ocean_transport.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this notebook, we will calculate the change in oceanic heat flux convergence\n",
    "\n",
    "# By: Ty Janoski\n",
    "# edited: 06.02.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import statements\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import dask.array as da\n",
    "import xesmf as xe\n",
    "from dask.diagnostics import ProgressBar\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MIROC-ESM\n",
      "0\n",
      "-1788 -1668\n",
      "[########################################] | 100% Completed |  2.5s\n",
      "1\n",
      "-1668 -1548\n",
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      "2\n",
      "-1548 -1428\n",
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      "3\n",
      "-1428 -1308\n",
      "[########################################] | 100% Completed |  2.5s\n",
      "4\n",
      "-1308 -1188\n",
      "[########################################] | 100% Completed |  7.5s\n",
      "[########################################] | 100% Completed |  0.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tylerj/miniconda3/envs/xesmf_env/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MIROC-ESM\n",
      "0\n",
      "[########################################] | 100% Completed |  2.5s\n",
      "1\n",
      "[########################################] | 100% Completed |  2.6s\n",
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      "[########################################] | 100% Completed |  2.6s\n"
     ]
    }
   ],
   "source": [
    "# List to store the climatology\n",
    "# excluded models: bcc-csm1-1, GFDL-ESM2G, IPSL-CM5B-LR, FGOALS-s2, CanESM2, inmcm4, IPSL-CM5A-LR\n",
    "\n",
    "models = ['MIROC-ESM']\n",
    "for mod in models:\n",
    "    hold = []\n",
    "    print(mod)\n",
    "    for i in range(5):\n",
    "        print(i)\n",
    "        start = -600 + (i*120)\n",
    "        end = start+120\n",
    "        if(i==4):\n",
    "            end = None\n",
    "        if mod in ['ACCESS1-0','ACCESS1-3','CNRM-CM5']:\n",
    "            # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "            mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_pic.nc',\n",
    "                                 use_cftime=True).mlotst.isel(time=slice(start,end)).load()\n",
    "            # read potential temperature from last 50 years of piControl\n",
    "            thetao = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/thetao_Omon_'+mod+'_*.nc',\n",
    "                                      parallel=True,combine='by_coords',\n",
    "                                  use_cftime=True).thetao.isel(time=slice(start,end))\n",
    "            if(i==4):\n",
    "                hfds = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/hfds_Omon_'+mod+'_*.nc',\n",
    "                                        parallel=True,combine='by_coords',\n",
    "                                        use_cftime=True).hfds.isel(time=slice(-600,None))\n",
    "        elif mod in ['MIROC-ESM']:\n",
    "            # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "            mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_pic.nc',\n",
    "                                 use_cftime=True).mlotst.isel(time=slice(start,end)).load()\n",
    "            if(i==4):\n",
    "                end=0\n",
    "            print(start-1188,end-1188)\n",
    "            # read potential temperature from last 50 years of piControl\n",
    "            thetao = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/thetao_Omon_'+mod+'_*.nc',\n",
    "                                      parallel=True,combine='by_coords',\n",
    "                                  use_cftime=True).thetao.isel(time=slice(start-1188,end-1188))\n",
    "            if(i==4):\n",
    "                hfds = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/hfds_Omon_'+mod+'_*.nc',\n",
    "                                        parallel=True,combine='by_coords',\n",
    "                                        use_cftime=True).hfds.isel(time=slice(-600-1188,0-1188))\n",
    "            mlotst['time'] = thetao.time\n",
    "        # FGOALS-g2 time axis isn't formatted correctly, so we can't decode the time axis\n",
    "        elif mod in ['FGOALS-g2']:\n",
    "            # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "            mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_pic.nc',\n",
    "                                 decode_times=False).mlotst.isel(time=slice(start,end))\n",
    "            # read potential temperature from last 50 years of piControl\n",
    "            thetao = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/thetao_Omon_'+mod+'_*.nc',\n",
    "                                      parallel=True,combine='by_coords',\n",
    "                                  decode_times=False).thetao.isel(time=slice(start,end))\n",
    "            if(i==4):\n",
    "                hfds = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/hfds_Omon_'+mod+'_*.nc',\n",
    "                                          parallel=True,combine='by_coords',\n",
    "                                      decode_times=False).hfds.isel(time=slice(-600,None))\n",
    "        else:\n",
    "            # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "            mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_pic.nc',\n",
    "                                 use_cftime=True).mlotst.isel(time=slice(start,end))\n",
    "            path = glob.glob('/dx07/tylerj/CMIP5_output/piControl/thetao_Omon_'+mod+'*.nc')[0]\n",
    "            thetao = xr.open_dataset(path,use_cftime=True).thetao.isel(\n",
    "                time=slice(start,end))\n",
    "            if(i==4):\n",
    "                path = glob.glob('/dx07/tylerj/CMIP5_output/piControl/hfds_Omon_'+mod+'*.nc')[0]\n",
    "                hfds = xr.open_dataset(path,use_cftime=True).hfds.isel(\n",
    "                    time=slice(-600,None))\n",
    "\n",
    "\n",
    "        # Next: establish which points are actually includes in the previously-defined mixed-layer\n",
    "        mlotst_4D = xr.broadcast(mlotst,thetao)[0]\n",
    "        ml = (mlotst_4D.lev <= mlotst_4D).load()\n",
    "        del mlotst_4D\n",
    "        \n",
    "        start = -600 + (i*120)\n",
    "        end = start+120\n",
    "        if(i==4):\n",
    "            end = None\n",
    "\n",
    "        if mod in ['ACCESS1-0','ACCESS1-3','GFDL-CM3','MIROC5','MPI-ESM-LR','MPI-ESM-P','MIROC-ESM']:\n",
    "            # read in thkcello\n",
    "            thkcello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/thkcello_Omon_'+mod+'_*.nc',\n",
    "                                use_cftime=True, combine='by_coords').thkcello.isel(time=slice(start,end)).compute()\n",
    "            thkcello = thkcello.where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "\n",
    "\n",
    "        elif mod in ['BNU-ESM','CCSM4','FGOALS-s2','MRI-CGCM3','FGOALS-g2']:\n",
    "            volcello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/volcello_fx_'+mod+'*.nc',\n",
    "                                         use_cftime=True,combine='by_coords')['volcello'].load()\n",
    "            areacello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/areacello_fx_'+mod+'*.nc',\n",
    "                                          use_cftime=True,combine='by_coords')['areacello'].load()\n",
    "\n",
    "            thkcello = volcello/areacello\n",
    "            thkcello = xr.broadcast(thkcello,thetao)[0].where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "\n",
    "        elif mod in ['CNRM-CM5']:\n",
    "            thkcello = xr.open_dataset('/dx07/tylerj/CMIP5_output/piControl/thkcello_fx_CNRM-CM5_piControl_r0i0p0.nc').thkcello.load()\n",
    "            thkcello = xr.broadcast(thkcello,thetao)[0].where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "\n",
    "        elif mod in ['NorESM1-M']:\n",
    "            # NorESM1-M is a special case where they forgot to multiply the cell thickness by areacello to get volcello\n",
    "            # Therefore, volcello actually is thkcello\n",
    "\n",
    "            thkcello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/volcello_fx_'+mod+'*.nc',\n",
    "                                         use_cftime=True,combine='by_coords')['volcello'].load()\n",
    "\n",
    "            # We need to make weights so we can take the vertical average later\n",
    "            thkcello = xr.broadcast(thkcello,thetao)[0].where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "\n",
    "        elif mod in ['CSIRO-Mk3-6-0']:\n",
    "            # CSIRO-Mk3-6-0 has a climatological thkcello. We must tile it to match the length of thetao.\n",
    "\n",
    "            thkcello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/thkcello_Omon_'+mod+'*.nc',\n",
    "                                       decode_times=False,combine='by_coords')['thkcello'].load()\n",
    "\n",
    "            # We need to make weights so we can take the vertical average later\n",
    "            thkcello_with_time = xr.concat([thkcello for i in range(int(len(thetao.time)/12))],dim='time')\n",
    "            thkcello_with_time['time'] = thetao['time']\n",
    "            thkcello = thkcello_with_time.where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "        \n",
    "        with ProgressBar():\n",
    "            T_ml = (thetao * weights).sum(dim='lev').compute()\n",
    "        del thetao\n",
    "        T_ml = T_ml.where(T_ml!=0)\n",
    "        hold.append(T_ml)\n",
    "    T_ml = xr.concat(hold,dim='time')\n",
    "    # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "    mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_pic.nc',\n",
    "                             decode_times=False).mlotst.isel(time=slice(-600,None))\n",
    "    if mod in ['FGOALS-g2']:\n",
    "        T_ml['time'] = T_ml.time*86400\n",
    "        dMLHC = T_ml.differentiate(coord='time',edge_order=2) * 3895 * np.array(mlotst) * 1025\n",
    "        hfds['time'] = dMLHC.time\n",
    "    else:\n",
    "        dMLHC = T_ml.differentiate(coord='time',edge_order=2,datetime_unit='s') * 3895 * np.array(mlotst) * 1025\n",
    "    with ProgressBar():\n",
    "        if(mod!='GFDL-CM3'):\n",
    "            Fconv = (dMLHC - hfds).compute()\n",
    "        else:\n",
    "            Fconv = (dMLHC - np.array(hfds)).compute()\n",
    "    if mod in ['FGOALS-g2']:\n",
    "        Fconv['time'] = np.arange(0,600,1)\n",
    "        Fconv['month'] = Fconv.time%12\n",
    "        clim = Fconv.groupby('month').mean(dim='time')\n",
    "    else:\n",
    "        clim = Fconv.groupby(Fconv.time.dt.month).mean(dim='time')\n",
    "\n",
    "###################################################################################\n",
    "################# abrupt4xCO2 below this line #################################### \n",
    "###################################################################################\n",
    "    hold = []\n",
    "    print(mod)\n",
    "    for i in range(15):\n",
    "        print(i)\n",
    "        start = i * 120\n",
    "        end = start+120\n",
    "        if mod in ['ACCESS1-0','ACCESS1-3','CNRM-CM5','MIROC-ESM']:\n",
    "            # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "            mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_4xCO2.nc',\n",
    "                                 use_cftime=True).mlotst.isel(time=slice(start,end)).load()\n",
    "            # read potential temperature from last 50 years of piControl\n",
    "            thetao = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/abrupt4xCO2/thetao_Omon_'+mod+'_*.nc',\n",
    "                                      parallel=True,combine='by_coords',\n",
    "                                  use_cftime=True).thetao.isel(time=slice(start,end))\n",
    "            if(i==14):\n",
    "                hfds = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/abrupt4xCO2/hfds_Omon_'+mod+'_*.nc',\n",
    "                                        parallel=True,combine='by_coords',\n",
    "                                        use_cftime=True).hfds.isel(time=slice(None,1800))\n",
    "        # FGOALS-g2 time axis isn't formatted correctly, so we can't decode the time axis\n",
    "        elif mod in ['FGOALS-g2']:\n",
    "            # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "            mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_4xCO2.nc',\n",
    "                                 decode_times=False).mlotst.isel(time=slice(start,end))\n",
    "            # read potential temperature from last 50 years of piControl\n",
    "            thetao = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/abrupt4xCO2/thetao_Omon_'+mod+'_*.nc',\n",
    "                                      parallel=True,combine='by_coords',\n",
    "                                  decode_times=False).thetao.isel(time=slice(start,end))\n",
    "            if(i==14):\n",
    "                hfds = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/abrupt4xCO2/hfds_Omon_'+mod+'_*.nc',\n",
    "                                          parallel=True,combine='by_coords',\n",
    "                                      decode_times=False).hfds.isel(time=slice(None,1800))\n",
    "        else:\n",
    "            # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "            mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_4xCO2.nc',\n",
    "                                 use_cftime=True).mlotst.isel(time=slice(start,end))\n",
    "            path = glob.glob('/dx07/tylerj/CMIP5_output/abrupt4xCO2/thetao_Omon_'+mod+'*.nc')[0]\n",
    "            thetao = xr.open_dataset(path,use_cftime=True).thetao.isel(\n",
    "                time=slice(start,end))\n",
    "            if(i==14):\n",
    "                path = glob.glob('/dx07/tylerj/CMIP5_output/abrupt4xCO2/hfds_Omon_'+mod+'*.nc')[0]\n",
    "                hfds = xr.open_dataset(path,use_cftime=True).hfds.isel(\n",
    "                    time=slice(None,1800))\n",
    "\n",
    "\n",
    "        # Next: establish which points are actually includes in the previously-defined mixed-layer\n",
    "        mlotst_4D = xr.broadcast(mlotst,thetao)[0]\n",
    "        ml = (mlotst_4D.lev <= mlotst_4D).load()\n",
    "        del mlotst_4D\n",
    "\n",
    "        if mod in ['ACCESS1-0','ACCESS1-3','GFDL-CM3','MIROC5','MPI-ESM-LR','MPI-ESM-P','MIROC-ESM']:\n",
    "            # read in thkcello\n",
    "            thkcello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/abrupt4xCO2/thkcello_Omon_'+mod+'_*.nc',\n",
    "                                use_cftime=True, combine='by_coords').thkcello.isel(time=slice(start,end)).compute()\n",
    "            thkcello = thkcello.where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "            \n",
    "        elif mod in ['CNRM-CM5']:\n",
    "            thkcello = xr.open_dataset('/dx07/tylerj/CMIP5_output/piControl/thkcello_fx_CNRM-CM5_piControl_r0i0p0.nc').thkcello.load()\n",
    "            thkcello = xr.broadcast(thkcello,thetao)[0].where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "\n",
    "        elif mod in ['BNU-ESM','CCSM4','FGOALS-s2','MRI-CGCM3','FGOALS-g2']:\n",
    "            volcello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/volcello_fx_'+mod+'*.nc',\n",
    "                                         use_cftime=True,combine='by_coords')['volcello'].load()\n",
    "            areacello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/areacello_fx_'+mod+'*.nc',\n",
    "                                          use_cftime=True,combine='by_coords')['areacello'].load()\n",
    "\n",
    "            thkcello = volcello/areacello\n",
    "            thkcello = xr.broadcast(thkcello,thetao)[0].where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "\n",
    "\n",
    "        elif mod in ['NorESM1-M']:\n",
    "            # NorESM1-M is a special case where they forgot to multiply the cell thickness by areacello to get volcello\n",
    "            # Therefore, volcello actually is thkcello\n",
    "\n",
    "            thkcello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/volcello_fx_'+mod+'*.nc',\n",
    "                                         use_cftime=True,combine='by_coords')['volcello'].load()\n",
    "\n",
    "            # We need to make weights so we can take the vertical average later\n",
    "            thkcello = xr.broadcast(thkcello,thetao)[0].where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "\n",
    "        elif mod in ['CSIRO-Mk3-6-0']:\n",
    "            # CSIRO-Mk3-6-0 has a climatological thkcello. We must tile it to match the length of thetao.\n",
    "\n",
    "            thkcello = xr.open_mfdataset('/dx07/tylerj/CMIP5_output/piControl/thkcello_Omon_'+mod+'*.nc',\n",
    "                                       decode_times=False,combine='by_coords')['thkcello'].load()\n",
    "\n",
    "            # We need to make weights so we can take the vertical average later\n",
    "            thkcello_with_time = xr.concat([thkcello for i in range(int(len(thetao.time)/12))],dim='time')\n",
    "            thkcello_with_time['time'] = thetao['time']\n",
    "            thkcello = thkcello_with_time.where(ml)\n",
    "            del ml\n",
    "            weights = thkcello / thkcello.sum(dim='lev')\n",
    "            del thkcello\n",
    "        \n",
    "        with ProgressBar():\n",
    "            T_ml = (thetao * weights).sum(dim='lev').compute()\n",
    "        del thetao\n",
    "        T_ml = T_ml.where(T_ml!=0)\n",
    "        hold.append(T_ml)\n",
    "    T_ml = xr.concat(hold,dim='time')\n",
    "    # read in mixed-layer depth, as calculated in mixed_layer.ipynb\n",
    "    mlotst = xr.open_dataset('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_mlotst_4xCO2.nc',\n",
    "                             decode_times=False).mlotst.isel(time=slice(None,1800))\n",
    "    if mod in ['FGOALS-g2']:\n",
    "        T_ml['time'] = T_ml.time*86400\n",
    "        dMLHC = T_ml.differentiate(coord='time',edge_order=2) * 3895 * np.array(mlotst) * 1025\n",
    "        hfds['time'] = dMLHC.time\n",
    "    else:\n",
    "        dMLHC = T_ml.differentiate(coord='time',edge_order=2,datetime_unit='s') * 3895 * np.array(mlotst) * 1025\n",
    "    if mod in ['CCSM4']:\n",
    "        hfds['time'] = dMLHC.time\n",
    "    with ProgressBar():\n",
    "        if(mod!='GFDL-CM3'):\n",
    "            Fconv = (dMLHC - hfds).compute()\n",
    "        else:\n",
    "            Fconv = (dMLHC - np.array(hfds)).compute()\n",
    "    diff = Fconv - np.tile(clim,(150,1,1))\n",
    "    diff.to_netcdf('/dx07/tylerj/CMIP5_output/CMIP5_feedbacks/'+mod+'_ocean_conv.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'thkcello'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 0</li><li><span class='xr-has-index'>lev</span>: 44</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 256</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5ff1781a-3ad9-42ed-843f-890307594b1b' class='xr-array-in' type='checkbox' ><label for='section-5ff1781a-3ad9-42ed-843f-890307594b1b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span></span></div><pre class='xr-array-data'>array([], shape=(0, 44, 192, 256), dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-2eaa6f94-96ee-47eb-a8d6-5fe32cb0b3fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2eaa6f94-96ee-47eb-a8d6-5fe32cb0b3fb' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'></div><input id='attrs-428b1bd0-2054-41b2-b841-4d52243964d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-428b1bd0-2054-41b2-b841-4d52243964d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50a49158-6ba6-413a-8a14-2531102e0f22' class='xr-var-data-in' type='checkbox'><label for='data-50a49158-6ba6-413a-8a14-2531102e0f22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array([], dtype=object)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5 7.5 12.5 ... 5.275e+03 5.45e+03</div><input id='attrs-7a0ae849-875b-4a4e-ba8e-5da57eb5c0eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a0ae849-875b-4a4e-ba8e-5da57eb5c0eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db4b3d08-9a03-4cb3-914c-589a9fabc54a' class='xr-var-data-in' type='checkbox'><label for='data-db4b3d08-9a03-4cb3-914c-589a9fabc54a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>ocean sigma over z coordinate</dd><dt><span>standard_name :</span></dt><dd>ocean_sigma_z</dd><dt><span>formula :</span></dt><dd>for k &lt;= nsigma: z = eta + sigma*(min(depth_c,depth)+eta) ; for k &gt; nsigma: z = zlev</dd><dt><span>formula_terms :</span></dt><dd>sigma: sigma eta: eta depth: depth depth_c: depth_c nsigma: nsigma zlev: zlev</dd></dl></div><pre class='xr-var-data'>array([2.500e+00, 7.500e+00, 1.250e+01, 1.750e+01, 2.250e+01, 2.750e+01,\n",
       "       3.375e+01, 4.125e+01, 5.000e+01, 6.000e+01, 7.000e+01, 8.000e+01,\n",
       "       9.250e+01, 1.100e+02, 1.325e+02, 1.575e+02, 1.850e+02, 2.250e+02,\n",
       "       2.875e+02, 3.625e+02, 4.500e+02, 5.500e+02, 6.750e+02, 8.250e+02,\n",
       "       9.750e+02, 1.125e+03, 1.300e+03, 1.525e+03, 1.775e+03, 2.025e+03,\n",
       "       2.275e+03, 2.525e+03, 2.775e+03, 3.025e+03, 3.275e+03, 3.525e+03,\n",
       "       3.775e+03, 4.025e+03, 4.275e+03, 4.525e+03, 4.775e+03, 5.025e+03,\n",
       "       5.275e+03, 5.450e+03])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7031 2.109 3.516 ... 357.9 359.3</div><input id='attrs-d6c8b102-baa5-4da7-9919-684b368092ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6c8b102-baa5-4da7-9919-684b368092ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceece4a4-b8a0-4ba9-b3f8-03fe8810c72c' class='xr-var-data-in' type='checkbox'><label for='data-ceece4a4-b8a0-4ba9-b3f8-03fe8810c72c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><pre class='xr-var-data'>array([  0.703125,   2.109375,   3.515625, ..., 356.484375, 357.890625,\n",
       "       359.296875])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.14 -87.43 ... 87.43 89.14</div><input id='attrs-a8cda28e-6e98-460f-a7a5-90e29f7ec10f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8cda28e-6e98-460f-a7a5-90e29f7ec10f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10883ec1-fb3b-4c91-8fd7-0f279e49b372' class='xr-var-data-in' type='checkbox'><label for='data-10883ec1-fb3b-4c91-8fd7-0f279e49b372' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><pre class='xr-var-data'>array([-89.14444 , -87.433311, -85.872566, -84.462212, -83.056526, -81.655525,\n",
       "       -80.255852, -78.857513, -77.459747, -76.062538, -74.665627, -73.269012,\n",
       "       -71.872559, -70.476288, -69.080116, -67.684059, -66.288071, -64.892159,\n",
       "       -63.728931, -62.798389, -61.867851, -60.937336, -60.006847, -59.076359,\n",
       "       -58.145885, -57.215435, -56.284985, -55.354549, -54.424126, -53.493706,\n",
       "       -52.563293, -51.632896, -50.702496, -49.772106, -48.841724, -47.911346,\n",
       "       -46.980972, -46.050606, -45.120239, -44.18988 , -43.259529, -42.329174,\n",
       "       -41.398827, -40.468483, -39.538139, -38.6078  , -37.677467, -36.747131,\n",
       "       -35.816799, -34.886471, -33.956146, -33.025818, -32.095497, -31.165176,\n",
       "       -30.234858, -29.304541, -28.374224, -27.443909, -26.513596, -25.583284,\n",
       "       -24.652973, -23.722664, -22.792355, -21.862047, -20.931742, -20.001436,\n",
       "       -19.071131, -18.140827, -17.210524, -16.396509, -15.698783, -15.001057,\n",
       "       -14.303331, -13.605605, -12.907881, -12.210155, -11.512431, -10.814707,\n",
       "       -10.116982,  -9.419259,  -8.721535,  -8.093584,  -7.535406,  -6.977227,\n",
       "        -6.419048,  -5.86087 ,  -5.302691,  -4.744514,  -4.186335,  -3.628157,\n",
       "        -3.069979,  -2.511801,  -1.953623,  -1.395445,  -0.837267,  -0.279089,\n",
       "         0.279089,   0.837267,   1.395445,   1.953623,   2.511801,   3.069979,\n",
       "         3.628157,   4.186335,   4.744514,   5.302691,   5.86087 ,   6.419048,\n",
       "         6.977227,   7.535406,   8.093584,   8.721535,   9.419259,  10.116982,\n",
       "        10.814707,  11.512431,  12.210155,  12.907881,  13.605605,  14.303331,\n",
       "        15.001057,  15.698783,  16.396509,  17.210524,  18.140827,  19.071131,\n",
       "        20.001436,  20.931742,  21.862047,  22.792355,  23.722664,  24.652973,\n",
       "        25.583284,  26.513596,  27.443909,  28.374224,  29.304541,  30.234858,\n",
       "        31.165176,  32.095497,  33.025818,  33.956146,  34.886471,  35.816799,\n",
       "        36.747131,  37.677467,  38.6078  ,  39.538139,  40.468483,  41.398827,\n",
       "        42.329174,  43.259529,  44.18988 ,  45.120239,  46.050606,  46.980972,\n",
       "        47.911346,  48.841724,  49.772106,  50.702496,  51.632896,  52.563293,\n",
       "        53.493706,  54.424126,  55.354549,  56.284985,  57.215435,  58.145885,\n",
       "        59.076359,  60.006847,  60.937336,  61.867851,  62.798389,  63.728931,\n",
       "        64.892159,  66.288071,  67.684059,  69.080116,  70.476288,  71.872559,\n",
       "        73.269012,  74.665627,  76.062538,  77.459747,  78.857513,  80.255852,\n",
       "        81.655525,  83.056526,  84.462212,  85.872566,  87.433311,  89.14444 ])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-4783c9a2-49a4-409e-940f-79e85d0b7e3e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4783c9a2-49a4-409e-940f-79e85d0b7e3e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'thkcello' (time: 0, lev: 44, lat: 192, lon: 256)>\n",
       "array([], shape=(0, 44, 192, 256), dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object \n",
       "  * lev      (lev) float64 2.5 7.5 12.5 17.5 ... 5.025e+03 5.275e+03 5.45e+03\n",
       "  * lon      (lon) float64 0.7031 2.109 3.516 4.922 ... 355.1 356.5 357.9 359.3\n",
       "  * lat      (lat) float64 -89.14 -87.43 -85.87 -84.46 ... 85.87 87.43 89.14"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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