# ======================================================= # Custom Functions # ======================================================= def readVarible(filename, variable, vertical=False): fileName ='ModelData/' + filename f = nc.Dataset(fileName) if vertical: z = f.variables['depth'][:] z = np.array(z) f.close() return z else: var = f.variables[variable][:,:,:] var = np.array(var) f.close() return var def getNewLatLon(): lat = [-50, 65] lon = [122, 290] newlat = np.arange((lat[1]-lat[0])+1)+lat[0] newlon = np.arange((lon[1]-lon[0])/1.5+1)*1.5+lon[0] return [newlat, newlon] def getNino34Index(ssta, lat=None, lon=None): if lat is None: lat = [-20, 20] if lon is None: lon = [122, 290] newlat = np.arange(lat[1]-(lat[0])+1)+lat[0] newlon = np.arange((lon[1]-lon[0])/1.5+1)*1.5+lon[0] maskLat = np.where((newlat < 5) & (newlat > -5))[0] maskLon = np.where((newlon < 240) & (newlon > 190))[0] if len(ssta.shape) == 3: nino34 = ssta[:, maskLat, :] nino34 = nino34[:, :, maskLon] avg = np.nanmean(nino34, axis=2) avg = np.nanmean(avg, axis=1) else: nino34 = ssta[maskLat, :] nino34 = nino34[:, maskLon] avg = np.nanmean(nino34) return avg def getEasternSSTIndex(sst, lat=None, lon=None): if lat is None: lat = [-20, 20] if lon is None: lon = [122, 290] newlat = np.arange(lat[1]-(lat[0])+1)+lat[0] newlon = np.arange((lon[1]-lon[0])/1.5+1)*1.5+lon[0] maskLat = np.where((newlat <= 5) & (newlat >= -5))[0] maskLon = np.where((newlon <= 270) & (newlon >= 240))[0] if len(sst.shape) == 3: eSSTInd = sst[:, maskLat, :] eSSTInd = eSSTInd[:, :, maskLon] avg = np.nanmean(eSSTInd, axis=2) avg = np.nanmean(avg, axis=1) else: eSSTInd = sst[maskLat, :] eSSTInd = eSSTInd[:, maskLon] avg = np.nanmean(eSSTInd) return avg def getWesternSSTIndex(sst, lat=None, lon=None): if lat is None: lat = [-20, 20] if lon is None: lon = [122, 290] newlat = np.arange(lat[1]-(lat[0])+1)+lat[0] newlon = np.arange((lon[1]-lon[0])/1.5+1)*1.5+lon[0] maskLat = np.where((newlat <= 5) & (newlat >= -5))[0] maskLon = np.where((newlon <= 190) & (newlon >= 160))[0] if len(sst.shape) == 3: eSSTInd = sst[:, maskLat, :] eSSTInd = eSSTInd[:, :, maskLon] avg = np.nanmean(eSSTInd, axis=2) avg = np.nanmean(avg, axis=1) else: eSSTInd = sst[maskLat, :] eSSTInd = eSSTInd[:, maskLon] avg = np.nanmean(eSSTInd) return avg def plotFigure1(eSST, wSST, ssta, winda, sst, sf_mode): unCom = UnclassifiedComputations() unCom.plotSchematicOfENSOComplexityNaturePaper1(eSST, wSST, ssta, winda, sst, sf_mode) def plotExtendedFigure1(ssta, lon_grid, meof, nino34): onWOENSO = OnsetWithoutENSO(1) onWOENSO.plotThreeSFEvolutionsForENSOComplexityNaturePaper1(ssta, lon_grid, meof, nino34) def plotExtendedFigure2(eSST, negsf, ePrep, ssta, winda, hgt200a): unCom = UnclassifiedComputations() unCom.plotFigure2ForSFAsymmetryGRL(eSST, negsf, ePrep, ssta, winda, hgt200a, lat=[-30,30]) def plotExtendedFigure3(wSST, sf, wPrep, ssta, winda, hgt850a): unCom = UnclassifiedComputations() unCom.plotFigure3ForSFAsymmetryGRL(wSST, sf, wPrep, ssta, winda, hgt850a, lat=[-30,30]) # ======================================================= # Import Packages # ======================================================= import netCDF4 as nc import numpy as np import os.path #from scipy.interpolate import griddata import glob # ======================================================= # Custom Packages # ======================================================= from EOF import EOF from MEOF import MEOF from OnsetWithoutENSO import OnsetWithoutENSO from UnclassifiedComputations import UnclassifiedComputations # ======================================================= # Main Codes # ======================================================= # Prepare data for plot grid = getNewLatLon() lat = np.where((grid[0] <= 20) & (grid[0] >= -20))[0] sst_65N50S = readVarible('HADISST.sst0.nc', 'tos') sst_anomaly_65N50S = EOF.removeSeasonal(sst_65N50S, False, True) ssta_65N50S = sst_anomaly_65N50S.reshape((sst_65N50S.shape[0], sst_65N50S.shape[1], sst_65N50S.shape[2])) ssta_20N20S = ssta_65N50S[:, lat, :] uwind_65N50S = readVarible('NCEP.uwind0.nc', 'uwind') vwind_65N50S = readVarible('NCEP.vwind0.nc', 'vwind') wind_65N50S = np.concatenate((uwind_65N50S, vwind_65N50S), axis=2) wind_anomaly_65N50S = EOF.removeSeasonal(wind_65N50S, False, True) winda_65N50S = wind_anomaly_65N50S.reshape((wind_65N50S.shape[0], wind_65N50S.shape[1], wind_65N50S.shape[2])) winda_20N20S = winda_65N50S[:, lat, :] precip_65N50S = readVarible('NCEP.precip0.nc', 'precip') hgt_65N50S = readVarible('NCEP.hgt0.nc', 'hgt') hgt850_anomaly_65N50S = EOF.removeSeasonal(hgt_65N50S[:,:,:,2], False, True) hgt850a_65N50S = hgt850_anomaly_65N50S.reshape((hgt_65N50S.shape[0], hgt_65N50S.shape[1], hgt_65N50S.shape[2])) hgt200_anomaly_65N50S = EOF.removeSeasonal(hgt_65N50S[:,:,:,9], False, True) hgt200a_65N50S = hgt200_anomaly_65N50S.reshape((hgt_65N50S.shape[0], hgt_65N50S.shape[1], hgt_65N50S.shape[2])) nino34 = getNino34Index(ssta_20N20S) eSSTIndex = getEasternSSTIndex(sst_65N50S[:,lat,:]) wSSTIndex = getWesternSSTIndex(sst_65N50S[:,lat,:]) ePrepIndex = getEasternSSTIndex(precip_65N50S[:,lat,:]) wPrepIndex = getWesternSSTIndex(precip_65N50S[:,lat,:]) eof_sst_0 = EOF('HA_NC_GE_0_HADISST_sst_eof_0.nc') eof_wind_0 = EOF('HA_NC_GE_0_NCEP_wind_eof_0.nc') eof_sl_0 = EOF('HA_NC_GE_0_GECCO2_sl_eof_0.nc') meof1_0 = MEOF(eof_sst_0, eof_wind_0, eof_sl_0, np.array([1, 1, 1]), 'HA_NC_GE_0', '(SST, TAU, SSH)') # plot Figure 1 #plotFigure1(eSSTIndex, wSSTIndex, ssta_20N20S, winda_20N20S, sst_65N50S[:,lat,:], meof1_0.pc[1]) # plot Extended Figures plotExtendedFigure1(ssta_20N20S, grid[1], meof1_0, nino34) #lat_30N30S = np.where((grid[0] <= 30) & (grid[0] >= -30))[0] #lat_65N20S = np.where((grid[0] <= 65) & (grid[0] >= -20))[0] #plotExtendedFigure2(eSSTIndex, -1*meof1_0.pc[1], ePrepIndex, ssta_65N50S[:,lat_30N30S,:], winda_65N50S[:,lat_30N30S,:], hgt200a_65N50S[:,lat_30N30S,:]) #plotExtendedFigure3(wSSTIndex, meof1_0.pc[1], wPrepIndex, ssta_65N50S[:,lat_30N30S,:], winda_65N50S[:,lat_30N30S,:], hgt850a_65N50S[:,lat_65N20S,:])