numask = ctl.regrid_dataset(okpino, lats, lons)
okmask = np.array(numask['okmask']).astype(bool)
rad_flds['net_srf'] = rad_flds['chflux']
for var in 'clwfl cqflux cswfl'.split():
rad_flds['net_srf'] += rad_flds[var]
#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():
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_wcmip5 + 'tas_corrmap_{}_{}_{}.pdf'.format(
area, reg, seas)
ctl.plot_map_contour(corr_map,
lat,
lon,
filename=fnam,
add_hatching=pval_map <= 0.05,
cbar_range=(-1, 1),
cb_label='Correlation',
title='area: {}, regime: {}, seas: {}'.format(
area, reg_names_area[area][reg], seas),
draw_grid=True)
fnam = cart_out_wcmip5 + 'tas_pvalmap_{}_{}_{}.pdf'.format(
area, reg, seas)
ctl.plot_map_contour(pval_map,
lat,
lon,
filename=fnam,
cbar_range=(0., 0.1),
plot_anomalies=False,
extend_opt='neither',
draw_grid=True,
# fig.legend(handles, labels, loc='lower center', ncol = 7, fontsize = 10)
# fig.savefig(cart_out + finam + '_area{}_modres.pdf'.format(anam))
#
# sys.exit()
blat = 0
projcos = 'nearside'
clatlo = (70, -20)
filename = cart_out + 'mean_field_era_zg500.pdf'
fig = ctl.plot_map_contour(mean_field,
lat,
lon,
title='',
filename=filename,
visualization=projcos,
bounding_lat=0,
plot_anomalies=False,
cbar_range=[5000., 5800.],
draw_grid=True,
cb_label='m',
central_lat_lon=clatlo)
all_figs_mf.append(fig)
filename = cart_out + 'low_fr_var_era_zg500.pdf'
fig = ctl.plot_map_contour(lowfr_variab,
lat,
lon,
title='',
filename=filename,
visualization=projcos,
bounding_lat=0,
plot_type='filled_contour',
verbose=False,
lw_contour=0.5)
ctl.plot_map_contour(sspcoso - histcoso,
lat,
lon,
filename=cart_out_orig + 'mapdiff_rebase_diff.pdf',
visualization='standard',
central_lat_lon=None,
cmap='RdBu_r',
title=None,
xlabel=None,
ylabel=None,
cb_label=None,
cbar_range=None,
plot_anomalies=True,
n_color_levels=21,
draw_contour_lines=False,
n_lines=5,
color_percentiles=(0, 100),
bounding_lat=30,
plot_margins='EAT',
add_rectangles=None,
draw_grid=True,
plot_type='filled_contour',
verbose=False,
lw_contour=0.5)
figs = []
for mod in reshist.keys():
histbase = np.mean(reshist[mod]['climate_mean'], axis=0)
plt.title('Meridional heat fluxes - {} {} {}'.format(exp, year, seas))
for flun in fluxnames:
plt.plot(era_lat, era_fluxes_zonal[(seas, flun)], label=shortnam[flun])
total = np.sum([era_fluxes_zonal[(seas, flun)] for flun in fluxnames],
axis=0)
plt.plot(era_lat, total, label='Total')
plt.legend()
plt.grid()
plt.xlabel('Latitude')
plt.ylabel('Integrated Net Heat Flux (W)')
figures_era.append(fig)
for flun in fluxnames:
fig = ctl.plot_map_contour(era_fluxes_maps[(seas, flun)],
era_lat,
era_lon,
title=fluxlongnames[flun] +
' - {} {} {}'.format(exp, year, seas))
figures_era.append(fig)
total = np.sum([era_fluxes_maps[(seas, flun)] for flun in fluxnames],
axis=0)
fig = ctl.plot_map_contour(total,
era_lat,
era_lon,
title='Total meridional flux - {} {} {}'.format(
exp, year, seas))
figures_era.append(fig)
file_era = cart_out + 'ERA_reference_fluxes_1988.pdf'
ctl.plot_pdfpages(file_era, figures_era)
for an, th in zip(year_thres_stoc, thress):
mask = abs(years_pdh.year - an) <= 10
yas_s.append(np.mean(yearly_anom_s[mask, ...], axis=0))
yas_b = np.stack(yas_b)
yas_s = np.stack(yas_s)
(cmin, cmax) = ctl.get_cbar_range(yas_b, symmetrical=True)
for okanom, an, th in zip(yas_b, year_thres_base, thress):
nomfi = cart + 'map_anom_{}_th{}_base.pdf'.format(an, int(10 * th))
tit = 'Year {} - 20yr ave anomaly - +{:3.1f} K globally'.format(an, th)
ctl.plot_map_contour(okanom,
lat,
lon,
filename=nomfi,
visualization='polar',
central_lat_lon=(30, 0),
cbar_range=(cmin, cmax),
title=tit,
cb_label='Temp. anomaly (K)')
for okanom, an, th in zip(yas_s, year_thres_stoc, thress):
tit = 'Year {} - 20yr averaged anomaly - +{:3.1f} K globally'.format(
an, th)
nomfi = cart + 'map_anom_{}_th{}_stoc.pdf'.format(an, int(10 * th))
ctl.plot_map_contour(okanom,
lat,
lon,
filename=nomfi,
visualization='polar',
central_lat_lon=(30, 0),
filok = 'bf.5day.daily.daily_mean.ERA.ERA.1958-2017.nc'
blocked_days, datacoords, aux_info = ctl.readxDncfield(cart_bloc + filok)
dates_block = datacoords['dates']
lat = datacoords['lat']
lon = datacoords['lon']
WR_index = results_refEOF[mod]['labels']
WR_dates = results_refEOF[mod]['dates']
blok, wri, datcom = ctl.extract_common_dates(dates_block, WR_dates,
blocked_days, WR_index)
ERA_map_full = np.mean(blok, axis=0)
ctl.plot_map_contour(ERA_map_full,
lat,
lon,
visualization='Nstereo',
plot_anomalies=False,
filename=cart_out_maps + 'map_full_ERA.pdf')
def func_sum(blok_ind, reg_ind):
alsums = []
for reg in range(4):
blokok = blok_ind[reg_ind == reg]
okmap = np.mean(blokok, axis=0)
alsums.append(np.mean(okmap))
return np.array(alsums)
blok_anom = blok - ERA_map_full
# num_members[mod] = len(climmeans)
#
# pickle.dump([climate_mean, climate_mean_dates, climate_std, num_members], open(cart_out + 'climate_mean_hist_p2.p', 'wb'))
climate_mean, climate_mean_dates, climate_std, num_members = pickle.load(open(cart_out + 'climate_mean_hist_p2.p', 'rb'))
for area in ['EAT', 'PNA']:
res_old, _ = ctl.load_wrtool('/data-hobbes/fabiano/WR_CMIP6/out_NEW_cmip6_hist_NDJFM_{}_4clus_4pcs_1964-2014_refCLUS_dtr_light.p'.format(area))
figs = []
for mod in okmods_mo:
modmem = [ke for ke in res_old.keys() if mod in ke][0]
lat = res_old[modmem]['lat']
lon = res_old[modmem]['lon']
newcoso = np.mean(climate_mean[mod], axis = 0).squeeze()
oldcoso = np.mean(res_old[modmem]['climate_mean'], axis = 0)
fig = ctl.plot_map_contour(newcoso-oldcoso, lat, lon, filename = None, visualization = 'standard', central_lat_lon = None, cmap = 'RdBu_r', title = mod, xlabel = None, ylabel = None, cb_label = None, cbar_range = (-60, 60), plot_anomalies = True, n_color_levels = 21, draw_contour_lines = False, n_lines = 5, color_percentiles = (0,100), bounding_lat = 30, plot_margins = area, add_rectangles = None, draw_grid = True, plot_type = 'filled_contour', verbose = False, lw_contour = 0.5)
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'map_ensrebase_diff_{}.pdf'.format(area), figs)
for area in ['EAT', 'PNA']:
res = dict()
for mod in okmods_mo:
res[mod+'_ensmean'] = dict()
#climmean_area, _, _ = ctl.sel_area(lat, lon, climate_mean[(area, mod)], area)
res[mod+'_ensmean']['climate_mean'] = climate_mean[mod].squeeze() #climmean_area
res[mod+'_ensmean']['climate_mean_dates'] = climate_mean_dates[mod]
pickle.dump([res, dict()], open(cart_out + 'dict_climate_mean_hist_{}.p'.format(area), 'wb'))
subtitles=oktit,
add_hatching=okha,
cmap=cmap,
n_color_levels=17,
hatch_styles=['////', '', ''])
for ru in ['pi'] + allru2[-2:]:
var_trend = trendz[(var, ru)]
var_hatch = trendz[(var, ru, 'pval')] < 0.05
ctl.plot_map_contour(var_trend,
coso.lat,
coso.lon,
filename=cart_out + var +
'_trend_{}.pdf'.format(ru),
figsize=(16, 9),
cbar_range=(c5, c95),
color_norm=divnorm,
add_hatching=var_hatch,
cmap=cmap,
n_color_levels=17,
hatch_styles=['///', '', ''])
#### Now fig with ssp585 and all botts
okfi = [trendz[(var, ru)] for ru in ['ssp585'] + allru[1:]]
okha = [trendz[(var, ru, 'pval')] < 0.05 for ru in ['ssp585'] + allru[1:]]
oktit = ['ssp585'] + allru[1:]
ctl.plot_multimap_contour(okfi,
coso.lat,
coso.lon,
filename=cart_out + var +
plt.plot(annme, stoc_glob - base_glob, label='diff stoc-base')
plt.xlabel('Year')
plt.ylabel('Rad. forcing (W/m^2)')
plt.grid()
plt.legend()
figures.append(fig)
for ann in annme:
base = radclim[('base', 'map', varna, ann)]
stoc = radclim[('stoc', 'map', varna, ann)]
print(base.shape, stoc.shape)
figures.append(
ctl.plot_map_contour(
stoc - base,
lat,
lon,
title=titlevar[varna] +
' (stoc-base diff): {}-{}'.format(ann - 5, ann + 5),
cb_label='Forcing (W/m^2)',
cbar_range=(-20., 20.)))
mino = np.min([radclim[('stoc', 'zonal', varna, ann)] for ann in annme])
maxo = np.max([radclim[('base', 'zonal', varna, ann)] for ann in annme])
mino = mino - 0.1 * abs(maxo - mino)
maxo = maxo + 0.1 * abs(maxo - mino)
mino_diff = 10.1 * np.min([
radclim[('stoc', 'zonal', varna, ann)] -
radclim[('base', 'zonal', varna, ann)] for ann in annme
])
maxo_diff = 10.1 * np.max([
radclim[('stoc', 'zonal', varna, ann)] -
plt.xlabel('Year')
plt.ylabel('Cloud cover')
plt.grid()
plt.legend()
figures.append(fig)
for ann in annme:
print(varna, ann)
base = radclim[('labm', 'map', varna, ann)]
stoc = radclim[('lasm', 'map', varna, ann)]
print(base.shape, stoc.shape)
figures.append(
ctl.plot_map_contour(
stoc - base,
lat,
lon,
title=titlevar[varna] +
' (stoc-base diff): {}-{}'.format(ann - 5, ann + 5),
cb_label='cloud cover')) #, cbar_range = (-20.,20.)))
mino = np.min([radclim[('lasm', 'zonal', varna, ann)] for ann in annme])
maxo = np.max([radclim[('labm', 'zonal', varna, ann)] for ann in annme])
mino = mino - 0.1 * abs(maxo - mino)
maxo = maxo + 0.1 * abs(maxo - mino)
mino_diff = 10.1 * np.min([
radclim[('lasm', 'zonal', varna, ann)] -
radclim[('labm', 'zonal', varna, ann)] for ann in annme
])
maxo_diff = 10.1 * np.max([
radclim[('lasm', 'zonal', varna, ann)] -
fix_subplots_shape=(3, 5),
figsize=(18, 12),
subtitles=allmods_MM,
cb_label='m/year',
verbose=True,
draw_grid=True)
filename = cart_out_orig + 'trend_anom_mmm_vs_hist.pdf'
ctl.plot_map_contour(trendsanom[-1],
lat,
lon,
filename,
plot_anomalies=True,
plot_margins=(-180, 180, 20, 90),
cbar_range=(-1, 1),
add_contour_field=meanfields[-1],
figsize=(24, 12),
cb_label='m/year',
draw_grid=True,
add_hatching=hatchs[-1],
n_lines=8,
add_contour_same_levels=False,
add_contour_plot_anomalies=False)
filename = cart_out_orig + 'trend_anom_mmm_vs_hist_EAT.pdf'
ctl.plot_map_contour(trendsanom[-1],
lat,
lon,
filename,
plot_anomalies=True,
visualization='nearside',
axis=0)
tam = np.mean(ta_, axis=0)
quiver_scale = 1000
vec_every = 10
figsize = (16, 8)
figs = []
for i, lev in enumerate(levs):
fig = ctl.plot_map_contour(zg_[i],
lat,
lon,
add_vector_field=[u_[i], v_[i]],
title='real winds - lev {} hPa'.format(lev),
plot_anomalies=False,
plot_margins=area,
quiver_scale=quiver_scale,
vec_every=vec_every,
plot_type='pcolormesh',
figsize=figsize)
figs.append(fig)
fig = ctl.plot_map_contour(
zg_[i],
lat,
lon,
add_vector_field=[ug[lev], vg[lev]],
title='geostrophic winds - lev {} hPa'.format(lev),
plot_anomalies=False,
plot_margins=area,
quiver_scale=quiver_scale,
plt.legend()
plt.grid()
plt.ylim(limits[('zonal', flun)])
plt.xlabel('Latitude')
plt.ylabel('Integrated Net Heat Flux (W)')
figures.append(fig)
ctl.plot_pdfpages(figure_file, figures)
figure_file = cart_out + 'maps_hfc_lcb0_vs_lcs0_NEW.pdf'
figures = []
for flun in fluxnames:
for ann in annme:
data = fluxes_model[('lcs0', flun, seas, 'map', ann)] - fluxes_model[('lcb0', flun, seas, 'map', ann)]
fig = ctl.plot_map_contour(data, lat, lon, title = 'stoc-base diff: {} - {}'.format(flun, ann), cb_label = 'W/m', cbar_range = limits[('map', flun)])
figures.append(fig)
ctl.plot_pdfpages(figure_file, figures)
figure_file = cart_out + 'cross_hfc_lcb0_vs_lcs0_NEW.pdf'
figures = []
for flun in fluxnames:
for ann in annme:
data = fluxes_model[('lcs0', flun, seas, 'cross', ann)] - fluxes_model[('lcb0', flun, seas, 'cross', ann)]
fig = ctl.plot_lat_crosssection(data, lat, level, title = 'stoc-base diff: {} - {}'.format(flun, ann), cb_label = 'W/m', cbar_range = limits[('cross', flun)])
figures.append(fig)
ctl.plot_pdfpages(figure_file, figures)
ax.set_ylabel('m')
fig.savefig(
cart_out +
'global_{}_trend_{}-{}_DJF.pdf'.format(modnam, yearange[0], yearange[1]))
############## PLOT TREND MAPS ######################
m, c, merr, cerr = ctl.calc_trend_climatevar(years, var_set)
ctl.plot_map_contour(
m,
lat,
lon,
plot_anomalies=True,
cbar_range=cbar_range,
n_color_levels=11,
n_lines=11,
draw_contour_lines=True,
draw_grid=True,
cb_label='Trend (m/year)',
filename=cart_out +
'{}_trend_{}-{}_DJF.pdf'.format(modnam, yearange[0], yearange[1]))
var_set_notr = []
for ye, va, glo in zip(years, var_set, glob_mea):
var_set_notr.append(va - glo)
var_set_notr = np.stack(var_set_notr)
m, c, merr, cerr = ctl.calc_trend_climatevar(years, var_set_notr)
ctl.plot_map_contour(
for per in periods:
for cos in ['base', 'stoc', 'diff']:
trenddict[('toa_net', per, cos)] = -(trenddict[('rsus', per, cos)] +
trenddict[('rlut', per, cos)])
for varnam in allvars + ['toa_net']:
figs = []
for per in periods:
for cos in ['base', 'stoc', 'diff']:
if varnam == 'tas':
cbar_range = (-2., 2.)
elif 'cc' in varnam:
cbar_range = (-0.09, 0.09)
else:
cbar_range = (-20., 20.)
if cos == 'diff' and cbar_range is not None:
cbar_range = (cbar_range[0] / 4., cbar_range[1] / 4.)
fig = ctl.plot_map_contour(trenddict[(varnam, per, cos)],
lat,
lon,
visualization='Robinson',
central_lat_lon=(0., 180.),
title='{} - {} - {}'.format(
varnam, per, cos),
cbar_range=cbar_range)
figs.append(fig)
ctl.plot_pdfpages(cart_out + 'trend_patterns_v2_{}.pdf'.format(varnam),
figs)
results_ref['dates'],
dates_range=(dateprec[0], dateprec[-1]))
allfigs = []
# Calculate and visualize composites. NO FILTERS
compos = dict()
compos['temp'] = []
compos['prec'] = []
for reg in range(kwar['numclus']):
labok = results_ref['labels'] == reg
cosa = np.mean(temp_anoms[labok, ...], axis=0)
compos['temp'].append(cosa)
fig = ctl.plot_map_contour(cosa,
coords_temp['lat'],
coords_temp['lon'],
title='Temp anom - regime {}'.format(reg),
plot_margins=(-120, 120, 20, 90),
cbar_range=(-5, 5))
allfigs.append(fig)
labok = oklabels == reg
cosa = np.mean(prec_anoms[labok, ...], axis=0)
compos['prec'].append(cosa)
fig = ctl.plot_map_contour(cosa,
coords_prec['lat'],
coords_prec['lon'],
title='Prec anom - regime {}'.format(reg),
plot_margins=(-120, 120, 20, 90),
cbar_range=(-5, 5),
cmap='RdBu')
# mappeanom = [ma-ctl.global_mean(ma, coso.lat) for ma in mappe]
# ctl.plot_multimap_contour(mappeanom, coso.lat, coso.lon, cmap = cmappa, plot_anomalies = True, cbar_range = [-10., 10.])
cmap = mcolors.LinearSegmentedColormap.from_list(
'bau', ['violet', 'white', 'lightgreen'])
cmap.set_under('violet')
cmap.set_over('lightgreen')
if varnam == 'tas':
print('entro tas')
mappa = (equi - transient) / (equi - pimap)
ctl.plot_map_contour(mappa,
coso.lat,
coso.lon,
filename=cart_out +
'stabtransratio_{}_{}.pdf'.format(varnam, ru),
plot_anomalies=True,
cbar_range=ext_rel,
cmap=cmap,
cb_label='Fraction of residual warming')
fig_ratio.append(mappa)
print(len(fig_ratio))
# elif varnam == 'pr_rel':
else:
# cmap = cm.get_cmap('viridis').copy()
# cmap.set_under('violet')
# cmap.set_bad('lightslategray', alpha = 0.5)
#mappa = (equi-transient)/(transient-pimap)
mappa = (equi - transient) / (equi - pimap)