THAI-TL-Streamfunction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TL streamfunction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")  # noqa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data analysis and viz libraries\n",
    "import aeolus.calc as acalc\n",
    "import aeolus.coord as acoord\n",
    "import aeolus.meta as ameta\n",
    "import aeolus.plot as aplt\n",
    "import iris\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from matplotlib.offsetbox import AnchoredText\n",
    "from windspharm.iris import VectorWind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local modules\n",
    "import mypaths\n",
    "from calc import (\n",
    "    hdiv,\n",
    "    integral,\n",
    "    mass_weighted_vertical_integral,\n",
    "    meridional_mean,\n",
    "    moist_static_energy,\n",
    "    spatial_mean,\n",
    "    vert_mer_mean_of_mse_flux,\n",
    "    wind_rot_div,\n",
    "    zonal_mean,\n",
    ")\n",
    "from commons import MODELS\n",
    "from grid import reverse_along_dim\n",
    "from load_thai import LOAD_CONF\n",
    "from names import names\n",
    "from plot_func import (\n",
    "    KW_AUX_TTL,\n",
    "    KW_AXGR,\n",
    "    KW_MAIN_TTL,\n",
    "    KW_SBPLT_LABEL,\n",
    "    XLOCS,\n",
    "    YLOCS,\n",
    "    figsave,\n",
    "    linspace_pm1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key legend.labelcolor in file paper.mplstyle, line 10 ('legend.labelcolor : linecolor')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.4.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "plt.style.use(\"paper.mplstyle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "THAI_cases = [\"Ben1\"]\n",
    "\n",
    "THAI_case = THAI_cases[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants\n",
    "\n",
    "Define atmospheric and planetary constants. Note the planet's radius is in meters!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "if THAI_case.endswith(\"1\"):\n",
    "    import const_ben1_hab1 as const\n",
    "else:\n",
    "    import const_ben2_hab2 as const"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the time-averaged data previously preprocessed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "datasets = {}  # Create an empty dictionary to store all data\n",
    "# Loop over THAI cases (this loop can swapped places with the loop below if needed)\n",
    "for THAI_case in THAI_cases:\n",
    "    # for each of the THAI cases, create a nested directory for models\n",
    "    datasets[THAI_case] = {}\n",
    "    for model_key in MODELS.keys():\n",
    "        datasets[THAI_case][model_key] = xr.open_dataset(\n",
    "            mypaths.datadir / model_key / f\"{THAI_case}_time_mean_{model_key}.nc\"\n",
    "        )\n",
    "    datasets[THAI_case][\"ExoCAM\"][names.exocam.lev].attrs.update(units=\"hPa\")\n",
    "    datasets[THAI_case][\"ROCKE3D\"][names.rocke3d.pres].attrs.update(units=\"hPa\")\n",
    "    datasets[THAI_case][\"ROCKE3D\"][names.rocke3d.lev].attrs.update(positive=\"up\", units=\"1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regrid to a common grid (otherwise `regrid_to_rotated_pole_coordinates()` returns huge numbers for the antistellar hemisphere)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_lats = datasets[THAI_case][\"UM\"].latitude.values  # regrid to the UM grid\n",
    "new_lons = datasets[THAI_case][\"UM\"].longitude.values\n",
    "for THAI_case in THAI_cases:\n",
    "    for model_key in MODELS.keys():\n",
    "        model_names = names[model_key]\n",
    "        if model_key == \"UM\":\n",
    "            continue\n",
    "        elif model_key == \"LMDG\":\n",
    "            indexers = {model_names.y: slice(1, -1), model_names.x: slice(None, -1)}\n",
    "        else:\n",
    "            indexers = {model_names.y: slice(1, -1)}\n",
    "\n",
    "        ds = datasets[THAI_case][model_key]\n",
    "        new_ds = {}\n",
    "        for d in ds.data_vars:\n",
    "            vrbl = ds[d]\n",
    "            if (model_names.y in vrbl.dims) and (model_names.x in vrbl.dims):\n",
    "                # print(model_key, vrbl.name)\n",
    "                vrbl = vrbl.isel(**indexers).interp(\n",
    "                    coords={\n",
    "                        model_names.y: new_lats,\n",
    "                        model_names.x: new_lons,\n",
    "                    },\n",
    "                    kwargs={\"fill_value\": \"extrapolate\"},\n",
    "                )\n",
    "                new_ds[d] = vrbl\n",
    "        datasets[THAI_case][model_key] = xr.Dataset(new_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_coord_system(cube, model_key, radius):\n",
    "    model_names = names[model_key]\n",
    "    _coord_system = iris.coord_systems.GeogCS(semi_major_axis=radius, longitude_of_prime_meridian=0)\n",
    "    for dim, standard_name in zip([\"x\", \"y\"], [\"longitude\", \"latitude\"]):\n",
    "        dim_name = getattr(model_names, dim)\n",
    "        try:\n",
    "            cube.coord(dim_name).units = \"degrees\"\n",
    "            cube.coord(dim_name).rename(standard_name)\n",
    "            cube.coord(dim_name).coord_system = _coord_system\n",
    "        except iris.exceptions.CoordinateNotFoundError:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def sf_const_factor(ds, model_key, const):\n",
    "    \"\"\"Combine constants for the mass streamfunction.\"\"\"\n",
    "    model_names = names[model_key]\n",
    "    # Convert constants to iris cubes to preserve units\n",
    "    RPLANET = iris.cube.Cube(const.rplanet_m, units=\"m\")\n",
    "    lat_cos_cube = np.cos(np.deg2rad(ds[model_names.y])).to_iris()\n",
    "    lat_cos_cube.rename(model_names.y)\n",
    "    if model_key == \"LMDG\":\n",
    "        lat_cos_cube.coord(\"North latitude\").rename(model_names.y)\n",
    "    lat_cos_cube.units = \"1\"\n",
    "    add_coord_system(lat_cos_cube, model_key, float(RPLANET.data))\n",
    "    # Constant factor outside the integral\n",
    "    return 2 * np.pi * RPLANET * lat_cos_cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "@ameta.update_metadata(name=\"tidally_locked_streamfunction\", units=\"1e11 kg s-1\")\n",
    "def tl_streamfunction(ds, model_key, const, truncation=None):\n",
    "    model_names = names[model_key]\n",
    "    # Convert constants to iris cubes to preserve units\n",
    "    RGAS = iris.cube.Cube(const.rgas, units=\"J kg-1 K-1\")\n",
    "    RPLANET = iris.cube.Cube(const.rplanet_m, units=\"m\")\n",
    "    GPLANET = iris.cube.Cube(const.gplanet, units=\"m s-2\")\n",
    "    # Convert data arrays to iris cubes\n",
    "    u_cube = ds[model_names.u].to_iris()\n",
    "    v_cube = ds[model_names.v].to_iris()\n",
    "    temp_cube = ds[model_names.temp].to_iris()\n",
    "    pres_cube = ds[model_names.pres].to_iris()\n",
    "    pres_cube.convert_units(\"Pa\")\n",
    "    rho_cube = pres_cube / (RGAS * temp_cube)\n",
    "    # Calculate divergent components of the wind\n",
    "    vec = VectorWind(u_cube, v_cube, rsphere=float(RPLANET.data))\n",
    "    u_cube, v_cube = vec.irrotationalcomponent(truncation=truncation)\n",
    "    # Make the lon/lat coordinates CF-compliant\n",
    "    for cube in [u_cube, v_cube, rho_cube]:\n",
    "        if model_key == \"LMDG\":\n",
    "            try:\n",
    "                cube.coord(\"North latitude\").rename(model_names.y)\n",
    "                cube.coord(\"East longitude\").rename(model_names.x)\n",
    "            except iris.exceptions.CoordinateNotFoundError:\n",
    "                pass\n",
    "        add_coord_system(cube, model_key, float(RPLANET.data))\n",
    "    # Transform to tidally-locked coordinates\n",
    "    _, v_tl_cube = acalc.rotate_winds_to_tidally_locked_coordinates(u_cube, v_cube)\n",
    "    v_tl_cube = acalc.regrid_to_tidally_locked_coordinates(v_tl_cube)\n",
    "    v_tl_zm = acalc.zonal_mean(v_tl_cube)\n",
    "    rho_tl_zm = acalc.zonal_mean(acalc.regrid_to_tidally_locked_coordinates(rho_cube))\n",
    "    # Calculate the integral\n",
    "    if model_key == \"ExoCAM\":\n",
    "        # alt_cube = ds[model_names.z].to_iris()\n",
    "        # add_coord_system(alt_cube, model_key, float(rplanet.data))\n",
    "        # alt_tl_zm = acalc.zonal_mean(acalc.regrid_to_tidally_locked_coordinates(alt_cube))\n",
    "        # dzdp = acalc.deriv(alt_tl_zm, \"atmosphere_hybrid_sigma_pressure_coordinate\")\n",
    "        sf_tl = -1 * acalc.cumsum(v_tl_zm, \"z\", True) / GPLANET\n",
    "    elif model_key == \"LMDG\":\n",
    "        sf_tl = acalc.cumsum(v_tl_zm * rho_tl_zm, \"z\", True)\n",
    "    elif model_key == \"ROCKE3D\":\n",
    "        # ysub = slice(None, None)\n",
    "        alt_cube = ds[model_names.z].to_iris()\n",
    "        add_coord_system(alt_cube, model_key, float(RPLANET.data))\n",
    "        alt_tl_zm = acalc.zonal_mean(acalc.regrid_to_tidally_locked_coordinates(alt_cube))\n",
    "        dzdp = acalc.deriv(alt_tl_zm, model_names.lev)\n",
    "        sf_tl = acalc.cumsum(v_tl_zm * rho_tl_zm * dzdp, \"z\", True)\n",
    "    elif model_key == \"UM\":\n",
    "        sf_tl = acalc.cumsum(v_tl_zm * rho_tl_zm, \"z\", True)\n",
    "    sf_tl *= sf_const_factor(ds, model_key, const)\n",
    "    return sf_tl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "VRBLS = {}\n",
    "for THAI_case in THAI_cases:\n",
    "    VRBLS[THAI_case] = {}\n",
    "    for model_key in LOAD_CONF.keys():\n",
    "        model_names = names[model_key]\n",
    "        VRBLS[THAI_case][model_key] = {}\n",
    "        VRBLS[THAI_case][model_key][\"sf_tl\"] = tl_streamfunction(\n",
    "            datasets[THAI_case][model_key], model_key, const=const\n",
    "        )\n",
    "        _pres = datasets[THAI_case][model_key][model_names.pres].to_iris()\n",
    "        _pres.convert_units(\"hPa\")\n",
    "        if model_key == \"LMDG\":\n",
    "            _pres.coord(\"North latitude\").rename(model_names.y)\n",
    "            _pres.coord(\"East longitude\").rename(model_names.x)\n",
    "        add_coord_system(_pres, model_key, const.rplanet_m)\n",
    "        VRBLS[THAI_case][model_key][\"mean_pres\"] = acalc.spatial_mean(_pres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "kw_plt = dict(levels=linspace_pm1(6) * 6, cmap=\"PuOr_r\", extend=\"both\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tl_ticks = np.arange(-90, 91, 30)\n",
    "tl_ticklabels = [f\"{i}\" for i in tl_ticks]\n",
    "tl_ticklabels[0] += \"\\nAS\"\n",
    "tl_ticklabels[-1] += \"\\nSS\"\n",
    "tl_ticklabels[tl_ticklabels.index(\"0\")] += \"\\nT\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved to ../plots/Ben1__tl_sf.png\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 3840x540 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncols = 4\n",
    "nrows = 1\n",
    "fig, axs = plt.subplots(ncols=ncols, nrows=nrows, figsize=(6.4 * ncols, 3.6 * nrows), squeeze=False)\n",
    "\n",
    "iletters = aplt.subplot_label_generator()\n",
    "for (model_key, model_dict), ax in zip(MODELS.items(), axs.flat):\n",
    "    model_names = names[model_key]\n",
    "    cube = VRBLS[THAI_case][model_key][\"sf_tl\"]\n",
    "    ypoints = cube.coord(model_names.y).points\n",
    "    zpoints = VRBLS[THAI_case][model_key][\"mean_pres\"].data\n",
    "    arr = cube.data\n",
    "    cntrf = ax.contourf(ypoints, zpoints, arr, **kw_plt)\n",
    "    ax.set_title(f\"{next(iletters)}\", **KW_SBPLT_LABEL)\n",
    "    ax.set_title(model_dict[\"title\"], **KW_MAIN_TTL)\n",
    "    ax.set_ylim(1000, 0)\n",
    "    ax.set_xlim(-90, 90)\n",
    "    ax.set_xticks(tl_ticks)\n",
    "    ax.set_xticklabels(tl_ticklabels)\n",
    "    ax.invert_xaxis()  # so that clockwise is positive\n",
    "    if ax.is_first_col():\n",
    "        ax.set_ylabel(\"Pressure [$hPa$]\", fontsize=\"x-large\")\n",
    "    if ax.is_last_row():\n",
    "        ax.set_xlabel(\"Tidally locked latitude [$\\degree$]\", fontsize=\"x-large\")\n",
    "    # ax.set_yscale(\"symlog\")\n",
    "\n",
    "    ax.add_artist(\n",
    "        AnchoredText(\n",
    "            f\"$\\Psi^{{TL}}_{{max}}$={float(arr.max()):.1f}\",\n",
    "            loc=\"upper right\",\n",
    "            prop=dict(size=\"large\"),\n",
    "        )\n",
    "    )\n",
    "cbar = fig.colorbar(cntrf, ax=axs, pad=0.01, orientation=\"vertical\", aspect=20)\n",
    "cbar.ax.set_ylabel(\"Streamfunction [$10^{11}$ $kg$ $s^{-1}$]\")\n",
    "cbar.set_ticks(linspace_pm1(6) * 6)\n",
    "fig.suptitle(f\"{THAI_case}\", fontsize=\"xx-large\", fontweight=\"bold\", x=0.45, y=1.1)\n",
    "figsave(fig, mypaths.plotdir / f\"{THAI_case}__tl_sf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# lons = v_tl_cube.coord(model_names.x).points\n",
    "# lats = v_tl_cube.coord(model_names.y).points\n",
    "# lons2d, lats2d = np.meshgrid(lons, lats)\n",
    "# skip = slice(None, None, 1), slice(None, None, 1)\n",
    "\n",
    "# iz = 35\n",
    "# plt.subplot(211)\n",
    "# plt.title(u_cube.coord(\"level\").points[iz])\n",
    "# plt.contourf(\n",
    "#     lons,\n",
    "#     lats,\n",
    "#     (u_cube.data[iz, ...][ysub, :] ** 2 + v_cube.data[iz, ...][ysub, :] ** 2) ** 0.5,\n",
    "# )\n",
    "# plt.colorbar()\n",
    "# plt.quiver(\n",
    "#     lons2d[skip],\n",
    "#     lats2d[skip],\n",
    "#     u_cube.data[iz, ...][ysub, :][skip],\n",
    "#     v_cube.data[iz, ...][ysub, :][skip],\n",
    "# )\n",
    "\n",
    "# plt.subplot(212)\n",
    "# plt.contourf(\n",
    "#     lons, lats, (u_tl_cube.data[iz, ...] ** 2 + v_tl_cube.data[iz, ...] ** 2) ** 0.5\n",
    "# )\n",
    "# plt.colorbar()\n",
    "# plt.quiver(\n",
    "#     lons2d[skip],\n",
    "#     lats2d[skip],\n",
    "#     u_tl_cube.data[iz, ...][skip],\n",
    "#     v_tl_cube.data[iz, ...][skip],\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # Load data\n",
    "# datasets = {}  # Create an empty dictionary to store all data\n",
    "# # Loop over THAI cases (this loop can swapped places with the loop below if needed)\n",
    "# for THAI_case in [THAI_case]:\n",
    "#     # for each of the THAI cases, create a nested directory for models\n",
    "#     datasets[THAI_case] = {}\n",
    "#     for model_key in MODELS.keys():\n",
    "#         model_names = names[model_key]\n",
    "#         ds = xr.open_dataset(mypaths.datadir / model_key / f\"{THAI_case}_time_mean_{model_key}.nc\")\n",
    "#         if model_key in [\"ExoCAM\", \"LMDG\", \"ROCKE3D\"]:\n",
    "#             # Regrid ExoCAM, LMD-G and ROCKE-3D data to be compatible with `windspharm`:\n",
    "#             # if latitudes are equally-spaced and even-numbered, they should not include poles.\n",
    "#             nlat = 50  # new number of latitudes: 50\n",
    "#             delta_lat = 180 / nlat\n",
    "#             new_lats = np.linspace(90 - 0.5 * delta_lat, -90 + 0.5 * delta_lat, nlat)\n",
    "#         else:\n",
    "#             new_lats = None\n",
    "#         if model_key in [\"ExoCAM\", \"LMDG\"]:\n",
    "#             lon_slice = slice(None, -1)  # omit the 180E longitude which is repeated\n",
    "#         else:\n",
    "#             lon_slice = slice(None, None)\n",
    "#         ds_regr = {}\n",
    "#         for d in ds.data_vars:\n",
    "#             vrbl = ds[d]\n",
    "#             if model_names.x in ds[d].dims:\n",
    "#                 vrbl = vrbl.isel(**{model_names.x: lon_slice})\n",
    "#             if (model_names.y in ds[d].dims) and (new_lats is not None):\n",
    "#                 vrbl = vrbl.isel(**{model_names.y: slice(1, -1)}).interp(\n",
    "#                     **{\n",
    "#                         model_names.y: new_lats,\n",
    "#                         \"kwargs\": {\"fill_value\": \"extrapolate\"},\n",
    "#                     },\n",
    "#                 )\n",
    "#             ds_regr[d] = vrbl\n",
    "#         datasets[THAI_case][model_key] = xr.Dataset(ds_regr)\n",
    "#         del ds\n",
    "#     datasets[THAI_case][\"ExoCAM\"][names.exocam.lev].attrs.update(units=\"hPa\")\n",
    "#     datasets[THAI_case][\"ROCKE3D\"][names.rocke3d.pres].attrs.update(units=\"hPa\")\n",
    "#     datasets[THAI_case][\"ROCKE3D\"][names.rocke3d.lev].attrs.update(positive=\"up\", units=\"1\")"
   ]
  }
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