heatbudget_debug_github.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2c7d6d9-3f68-4938-a542-1f143e6cd6e4",
   "metadata": {},
   "source": [
    "### Compute time weights to obtain the time mean from the monthly files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79e98f15-ccab-4313-82be-d7fdede42c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights=ds.time.dt.days_in_month/ds.time.dt.days_in_month.sum()\n",
    "weights=weights.compute()\n",
    "weights.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0089c690-0012-4c30-b23f-1021438098b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For 0.1 degree POP output\n",
    "#ds['DZBC']=xr.broadcast(ds.dz,ds.TLONG)[0]\n",
    "#ds['DZBC']=ds['DZBC'][-1,:,:]\n",
    "#dzu, dzt = pop_tools.grid.calc_dzu_dzt(ds)\n",
    "#ds['DZU']=dzu; ds['DZT']=dzt\n",
    "\n",
    "# For 1 deg POP output\n",
    "ds['DZU']=xr.broadcast(ds.dz,ds.TLONG)[0]; ds['DZT']=ds['DZU']\n",
    "\n",
    "ds.DZT.attrs[\"long_name\"] = \"Thickness of T cells\"\n",
    "ds.DZT.attrs[\"units\"] = \"centimeter\"\n",
    "ds.DZT.attrs[\"grid_loc\"] = \"3111\"\n",
    "ds.DZU.attrs[\"long_name\"] = \"Thickness of U cells\"\n",
    "ds.DZU.attrs[\"units\"] = \"centimeter\"\n",
    "ds.DZU.attrs[\"grid_loc\"] = \"3221\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1f31ecd4-3ccf-4ac5-9fcd-26f7a8031687",
   "metadata": {},
   "outputs": [],
   "source": [
    "volfile=Path(common_path + 'popvol.nc')\n",
    "\n",
    "if volfile.is_file():\n",
    "    ds_VOL=xr.open_dataset(volfile)\n",
    "    VOL=ds_VOL.VOL\n",
    "else:    \n",
    "# make sure we have the cell volumne for calculations\n",
    "    VOL = (ds.DZT * ds.DXT * ds.DYT).compute()\n",
    "    KMT = ds.KMT.compute()\n",
    "\n",
    "    for j in tqdm(range(len(KMT.nlat))):\n",
    "        for i in range(len(KMT.nlon)):\n",
    "            k = KMT.values[j, i].astype(int)\n",
    "            VOL.values[k:, j, i] = 0.0\n",
    "    VOL.to_dataset(name='VOL').to_netcdf(volfile,mode='w')\n",
    "            \n",
    "ds[\"VOL\"] = VOL\n",
    "\n",
    "ds.VOL.attrs[\"long_name\"] = \"volume of T cells\"\n",
    "ds.VOL.attrs[\"units\"] = \"centimeter^3\"\n",
    "\n",
    "ds.VOL.attrs[\"grid_loc\"] = \"3111\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "504a302b-7c57-4887-9fea-4ef789b0aab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 235 ms, sys: 42.1 ms, total: 277 ms\n",
      "Wall time: 258 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "metrics = {\n",
    "    (\"X\",): [\"DXU\", \"DXT\"],  # X distances\n",
    "    (\"Y\",): [\"DYU\", \"DYT\"],  # Y distances\n",
    "    (\"Z\",): [\"DZU\", \"DZT\"],  # Z distances\n",
    "    (\"X\", \"Y\"): [\"UAREA\", \"TAREA\"],\n",
    "}\n",
    "\n",
    "# here we get the xgcm compatible dataset\n",
    "gridxgcm, dsxgcm = pop_tools.to_xgcm_grid_dataset(\n",
    "    ds,\n",
    "    periodic=['X','Y'],\n",
    "    metrics=metrics,\n",
    "#    boundary={\"X\":\"extend\",\"Y\":\"extend\",\"Z\": \"extend\"},\n",
    "    boundary={\"Z\": \"extend\"}\n",
    ") \n",
    "\n",
    "for coord in [\"nlat\", \"nlon\"]:\n",
    "    if coord in dsxgcm.coords:\n",
    "        dsxgcm = dsxgcm.drop_vars(coord)\n",
    "        \n",
    "#ds.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "633a94d0-bf51-4503-9e44-c1c39bc4fea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "vol=dsxgcm.VOL.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0824ebcb-1152-49e4-8228-becab486b265",
   "metadata": {},
   "source": [
    "### Compute the time means of all the terms in the budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "251ee477-4205-4355-82e3-97b93c0f053a",
   "metadata": {},
   "outputs": [],
   "source": [
    "uet_tmean=(dsxgcm.UET*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "054eb833-7b52-4b81-b3a3-2b3cfef29860",
   "metadata": {},
   "outputs": [],
   "source": [
    "vnt_tmean=(dsxgcm.VNT*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39d66fa3-c3c3-4e32-8210-dbc774686eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "wtt_tmean=(dsxgcm.WTT*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0bc3e5a2-48c6-45e7-b02e-b0dd88079a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "diapyc_tmean=(dsxgcm.DIA_IMPVF_TEMP*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "94221521-816d-4f1f-836a-517b9db36019",
   "metadata": {},
   "outputs": [],
   "source": [
    "kppnl_tmean=(dsxgcm.KPP_SRC_TEMP*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d3e16446-5a75-4916-acad-b6cfcd8e80ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdife_tmean=(dsxgcm.HDIFE_TEMP*weights).sum(dim='time',skipna=True).persist()\n",
    "hdifn_tmean=(dsxgcm.HDIFN_TEMP*weights).sum(dim='time',skipna=True).persist()\n",
    "hdifb_tmean=(dsxgcm.HDIFB_TEMP*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b977c2c4-8275-494b-aee1-b8c52d179aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "shf_tmean=(dsxgcm.SHF*weights).sum(dim='time',skipna=True).persist()\n",
    "#shf_tmean.to_dataset(name='shf').to_netcdf(savedir+'shf.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f49bbe45-d95d-4c7e-a2c2-be70a877e4a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "qflux_minus_shfsw_tmean=((dsxgcm.QFLUX-dsxgcm.SHF_QSW)*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "87738636-8c7b-4dcc-a40a-649fe9bd771b",
   "metadata": {},
   "outputs": [],
   "source": [
    "qsw3d_tmean=(dsxgcm.QSW_3D*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9952de37-51ea-4be9-9ad5-7541aa106da6",
   "metadata": {},
   "outputs": [],
   "source": [
    "wtt_GM_tmean=(dsxgcm.WTT_ISOP*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fc655de7-fd2d-48b1-b3d9-1b8d039d77cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "wtt_submeso_tmean=(dsxgcm.WTT_SUBM*weights).sum(dim='time',skipna=True).persist()\n",
    "#submeso_tmean.to_dataset(name='submeso').to_netcdf(savedir+'submeso_3d.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "119011a3-7525-49af-931a-ea4b52ee34e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Ttend_tmean=(dsxgcm.TEND_TEMP*weights).sum(dim='time',skipna=True).persist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adfae37c-637c-4549-a823-5e005dfd2211",
   "metadata": {},
   "source": [
    "### Create array for storing time-averaged budget terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "091fe779-7600-4b78-85ac-78d1f0d439e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "budget=xr.Dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db9dc0b-ff99-4329-8f57-441905cb0cf3",
   "metadata": {},
   "source": [
    "#### Total tendency (saved term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2b01eb68-a912-416a-8d9e-52b73772d482",
   "metadata": {},
   "outputs": [],
   "source": [
    "budget['TEND_TEMP'] = Ttend_tmean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a21b3b-2cf3-47ee-a8d0-df868f68beb4",
   "metadata": {},
   "source": [
    "#### Divergence of resolved advective fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cff1181d-e213-40a7-a6d0-62bc7c400cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "budget[\"UET\"] = -(gridxgcm.diff(uet_tmean * dsxgcm.VOL.values, axis=\"X\") / dsxgcm.VOL)\n",
    "budget[\"VNT\"] = -(gridxgcm.diff(vnt_tmean * dsxgcm.VOL.values, axis=\"Y\") / dsxgcm.VOL)\n",
    "budget[\"WTT\"] = (\n",
    "    gridxgcm.diff(wtt_tmean.fillna(0) * (dsxgcm.dz * dsxgcm.DXT * dsxgcm.DYT).values, axis=\"Z\")\n",
    "    / dsxgcm.VOL\n",
    ")\n",
    "budget[\"TOT_ADV\"] = budget[\"UET\"] + budget[\"VNT\"] + budget[\"WTT\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2596a07-38b7-4c8a-ab73-af9afe0ae94a",
   "metadata": {},
   "source": [
    "#### Local and nonlocal terms in KPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2f07dac3-5df7-46f0-963c-b00724db0421",
   "metadata": {},
   "outputs": [],
   "source": [
    "budget[\"DIA_IMPVF_TEMP\"] = -(\n",
    "    gridxgcm.diff(diapyc_tmean * dsxgcm.TAREA, axis=\"Z\") / dsxgcm.VOL\n",
    ")\n",
    "\n",
    "# set surface flux at 0th layer\n",
    "SRF_TEMP_FLUX = (shf_tmean + qflux_minus_shfsw_tmean) * dsxgcm.hflux_factor\n",
    "\n",
    "budget[\"DIA_IMPVF_TEMP\"][0, :, :] = (\n",
    "    SRF_TEMP_FLUX * dsxgcm.TAREA - diapyc_tmean.isel(z_w_bot=0) * dsxgcm.TAREA\n",
    ") / dsxgcm.VOL.values[0, :, :]\n",
    "\n",
    "budget[\"KPP_SRC_TMP\"] = kppnl_tmean\n",
    "\n",
    "budget[\"VDIF\"] = budget[\"DIA_IMPVF_TEMP\"] + budget[\"KPP_SRC_TMP\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b08a809a-36f0-4c82-971a-1956a1cd8b27",
   "metadata": {},
   "source": [
    "#### Divergence of vertical fluxes arising from Redi isopycnal stirring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0fc03599-7c39-4886-aa2f-07d2d320c266",
   "metadata": {},
   "outputs": [],
   "source": [
    "budget[\"HDIFE_TEMP\"] = gridxgcm.diff(hdife_tmean * dsxgcm.VOL.values, axis=\"X\") / dsxgcm.VOL\n",
    "budget[\"HDIFN_TEMP\"] = gridxgcm.diff(hdifn_tmean * dsxgcm.VOL.values, axis=\"Y\") / dsxgcm.VOL\n",
    "\n",
    "budget[\"HDIFB_TEMP\"] = -(\n",
    "    gridxgcm.diff(hdifb_tmean * dsxgcm.TAREA, axis=\"Z\") / dsxgcm.VOL\n",
    ")\n",
    "budget[\"HDIFB_TEMP\"][0, :, :] = (\n",
    "    0 - hdifb_tmean.isel(z_w_bot=0) * dsxgcm.TAREA\n",
    ") / dsxgcm.VOL.values[0, :, :]\n",
    "\n",
    "budget[\"HDIF\"] = budget[\"HDIFE_TEMP\"] + budget[\"HDIFN_TEMP\"] + budget[\"HDIFB_TEMP\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c0e960-dda0-4d94-ba9e-3224c20264d7",
   "metadata": {},
   "source": [
    "#### Divergence of vertical fluxes from submesoscale and GM parameterizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "31ef5788-16a4-4ded-98d7-606a73e6d7fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "budget[\"SUBMESO\"] = (\n",
    "    gridxgcm.diff(wtt_submeso_tmean.fillna(0) * (dsxgcm.dz * dsxgcm.DXT * dsxgcm.DYT).values, axis=\"Z\")\n",
    "    / dsxgcm.VOL )\n",
    "    \n",
    "budget[\"GM\"] = (\n",
    "    gridxgcm.diff(wtt_GM_tmean.fillna(0) * (dsxgcm.dz * dsxgcm.DXT * dsxgcm.DYT).values, axis=\"Z\")\n",
    "    / dsxgcm.VOL )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c93bd8-50af-44c6-b53a-7d9fd68ae310",
   "metadata": {},
   "source": [
    "#### Solar penetration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c55350ac-c24a-4f39-a97e-cbf68a53dd1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "budget[\"QSW_3D\"] = -gridxgcm.diff((qsw3d_tmean * dsxgcm.hflux_factor), axis=\"Z\") / dsxgcm.DZT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c99bbd-8a55-4c10-83b3-5f6e2131c6d5",
   "metadata": {},
   "source": [
    "#### Select (lon,lat) for plotting budget terms at one location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "93b14624-faaa-46b7-bcc9-0fea715849cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = budget.isel(nlon_t=200, nlat_t=180) * (86400*365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "47938478-cb46-4cb2-88f4-162b439b9423",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.42 s, sys: 468 ms, total: 1.89 s\n",
      "Wall time: 3.22 s\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "subset['z_t'] = ds.z_t / m2cm\n",
    "total_rhs = subset.QSW_3D + subset.HDIF + subset.VDIF + subset.TOT_ADV \n",
    "\n",
    "ymin = 300\n",
    "\n",
    "plt.rc('xtick',labelsize=15)\n",
    "plt.rc('ytick',labelsize=15)\n",
    "plt.rc('figure',titlesize=18)\n",
    "mpl.rcParams['lines.linewidth'] = 3\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(13, 6), sharey=True)\n",
    "\n",
    "# plot individual components\n",
    "plt.grid()\n",
    "subset.VDIF.plot(y=\"z_t\", ylim=(ymin, 0), color='b',label=\"VDIF\", ax=ax[0])\n",
    "subset.HDIF.plot(y=\"z_t\", ylim=(ymin, 0), color='b',label=\"HDIFB\",ls='--', ax=ax[0])\n",
    "subset.QSW_3D.plot(y=\"z_t\", ylim=(ymin, 0), color='r', label=\"QSW_3D\", ax=ax[0])\n",
    "subset.TOT_ADV.plot(y=\"z_t\", ylim=(ymin, 0), color='m',label=\"ADV\", ax=ax[0])\n",
    "#subset.SUBMESO.plot(y=\"z_t\", ylim=(ymin, 0), color='g',label=\"SUBM\", ax=ax[0])\n",
    "#subset.GM.plot(y=\"z_t\", ylim=(ymin, 0), color='c',label=\"GM\", ax=ax[0])\n",
    "# plot sum\n",
    "total_rhs.plot(y=\"z_t\", ylim=(ymin, 0), color='k', label=\"SUM\", ls=\"--\", ax=ax[0] )\n",
    "ax[0].grid(axis='both')\n",
    "ax[0].set_title('POP heat budget',fontsize=16)\n",
    "ax[0].set_xlabel('degC / year',fontsize=16)\n",
    "ax[0].set_ylabel('depth (m)',fontsize=16)\n",
    "leg=ax[0].legend(fontsize=12)\n",
    "leg.get_frame().set_edgecolor('k')\n",
    "\n",
    "# Compare sum of terms on RHS and TEND_TEMP (saved online)\n",
    "plt.grid()\n",
    "total_rhs.plot(y=\"z_t\", ylim=(ymin, 0), color='k', label=\"SUM\", ls=\"--\", ax=ax[1] )\n",
    "# plot tendency\n",
    "subset.TEND_TEMP.plot(y=\"z_t\", ylim=(ymin, 0), color='k', label=\"TEND_TEMP\", ax=ax[1])\n",
    "ax[1].grid(axis='both')\n",
    "ax[1].set_title('Computed vs saved tendency',fontsize=16)\n",
    "ax[1].set_xlabel('degC / year',fontsize=16)\n",
    "ax[1].set_ylabel('',fontsize=16)\n",
    "leg=ax[1].legend(fontsize=12,loc='lower left',edgecolor='k');\n",
    "leg.get_frame().set_edgecolor('k')\n",
    "\n",
    "fig.set_facecolor(\"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90c26524-e5bd-4991-a279-1a6bf5aa66e7",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}