axes.append(ax)
allpercs = dict()
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [
np.percentile(freqs[(ssp, 'all', cos)][:, reg], nu)
for ssp in allsims
]
ax.axhline(allpercs['p50'][0], color='gray', linewidth=0.5)
ctl.boxplot_on_ax(ax,
allpercs,
allsims,
colsim,
plot_mean=False,
plot_ensmeans=False,
plot_minmax=False)
ax.set_xticks([])
ax.set_title(reg_names[reg])
if reg == 0: ax.set_ylabel('Regime frequency')
ax.scatter(0,
results_ref['freq_clus'][reg],
color='black',
marker='*',
s=5)
ctl.custom_legend(figall, colsim, allsims, ncol=4)
figall.savefig(cart_out + 'WRfreq_cmip5_{}.pdf'.format(area))
cosi = [runfreq[(tip, mem, reg)] for mem in okmods]
coso = np.mean(cosi, axis=0)
runfreq[(tip, reg)] = coso
coserr = np.std(cosi, axis=0) / np.sqrt(len(okmods) - 1)
ax.fill_between(yr,
coso - coserr,
coso + coserr,
color=col,
alpha=0.3)
ax.plot(yr, coso, label=tip, color=col)
ax.set_title(reg_names_area[area][reg])
#ax.legend()
ctl.custom_legend(fig,
colorz, ['MPI-ESM1-2-LR', 'UKESM1-0-LL'],
ncol=2,
add_space_below=0.12)
fig.savefig(cart_out + 'check_ece_vs_mpi_{}.pdf'.format(area))
trend_ssp = dict()
for tip in alltips:
okmods = resdict[tip].keys()
for reg in range(4):
for mem in okmods:
seasfr = seasfreq[(tip, mem, reg)]
seas10 = np.array(ctl.running_mean(seasfr, 10))
okcose = ~(np.isnan(seas10)) & (yr >= 2015)
m, c, err_m, err_c = ctl.linear_regre_witherr(
np.array(yr[okcose]), np.array(seas10[okcose]))
ax.scatter(res, val, color=col, marker=mark, s=100, linewidth=lw)
ax.errorbar(res,
val,
yerr=errval,
color=col,
linewidth=lw,
capsize=4)
allvals_mods.append(val)
allerr_mods.append(errval)
ax.axhline(results['ERA_0'][cos], color='grey', alpha=0.6)
ax.set_ylabel(tit)
ax.set_xlabel('Eff. atm. resolution (km)')
fig = ctl.custom_legend(fig, colors, model_names, ncol=6)
fig.savefig(cart_out + cos + '_{}_atmresolution.pdf'.format(vtag))
figures.append(fig)
# WITH OCEAN RESOLUTION
fig = plt.figure(figsize=(16, 12))
ax = plt.subplot(111)
for mod, res, col, mark in zip(model_names, oce_resolution, colors,
sym_all):
allvals = []
for ke in results.keys():
if mod == ke.split('_')[0]:
allvals.append(results[ke][cos])
if len(allvals) == 1:
i += 0.7
ax.scatter(i,
ref_corrs[(ind, reg, seas)],
marker='D',
color='black',
s=25)
i += 1
ax.scatter(i, np.mean(LRs), marker='D', color=col_LR, s=25)
i += 1
ax.scatter(i, np.mean(HRs), marker='D', color=col_HR, s=25)
ax.set_xticks([])
ax.set_title(reg_names[reg])
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, coloks + ['black', col_LR, col_HR],
modoks + ['ERA', 'LR', 'HR'])
fig.suptitle('Correlation of WR freq. with {} index - {}'.format(
ind, seas))
fig.savefig(cou + 'corr_models_WRfreq_vs_{}_{}.pdf'.format(ind, seas))
# PLOT 2: freq diff per ogni regime pos/neg
fig = plt.figure(figsize=(16, 12))
axes = []
for reg in range(4):
modoks = []
coloks = []
ax = fig.add_subplot(2, 2, reg + 1)
axes.append(ax)
ax = plt.subplot(5, 4, ind)
allvals = np.array([all_stats_choices[(n_choice, 'multi_cramer',reg)][i][0][0] for i in range(6)])
ax.scatter(allks_ok, allvals, marker = 'D', color = colors[4])
all_cramer_compare.append(allvals)
allvalspval = np.array([all_stats_choices[(n_choice, 'multi_cramer',reg)][i][1][0] for i in range(6)])
all_cramer_pvals.append(allvalspval)
#ax.scatter(allks_ok_max, allvals, marker = 'D', color = colors[4])
ax.set_xlabel('1D KolmogSmir')
ax.set_ylabel('2D cramer')
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.tight_layout()
fig = ctl.custom_legend(fig, colors, labels = ['KolmogSmir', 'MannWhit', 'Anderson', 'peacock2', 'cramer'])
fig.savefig(cart_out + 'all_stats_ERA_pdfs_{}yr.pdf'.format(n_choice))
# Only KolmogSmir 1D
fig = plt.figure(figsize = (16, 12))
#yrcho = [5, 10, 15, 20, 25, 30, 40, 50, 60]
yrcho = [5, 10, 20, 30, 40, 50, 60]
colors = ctl.color_set(len(yrcho))
ind = 0
axes = []
for reg in range(4):
ind += 1
ax = plt.subplot(2, 2, ind)
for n_choice, col in zip(yrcho, colors):
allvals = np.array([np.mean([cos[0] for cos in all_stats_choices[(n_choice, 'KolmogSmir', reg)][i]]) for i in range(100)])
#xvec = np.linspace(0., 0.4, 500)
if 'KS' in nam:
xvec = np.linspace(0., 0.5, 500)
ax.axvline(0.06, color = 'grey', alpha = 0.6)
elif nam == 'Clus radius':
xvec = np.linspace(1000, 2200, 500)
ax.axvline(ref_cent_dist[reg], color = 'grey', alpha = 0.6)
elif nam == 'Clus std':
xvec = np.linspace(300, 800, 500)
ax.axvline(ref_cent_std[reg], color = 'grey', alpha = 0.6)
elif nam == 'dist centocen':
xvec = np.linspace(0, 2300, 500)
else:
xvec = np.linspace(0., 1.2*allvals.max(), 500)
pdfxvec = kpdf(xvec)
ax.plot(xvec, pdfxvec, color = col)
ax.set_title('Reg {}'.format(reg))
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.suptitle(tit)
plt.subplots_adjust(top = 0.9)
fig = ctl.custom_legend(fig, colors, ['{} yr'.format(yr) for yr in yrcho])
fig.savefig(cart_out + '_'.join(nam.split())+'_ERA_allyears.pdf')
pickle.dump(ERA_ref_thresholds, open(cart_out + 'ERA_ref_thresholds_allstats.p', 'wb'))
ax.bar(i, okrms, width = wi, color = col, yerr = yerr, capsize = 5)
i += 0.7
if vv == 'HR': i += 0.2
all_LR = np.mean([allrms[mod] for mod, vv in zip(model_names, vers) if vv == 'LR'])
all_HR = np.mean([allrms[mod] for mod, vv in zip(model_names, vers) if vv == 'HR'])
i+=0.2
col_LR = 'grey'
col_HR = 'black'
ax.bar(i, all_LR, width = wi, color = col_LR)
i += 0.7
ax.bar(i, all_HR, width = wi, color = col_HR)
ctl.custom_legend(fig, colors, model_names, ncol = 6)
fig.savefig(cart_out + 'sst_bias_RMS_DJF_EAT.pdf')
fig = plt.figure(figsize = (16, 12))
ax = plt.subplot()
ax.set_ylabel('(1-patcor)')
ax.set_title('Pattern correlation with observed SSTs in the North Atlantic')
ax.set_xticks([])
i = 0
wi = 0.6
#for mod, col, vv, mem in zip(model_names, colors, vers, ens_mems):
#ax.bar(i, allrms[(mod, mem)], width = wi, color = col)
for mod, col, vv in zip(model_names, colors, vers):
rmsall = []
all_LR_std = np.std([
allrms[(mod, reg)] for mod, vv in zip(model_names_all, vers)
if vv == 'LR'
])
all_HR_std = np.std([
allrms[(mod, reg)] for mod, vv in zip(model_names_all, vers)
if vv == 'HR'
])
i += 0.4
ax.bar(i, all_LR, width=wi, color=col_LR, yerr=all_LR_std, capsize=5)
i += 0.7
ax.bar(i, all_HR, width=wi, color=col_HR, yerr=all_HR_std, capsize=5)
ctl.custom_legend(fig, colors + ens_colors, model_names + ens_names)
#fig.suptitle('RMS of blocking anomalies')
fig.savefig(cart_out + 'block_allregs_rms{}.pdf'.format(tag))
fig = plt.figure(figsize=(16, 12))
axes = []
for reg in range(4):
ax = plt.subplot(2, 2, reg + 1)
axes.append(ax)
#ax.set_xlabel('Latitude')
ax.set_ylabel('patcor')
ax.set_title(patnames[reg])
ax.set_xticks([])
i = 0
ctl.boxplot_on_ax(ax,
allpercs,
alltips,
colorz,
plot_mean=False,
plot_ensmeans=False,
plot_minmax=False)
ax.axhline(0, color='gray', linewidth=0.5)
ax.set_xticks([])
ax.set_title(reg_names[reg])
if reg == 0 or reg == 2: ax.set_ylabel('Regime frequency anomaly')
ctl.adjust_ax_scale(axes)
ctl.custom_legend(figall, colorz, alltips, ncol=2)
figall.savefig(cart_out + 'check_WRfreq_alltips_{}.pdf'.format(area))
figall = plt.figure(figsize=(16, 12))
axes = []
for reg in range(4):
ax = figall.add_subplot(2, 2, reg + 1)
axes.append(ax)
histmean = dict()
for tip in alltips:
histmean[tip] = np.mean(freqs[('hist', tip)][:, reg])
allpercs = dict()
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [
np.mean(clm_fullperiod[(name, num)][:, lla1:lla2,
llo1:llo2],
axis=(1, 2)),
color=col,
linewidth=lw)
if lla2 is None: lla2 = -1
if llo2 is None: llo2 = -1
ax.set_title('lat {:2.0f}/{:2.0f}, lon {:3.0f}/{:3.0f}'.format(
lat_area[lla1], lat_area[lla2], lon_area[llo1],
lon_area[llo2]))
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.tight_layout()
fig = ctl.custom_legend(fig, colors, model_names)
fig.savefig(cart_out + 'mean_climatology_zg_models_{}days.pdf'.format(num))
fig = plt.figure(figsize=(16, 12))
i = 0
for lla1, lla2 in lat_sect:
axes = []
for llo1, llo2 in lon_sect:
lw = 0.7
i += 1
ax = plt.subplot(3, 3, i)
for name, col in zip(model_names, colors):
if name == 'ERA': lw = 2.0
ax.plot(datesall[name].dayofyear,
np.mean(climate_mean_dict[name][:, lla1:lla2, llo1:llo2],
axis=(1, 2)),
rotation='horizontal',
transform=ax.transAxes,
fontsize=20)
ax1.scatter(0.35,
plocos[('var_ratio', 'ref', 'EAT')],
color='black',
marker='*',
s=100)
ax1.scatter(2.15,
plocos[('var_ratio', 'ref', 'PNA')],
color='black',
marker='*',
s=100)
ctl.custom_legend(fig, colorzi, nomi, ncol=2, add_space_below=0.1)
ax1.set_ylabel('Variance ratio')
ax2.set_ylabel('Frequency bias')
#plt.title('cmip6 vs cmip6 var_fr '+tip)
fig.savefig(cart_out + 'var_fr_boxes_{}.pdf'.format(tip))
# taylor with clouds
for tip in ['', '_refEOF']:
fig = plt.figure(figsize=(24, 12))
for i, area in enumerate(['EAT', 'PNA']):
patnames = reg_names_area[area]
for num, patt in enumerate(patnames):
ax = plt.subplot(2, 4, num + 1 + 4 * i, polar=True)
obs = plocos[('patterns', 'ref', area, num)]
meapats = dict()
for var in allvars:
print(var)
fig = plt.figure(figsize = (16,12))
for col, nam in zip(colors, resuu.keys()):
coso = resuu[nam][var].mean('lon').groupby("time.year").mean()
glomean = np.average(coso, weights = abs(np.cos(np.deg2rad(coso.lat))), axis = -1)
if var == 'evspsbl':
plt.plot(coso.year.data, np.abs(glomean), label = nam, color = col, linewidth = 2)
else:
plt.plot(coso.year.data, glomean, label = nam, color = col, linewidth = 2)
plt.grid()
plt.title(var)
#plt.legend()
ctl.custom_legend(fig, colors, resuu.keys(), ncol = 4, add_space_below = 0.03)
figs_global.append(fig)
ctl.plot_pdfpages(cart + 'global_timeseries_allmems.pdf', figs_global)
sys.exit()
#srf_net = ssr + str + sshf + slhf
surf_fluxs = ['rsds', 'rlds', 'rsus', 'rlus', 'hfss', 'hfls']
toa_fluxs = ['rlut', 'rsut', 'rsdt']
allvars = surf_fluxs + toa_fluxs + ['clt', 'clwvi', 'evspsbl']
fir_HR = '/home/paolo/work/data/REFORGE/EC-Earth3-TL799/rfrg-orog255-noparam/r2i1p1f1/mon/{}/{}_Amon_EC-Earth3-TL799_rfrg-orog255-noparam_r2i1p1f1_r144x73_*nc'
fir_LR = '/home/paolo/work/data/REFORGE/EC-Earth3/rfrg-ctrl-noparam/r1i1p1f1/mon/{}/{}_Amon_EC-Earth3_rfrg-ctrl-noparam_r1i1p1f1_r144x73_*nc'
fils_HR = np.concatenate([glob.glob(fir_HR.format(var, var)) for var in allvars])
fils_LR = np.concatenate([glob.glob(fir_LR.format(var, var)) for var in allvars])
rucaz = np.concatenate(rugi)
rumin, rumax = (np.min(rucaz), np.max(rucaz))
ruvec = np.linspace(rumin-0.2*abs(rumin), rumax+0.2*abs(rumax), 100)
for ru, gi, col in zip(allru, rugi, colors):
pdf = ctl.calc_pdf(gi)
pdfvec = pdf(ruvec)
pdfvec = pdfvec/np.sum(pdfvec)
ax.plot(ruvec, pdfvec, color = col)
ax.grid()
ax.set_title('{} - {}'.format(anam, cos))
fig.suptitle('{} - {}'.format(varna, seas))
ctl.custom_legend(fig, colors, allru, ncol = 4, add_space_below = 0.06)
fig.savefig(cart_out + '{}_{}_{}dist_{}.pdf'.format(varna, seas, pio, typet))
#### Now with smaller regions
for varna in allvars_2D:
for seas in seasons:
for pio, gipio in zip(['mon', 'sea'], [800, 200]):
if pio == 'mon' and seas == 'year': continue
fig, axs = plt.subplots(3, 5, figsize = (24,12))
for ia, anam in enumerate(areas_ls_names[:-3]):
ax = axs.T.flatten()[ia]
rugi = []
for na, ru, col in zip(allnams, allru, colors):
if typet == 'rel':
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 50)], color = 'grey', alpha = 0.6)
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 10)], color = 'grey', alpha = 0.6, linestyle = '--')
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 90)], color = 'grey', alpha = 0.6, linestyle = '--')
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 1)], color = 'grey', alpha = 0.6, linestyle = ':')
ax.axhline(ERA_ref_thresholds[(nam, 60, reg, 99)], color = 'grey', alpha = 0.6, linestyle = ':')
#ax.set_xticklabels(model_names, size='small', rotation = 60)
ax.set_xticklabels([])
ax.set_title('Reg {}'.format(reg))
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.suptitle(tit)
plt.subplots_adjust(top = 0.9)
fig = ctl.custom_legend(fig, colors, model_names)
fig.savefig(cart_out + '_'.join(nam.split())+'_models_primacoup_{}.pdf'.format(tag))
allfigs.append(fig)
fig = plt.figure(figsize = (16, 12))
ind = 0
axes = []
for reg in range(4):
ind += 1
ax = plt.subplot(2, 2, ind)
eraval = ERA_ref_thresholds[(nam, 60, reg, 50)]
allvals = np.array([all_mod_stats[(nam, mod, reg)]-eraval for mod in model_names])
allvalsdiff = np.array([abs(cu)-abs(ci) for cu, ci in zip(allvals[::2], allvals[1::2])])
modskill.append(ctl.Rcorr(obserie, modserie))
modskill_p50.append(ctl.Rcorr(obserie, modserie_p50))
xs = np.arange(len(modskill))
ax.scatter(xs, modskill, c=colors, s=100)
ax.scatter(xs, modskill_p50, c=colors, s=100, marker='x')
ax.grid()
ax.set_xticks([])
ax.set_title(patnames[reg])
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.suptitle('Seasonal skill')
ctl.custom_legend(fig, colors, exps)
fig.savefig(cart_out + 'seas_skill.pdf')
figs.append(fig)
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111)
allye = results['ERA']['freq_clus_seasonal_years']
obsfr = results['ERA']['freq_clus_seasonal'].T
modskill = []
modskill_p50 = []
modskill_p75 = []
modskill_p25 = []
for mod in exps:
modserie = []
regnames = ['NAO+', 'SBL', 'NAO-', 'AR']
freq_ref = results_ref['freq_clus']
fig = plt.figure(figsize=(16,12))
axes = []
for reg in range(4):
ax = plt.subplot(2,2,reg+1)
frqall = np.array([freqs[cos][reg] for cos in keall])
ax.bar(np.arange(4), frqall-freq_ref[reg], color = colors)
ax.set_title(regnames[reg])
ax.set_xticks([])
axes.append(ax)
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, colors, laball, ncol = 2)
fig.savefig(cart_out+'Regime_frequency.pdf')
# Hist composite difference (HR, LR)
compdiffs = dict()
for varnam in ['tas', 'pr']:
comps = []
for mod in okmods_hist_L:
comps.append(composites[('present', mod, varnam, 'mean')]-composites[('present', 'ref', varnam, 'mean')])
print('CHECK', mod, np.mean(composites[('present', mod, varnam, 'mean')]))
compdiffs[(varnam, 'LR')] = np.mean(comps, axis = 0)
comps = []
for mod in okmods_hist_H:
comps.append(composites[('present', mod, varnam, 'mean')]-composites[('present', 'ref', varnam, 'mean')])
print('CHECK', mod, np.mean(composites[('present', mod, varnam, 'mean')]))
ctl.primavera_boxplot_on_ax(ax,
allpercs,
model_names_all,
colors_wERA,
color_main,
vers,
ens_names,
ens_colors,
plot_mean=plot_mean)
ax.set_ylabel(allylabels[nam])
#plt.title(alltits[nam])
#fig.suptitle(alltits[nam], fontsize = titlefont)
ctl.custom_legend(fig,
colors_wERA + ens_colors,
model_names_all + ens_names,
ncol=6)
fig.savefig(cart_out +
'{}_bootstraps_v7_k{}.pdf'.format(nam, numclus))
allfigs.append(fig)
for nam in [
'freq', 'dist_cen', 'resid_times_av', 'resid_times_90', 'patcor'
]: #, 'filt_dist_cen', 'filt_relative_entropy', 'filt_patcor']:
fig = plt.figure(figsize=(20, 12))
axes = []
for ii, reg in enumerate(regnam):
ax = plt.subplot(2, 3, ii + 1)
allpercs = dict()
for cos in ['mean', 'std', 'p10', 'p25', 'p50', 'p75', 'p90']:
#### Figurez
# only stab
fig, ax = ctl.get_cartopy_fig_ax(coast_lw=0.1)
proj = ccrs.PlateCarree()
for ru, col in zip(allru, colors):
coso = monreg[(ru, 'stab')]
ctl.plot_mapc_on_ax(ax,
coso.values,
coso.lat.values,
coso.lon.values,
proj,
None, (0, 1),
plot_type='binary_contour',
lw_contour=2.,
line_color=col)
ctl.custom_legend(fig, colors, allru, ncol=4)
fig.savefig(cart_out + 'monsoon_stab.pdf')
cosi = [mpi[(ru, 'stab')] for ru in allru]
for co in cosi:
co.values[co < 0.5] = np.nan
ctl.plot_multimap_contour(cosi,
filename=cart_out + 'monsoon_all.pdf',
cbar_range=(0.5, 1.),
subtitles=allru,
cmap='viridis')
ax=ax,
colors=cols,
markers=marks,
only_first_quarter=True,
plot_ellipse=False,
ellipse_color=colors[0],
max_val_sd=1.6,
title=patt,
alpha_markers=0.8)
#ax.text(0.95, 0.95, patt, horizontalalignment='center', verticalalignment='center', rotation='horizontal',transform=ax.transAxes, fontsize = 18)
ctl.custom_legend(fig,
cols,
mods_all,
markers=marks,
ncol=4,
add_space_below=0.05,
loc='right',
fontsize=14)
fig.savefig(cart_out + 'taylor_{}_{}.pdf'.format(tip.split()[0], aaa))
# Taylor con ellipse
for tip in ['tot LWA', 'trans LWA', 'Montg streamf']:
patt_ref = resu['ERA5'][tip]
fig = plt.figure(figsize=(16, 12))
for num, patt in enumerate(patnames):
ax = plt.subplot(2, 2, num + 1, polar=True)
obs = patt_ref[num]
obs, lat_area, lon_area = ctl.sel_area(olat, olon, obs, areas[aaa])
pdf = ctl.calc_pdf(okjet)
if vv in ['LR', 'OBS']:
axes[reg].plot(latgri, pdf(latgri), color = col, label = mod, linewidth = linewidth, linestyle = sty)
if vv in ['HR', 'OBS']:
axes_HR[reg].plot(latgri, pdf(latgri), color = col, label = mod, linewidth = linewidth, linestyle = sty)
pdf = ctl.calc_pdf(jetind)
if vv in ['LR', 'OBS']:
ax_all.plot(latgri, pdf(latgri), color = col, label = mod, linewidth = linewidth, linestyle = sty)
if vv in ['HR', 'OBS']:
ax_all_HR.plot(latgri, pdf(latgri), color = col, label = mod, linewidth = linewidth, linestyle = sty)
ctl.adjust_ax_scale(axes+axes_HR)
# ctl.custom_legend(fig, colors_wERA, model_names_all)
ctl.custom_legend(fig, colors_wERA2[0::2], model_coups + ['OBS'])
ctl.custom_legend(fig_HR, colors_wERA2[0::2], model_coups + ['OBS'])
#fig.suptitle('Jet Latitude and Weather Regimes (SR)')
fig.savefig(cart_out + 'jetlat_compos_LR{}.pdf'.format(tag))
#fig_HR.suptitle('Jet Latitude and Weather Regimes (HR)')
fig_HR.savefig(cart_out + 'jetlat_compos_HR{}.pdf'.format(tag))
# fig.savefig(cart_out + 'jetlat_compos_wECEstream2.pdf')
ctl.adjust_ax_scale([ax_all, ax_all_HR])
ctl.custom_legend(fig_all, colors_wERA2[0::2], model_coups + ['OBS'])
#ctl.custom_legend(fig_all, colors_wERA, model_names_all)
fig_all.savefig(cart_out + 'jetlat_allregs_LR{}.pdf'.format(tag))
ctl.custom_legend(fig_all_HR, colors_wERA2[0::2], model_coups + ['OBS'])
fig_all_HR.savefig(cart_out + 'jetlat_allregs_HR{}.pdf'.format(tag))
pickle.dump(jetlat_comp, open(cart_out + 'jetlat_compos_all{}.p'.format(tag), 'wb'))
coso = runfreq[(ssp, 'run20', reg)] - np.mean(
freqs[('hist', 'all', 'tot50')][:, reg])
coserr = runfreq[(ssp, 'run20err', reg)]
ax.fill_between(yr,
coso - coserr,
coso + coserr,
color=col,
alpha=0.15)
ax.plot(yr, coso, label=ssp, color=col, linewidth=2)
ax.set_title(reg_names_area[area][reg])
ax.axvline(2015, color='lightslategray', linewidth=0.2, linestyle='--')
ax.axhline(0., color='lightslategray', linewidth=0.2)
axes.append(ax)
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, colssp, allssps, ncol=3)
fig.savefig(cart_out + 'allssps_freq20_{}_anom_wcmip5.pdf'.format(area))
fig = plt.figure(figsize=(27, 6))
axes = []
for reg in range(4):
ax = fig.add_subplot(1, 4, reg + 1)
for ssp in allssps:
col = coldic[ssp]
coso = runfreq[(ssp, 'run20', reg)] - np.mean(
freqs[('hist', 'all', 'tot50')][:, reg])
coserr = runfreq[(ssp, 'run20err', reg)]
ax.fill_between(yr,
coso - coserr,
coso + coserr,
color=col,
ax = plt.subplot(3, 3, i)
for num, col in zip(allnums, colors):
ax.plot(dtclmpd.dayofyear,
np.mean(clm_fullperiod[num][:, lla1:lla2, llo1:llo2],
axis=(1, 2)),
color=col)
if lla2 is None: lla2 = -1
if llo2 is None: llo2 = -1
ax.set_title('lat {:2.0f}/{:2.0f}, lon {:3.0f}/{:3.0f}'.format(
lat_area[lla1], lat_area[lla2], lon_area[llo1], lon_area[llo2]))
axes.append(ax)
ctl.adjust_ax_scale(axes)
fig.tight_layout()
fig = ctl.custom_legend(fig, colors, ['{}'.format(i) for i in allnums])
fig.savefig(cart_out + 'mean_climatology_zg.pdf')
figs = []
axaxes = [[], [], []]
colors = ctl.color_set(len(climate_mean_area))
for num in allnums:
cosone = climate_mean_dict[num]
ref = clm_fullperiod[num]
fig = plt.figure(figsize=(16, 12))
i = 0
for uu, (lla1, lla2) in enumerate(lat_sect):
for llo1, llo2 in lon_sect:
i += 1
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [np.percentile(trend_ssp[(ssp, 'all', pio, cos, reg)], nu) for ssp in allsims[1:]]
allpercs['ens_min'] = [np.min(trend_ssp[(ssp, 'all', pio, cos, reg)]) for ssp in allsims[1:]]
allpercs['ens_max'] = [np.max(trend_ssp[(ssp, 'all', pio, cos, reg)]) for ssp in allsims[1:]]
ctl.boxplot_on_ax(ax, allpercs, allsims[1:], colsim[1:], plot_mean = False, plot_ensmeans = False, plot_minmax = True)
ax.axhline(0, color = 'gray', linewidth = 0.5)
ax.set_xticks([])
ax.set_title(reg_names[reg])
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, colsim[1:], allsims[1:], ncol = 4)
#fig.suptitle('30yr bsp of WR freq. in 2050-2100 wrt 1964-2014')
fig.savefig(cart_out + '{}_WRfreq_allssp_{}_{}.pdf'.format(pio, area, cos))
fig = plt.figure(figsize = (16,12))
axes = []
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
axes.append(ax)
allpercs = dict()
allpercs['mean'] = [np.mean(residtime_ssp[(ssp, 'all', pio, reg)]) for ssp in allsims[1:]]
for nu in [10, 25, 50, 75, 90]:
allpercs['p{}'.format(nu)] = [np.percentile(residtime_ssp[(ssp, 'all', pio, reg)], nu) for ssp in allsims[1:]]
for mod, col in zip(modoks, coloks):
frdiff = results_ssp585[mod]['freq_clus'][reg] - results_hist[mod][
'freq_clus'][reg]
ax.scatter(i, frdiff, marker='D', color=col, s=25)
allfreqs[(ssp, area, season, tip,
reg)].append(results_ssp585[mod]['freq_clus'][reg] -
results_hist[mod]['freq_clus'][reg])
# allfreqs[('hist', tip, reg)].append(results_hist[mod]['freq_clus'][reg])
i += 1
ax.axhline(0, color='gray', linewidth=0.5)
ax.set_xticks([])
ax.set_title(reg_names[reg])
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, coloks, modoks, ncol=4)
fig.suptitle('Change of WR freq. in 2050-2100 wrt 1964-2014')
fig.savefig(cart_out + 'WR_freq_change_dtr_{}_{}_{}_{}.pdf'.format(*ke))
# Same but only for long-lasting (> 5 days) regimes
for mod in modoks:
results_ssp585[mod]['av_res'] = np.array([
np.mean(results_ssp585[mod]['resid_times'][reg])
for reg in range(4)
])
results_hist[mod]['av_res'] = np.array([
np.mean(results_hist[mod]['resid_times'][reg]) for reg in range(4)
])
labs = ctl.regime_filter_long(results_ssp585[mod]['labels'],
# ok200 = np.array([da.year > dates[-1].year-200 for da in dates])
# varok = var[ok200]
# dateok = dates[ok200]
# else:
# varok = var
# dateok = dates
#
# resdict[(ru, varnam, 'mean', 'mar')], resdict[(ru, varnam, 'std', 'mar')] = ctl.seasonal_climatology(varok, dateok, 'Mar')
# resdict[(ru, varnam, 'mean', 'sep')], resdict[(ru, varnam, 'std', 'sep')] = ctl.seasonal_climatology(varok, dateok, 'Sep')
axs[0, 0].set_title('March')
axs[0, 1].set_title('September')
axs[0, 0].set_ylabel(r'Sea ice extent (m$^2$)')
axs[1, 0].set_ylabel(r'Sea ice extent (m$^2$)')
#axs[1,1].legend()
ctl.custom_legend(fig, colors2, allru2, ncol=3)
fig.savefig(cart_out + 'bottseaice.pdf')
sys.exit()
#
# miptab = 'SImon_r1'
# var_map_200 = ['siconc', 'sithick']
#
# mapmean = dict()
# for na, ru, col in zip(allnams, allru, colors):
# mem = 'r1'
# filist = np.concatenate([glob.glob(filna.format(na, mem, miptab, varnam)) for varnam in var_map_200])
# gigi = xr.open_mfdataset(filist, use_cftime=True)
#
# if ru != 'pi':
