var, coords, aux_info = ctl.read_iris_nc(ref_file, extract_level_hPa=500)
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
var, dates = ctl.sel_time_range(var, dates,
ctl.range_years(yearange[0], yearange[1]))
var_set, dates_set = ctl.seasonal_set(var, dates, 'DJF', seasonal_average=True)
years = np.array([da.year for da in dates_set])
############## PLOT GLOBAL TRENDS ######################
fig, ax = plt.subplots()
glob_mea = ctl.global_mean(var_set, lat)
g0 = glob_mea[0]
m, c = ctl.linear_regre(years, glob_mea)
ax.scatter(years, glob_mea - g0, label='Global', color='blue')
ax.plot(years, c + m * years - g0, color='blue')
var_area, lat_area, lon_area = ctl.sel_area(lat, lon, var_set, 'EAT')
eat_mea = ctl.global_mean(var_area, lat_area)
g0 = eat_mea[0]
m, c = ctl.linear_regre(years, eat_mea)
ax.scatter(years, eat_mea - g0, label='EAT', color='green')
ax.plot(years, c + m * years - g0, color='green')
var_area, lat_area, lon_area = ctl.sel_area(lat, lon, var_set, 'NH')
eat_mea = ctl.global_mean(var_area, lat_area)
g0 = eat_mea[0]
glomeans_245, yeamean_245 = pickle.load(open(cart_out + 'yeamean_245.p', 'rb'))
yeamean_126 = dict()
glomeans_126 = dict()
ru = 'ssp126'
for var in ['tas', 'pr']:
print(var)
fils = glob.glob(filna.format(ru, var, var))
kose = xr.open_mfdataset(fils, use_cftime=True)
kose = kose.drop_vars('time_bnds')
cosoye = kose[var].groupby("time.year").mean().compute()
yeamean_126[(ru, var)] = cosoye
glomeans_126[(ru, var)] = (cosoye.year.values, ctl.global_mean(cosoye))
pickle.dump([glomeans_126, yeamean_126], open(cart_out + 'yeamean_126.p',
'wb'))
#glomeans_126, yeamean_126 = pickle.load(open(cart_out + 'yeamean_126.p', 'rb'))
########################
glomeans, pimean, yeamean, mapmean = pickle.load(
open(cart_in + 'bottino_seasmean_2D.p', 'rb'))
glomeans.update(glomeans_245)
yeamean.update(yeamean_245)
glomeans.update(glomeans_126)
yeamean.update(yeamean_126)
var = 'tas'
for varnam in ['tas', 'pr', 'uas']:
print(varnam)
for ru, col in zip(allru, colors):
print(ru)
filist = glob.glob(filna.format(ru, ru[1:], miptab, varnam))
gigi = xr.open_mfdataset(filist, use_cftime=True)
var = np.array(gigi[varnam].data)
lat = np.array(gigi.lat.data)
lon = np.array(gigi.lon.data)
dates = np.array(gigi.time.data)
varye, datye = ctl.yearly_average(var, dates)
glomean = ctl.global_mean(varye, lat)
resdict[(ru, varnam, 'glomean')] = glomean
ok200 = np.array([da.year > dates[-1].year - 200 for da in dates])
varok = var[ok200]
dateok = dates[ok200]
resdict[(ru, varnam,
'mean200')], resdict[(ru, varnam,
'std200')] = ctl.seasonal_climatology(
varok, dateok, 'year')
resdict[(ru, varnam, 'mean200',
'DJFM')], resdict[(ru, varnam, 'std200',
'DJFM')] = ctl.seasonal_climatology(
varok, dateok, 'DJFM')
resdict[(ru, varnam, 'mean200',
#ctl.plot_multimap_contour(rad_flds['net_srf'], lats, lons, plot_anomalies=False, color_percentiles=(1,99), title='SRF_NET', cmap='viridis', plot_type='pcolormesh')
ok_coso = np.mean(rad_flds['net_srf'][1:], axis=0)
avfld[(expnam, 'net_srf')] = ok_coso
ctl.plot_map_contour(np.mean(rad_flds['net_srf'][1:], axis=0),
lats,
lons,
plot_anomalies=True,
color_percentiles=(1, 99),
title='SRF_NET',
cmap='viridis',
plot_type='pcolormesh',
filename=cart + 'map_net_srf.pdf')
print('NET SRF', ctl.global_mean(ok_coso, lats))
## over land and ocean
ok_coso = avfld[(expnam, 'net_srf')]
ok_land = ctl.global_mean(ok_coso, lats, okmask)
ok_ocean = ctl.global_mean(ok_coso, lats, ~okmask)
print('NET SRF - LAND', ok_land)
print('NET SRF - OCEAN', ok_ocean)
for var in 'osrtotc olrtotc'.split():
var_name, nlon, nlat, cose = ctl.read_globo_plotout(cart + var)
rad_flds[var] = cose[init:] / 86400.
print(nlon, nlat)
rad_flds['net_toa'] = rad_flds['osrtotc'] + rad_flds['olrtotc']
from matplotlib.animation import ImageMagickFileWriter
import cartopy.crs as ccrs
#cart = '/home/fabiano/Research/lavori/SPHINX_for_lisboa/'
cart = '/home/fedefab/Scrivania/Research/Post-doc/SPHINX/'
ref_period = ctl.range_years(1850, 1900)
filena = 'lcb0-1850-2100-tas_mon.nc'
var, lat, lon, dates, time_units, var_units = ctl.read3Dncfield(cart + filena)
dates_pdh = pd.to_datetime(dates)
# Global stuff
global_mean = ctl.global_mean(var, lat)
zonal_mean = ctl.zonal_mean(var)
climat_mon, dates_mon, climat_std = ctl.monthly_climatology(
var, dates, dates_range=ref_period)
climat_year = np.mean(climat_mon, axis=0)
yearly_anom, years = ctl.yearly_average(var, dates)
yearly_anom = yearly_anom - climat_year
zonal_anom = ctl.zonal_mean(yearly_anom)
global_anom = ctl.global_mean(yearly_anom, lat)
del var
# GIF animation
cart_out_wcmip5 = cart_out + 'wcmip5/'
ctl.mkdir(cart_out_wcmip5)
corrmaps = dict()
for seas in ['NDJFM', 'year']:
for area in ['EAT', 'PNA']:
for reg in range(4):
trendmat = tas_trends[('ssp585', okmods[0], seas)]
corr_map = np.empty_like(trendmat)
pval_map = np.empty_like(trendmat)
nlat, nlon = trendmat.shape
lat, lon = ctl.genlatlon(nlat, nlon)
ssp = 'ssp585'
gw_cmip6 = np.array([
ctl.global_mean(tas_trends[(ssp, mod, seas)], lat)
for mod in okmods
])
frok_cmip6 = np.array(
[cose[(ssp, area, mod, 'trend', reg)] for mod in okmods])
ssp = 'rcp85_cmip5'
gw_cmip5 = np.array([
ctl.global_mean(tas_trends[(ssp, mod, seas)], lat)
for mod in okmods_cmip5
])
frok_cmip5 = np.array(
[cose[(ssp, area, mod, 'trend', reg)] for mod in okmods_cmip5])
gw = np.concatenate([gw_cmip5, gw_cmip6])
frok = np.concatenate([frok_cmip5, frok_cmip6])
for varna in varnames:
vardict[varna] = np.stack(vardict[varna])
for key in vardict:
print(vardict[key].shape)
radclim[(exp, 'map', key, am)] = np.mean(vardict[key], axis=0)
radclim[(exp, 'map_std', key, am)] = np.std(vardict[key], axis=0)
radclim[(exp, 'zonal', key, am)] = np.mean(radclim[(exp, 'map',
key, am)],
axis=-1)
radclim[(exp, 'zonal_std', key,
am)] = np.mean(radclim[(exp, 'map_std', key, am)],
axis=-1)
radclim[(exp, 'global', key,
am)] = ctl.global_mean(radclim[(exp, 'map', key, am)],
lat)
radclim[(exp, 'global_std', key,
am)] = ctl.global_mean(radclim[(exp, 'map_std', key, am)],
lat)
pickle.dump(radclim, open(cart_out + 'cloudcover_allens.p', 'wb'))
# radclim = pickle.load(open(cart_out+'cloudcover_allens.p'))
# varniuu, lat, lon, dates, time_units, var_units = ctl.read3Dncfield(cart_in+namefi.format('lcb0',1988,'hcc'))
# del varniuu
# figure
#voglio figura con globalmean base e stoc anno per anno (con err da std? forse)
titlevar = dict()
titlevar['hcc'] = 'High clouds cover'
titlevar['lcc'] = 'Low clouds cover'
titlevar['mcc'] = 'Mid clouds cover'
# sennò non ha molto senso, cioè beccherò le zone che si scaldano di più
# devo dividere sst_trend e freq_trend per il global tas trend di ogni modello
corrmaps = dict()
ssp = 'ssp585'
for seas in ['NDJFM', 'year']:
for area in ['EAT', 'PNA']:
for reg in range(4):
# trendmat = tas_trends[(ssp, okmods[0])]
trendmat = field_trends[(ssp, okmok[0].split('_')[0], seas)]
corr_map = np.empty_like(trendmat)
pval_map = np.empty_like(trendmat)
nlat, nlon = trendmat.shape
lat, lon = ctl.genlatlon(nlat, nlon)
gw = np.array([ctl.global_mean(tas_trends[(ssp, mod, seas)], lat_180) for mod in okmok])
#gw = np.array([ctl.global_mean(tas_trends[(ssp, mod.split('_')[0])], lat) for mod in okmok])
frok = np.array([cose[(ssp, area, mod, 'trend', reg)] for mod in okmok])
for la in range(nlat):
for lo in range(nlon):
tastr = np.array([field_trends[(ssp, mod.split('_')[0], seas)][la, lo] for mod in okmok])
pears, pval = stats.pearsonr(frok/gw, tastr/gw)
corr_map[la, lo] = pears
pval_map[la, lo] = pval
corrmaps[('corr', area, reg)] = corr_map
corrmaps[('pval', area, reg)] = pval_map
fnam = cart_out + '{}_corrmap_{}_{}_{}.pdf'.format(fieldnam, area, reg, seas)
print(annme)
years = np.arange(1850,2101)
ensmems = ['lcb0', 'lcb1', 'lcb2', 'lcs0', 'lcs1', 'lcs2']
radclim_yr = dict()
for exp in ensmems:
for varna in varnames:
radclim_yr[('zonal', exp, varna)] = []
radclim_yr[('global', exp, varna)] = []
for year in years:
vardict = dict()
for varna in varnames:
varniuu, lat, lon, dates, time_units, var_units = ctl.read3Dncfield(cart_in+namefi.format(exp,year,varna))
vardict[varna] = np.mean(varniuu, axis = 0)
radclim_yr[('global', exp, varna)].append(ctl.global_mean(vardict[varna], lat))
radclim_yr[('zonal', exp, varna)].append(ctl.zonal_mean(vardict[varna]))
for varna in varnames:
radclim_yr[('global', exp, varna)] = np.array(radclim_yr[('global', exp, varna)])
radclim_yr[('zonal', exp, varna)] = np.stack(radclim_yr[('zonal', exp, varna)])
for varna in varnames:
radclim_yr[('global', 'base', varna)] = np.mean([radclim_yr[('global', exp, varna)] for exp in ensmems if 'lcb' in exp], axis = 0)
radclim_yr[('global', 'stoc', varna)] = np.mean([radclim_yr[('global', exp, varna)] for exp in ensmems if 'lcs' in exp], axis = 0)
radclim_yr[('zonal', 'base', varna)] = np.mean([radclim_yr[('zonal', exp, varna)] for exp in ensmems if 'lcb' in exp], axis = 0)
radclim_yr[('zonal', 'stoc', varna)] = np.mean([radclim_yr[('zonal', exp, varna)] for exp in ensmems if 'lcs' in exp], axis = 0)
pickle.dump(radclim_yr, open(cart_out+'cloudcover_yearly.p', 'wb'))
sys.exit()