+NI+L7ERHA6b00xhhj1gwlRmMr4JbK/q35WInMsLRbRMTtAPl38wI9bm3QAwBJiyQtk7RsxYoVDdkaY4wppcRo1I0bRKFMSdpSivOKiFMjYn5EzJ8zZ84kL2eMMaafEqNxK7BNZX9r4LZCmWFp78hdTr2upzsL9Ni6QQ9jjDEdUmI0LgPmStpB0mzgQGBpn8xS4JA8i2pP4J7c5TQs7VLg0Pz3ocA5w5TI+d0rac88a+qQpjTGGGNGS+OK8Ih4QNJRwHnALOATEXGtpCPy+cXAucC+wHLgPuCwYWlz1icAZ0o6HLgZOKB3TUk3ARsDsyXtD+wdET8GXg98EngY8LW8GWOMWUMUuRGJiHNJhqF6bHHl7wCOLE2bj98NPH9Amu0HHF8G7FSiszHGmNHjFeHGGGOKsdEwxhhTjI2GMcaYYmw0jDHGFGOjYYwxphgbDWOMMcXYaBhjjCnGRsMYY0wxNhrGGGOKsdEwxhhTjI2GMcaYYmw0jDHGFGOjYYwxphgbDWOMMcXYaBhjjCnGRsMYY0wxNhrGGGOKsdEwxhhTjI2GMcaYYmw0jDHGFGOjYYwxphgbDWOMMcXYaBhjjCnGRsMYY0wxNhrGGGOKsdEwxhhTTJHRkLRA0nWSlks6pua8JJ2Uz18tabemtJI2k3S+pOvz76aVc8dm+eskvahy/KJ87Mq8bT75WzfGGNOWRqMhaRZwMrAPMA84SNK8PrF9gLl5WwScUpD2GOCCiJgLXJD3yecPBJ4MLAA+kvPpcXBE7Jq3O9vfsjHGmMlS0tLYA1geETdExB+BM4CFfTILgdMjcQmwiaQtG9IuBJbkv5cA+1eOnxER90fEjcDynI8xxphppsRobAXcUtm/NR8rkRmWdouIuB0g//a6mpqud1rumjpOkuoUlrRI0jJJy1asWNF0f8YYYwopMRp1FXMUypSkbXO9gyPiKcCz8vaqugwi4tSImB8R8+fMmdNwOWOMMaWUGI1bgW0q+1sDtxXKDEt7R+7CIv/2xicGpomIn+ffe4HP4m4rY4xZo5QYjcuAuZJ2kDSbNEi9tE9mKXBInkW1J3BP7nIalnYpcGj++1DgnMrxAyVtKGkH0uD6pZLWl/QYAEkbAPsBP5rEPRtjjJkk6zcJRMQDko4CzgNmAZ+IiGslHZHPLwbOBfYlDVrfBxw2LG3O+gTgTEmHAzcDB+Q010o6E/gx8ABwZEQ8KOkRwHnZYMwCvgl8dBQPwRhjTBmNRgMgIs4lGYbqscWVvwM4sjRtPn438PwBad4LvLfv2O+A3Uv0NcYY0w1eEW6MMaYYGw1jjDHF2GgYY4wpxkbDGGNMMTYaxhhjirHRMMYYU4yNhjHGmGJsNIwxxhRjo2GMMaYYGw1jjDHF2GgYY4wpxkbDGGNMMTYaxhhjirHRMMYYU4yNhjHGmGJsNIwxxhRjo2GMMaYYGw1jjDHF2GgYY4wpxkbDGGNMMTYaxhhjirHRMMYYU4yNhjHGmGJsNIwxxhRjo2GMMaYYGw1jjDHFFBkNSQskXSdpuaRjas5L0kn5/NWSdmtKK2kzSedLuj7/blo5d2yWv07SiyrHd5d0TT53kiRN/taNMca0pdFoSJoFnAzsA8wDDpI0r09sH2Bu3hYBpxSkPQa4ICLmAhfkffL5A4EnAwuAj+R8yPkuqlxrQftbNsYYM1lKWhp7AMsj4oaI+CNwBrCwT2YhcHokLgE2kbRlQ9qFwJL89xJg/8rxMyLi/oi4EVgO7JHz2zgivh8RAZxeSWOMMWYNoFT/DhGQXgEsiIjX5v1XAU+PiKMqMl8BToiI7+T9C4C3AdsPSivp1xGxSSWPX0XEppL+G7gkIj6dj38c+BpwU77GC/LxZwFvi4j9anReRGqRADwBuK7VUxnMY4C7LGvZGSg73de37HjJlnBXRKzWm7N+QcK6cYN+SzNIpiRt6fWK84qIU4FTG67TGknLImK+ZS0702Sn+/qWHS/ZqVDSPXUrsE1lf2vgtkKZYWnvyF1O5N87C/LaukEPY4wxHVJiNC4D5kraQdJs0iD10j6ZpcAheRbVnsA9EXF7Q9qlwKH570OBcyrHD5S0oaQdSAPel+b87pW0Z541dUgljTHGmDVAY/dURDwg6SjgPGAW8ImIuFbSEfn8YuBcYF/SoPV9wGHD0uasTwDOlHQ4cDNwQE5zraQzgR8DDwBHRsSDOc3rgU8CDyONc3xtarffmjZdXpa17DjJTvf1LTtespOmcSDcGGOM6eEV4cYYY4qx0TDGGFOMjYYxxphibDRGQJ7l1TbN7jXHXtK3f0H+PXHy2tVe+6/y74YjzHPSeUmaLWlnSU/Js+z6z7d+DpI2qzm2Q9/+ifn3gPZaD71262chafuaY0/r2/9U/j160sqtfo11vux2kV8l3xmlbxER4a1hA3YD3gj8A7BbzfnL8+8FLfL8IfCUyv5BwA/6ZH4M7AX8BHhq1mPlNiTvTUkuXJ7d2wbo+8NCXfepOXZE//3k30+1fLYvBm4BLgIuJs2k26dPpvVzAL5LcjvT258H/KhP5hpgg9Ln0OZ/2/ZZ5PKwVWV/L+CamuewHXBV/h9vVt1cdqel/L4c+ADwfuBlQ57vWOg7iq1kRfg6jaS3k6YDfykfOk3S5yPiPRWx9SQdDzxe0j/25xERH6jJ+hXAFyQdDDyTtO5k7z6Zt5McOW5NKpTVVfEBPK9G39cCR+c0VwJ7At/vk/2TpNOArSWdVKPvG/sOHSfp/oj4Vr7G24DnAIsrMrMlHQr8paSX1+T5pf5jmfcDz42I5TnvxwJfZeJ06tbPAfg34MuSXkxyJXM6cHCfzNdJbhceIek3leNKKsfG/ZnmdUgfBp4EzCZNJf9dn+xknsXrgLPzF/tuWf99+2QWZ513BC5n9eewY5+uLruJTsqvpI8AjwP+bz70OkkviIgjx1HfUWGj0cxBwFMj4g8Akk4gfWlVX7wDSc4T1wceVZJpRNwg6UDgbNKX9t4R8fs+sdsjYh9Jb4+IdxXqezTwNJL/rudKeiLwzj6Z/YAXkF7GywvyfCnwFUlvJXkWfmI+VuUIUqW8CfCSvnPBqoqrnzt7BiNzA6u8A/Ro/Rwi4quSNgC+Qfqf7B8R1/eJ/WtEvFXSORHR74RzEP9N+n9/HphPqjAf1yfT+llExGWS3pj1/QPwwohY0Sf25Yg4SdIpEfH6Al1ddhNdld+9gJ0if/JLWkJqvY6rviPBRqOZm4CNSC8ywIbAz/pkFkTEiZI2bHpBJF3DRJ9Zm5G+Vn8giYjYuXLuJGB30ktd+uL9ISL+IImsz08lPaFP5q0R8TZJ20bEktpcKkTEXZJeCnyTVPBf0XtRKmwZEa+XdEUk31+lXCvpXOBM0nM5ALis9/WUv5iKn4OkDzPx+W5MMkT/kJ9v9cvu+6Sv+moro5GIWC5pVqRFp6dJ+l6fSPGzkPTlPn0fDtwDfDzrW60svkB6Do8vVPUm1vGyC52W3+uAbYH/zfvbAFePsb4jwYv7GpB0Nunr53zSC/NC4Dvkr+GIeKOkKyNiV0k/jIjdBmaW8ttu2PmI+N+K7CWkPuF9gc/VyK7WtJV0FmlF/ptIXze/AjaIiH0rMteQKssfDNNX0r1MrCRmk1bpB33dN717L3kGfdc4bcjpiIjXtHkOuck+LMMlFdkfAf9B6kp5a41sXZfEt0lfjh8DfgHcDrw6InapyBQ/C0l7Neh7cUX2CtLX/WuBD9bITuhKWpfLbpbttPxKupj0fC/Nh55G+hC5L9/jS8dJ31HhlkYzZ+Wtx0U1Mj+RdBMwR1L1S6PXN77yC6z6Yq0Ukl4aEf3+vGASTduIeFn+8x2SLgT+jNQPXqW/L1+s8iS8snBGRFF3RebufL0dJK12L31fzNXjhxXkXfwc6r7mJG0eEf1dXjC5Jv6rSF/XRwFvJn1d/nWfTPGzqBqFir67RcQPa67dtitpnS27Ob+uy+/bC/IdJ31HglsaI0LSn5N8bK32z+r7AusftBIpuuEbsmzd1+0uEXFVoR7b1h2PiJtrZNv05ffSvCEiPlJzfDbpi+pTpC/h/utf3Cf/TxHxvprupJ583Zdo43NQzVRbUj/+U0nl/Zc1aQ6PiI8Py7cNbZ6FKqGRKywlGTHVGQ9J+0TEyPyurStlN6cbSfmdxHVnlL5DdbLRqEfSmRHxypp+XID+/ttqutms6nO+LiL+1Hf+AdLXx52smlHyClJ/dUTEa2ry3Jo0Y+evsi7fAY6OiFtrZHv6itSfvUPW48kD9N2C1KyG1IReUTnXP5tGwLGkmT21M2skzYmIFZIele/ntwOu+5KI+PKg7qQBLYbG5yDpIVb1MffYmuRaPyJix75zvf/ZEaQpnpCm/i7u/99l2RupLw91+TY+i6zvJcD9lcN75mMREXWzjP4MOL5P33dFxD35/DpfdvP5TsqvpO9ExDNrupMGzrqbTn1HTqzhOb4zZSMNNEGaF7/aNiDNXqQK62Lg28CNrD7P/GmkmOivZ5XRvrFBl/NJfb3r5+3VwPmF97Eb8D8Dzh2Q9V1CmpJ6I2nQrXf+XlJ/9NtJldTxpH7m44HjB+S5E3BFzvdmUtfETiP6nzQ+B+AtpIqtuo6g6fl+LD+D5+XtNOBjA2QfXdm2IvW/v2uyz4JU6V4M7NtC3y+SZhXtmLfjgS+57K4qu+NWfmeavkPvpcvM16aNNAunaSHV5cATKvuPJy/u6ZNbjzS98ELSQqYbGq59Vc2xK1voXruwiLRIbPPK/pzqtUgzQ74AnAg8PB9r0vV7pHUXvf3nAN8bIj+f1O/+Q9LMk6uBq6fyHEgti8+TFl09apLPd7VjQ9J/ZyrPAngkaWD78/mZN+lbd88Dy8O6WHbXYPndFNiZ5sWmY6HvKDYPhDcg6XWkKYO/Z1VTNOhbSJXZICJWxiOPiP9Paa3ABCLiIeBDkj4P/FeBGisk/R2rFhEdBNw9QN9qE3c9UkHun++/8nxMHCC+m4prmUh9ya+QtBA4X9IHC3R9RERcWMnjIkmPGCL/GdLMpWuAhxryLnoOkbo+DlBaKHc+aRrrMB6U9NiI+BmApB2BB+sE+8Yg1iMZvUEDmEXPIlKXwpsl7Ur6Em0aEP29pGdGxHeyTn9FKp/9uq6zZTfr2mn5lfRuUsvpBlaV3aB+sem06zsqbDSaeQvw5IgoCdi+TNLHSYNTkGbmDJw5EhG3Aa9UDns7hNeQFpV9kFQov5eP1VGtcB4gra7+4gDZr0s6j1Uv9N+QAmr163mOpPNJXSKr9UX3cYOk41j1DP6O1BQfxIqon31TR5vnQKQxk28Cj23I963AhZJuIPUjb0cOJFbD+yt/P0BaC/HKAbKtnkVEXCnpeTQbjSOA0/PYhoBfsioKZpV1vuxmXbsqv68EHhsRf2zIc1z0HQkeCG9A0teBl0fEfQWyGwJHklwriNQ3/JGIuL8h3Rqfa1259sup6BsRZzXID5oO2ju/Kamw9/K8GHhnRPxqgPzzSV+fF1AZDI4RukGQ9JWI2K9BZkOSuxEBP236nxVet9WzqKQrKg+SelOjaxcnuuzWphlZ+ZX0ReD1UT+de+z0HRU2Gg1IeippYPQHTKzU6nzGVNNtGSmueck1roiIpxbKDq0AVTNnu0oMmL/dVDgrciOtJCR9muQm4VoqTfyomYnTl67REFRk2zzfd0TEO4ac75/hMoGo99XUipb6nhoRiwacc9ldXXZk5VfSfOAc4EdMfL5D10hMl76jwt1TzfwP8C3K+tyrfJXUJ1vCR1vku1XD+RuBPwc+nfcPInWhnNeQ7mOU6atmkSxYVuB3iYinlOZZoek5VLmihexLgXcMOT+fNIuoV8G9hPRVfsuwTFu+/F8tlOvpMwiX3dUZZfldQhq0bvt8p0vfkWCj0cwDETH063IAjf/sPKj6TCBafH00VYBPjYhnV/a/LOnbEfHPTeoUXBtWdyA31TwvkTQvIn7cIl8oMAR53cETgQ9Kml3Y99yk82NIM2Tuzdd4B/D5iFhtgVXLfFcSEf9aKsvqzh2ruOyuzijL710RcVKL/ErzrTLq923KrNcsss5zoaRFkraUtFlvK0g39AtMyW31EtJ8/8eQHN/VVhaqBL3pdduoL+hNhTl59k8v7Q6k6X1NDC2ckjaVtAfwS0nPlvTsYfKZki/mZwJXSrpO0tWSrtFEdxb9esyWtDPZEAyRezHJOd9JpIHY5ZL2GSBb/X/uno/tMCDrbYGq8fkjsP0gPSoMfRaS9pR0maTfSvqjpAc10V17VXbl9SJiQT72tBpRl91VeXVRfi+X9O+SniFpt942In1fLukDwLMkvaxJPtOmhTp5RjFvd23eSE3m/m3g3Gkagt5U5H4CbFTZfxjwkwGyP6Qh6E3l3ALSIp+L8nYT8KIhemzKkKA3Wea1pCb4r0jz838PfGtAfo0BZPrObVe3DZBtDNhUkf0p8LjK/mNJA9x1so0Bmyrn/oU05/4dpEVXVwL/PIJytozkYv0Kkm+rw4D3DikPQwM2uex2X35zXv1bbb5Z/uUMCdhUkfsIyUX+YXn7OnDyVPUd1dZZxuviRlrJeQ3pS+KduXL51wGyXwM2qexvAnxlgOyO+eV7EvD3wP8D/myIHhsCu+RtwyFyRS9TltmIvCiL1OXzuQF5fg94XmX/bcDXCp7d5qSv+G2BbQfItDEE3+7bV/+xyrkXk4zQI0ktjWuBXYfoujtpgdvRpC6VQXJ7ApcBvyW1SB4EfjNAdln+vbpyrHaRFmlM5TJS//++JMO1jcvuQEPQefkteL5tDMG15ElKeX894No1qe+wzWMaBUjaifT1uVHvWEScXiNaEvSmx/2kWBIT3FYrR/eKygyXKAt6U2UuafroRsAuSrEO6vQ9muagN1AW56BHSQCZlSjFDXg/8Bek/vntSF+ydf6GSgI29SiJ00H+uyRgExX5yyXdQi4PSrESVnOqR1nAph735e62KyW9j+RyvXaRVpQFbCLrtq6XXei2/L6YVFarz/ddNaJ70RywqUdJnI5J6TsKbDQaUAqF+RzSi3cusA/J6VpdQb6J5qA3Pc6iwW212gW9mYy+pS/TrZI2Ib3450v6FXBb3U1FWQCZKu8mfZF/MyKeKum5pAqsjmJDQPo/3EF6WSGtLN6MNNspgC+pXcAmoNbIbUtqAdU61YvmgE09Gl2uq13AJpfdVXRSfiUtJv0PnkuaEfUKVsXW6KeNIXg0yWX9hDgdylOSY6Jr/bbv25TxOo0GcuHfBbgiInZR8lT5sYhYbTBPBUFvWl57u2Hnoz6+QRt9z6Ih6E1Nmr3IcQ6iMhtJLQLI9OW3LCLmS7qK9KX7kKRLI2KPGtnTBulFwdqOmvwOHXY+6j3tXkV6VhOMXNSslVBBwKaW+u417Hys7n7eZXf1dCMrv5KujoidK7+PJDmO7I+XjgoCNvXpOJCIuHiy79socEujmd/niuwBpRW4d1LvuwfKvsCK3VYPeLEGBb1prW+UBb1BE+Mc3Jh//5w0aNnLq00AmSq/zi/bt4HPSLqTVPjr9B3k2qOqa3GcjgFGYVDAph5/ioi7Ja0nab2IuFDSiQNkSwI29a574wB9d6z8fXH/eQ2f7rrOl9183a7Kb6+b7T5Jf0HyJzVo1t3bSzOt+z/XyEz2fZsyNhrNLMtN24+Smn+/ZUATtK4SquHo/Nu4mlkDgt5IWj9fr87VRrG+JS9T5qvUxDlgQJdMJf/aADJ9LCS9fG8m+Tv6M/piSrcxBKTxEEizkYai+umnlyqtpK4N2EQ7I9erOH9P8zTL6iK9jUhdbxP004CATUpTWOsCNrnsJroqv1/J+v4HafwnGDBducQQaJJxOlroOxLcPdUCpfnxG0fE1X3HJxX0JqfdmIrxrlZUmkTQmxJ9K+dbBb2ppNsNeF1EvK5yrH8RmWgIIFOT734R8ZWa460DNpWgyQVsegTJCKzHKiP3mYhYzXNrSeuhQb/vRMQz+/RtFbCpknZ7XHZ7aUdefpV8d20UORBW5fiUDMGQ6035fZssbmm049VR75eo+Aush8rcVj8DOIE0vXJxRISk55R00zTomy7W576j9zI1ZRoRP9Tqi8neSRq8vJZVlcQsymJZ93gXsJrRiIgv599i46DkF+hfSLOxqhVbtRL8J9KYw1sj4pqc7saIGNTFQET8Lv/5kKS7G3RqbD1U9K22Itaj3uX6K0lrKP4jIs6t6PvcITr0cNldlbaL8vvhunGtntFv252k5IhwGyaW3WpLchTv26RwS6MFaud9tPYLrCJzPfCMaHBbLWk9UkWxP2kO9hktvlRb+6KpS6P6OAePjogXVWS2JS1c+hnJy+Z9km4o1TXncUUMcX5XaAh6stdRE6ejv69dKRzpB0nTQY8nBcbp8vlOaD1Ujl9Y2e25XP/PqMS4yHKPJM042xr4P8BFJfquq2U3H++8/JboW2AIenK1cTqqLclRvG+TxS2Ndgz17VL4BdbjZ+SZE8OI9kFvJqg09GR50JvGOAcxuQAy/TR9KbYK2NQw6ApAtA/YVKXp+Za0Hnp6lLQWiPYBm1aqM/Tk2lt2Yc2U36Hu0QcZAuoDNjXG6RjR+zYp3NJogdJsmYGVVekXWJZ9Kt27rW7S9/jKbu/r9ouRF3hNFkkPJzWfnx4THdBVZfoHSidQN1A66Ct9QP6t43RIehjpZf1R4TX2iIhB8/KLWw9Ztr+PegJ1fdSSBDwqBsTT6JN12S2kpPxOIs/rSO5UGp1mqn2cjpHrO/R6NhrDUXKa9g8kp3TVZuVqqy7VLujNpaR58P3dJ0P77ZuawW30LUUdxDnQJNZctDEEGnGcjskYuTZI+ixDXK5HRO3sq2HlwWV3Zb4jLb+S/isi3qTVF1sOzK+NIdAaiNMxFdw91czZwMeBL9PcJXIs8D1JJV9gXbmtPptCfVu8TDcy4jgHLQZEqxxGMgQbMLGJX1dh79I/WFrIoJgPgzyzDtShZeuhC5frZ+OyC6Mvv73Qqv/ZkL7KvwNXSCoxBEvoPk7HpLHRaOYPUe4zv03QmwslLSK9INVCVLc2oEpT0Js2+pa+TJ3FOZB0NKmr417Sve0GHBMR36gRb2MIRhqnY5JGrk3Apm0Zvct1l93ESMtvJN9js4C/j4i/K9S3jSFYE3E6Jo27pxqQ9LckJ2rfYOILUtds/V5E/GVhvjfWHI5Bsx9UCXoDfHdIt08bfb/d3wc64NhPgBdHxA15fwfg3Ih40pBbRNL+EXF2g8xVkVxGvIgUo/o44LQBs2A+CnywxBBknR9Lqlzuh5Xz4mvXHWhVwKYgzfev7XtuY+QkfQP460rr4VGk1sOCGtl/IQ2AnpV1eBlwZkT8W9O9DsJld+XxTsqvpPOAlxSOU1wcEUPdg1RkP0C6/6U0PIc2+o4KtzSaeQrJHcTzaJ71UPwFFkPWAvSjFPTmAFZ1gZwm6fMRUeeBtI2+cyTt2Pcy1QW9eTNwkaQb8v72DJnplKcWziUHvAGIiG8PEs+/+5KMxVV5gLeOZwKH5kqryRCsVjEP0ffFwGLSrCABO0h6XUR8rUb8NRHxoWzkNid1mZ1Gquj6KW49RMR787hCb6D/sIiobfVI2hP4MMnd+GzS/PzfxeoLxVx2E12V35uA7+aust76nUEL6y6X9O+UGYLetPM9K8cGPYfeeFsviqIi4qw6uVHhlkYDkn4K7Fz4NdH2C6zIbXX+Uqq6rX4Y8MO6L6WW+i4ATiVNA4T8MkXEak18pRWvT8y7P42I+/tlstxrSQvGtibFedgT+H4MWK2cB8S3Iq3o3YVUAV4UEbvXyNY6wYsaP0eVNJsz8fmu5mYiP7P9Irtdl/RY4KsR8cQa2Z5zug9lPc/SgPUlk2k9FOq7jBqX6xHxLzX3tc6X3Sw/8vKrVTO4JlSiUeMaXRNn0lVEB6/iL0HSR0ju9v9vPvQ3wM8i4sip5DuU6DBYx9qwAZ8DNu8g3+NJwWPuIH2p/gL4wgDZNkFvWulLedCbnUgV4CG9bYBcccCbfL43x36TvP9oUsUxTOeSgE0vBa4nfQHeSPpyHRTIpk3Apl6r4nrSmo5HAZcP0bU0YFO/vg8O0bcoYJPLbrfllzRedRZpHOyavF1dqn+Dvi8meSx4e28bIFccsGlUm7unmtkC+KmkyyiY/lb6BUbyw7MLyQ30YcpuoAfoUBz0pq2+FAS9UXdxDojk1XR74O8kBfCdGNC8VruATV3F6Tgc2JUUNvU+SY8mdVENur/SgE1t9C0N2LTOl918X12V308DbyFNjW2c5aTCgE3qLk7HSLDRaOb4ZpFEy8I5UrfVHevbppIoDniTdehvXr9O0guivnndpmJt48K8MWBTT3CKRm5bBgds6sLlustuoqvyuyKyX7QCfdsYgr+MVXE63inp/dRPK4cWAZtGRpfNmLVhI33BrZf/fjypG2GDAbLXkJqHV+X9LYAvD5D9CKmpfgSpW+IK0kDw2OkLXJp/LydFtxMFTWBSJfxSYPYQmTbxkHvdMldV7vHSAbLfJMX8/jDJIH2IATG3Wz7fNrGeryK91Ffk/ecCp64JffNz/FEL+Wktux3r20n5BZ5PMgAHAS/vbQNkr+77fSTwjQGyP8i/l5A+ODYErh+i48BtquW9bnNLo5lvA8/KMyouIMVp+BuSW+x+2gSReUP+c3GeNTMqt9Vd6NtVnANo17z+tQpjWTD6OB099qI81nOb1kOjvhW9bxyg746Vvx+SdNWQ7rB+prXsdqkv3ZXfNotN2wRsGmmcjlFjo9GMIvVdH05yf/w+SVcOkC0unH28OkbktroLfUsqiQptA960aV4XV6xR5sK8OGBThU6MXKG+PUpdrm9JGk+4lIlTQuu6LMah7Haib4fld5coX2zaxhC8O//5RUlfYQ3G6SjBU24bkHQF8AaS++zDI+JaSdc0FRY1BJHpkx2l2+pO9ZX0jgGVxCD53egLeNN3fq+64z0GfUlpQMCmAbKt3WwPyetiymM9Fwdsmqq+qnHmOOjZNn2dTmPZXRP6jqz8qsVi0750tQGbBsieGjVxOqYTtzSaeRPJL89ZuQLekTTdsIlBX2B1jNJtddf6vhQokQPS4iWtHvCmen6yzet3UROwaQBNz7c4TgftYj23aT1MUGnoyUKX61N4ttNSdteQvqMsv20Wm1apDdg0gPlNAiqM0zEq3NIoRNIjKpVAiXzx16JG6La6kqYTfdUcKKnqyK63BmNCwJssN9V4yEP16JNtcmFeFLBpKrQsDyNxua40ZfhE0roWUf5sp6XsriF9R1J+s+x2dXk0lZuW+n49atzOVM43BmwaNW5pNCDpGSTPm48EtpW0C6m5+obhKRu/wCa4gVb2nDGg/7Yo6E2X+lZYbaV2H40BbwBikmEwKwzq7nr5gONb5+vVDVI2BmwaQR9y7fOdjL5RGLAJeB/JN9JPGiX7Lj/0ZEdll4707WMk5Rem9FFRFCcjX6PJHU5jwKZR45ZGA0quol8BLO19oUj6UUTs1JCu6QvsKlLl3v91u1oTXS2C3nShb38lUbn+6OeA11+/tmKt6PGliuxpw0WnFqdjsgxqPUxS33+sE64k+kCW+25E/NUkdJ2ustuVvtNafktRx3E6RoVbGgVExC2a6EPvwTq5ll9gXbmt7kLfs+kmzkEpxbEsovs4HY20aT1MUt9Sl+vLJH2O9P9rClo1DmW3K33PZprKb0tD0HWcjpFgo9HMLZL+kuRBcjbwRlZN1eznbMqD3nxIaVVroxto2gW96ULfruIcFDGZilXdxekoYTIBm9roWxqwaWNS19DeTddnPMpuV/pOZ/ktNgTRfZyO0RAdrBhcmzbSC/oZkpuJO0mFabMBsj9oke+/A7cCF5NmN10IfGuA7HuBRaR57Jv1tjWlL/C3JBcPzyBVZruRKq062dUc/dUdm+T/4mhWrej9GGm++94DZHsrhV9E+iLfheRdtU72o8C8aS5nbfT9KRUHfaQVwz+tkVvCRGeBmwKfGOOy25W+01p+SS5fPt1C/jyGeFHok714TZdVtzSaeT9wVET8ClZOb3s/sFpfM+2+wF4G7BhlA1h/m3+PrRwbNOW2C327inPQljaxLHr9c6OO01FMy9ZDG30/BVwq6SxY6XK9zufSzhHx695ORPwqjzHUMQ5ltyt9p7X8RsSDkuZIml34zG6imzgdI8FGo5mdexUwNBbkNoXzKpL/nsYBrGgR9KYjfdtUEm+mRcCblrSpWC9Xipy3A3CsUtS8Qc334oBNLWlj5Ir1jfKATetJ2rTyAbEZg9/5cSi7Xek7DuX3JsoNwW15E2kW5DB673ZRwKZRYKPRTJuC3KZwduW2ugt921QSX5c0l4KAN5OgjSEodmEeeeqk+gIgjYA2Rq4Ll+vvB74n6QukiuSVpO6iOsah7Hal7ziU3zaG4Fzgn5k42yuocZkT5dOvR4aNRjNtCnJx4aQ7N9Bd6NtJnINJ0MYQdBWnow1tWg8jd7keEacrRfl7HqmyenkMdnkx7WW3Q33HofwWGwI6itMxKrxOowBJ81hVkC8YVJAlXQTsDDQWTmW/RLmyeDzpy+ZrEfGnGtlrWBUPYBfleAARUTtLZ9T6qoVPoEGVRES8oi6PtqgSD5nhFWtxGEyldQfPoy9OR0zR54+k9Vhl5H6djdxWUeMjabr1bVEW1iO59x667qci36rsjlrfLDvt5VfJ68BqhiBqFgiqxo/YkHxr43RExOFT0XcYbmkUkCvdEqdkxV9gdORyvSN9l1Fj4AbItgl404qainVYwKa96MaFeTFtWg9joG9RWYhuXZi3oUjfbOROLjVydFd+iwM2AcdL+hhli03bBGwaCTYao6VN5bomXK6PSt/ODFxL2lSsXcXpKKalkZs2fSdRsa4Jl+sj0XecjFwLQ9BmsWmbOB0jwUZjtLSpXKXkJ+pgUl89pPncqxHt4gF0oe84GDhoV7F2EqejJW2M3LTpO4mK9Z0t8h552Z2EvtNq5DJtDMEu0UGcjlFhozFa2lSub6J7l+tNlOo7DgYO2lWsa8KFeRNtjNx061tcsdaNBRQyyrLbxhBMq5HLtDEEl0iaN2QSwEqiIGDTqPFA+AjRJAIgqUOX6wV5FembBxL/D/DdiDgxG7g3RX041Gq6d4ywkhg4oNljCpVZ9RrTErBpCtcYib4tB4s7d2HeRBt9p3CNkZVftQjYJOknwGNJLk2KF5tqDQVsstEYIW0qV1VcmEdEsQtztYgjMUp9s/y0GbgW11xjcToKdGk0cuOkbymSljMJF+bToWu+7jgYuWJDoDUQp2Mq2Gh0QEnlqo5crk+GJn2n28BNtWJtea2hAZDGjVHp26ZiVUcuzFvm1UbfaTdykzUELa8xNGDTyK5jozE62lSukn4QEU+vFkxJV0XELjWyO9BBPIBSfcfJwI0StYjT0TLfToxcV/rmvIsrVkkfInmCPZuWLswrslMtu230nXYjtzbhgfDR8l+s8lJKJLcRzx4g25XL9U70jW5iikw3rV2YlxBTj0o4iE70zdzR4ku8KxfmbWijb1dxOqYNTSJg06iw0RgxpZUrcATwIWArkpvpb5AGpetoEw+gFYX6joOBGzkxuQBI00bH+hZXrKTY2UdH9kirVZ6U6+iq7LbRdxyM3KiZTMCmkWCjMVraVK5duVzvQt+xMHBdoXYuzKedjvRtU7F25cK8K33HwciNlJhcwKaRYKMxWtpUrl25XO9C33EwcF3SxoX5ONCFvm0q1q5cmHel7zgYuZET7eN0jAQbjdHSpnLtyuV6F/qOg4HrkjYuzMeBLvRtU7F25cK8DW30HQcj1xU3UR6nYyTYaIyWNpVrVy7X21Cq7zgYuC5pE6djHOhC3+L/cXTnwrwTfRkPI9cVbeJ0jAQbjdHS1YvXNh7AqPUdBwPXJa0CII0BXejb5n/cxpNyV2W3WN8xMXJd0SZOx0jwOo0RIukQkj+pCQU5Ij41NGFzvp24TGijrzqIKTJOqDBOx7jQhb6l/+OWeXbm7qMjfS9iBpVftYjTMbJr2miMlo4KcnHApknkPVJ9u6wkukItAiCNAzNFX7UM2DQOzLTyqxYBm0Z2TRuN8UfS5cCzgE2BS0guzO+LiDqX69NKlwauKyRdy0QX5usB10TEVMO9dsJM0lfSZ4Bjo8yF+bQyQ43c84GDKIvTMRI8pjEzaONyfbppE1NkXGjjwnwcmEn6tnFhPq1E+zgd40CbOB0jwUZjZiAVxrMYA2aSgevRJk7HODCT9C2OZTEmzBgjl9klyuN0jAQbjZnBm5hcwKbpYCYZuB7FAZDGhBmj77iOBQxhphm54oBNo8JjGjMItYxnMR1okgGbzNqJJhnLwpShSQZsmtI1bTTGH00ynsV0MkMM3BqL0zEKZpq+MPlYFtPFTDNyWgNxOla7po3G+KNJxrOYDmaigTPdoUnGspguZpqRmw48pjFDiHKX69PNf1EeU8Ss/bRxYT4OtInTsU5iozEzaONyfdqZQQbOdE8bF+bjwEwzcmscG42ZQRuX69PNjDJwpnPauDAfB2aakVvj2GjMDNq4XJ9uZpKBM93TxoX5ODDTjNwaZ73pVsAUsZoLc2BcX7yegdsiIjYnxVte4yEpzdiwXq54gUYX5uPAakaO8X3XpoVx/ueZVbSJHTDdtIkpYtZ+WrlcHwNm0rs2LfhhzAxm0ovnl86spGUsi3FgJr1r04LXacwQunC53gVdxRQxZk0xU9616cJGw4wcv3TGrL3YaBhjjCnGs6eMMcYUY6NhjDGmGBsNY4wxxdhoGGOMKeb/B6gFQVeoPLx6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.bar(np.arange(1, len(R)+1), [R[r].p95 for r in R])\n",
    "ax.set_xticks(np.arange(1, len(R)+1))\n",
    "ax.set_xticklabels(list(R.keys()), rotation=90)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.set_title(\"95th Percentile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('ggplot'):\n",
    "    fig, ax = plt.subplots(figsize=(12,12), nrows=4, sharex=True, constrained_layout=True)\n",
    "    ax[0].bar(np.arange(1, len(R)+1), [R[r].goavg for r in R], width=0.4, alpha=1, label='all')\n",
    "    ax[0].bar(0.4+np.arange(1, len(R)+1), [R[r].gavg for r in R], width=0.4, alpha=1, label='ocn')\n",
    "    ax[0].set_xticks(0.2+np.arange(1, len(R)+1))\n",
    "    ax[0].set_xticklabels(list(R.keys()), rotation=90)\n",
    "    ax[0].spines['top'].set_visible(False)\n",
    "    ax[0].spines['right'].set_visible(False)\n",
    "    ax[0].set_title(\"Global Average Precipitation\")\n",
    "    ax[0].legend(loc='lower right')\n",
    "    \n",
    "    ax[1].bar(np.arange(1, len(R)+1), [R[r].toavg for r in R], width=0.4, alpha=1)\n",
    "    ax[1].bar(0.4+np.arange(1, len(R)+1), [R[r].tavg for r in R], width=0.4, alpha=1)\n",
    "    ax[1].set_xticks(0.2+np.arange(1, len(R)+1))\n",
    "    ax[1].set_xticklabels(list(R.keys()), rotation=90)\n",
    "    ax[1].spines['top'].set_visible(False)\n",
    "    ax[1].spines['right'].set_visible(False)\n",
    "    ax[1].set_title(\"Tropical Average Precipitation\")\n",
    "    \n",
    "    ax[2].bar(0.2+np.arange(1, len(R)+1), [R[r].p95 for r in R], width=0.4, alpha=1, color='darkgray')\n",
    "    ax[2].set_xticks(0.2+np.arange(1, len(R)+1))\n",
    "    ax[2].set_xticklabels(list(R.keys()), rotation=90)\n",
    "    ax[2].spines['top'].set_visible(False)\n",
    "    ax[2].spines['right'].set_visible(False)\n",
    "    ax[2].set_title(\"95th Percentile Precipitation\")\n",
    "    \n",
    "    ax[3].bar(0.2+np.arange(1, len(R)+1), [R[r].xsum/R[r].tsum for r in R], width=0.4, alpha=1, color='darkgray')\n",
    "    ax[3].set_xticks(0.2+np.arange(1, len(R)+1))\n",
    "    ax[3].set_xticklabels(list(R.keys()), rotation=90)\n",
    "    ax[3].spines['top'].set_visible(False)\n",
    "    ax[3].spines['right'].set_visible(False)\n",
    "    ax[3].set_title(\"Extreme Precipitation Fraction\")\n",
    "\n",
    "    fig.savefig('/Users/brianpm/Dropbox/Manuscripts/M_cre_pex/summary_bars_base_and_p4K_and_LWoff.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}