{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "6c6379c0", "metadata": {}, "outputs": [], "source": [ "#Import libraries\n", "import numpy as np\n", "import xarray as xr\n", "import scipy\n", "from scipy import stats\n", "import scipy.special as sp\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "from matplotlib.colors import ListedColormap\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "from glob import glob\n", "import os\n", "import warnings\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "id": "338d98b6", "metadata": {}, "outputs": [], "source": [ "dir_data = '/Users/jzhang/Documents/Data/CMIP6/'\n", "dir_proj = '/Users/jzhang/Documents/Projects/Albedo_Symmetry_WE/'" ] }, { "cell_type": "code", "execution_count": 3, "id": "d5684b95", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsut' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsut' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsutcs' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsutcs' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsut' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsutcs' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'siconc' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'siconc' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'siconc' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'areacello' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'areacella' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n" ] } ], "source": [ "#\n", "# CESM2-WACCM\n", "#\n", "flx = xr.open_mfdataset(sorted(glob(dir_data+'CESM2-WACCM/historical/*_Amon_*.nc') + glob(dir_data+'CESM2-WACCM/SSP370/*_Amon_*.nc')))\n", "sic = xr.open_mfdataset(sorted(glob(dir_data+'CESM2-WACCM/historical/*_SImon_*.nc') + glob(dir_data+'CESM2-WACCM/SSP370/*_SImon_*.nc')))\n", "siconc = sic['siconc']/100.\n", "ofx = xr.open_mfdataset(sorted(glob(dir_data+'CESM2-WACCM/areacello_Ofx_*.nc')))\n", "fx = xr.open_mfdataset(sorted(glob(dir_data+'CESM2-WACCM/areacella_fx_*.nc')))\n", "\n", "time_bounds = flx['time_bnds'].values\n", "Mwts = np.array([(tb[1] - tb[0]).days for tb in time_bounds], dtype=float)\n", "# calculate ocean fraction\n", "oce_frac = 1. - siconc\n", "oce_frac = oce_frac.fillna(0.)\n", "# get fluxes\n", "R = flx['rsut']\n", "R = R.where(R>0,0)\n", "R_clr = flx['rsutcs']\n", "R_clr = R_clr.where(R_clr>0,0)\n", "R_cld = R - R_clr\n", "\n", "lon2 = sic.lon[0,:,:].values\n", "lon = flx.lon.values\n", "area = fx['areacella'].values\n", "oarea = ofx['areacello'].fillna(0.)\n", "\n", "xM = xr.Dataset()\n", "xM['Mwts'] = (('time',), Mwts)" ] }, { "cell_type": "code", "execution_count": 7, "id": "f50eb814", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fraction of missing values in R: 0.0\n" ] } ], "source": [ "# calculate the fraction of missing values in R\n", "missing_frac = R.isnull().sum(dim='time') / R.sizes['time']\n", "print(\"Fraction of missing values in R:\", np.sum(missing_frac.values))" ] }, { "cell_type": "code", "execution_count": 4, "id": "8c8cf68d", "metadata": {}, "outputs": [], "source": [ "def geoavg_sic(data,hemi='both',div=180):\n", " \n", " sarea = data * oarea\n", " \n", " mask = lon<1e10\n", " mask2 = lon2<1e10\n", " \n", " if hemi == 'WH':\n", " mask2 = (lon2(180+div))\n", " mask = (lon(180+div))\n", " elif hemi == 'EH':\n", " mask2 = (lon2>div) & (lon2<(180+div))\n", " mask = (lon>div) & (lon<(180+div))\n", "\n", " data_flat = sarea.values.flatten()\n", " mask2_flat = mask2.flatten()\n", " return np.sum(data_flat[mask2_flat]) / np.sum(area[:,mask])\n", " #return np.sum(sarea[:,mask2]) / np.sum(area[:,mask])\n", "\n", "def geoavg_flx(data,hemi='both',div=180):\n", "\n", " mask2 = lon<1e10\n", "\n", " if hemi == 'WH':\n", " mask2 = (lon(180+div))\n", " elif hemi == 'EH':\n", " mask2 = (lon>div) & (lon<(180+div))\n", "\n", " return np.sum(data[:,mask2]*area[:,mask2]/np.sum(area[:,mask2],axis=(0,1)),axis=(0,1))\n", " #return np.average(data[:,:,mask2],weights=area[:,:,mask2],axis=(1,2))" ] }, { "cell_type": "code", "execution_count": null, "id": "7b10ac61", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing period PI, division 1...\n", "Processing period PI, division 2...\n", "Processing period PI, division 3...\n", "Processing period PI, division 4...\n", "Processing period PI, division 5...\n", "Processing period PI, division 6...\n", "Processing period PI, division 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"ewR_sym_cld = np.nan*np.ones((2,4))\n", "ewR_sym_clr = np.nan*np.ones((2,4))\n", "ewO_sym = np.nan*np.ones((2,4))\n", "ewR_27 = np.nan*np.ones((2,4))\n", "ewR_27_cld = np.nan*np.ones((2,4))\n", "ewR_27_clr = np.nan*np.ones((2,4))\n", "ewO_27 = np.nan*np.ones((2,4))\n", "\n", "for period, tidx in zip(['PI','MC','PD','EOC'],tindex):\n", " idx = ['PI','MC','PD','EOC'].index(period)\n", " w = xM['Mwts'].isel(time=tidx)\n", " Rs = R.isel(time=tidx).weighted(w).mean(dim='time').values\n", " R_clds = R_cld.isel(time=tidx).weighted(w).mean(dim='time').values\n", " R_clrs = R_clr.isel(time=tidx).weighted(w).mean(dim='time').values\n", " oce_fracs = oce_frac.isel(time=tidx).weighted(w).mean(dim='time').values\n", " for dvd in range(1, 181, 1):\n", " print(f'Processing period {period}, division {dvd}...')\n", " dR[dvd-1, idx] = geoavg_flx(Rs,hemi='EH',div=dvd) - geoavg_flx(Rs,hemi='WH',div=dvd)\n", " dR_cld[dvd-1, idx] = geoavg_flx(R_clds,hemi='EH',div=dvd) - geoavg_flx(R_clds,hemi='WH',div=dvd)\n", " dR_clr[dvd-1, idx] = geoavg_flx(R_clrs,hemi='EH',div=dvd) - geoavg_flx(R_clrs,hemi='WH',div=dvd)\n", " dO[dvd-1, idx] = geoavg_sic(oce_fracs,hemi='EH',div=dvd) - geoavg_sic(oce_fracs,hemi='WH',div=dvd)\n", " symmetry[idx] = np.argmin(np.abs(dR[:,idx])) + 1\n", " ewR_sym[0, idx] = geoavg_flx(Rs,hemi='EH',div=symmetry[idx])\n", " ewR_sym[1, idx] = geoavg_flx(Rs,hemi='WH',div=symmetry[idx])\n", " ewR_sym_cld[0, idx] = geoavg_flx(R_clds,hemi='EH',div=symmetry[idx])\n", " ewR_sym_cld[1, idx] = geoavg_flx(R_clds,hemi='WH',div=symmetry[idx])\n", " ewR_sym_clr[0, idx] = geoavg_flx(R_clrs,hemi='EH',div=symmetry[idx])\n", " ewR_sym_clr[1, idx] = geoavg_flx(R_clrs,hemi='WH',div=symmetry[idx])\n", " ewO_sym[0, idx] = geoavg_sic(oce_fracs,hemi='EH',div=symmetry[idx])\n", " ewO_sym[1, idx] = geoavg_sic(oce_fracs,hemi='WH',div=symmetry[idx])\n", " ewR_27[0, idx] = geoavg_flx(Rs,hemi='EH',div=27)\n", " ewR_27[1, idx] = geoavg_flx(Rs,hemi='WH',div=27)\n", " ewR_27_cld[0, idx] = geoavg_flx(R_clds,hemi='EH',div=27)\n", " ewR_27_cld[1, idx] = geoavg_flx(R_clds,hemi='WH',div=27)\n", " ewR_27_clr[0, idx] = geoavg_flx(R_clrs,hemi='EH',div=27)\n", " ewR_27_clr[1, idx] = geoavg_flx(R_clrs,hemi='WH',div=27)\n", " ewO_27[0, idx] = geoavg_sic(oce_fracs,hemi='EH',div=27)\n", " ewO_27[1, idx] = geoavg_sic(oce_fracs,hemi='WH',div=27)\n", " for yr in range(20):\n", " wy = xM['Mwts'].isel(time=tidx)[yr*12:(yr+1)*12]\n", " Rsy = R.isel(time=tidx)[yr*12:(yr+1)*12].weighted(wy).mean(dim='time').values\n", " dR_intann_sym[yr, idx] = geoavg_flx(Rsy,hemi='EH',div=symmetry[idx]) - geoavg_flx(Rsy,hemi='WH',div=symmetry[idx])\n", " dR_intann_27[yr, idx] = geoavg_flx(Rsy,hemi='EH',div=27) - geoavg_flx(Rsy,hemi='WH',div=27)" ] }, { "cell_type": "code", "execution_count": null, "id": "dc83d8d9", "metadata": {}, "outputs": [], "source": [ "# save results\n", "np.savez(dir_data+'CESM2-WACCM/CESM2_Symmetry_LON_PI_MC_PD_EOC_20yrs.npz', dR=dR, dR_cld=dR_cld, dR_clr=dR_clr, dO=dO, symmetry=symmetry, dR_intann_sym=dR_intann_sym, dR_intann_27=dR_intann_27, ewR_sym=ewR_sym, ewR_sym_cld=ewR_sym_cld, ewR_sym_clr=ewR_sym_clr, ewO_sym=ewO_sym, ewR_27=ewR_27, ewR_27_cld=ewR_27_cld, ewR_27_clr=ewR_27_clr, ewO_27=ewO_27)" ] }, { "cell_type": "code", "execution_count": 3, "id": "e59835fe", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsut' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsut' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsutcs' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'rsutcs' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'siconc' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'siconc' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'areacello' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n", "/opt/anaconda3/envs/tf215env/lib/python3.11/site-packages/xarray/conventions.py:204: SerializationWarning: variable 'areacella' has multiple fill values {np.float32(1e+20), np.float64(1e+20)} defined, decoding all values to NaN.\n", " var = coder.decode(var, name=name)\n" ] } ], "source": [ "#\n", "# CESM2-WACCM G6sulfur\n", "#\n", "flx = xr.open_mfdataset(sorted(glob(dir_data+'CESM2-WACCM/G6sulfur/*_Amon_*.nc')))\n", "sic = xr.open_mfdataset(sorted(glob(dir_data+'CESM2-WACCM/G6sulfur/*_SImon_*.nc')))\n", "siconc = sic['siconc']/100.\n", "ofx = xr.open_mfdataset(sorted(glob(dir_data+'CESM2-WACCM/areacello_Ofx_*.nc')))\n", "fx = xr.open_mfdataset(sorted(glob(dir_data+'CESM2-WACCM/areacella_fx_*.nc')))\n", "\n", "time_bounds = flx['time_bnds'].values\n", "Mwts = np.array([(tb[1] - tb[0]).days for tb in time_bounds], dtype=float)\n", "# calculate ocean fraction\n", "oce_frac = 1. - siconc\n", "oce_frac = oce_frac.fillna(0.)\n", "# get fluxes\n", "R = flx['rsut']\n", "R = R.where(R>0,0)\n", "R_clr = flx['rsutcs']\n", "R_clr = R_clr.where(R_clr>0,0)\n", "R_cld = R - R_clr\n", "\n", "lon2 = sic.lon.values\n", "lon = flx.lon.values\n", "area = fx['areacella'].values\n", "oarea = ofx['areacello'].fillna(0.)\n", "\n", "xM = xr.Dataset()\n", "xM['Mwts'] = (('time',), Mwts)" ] }, { "cell_type": "code", "execution_count": 26, "id": "a27f4c0f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing period MC-SAI, division 1...\n", "Processing period MC-SAI, division 2...\n", "Processing period 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"Processing period EOC-SAI, division 164...\n", "Processing period EOC-SAI, division 165...\n", "Processing period EOC-SAI, division 166...\n", "Processing period EOC-SAI, division 167...\n", "Processing period EOC-SAI, division 168...\n", "Processing period EOC-SAI, division 169...\n", "Processing period EOC-SAI, division 170...\n", "Processing period EOC-SAI, division 171...\n", "Processing period EOC-SAI, division 172...\n", "Processing period EOC-SAI, division 173...\n", "Processing period EOC-SAI, division 174...\n", "Processing period EOC-SAI, division 175...\n", "Processing period EOC-SAI, division 176...\n", "Processing period EOC-SAI, division 177...\n", "Processing period EOC-SAI, division 178...\n", "Processing period EOC-SAI, division 179...\n", "Processing period EOC-SAI, division 180...\n" ] } ], "source": [ "tindex = [np.arange(240)+240, np.arange(240)+720]\n", "dR = np.nan*np.ones((180,2))\n", "dR_cld = np.nan*np.ones((180,2))\n", "dR_clr = np.nan*np.ones((180,2))\n", "dO = np.nan*np.ones((180,2))\n", "symmetry = np.nan*np.ones(2)\n", "dR_intann_sym = np.nan*np.ones((20,2))\n", "dR_intann_27 = np.nan*np.ones((20,2))\n", "ewR_sym = np.nan*np.ones((2,2))\n", "ewR_sym_cld = np.nan*np.ones((2,2))\n", "ewR_sym_clr = np.nan*np.ones((2,2))\n", "ewO_sym = np.nan*np.ones((2,2))\n", "ewR_27 = np.nan*np.ones((2,2))\n", "ewR_27_cld = np.nan*np.ones((2,2))\n", "ewR_27_clr = np.nan*np.ones((2,2))\n", "ewO_27 = np.nan*np.ones((2,2))\n", "for period, tidx in zip(['MC-SAI','EOC-SAI'],tindex):\n", " idx = ['MC-SAI','EOC-SAI'].index(period)\n", " w = xM['Mwts'].isel(time=tidx)\n", " Rs = R.isel(time=tidx).weighted(w).mean(dim='time').values\n", " R_clds = R_cld.isel(time=tidx).weighted(w).mean(dim='time').values\n", " R_clrs = R_clr.isel(time=tidx).weighted(w).mean(dim='time').values\n", " oce_fracs = oce_frac.isel(time=tidx).weighted(w).mean(dim='time').values\n", " for dvd in range(1, 181, 1):\n", " print(f'Processing period {period}, division {dvd}...')\n", " dR[dvd-1, idx] = geoavg_flx(Rs,hemi='EH',div=dvd) - geoavg_flx(Rs,hemi='WH',div=dvd)\n", " dR_cld[dvd-1, idx] = geoavg_flx(R_clds,hemi='EH',div=dvd) - geoavg_flx(R_clds,hemi='WH',div=dvd)\n", " dR_clr[dvd-1, idx] = geoavg_flx(R_clrs,hemi='EH',div=dvd) - geoavg_flx(R_clrs,hemi='WH',div=dvd)\n", " dO[dvd-1, idx] = geoavg_sic(oce_fracs,hemi='EH',div=dvd) - geoavg_sic(oce_fracs,hemi='WH',div=dvd)\n", " symmetry[idx] = np.argmin(np.abs(dR[:,idx])) + 1\n", " ewR_sym[0, idx] = geoavg_flx(Rs,hemi='EH',div=symmetry[idx])\n", " ewR_sym[1, idx] = geoavg_flx(Rs,hemi='WH',div=symmetry[idx])\n", " ewR_sym_cld[0, idx] = geoavg_flx(R_clds,hemi='EH',div=symmetry[idx])\n", " ewR_sym_cld[1, idx] = geoavg_flx(R_clds,hemi='WH',div=symmetry[idx])\n", " ewR_sym_clr[0, idx] = geoavg_flx(R_clrs,hemi='EH',div=symmetry[idx])\n", " ewR_sym_clr[1, idx] = geoavg_flx(R_clrs,hemi='WH',div=symmetry[idx])\n", " ewO_sym[0, idx] = geoavg_sic(oce_fracs,hemi='EH',div=symmetry[idx])\n", " ewO_sym[1, idx] = geoavg_sic(oce_fracs,hemi='WH',div=symmetry[idx])\n", " ewR_27[0, idx] = geoavg_flx(Rs,hemi='EH',div=27)\n", " ewR_27[1, idx] = geoavg_flx(Rs,hemi='WH',div=27)\n", " ewR_27_cld[0, idx] = geoavg_flx(R_clds,hemi='EH',div=27)\n", " ewR_27_cld[1, idx] = geoavg_flx(R_clds,hemi='WH',div=27)\n", " ewR_27_clr[0, idx] = geoavg_flx(R_clrs,hemi='EH',div=27)\n", " ewR_27_clr[1, idx] = geoavg_flx(R_clrs,hemi='WH',div=27)\n", " ewO_27[0, idx] = geoavg_sic(oce_fracs,hemi='EH',div=27)\n", " ewO_27[1, idx] = geoavg_sic(oce_fracs,hemi='WH',div=27)\n", " for yr in range(20):\n", " wy = xM['Mwts'].isel(time=tidx)[yr*12:(yr+1)*12]\n", " Rsy = R.isel(time=tidx)[yr*12:(yr+1)*12].weighted(wy).mean(dim='time').values\n", " dR_intann_sym[yr, idx] = geoavg_flx(Rsy,hemi='EH',div=symmetry[idx]) - geoavg_flx(Rsy,hemi='WH',div=symmetry[idx])\n", " dR_intann_27[yr, idx] = geoavg_flx(Rsy,hemi='EH',div=27) - geoavg_flx(Rsy,hemi='WH',div=27)" ] }, { "cell_type": "code", "execution_count": 27, "id": "afdae3bf", "metadata": {}, "outputs": [], "source": [ "# save results\n", "np.savez(dir_data+'CESM2-WACCM/CESM2_Symmetry_LON_MC-SAI_EOC-SAI_20yrs.npz', dR=dR, dR_cld=dR_cld, dR_clr=dR_clr, dO=dO, symmetry=symmetry, dR_intann_sym=dR_intann_sym, dR_intann_27=dR_intann_27, ewR_sym=ewR_sym, ewR_sym_cld=ewR_sym_cld, ewR_sym_clr=ewR_sym_clr, ewO_sym=ewO_sym, ewR_27=ewR_27, ewR_27_cld=ewR_27_cld, ewR_27_clr=ewR_27_clr, ewO_27=ewO_27)" ] }, { "cell_type": "code", "execution_count": null, "id": "7af84b26", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Finished processing period PI.\n", "Finished processing period MC.\n", "Finished processing period PD.\n", "Finished processing period EOC.\n" ] } ], "source": [ "lat = flx.lat.values\n", "def geoavg_ns(data,hemi='both'):\n", "\n", " mask = lat<1e10\n", "\n", " #Hemisphere mask\n", " if hemi == 'NH':\n", " mask = lat>0\n", " elif hemi == 'SH':\n", " mask = lat<0\n", "\n", " return np.sum(data[mask,:]*area[mask,:]/np.sum(area[mask,:],axis=(0,1)),axis=(0,1))\n", "\n", "tindex = [np.arange(240), np.arange(240)+1080, np.arange(240)+1740, np.arange(240)+2760]\n", "#tindex = [np.arange(240), np.arange(240)+1080, np.arange(168)+1812, np.arange(240)+2760]\n", "dR = np.nan*np.ones(4)\n", "dR_cld = np.nan*np.ones(4)\n", "dR_clr = np.nan*np.ones(4)\n", "dR_intann_sym = np.nan*np.ones((20,4))\n", "nsR_sym = np.nan*np.ones((2,4))\n", "nsR_sym_cld = np.nan*np.ones((2,4))\n", "nsR_sym_clr = np.nan*np.ones((2,4))\n", "\n", "for period, tidx in zip(['PI','MC','PD','EOC'],tindex):\n", " idx = ['PI','MC','PD','EOC'].index(period)\n", " w = xM['Mwts'].isel(time=tidx)\n", " Rs = R.isel(time=tidx).weighted(w).mean(dim='time').values\n", " R_clds = R_cld.isel(time=tidx).weighted(w).mean(dim='time').values\n", " R_clrs = R_clr.isel(time=tidx).weighted(w).mean(dim='time').values\n", "\n", " dR[idx] = geoavg_ns(Rs,hemi='NH') - geoavg_ns(Rs,hemi='SH')\n", " dR_cld[idx] = geoavg_ns(R_clds,hemi='NH') - geoavg_ns(R_clds,hemi='SH')\n", " dR_clr[idx] = geoavg_ns(R_clrs,hemi='NH') - geoavg_ns(R_clrs,hemi='SH')\n", "\n", " nsR_sym[0, idx] = geoavg_ns(Rs,hemi='NH')\n", " nsR_sym[1, idx] = geoavg_ns(Rs,hemi='SH')\n", " nsR_sym_cld[0, idx] = geoavg_ns(R_clds,hemi='NH')\n", " nsR_sym_cld[1, idx] = geoavg_ns(R_clds,hemi='SH')\n", " nsR_sym_clr[0, idx] = geoavg_ns(R_clrs,hemi='NH')\n", " nsR_sym_clr[1, idx] = geoavg_ns(R_clrs,hemi='SH')\n", " for yr in range(20):\n", " wy = xM['Mwts'].isel(time=tidx)[yr*12:(yr+1)*12]\n", " Rsy = R.isel(time=tidx)[yr*12:(yr+1)*12].weighted(wy).mean(dim='time').values\n", " dR_intann_sym[yr, idx] = geoavg_ns(Rsy,hemi='NH') - geoavg_ns(Rsy,hemi='SH')\n", " print(f'Finished processing period {period}.')" ] }, { "cell_type": "code", "execution_count": null, "id": "6628d025", "metadata": {}, "outputs": [], "source": [ "# save results\n", "np.savez(dir_data+'CESM2-WACCM/CESM2_Symmetry_NS_PI_MC_PD_EOC_20yrs.npz', dR=dR, dR_cld=dR_cld, dR_clr=dR_clr, dR_intann_sym=dR_intann_sym, nsR_sym=nsR_sym, nsR_sym_cld=nsR_sym_cld, nsR_sym_clr=nsR_sym_clr)" ] }, { "cell_type": "code", "execution_count": 4, "id": "12afd5c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Finished processing period MC-SAI.\n", "Finished processing period EOC-SAI.\n" ] } ], "source": [ "lat = flx.lat.values\n", "def geoavg_ns(data,hemi='both'):\n", "\n", " mask = lat<1e10\n", "\n", " #Hemisphere mask\n", " if hemi == 'NH':\n", " mask = lat>0\n", " elif hemi == 'SH':\n", " mask = lat<0\n", "\n", " return np.sum(data[mask,:]*area[mask,:]/np.sum(area[mask,:],axis=(0,1)),axis=(0,1))\n", "\n", "tindex = [np.arange(240)+240, np.arange(240)+720]\n", "dR = np.nan*np.ones(2)\n", "dR_cld = np.nan*np.ones(2)\n", "dR_clr = np.nan*np.ones(2)\n", "dR_intann_sym = np.nan*np.ones((20,2))\n", "nsR_sym = np.nan*np.ones((2,2))\n", "nsR_sym_cld = np.nan*np.ones((2,2))\n", "nsR_sym_clr = np.nan*np.ones((2,2))\n", "\n", "for period, tidx in zip(['MC-SAI','EOC-SAI'],tindex):\n", " idx = ['MC-SAI','EOC-SAI'].index(period)\n", " w = xM['Mwts'].isel(time=tidx)\n", " Rs = R.isel(time=tidx).weighted(w).mean(dim='time').values\n", " R_clds = R_cld.isel(time=tidx).weighted(w).mean(dim='time').values\n", " R_clrs = R_clr.isel(time=tidx).weighted(w).mean(dim='time').values\n", "\n", " dR[idx] = geoavg_ns(Rs,hemi='NH') - geoavg_ns(Rs,hemi='SH')\n", " dR_cld[idx] = geoavg_ns(R_clds,hemi='NH') - geoavg_ns(R_clds,hemi='SH')\n", " dR_clr[idx] = geoavg_ns(R_clrs,hemi='NH') - geoavg_ns(R_clrs,hemi='SH')\n", "\n", " nsR_sym[0, idx] = geoavg_ns(Rs,hemi='NH')\n", " nsR_sym[1, idx] = geoavg_ns(Rs,hemi='SH')\n", " nsR_sym_cld[0, idx] = geoavg_ns(R_clds,hemi='NH')\n", " nsR_sym_cld[1, idx] = geoavg_ns(R_clds,hemi='SH')\n", " nsR_sym_clr[0, idx] = geoavg_ns(R_clrs,hemi='NH')\n", " nsR_sym_clr[1, idx] = geoavg_ns(R_clrs,hemi='SH')\n", " for yr in range(20):\n", " wy = xM['Mwts'].isel(time=tidx)[yr*12:(yr+1)*12]\n", " Rsy = R.isel(time=tidx)[yr*12:(yr+1)*12].weighted(wy).mean(dim='time').values\n", " dR_intann_sym[yr, idx] = geoavg_ns(Rsy,hemi='NH') - geoavg_ns(Rsy,hemi='SH')\n", " print(f'Finished processing period {period}.')\n", "\n", "# save results\n", "np.savez(dir_data+'CESM2-WACCM/CESM2_Symmetry_NS_MC-SAI_EOC-SAI_20yrs.npz', dR=dR, dR_cld=dR_cld, dR_clr=dR_clr, dR_intann_sym=dR_intann_sym, nsR_sym=nsR_sym, nsR_sym_cld=nsR_sym_cld, nsR_sym_clr=nsR_sym_clr)" ] } ], "metadata": { "kernelspec": { "display_name": "tf215env", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }