{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "35f20839-60b7-4be8-9ff9-6caaf7f0119d",
   "metadata": {},
   "source": [
    "# Figure 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43a5405-db85-4c94-bfe5-4235334539ae",
   "metadata": {},
   "source": [
    "[Skip code and jump to the figure](#Show-the-figure)\n",
    "\n",
    "----------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a0a24d3-8036-4e62-aeea-dddbd3f5daf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")  # noqa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "868d9af2-093d-44f3-96f8-833caa2007c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scientific and datavis stack\n",
    "import iris\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5978d8c7-9c89-48bb-80f5-8a826ff8b7e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# My packages\n",
    "from aeolus.calc import deriv, spatial_mean, water_path\n",
    "from aeolus.const import add_planet_conf_to_cubes, init_const\n",
    "from aeolus.coord import get_cube_rel_days, isel, roll_cube_pm180\n",
    "from aeolus.core import AtmoSim\n",
    "from aeolus.io import load_data\n",
    "from aeolus.model import um, um_stash\n",
    "from aeolus.plot import add_custom_legend, subplot_label_generator, tex2cf_units\n",
    "from pouch.clim_diag import calc_derived_cubes\n",
    "from pouch.plot import KW_MAIN_TTL, KW_SBPLT_LABEL, figsave, use_style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f6bdb19-a630-478c-9573-79fc78b5c65f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local modules\n",
    "import mypaths\n",
    "from commons import GLM_SUITE_ID, SIM_LABELS, cold_traps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3048fe00-c4f2-48ae-9582-edf6d1ca3e5f",
   "metadata": {},
   "source": [
    "Apply custom matplotlib style sheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47402b50-1944-42c5-a587-aa7342417b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "use_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db456b8f-cd2f-41ba-b435-07b954aeff46",
   "metadata": {},
   "source": [
    "## Load the data for the two key experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f6a1e3-23f8-41e6-b874-cb2dbe547ecd",
   "metadata": {},
   "source": [
    "Define paths to input data and results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9cfddd26-1e81-4961-8980-c221ba5c4c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_prefix = f\"{GLM_SUITE_ID}_spinup\"\n",
    "inp_dir = mypaths.sadir / f\"{GLM_SUITE_ID}_spinup\"\n",
    "time_prof = \"mean_days0_499\"\n",
    "plotdir = mypaths.plotdir / img_prefix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf171692-609c-4a63-a6df-db92f048e7ba",
   "metadata": {},
   "source": [
    "Load processed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "005fac1d-cc49-4e7f-abf1-107708b72d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = {}\n",
    "for sim_label, sim_prop in SIM_LABELS.items():\n",
    "    planet = sim_prop[\"planet\"]\n",
    "    const = init_const(planet, directory=mypaths.constdir)\n",
    "\n",
    "    cl = load_data(\n",
    "        files=inp_dir / f\"{GLM_SUITE_ID}_{sim_label}_raddiag.nc\",\n",
    "    )\n",
    "    cl = iris.cube.CubeList([roll_cube_pm180(cube) for cube in cl])\n",
    "    lw_up_forcing = cl.extract_cube(um_stash.lw_up_forcing)\n",
    "    lw_up_forcing.rename(um.lw_up_forcing)\n",
    "    lw_up_forcing.units = cl.extract_cube(um.lw_up).units\n",
    "    lw_dn_forcing = cl.extract_cube(um_stash.lw_dn_forcing)\n",
    "    lw_dn_forcing.rename(um.lw_dn_forcing)\n",
    "    lw_dn_forcing.units = cl.extract_cube(um.lw_dn).units\n",
    "    cl += load_data(\n",
    "        files=inp_dir / f\"{GLM_SUITE_ID}_{sim_label}_{time_prof}.nc\",\n",
    "    )\n",
    "    cldtop = cl.extract_cube(\"m01s09i223\")\n",
    "    cldtop.rename(\"total_cloud_top_height\")\n",
    "    cldtop.units = \"kft\"\n",
    "\n",
    "    add_planet_conf_to_cubes(cl, const)\n",
    "    # Derive additional fields\n",
    "    calc_derived_cubes(cl, const=const, model=um)\n",
    "    # Use the cube list to initialise an AtmoSim object\n",
    "    runs[sim_label] = AtmoSim(\n",
    "        cl,\n",
    "        name=sim_label,\n",
    "        planet=planet,\n",
    "        const_dir=mypaths.constdir,\n",
    "        timestep=cl[-1].attributes[\"timestep\"],\n",
    "        model=um,\n",
    "        vert_coord=\"z\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae1e5b7-a013-49c6-8a92-6fe9c76b55e8",
   "metadata": {},
   "source": [
    "Define diagnostics for the night-side surface heat balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b54d7eb1-2622-4010-ae83-cdfe0fd24a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "DIAGS = {\n",
    "    \"dt_sfc_dt\": {\n",
    "        \"cube\": lambda AS: deriv(spatial_mean(AS.t_sfc.extract(cold_traps)), um.t),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:blue\",\n",
    "        ),\n",
    "        \"title\": \"Change in surface temperature\",\n",
    "        \"tex_units\": \"$K$ $day^{-1}$\",\n",
    "        \"lim\": [-1, 1],\n",
    "        \"ax\": 0,\n",
    "    },\n",
    "    \"sfc_net_down_lw\": {\n",
    "        \"cube\": lambda AS: spatial_mean(AS.sfc_net_down_lw.extract(cold_traps)),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:green\",\n",
    "        ),\n",
    "        \"title\": \"Net downward LW radiation\",\n",
    "        \"ylabel\": \"Air-sea energy flux\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"lim\": [-50, 50],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"sfc_shf\": {\n",
    "        \"cube\": lambda AS: -1 * spatial_mean(AS.sfc_shf.extract(cold_traps)),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(color=\"tab:green\", linestyle=\"--\", dash_capstyle=\"round\"),\n",
    "        \"title\": \"Downward sensible heat flux\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"ylabel\": \"Air-sea energy flux\",\n",
    "        \"lim\": [-50, 50],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"sfc_lhf\": {\n",
    "        \"cube\": lambda AS: -1 * spatial_mean(AS.sfc_lhf.extract(cold_traps)),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(color=\"tab:green\", linestyle=\":\", dash_capstyle=\"round\"),\n",
    "        \"title\": \"Downward latent heat flux\",\n",
    "        \"ylabel\": \"Air-sea energy flux\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"lim\": [-50, 50],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"sfc_down_lw\": {\n",
    "        \"cube\": lambda AS: spatial_mean(AS.sfc_dn_lw.extract(cold_traps)),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:orange\",\n",
    "        ),\n",
    "        \"title\": \"Downward LW radiation\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"lim\": [0, 220],\n",
    "        \"ax\": 2,\n",
    "    },\n",
    "    ######################\n",
    "    \"wvp\": {\n",
    "        \"cube\": lambda AS: spatial_mean(water_path(AS._cubes.extract(cold_traps))),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:purple\",\n",
    "        ),\n",
    "        \"title\": \"Water vapour path\",\n",
    "        \"ylabel\": \"Water path\",\n",
    "        \"tex_units\": \"$kg$ $m^{-2}$\",\n",
    "        \"lim\": [0, 15],\n",
    "        \"ax\": 0,\n",
    "    },\n",
    "    \"cwp\": {\n",
    "        \"cube\": lambda AS: spatial_mean(\n",
    "            water_path(AS._cubes.extract(cold_traps), kind=\"cloud_water\")\n",
    "        )\n",
    "        * 10,\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:purple\",\n",
    "            linestyle=\":\",\n",
    "            dash_capstyle=\"round\",\n",
    "        ),\n",
    "        \"title\": r\"Cloud water path ($\\times 10$)\",\n",
    "        \"ylabel\": \"Water path\",\n",
    "        \"tex_units\": \"$kg$ $m^{-2}$\",\n",
    "        \"lim\": [0, 10],\n",
    "        \"ax\": 0,\n",
    "    },\n",
    "    \"wvre_lw_sfc\": {\n",
    "        \"cube\": lambda AS: spatial_mean(\n",
    "            isel(AS.lw_dn_forcing - AS.lw_dn, um.z, 0).extract(cold_traps)\n",
    "        ),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:red\",\n",
    "        ),\n",
    "        \"title\": \"$WVRE_{LW}^{sfc}$\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"lim\": [-150, 150],\n",
    "        \"ticks\": np.arange(-150, 151, 25),\n",
    "        \"ylabel\": \"LW radiative effect\",\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"cre_lw_sfc\": {\n",
    "        \"cube\": lambda AS: spatial_mean(\n",
    "            (AS.sfc_dn_lw_cs - AS.sfc_dn_lw).extract(cold_traps)\n",
    "        ),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:red\",\n",
    "            linestyle=\":\",\n",
    "            dash_capstyle=\"round\",\n",
    "        ),\n",
    "        \"title\": \"$CRE_{LW}^{sfc}$\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"ylabel\": \"LW radiative effect\",\n",
    "        \"lim\": [-150, 150],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"t_sfc\": {\n",
    "        \"cube\": lambda AS: spatial_mean(AS.t_sfc.extract(cold_traps)),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:blue\",\n",
    "        ),\n",
    "        \"title\": \"Surface temperature\",\n",
    "        \"tex_units\": \"$K$\",\n",
    "        \"lim\": [170, 260],\n",
    "        \"ax\": 2,\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dba10c0-ee81-457d-b370-89d478ef1ebb",
   "metadata": {},
   "source": [
    "Define which diagnostics to show."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae8d8940-1840-4c77-bb74-1591787e08f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "vrbls_to_show = [\"wvp\", \"cwp\", \"wvre_lw_sfc\", \"cre_lw_sfc\", \"t_sfc\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855b03c5-b6f8-4ae2-9b27-c9419c5ec7a9",
   "metadata": {},
   "source": [
    "Do the calculations and store results in a separate dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e3f7043-7ea5-4e3d-86bc-50e516d819c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "RESULTS = {}\n",
    "for sim_label in SIM_LABELS.keys():\n",
    "    the_run = runs[sim_label]\n",
    "    RESULTS[sim_label] = {}\n",
    "    for vrbl_key in vrbls_to_show:\n",
    "        vrbl_prop = DIAGS[vrbl_key]\n",
    "        cube = vrbl_prop[\"cube\"](the_run)\n",
    "        cube.convert_units(tex2cf_units(vrbl_prop[\"tex_units\"]))\n",
    "        RESULTS[sim_label][vrbl_key] = cube"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3052589f-6ae5-418c-80ea-6cfb406084e4",
   "metadata": {},
   "source": [
    "### Create a figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f1d5da6-9a53-4d01-b469-de3a11c0b12d",
   "metadata": {},
   "outputs": [],
   "source": [
    "imgname = f\"{img_prefix}__{'_'.join(SIM_LABELS)}__{'_'.join(vrbls_to_show)}__cold_traps\"\n",
    "xlim = [0, 500]\n",
    "ncols = 2\n",
    "nrows = 1\n",
    "window = 10\n",
    "fig, axs = plt.subplots(ncols=ncols, nrows=nrows, figsize=(ncols * 8.5, nrows * 4))\n",
    "iletters = subplot_label_generator()\n",
    "for sim_label, ax in zip(SIM_LABELS.keys(), axs.flat):\n",
    "    ax.set_title(f\"{next(iletters)}\", **KW_SBPLT_LABEL)\n",
    "    ax.set_title(SIM_LABELS[sim_label][\"title\"], **KW_MAIN_TTL)\n",
    "    ax.set_xlabel(\"Time [day]\")\n",
    "    n_axes = len(set([DIAGS[vrbl_key].get(\"ax\", 0) for vrbl_key in vrbls_to_show]))\n",
    "    twinx_axes = [ax]\n",
    "    for _ in range(n_axes - 1):\n",
    "        twinx_axes.append(ax.twinx())\n",
    "    if len(twinx_axes) >= 3:\n",
    "        for i, ax in enumerate(twinx_axes[2:]):\n",
    "            if ax.is_last_col():\n",
    "                x_off = 1.14 + i * 0.05\n",
    "            else:\n",
    "                x_off = 1.08 + i * 0.05\n",
    "            ax.spines[\"right\"].set_position((\"axes\", x_off))\n",
    "\n",
    "    for i, vrbl_key in enumerate(vrbls_to_show):\n",
    "        vrbl_prop = DIAGS[vrbl_key]\n",
    "        the_run = runs[sim_label]\n",
    "        # Calculate diagnostics\n",
    "        cube = RESULTS[sim_label][vrbl_key]\n",
    "        # cube_rm = rolling_mean(cube, um.t, window=window)\n",
    "        # Plot diagnostics\n",
    "        _ax = twinx_axes[vrbl_prop.get(\"ax\", 0)]\n",
    "\n",
    "        if (_ax.is_first_col() and (vrbl_prop.get(\"ax\", 0) == 0)) or (\n",
    "            not _ax.is_first_col() and (vrbl_prop.get(\"ax\", 0) != 0)\n",
    "        ):\n",
    "            _ax.set_ylabel(\n",
    "                f'{vrbl_prop.get(\"ylabel\", vrbl_prop[\"title\"])} [{vrbl_prop[\"tex_units\"]}]',\n",
    "                color=vrbl_prop[\"kw_plt\"][\"color\"],\n",
    "                fontsize=\"medium\",\n",
    "            )\n",
    "        _ax.set_ylim(vrbl_prop[\"lim\"])\n",
    "        if \"ticks\" in vrbl_prop:\n",
    "            _ax.set_yticks(vrbl_prop[\"ticks\"])\n",
    "        _ax.set_xlim(xlim)\n",
    "        if min(vrbl_prop[\"lim\"]) < 0 and max(vrbl_prop[\"lim\"]) > 0:\n",
    "            _ax.hlines(0, *xlim, alpha=0.25, color=vrbl_prop[\"kw_plt\"][\"color\"])\n",
    "        _ax.tick_params(\n",
    "            axis=\"y\", labelcolor=vrbl_prop[\"kw_plt\"][\"color\"], labelsize=\"x-small\"\n",
    "        )\n",
    "        _ax.plot(\n",
    "            get_cube_rel_days(cube),\n",
    "            cube.data,  # cube_rm\n",
    "            linewidth=1.5,\n",
    "            **vrbl_prop[\"kw_plt\"],\n",
    "            label=vrbl_prop[\"title\"],\n",
    "        )\n",
    "add_custom_legend(\n",
    "    fig,\n",
    "    {\n",
    "        DIAGS[vrbl_key][\"title\"]: {\"linewidth\": 3, **DIAGS[vrbl_key][\"kw_plt\"]}\n",
    "        for vrbl_key in vrbls_to_show\n",
    "    },\n",
    "    loc=\"upper center\",\n",
    "    bbox_to_anchor=(0.5, 1.25),\n",
    "    frameon=False,\n",
    "    ncol=3,\n",
    "    title=\"Spin-up time series for the cold traps region\",\n",
    ")\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4000a87-5c6b-439c-bd83-5550f0fabc8f",
   "metadata": {},
   "source": [
    "# Show the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02102d0c-e80d-4b12-a26d-ea99deb52e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x288 with 6 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc28ca2f-1d26-45bd-8081-57cf1c4dff7e",
   "metadata": {},
   "source": [
    "* **Time series of diagnostics for the night-side cold traps, defined as the region bounded by $45^\\circ$ and $55^\\circ$ in the latitude and $160^\\circ-140^\\circ$W in the longitude.**\n",
    "* **The panels for the (left) SJ and (right) DJ regime show: (purple, solid) water vapor path in $kg\\,m^{-2}$, (purple, dashed) cloud water path in $\\times 10\\,kg\\,m^{-2}$, (red, solid) water vapor radiative effect $WVRE_{LW}^{sfc}$ in $W\\,m^{-2}$, (red, dashed) cloud radiative effect $CRE_{LW}^{sfc}$ in $W\\,m^{-2}$, and (blue) surface temperature in $K$.**\n",
    "* **$WVRE_{LW}^{sfc}$ is defined as the difference between the radiative fluxes at the surface calculated with and without the water vapor opacity.**\n",
    "* **Likewise, the $CRE_{LW}^{sfc}$ is defined as the difference between the \"clear-sky\" and \"cloudy\" radiative fluxes at the surface.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a07ee58f-14e1-4b28-8d27-84907d11763e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved to ../plots/ch111_spinup/ch111_spinup__base_sens-t280k__wvp_cwp_wvre_lw_sfc_cre_lw_sfc_t_sfc__cold_traps.png\n"
     ]
    }
   ],
   "source": [
    "figsave(fig, plotdir / imgname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26311c51-67da-4db6-850a-6a87a8f9fc5a",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb82210-27c5-4d7a-8ec5-210e6c10851f",
   "metadata": {},
   "source": [
    "## Extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa6edb4a-e8bf-467d-8f69-140e28e47f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "def temperature_at_cloud_top(AS, model=um):\n",
    "    import xarray as xr\n",
    "\n",
    "    cldtop = AS._cubes.extract_cube(\"m01s09i223\")\n",
    "    cldtop.convert_units(\"m\")\n",
    "    cldtop_xr = xr.DataArray.from_iris(cldtop)\n",
    "    temp_at_cldtop_xr = xr.DataArray.from_iris(AS.temp).sel(\n",
    "        {model.z: cldtop_xr}, method=\"nearest\"\n",
    "    )\n",
    "    return temp_at_cldtop_xr.where(cldtop_xr > 0, np.nan).to_iris()\n",
    "\n",
    "\n",
    "def cloud_top_height_masked(AS, model=um):\n",
    "    cldtop = AS._cubes.extract_cube(\"m01s09i223\").copy()\n",
    "    cldtop_m = iris.util.mask_cube(cldtop, cldtop.core_data() < 0)\n",
    "    return cldtop_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "087f3abc-4eea-4e2f-861a-4b369ce13fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import prod\n",
    "\n",
    "from aeolus.calc import integrate\n",
    "\n",
    "\n",
    "@update_metadata(units=\"kg m-2\")\n",
    "def sum_cwp_ccp_rwp(cubes):\n",
    "    # cwp = water_path(cubes, kind=\"cloud_water\")\n",
    "    cwp = 0\n",
    "    rwp = integrate(prod(cubes.extract_cubes([um.rain_mf, um.dens])), um.z)\n",
    "    # ccw = cubes.extract_cube(um_stash.ccw_rad)\n",
    "    # cca = cubes.extract_cube(um_stash.cca_anvil)\n",
    "    # cc_mr = ccw * cca\n",
    "    # cc_mr.units = \"kg kg-1\"\n",
    "    # ccp = integrate(\n",
    "    #     cc_mr * cubes.extract_cube(um.dens),\n",
    "    #     um.z,\n",
    "    # )\n",
    "    ccp = 0\n",
    "    return cwp + ccp + rwp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3e250a1-6f53-4018-a8a6-63a1ba36dc39",
   "metadata": {},
   "outputs": [],
   "source": [
    "DIAGS = {\n",
    "    \"cwp_ccp_rwp\": {\n",
    "        \"cube\": lambda AS: spatial_mean(sum_cwp_ccp_rwp(AS._cubes.extract(cold_traps)))\n",
    "        * 10,\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:purple\",\n",
    "            linestyle=\":\",\n",
    "            dash_capstyle=\"round\",\n",
    "        ),\n",
    "        \"title\": r\"Cloud water path ($\\times 10$)\",\n",
    "        \"ylabel\": \"Water path\",\n",
    "        \"tex_units\": \"$kg$ $m^{-2}$\",\n",
    "        \"lim\": [0, 10],\n",
    "        \"ax\": 0,\n",
    "    },\n",
    "    \"wvre_lw\": {\n",
    "        \"cube\": lambda AS: spatial_mean(\n",
    "            isel(AS.lw_up_forcing - AS.lw_up, um.z, -1).extract(cold_traps)\n",
    "        ),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:red\",\n",
    "        ),\n",
    "        \"title\": \"$WVRE_{LW}$\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"lim\": [-50, 50],\n",
    "        \"ticks\": np.arange(-40, 41, 10),\n",
    "        \"ylabel\": \"LW radiative effect\",\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"cre_lw\": {\n",
    "        \"cube\": lambda AS: spatial_mean(\n",
    "            (AS.toa_olr_cs - AS.toa_olr).extract(cold_traps)\n",
    "        ),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:red\",\n",
    "            linestyle=\":\",\n",
    "            dash_capstyle=\"round\",\n",
    "        ),\n",
    "        \"title\": \"$CRE_{LW}$\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"ylabel\": \"LW radiative effect\",\n",
    "        \"lim\": [-30, 30],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"t_sfc\": {\n",
    "        \"cube\": lambda AS: spatial_mean(AS.t_sfc.extract(cold_traps)),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:blue\",\n",
    "        ),\n",
    "        \"title\": \"Surface temperature\",\n",
    "        \"tex_units\": \"$K$\",\n",
    "        \"lim\": [170, 260],\n",
    "        \"ax\": 0,\n",
    "    },\n",
    "    \"sfc_down_lw\": {\n",
    "        \"cube\": lambda AS: spatial_mean(AS.sfc_dn_lw.extract(cold_traps)),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:red\",\n",
    "        ),\n",
    "        \"title\": \"All-sky LW flux\",\n",
    "        \"ylabel\": \"Downward LW radiation\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"lim\": [0, 220],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"sfc_down_lw_forcing\": {\n",
    "        \"cube\": lambda AS: spatial_mean(\n",
    "            isel(AS.lw_dn_forcing, um.z, 0).extract(cold_traps)\n",
    "        ),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:red\",\n",
    "            linestyle=\"--\",\n",
    "            dash_capstyle=\"round\",\n",
    "        ),\n",
    "        \"title\": \"Dry-sky LW flux\",\n",
    "        \"ylabel\": \"Downward LW radiation\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"lim\": [0, 220],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"sfc_down_lw_cs\": {\n",
    "        \"cube\": lambda AS: spatial_mean(AS.sfc_dn_lw_cs.extract(cold_traps)),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:red\",\n",
    "            linestyle=\":\",\n",
    "            dash_capstyle=\"round\",\n",
    "        ),\n",
    "        \"title\": \"Clear-sky LW flux\",\n",
    "        \"ylabel\": \"Downward LW radiation\",\n",
    "        \"tex_units\": \"$W$ $m^{-2}$\",\n",
    "        \"lim\": [0, 220],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    ##############\n",
    "    \"cldtop_height\": {\n",
    "        \"cube\": lambda AS: spatial_mean(\n",
    "            cloud_top_height_masked(AS).extract(cold_traps)\n",
    "        ),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:orange\",\n",
    "        ),\n",
    "        \"title\": \"Cloud top height\",\n",
    "        \"tex_units\": \"$km$\",\n",
    "        \"lim\": [0, 20],\n",
    "        \"ax\": 1,\n",
    "    },\n",
    "    \"cldtop_temp\": {\n",
    "        \"cube\": lambda AS: spatial_mean(\n",
    "            temperature_at_cloud_top(AS).extract(cold_traps)\n",
    "        ),\n",
    "        \"method\": \"plot\",\n",
    "        \"kw_plt\": dict(\n",
    "            color=\"tab:green\",\n",
    "        ),\n",
    "        \"title\": \"Cloud top temperature\",\n",
    "        \"tex_units\": \"$K$\",\n",
    "        \"lim\": [200, 260],\n",
    "        \"ax\": 2,\n",
    "    },\n",
    "}"
   ]
  }
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