docs/examples/layered.ipynb

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   "metadata": {},
   "source": [
    "# Fully developed baroclinic instability of a 3-layer flow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import pi\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import pyqg\n",
    "from pyqg import diagnostic_tools as tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "L =  1000.e3     # length scale of box    [m]\n",
    "Ld = 15.e3       # deformation scale      [m]\n",
    "kd = 1./Ld       # deformation wavenumber [m^-1]\n",
    "Nx = 64          # number of grid points\n",
    "\n",
    "H1 = 500.        # layer 1 thickness  [m]\n",
    "H2 = 1750.       # layer 2 \n",
    "H3 = 1750.       # layer 3 \n",
    "\n",
    "U1 = 0.05          # layer 1 zonal velocity [m/s]\n",
    "U2 = 0.025         # layer 2\n",
    "U3 = 0.00          # layer 3\n",
    "\n",
    "rho1 = 1025.\n",
    "rho2 = 1025.275\n",
    "rho3 = 1025.640\n",
    "\n",
    "rek = 1.e-7       # linear bottom drag coeff.  [s^-1]\n",
    "f0  = 0.0001236812857687059 # coriolis param [s^-1]\n",
    "beta = 1.2130692965249345e-11 # planetary vorticity gradient [m^-1 s^-1]\n",
    "\n",
    "Ti = Ld/(abs(U1))  # estimate of most unstable e-folding time scale [s]\n",
    "dt = Ti/200.   # time-step [s]\n",
    "tmax = 500*Ti      # simulation time [s]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:  Logger initialized\n"
     ]
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   "source": [
    "m = pyqg.LayeredModel(nx=Nx, nz=3, U = [U1,U2,U3],V = [0.,0.,0.],L=L,f=f0,beta=beta,\n",
    "                         H = [H1,H2,H3], rho=[rho1,rho2,rho3],rek=rek,\n",
    "                        dt=dt,tmax=tmax, twrite=10000, tavestart=Ti*200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig = 1.e-7\n",
    "qi = sig*np.vstack([np.random.randn(m.nx,m.ny)[np.newaxis,],\n",
    "                    np.random.randn(m.nx,m.ny)[np.newaxis,],\n",
    "                    np.random.randn(m.nx,m.ny)[np.newaxis,]])\n",
    "m.set_q(qi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Step: 10000, Time: 1.50e+07, KE: 2.38e-04, CFL: 0.009\n",
      "INFO: Step: 20000, Time: 3.00e+07, KE: 3.59e-02, CFL: 0.126\n",
      "INFO: Step: 30000, Time: 4.50e+07, KE: 1.70e-01, CFL: 0.191\n",
      "INFO: Step: 40000, Time: 6.00e+07, KE: 3.59e-01, CFL: 0.213\n",
      "INFO: Step: 50000, Time: 7.50e+07, KE: 1.91e-01, CFL: 0.192\n",
      "INFO: Step: 60000, Time: 9.00e+07, KE: 2.24e-01, CFL: 0.207\n",
      "INFO: Step: 70000, Time: 1.05e+08, KE: 5.46e-01, CFL: 0.307\n",
      "INFO: Step: 80000, Time: 1.20e+08, KE: 4.94e-01, CFL: 0.252\n",
      "INFO: Step: 90000, Time: 1.35e+08, KE: 5.62e-01, CFL: 0.210\n",
      "INFO: Step: 100000, Time: 1.50e+08, KE: 2.70e-01, CFL: 0.218\n"
     ]
    }
   ],
   "source": [
    "m.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Xarray Dataset ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the conversion to an xarray dataset requires xarray to be installed on your machine. See here for installation instructions: http://xarray.pydata.org/en/stable/getting-started-guide/installing.html#instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (time: 1, lev: 3, y: 64, x: 64, l: 64, k: 33, lev_mid: 2)\n",
       "Coordinates:\n",
       "  * time               (time) float64 1.5e+08\n",
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       "  * lev_mid            (lev_mid) float64 1.5 2.5\n",
       "  * x                  (x) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "  * y                  (y) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
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       "  * k                  (k) float64 0.0 6.283e-06 ... 0.0001948 0.0002011\n",
       "Data variables: (12/34)\n",
       "    q                  (time, lev, y, x) float64 0.0001383 ... -8.462e-06\n",
       "    u                  (time, lev, y, x) float64 0.0413 -0.05882 ... -0.1616\n",
       "    v                  (time, lev, y, x) float64 -0.357 -0.3027 ... -0.1369\n",
       "    ufull              (time, lev, y, x) float64 0.0913 -0.008824 ... -0.1616\n",
       "    vfull              (time, lev, y, x) float64 -0.357 -0.3027 ... -0.1369\n",
       "    qh                 (time, lev, l, k) complex128 (9.956089311189431e-06+0j...\n",
       "    ...                 ...\n",
       "    APEgenspec         (time, l, k) float64 0.0 1.724e-08 ... 5.211e-50\n",
       "    KEspec_modal       (time, lev, l, k) float64 0.0 0.1616 ... 2.567e-41\n",
       "    PEspec_modal       (time, lev_mid, l, k) float64 0.0 0.02742 ... 9.974e-42\n",
       "    APEspec            (time, l, k) float64 0.0 0.04085 ... 2.995e-32 1.117e-41\n",
       "    KEflux_div         (time, l, k) float64 0.0 6.588e-09 ... 1.817e-24 3.27e-29\n",
       "    APEflux_div        (time, l, k) float64 0.0 -1.748e-08 ... 1.176e-29\n",
       "Attributes: (12/24)\n",
       "    pyqg:beta:       1.2130692965249345e-11\n",
       "    pyqg:delta:      None\n",
       "    pyqg:dt:         1500.0\n",
       "    pyqg:filterfac:  23.6\n",
       "    pyqg:L:          1000000.0\n",
       "    pyqg:M:          4096\n",
       "    ...              ...\n",
       "    pyqg:tc:         100000\n",
       "    pyqg:tmax:       150000000.0\n",
       "    pyqg:twrite:     10000\n",
       "    pyqg:W:          1000000.0\n",
       "    title:           pyqg: Python Quasigeostrophic Model\n",
       "    reference:       https://pyqg.readthedocs.io/en/latest/index.html</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-10bc5011-95d9-4b88-baeb-1ccdf907a487' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-10bc5011-95d9-4b88-baeb-1ccdf907a487' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lev</span>: 3</li><li><span class='xr-has-index'>y</span>: 64</li><li><span class='xr-has-index'>x</span>: 64</li><li><span class='xr-has-index'>l</span>: 64</li><li><span class='xr-has-index'>k</span>: 33</li><li><span class='xr-has-index'>lev_mid</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fc99acee-aad9-4e1b-be4b-dab01312aeaf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc99acee-aad9-4e1b-be4b-dab01312aeaf' class='xr-section-summary' >Coordinates: <span>(7)</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'>float64</div><div class='xr-var-preview xr-preview'>1.5e+08</div><input id='attrs-b828c768-94bd-4c0d-a8ce-d7233a55631f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b828c768-94bd-4c0d-a8ce-d7233a55631f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5634c42e-bbe7-4d30-82d1-c8e843f3d655' class='xr-var-data-in' type='checkbox'><label for='data-5634c42e-bbe7-4d30-82d1-c8e843f3d655' 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>long_name :</span></dt><dd>model time</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([1.5e+08])</pre></div></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'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-8e74194a-1b93-4a65-95ae-7132ac13a173' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e74194a-1b93-4a65-95ae-7132ac13a173' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcd1006e-068a-41bb-8d35-81d0f7eefce6' class='xr-var-data-in' type='checkbox'><label for='data-bcd1006e-068a-41bb-8d35-81d0f7eefce6' 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>long_name :</span></dt><dd>vertical levels</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev_mid</span></div><div class='xr-var-dims'>(lev_mid)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 2.5</div><input id='attrs-d4d46c78-b4eb-4693-808f-1966df396a13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4d46c78-b4eb-4693-808f-1966df396a13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e4d6e2b-f6a7-46f4-92ca-1961d8cff5f9' class='xr-var-data-in' type='checkbox'><label for='data-4e4d6e2b-f6a7-46f4-92ca-1961d8cff5f9' 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>long_name :</span></dt><dd>vertical level interface</dd></dl></div><div class='xr-var-data'><pre>array([1.5, 2.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.812e+03 2.344e+04 ... 9.922e+05</div><input id='attrs-d535b184-1602-4789-8f50-6df944d34068' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d535b184-1602-4789-8f50-6df944d34068' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acfe6b51-e26d-45ad-898e-20f8c6b17075' class='xr-var-data-in' type='checkbox'><label for='data-acfe6b51-e26d-45ad-898e-20f8c6b17075' 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>long_name :</span></dt><dd>real space grid points in the x direction</dd><dt><span>units :</span></dt><dd>grid point</dd></dl></div><div class='xr-var-data'><pre>array([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.812e+03 2.344e+04 ... 9.922e+05</div><input id='attrs-0022d7a7-0df5-487b-b9b3-6d3c840ea156' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0022d7a7-0df5-487b-b9b3-6d3c840ea156' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5809ee7b-8a9f-44ec-8653-5cb803c9f1a0' class='xr-var-data-in' type='checkbox'><label for='data-5809ee7b-8a9f-44ec-8653-5cb803c9f1a0' 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>long_name :</span></dt><dd>real space grid points in the y direction</dd><dt><span>units :</span></dt><dd>grid point</dd></dl></div><div class='xr-var-data'><pre>array([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>l</span></div><div class='xr-var-dims'>(l)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.283e-06 ... -6.283e-06</div><input id='attrs-b5d783cc-0115-42be-a9e1-5d2320a43146' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5d783cc-0115-42be-a9e1-5d2320a43146' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77717f8e-dcba-4698-ba35-de543e841858' class='xr-var-data-in' type='checkbox'><label for='data-77717f8e-dcba-4698-ba35-de543e841858' 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>long_name :</span></dt><dd>spectal space grid points in the l direction</dd><dt><span>units :</span></dt><dd>meridional wavenumber</dd></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00,  6.283185e-06,  1.256637e-05,  1.884956e-05,\n",
       "        2.513274e-05,  3.141593e-05,  3.769911e-05,  4.398230e-05,\n",
       "        5.026548e-05,  5.654867e-05,  6.283185e-05,  6.911504e-05,\n",
       "        7.539822e-05,  8.168141e-05,  8.796459e-05,  9.424778e-05,\n",
       "        1.005310e-04,  1.068142e-04,  1.130973e-04,  1.193805e-04,\n",
       "        1.256637e-04,  1.319469e-04,  1.382301e-04,  1.445133e-04,\n",
       "        1.507964e-04,  1.570796e-04,  1.633628e-04,  1.696460e-04,\n",
       "        1.759292e-04,  1.822124e-04,  1.884956e-04,  1.947787e-04,\n",
       "       -2.010619e-04, -1.947787e-04, -1.884956e-04, -1.822124e-04,\n",
       "       -1.759292e-04, -1.696460e-04, -1.633628e-04, -1.570796e-04,\n",
       "       -1.507964e-04, -1.445133e-04, -1.382301e-04, -1.319469e-04,\n",
       "       -1.256637e-04, -1.193805e-04, -1.130973e-04, -1.068142e-04,\n",
       "       -1.005310e-04, -9.424778e-05, -8.796459e-05, -8.168141e-05,\n",
       "       -7.539822e-05, -6.911504e-05, -6.283185e-05, -5.654867e-05,\n",
       "       -5.026548e-05, -4.398230e-05, -3.769911e-05, -3.141593e-05,\n",
       "       -2.513274e-05, -1.884956e-05, -1.256637e-05, -6.283185e-06])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.283e-06 ... 0.0002011</div><input id='attrs-ecbe222b-c151-4437-99f1-e48894f9df97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ecbe222b-c151-4437-99f1-e48894f9df97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75ffe0a0-a5b2-4947-96df-c6ebdb1f3903' class='xr-var-data-in' type='checkbox'><label for='data-75ffe0a0-a5b2-4947-96df-c6ebdb1f3903' 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>long_name :</span></dt><dd>spectal space grid points in the k direction</dd><dt><span>units :</span></dt><dd>zonal wavenumber</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 6.283185e-06, 1.256637e-05, 1.884956e-05, 2.513274e-05,\n",
       "       3.141593e-05, 3.769911e-05, 4.398230e-05, 5.026548e-05, 5.654867e-05,\n",
       "       6.283185e-05, 6.911504e-05, 7.539822e-05, 8.168141e-05, 8.796459e-05,\n",
       "       9.424778e-05, 1.005310e-04, 1.068142e-04, 1.130973e-04, 1.193805e-04,\n",
       "       1.256637e-04, 1.319469e-04, 1.382301e-04, 1.445133e-04, 1.507964e-04,\n",
       "       1.570796e-04, 1.633628e-04, 1.696460e-04, 1.759292e-04, 1.822124e-04,\n",
       "       1.884956e-04, 1.947787e-04, 2.010619e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d97daad-06eb-403e-b4ae-431360f7f47f' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d97daad-06eb-403e-b4ae-431360f7f47f' class='xr-section-summary' >Data variables: <span>(34)</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>q</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0001383 0.0001531 ... -8.462e-06</div><input id='attrs-a1a38035-8bd1-4f15-aafe-c7658635079c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1a38035-8bd1-4f15-aafe-c7658635079c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31927dc5-176f-41c1-a677-187e7023ff2f' class='xr-var-data-in' type='checkbox'><label for='data-31927dc5-176f-41c1-a677-187e7023ff2f' 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>units :</span></dt><dd>s^-1</dd><dt><span>long_name :</span></dt><dd>potential vorticity in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 1.38346980e-04,  1.53111435e-04,  1.54358625e-04, ...,\n",
       "           2.85784794e-05,  5.97597167e-05,  1.03073039e-04],\n",
       "         [ 1.55526986e-04,  1.53524409e-04,  1.50892647e-04, ...,\n",
       "           8.25769230e-05,  1.23698526e-04,  1.48529939e-04],\n",
       "         [ 1.48367526e-04,  1.48205705e-04,  1.45692383e-04, ...,\n",
       "           1.31462197e-04,  1.48099176e-04,  1.50191465e-04],\n",
       "         ...,\n",
       "         [ 5.19204079e-05,  4.33539874e-05,  4.83763052e-05, ...,\n",
       "           4.77864081e-05,  6.16317812e-05,  6.16767724e-05],\n",
       "         [ 3.33338205e-05,  5.56122936e-05,  9.05724656e-05, ...,\n",
       "           5.60070300e-05,  4.76475925e-05,  3.28277906e-05],\n",
       "         [ 8.09059461e-05,  1.19277378e-04,  1.43866140e-04, ...,\n",
       "           3.26033695e-05,  2.64745657e-05,  4.31862319e-05]],\n",
       "\n",
       "        [[-3.14821604e-06, -3.34691392e-06, -3.22156397e-06, ...,\n",
       "          -9.07885590e-07, -1.41760803e-06, -2.31118202e-06],\n",
       "         [-3.43804498e-06, -3.22687252e-06, -3.12563266e-06, ...,\n",
       "          -1.93103248e-06, -2.77719890e-06, -3.35298787e-06],\n",
       "         [-3.30883090e-06, -3.10657612e-06, -2.94249370e-06, ...,\n",
       "          -3.23082253e-06, -3.57502854e-06, -3.51541971e-06],\n",
       "...\n",
       "         [-1.09967043e-06, -8.33961033e-07, -1.19503346e-06, ...,\n",
       "           6.64086608e-07, -8.61071587e-07, -1.61226457e-06],\n",
       "         [-7.83581410e-07, -1.62385151e-06, -2.69049401e-06, ...,\n",
       "          -8.85270582e-07, -1.38981526e-06, -9.49966290e-07],\n",
       "         [-2.10269083e-06, -3.13377258e-06, -3.59703201e-06, ...,\n",
       "          -1.07142210e-06, -1.02601363e-06, -1.16995754e-06]],\n",
       "\n",
       "        [[-2.22922586e-05, -2.51085147e-05, -2.66009485e-05, ...,\n",
       "          -4.67433239e-06, -1.15018987e-05, -1.79691287e-05],\n",
       "         [-2.41152832e-05, -2.46907691e-05, -2.43284654e-05, ...,\n",
       "          -1.41184427e-05, -1.95993344e-05, -2.25040809e-05],\n",
       "         [-2.40001476e-05, -2.30415302e-05, -2.22772635e-05, ...,\n",
       "          -2.05670633e-05, -2.27250347e-05, -2.36026284e-05],\n",
       "         ...,\n",
       "         [-3.57858065e-06, -3.97846279e-06, -8.50311529e-06, ...,\n",
       "          -1.81871331e-05, -1.42513043e-05, -8.15398434e-06],\n",
       "         [-5.97262082e-06, -1.19213155e-05, -1.75003166e-05, ...,\n",
       "          -1.26181190e-05, -5.97364665e-06, -3.31086388e-06],\n",
       "         [-1.48583133e-05, -2.02847930e-05, -2.36636303e-05, ...,\n",
       "          -3.58576749e-06, -3.52490374e-06, -8.46207095e-06]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0413 -0.05882 ... -0.1225 -0.1616</div><input id='attrs-342ac4ee-91fb-4f8a-8f08-1cf6a40a7bd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-342ac4ee-91fb-4f8a-8f08-1cf6a40a7bd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b7310fc-e5c3-434a-8ece-680113539fc0' class='xr-var-data-in' type='checkbox'><label for='data-2b7310fc-e5c3-434a-8ece-680113539fc0' 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>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>zonal velocity anomaly</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.041303  , -0.05882397, -0.14417444, ..., -0.03893048,\n",
       "           0.08009587,  0.10676382],\n",
       "         [-0.21434096, -0.27255329, -0.31073178, ...,  0.00168405,\n",
       "          -0.0239985 , -0.1179842 ],\n",
       "         [-0.44955337, -0.46571795, -0.47602383, ..., -0.21873546,\n",
       "          -0.32589967, -0.40809609],\n",
       "         ...,\n",
       "         [-0.01410377,  0.05719979,  0.17679678, ...,  0.00577776,\n",
       "           0.01378183, -0.01427468],\n",
       "         [ 0.07562583,  0.19553651,  0.25139978, ..., -0.08365346,\n",
       "          -0.09865378, -0.04424568],\n",
       "         [ 0.19058157,  0.18033734,  0.101034  , ..., -0.14784369,\n",
       "          -0.03707588,  0.10321181]],\n",
       "\n",
       "        [[-0.12441311, -0.14451521, -0.17005709, ..., -0.14611402,\n",
       "          -0.12220565, -0.11510011],\n",
       "         [-0.25620509, -0.27609834, -0.29449047, ..., -0.21504821,\n",
       "          -0.21719475, -0.23419056],\n",
       "         [-0.39456666, -0.40474374, -0.41073194, ..., -0.33015652,\n",
       "          -0.35382216, -0.37732093],\n",
       "...\n",
       "         [ 0.03556206,  0.05626955,  0.08283948, ...,  0.00827399,\n",
       "           0.01370126,  0.0225854 ],\n",
       "         [ 0.00337721,  0.02488118,  0.03822425, ..., -0.04400576,\n",
       "          -0.03401513, -0.01786722],\n",
       "         [-0.03496625, -0.0338044 , -0.04746255, ..., -0.09951442,\n",
       "          -0.07464661, -0.05052528]],\n",
       "\n",
       "        [[-0.24040205, -0.22504037, -0.21250011, ..., -0.2253834 ,\n",
       "          -0.25120238, -0.25260016],\n",
       "         [-0.30321383, -0.28771521, -0.27835132, ..., -0.3431754 ,\n",
       "          -0.33567124, -0.31944543],\n",
       "         [-0.38748786, -0.3742275 , -0.36915623, ..., -0.42482371,\n",
       "          -0.41439939, -0.40208503],\n",
       "         ...,\n",
       "         [ 0.05780543,  0.01524783, -0.01522518, ...,  0.08699704,\n",
       "           0.09738834,  0.09059418],\n",
       "         [-0.07500959, -0.09905042, -0.10214846, ...,  0.0342105 ,\n",
       "           0.01357364, -0.03103424],\n",
       "         [-0.17617819, -0.17242753, -0.16214874, ..., -0.07308837,\n",
       "          -0.1224911 , -0.16160678]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.357 -0.3027 ... -0.1296 -0.1369</div><input id='attrs-4e63e417-f3a8-4311-91ef-6e4f6cd6d4b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e63e417-f3a8-4311-91ef-6e4f6cd6d4b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dba867dd-bdfd-4722-b7fb-5e4ed0979e0e' class='xr-var-data-in' type='checkbox'><label for='data-dba867dd-bdfd-4722-b7fb-5e4ed0979e0e' 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>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>meridional velocity anomaly</dd></dl></div><div class='xr-var-data'><pre>array([[[[-0.35695727, -0.3027412 , -0.27233807, ..., -0.22127154,\n",
       "          -0.33463249, -0.38214607],\n",
       "         [-0.25947697, -0.23236602, -0.21556218, ..., -0.31879766,\n",
       "          -0.33522146, -0.30198907],\n",
       "         [-0.21094371, -0.20971568, -0.18681966, ..., -0.26903515,\n",
       "          -0.23848051, -0.2142148 ],\n",
       "         ...,\n",
       "         [-0.19903035, -0.25283605, -0.33252204, ..., -0.08358111,\n",
       "          -0.14830597, -0.16808342],\n",
       "         [-0.29123028, -0.37164571, -0.41181148, ..., -0.07901642,\n",
       "          -0.12613017, -0.19524749],\n",
       "         [-0.3934971 , -0.39232027, -0.36279303, ..., -0.09270966,\n",
       "          -0.20811628, -0.32577982]],\n",
       "\n",
       "        [[-0.24485576, -0.24854691, -0.24851126, ..., -0.13912785,\n",
       "          -0.19398269, -0.22899958],\n",
       "         [-0.22424936, -0.22630952, -0.2244862 , ..., -0.16036632,\n",
       "          -0.19656565, -0.21589393],\n",
       "         [-0.20652508, -0.21348054, -0.21226952, ..., -0.15273056,\n",
       "          -0.17673665, -0.1935862 ],\n",
       "...\n",
       "         [-0.21122701, -0.23503801, -0.26297337, ..., -0.08961803,\n",
       "          -0.14186602, -0.1819652 ],\n",
       "         [-0.23092939, -0.2587497 , -0.28092298, ..., -0.09512978,\n",
       "          -0.15065944, -0.19575631],\n",
       "         [-0.24892488, -0.26561068, -0.27375455, ..., -0.10989494,\n",
       "          -0.17058242, -0.21804471]],\n",
       "\n",
       "        [[-0.15987948, -0.18727619, -0.21052377, ..., -0.07680151,\n",
       "          -0.09681561, -0.12844598],\n",
       "         [-0.17623852, -0.2020838 , -0.21770841, ..., -0.05741993,\n",
       "          -0.10059158, -0.14279443],\n",
       "         [-0.19169735, -0.21373071, -0.21953965, ..., -0.06298255,\n",
       "          -0.11491794, -0.1576389 ],\n",
       "         ...,\n",
       "         [-0.21507356, -0.20191583, -0.19891015, ..., -0.13956753,\n",
       "          -0.19139942, -0.21661234],\n",
       "         [-0.17185204, -0.17488574, -0.19128171, ..., -0.1481871 ,\n",
       "          -0.17688261, -0.17825497],\n",
       "         [-0.15222788, -0.17475455, -0.19995156, ..., -0.1228191 ,\n",
       "          -0.12958358, -0.1369193 ]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ufull</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0913 -0.008824 ... -0.1616</div><input id='attrs-b73ad2af-c43a-4bc1-8837-5a5983990867' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b73ad2af-c43a-4bc1-8837-5a5983990867' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a68af974-b5cb-4632-9139-eb02b6ffef17' class='xr-var-data-in' type='checkbox'><label for='data-a68af974-b5cb-4632-9139-eb02b6ffef17' 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>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>zonal full velocities in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.091303  , -0.00882397, -0.09417444, ...,  0.01106952,\n",
       "           0.13009587,  0.15676382],\n",
       "         [-0.16434096, -0.22255329, -0.26073178, ...,  0.05168405,\n",
       "           0.0260015 , -0.0679842 ],\n",
       "         [-0.39955337, -0.41571795, -0.42602383, ..., -0.16873546,\n",
       "          -0.27589967, -0.35809609],\n",
       "         ...,\n",
       "         [ 0.03589623,  0.10719979,  0.22679678, ...,  0.05577776,\n",
       "           0.06378183,  0.03572532],\n",
       "         [ 0.12562583,  0.24553651,  0.30139978, ..., -0.03365346,\n",
       "          -0.04865378,  0.00575432],\n",
       "         [ 0.24058157,  0.23033734,  0.151034  , ..., -0.09784369,\n",
       "           0.01292412,  0.15321181]],\n",
       "\n",
       "        [[-0.09941311, -0.11951521, -0.14505709, ..., -0.12111402,\n",
       "          -0.09720565, -0.09010011],\n",
       "         [-0.23120509, -0.25109834, -0.26949047, ..., -0.19004821,\n",
       "          -0.19219475, -0.20919056],\n",
       "         [-0.36956666, -0.37974374, -0.38573194, ..., -0.30515652,\n",
       "          -0.32882216, -0.35232093],\n",
       "...\n",
       "         [ 0.06056206,  0.08126955,  0.10783948, ...,  0.03327399,\n",
       "           0.03870126,  0.0475854 ],\n",
       "         [ 0.02837721,  0.04988118,  0.06322425, ..., -0.01900576,\n",
       "          -0.00901513,  0.00713278],\n",
       "         [-0.00996625, -0.0088044 , -0.02246255, ..., -0.07451442,\n",
       "          -0.04964661, -0.02552528]],\n",
       "\n",
       "        [[-0.24040205, -0.22504037, -0.21250011, ..., -0.2253834 ,\n",
       "          -0.25120238, -0.25260016],\n",
       "         [-0.30321383, -0.28771521, -0.27835132, ..., -0.3431754 ,\n",
       "          -0.33567124, -0.31944543],\n",
       "         [-0.38748786, -0.3742275 , -0.36915623, ..., -0.42482371,\n",
       "          -0.41439939, -0.40208503],\n",
       "         ...,\n",
       "         [ 0.05780543,  0.01524783, -0.01522518, ...,  0.08699704,\n",
       "           0.09738834,  0.09059418],\n",
       "         [-0.07500959, -0.09905042, -0.10214846, ...,  0.0342105 ,\n",
       "           0.01357364, -0.03103424],\n",
       "         [-0.17617819, -0.17242753, -0.16214874, ..., -0.07308837,\n",
       "          -0.1224911 , -0.16160678]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vfull</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.357 -0.3027 ... -0.1296 -0.1369</div><input id='attrs-8eafca44-db58-4e28-99f2-e4b50fd9c63a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8eafca44-db58-4e28-99f2-e4b50fd9c63a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-619e8578-594a-4c93-96f5-5c208a677bcc' class='xr-var-data-in' type='checkbox'><label for='data-619e8578-594a-4c93-96f5-5c208a677bcc' 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>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>meridional full velocities in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[-0.35695727, -0.3027412 , -0.27233807, ..., -0.22127154,\n",
       "          -0.33463249, -0.38214607],\n",
       "         [-0.25947697, -0.23236602, -0.21556218, ..., -0.31879766,\n",
       "          -0.33522146, -0.30198907],\n",
       "         [-0.21094371, -0.20971568, -0.18681966, ..., -0.26903515,\n",
       "          -0.23848051, -0.2142148 ],\n",
       "         ...,\n",
       "         [-0.19903035, -0.25283605, -0.33252204, ..., -0.08358111,\n",
       "          -0.14830597, -0.16808342],\n",
       "         [-0.29123028, -0.37164571, -0.41181148, ..., -0.07901642,\n",
       "          -0.12613017, -0.19524749],\n",
       "         [-0.3934971 , -0.39232027, -0.36279303, ..., -0.09270966,\n",
       "          -0.20811628, -0.32577982]],\n",
       "\n",
       "        [[-0.24485576, -0.24854691, -0.24851126, ..., -0.13912785,\n",
       "          -0.19398269, -0.22899958],\n",
       "         [-0.22424936, -0.22630952, -0.2244862 , ..., -0.16036632,\n",
       "          -0.19656565, -0.21589393],\n",
       "         [-0.20652508, -0.21348054, -0.21226952, ..., -0.15273056,\n",
       "          -0.17673665, -0.1935862 ],\n",
       "...\n",
       "         [-0.21122701, -0.23503801, -0.26297337, ..., -0.08961803,\n",
       "          -0.14186602, -0.1819652 ],\n",
       "         [-0.23092939, -0.2587497 , -0.28092298, ..., -0.09512978,\n",
       "          -0.15065944, -0.19575631],\n",
       "         [-0.24892488, -0.26561068, -0.27375455, ..., -0.10989494,\n",
       "          -0.17058242, -0.21804471]],\n",
       "\n",
       "        [[-0.15987948, -0.18727619, -0.21052377, ..., -0.07680151,\n",
       "          -0.09681561, -0.12844598],\n",
       "         [-0.17623852, -0.2020838 , -0.21770841, ..., -0.05741993,\n",
       "          -0.10059158, -0.14279443],\n",
       "         [-0.19169735, -0.21373071, -0.21953965, ..., -0.06298255,\n",
       "          -0.11491794, -0.1576389 ],\n",
       "         ...,\n",
       "         [-0.21507356, -0.20191583, -0.19891015, ..., -0.13956753,\n",
       "          -0.19139942, -0.21661234],\n",
       "         [-0.17185204, -0.17488574, -0.19128171, ..., -0.1481871 ,\n",
       "          -0.17688261, -0.17825497],\n",
       "         [-0.15222788, -0.17475455, -0.19995156, ..., -0.1228191 ,\n",
       "          -0.12958358, -0.1369193 ]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qh</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(9.956089311189431e-06+0j) ... (...</div><input id='attrs-9243a65d-e386-470e-95b6-a3037e0799c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9243a65d-e386-470e-95b6-a3037e0799c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f006c6-2efb-4ce9-b9ab-cc8cca66b386' class='xr-var-data-in' type='checkbox'><label for='data-85f006c6-2efb-4ce9-b9ab-cc8cca66b386' 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>units :</span></dt><dd>s^-1</dd><dt><span>long_name :</span></dt><dd>potential vorticity in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 9.95608931e-06+0.00000000e+00j,\n",
       "          -4.32326894e-02-4.27921198e-02j,\n",
       "          -1.00421399e-02+6.16019405e-03j, ...,\n",
       "           1.38458920e-12-2.48029048e-13j,\n",
       "           3.93230287e-16+7.62593058e-17j,\n",
       "          -7.50357234e-39+2.95576129e-21j],\n",
       "         [ 2.37954035e-01+4.60146769e-02j,\n",
       "           4.71180504e-03+4.54586302e-04j,\n",
       "           5.50009274e-03+2.45759024e-02j, ...,\n",
       "           1.75982307e-13+8.14350640e-13j,\n",
       "          -7.02914975e-17+1.76033711e-16j,\n",
       "           8.30966237e-22-1.03367972e-21j],\n",
       "         [ 3.23110173e-02+4.99472307e-03j,\n",
       "          -9.63865591e-03-7.20626758e-03j,\n",
       "           1.20715405e-02+5.66294150e-03j, ...,\n",
       "          -9.18743244e-13+1.03968925e-12j,\n",
       "          -4.03490659e-16-1.39972897e-16j,\n",
       "          -1.46168671e-21-3.50495213e-21j],\n",
       "         ...,\n",
       "         [ 3.23376276e-03+1.22926115e-02j,\n",
       "...\n",
       "          -9.99431629e-23-5.05643998e-23j],\n",
       "         ...,\n",
       "         [-3.34407198e-03-3.30350221e-03j,\n",
       "           8.56289178e-04-1.45655471e-03j,\n",
       "          -5.81550966e-04-1.08357119e-03j, ...,\n",
       "          -9.87207704e-15+2.11194454e-15j,\n",
       "          -4.28645708e-18-4.92580498e-18j,\n",
       "           8.00704917e-23+4.83310570e-23j],\n",
       "         [-6.14964212e-03+1.25593405e-03j,\n",
       "           4.23293687e-03-2.26290334e-03j,\n",
       "          -1.90530127e-03+2.20227866e-03j, ...,\n",
       "           1.85230017e-14-4.86120473e-14j,\n",
       "           8.42549771e-18+8.69134357e-18j,\n",
       "          -1.42885937e-22-1.01362064e-22j],\n",
       "         [-3.55660402e-02+6.85712269e-03j,\n",
       "          -3.56241190e-03+1.13203289e-02j,\n",
       "          -1.10376810e-03-2.21576637e-04j, ...,\n",
       "          -1.32259270e-14+4.85691992e-14j,\n",
       "          -4.35423529e-18+2.96871909e-17j,\n",
       "          -4.43777174e-22+1.30032994e-22j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uh</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-377.8057880393274+0j) ... (2.5...</div><input id='attrs-a4c8973b-c6b6-4c5c-861e-efbca97458ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4c8973b-c6b6-4c5c-861e-efbca97458ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bead819-8308-49ee-9300-08c0624100e1' class='xr-var-data-in' type='checkbox'><label for='data-7bead819-8308-49ee-9300-08c0624100e1' 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>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>zonal velocity anomaly in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[-3.77805788e+02+0.00000000e+00j,\n",
       "          -2.70076567e+02-9.41934650e+01j,\n",
       "          -1.44755687e+02+3.59323069e+01j, ...,\n",
       "          -2.63722281e-10-1.08157244e-10j,\n",
       "           8.38508824e-14-3.32305323e-14j,\n",
       "           6.09774061e-19+6.17433753e-19j],\n",
       "         [-8.00290483e+02+0.00000000e+00j,\n",
       "          -2.99395447e+02-2.25459743e+01j,\n",
       "           4.35642953e+00-2.41254317e+02j, ...,\n",
       "          -9.88984902e-11-1.46453680e-10j,\n",
       "           6.48912308e-14-6.74579179e-15j,\n",
       "           6.55370971e-19+4.46049045e-19j],\n",
       "         [-1.50764815e+03+0.00000000e+00j,\n",
       "          -1.43028584e+02+4.79852786e+01j,\n",
       "           1.26416072e+02-2.72660955e+02j, ...,\n",
       "           4.96061960e-11-1.55446816e-10j,\n",
       "           4.17340137e-14+1.38036293e-14j,\n",
       "           7.25137579e-19+2.73741848e-19j],\n",
       "         ...,\n",
       "         [ 5.63093264e+02+0.00000000e+00j,\n",
       "...\n",
       "           2.86244322e-20+4.23759781e-20j],\n",
       "         ...,\n",
       "         [ 5.16376010e+02+0.00000000e+00j,\n",
       "          -1.06224873e+02-1.93796447e+02j,\n",
       "          -2.69259489e+01+2.28616370e+02j, ...,\n",
       "          -1.89224298e-11+3.10791739e-12j,\n",
       "           4.52220691e-15-2.44939646e-15j,\n",
       "          -3.72172813e-21+5.73608872e-20j],\n",
       "         [ 1.18862288e+02+0.00000000e+00j,\n",
       "          -7.90969430e+01-1.85243767e+02j,\n",
       "          -1.69195293e+01+2.18326820e+02j, ...,\n",
       "          -1.51580725e-11+5.00995905e-12j,\n",
       "           3.67498412e-15-2.47854291e-15j,\n",
       "           1.38743807e-20+4.71916603e-20j],\n",
       "         [-3.36729799e+02+0.00000000e+00j,\n",
       "          -7.47650620e+01-1.36164485e+02j,\n",
       "           6.61278308e+00+2.13566627e+02j, ...,\n",
       "          -1.06628903e-11+6.29590944e-12j,\n",
       "           3.10059962e-15-2.02574926e-15j,\n",
       "           2.50589254e-20+4.03090610e-20j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vh</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>0j ... (2.2258960601546507e-19+2...</div><input id='attrs-330daf86-eb26-4075-96ca-1e56720d9ea5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-330daf86-eb26-4075-96ca-1e56720d9ea5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78a7a97e-ee1b-4f38-a446-0fd8341d53bd' class='xr-var-data-in' type='checkbox'><label for='data-78a7a97e-ee1b-4f38-a446-0fd8341d53bd' 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>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>meridional velocity anomaly in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "          -5.80817096e+02+5.28850119e+02j,\n",
       "           2.95793574e+02+1.66925408e+02j, ...,\n",
       "           3.12406826e-09+1.61955067e-09j,\n",
       "          -4.10204922e-13+1.84371442e-13j,\n",
       "           1.44176362e-17+1.27575862e-17j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "          -5.86697110e+02+5.57694129e+02j,\n",
       "           2.73764384e+02+1.82898200e+02j, ...,\n",
       "           2.74213591e-09+2.15062951e-09j,\n",
       "          -4.69664531e-13-4.36038528e-14j,\n",
       "           1.60923360e-17+1.07766971e-17j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "          -5.85370687e+02+5.81264592e+02j,\n",
       "           2.18357602e+02+1.67413454e+02j, ...,\n",
       "           2.29043675e-09+2.21820927e-09j,\n",
       "          -4.57327475e-13-2.06352411e-13j,\n",
       "           1.72193027e-17+8.61422686e-18j],\n",
       "         ...,\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "...\n",
       "           5.84594730e-19+2.50946517e-18j],\n",
       "         ...,\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "          -4.22096506e+02+4.38812244e+02j,\n",
       "           1.26661043e+01+1.43779335e+02j, ...,\n",
       "          -1.13834495e-10+1.28531638e-10j,\n",
       "           4.70861947e-14-1.22395964e-14j,\n",
       "          -7.73696946e-20+2.87018691e-18j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "          -4.41247168e+02+4.47717995e+02j,\n",
       "           5.64582584e+01+1.48651216e+02j, ...,\n",
       "          -1.01728344e-10+1.79024149e-10j,\n",
       "           3.94462903e-14-2.46635450e-14j,\n",
       "           8.60590403e-20+2.85254888e-18j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "          -4.57053329e+02+4.55082903e+02j,\n",
       "           9.89680158e+01+1.49450078e+02j, ...,\n",
       "          -8.49293415e-11+2.17102486e-10j,\n",
       "           3.24906985e-14-3.48865551e-14j,\n",
       "           2.22589606e-19+2.78975288e-18j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ph</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>0j ... (1.3885920480162288e-14-4...</div><input id='attrs-532d5a84-3486-40d4-a534-97de47701e21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-532d5a84-3486-40d4-a534-97de47701e21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fd64aae-cd96-47eb-b35d-922606d781ef' class='xr-var-data-in' type='checkbox'><label for='data-7fd64aae-cd96-47eb-b35d-922606d781ef' 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>units :</span></dt><dd>m^2 s^-1</dd><dt><span>long_name :</span></dt><dd>streamfunction in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "           8.33000202e+07+5.10064301e+07j,\n",
       "           1.06589563e+07+3.05923395e+06j, ...,\n",
       "          -3.10460034e-05+1.76030846e-06j,\n",
       "          -1.08046274e-08-3.41402514e-09j,\n",
       "           2.28829946e-31-1.02113164e-13j],\n",
       "         [-3.25075958e+08-6.58532210e+07j,\n",
       "          -7.47919064e+06+1.54389013e+05j,\n",
       "          -1.36830223e+06-1.22583001e+07j, ...,\n",
       "           7.17438827e-08-1.79884605e-05j,\n",
       "           2.73708926e-09-5.71504458e-09j,\n",
       "          -3.90791370e-16+5.16918933e-15j],\n",
       "         [-1.07790749e+07+5.38361013e+05j,\n",
       "           4.88701679e+06+3.75716766e+06j,\n",
       "          -3.26128992e+06+1.49309229e+06j, ...,\n",
       "           2.07914153e-05-1.82958820e-05j,\n",
       "           1.00991338e-08+4.11842196e-09j,\n",
       "           2.54618287e-14+9.56287894e-14j],\n",
       "         ...,\n",
       "         [ 2.55166310e+06-1.04695730e+06j,\n",
       "...\n",
       "           2.74950524e-15+2.41416456e-16j],\n",
       "         ...,\n",
       "         [ 3.71112507e+06+1.13212073e+06j,\n",
       "          -1.48949905e+06+5.63958417e+06j,\n",
       "           5.85480333e+05-1.57651509e+06j, ...,\n",
       "           6.60180680e-07+2.46173993e-08j,\n",
       "           1.14073239e-10+1.92222565e-10j,\n",
       "          -2.94505461e-15-1.36625509e-15j],\n",
       "         [-5.34989191e+06-1.39926422e+06j,\n",
       "           3.08157073e+05-1.30385518e+07j,\n",
       "           3.50086868e+06-7.65791855e+06j, ...,\n",
       "          -7.10365663e-07+1.51796557e-06j,\n",
       "          -2.22033274e-10-2.75979881e-10j,\n",
       "           2.74548513e-15+2.13848579e-15j],\n",
       "         [-2.86971457e+08+5.84628818e+07j,\n",
       "           3.07122964e+05+3.23101111e+07j,\n",
       "           4.94264980e+05+4.80874705e+06j, ...,\n",
       "           2.90482290e-08-1.10514865e-06j,\n",
       "           5.69362640e-11-8.23983804e-10j,\n",
       "           1.38859205e-14-4.20452021e-15j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dqhdt</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0-0j) ... (-4.0876124388172813...</div><input id='attrs-722be59e-164f-4be7-9569-639abdb79eba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-722be59e-164f-4be7-9569-639abdb79eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5d024ff-8fad-4312-a10d-cd1415c12d66' class='xr-var-data-in' type='checkbox'><label for='data-f5d024ff-8fad-4312-a10d-cd1415c12d66' 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>units :</span></dt><dd>s^-2</dd><dt><span>long_name :</span></dt><dd>previous partial derivative of potential vorticity wrt. time in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[-0.00000000e+00-0.00000000e+00j,\n",
       "           1.38090053e-07-2.45090198e-07j,\n",
       "          -7.88627637e-09-1.54459458e-07j, ...,\n",
       "           6.26178851e-09-7.03325382e-10j,\n",
       "           6.29449831e-09+1.66471275e-09j,\n",
       "          -6.21544739e-27+2.63303558e-09j],\n",
       "         [-9.04852908e-09+2.69128899e-08j,\n",
       "           6.53669332e-08+1.22514521e-08j,\n",
       "          -1.19034337e-07+1.56023975e-08j, ...,\n",
       "           4.00360451e-10+4.09978088e-09j,\n",
       "          -1.63081314e-09+3.66653563e-09j,\n",
       "           3.06802619e-10-5.07629472e-10j],\n",
       "         [ 4.13086469e-09-5.37672124e-08j,\n",
       "           4.10328928e-08+6.10880445e-09j,\n",
       "           4.46967951e-08-4.77688948e-09j, ...,\n",
       "          -6.62041967e-09+6.58083382e-09j,\n",
       "          -1.12256002e-08-4.24780454e-09j,\n",
       "          -1.90816775e-09-5.89386008e-09j],\n",
       "         ...,\n",
       "         [ 2.86359040e-08+4.30835174e-09j,\n",
       "...\n",
       "          -1.49250152e-10-2.48761511e-11j],\n",
       "         ...,\n",
       "         [-3.86940505e-09-8.40568457e-09j,\n",
       "          -9.03972769e-09+1.99808196e-08j,\n",
       "           4.98632620e-09-2.24173282e-08j, ...,\n",
       "          -1.97806704e-10+8.72708095e-12j,\n",
       "          -2.53706482e-10-3.46711642e-10j,\n",
       "           3.87535545e-10+1.93198349e-10j],\n",
       "         [ 3.30992487e-09-5.69183250e-09j,\n",
       "           1.87596029e-08-1.60077169e-08j,\n",
       "           5.59047871e-09+6.43229927e-10j, ...,\n",
       "           1.73032032e-10-3.74673942e-10j,\n",
       "           2.25883182e-10+2.46638603e-10j,\n",
       "          -1.71972551e-10-1.31314278e-10j],\n",
       "         [ 8.05846966e-10+4.76555115e-09j,\n",
       "           1.03344501e-08-7.81035440e-09j,\n",
       "           7.43874904e-10+2.34682966e-08j, ...,\n",
       "          -4.10251269e-11+2.32194363e-10j,\n",
       "          -4.86155641e-11+5.04845444e-10j,\n",
       "          -4.08761244e-10+1.20700578e-10j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ubg</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05 0.025 0.0</div><input id='attrs-9958b5db-f271-44a1-b6c6-21074c21dcf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9958b5db-f271-44a1-b6c6-21074c21dcf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-470cc8f9-ecde-42b9-aadd-9e0dcc0cb836' class='xr-var-data-in' type='checkbox'><label for='data-470cc8f9-ecde-42b9-aadd-9e0dcc0cb836' 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>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>background zonal velocity</dd></dl></div><div class='xr-var-data'><pre>array([0.05 , 0.025, 0.   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Qy</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.027e-10 -8.326e-12 -5.044e-11</div><input id='attrs-33d1d97f-e350-4378-8ef3-9b1cde4be644' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33d1d97f-e350-4378-8ef3-9b1cde4be644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-649ffe74-0e65-40c4-8022-bf936682ac22' class='xr-var-data-in' type='checkbox'><label for='data-649ffe74-0e65-40c4-8022-bf936682ac22' 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>units :</span></dt><dd>m^-1 s^-1</dd><dt><span>long_name :</span></dt><dd>background potential vorticity gradient</dd></dl></div><div class='xr-var-data'><pre>array([ 3.02733732e-10, -8.32553612e-12, -5.04425176e-11])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dqdt</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.211e-10 4.369e-10 ... -1.523e-10</div><input id='attrs-41b84b18-9f57-4373-8890-e4ba5f062783' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41b84b18-9f57-4373-8890-e4ba5f062783' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e466ded-50a8-47db-a3b8-1b4a0cef17f4' class='xr-var-data-in' type='checkbox'><label for='data-9e466ded-50a8-47db-a3b8-1b4a0cef17f4' 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>units :</span></dt><dd>s^-2</dd><dt><span>long_name :</span></dt><dd>previous partial derivative of potential vorticity wrt. time in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 8.21059746e-10,  4.36940426e-10,  5.33408207e-11, ...,\n",
       "           6.91137238e-10,  8.76469807e-10,  8.92141076e-10],\n",
       "         [ 1.75071807e-10, -1.14789679e-10, -1.18048056e-11, ...,\n",
       "           1.06340712e-09,  1.35622612e-09,  9.43040697e-10],\n",
       "         [-2.64376580e-10,  1.07206222e-11, -2.56954768e-10, ...,\n",
       "           9.90237412e-10,  1.43867397e-11, -4.60367842e-10],\n",
       "         ...,\n",
       "         [-2.82638928e-10, -5.66042205e-10,  1.36028000e-10, ...,\n",
       "          -2.44009548e-10,  2.32241110e-10,  4.84423232e-10],\n",
       "         [ 2.50238082e-10,  1.02534999e-09,  1.08083243e-09, ...,\n",
       "           2.57049937e-10, -3.89610837e-10, -7.21728048e-10],\n",
       "         [ 1.16818851e-09,  9.52936004e-10,  6.91115306e-10, ...,\n",
       "          -4.98831256e-10,  2.27805158e-10,  1.09305209e-09]],\n",
       "\n",
       "        [[-1.48077373e-11,  1.14388238e-12,  4.40907252e-12, ...,\n",
       "          -7.66532990e-12, -1.79811131e-11, -2.51441361e-11],\n",
       "         [-1.43921529e-12,  9.36802766e-13, -2.26794727e-12, ...,\n",
       "          -2.63102952e-11, -2.90490403e-11, -1.76292344e-11],\n",
       "         [ 8.74799453e-12,  7.58848413e-12,  9.72129191e-12, ...,\n",
       "          -2.33339332e-11, -4.57974162e-12,  7.71150951e-12],\n",
       "...\n",
       "         [ 9.03050039e-12,  3.19321187e-12, -1.24858840e-11, ...,\n",
       "          -7.87840877e-12, -1.11629067e-11, -3.51405940e-13],\n",
       "         [-1.14072122e-11, -2.66449188e-11, -2.86415350e-11, ...,\n",
       "          -8.78997104e-12,  1.10781934e-12,  5.82850356e-12],\n",
       "         [-2.63149126e-11, -1.77789157e-11, -3.24223662e-12, ...,\n",
       "           2.12167695e-12,  1.06427340e-12, -1.54485517e-11]],\n",
       "\n",
       "        [[-1.11847987e-10, -6.74707980e-11, -3.30047338e-11, ...,\n",
       "          -1.18216120e-10, -1.85776836e-10, -1.57291272e-10],\n",
       "         [-3.49342317e-11,  1.07721421e-11,  4.79705258e-11, ...,\n",
       "          -1.78402504e-10, -1.28651859e-10, -6.82736914e-11],\n",
       "         [-7.93697373e-12,  4.58232572e-11, -1.30213943e-11, ...,\n",
       "          -1.21576009e-10, -5.81304835e-11, -4.00150889e-11],\n",
       "         ...,\n",
       "         [ 3.28145410e-11, -6.65651779e-11, -1.29711293e-10, ...,\n",
       "          -3.10072381e-12,  4.82871775e-11,  7.55418612e-11],\n",
       "         [-1.11871166e-10, -1.47644977e-10, -1.36480584e-10, ...,\n",
       "           6.48680636e-11,  6.91554518e-11, -8.06025232e-12],\n",
       "         [-1.65464504e-10, -1.41418290e-10, -1.01319117e-10, ...,\n",
       "           5.70630825e-11, -5.69630486e-11, -1.52320610e-10]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.216e+05 ... -1.029e+05</div><input id='attrs-b45aac38-3ee0-4136-950f-4cb688859eb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b45aac38-3ee0-4136-950f-4cb688859eb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c915e82d-2fa5-481a-a62d-c514e4c0ccf0' class='xr-var-data-in' type='checkbox'><label for='data-c915e82d-2fa5-481a-a62d-c514e4c0ccf0' 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>units :</span></dt><dd>m^2 s^-1</dd><dt><span>long_name :</span></dt><dd>streamfunction in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[-121554.75270958, -126704.50691079, -131152.64241816, ...,\n",
       "          -105593.49644487, -110001.86303669, -115703.44598756],\n",
       "         [-120207.4600794 , -124025.74691243, -127527.64965551, ...,\n",
       "          -105618.249784  , -110815.13986657, -115831.59510844],\n",
       "         [-114972.91603201, -118275.44560719, -121408.93646799, ...,\n",
       "          -104183.05070148, -108162.19483021, -111672.27204856],\n",
       "         ...,\n",
       "         [-116928.42438818, -120419.07846912, -124976.48995466, ...,\n",
       "          -109675.53705332, -111608.31172261, -114082.61633544],\n",
       "         [-117090.11996203, -122310.59420267, -128491.34075096, ...,\n",
       "          -109220.1263531 , -110840.62614486, -113295.78240094],\n",
       "         [-119441.42235797, -125652.28554747, -131556.01707313, ...,\n",
       "          -107199.37386027, -109528.80308901, -113727.1390795 ]],\n",
       "\n",
       "        [[-111370.71566964, -115235.28131731, -119119.37504178, ...,\n",
       "          -101689.57114207, -104315.51717355, -107647.18983849],\n",
       "         [-108428.8736821 , -111955.21126567, -115480.76204723, ...,\n",
       "           -98925.77252422, -101738.97493934, -104979.74863771],\n",
       "         [-103331.05094749, -106622.91649042, -109957.88528687, ...,\n",
       "           -94714.43361257,  -97300.58729943, -100200.04710953],\n",
       "...\n",
       "         [-112457.8449807 , -115943.25171871, -119827.93321849, ...,\n",
       "          -105000.8639344 , -106828.87812512, -109372.33326101],\n",
       "         [-112743.54064528, -116576.10951258, -120801.27425567, ...,\n",
       "          -104742.73661398, -106678.64743316, -109397.72965768],\n",
       "         [-112540.94030851, -116575.56381873, -120797.12002035, ...,\n",
       "          -103609.03780069, -105814.74069588, -108871.15336395]],\n",
       "\n",
       "        [[-101870.98928874, -104587.68759904, -107701.73157397, ...,\n",
       "           -96523.4990232 ,  -97860.57246824,  -99612.85911899],\n",
       "         [ -97651.37175381, -100618.10267079, -103910.71305809, ...,\n",
       "           -92011.29449176,  -93236.93187089,  -95148.37982723],\n",
       "         [ -92270.99793127,  -95460.62744344,  -98859.47487308, ...,\n",
       "           -85991.58389714,  -87388.63003384,  -89531.54768901],\n",
       "         ...,\n",
       "         [-107326.46509099, -110582.941234  , -113694.03861879, ...,\n",
       "           -98083.62455753, -100699.54745021, -103924.42002521],\n",
       "         [-107176.28621131, -109865.55213176, -112714.35889792, ...,\n",
       "           -99059.81579189, -101645.9852423 , -104443.62489077],\n",
       "         [-105158.17188258, -107704.84431958, -110633.70938772, ...,\n",
       "           -98853.42111531, -100836.84217333, -102909.5471078 ]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ensspec</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.908e-18 2.484e-09 ... 1.944e-49</div><input id='attrs-b78a777a-dd21-4adc-94fa-390381843bd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b78a777a-dd21-4adc-94fa-390381843bd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5236cd73-13d4-45ad-b2cb-52d23292daf9' class='xr-var-data-in' type='checkbox'><label for='data-5236cd73-13d4-45ad-b2cb-52d23292daf9' 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>long_name :</span></dt><dd>enstrophy spectrum</dd><dt><span>units :</span></dt><dd>s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[[5.90823378e-18, 2.48377414e-09, 1.10099289e-10, ...,\n",
       "          6.30072193e-31, 4.58081439e-38, 2.13919835e-47],\n",
       "         [1.88822103e-09, 2.77447492e-10, 1.08946799e-10, ...,\n",
       "          6.20427127e-31, 4.97997451e-38, 2.24437498e-47],\n",
       "         [1.06975106e-10, 1.03699620e-10, 8.15427283e-11, ...,\n",
       "          4.46674637e-31, 3.98300263e-38, 1.47489995e-47],\n",
       "         ...,\n",
       "         [4.57271268e-11, 4.94522737e-11, 4.14033503e-11, ...,\n",
       "          2.24982689e-31, 1.65691991e-38, 4.11998463e-48],\n",
       "         [1.06975106e-10, 1.05290261e-10, 7.05741257e-11, ...,\n",
       "          5.26839460e-31, 4.01059360e-38, 1.63621147e-47],\n",
       "         [1.88822103e-09, 2.53934165e-10, 1.05432923e-10, ...,\n",
       "          5.59662777e-31, 5.58256532e-38, 2.28928354e-47]],\n",
       "\n",
       "        [[3.30413329e-18, 1.61607182e-12, 7.01872780e-14, ...,\n",
       "          3.47439952e-34, 2.79905784e-41, 1.22095678e-50],\n",
       "         [1.04573645e-12, 2.17863279e-13, 7.90492106e-14, ...,\n",
       "          2.93264583e-34, 1.96036896e-41, 8.06708889e-51],\n",
       "         [7.33454380e-14, 6.62951176e-14, 5.06740308e-14, ...,\n",
       "          1.38528507e-34, 9.01137502e-42, 2.71960349e-51],\n",
       "...\n",
       "         [2.69776413e-14, 2.75659007e-14, 2.40740261e-14, ...,\n",
       "          4.49816768e-35, 1.94510089e-42, 4.22137182e-52],\n",
       "         [7.33454380e-14, 6.76849134e-14, 4.50350961e-14, ...,\n",
       "          1.46780013e-34, 9.22598566e-42, 2.68762056e-51],\n",
       "         [1.04573645e-12, 2.03592983e-13, 7.62244807e-14, ...,\n",
       "          2.84502289e-34, 2.09341344e-41, 8.08536310e-51]],\n",
       "\n",
       "        [[2.99145570e-21, 5.87046886e-11, 3.30781811e-12, ...,\n",
       "          6.44861060e-33, 5.05418706e-40, 1.81683285e-49],\n",
       "         [3.68187740e-11, 9.05622497e-12, 3.71724111e-12, ...,\n",
       "          5.76710254e-33, 5.23018807e-40, 1.96212263e-49],\n",
       "         [3.25918016e-12, 2.92868293e-12, 2.26820157e-12, ...,\n",
       "          3.66781567e-33, 2.86149903e-40, 9.58524037e-50],\n",
       "         ...,\n",
       "         [1.38033740e-12, 1.40054050e-12, 1.29940228e-12, ...,\n",
       "          1.60729988e-33, 9.19557761e-41, 2.23406943e-50],\n",
       "         [3.25918016e-12, 3.19457003e-12, 2.27037741e-12, ...,\n",
       "          3.80141171e-33, 2.45746947e-40, 1.06987045e-49],\n",
       "         [3.68187740e-11, 8.68921408e-12, 3.56851151e-12, ...,\n",
       "          5.67997379e-33, 4.59235542e-40, 1.94378914e-49]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KEspec</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1877 ... 1.071e-32 4.295e-42</div><input id='attrs-452ca8ec-8eb5-4c47-925d-f9eb9549138c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-452ca8ec-8eb5-4c47-925d-f9eb9549138c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-214f9ad0-4679-4251-ad9a-a5649d654f97' class='xr-var-data-in' type='checkbox'><label for='data-214f9ad0-4679-4251-ad9a-a5649d654f97' 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>long_name :</span></dt><dd>kinetic energy spectrum</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.00000000e+00, 1.87692391e-01, 4.28494217e-03, ...,\n",
       "          1.04312694e-23, 7.30356618e-31, 3.28402181e-40],\n",
       "         [1.79563988e-01, 1.24294288e-02, 3.52441311e-03, ...,\n",
       "          1.02616338e-23, 7.93671572e-31, 3.44642518e-40],\n",
       "         [4.68796818e-03, 3.49463370e-03, 2.31695389e-03, ...,\n",
       "          7.38154209e-24, 6.34372873e-31, 2.26214301e-40],\n",
       "         ...,\n",
       "         [1.55322059e-03, 1.59032938e-03, 1.14723144e-03, ...,\n",
       "          3.71015518e-24, 2.63200234e-31, 6.30294187e-41],\n",
       "         [4.68796818e-03, 3.41541673e-03, 2.06739547e-03, ...,\n",
       "          8.70909501e-24, 6.38692219e-31, 2.50996155e-40],\n",
       "         [1.79563988e-01, 1.04063433e-02, 3.30124506e-03, ...,\n",
       "          9.25890369e-24, 8.90110341e-31, 3.51626753e-40]],\n",
       "\n",
       "        [[0.00000000e+00, 1.67234494e-01, 3.25136572e-03, ...,\n",
       "          6.03162017e-26, 3.86576476e-33, 1.53411303e-42],\n",
       "         [1.61968328e-01, 1.04004758e-02, 2.53918601e-03, ...,\n",
       "          5.68257267e-26, 4.03409414e-33, 1.60757389e-42],\n",
       "         [3.65586491e-03, 2.46238052e-03, 1.39783253e-03, ...,\n",
       "          4.15203626e-26, 3.29587160e-33, 1.03885693e-42],\n",
       "...\n",
       "         [9.85661391e-04, 9.52727381e-04, 6.00530062e-04, ...,\n",
       "          2.13879737e-26, 1.34789137e-33, 2.88376112e-43],\n",
       "         [3.65586491e-03, 2.40612739e-03, 1.29671536e-03, ...,\n",
       "          4.95038076e-26, 3.29995048e-33, 1.16068404e-42],\n",
       "         [1.61968328e-01, 8.64600025e-03, 2.35384572e-03, ...,\n",
       "          5.25651352e-26, 4.60349663e-33, 1.66749075e-42]],\n",
       "\n",
       "        [[0.00000000e+00, 1.49182766e-01, 2.62668246e-03, ...,\n",
       "          1.59055697e-25, 1.18151137e-32, 4.02510902e-42],\n",
       "         [1.46697691e-01, 8.90494723e-03, 2.02390735e-03, ...,\n",
       "          1.41853742e-25, 1.21989180e-32, 4.32066750e-42],\n",
       "         [3.06851955e-03, 1.86054308e-03, 9.52800139e-04, ...,\n",
       "          8.99018265e-26, 6.69095943e-33, 2.11598218e-42],\n",
       "         ...,\n",
       "         [7.63476841e-04, 7.01043792e-04, 4.25576355e-04, ...,\n",
       "          3.94723210e-26, 2.13161045e-33, 4.88959029e-43],\n",
       "         [3.06851955e-03, 1.83081856e-03, 9.65901748e-04, ...,\n",
       "          9.38632613e-26, 5.74531751e-33, 2.35812305e-42],\n",
       "         [1.46697691e-01, 7.34589562e-03, 1.84942290e-03, ...,\n",
       "          1.39910330e-25, 1.07143634e-32, 4.29510338e-42]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EKEdiss</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.031e-08</div><input id='attrs-a2d9b466-278c-4d5c-8d33-ef9f1e64321f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2d9b466-278c-4d5c-8d33-ef9f1e64321f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-add6bc98-949f-4b66-a731-1abc107cc8ea' class='xr-var-data-in' type='checkbox'><label for='data-add6bc98-949f-4b66-a731-1abc107cc8ea' 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>long_name :</span></dt><dd>total energy dissipation by bottom drag</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([3.0307862e-08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KEfrictionspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 -6.527e-09 ... -1.879e-49</div><input id='attrs-768472f3-86ee-4805-9841-e149d8f6d687' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-768472f3-86ee-4805-9841-e149d8f6d687' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4efe4b40-4071-4a82-8935-06c780500655' class='xr-var-data-in' type='checkbox'><label for='data-4efe4b40-4071-4a82-8935-06c780500655' 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>long_name :</span></dt><dd>total energy dissipation spectrum by bottom drag</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.00000000e+00, -6.52674600e-09, -1.14917358e-10, ...,\n",
       "         -6.95868676e-33, -5.16911226e-40, -1.76098520e-49],\n",
       "        [-6.41802399e-09, -3.89591442e-10, -8.85459467e-11, ...,\n",
       "         -6.20610123e-33, -5.33702665e-40, -1.89029203e-49],\n",
       "        [-1.34247730e-10, -8.13987599e-11, -4.16850061e-11, ...,\n",
       "         -3.93320491e-33, -2.92729475e-40, -9.25742205e-50],\n",
       "        ...,\n",
       "        [-3.34021118e-11, -3.06706659e-11, -1.86189655e-11, ...,\n",
       "         -1.72691404e-33, -9.32579574e-41, -2.13919575e-50],\n",
       "        [-1.34247730e-10, -8.00983118e-11, -4.22582015e-11, ...,\n",
       "         -4.10651768e-33, -2.51357641e-40, -1.03167884e-49],\n",
       "        [-6.41802399e-09, -3.21382934e-10, -8.09122521e-11, ...,\n",
       "         -6.12107694e-33, -4.68753401e-40, -1.87910773e-49]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EKE</span></div><div class='xr-var-dims'>(time, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.502 0.3911 0.3464</div><input id='attrs-1a517dba-a9b3-44b3-8e7b-0794be2253d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a517dba-a9b3-44b3-8e7b-0794be2253d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4580e9cf-52ed-4aa5-bf8f-0c9077167791' class='xr-var-data-in' type='checkbox'><label for='data-4580e9cf-52ed-4aa5-bf8f-0c9077167791' 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>long_name :</span></dt><dd>mean eddy kinetic energy</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[0.5020376 , 0.39110979, 0.34637557]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dissspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 -0.0 ... -2.275e-24 -4.371e-29</div><input id='attrs-e4254ce7-7d1f-4eac-b481-29217aefd46c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4254ce7-7d1f-4eac-b481-29217aefd46c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaec3e78-873f-4607-89a9-52581b36964e' class='xr-var-data-in' type='checkbox'><label for='data-aaec3e78-873f-4607-89a9-52581b36964e' 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>long_name :</span></dt><dd>Spectral contribution of filter dissipation to total energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -7.79369383e-21, -1.64440930e-24, -3.33389652e-29],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -8.41085727e-21, -2.05666521e-24, -4.30022298e-29],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -8.52225945e-21, -2.55750638e-24, -5.09981130e-29],\n",
       "        ...,\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -7.61017538e-21, -2.28359698e-24, -3.71654913e-29],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -1.00746126e-20, -2.55210510e-24, -5.57567599e-29],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -7.73629613e-21, -2.27497291e-24, -4.37091485e-29]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENSDissspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -1.106e-31 -2.233e-36</div><input id='attrs-28b39b7d-d5dc-4163-b3ac-61fec8c81b89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b39b7d-d5dc-4163-b3ac-61fec8c81b89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe2468b-2a6f-455a-a0d9-d59f5cfbe17c' class='xr-var-data-in' type='checkbox'><label for='data-dbe2468b-2a6f-455a-a0d9-d59f5cfbe17c' 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>long_name :</span></dt><dd>Spectral contribution of filter dissipation to barotropic enstrophy</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.58982763e-28, -7.97305982e-32, -1.70370748e-36],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.88146602e-28, -9.98171512e-32, -2.19749778e-36],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.94308719e-28, -1.24599539e-31, -2.61496231e-36],\n",
       "        ...,\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.53577185e-28, -1.11807909e-31, -1.91384739e-36],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -4.66289040e-28, -1.24430603e-31, -2.85828034e-36],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.56806252e-28, -1.10552194e-31, -2.23344842e-36]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paramspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 -0.0 -0.0 ... -0.0 -0.0 -0.0</div><input id='attrs-5d2e1367-6025-47bf-9a6c-56b78286ad4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d2e1367-6025-47bf-9a6c-56b78286ad4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0188198e-a787-4d96-9a15-86f72c46906c' class='xr-var-data-in' type='checkbox'><label for='data-0188198e-a787-4d96-9a15-86f72c46906c' 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>long_name :</span></dt><dd>Spectral contribution of subgrid parameterization (if present)</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[-0., -0., -0., ..., -0., -0., -0.],\n",
       "        [-0., -0., -0., ..., -0., -0., -0.],\n",
       "        [-0., -0., -0., ..., -0., -0., -0.],\n",
       "        ...,\n",
       "        [-0., -0., -0., ..., -0., -0., -0.],\n",
       "        [-0., -0., -0., ..., -0., -0., -0.],\n",
       "        [-0., -0., -0., ..., -0., -0., -0.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>entspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.656e-19 6.38e-12 ... 3.817e-49</div><input id='attrs-8459217d-0d27-401a-a4b2-ce327adcd09a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8459217d-0d27-401a-a4b2-ce327adcd09a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31b73b14-953c-4ec4-94f1-3d184702d651' class='xr-var-data-in' type='checkbox'><label for='data-31b73b14-953c-4ec4-94f1-3d184702d651' 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>long_name :</span></dt><dd>barotropic enstrophy spectrum</dd><dt><span>units :</span></dt><dd>m s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[2.65584349e-19, 6.37988957e-12, 4.82787649e-13, ...,\n",
       "         1.00461388e-32, 7.70011950e-40, 3.56843291e-49],\n",
       "        [6.20907823e-12, 7.84154431e-13, 4.68595879e-13, ...,\n",
       "         9.71240647e-33, 8.17599449e-40, 3.65624599e-49],\n",
       "        [5.49555671e-13, 4.48315022e-13, 3.95023135e-13, ...,\n",
       "         6.97771868e-33, 6.61082276e-40, 2.43857161e-49],\n",
       "        ...,\n",
       "        [3.26466662e-13, 3.45407416e-13, 2.77788350e-13, ...,\n",
       "         3.64710209e-33, 2.65380175e-40, 6.56784375e-50],\n",
       "        [5.49555671e-13, 4.38493237e-13, 3.75324938e-13, ...,\n",
       "         8.51958375e-33, 6.50920849e-40, 2.67917890e-49],\n",
       "        [6.20907823e-12, 6.50089117e-13, 4.32395701e-13, ...,\n",
       "         8.91045811e-33, 9.08561809e-40, 3.81686363e-49]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paramspec_APEflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 -0.0 -0.0 ... -0.0 -0.0 -0.0</div><input id='attrs-dbca65c9-36b1-47f1-826a-e851b18e2dd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbca65c9-36b1-47f1-826a-e851b18e2dd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9683d478-699c-4d92-bccd-7c0099dee538' class='xr-var-data-in' type='checkbox'><label for='data-9683d478-699c-4d92-bccd-7c0099dee538' 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>long_name :</span></dt><dd>total additional APE flux due to subgrid parameterization</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[-0., -0., -0., ..., -0., -0., -0.],\n",
       "        [-0., -0., -0., ..., -0., -0., -0.],\n",
       "        [-0., -0., -0., ..., -0., -0., -0.],\n",
       "        ...,\n",
       "        [-0., -0., -0., ..., -0., -0., -0.],\n",
       "        [-0., -0., -0., ..., -0., -0., -0.],\n",
       "        [-0., -0., -0., ..., -0., -0., -0.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paramspec_KEflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-c8bf171f-d9fd-47d8-b642-021d34d3e246' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8bf171f-d9fd-47d8-b642-021d34d3e246' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cda4b57-d5e1-4623-b1b5-56acbe18281c' class='xr-var-data-in' type='checkbox'><label for='data-6cda4b57-d5e1-4623-b1b5-56acbe18281c' 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>long_name :</span></dt><dd>total additional KE flux due to subgrid parameterization</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENSflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -1.29e-16 ... 2.272e-36</div><input id='attrs-8cfaeaf2-01f3-49a4-a06b-11dc97a45f2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cfaeaf2-01f3-49a4-a06b-11dc97a45f2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbf6f854-72f5-4e5f-92ab-0a3532fe0875' class='xr-var-data-in' type='checkbox'><label for='data-dbf6f854-72f5-4e5f-92ab-0a3532fe0875' 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>long_name :</span></dt><dd>barotropic enstrophy flux</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.00000000e+00, -1.29046085e-16, -1.75595911e-18, ...,\n",
       "          3.61281572e-28,  8.04230756e-32,  1.74248260e-36],\n",
       "        [ 5.37049449e-19,  4.96352317e-20, -4.15413098e-19, ...,\n",
       "          3.94205684e-28,  1.01018228e-31,  2.23096047e-36],\n",
       "        [ 6.66720048e-21, -9.57370164e-19, -2.60729287e-19, ...,\n",
       "          4.00021083e-28,  1.25965618e-31,  2.62814841e-36],\n",
       "        ...,\n",
       "        [ 2.75185516e-20, -5.02683957e-20, -2.38729056e-19, ...,\n",
       "          3.59920095e-28,  1.12854628e-31,  1.94268652e-36],\n",
       "        [ 6.66720048e-21, -5.55973257e-19, -5.08588832e-19, ...,\n",
       "          4.71442513e-28,  1.26094624e-31,  2.89581662e-36],\n",
       "        [ 5.37049449e-19,  3.96917439e-19, -1.01894832e-18, ...,\n",
       "          3.59404220e-28,  1.12146241e-31,  2.27158454e-36]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENSgenspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.284e-16 ... 5.883e-58</div><input id='attrs-345f3eb6-9623-41fe-a348-deda90ea8ca0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-345f3eb6-9623-41fe-a348-deda90ea8ca0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-325bed7a-808f-494f-9ee2-583aeb703317' class='xr-var-data-in' type='checkbox'><label for='data-325bed7a-808f-494f-9ee2-583aeb703317' 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>long_name :</span></dt><dd>the spectrum of the rate of generation of barotropic enstrophy</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.00000000e+00,  1.28422491e-16,  1.73627197e-18, ...,\n",
       "          8.45577552e-41,  2.09252731e-48, -9.78454951e-75],\n",
       "        [ 0.00000000e+00, -4.83882906e-19,  3.40128808e-19, ...,\n",
       "          3.97825911e-41,  1.04537592e-48, -1.75296827e-58],\n",
       "        [ 0.00000000e+00,  7.83438658e-19,  1.91517989e-19, ...,\n",
       "          2.66622425e-43, -1.39210269e-48,  4.51162530e-59],\n",
       "        ...,\n",
       "        [ 0.00000000e+00,  2.27897422e-20,  1.97728800e-19, ...,\n",
       "         -7.06991934e-42, -9.35491387e-50, -3.88741684e-59],\n",
       "        [ 0.00000000e+00,  5.91721014e-19,  5.16166714e-19, ...,\n",
       "          1.04805990e-41, -7.04327330e-49, -6.00069777e-58],\n",
       "        [ 0.00000000e+00, -3.15708861e-19,  9.97205079e-19, ...,\n",
       "          1.72847560e-41,  5.66578342e-49,  5.88309103e-58]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENSfrictionspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.974e-19 ... -8.036e-57</div><input id='attrs-2ea900a9-0d9e-48aa-8793-4fa0e4da62b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ea900a9-0d9e-48aa-8793-4fa0e4da62b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2aaf3af-bb01-4f84-8ab2-97eb5e185260' class='xr-var-data-in' type='checkbox'><label for='data-d2aaf3af-bb01-4f84-8ab2-97eb5e185260' 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>long_name :</span></dt><dd>the spectrum of the rate of dissipation of barotropic enstrophy due to bottom friction</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.00000000e+00,  6.97407461e-19,  1.02697516e-20, ...,\n",
       "         -2.63894482e-40, -2.08110601e-47, -7.51755672e-57],\n",
       "        [ 5.59195751e-19,  4.30296457e-20,  3.36650053e-21, ...,\n",
       "         -2.35799734e-40, -2.15224400e-47, -8.09812876e-57],\n",
       "        [ 5.44796034e-21,  1.03794728e-20,  3.64906776e-21, ...,\n",
       "         -1.49933606e-40, -1.18048600e-47, -3.96602557e-57],\n",
       "        ...,\n",
       "        [-4.23109249e-21, -3.55476257e-21, -5.63020435e-21, ...,\n",
       "         -6.59380309e-41, -3.78591796e-48, -9.22496357e-58],\n",
       "        [ 5.44796034e-21,  8.85531480e-21, -2.08011952e-21, ...,\n",
       "         -1.55945379e-40, -1.01356780e-47, -4.42328247e-57],\n",
       "        [ 5.59195751e-19,  3.82057881e-20,  4.50515761e-21, ...,\n",
       "         -2.32415861e-40, -1.88969107e-47, -8.03614076e-57]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>APEgenspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.724e-08 ... 5.211e-50</div><input id='attrs-7144e323-6813-47f7-add6-1d433064d410' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7144e323-6813-47f7-add6-1d433064d410' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95967902-1843-4bcd-bfd8-e96857c46590' class='xr-var-data-in' type='checkbox'><label for='data-95967902-1843-4bcd-bfd8-e96857c46590' 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>long_name :</span></dt><dd>the spectrum of the rate of generation of available potential energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.00000000e+00,  1.72406122e-08,  2.33539252e-10, ...,\n",
       "          6.56762708e-33,  1.27119277e-40, -7.74174196e-67],\n",
       "        [ 0.00000000e+00, -6.09445265e-11,  4.59132921e-11, ...,\n",
       "          3.95064954e-33,  1.12815998e-40, -8.67346826e-51],\n",
       "        [ 0.00000000e+00,  1.06097067e-10,  2.65786239e-11, ...,\n",
       "          5.12009690e-35, -1.25774079e-40,  1.07973105e-50],\n",
       "        ...,\n",
       "        [ 0.00000000e+00,  2.84707599e-12,  2.64393450e-11, ...,\n",
       "         -5.78901584e-34, -6.96701920e-42, -1.71435537e-51],\n",
       "        [ 0.00000000e+00,  7.95817721e-11,  6.98751714e-11, ...,\n",
       "          4.31271662e-34, -4.81323254e-41, -5.54562856e-50],\n",
       "        [ 0.00000000e+00, -4.23612751e-11,  1.35558535e-10, ...,\n",
       "          1.53311758e-33,  2.25534694e-41,  5.21125130e-50]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KEspec_modal</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1616 ... 6.438e-32 2.567e-41</div><input id='attrs-ff965b9e-27b4-47e4-b946-bcd162025c61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff965b9e-27b4-47e4-b946-bcd162025c61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f79b650-d5f7-4e5f-8981-e887be681a2f' class='xr-var-data-in' type='checkbox'><label for='data-7f79b650-d5f7-4e5f-8981-e887be681a2f' 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>long_name :</span></dt><dd>modal kinetic energy spectra</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.00000000e+00, 1.61604491e-01, 3.05728850e-03, ...,\n",
       "          2.82746299e-25, 2.02961816e-32, 8.82709586e-42],\n",
       "         [1.57277789e-01, 9.93143188e-03, 2.37393445e-03, ...,\n",
       "          2.73050089e-25, 2.15281039e-32, 9.03549197e-42],\n",
       "         [3.48010194e-03, 2.27119043e-03, 1.25075661e-03, ...,\n",
       "          1.95517344e-25, 1.73527561e-32, 6.00872962e-42],\n",
       "         ...,\n",
       "         [9.18833017e-04, 8.74927205e-04, 5.41266238e-04, ...,\n",
       "          1.01630556e-25, 6.93006016e-33, 1.61050751e-42],\n",
       "         [3.48010194e-03, 2.22143269e-03, 1.18838647e-03, ...,\n",
       "          2.38720771e-25, 1.70860287e-32, 6.60159478e-42],\n",
       "         [1.57277789e-01, 8.23347485e-03, 2.19054221e-03, ...,\n",
       "          2.50504485e-25, 2.39232219e-32, 9.43241807e-42]],\n",
       "\n",
       "        [[0.00000000e+00, 2.55861526e-04, 4.41005771e-05, ...,\n",
       "          3.70398162e-25, 2.47977938e-32, 1.04875863e-41],\n",
       "         [1.83393012e-04, 6.08942665e-05, 5.57457327e-05, ...,\n",
       "          3.60583386e-25, 2.73146508e-32, 1.14893252e-41],\n",
       "         [4.20822460e-05, 5.00182153e-05, 5.87035438e-05, ...,\n",
       "          2.58035514e-25, 2.05083125e-32, 7.13963772e-42],\n",
       "...\n",
       "         [3.51734232e-05, 4.09396341e-05, 4.41647278e-05, ...,\n",
       "          1.24399678e-25, 8.55372647e-33, 2.02914194e-42],\n",
       "         [4.20822460e-05, 5.21715619e-05, 5.25380575e-05, ...,\n",
       "          2.89861985e-25, 2.06097183e-32, 8.03631133e-42],\n",
       "         [1.83393012e-04, 5.69436089e-05, 5.40533290e-05, ...,\n",
       "          3.27687973e-25, 2.96611488e-32, 1.14556754e-41]],\n",
       "\n",
       "        [[0.00000000e+00, 3.37477983e-05, 5.87476945e-06, ...,\n",
       "          7.46739414e-25, 5.30609861e-32, 2.41677500e-41],\n",
       "         [2.57000656e-05, 7.47502096e-06, 7.22479665e-06, ...,\n",
       "          7.35993021e-25, 5.74681346e-32, 2.51491032e-41],\n",
       "         [5.73003895e-06, 6.89964479e-06, 8.56087214e-06, ...,\n",
       "          5.26637112e-25, 4.58047791e-32, 1.65086624e-41],\n",
       "         ...,\n",
       "         [5.39410977e-06, 6.44922228e-06, 6.89452177e-06, ...,\n",
       "          2.64365543e-25, 1.89385247e-32, 4.57911202e-42],\n",
       "         [5.73003895e-06, 6.98669016e-06, 7.39489372e-06, ...,\n",
       "          6.22777212e-25, 4.60980851e-32, 1.82760914e-41],\n",
       "         [2.57000656e-05, 6.82890226e-06, 6.99011192e-06, ...,\n",
       "          6.63378519e-25, 6.43809857e-32, 2.56738856e-41]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PEspec_modal</span></div><div class='xr-var-dims'>(time, lev_mid, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.02742 ... 2.665e-32 9.974e-42</div><input id='attrs-bac73cdc-7194-44f1-9ce6-5f676ec9df7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bac73cdc-7194-44f1-9ce6-5f676ec9df7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-389e7273-1eb6-4fa7-a113-f8e143e4e15c' class='xr-var-data-in' type='checkbox'><label for='data-389e7273-1eb6-4fa7-a113-f8e143e4e15c' 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>long_name :</span></dt><dd>modal potential energy spectra</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[[0.00000000e+00, 2.74153236e-02, 1.18133391e-03, ...,\n",
       "          4.40975746e-26, 2.76489120e-33, 1.09739754e-42],\n",
       "         [1.96503899e-02, 3.26238189e-03, 1.19462064e-03, ...,\n",
       "          4.28814351e-26, 3.04234856e-33, 1.20104435e-42],\n",
       "         [1.12726834e-03, 1.07188101e-03, 7.86253736e-04, ...,\n",
       "          3.05843632e-26, 2.27714666e-33, 7.44168787e-43],\n",
       "         ...,\n",
       "         [4.18755493e-04, 4.38664356e-04, 3.64016103e-04, ...,\n",
       "          1.46637066e-26, 9.44869899e-34, 2.10474989e-43],\n",
       "         [1.12726834e-03, 1.11802683e-03, 7.03675474e-04, ...,\n",
       "          3.43566824e-26, 2.28840629e-33, 8.37629623e-43],\n",
       "         [1.96503899e-02, 3.05072726e-03, 1.15835274e-03, ...,\n",
       "          3.89694342e-26, 3.30370518e-33, 1.19752675e-42]],\n",
       "\n",
       "        [[0.00000000e+00, 1.34390354e-02, 5.84862113e-04, ...,\n",
       "          3.30406983e-25, 2.19874356e-32, 9.39850802e-42],\n",
       "         [1.02342703e-02, 1.48835000e-03, 5.75411154e-04, ...,\n",
       "          3.25290633e-25, 2.37889175e-32, 9.77060125e-42],\n",
       "         [5.70453482e-04, 5.49514784e-04, 4.26138405e-04, ...,\n",
       "          2.31988086e-25, 1.89019292e-32, 6.39501287e-42],\n",
       "         ...,\n",
       "         [2.38671149e-04, 2.56820684e-04, 2.11194910e-04, ...,\n",
       "          1.15814682e-25, 7.77494030e-33, 1.76523934e-42],\n",
       "         [5.70453482e-04, 5.56447419e-04, 3.68098970e-04, ...,\n",
       "          2.74338611e-25, 1.90229657e-32, 7.07966748e-42],\n",
       "         [1.02342703e-02, 1.35970143e-03, 5.56719941e-04, ...,\n",
       "          2.93196827e-25, 2.66504902e-32, 9.97448287e-42]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>APEspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.04085 ... 2.995e-32 1.117e-41</div><input id='attrs-584bee4b-596d-44ba-b37f-19bf5ec32072' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-584bee4b-596d-44ba-b37f-19bf5ec32072' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ced7d9f-ff0e-409d-95bd-c7f0eff75b7d' class='xr-var-data-in' type='checkbox'><label for='data-4ced7d9f-ff0e-409d-95bd-c7f0eff75b7d' 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>long_name :</span></dt><dd>available potential energy spectrum</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[0.00000000e+00, 4.08543589e-02, 1.76619602e-03, ...,\n",
       "         3.74504558e-25, 2.47523269e-32, 1.04959056e-41],\n",
       "        [2.98846603e-02, 4.75073189e-03, 1.77003179e-03, ...,\n",
       "         3.68172068e-25, 2.68312661e-32, 1.09716456e-41],\n",
       "        [1.69772182e-03, 1.62139579e-03, 1.21239214e-03, ...,\n",
       "         2.62572449e-25, 2.11790759e-32, 7.13918166e-42],\n",
       "        ...,\n",
       "        [6.57426642e-04, 6.95485040e-04, 5.75211013e-04, ...,\n",
       "         1.30478388e-25, 8.71981020e-33, 1.97571433e-42],\n",
       "        [1.69772182e-03, 1.67447424e-03, 1.07177444e-03, ...,\n",
       "         3.08695293e-25, 2.13113720e-32, 7.91729710e-42],\n",
       "        [2.98846603e-02, 4.41042868e-03, 1.71507268e-03, ...,\n",
       "         3.32166261e-25, 2.99541954e-32, 1.11720096e-41]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KEflux_div</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.588e-09 ... 3.27e-29</div><input id='attrs-289562e6-962e-4867-9077-e62b939d34c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-289562e6-962e-4867-9077-e62b939d34c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93524aaa-a5a8-45b2-b06f-d8efb85287bb' class='xr-var-data-in' type='checkbox'><label for='data-93524aaa-a5a8-45b2-b06f-d8efb85287bb' 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>long_name :</span></dt><dd>spectral divergence of flux of kinetic energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.00000000e+00,  6.58838173e-09,  8.73985657e-11, ...,\n",
       "          6.81916012e-21,  1.45135084e-24,  3.00150387e-29],\n",
       "        [ 6.62908849e-09,  2.88623787e-10, -3.74700595e-11, ...,\n",
       "          6.82704363e-21,  1.63214702e-24,  3.22634770e-29],\n",
       "        [ 1.06172004e-10, -7.63539389e-11, -1.29272151e-10, ...,\n",
       "          6.23559328e-21,  1.73065664e-24,  3.24313471e-29],\n",
       "        ...,\n",
       "        [-2.65036905e-11, -5.83562934e-11, -1.25748580e-10, ...,\n",
       "          5.07627043e-21,  1.40228514e-24,  2.17651042e-29],\n",
       "        [ 1.06172004e-10, -2.35277397e-11, -1.24654772e-10, ...,\n",
       "          7.20257303e-21,  1.70157129e-24,  3.59024403e-29],\n",
       "        [ 6.62908849e-09,  2.48062062e-10, -4.51965371e-11, ...,\n",
       "          6.28519443e-21,  1.81737485e-24,  3.26960591e-29]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>APEflux_div</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -1.748e-08 ... 1.176e-29</div><input id='attrs-a81791ba-01f7-47c1-8e05-14af0ec16ab3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a81791ba-01f7-47c1-8e05-14af0ec16ab3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-def0733d-21cd-46a7-b01d-8a036313f7e4' class='xr-var-data-in' type='checkbox'><label for='data-def0733d-21cd-46a7-b01d-8a036313f7e4' 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>long_name :</span></dt><dd>spectral divergence of flux of available potential energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.00000000e+00, -1.74817261e-08, -1.99384139e-10, ...,\n",
       "          1.02430340e-21,  2.07315881e-25,  4.08020526e-30],\n",
       "        [-2.82092284e-10,  6.34009082e-11,  7.26536079e-11, ...,\n",
       "          1.71463438e-21,  4.49173896e-25,  1.13924262e-29],\n",
       "        [ 3.17351849e-11, -1.46454690e-11,  1.38912859e-10, ...,\n",
       "          2.41016481e-21,  8.54906585e-25,  1.88240123e-29],\n",
       "        ...,\n",
       "        [ 6.46122367e-11,  8.40546420e-11,  1.15466391e-10, ...,\n",
       "          2.67055440e-21,  9.02731276e-25,  1.59608216e-29],\n",
       "        [ 3.17351849e-11,  3.75655285e-11,  1.06783864e-10, ...,\n",
       "          2.98294853e-21,  8.84588787e-25,  2.05851264e-29],\n",
       "        [-2.82092284e-10,  1.35001693e-10, -1.51630084e-11, ...,\n",
       "          1.50829924e-21,  4.90383023e-25,  1.17585064e-29]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-913e9e7f-d9ab-4e4e-b14c-39c834f9a23c' class='xr-section-summary-in' type='checkbox'  ><label for='section-913e9e7f-d9ab-4e4e-b14c-39c834f9a23c' class='xr-section-summary' >Attributes: <span>(24)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>pyqg:beta :</span></dt><dd>1.2130692965249345e-11</dd><dt><span>pyqg:delta :</span></dt><dd>None</dd><dt><span>pyqg:dt :</span></dt><dd>1500.0</dd><dt><span>pyqg:filterfac :</span></dt><dd>23.6</dd><dt><span>pyqg:L :</span></dt><dd>1000000.0</dd><dt><span>pyqg:M :</span></dt><dd>4096</dd><dt><span>pyqg:nk :</span></dt><dd>33</dd><dt><span>pyqg:nl :</span></dt><dd>64</dd><dt><span>pyqg:ntd :</span></dt><dd>1</dd><dt><span>pyqg:nx :</span></dt><dd>64</dd><dt><span>pyqg:ny :</span></dt><dd>64</dd><dt><span>pyqg:nz :</span></dt><dd>3</dd><dt><span>pyqg:pmodes :</span></dt><dd>[[ 1.          1.21826465  2.34858069]\n",
       " [ 1.          0.77493209 -0.82776478]\n",
       " [ 1.         -1.12300771  0.15674173]]</dd><dt><span>pyqg:radii :</span></dt><dd>[1601623.77840311   15375.38278599    7975.516272  ]</dd><dt><span>pyqg:rd :</span></dt><dd>15000.0</dd><dt><span>pyqg:rek :</span></dt><dd>1e-07</dd><dt><span>pyqg:taveint :</span></dt><dd>86400.0</dd><dt><span>pyqg:tavestart :</span></dt><dd>60000000.0</dd><dt><span>pyqg:tc :</span></dt><dd>100000</dd><dt><span>pyqg:tmax :</span></dt><dd>150000000.0</dd><dt><span>pyqg:twrite :</span></dt><dd>10000</dd><dt><span>pyqg:W :</span></dt><dd>1000000.0</dd><dt><span>title :</span></dt><dd>pyqg: Python Quasigeostrophic Model</dd><dt><span>reference :</span></dt><dd>https://pyqg.readthedocs.io/en/latest/index.html</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (time: 1, lev: 3, y: 64, x: 64, l: 64, k: 33, lev_mid: 2)\n",
       "Coordinates:\n",
       "  * time               (time) float64 1.5e+08\n",
       "  * lev                (lev) int64 1 2 3\n",
       "  * lev_mid            (lev_mid) float64 1.5 2.5\n",
       "  * x                  (x) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "  * y                  (y) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "  * l                  (l) float64 0.0 6.283e-06 ... -1.257e-05 -6.283e-06\n",
       "  * k                  (k) float64 0.0 6.283e-06 ... 0.0001948 0.0002011\n",
       "Data variables: (12/34)\n",
       "    q                  (time, lev, y, x) float64 0.0001383 ... -8.462e-06\n",
       "    u                  (time, lev, y, x) float64 0.0413 -0.05882 ... -0.1616\n",
       "    v                  (time, lev, y, x) float64 -0.357 -0.3027 ... -0.1369\n",
       "    ufull              (time, lev, y, x) float64 0.0913 -0.008824 ... -0.1616\n",
       "    vfull              (time, lev, y, x) float64 -0.357 -0.3027 ... -0.1369\n",
       "    qh                 (time, lev, l, k) complex128 (9.956089311189431e-06+0j...\n",
       "    ...                 ...\n",
       "    APEgenspec         (time, l, k) float64 0.0 1.724e-08 ... 5.211e-50\n",
       "    KEspec_modal       (time, lev, l, k) float64 0.0 0.1616 ... 2.567e-41\n",
       "    PEspec_modal       (time, lev_mid, l, k) float64 0.0 0.02742 ... 9.974e-42\n",
       "    APEspec            (time, l, k) float64 0.0 0.04085 ... 2.995e-32 1.117e-41\n",
       "    KEflux_div         (time, l, k) float64 0.0 6.588e-09 ... 1.817e-24 3.27e-29\n",
       "    APEflux_div        (time, l, k) float64 0.0 -1.748e-08 ... 1.176e-29\n",
       "Attributes: (12/24)\n",
       "    pyqg:beta:       1.2130692965249345e-11\n",
       "    pyqg:delta:      None\n",
       "    pyqg:dt:         1500.0\n",
       "    pyqg:filterfac:  23.6\n",
       "    pyqg:L:          1000000.0\n",
       "    pyqg:M:          4096\n",
       "    ...              ...\n",
       "    pyqg:tc:         100000\n",
       "    pyqg:tmax:       150000000.0\n",
       "    pyqg:twrite:     10000\n",
       "    pyqg:W:          1000000.0\n",
       "    title:           pyqg: Python Quasigeostrophic Model\n",
       "    reference:       https://pyqg.readthedocs.io/en/latest/index.html"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = m.to_dataset()\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Snapshots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "PV = ds.q + ds.Qy * ds.y\n",
    "PV['x'] = ds.x/ds.attrs['pyqg:rd']; PV.x.attrs = {'long_name': r'$x/L_d$'}\n",
    "PV['y'] = ds.y/ds.attrs['pyqg:rd']; PV.y.attrs = {'long_name': r'$y/L_d$'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,4))\n",
    "\n",
    "plt.subplot(131)\n",
    "PV.sel(lev=1).plot(cmap='Spectral_r')\n",
    "\n",
    "plt.subplot(132)\n",
    "PV.sel(lev=2).plot(cmap='Spectral_r')\n",
    "\n",
    "plt.subplot(133)\n",
    "PV.sel(lev=3).plot(cmap='Spectral_r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pyqg has a built-in method that computes the vertical modes. It is stored as an attribute in the Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first baroclinic deformation radius is 15.375382785987185 km\n",
      "The second baroclinic deformation radius is 7.975516271996243 km\n"
     ]
    }
   ],
   "source": [
    "print(f\"The first baroclinic deformation radius is {ds.attrs['pyqg:radii'][1]/1.e3} km\")\n",
    "print(f\"The second baroclinic deformation radius is {ds.attrs['pyqg:radii'][2]/1.e3} km\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can project the solution onto the modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pn = m.modal_projection(m.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,4))\n",
    "\n",
    "plt.subplot(131)\n",
    "plt.pcolormesh(m.x/m.rd, m.y/m.rd, pn[0]/(U1*Ld), cmap='Spectral_r', shading='auto')\n",
    "plt.xlabel(r'$x/L_d$')\n",
    "plt.ylabel(r'$y/L_d$')\n",
    "plt.colorbar()\n",
    "plt.title('Barotropic streamfunction')\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.pcolormesh(m.x/m.rd, m.y/m.rd, pn[1]/(U1*Ld), cmap='Spectral_r', shading='auto')\n",
    "plt.xlabel(r'$x/L_d$')\n",
    "plt.ylabel(r'$y/L_d$')\n",
    "plt.colorbar()\n",
    "plt.title('1st baroclinic streamfunction')\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.pcolormesh(m.x/m.rd, m.y/m.rd, pn[2]/(U1*Ld), cmap='Spectral_r', shading='auto')\n",
    "plt.xlabel(r'$x/L_d$')\n",
    "plt.ylabel(r'$y/L_d$')\n",
    "plt.colorbar()\n",
    "plt.title('2nd baroclinic streamfunction');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "kr, kespec_1 = tools.calc_ispec(m, ds.KEspec.sel(lev=1).data.squeeze())\n",
    "_ , kespec_2 = tools.calc_ispec(m, ds.KEspec.sel(lev=2).data.squeeze())\n",
    "_ , kespec_3 = tools.calc_ispec(m, ds.KEspec.sel(lev=3).data.squeeze())\n",
    "\n",
    "plt.loglog(kr, kespec_1, '.-' )\n",
    "plt.loglog(kr, kespec_2, '.-' )\n",
    "plt.loglog(kr, kespec_3, '.-' )\n",
    "\n",
    "plt.legend(['layer 1','layer 2', 'layer 3'], loc='lower left')\n",
    "plt.ylim([1e-12,4e-6]); \n",
    "plt.xlabel(r'k (m$^{-1}$)'); plt.grid()\n",
    "plt.title('Kinetic Energy Spectrum');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the modal KE and PE spectra are also calculated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "kr, modal_kespec_1 = tools.calc_ispec(m, ds.KEspec_modal.sel(lev=1).data.squeeze())\n",
    "_,  modal_kespec_2 = tools.calc_ispec(m, ds.KEspec_modal.sel(lev=2).data.squeeze())\n",
    "_,  modal_kespec_3 = tools.calc_ispec(m, ds.KEspec_modal.sel(lev=3).data.squeeze())\n",
    "\n",
    "_,  modal_pespec_2 = tools.calc_ispec(m, ds.PEspec_modal.sel(lev_mid=1.5).data.squeeze())\n",
    "_,  modal_pespec_3 = tools.calc_ispec(m, ds.PEspec_modal.sel(lev_mid=2.5).data.squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.loglog(kr, modal_kespec_1, '.-')\n",
    "plt.loglog(kr, modal_kespec_2, '.-')\n",
    "plt.loglog(kr, modal_kespec_3, '.-')\n",
    "\n",
    "plt.legend(['barotropic ','1st baroclinic', '2nd baroclinic'], loc='lower left')\n",
    "plt.ylim([1e-12,1e-5]); \n",
    "plt.xlabel(r'k (m$^{-1}$)'); plt.grid()\n",
    "plt.title('Kinetic Energy Spectra');\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.loglog(kr, modal_pespec_2, '.-')\n",
    "plt.loglog(kr, modal_pespec_3, '.-')\n",
    "\n",
    "plt.legend(['1st baroclinic', '2nd baroclinic'], loc='lower left')\n",
    "plt.ylim([1e-12,1e-5]); \n",
    "plt.xlabel(r'k (m$^{-1}$)'); plt.grid()\n",
    "plt.title('Potential Energy Spectra');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "_, APEgenspec = tools.calc_ispec(m, ds.APEgenspec.data.squeeze())\n",
    "_, APEflux    = tools.calc_ispec(m, ds.APEflux_div.data.squeeze())\n",
    "_, KEflux     = tools.calc_ispec(m, ds.KEflux_div.data.squeeze())\n",
    "_, Dissspec   = tools.calc_ispec(m, ds.Dissspec.squeeze().data)\n",
    "_, KEfrictionspec = tools.calc_ispec(m, ds.KEfrictionspec.squeeze().data)\n",
    "\n",
    "ebud = [ APEgenspec,\n",
    "         APEflux,\n",
    "         KEflux,\n",
    "         Dissspec, \n",
    "         KEfrictionspec]\n",
    "ebud.append(-np.vstack(ebud).sum(axis=0))\n",
    "ebud_labels = ['APE gen','APE flux div.','KE flux div.','Dissipation','Friction','Resid.']\n",
    "[plt.semilogx(kr, term) for term in ebud]\n",
    "plt.legend(ebud_labels, loc='lower left', ncol=2)\n",
    "plt.xlabel(r'k (m$^{-1}$)'); plt.grid()\n",
    "plt.title('Spectral Energy Transfers');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "_, ENSflux    = tools.calc_ispec(m, ds.ENSflux.data.squeeze())\n",
    "_, ENSgenspec = tools.calc_ispec(m, ds.ENSgenspec.data.squeeze())\n",
    "_, ENSfrictionspec = tools.calc_ispec(m, ds.ENSfrictionspec.data.squeeze())\n",
    "_, ENSDissspec = tools.calc_ispec(m, ds.ENSDissspec.data.squeeze())\n",
    "\n",
    "ebud = [ ENSgenspec,\n",
    "         ENSflux, \n",
    "         ENSDissspec,\n",
    "         ENSfrictionspec]\n",
    "ebud.append(-np.vstack(ebud).sum(axis=0))\n",
    "ebud_labels = ['ENS gen','ENS flux div.','Dissipation','Friction','Resid.']\n",
    "[plt.semilogx(kr, term) for term in ebud]\n",
    "plt.legend(ebud_labels, loc='lower left')\n",
    "plt.xlabel(r'k (m$^{-1}$)'); plt.grid()\n",
    "plt.title('Spectral Enstrophy Transfer');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dynamics here is similar to the reference experiment of [Larichev & Held (1995)](http://journals.ametsoc.org/doi/pdf/10.1175/1520-0485%281995%29025%3C2285%3AEAAFIA%3E2.0.CO%3B2). The APE generated through baroclinic instability is fluxed towards deformation length scales, where it is converted into KE. The KE the  experiments and inverse tranfer, cascading up to the scale of the domain. The mechanical bottom drag essentially removes the large scale KE.  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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