{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# True Zonal Mean\n", "\n", "Calculate the *true zonal mean* of a scalar quantity regardless of the horizontal mesh. \n", "\n", "Specifically, we calculate the volume weighted mean along all grid cells whose centres fall within finite latitude intervals rather than the arithmetic mean of cells along the model's curvilinear grid. The method presented can also be used to re-grid models onto the same latitudinal grid and the general principles can be used to define any multidimensional sum or average using the `xhistogram` package.\n", " \n", "**Requirements:** \n", "Select the `conda/analysis3-22.04` (or later) kernel.\n", "This code should work for just about any MOM5 configuration since all we are grabbing is temeprature and standard grid information. You can swap temperature with any other scalar variable. You can also in principle swap latitude with another scalar." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%config InlineBackend.figure_format='retina'\n", "\n", "import cosima_cookbook as cc\n", "import matplotlib.pyplot as plt\n", "import xarray as xr\n", "import numpy as np\n", "from dask.distributed import Client\n", "from xhistogram.xarray import histogram" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client = Client()\n", "client" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "session = cc.database.create_session()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Choose the run and the variable we want to average. I am using temperature but you can choose any scalar." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'temp' (time: 300, st_ocean: 50, yt_ocean: 1080,\n",
       "                          xt_ocean: 1440)> Size: 93GB\n",
       "dask.array<concatenate, shape=(300, 50, 1080, 1440), dtype=float32, chunksize=(2, 10, 216, 288), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "  * st_ocean  (st_ocean) float64 400B 1.152 3.649 6.565 ... 5.034e+03 5.254e+03\n",
       "  * time      (time) datetime64[ns] 2kB 1958-06-30T12:00:00 ... 2257-06-30T12...\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    units:          deg_C\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...
" ], "text/plain": [ " Size: 93GB\n", "dask.array\n", "Coordinates:\n", " * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n", " * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n", " * st_ocean (st_ocean) float64 400B 1.152 3.649 6.565 ... 5.034e+03 5.254e+03\n", " * time (time) datetime64[ns] 2kB 1958-06-30T12:00:00 ... 2257-06-30T12...\n", "Attributes:\n", " long_name: Conservative temperature\n", " units: deg_C\n", " valid_range: [-10. 500.]\n", " cell_methods: time: mean\n", " time_avg_info: average_T1,average_T2,average_DT\n", " ncfiles: ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "experiment = '025deg_jra55v13_iaf_gmredi6' #Choose any run\n", "variable = 'temp' # any scalar variable where volume weighted averaging makes sense\n", "variable_to_average = cc.querying.getvar(experiment, variable, session, ncfile='ocean.nc', chunks={'time': 6})\n", "variable_to_average" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we show the standard approach, which is to take the arithmetic mean of all grid cells along the quasi-longitudinal coordinate. For MOM5's tri-polar grid this approach is in principle \"okay\" for the southern hemisphere, where grid cell areas are constant at fixed latitude. It doesn't though, take into account partial cells.\n", "\n", "The `xarray`'s method `.mean(dim='dimension')` applies `numpy.mean()` across that dimension. This is simply the arithmetic mean.\n", "\n", "For some scalar $T$ the arithmetic mean, e.g., across dimension `i`, is given by\n", "\n", "$$ \\left_{j,k} = \\frac{1}{I}\\sum_{i=1}^{I} T_{i,j,k},$$\n", "\n", "where $i$, $j$, and $k$ are the indicies in the $x$, $y$, and $z$ directions respectively of the curvilinear grid and $I$ is the number of indicies along the $x$ axis. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 470, "width": 818 } }, "output_type": "display_data" } ], "source": [ "x_arith_mean = variable_to_average.groupby('time.year').mean(dim='time').mean(dim='xt_ocean')\n", "\n", "plt.figure(figsize=(10, 5))\n", "x_arith_mean.sel(year=2000).plot(yincrease=False, vmin=273, vmax=300, cmap='Oranges')\n", "plt.title('x-coordinate arithmetic mean');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The main issue with this average is that the 'latitude' coordinate may be meaningless near the north pole, particulalrly when comparing to observational analyses or other models which can have either a regular grid or a different curvilinear grid. Even different versions of MOM might have different grids! \n", "\n", "Let us consider what the true zonal average looks like. That is consider a set of latitude 'edges' $\\{\\phi'_{1/2},\\phi'_{1+1/2},...,\\phi'_{\\ell-1/2},\\phi'_{\\ell+1/2},...,\\phi'_{L+1/2}\\}$ between which we want to compute an average of $T$ at $\\{\\phi'_{1},\\phi'_{2},...,\\phi'_{\\ell},...,\\phi'_{L}\\}$ such that\n", "\n", "$$ \\overline{T}(\\phi'_\\ell,\\sigma) = \\dfrac{\\iint_{\\phi'_{\\ell-1/2} < \\phi \\leq \\phi'_{\\ell+1/2}} T(\\phi,\\lambda,\\sigma)\\frac{\\partial z}{\\partial \\sigma}(\\phi,\\lambda,\\sigma)\\,\\mathrm{d}A}{\\iint_{\\phi'_{\\ell-1/2} < \\phi \\leq \\phi'_{\\ell+1/2}}\\frac{\\partial z}{\\partial \\sigma}(\\phi,\\lambda,\\sigma)\\,\\mathrm{d}A},$$\n", "\n", "where $\\lambda$ is longitude and $\\sigma$ is an arbitrary vertical coordinate. \n", "\n", "In discrete form this average is\n", "\n", "$$\\overline{T}_{\\ell,k} = \\frac{\\sum_{i=1}^{I}\\sum_{j=1}^{J}\\delta_{i,j}T_{i,j,k}\\Delta Z_{i,j,k}\\Delta \\mathrm{Area}_{i,j}}{\\sum_{i=1}^{I}\\sum_{j=1}^{J}\\delta_{i,j,k}\\Delta Z_{i,j,k}\\Delta \\mathrm{Area}_{i,j}},$$\n", "\n", "where $\\delta_{i,j} = 1$ if $\\phi'_{\\ell-1/2}<\\phi_{i,j}\\leq \\phi'_{\\ell+1/2}$ and $\\delta_{i,j} = 0$ elsewhere, $\\Delta Z$ is the grid cell vertical thickness and $\\Delta \\mathrm{Area}$ is the grid cell horizontal area.\n", "\n", "For our purposes we will use the edges of the models `xt_ocean` coordinate to define $\\phi'_{\\ell+1/2}$ so the number of 'bins' $L$ will be the same as the length of the quasi-latitude coordinate ($J$). \n", "\n", "Fortunately, as you can see below, the two sums are weighted histograms (one for $T$ times volume and the other for just volume) and these can be rapidly computed using `xhistogram`.\n", "\n", "First lets load the scalar variable (latitude) we want to use as our coordinate then define the bin edges." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/xarray/core/dataset.py:271: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 1. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n" ] } ], "source": [ "coord = 'geolat_t' #can be any scalar (2D, 3D, eulerian, lagrangian etc)\n", "variable_as_coord = cc.querying.getvar(experiment, coord, session, n=1) # might need ncfile='ocean.nc' if a tracer\n", "\n", "# Define the coordinate bins as the latitude edges of the T-cells\n", "yu_ocean = cc.querying.getvar(experiment, 'yu_ocean', session, n=1)\n", "\n", "# make numpy array (using .values) and add 1st edge at -90\n", "bins = np.insert(yu_ocean.values, 0, np.array(-90), axis=0) \n", "\n", "# Alternatively we could just use some regular grid like this \n", "# coordbins = np.linspace(-80, 90, 50)\n", "# or use a grid from a different (coarser) model.\n", "\n", "dzt = cc.querying.getvar(experiment, 'dzt', session, ncfile='ocean.nc') #thickness of cells\n", "area_t = cc.querying.getvar(experiment, 'area_t', session, n=1) #area of cells\n", "\n", "dVol = dzt * area_t #Volume of cells" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's compute the numerator and denominator of the equation above using `xhistogram`, then the time mean and then the zonal mean." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "histVolCoordDepth = histogram(variable_as_coord.broadcast_like(dVol).where(~np.isnan(dVol)),\n", " bins=[bins], weights=dVol, dim=['yt_ocean', 'xt_ocean'])\n", "histTVolCoordDepth = histogram(variable_as_coord.broadcast_like(dVol).where(~np.isnan(dVol)),\n", " bins=[bins], weights=dVol * variable_to_average, dim=['yt_ocean', 'xt_ocean'])\n", "coord_mean = (histTVolCoordDepth/histVolCoordDepth).groupby('time.year').mean(dim='time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can plot the results which thankfully retain all the data-array info on variables and axis etc." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 470, "width": 818 } }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 5))\n", "coord_mean.sel(year=2000).plot(yincrease=False, vmin=273, vmax=300, cmap='Oranges')\n", "plt.title('True zonal mean');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we used the same bin edges as the standard `yt_ocean` coordinate we can take a difference between the arithmetic mean along the model's x-axis and our mean along grid cells within latitude bands. The main differences are near the North Pole where the grid is furthest for being regular. There are also differences near the Antacrtic Shelf suggesting partial cells also matter." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 470, "width": 825 } }, "output_type": "display_data" } ], "source": [ "zonal_minus_x_mean = coord_mean.sel(year=2000) - x_arith_mean.sel(year=2000).values\n", "\n", "plt.figure(figsize=(10, 5))\n", "zonal_minus_x_mean.plot(yincrease=False, vmin=-0.8, vmax=0.8, cmap='RdBu_r', extend='both')\n", "plt.title('True zonal minus $x$-coordinate arithmetic mean');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`xarray` has a new `weighted` functionality which allows it to do weighted means instead of arithmetic mean.\n", "\n", "Let's see how that works out... We chose `dVol` as the weights and we only do the comparisson for year 2000." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "variable_to_average_weighted = variable_to_average.copy().sel(time='2000').mean(dim='time')\n", "variable_to_average_weighted = variable_to_average_weighted.weighted(dVol.sel(time='2000').fillna(0))\n", "meanweighted_y2000 = variable_to_average_weighted.mean(dim='xt_ocean').groupby('time.year').mean(dim='time').sel(year=2000)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-24.01/lib/python3.10/site-packages/dask/core.py:127: RuntimeWarning: invalid value encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 471, "width": 825 } }, "output_type": "display_data" } ], "source": [ "zonal_minus_x_mean = coord_mean.sel(year=2000) - meanweighted_y2000.values\n", "\n", "plt.figure(figsize=(10, 5))\n", "zonal_minus_x_mean.plot(yincrease=False, vmin=-0.8, vmax=0.8, cmap='RdBu_r')\n", "plt.title(\"True zonal minus xarray's $x$-coordinate weighted mean\"); " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "South of 65N, where complications of the tripolar grid don't matter, `xarray`'s weighted mean does the job! But in the region of the tripolar we need to be more careful." ] } ], "metadata": { "extensions": { "jupyter_dashboards": { "activeView": "grid_default", "version": 1, "views": { "grid_default": { "cellMargin": 10, "defaultCellHeight": 20, "maxColumns": 12, "name": "grid", "type": "grid" }, "report_default": { "name": "report", "type": "report" } } } }, "kernelspec": { "display_name": "Python [conda env:analysis3-24.01]", "language": "python", "name": "conda-env-analysis3-24.01-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" }, "thumbnail_figure": 1 }, "nbformat": 4, "nbformat_minor": 4 }