{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Anomaly & Detrend\n", "This code shows as simple way to:\n", "- Read in a .nc file using xarray\n", "- Generate montly anomalies from climatology of monthly means\n", "- detrend data by grid cell\n", "\n", "Will Wieder Feb. 2020" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "import pandas as pd\n", "import esmlab\n", "from ctsm_py import utils\n", "import segment as sg\n", "import scipy # Try scipy to detrend\n", "from scipy import signal\n", "import cf_units as cf\n", "\n", "# some resources for plotting\n", "import matplotlib.pyplot as plt\n", "import cartopy\n", "import cartopy.crs as ccrs\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "## User defined options\n", "model = 'CLM5_GSWP3'\n", "variables = ['GPP','NBP','TWS']\n", "pattern = '/glade/p/cgd/tss/people/oleson/CLM_LAND_ONLY_RELEASE/CLM5/clm50_r270_1deg_GSWP3V1_iso_newpopd_hist/'\\\n", " 'lnd/proc/tseries/month_1/clm50_r270_1deg_GSWP3V1_iso_newpopd_hist.clm2.h0.{var}.185001-201412.nc'\n", "files = [pattern.format(var=var) for var in variables]\n", "\n", "# Read in the data \n", "# utils.time_set_mid corrects dates, to get Jan of first year\n", "ds = utils.time_set_mid(xr.open_mfdataset(files, combine='by_coords', decode_times=True), 'time') # combine needed for newer xarray versions\n", "\n", "var = variables #redundant, but we'll keep using this for now because 'var' is used more later on...\n", "area = ds.area\n", "landfrac = ds.landfrac\n", "#print(ds.var)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Frozen(SortedKeysDict({'time': 300, 'lat': 192, 'lon': 288, 'hist_interval': 2, 'levgrnd': 25, 'levlak': 10, 'levdcmp': 25}))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/glade/u/home/wwieder/miniconda3/envs/python-tutorial/lib/python3.7/site-packages/xarray/core/indexing.py:1302: PerformanceWarning: Slicing with an out-of-order index is generating 25 times more chunks\n", " return self.array[key]\n" ] } ], "source": [ "# Select the time slice to analize, here we'll do the last 25 years\n", "# Use esmlab to calculate climatology & anomalies\n", "# First we'll do this for every grid w/ monthly data\n", "\n", "years = 25\n", "months = years * 12\n", "\n", "ds2 = ds.isel(time=slice(-months,None)) # Select last N years of data\n", "ds_climo = esmlab.core.climatology(ds2,freq='mon') # Calculate climatology\n", "ds_anom = esmlab.core.anomaly(ds2, clim_freq='mon', time_coord_name='time') #not sure how to use slice_mon_clim_time\n", "ds_anom = ds_anom.where(ds_anom.get(var[0]).max(dim='time')) # mask out regions with no GPP for all variables\n", "print(ds_anom.dims)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/glade/u/home/wwieder/miniconda3/envs/python-tutorial/lib/python3.7/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n", " x = np.divide(x1, x2, out)\n", "/glade/u/home/wwieder/miniconda3/envs/python-tutorial/lib/python3.7/site-packages/dask/compatibility.py:107: RuntimeWarning: All-NaN slice encountered\n", " return func(*args, **kwargs)\n", "/glade/u/home/wwieder/miniconda3/envs/python-tutorial/lib/python3.7/site-packages/toolz/functoolz.py:488: RuntimeWarning: All-NaN slice encountered\n", " ret = f(ret)\n" ] } ], "source": [ "# now remove trends from each grid cell\n", "# detrend data using signal.detrend\n", "# use apply_ufunc\n", "# This works for just for one variable\n", "\n", "def dtrend(anom, dim):\n", " # note: apply always moves core dimensions to the end\n", " return xr.apply_ufunc(signal.detrend, anom,\n", " input_core_dims=[[dim]],\n", " output_core_dims=[[dim]],\n", " kwargs={'axis': -1})\n", "\n", "dt_anom = dtrend(ds_anom.GPP.load().fillna(0), 'time')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/glade/u/home/wwieder/miniconda3/envs/python-tutorial/lib/python3.7/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n", " x = np.divide(x1, x2, out)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# quick look at a grid cell\n", "\n", "ilat = 60\n", "ilon = 240\n", "plt.plot(ds2.time['time.month'], ds2['GPP'].sel(lat=ilat, lon=ilon, method='nearest'), 'o' )\n", "plt.plot(ds_climo.time['time.month'], ds_climo['GPP'].sel(lat=ilat, \n", " lon=ilon, method='nearest'), '-', lw=3 )\n", "plt.plot(ds_anom.time['time.month'], ds_anom['GPP'].sel(lat=ilat, \n", " lon=ilon, method='nearest'), 'o' )\n", "plt.plot(dt_anom.time['time.month'], dt_anom.sel(lat=ilat, lon=ilon, method='nearest'), '*' )\n", "plt.legend(labels=('raw','climo','anom','dt_anom'),frameon = False)\n", "plt.axhline(0, color='black', lw=1)\n", "plt.ylabel(\"GPP (gC/m2/s)\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Global means, anomalies, & detrended anomalies\n", "#### * These are still monthly results * " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# mask out area for gridcells with no GPP for weights?\n", "landUp = area * landfrac * 1e6 # area in km2, not m2\n", "wgt = landUp / landUp.sum() # weighting for each grid cell\n", "spd = 3600 * 24 # Convert to daily fluxes (gC/m2/d), from gc/m2/s \n", "spy = spd * 365\n", "\n", "global_ts = esmlab.statistics.weighted_sum(ds2.GPP*landUp, dim=('lat','lon'))*spd\n", "global_ts_anom = esmlab.statistics.weighted_sum(ds_anom.GPP*landUp, dim=('lat','lon'))*spd\n", "global_ts_dt = signal.detrend(global_ts_anom)\n", "\n", "# Then makes some plots\n", "plt.plot(global_ts.time, global_ts_anom, '-' )\n", "plt.plot(global_ts.time, global_ts_dt, '-' )\n", "plt.ylabel(\"Global monthly GPP anomalies (gC/m2/d)\")\n", "plt.legend(labels=(\"monthly anomalies\", \"detrended anomalies\"));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert monthly to annual" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "dask.array\n", "Coordinates:\n", " * lat (lat) float32 -90.0 -89.057594 -88.11518 ... 89.057594 90.0\n", " * lon (lon) float32 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.25 357.5 358.75\n", " * time (time) object 1990-12-16 12:00:00 ... 2014-12-16 12:00:00\n", "Attributes:\n", " units: gC m-2 y-1\n", "[115.83182242 115.15869757 112.85235243 114.92316293 114.05480004\n", " 115.1866071 114.62268845 117.13412568 115.49125931 117.26893797\n", " 119.334785 118.19690718 115.3763682 116.20857797 117.65856708\n", " 118.32954254 120.26782701 119.86430742 120.72266914 120.46586301\n", " 122.1697529 124.63990938 121.30831584 122.25063894 122.40474113]\n" ] }, { "data": { "text/plain": [ "Text(0, 0.5, 'Global annual GPP anomalies (PgC/y)')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# These values don't make any sense, let's have a closer look.\n", "\n", "# apply iterates through all variables in a dataset and applies the function to each variable.\n", "# ?? Would be nice to do this differently for fluxes & stocks ??\n", "ann_mean = utils.weighted_annual_mean(ds2['GPP']) * spy\n", "ann_mean.attrs['units'] = 'gC m-2 y-1'\n", "print(ann_mean)\n", "#print(ds2.apply_ufunc(utils.weighted_annual_mean))\n", "\n", "#ann_mean_anom = esmlab.core.anomaly(ann_mean, clim_freq='mon', time_coord_name='time') #not sure how to use slice_mon_clim_time\n", "\n", "global_ann_ts = esmlab.statistics.weighted_sum(ann_mean*landUp, dim=('lat','lon'))*1e-15 #PgC y-1\n", "global_ann_dt = signal.detrend(global_ann_ts)\n", "#global_ann_dt.attrs['units'] = 'PgC y-1'\n", "print(global_ann_ts.values)\n", "#plt.plot(global_ann_ts.time, global_ann_ts, '-' )\n", "plt.plot(global_ann_ts.time, global_ann_dt, '-' )\n", "plt.ylabel(\"Global annual GPP anomalies (PgC/y)\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:miniconda3-python-tutorial]", "language": "python", "name": "conda-env-miniconda3-python-tutorial-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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }