allru = ['pi', 'b025', 'b050', 'b100']
allnams = ['piControl', 'stabilization-ssp585-2025', 'stabilization-ssp585-2050', 'stabilization-ssp585-2100']
colors = ['black', 'forestgreen', 'orange', 'violet']
allnams2 = allnams + ['ssp585', 'historical']
allru2 = allru + ['ssp585', 'hist']
colors2 = colors + ['indianred', 'steelblue']
####################################################################################################
glomeans, pimean, yeamean, mapmean = pickle.load(open(cart_in + 'bottino_seasmean_2D.p', 'rb'))
var = 'tas'
latbins = np.arange(-90, 91, 20)
lacol = ctl.color_set(9, use_seaborn=False)
for var in ['tas', 'pr']:
fig, axs = plt.subplots(2, 3, figsize = (16,9))
for ru, ax in zip(allru2, axs.flatten()):
coso = yeamean[(ru, var)]
coso1 = coso[:10].mean(axis = 0)
cosoanom = coso-coso1
ax.set_title(ru)
smut = 50
if ru in ['ssp585', 'hist']:
smut = 20
for la1, la2, col in zip(latbins[:-1], latbins[1:], lacol):
print(la1,la2)
cosolat = cosoanom.sel(lat = slice(la1, la2)).mean(['lat','lon'])
ps_low = ctl.butter_filter(ps, 50)
#ps_low = ctl.running_mean(ps, 50)
ax.plot(freqs[idx], ps_low[idx], linewidth = 2)
ax.set_title(ru)
if ax in axs[1, :]:
ax.set_xlabel('freq (yr-1)')
#ax.set_ylabel('amplitude')
#ax.set_xlim(2, 10)
#ctl.adjust_ax_scale(axs.flatten())
fig.savefig(cart_out + 'enso_spectra_all500.pdf')
nchu = 5
nye = 100
colzz = ctl.color_set(5, cmap = 'viridis', use_seaborn=False)
ps_max = dict()
ps_ext = dict()
fig, axs = plt.subplots(2, 2, figsize = (16,12))
for ru, ax in zip(allru, axs.flatten()):
piuz = enso[ru]['tos'].groupby('time.year').mean()
data_all = piuz.values.flatten()
for ich, col in zip(range(nchu), colzz):
data = data_all[ich*nye:(ich+1)*nye]
ps = np.abs(np.fft.fft(data))**2
freqs = np.fft.fftfreq(data.size, 1)
ttests[(area, season, 'pos')] = np.stack(testyp)
ttests[(area, season, 'neg')] = np.stack(testyn)
deltapos = (pcpos - pcref) / koze_ref['pcs'].std(axis=0)
deltaneg = (pcneg - pcref) / koze_ref['pcs'].std(axis=0)
deltadist_all[(area, season, 'pos')] = deltapos
deltadist_all[(area, season, 'neg')] = deltaneg
ofidpc.write('------- {} - {} ----------\n'.format(area, season))
ofidpc.write(('pcs deltapos: ' + '{:10.2e}' * len(deltapos) +
'\n').format(*deltapos))
ofidpc.write(('pcs deltaneg: ' + '{:10.2e}' * len(deltaneg) +
'\n').format(*deltaneg))
ofidpc.write('---------------------------\n')
colors = ctl.color_set(4)
for ii, col in enumerate(colors):
# eventi positivi
okpo = coso_pos['labels'] == ii
plt.scatter(coso_pos['pcs'][okpo, 0],
coso_pos['pcs'][okpo, 1],
color=col,
s=10,
marker='o')
# eventi negativi
okpo = coso_neg['labels'] == ii
plt.scatter(coso_neg['pcs'][okpo, 0],
coso_neg['pcs'][okpo, 1],
color=col,
s=10,
gen_file_ssp = cart_data + 'out_NEW_cmip6_{}_NDJFM_{}_4clus_4pcs_2015-2100_refCLUS_dtr_histrebase.p'
yr10 = 10 # length of running mean
numclus = 4
reg_names_area = dict()
reg_names_area['EAT'] = ['NAO+', 'SBL', 'AR', 'NAO-']
reg_names_area['PNA'] = ['PT', 'PNA+', 'PNA-', 'BR']
#okmods = ['ACCESS-CM2_r1i1p1f1', 'BCC-CSM2-MR_r1i1p1f1', 'CanESM5_r1i1p1f1', 'CESM2-WACCM_r1i1p1f1','CNRM-CM6-1-HR_r1i1p1f2', 'CNRM-CM6-1_r1i1p1f2','CNRM-ESM2-1_r1i1p1f2', 'EC-Earth3_r1i1p1f1', 'FGOALS-g3_r1i1p1f1','INM-CM4-8_r1i1p1f1', 'INM-CM5-0_r1i1p1f1', 'IPSL-CM6A-LR_r1i1p1f1','MIROC6_r1i1p1f1', 'MPI-ESM1-2-HR_r1i1p1f1','MPI-ESM1-2-LR_r1i1p1f1', 'MRI-ESM2-0_r1i1p1f1','NorESM2-LM_r1i1p1f1', 'NorESM2-MM_r1i1p1f1', 'UKESM1-0-LL_r1i1p1f2']
# mancano = ['BCC-ESM1_r1i1p1f1', 'CESM2_r1i1p1f1', 'GFDL-CM4_r1i1p1f1', 'HadGEM3-GC31-LL_r1i1p1f3', 'KACE-1-0-G_r1i1p1f1', 'MPI-ESM-1-2-HAM_r1i1p1f1']
allssps = 'ssp126 ssp245 ssp370 ssp585'.split()
allsimcol = [
'hist', 'ssp126', 'ssp245', 'bau', 'ssp370', 'ssp585', 'rcp85_cmip5'
]
coldic = dict(zip(allsimcol, ctl.color_set(7)))
colssp = [coldic[ssp] for ssp in allssps]
# tas_anom = dict()
# tas_trends = dict()
# for ssp in ['ssp585']:
# print('SSP '+ssp)
#
# area = 'EAT'
# cart_lui = cart_in + 'Results_v5_rebase/{}_NDJFM/'.format(area)
# freqs, residtimes, patterns = pickle.load(open(cart_lui + 'allresults_dicts_{}_v3.p'.format(area), 'rb'))
#
# okmods = [ke[1] for ke in freqs if ssp in ke and 'tot50' in ke and 'all' not in ke and 'rel' not in ke]
# if ssp == 'ssp126':
# okmods = [mod for mod in okmods if mod != 'FGOALS-g3_r1i1p1f1']
# print(okmods)
all_years_ref = np.array([dat[0].year for dat in dates_climate_mean])
allnums = [1, 5, 10, 15, 20]
clm_fullperiod = dict()
for num in [1, 5, 10, 15, 20]:
clm, dtclm, _ = ctl.daily_climatology(var, dates, num)
clmarea, _, _ = ctl.sel_area(lat, lon, clm, 'EAT')
clm_fullperiod[num] = clmarea
dtclmpd = pd.to_datetime(dtclm)
lat_sect = [(16, None), (8, 16), (0, 8)]
lon_sect = [(0, 16), (16, 32), (32, None)]
colors = ctl.color_set(len(allnums))
fig = plt.figure(figsize=(16, 12))
i = 0
for lla1, lla2 in lat_sect:
axes = []
for llo1, llo2 in lon_sect:
i += 1
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
cart_out_ERA = '/home/fabiano/Research/lavori/WeatherRegimes/phasespace_ERA/' # cart = '/home/fabiano/Research/lavori/WeatherRegimes/primavera_coupled_1957-2014/' # filo = cart + 'out_primavera_coupled_1957-2014_DJF_EAT_4clus_4pcs_1957-2014_dtr.p' tag = 'v4' cart = '/home/fabiano/Research/lavori/WeatherRegimes/prima_coup_v4/' filo = cart + 'out_prima_coup_v4_DJF_EAT_4clus_4pcs_1957-2014_refEOF.p' model_names = ['CMCC-CM2-HR4', 'CMCC-CM2-VHR4', 'CNRM-CM6-1', 'CNRM-CM6-1-HR', 'EC-Earth3P', 'EC-Earth3P-HR', 'ECMWF-IFS-LR', 'ECMWF-IFS-HR', 'MPI-ESM1-2-HR', 'MPI-ESM1-2-XR', 'HadGEM3-GC31-MM', 'HadGEM3-GC31-HM', 'HadGEM3-GC31-LL-det', 'HadGEM3-GC31-LL-stoc', 'EC-Earth3P-det', 'EC-Earth3P-stoc'] model_coups = ['CMCC-CM2', 'CNRM-CM6-1', 'EC-Earth3P', 'ECMWF-IFS', 'MPI-ESM1-2', 'HadGEM3-GC31', 'HadGEM3-GC31 (det vs stoc)', 'EC-Earth3P (det vs stoc)'] model_names_all = model_names + ['ERA'] model_coups = ['CMCC-CM2', 'CNRM-CM6-1', 'EC-Earth3P', 'ECMWF-IFS', 'MPI-ESM1-2', 'HadGEM3-GC31', 'HadGEM3-GC31 (det vs stoc)', 'EC-Earth3P (det vs stoc)'] colors = ctl.color_set(len(model_names), sns_palette = 'Paired') colors_wERA = colors + ['black'] # # Ora tutte le statistiche resolution = np.array([100, 25, 250, 50, 100, 50, 50, 25, 100, 50, 100, 50, 250, 250, 250, 100, 100]) sym_all = ['o', 'P','o', 'P','o', 'P','o', 'P','o', 'P','o', 'P', 's', '*', 's', '*'] wi = 0.6 # # Ora tutte le statistiche stat_nams = ['KS 1D', 'Clus radius', 'Clus std', 'KS dist refcentroid', 'dist centocen'] titles = ['1D Kolmogorov-Smirnov statistics on regime pdfs', 'Cluster radius', 'Cluster internal std dev', '1D KS on distance to reference centroid', 'Distance of centroid to reference centroid'] allfigs = [] eof_axis_lim = (-3000., 3000.)
fil_jet = 'JetLatDist_850hPa_{}_hist-1950_DJF.npy'
file_ERA = '/home/fabiano/Research/lavori/Jet_latitude/jli_ERA_1957_2014.npy'
cart_bloc = '/data-hobbes/fabiano/PRIMAVERA/Reinhard_blocking/all_hist-1950/'
fil_bloc = 'bf.5day.daily.daily_mean.{}.{}.hist-1950.r1i1p1f1.v20170915.{}-{}.nc'
# model_names = ['AWI-CM-1-1-LR', 'AWI-CM-1-1-HR', 'CMCC-CM2-HR4', 'CMCC-CM2-VHR4', 'CNRM-CM6-1', 'CNRM-CM6-1-HR', 'EC-Earth3P', 'EC-Earth3P-HR', 'ECMWF-IFS-LR', 'ECMWF-IFS-HR', 'MPI-ESM1-2-HR', 'MPI-ESM1-2-XR', 'HadGEM3-GC31-LL', 'HadGEM3-GC31-MM']#'HadGEM3-GC31-HM']
model_names = ['CMCC-CM2-HR4', 'CMCC-CM2-VHR4', 'CNRM-CM6-1', 'CNRM-CM6-1-HR', 'EC-Earth3P', 'EC-Earth3P-HR', 'ECMWF-IFS-LR', 'ECMWF-IFS-HR', 'MPI-ESM1-2-HR', 'MPI-ESM1-2-XR', 'HadGEM3-GC31-LL', 'HadGEM3-GC31-MM']
model_mems = ['r1i1p1f1', 'r1i1p1f1', 'r1i1p1f2', 'r1i1p1f2', 'r3i1p2f1', 'r1i1p2f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', 'r1i1p1f1', '']
vers = 6*['LR', 'HR'] + ['OBS']
# model_coups = ['AWI-CM-1-1', 'CMCC-CM2', 'CNRM-CM6-1', 'EC-Earth3P', 'ECMWF-IFS', 'MPI-ESM1-2', 'HadGEM3-GC31']
model_coups = ['CMCC-CM2', 'CNRM-CM6-1', 'EC-Earth3P', 'ECMWF-IFS', 'MPI-ESM1-2', 'HadGEM3-GC31']
model_names_all = model_names + ['ERA']
colors2 = ctl.color_set(len(model_coups))
col22 = list(np.concatenate([[co, co] for co in colors2]))
colors_wERA2 = col22 + ['black']
# styall = np.empty(len(colors_wERA2), dtype = str)
# styall[0::2] = ':'
# styall[1::2] = '-'
# styall[-1] = '-'
colors = ctl.color_set(len(model_names), sns_palette = 'Paired')
colors_wERA = colors + ['black']
# colors_wERA2 = np.array(colors_wERA)
# colors_wERA2[0:-1:2] = colors_wERA2[1::2]
colors = ctl.color_set((len(model_coups)+1)*2, sns_palette = 'Paired')
colorscoup = [np.mean([col1, col2], axis = 0) for col1, col2 in zip(colors[:-1:2], colors[1::2])]
color_main = []
file_hist = cart_in + 'out_NEW_cmip6_hist_NDJFM_{}_4clus_4pcs_{}-{}_refCLUS_dtr.p'
file_hist_refEOF_cmip5 = '/home/fabiano/Research/lavori/CMIP6/cmip5_hist_reb/out_cmip5_hist_reb_NDJFM_{}_4clus_4pcs_{}-{}_refEOF_dtr.p'
file_hist_cmip5 = '/home/fabiano/Research/lavori/CMIP6/cmip5_hist_reb/out_cmip5_hist_reb_NDJFM_{}_4clus_4pcs_{}-{}_refCLUS_dtr.p'
numclus = 4
reg_names_area = dict()
reg_names_area['EAT'] = ['NAO+', 'SBL', 'AR', 'NAO-']
reg_names_area['PNA'] = ['PT', 'PNA+', 'PNA-', 'BR']
clatlo = dict()
clatlo['EAT'] = (70., -20.)
clatlo['PNA'] = (70., -120.)
colormip = dict()
colormip[('cmip5', 'EAT')] = ctl.color_set(7)[4]
colormip[('cmip6', 'EAT')] = ctl.color_set(7)[0]
colormip[('cmip5', 'PNA')] = ctl.color_set(7)[3]
colormip[('cmip6', 'PNA')] = ctl.color_set(7)[6]
area = 'EAT'
plocos = dict()
res_short = dict()
for area in ['EAT', 'PNA']:
print(area)
cart_out = cart_out_orig + '{}_NDJFM/'.format(area)
ctl.mkdir(cart_out)
results_hist, results_ref = ctl.load_wrtool(
file_hist.format(area, ye1, ye2))
results_hist_refEOF, _ = ctl.load_wrtool(
bauda = np.arange(1965, 2015)
gigi = results_ssp[mod]['var_dtr']
gigida = np.arange(2015, 2100)
fig = plt.figure()
plt.plot(bauda, bau)
plt.plot(bauda,
np.polyval(results_hist[mod]['coeffs_dtr'], bauda),
linestyle='--')
plt.plot(gigida, gigi)
plt.plot(gigida,
np.polyval(results_ssp[mod]['coeffs_dtr'], gigida),
linestyle='--')
fig.savefig(cart_out_orig + 'vardtr_check.pdf')
fig = plt.figure()
cols = ctl.color_set(len(results_ssp.keys()))
for mod, co in zip(results_ssp.keys(), cols):
try:
bau = results_hist[mod]['var_dtr']
bauda = np.arange(1965, 2015)
gigi = results_ssp[mod]['var_dtr']
gigida = np.arange(2015, 2100)
plt.scatter(bauda, bau, color=co, s=2)
plt.plot(bauda,
np.polyval(results_hist[mod]['coeffs_dtr'], bauda),
color=co,
linewidth=0.5)
plt.scatter(gigida, gigi, color=co, s=2)
plt.plot(gigida,
np.polyval(results_ssp[mod]['coeffs_dtr'], gigida),
color=co,
mem = allmems[0]
fils_HR = np.concatenate([glob.glob(fir_HR.format(mod, tip, mem, var, var)) for var in allvars])
flux_hr = xr.open_mfdataset(fils_HR, use_cftime = True)
flux_hr = flux_hr.assign(net_sfc = flux_hr.rsds + flux_hr.rlds - flux_hr.rsus - flux_hr.rlus - flux_hr.hfss - flux_hr.hfls) # net downward energy flux at surface
flux_hr = flux_hr.assign(net_toa = flux_hr.rsdt - flux_hr.rlut - flux_hr.rsut) # net downward energy flux at TOA
flux_hr = flux_hr.assign(in_atm = flux_hr.net_toa - flux_hr.net_sfc)
flux_hr = flux_hr.drop_vars('time_bnds')
resuu[na+'_'+tipna] = flux_hr
allvars = allvars + ['net_sfc', 'net_toa', 'in_atm']
colors = ctl.color_set(len(resuu))
### Add global mean timeseries
figs_global = []
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)
filename = cart_out_orig + 'zontrend_ssp585.pdf'
oklats = lat >= 30
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111)
for modmem, co in zip(okmods, zontrend):
ax.plot(co[oklats], lat[oklats], label=modmem)
ax.plot(np.mean(zontrend, axis=0)[oklats],
lat[oklats],
label='MMM',
color='black',
linewidth=3)
ax.axvline(0., color='lightslategray', linewidth=0.2)
plt.legend()
fig.savefig(filename)
cols = np.array(ctl.color_set(7))[1::2]
filename = cart_out_orig + 'zontrend_ssp585_MMM.pdf'
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111)
coso = np.mean(zontrend, axis=0)[oklats]
coserr = np.std(zontrend, axis=0)[oklats] / np.sqrt(len(zontrend) - 1)
ax.fill_betweenx(lat[oklats],
coso - coserr,
coso + coserr,
color=cols[0],
alpha=0.2)
ax.plot(coso, lat[oklats], color=cols[0], linewidth=2, label='NML')
coso = np.mean(zontrend_PNA, axis=0)
cyea = [np.mean(ran) for ran in yr_ranges]
histran = (1850, 2005)
futran = (2006, 2100)
cartsig = cart + 'sig/'
sigs_EAT = []
sigs_PNA = []
for ens in ensmem:
sig_a = np.array([results[(ens,'EAT',ran)]['significance'] for ran in yr_ranges])
sig_p = np.array([results[(ens,'PNA',ran)]['significance'] for ran in yr_ranges])
sigs_EAT.append(sig_a)
sigs_PNA.append(sig_p)
colors = ctl.color_set(6, bright_thres = 0.2)
for area in ['EAT', 'PNA']:
fig = plt.figure()
for ens in base_ens:
sig = np.array([results[(ens,area,ran)]['significance'] for ran in yr_ranges])
plt.plot(cyea, sig, label = ens)
# plt.scatter(cyea, sig, color = col, marker = sym, label = ens)
plt.legend()
plt.ylabel('Significance')
plt.xlabel('central year of 30yr period')
plt.title(area+' - base runs')
fig.savefig(cartsig+'Sig_{}_base_5yr.pdf'.format(area))
fig = plt.figure()
for ens in stoc_ens:
for flun in fluxnames:
for seas in seasons+['year']:
for taw in ['zonal', 'maps', 'cross']:
fluxes_model[(ens, flun, seas, taw, am)] = np.mean([coso[flun][taw][seas] for coso in results_year], axis = 0)
pickle.dump(fluxes_model, open(cart_out+'out_hfc_NEW_lcb0vslcs0.p', 'wb'))
seasonsall = ['year']+seasons
figure_file = cart_out + 'hfc_lcb0_vs_lcs0_NEW.pdf'
figures = []
for flun in fluxnames:
for ann in annme:
fig = plt.figure()
plt.title('{} fluxes - {}'.format(flun, ann))
cset = ctl.color_set(len(seasonsall), bright_thres = 1)
for seas, col in zip(seasonsall, cset):
plt.plot(lat, fluxes_model[('lcb0', flun, seas, 'zonal', ann)], label = 'base '+seas, color = col, linewidth = 2.)
plt.plot(lat, fluxes_model[('lcs0', flun, seas, 'zonal', ann)], label = 'stoc '+seas, color = col, linewidth = 2., linestyle = '--')
plt.plot(lat, era_fluxes_zonal[(flun, seas)], label = 'ref '+seas, color = col, linewidth = 0.7, linestyle = ':')
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'
global_anom_b = np.mean(global_anom_ens[:3, ...], axis=0)
global_anom_s = np.mean(global_anom_ens[3:, ...], axis=0)
# med_anom_b = np.mean(med_anom_ens[:3, ...], axis = 0)
# med_anom_s = np.mean(med_anom_ens[3:, ...], axis = 0)
# med_anom_sum_b = np.mean(med_anom_sum_ens[:3, ...], axis = 0)
# med_anom_sum_s = np.mean(med_anom_sum_ens[3:, ...], axis = 0)
yearly_anom_b = np.mean(yearly_anom_ens[:3, ...], axis=0)
yearly_anom_s = np.mean(yearly_anom_ens[3:, ...], axis=0)
#monthly_climat_b = np.mean(month_climat_ens[:3, ...], axis = 0)
#monthly_climat_s = np.mean(month_climat_ens[3:, ...], axis = 0)
#plt.ion()
fig = plt.figure()
years_pdh = pd.to_datetime(years)
cset = ctl.color_set(len(years), bright_thres=0., full_cb_range=True)
for year, col in zip(years_pdh.year, cset):
plt.plot(lat, zonal_anom_b[years_pdh.year == year].squeeze(), color=col)
plt.xlabel('Latitude')
plt.ylabel('Temperature anomaly [K]')
fig.subplots_adjust(right=0.8)
plt.grid()
plt.xlim(-90, 90)
plt.ylim(-3., 18.)
plt.title('Zonal temperature anomaly (base)')
cbar_ax = fig.add_axes([0.83, 0.12, 0.05, 0.7])
norm = mpl_colors.Normalize(vmin=years_pdh.year[0], vmax=years_pdh.year[-1])
trend_ssp[(ssp, 'all', 'errtrend', 'seafreq', reg)] = np.array([
trend_ssp[(ssp, mem, 'errtrend', 'seafreq', reg)] for mem in okmods
])
trend_ssp[(ssp, 'all', 'trend', 'freq10', reg)] = np.array(
[trend_ssp[(ssp, mem, 'trend', 'freq10', reg)] for mem in okmods])
trend_ssp[(ssp, 'all', 'errtrend', 'freq10', reg)] = np.array([
trend_ssp[(ssp, mem, 'errtrend', 'freq10', reg)] for mem in okmods
])
pickle.dump([freqs, trend_ssp, residtimes, num_event],
open(cart_out + 'freqs_cmip5_{}.p'.format(area), 'wb'))
pickle.dump([seasfreq, runfreq],
open(cart_out + 'seasfreqs_cmip5_{}.p'.format(area), 'wb'))
allsims = ['hist_cmip5', 'rcp85_cmip5']
colsim = ctl.color_set(2)
figall = plt.figure(figsize=(16, 12))
axes = []
cos = 'tot50'
for reg in range(4):
ax = figall.add_subplot(2, 2, reg + 1)
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
]
results = dict()
for ex in exps:
fiok = finam.format(ex, ex)
gigi = pickle.load(open(fiok, 'rb'))
modnam = list(gigi['models'].keys())[0]
results[ex] = gigi['models'][modnam]
results_ref = gigi['reference']
gigi = pickle.load(
open(
'/home/fedefab/Scrivania/Research/Post-doc/lavori/ecmwf_seasonal/ERA_test/out_ERA_test_DJF_EAT_4clus_4pcs_1981-2010_refEOF.p',
'rb'))
results['ERA'] = gigi['models']['ERA']
colors = ctl.color_set(len(exps))
colors_all = colors + ['black']
exps_all = exps + ['ERA']
patnames = ['NAO+', 'SBL', 'AR', 'NAO-']
## Diagnostics
## Plots: variance ratio, patcor & phase space, persistence, frequency
## Two versions: simple and with ensemble member spread
# Variance ratio
figs = []
fig = plt.figure(figsize=(16, 12))
#ax = fig.add_subplot(111)
rescoso[(tip, 'ssp585')][mem]['dates']
])
pcs = np.concatenate([
rescoso[(tip, 'hist')][mem]['pcs'],
rescoso[(tip, 'ssp585')][mem]['pcs']
],
axis=0)
cosone[mem]['labels'] = labs
cosone[mem]['dates'] = dats
cosone[mem]['pcs'] = pcs
resdict[tip] = cosone
alltips = tuple(resdict.keys())
colorz = ctl.color_set(len(alltips))
runfreq = dict()
seasfreq = dict()
figs = []
for tip, col in zip(alltips, colorz):
okmods = resdict[tip].keys()
fig = plt.figure(figsize=(16, 12))
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
cosi = []
for mem in okmods:
seasfr, yr = ctl.calc_seasonal_clus_freq(
resdict[tip][mem]['labels'], resdict[tip][mem]['dates'],
numclus)
plt.rcParams['axes.titlesize'] = 28
plt.rcParams['axes.labelsize'] = 18
plt.rcParams['axes.axisbelow'] = True
#############################################################################
# leggi cose
cart_in = '/home/fedef/Research/lavori/paolitos_LWA/LWA_timeseries/'
cart_out = '/home/fedef/Research/lavori/paolitos_LWA/'
filin = cart_in + 'timeseries_{}_{}.p'
areas = ['NH', 'EAT'] #, 'PNA']
versions = ['LR', 'MR', 'HR']
colors = ctl.color_set(12, sns_palette='Paired')
cose = dict()
for area in areas:
cose[area] = dict()
for vers in versions:
print(area, vers)
gigi = pickle.load(open(filin.format(area, vers), 'rb'))
print(gigi['domain'], gigi['resolution'])
#cose[(area, vers)] = gigi['timeseries']
mod_names = gigi['labels']
if 'ERA5' in mod_names:
cose[area]['obs'] = gigi['timeseries'][0]
mod_names = mod_names[1:]
ts = gigi['timeseries'][1:]
for mod, tise in zip(mod_names, ts):
ctl.mkdir(cart_out_gen.format(ensmod))
figscores = dict()
rsq_old = dict()
xssdi = dict()
for reg in regtip:
fig_score, axs = plt.subplots(figsize=(12, 8))
figscores[(reg, 0)] = (fig_score, axs)
fig_score, axs = plt.subplots(figsize=(12, 8))
figscores[(reg, 1)] = (fig_score, axs)
rsq_old[reg] = None
for nu, colnu in zip(np.arange(2, 8), ctl.color_set(6)):
print('Finding best {} drivers'.format(nu))
allcombs = list(itt.combinations(range(len(drilis)), nu))
okcombs = dict()
allscores = dict()
#faccio cose
for reg in regtip:
print(reg)
allscores[reg] = []
okcombs[reg] = []
# pesca i drivers, uno per tipo, e fai il model
for ii, comb in enumerate(allcombs):
# print(ii, comb)
])
trend_ssp[(ssp, 'all', 'errtrend', 'freq10', reg)] = np.array([
trend_ssp[(ssp, mem, 'errtrend', 'freq10', reg)]
for mem in okmods
])
residtime_ssp[(ssp, 'all', 'trend', reg)] = np.array(
[residtime_ssp[(ssp, mem, 'trend', reg)] for mem in okmods])
residtime_ssp[(ssp, 'all', 'errtrend', reg)] = np.array(
[residtime_ssp[(ssp, mem, 'errtrend', reg)] for mem in okmods])
pickle.dump([trend_ssp, residtime_ssp],
open(cart_out + 'trends_wrfreq_e_restime_{}.p'.format(area),
'wb'))
allsims = ['hist', 'ssp126', 'ssp245', 'ssp370', 'ssp585']
colsim = ctl.color_set(5)
reg_names = reg_names_area[area]
kess = [ke for ke in trend_ssp.keys() if 'all' in ke]
print(kess)
for pio in ['trend', 'errtrend']:
for cos in ['seafreq', 'freq10']:
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()
for loe, erol in enumerate(era_lon):
total_eragrid[lae, loe] = pinotot(eral, erol)
diff = total_eragrid - era_fluxes_maps[(seas, 'tot')]
fig = ctl.plot_map_contour(
diff,
era_lat,
era_lon,
title='Difference vs ERA flux - {} {} {}'.format(
exp, year, seas),
plot_anomalies=True)
figures_exp_maps.append(fig)
for flun in fluxnames:
fig = plt.figure()
plt.title('{} fluxes - {}'.format(shortnam[flun], year))
cset = ctl.color_set(3, bright_thres=1)
for seas, col in zip(press0.keys(), cset):
plt.plot(lat,
fluxes_zonal[(seas, flun)],
label=seas,
color=col,
linewidth=2.)
plt.plot(lat,
era_fluxes_zonal_itrp[(seas, flun)],
label='ERA ' + seas,
color=col,
linewidth=0.7,
linestyle='--')
plt.legend()
plt.grid()
plt.ylim(zonal_margins[flun])
for mod in all_res:
print(mod, all_res[mod][17].keys())
all_figures = []
# ellipse plot
nsqr = np.sqrt(results_ref['cluspattern_area'].size)
pattnames = ['NAO +', 'Sc. Blocking', 'Atl. Ridge', 'NAO -']
fig = plt.figure(figsize=(16, 12))
for i in range(4):
ax = fig.add_subplot(2, 2, i + 1)
ax.set_title(pattnames[i], fontsize=18, fontweight='bold')
ax.grid()
colors = ctl.color_set(len(all_res.keys()))
ctl.ellipse_plot(
[ens_mean[(mod, 'patcor')][i] for mod in all_res.keys()],
[ens_mean[(mod, 'RMS')][i] / nsqr for mod in all_res.keys()],
[ens_std[(mod, 'patcor')][i] for mod in all_res.keys()],
[ens_std[(mod, 'RMS')][i] / nsqr for mod in all_res.keys()],
alpha=0.7,
ax=ax,
colors=colors)
for mod, col in zip(all_res.keys(), colors):
pats = [coso['patcor'][i] for coso in all_res[mod]]
rmss = [coso['RMS'][i] / nsqr for coso in all_res[mod]]
ax.scatter(pats, rmss, color=col, s=5)
ax.set_xlim(0.4, 1.0)
all_stats = pickle.load(open(cart_out + 'KS_stat_1000.p'))
all_stats += [all_centtocent_dist]
stat_nams = ['KS 1D', 'Clus radius', 'Clus std', 'KS dist refcentroid', 'dist centocen']
titles = ['1D Kolmogorov-Smirnov statistics on regime pdfs', 'Cluster radius', 'Cluster internal std dev', '1D KS on distance to reference centroid', 'Distance of centroid to reference centroid']
ERA_ref_thresholds = dict()
for stat, nam, tit in zip(all_stats, stat_nams, titles):
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(stat[(n_choice, reg)])
for perc in [1, 5, 10, 100/3., 50, 200/3., 90, 95, 99]:
ERA_ref_thresholds[(nam, n_choice, reg, perc)] = np.percentile(allvals, perc)
kpdf = ctl.calc_pdf(allvals)
#xvec = np.linspace(0., 0.4, 500)
if 'KS' in nam:
seasfreq.update(seasfreq_cmip5)
runfreq.update(runfreq_cmip5)
num_event.update(num_event_cmip5)
allssps = ['ssp126', 'ssp245', 'ssp370', 'ssp585', 'rcp85_cmip5']
allsims = ['hist', 'ssp126', 'ssp245', 'ssp370', 'ssp585', 'rcp85_cmip5']
allsims_wcmip5 = [
'hist', 'ssp126', 'ssp245', 'ssp370', 'ssp585', 'hist_cmip5',
'rcp85_cmip5'
]
allsimcol = [
'hist', 'ssp126', 'ssp245', 'hist_cmip5', 'ssp370', 'ssp585',
'rcp85_cmip5'
]
colorzi = ctl.color_set(7)
allsimcol = [
'ssp370', 'ssp245', 'ssp126', 'bau', 'rcp85_cmip5', 'ssp585', 'bau2'
]
coldic = dict(zip(allsimcol, colorzi))
coldic['hist'] = 'darkslategray'
coldic['hist_cmip5'] = 'saddlebrown'
colsim = [coldic[ssp] for ssp in allsims]
colssp = [coldic[ssp] for ssp in allssps]
colsim_wcmip5 = [coldic[ssp] for ssp in allsims_wcmip5]
yr = np.arange(1965, 2100)
fig = plt.figure(figsize=(16, 12))
axes = []
for reg in range(4):
'MPI-ESM1-2-XR', 'HadGEM3-GC31-LL', 'HadGEM3-GC31-MM', 'HadGEM3-GC31-HM',
'HadGEM3-GC31-HH'
]
vers = [
'LR', 'HR', 'LR', 'HR', 'LR', 'HR', 'LR', 'HR', 'LR', 'MR', 'HR', 'LR',
'HR', 'LR', 'MR', 'MR', 'HR'
] + ['OBS']
model_coups = [
'AWI-CM-1-1', 'CMCC-CM2', 'CNRM-CM6-1', 'EC-Earth3P', 'ECMWF-IFS',
'MPI-ESM1-2', 'HadGEM3-GC31'
]
model_names_all = model_names + ['ERA']
colors = ctl.color_set(len(model_coups) * 2, sns_palette='Paired')
colorscoup = [
np.mean([col1, col2], axis=0)
for col1, col2 in zip(colors[:-1:2], colors[1::2])
]
mr_cos = np.where(np.array(vers) == 'MR')[0]
for gi in mr_cos:
colors.insert(gi, np.mean(colors[gi - 1:gi + 1], axis=0))
colors_wERA = colors + ['black']
# # Ora tutte le statistiche
# atm_resolution = np.array([250, 100, 100, 25, 250, 50, 100, 50, 50, 50, 25, 100, 50, 250, 100, 50, 50])
# oce_resolution = np.array([50, 25, 25, 25, 100, 25, 100, 25, 100, 25, 25, 40, 40, 100, 25, 25, 8])
atm_resolution = np.array(
for ssp in allssps:
for reg in range(4):
trend_ssp[(ssp, 'all', 'trend', 'seafreq', reg)] = np.array([trend_ssp[(ssp, mem, 'trend', 'seafreq', reg)] for mem in okmods_ssp])
trend_ssp[(ssp, 'all', 'errtrend', 'seafreq', reg)] = np.array([trend_ssp[(ssp, mem, 'errtrend', 'seafreq', reg)] for mem in okmods_ssp])
trend_ssp[(ssp, 'all', 'trend', 'freq10', reg)] = np.array([trend_ssp[(ssp, mem, 'trend', 'freq10', reg)] for mem in okmods_ssp])
trend_ssp[(ssp, 'all', 'errtrend', 'freq10', reg)] = np.array([trend_ssp[(ssp, mem, 'errtrend', 'freq10', reg)] for mem in okmods_ssp])
residtime_ssp[(ssp, 'all', 'trend', reg)] = np.array([residtime_ssp[(ssp, mem, 'trend', reg)] for mem in okmods_ssp])
residtime_ssp[(ssp, 'all', 'errtrend', reg)] = np.array([residtime_ssp[(ssp, mem, 'errtrend', reg)] for mem in okmods_ssp])
pickle.dump([trend_ssp, residtime_ssp], open(cart_out + 'trends_wrfreq_e_restime_{}.p'.format(area), 'wb'))
#allsims = ['hist', 'ssp126', 'ssp245', 'ssp370', 'ssp585']
allsims = ['hist', 'ssp585']
colsim = [ctl.color_set(5)[0], ctl.color_set(5)[-1]]
reg_names = reg_names_area[area]
kess = [ke for ke in trend_ssp.keys() if 'all' in ke]
print(kess)
for pio in ['trend', 'errtrend']:
for cos in ['seafreq', 'freq10']:
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()
cart_out = cart_in + 'FutWR_{}_{}_{}_{}/'.format(*ke)
ctl.mkdir(cart_out)
results_hist, results_ref = pickle.load(open(file_hist, 'rb'))
results_ssp585, results_ref = pickle.load(open(file_ssp585, 'rb'))
if tip == 'refCLUS':
res_hist_refEOF, _ = pickle.load(
open(gen_file_hist.format(season, area, 'refEOF'), 'rb'))
### plot 0: WR freq difference tra ssp e hist, per ogni WR
reg_names = reg_names_area[area]
modoks = [
mod for mod in results_ssp585.keys() if mod in results_hist.keys()
]
coloks = ctl.color_set(len(modoks))
fig = plt.figure(figsize=(16, 12))
axes = []
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
axes.append(ax)
i = 0
allfreqs[(ssp, area, season, tip, reg)] = []
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] -
lat=slice(-20., 20.)).mean(['lon', 'lat'])
coso_hr = flux_hr[var].sel(time=slice('1999-01-01', '1999-01-30'),
lat=slice(-20., 20.)).mean(['lon', 'lat'])
# guplo3 = coso_lr.plot(y = 'plev', col = 'time', col_wrap = 6, ylim = (1.e5, 1.e3), yscale = 'log', color = 'forestgreen')
# guplo2 = coso_hr.plot(ylim = (1.e5, 1.e3), yscale = 'log', color = 'indianred')
# plt.title(var)
# plt.savefig(cart + '{}_profs_1mo_799-cntrl.pdf'.format(var))
# figs_prof.append(fig)
lrko = coso_lr.data.compute()
lrko_diff = np.diff(lrko, axis=0)
hrko = coso_hr.data.compute()
hrko_diff = np.diff(hrko, axis=0)
levs = coso_hr.plev.data
cols = ctl.color_set(29, sns_palette='crest')
# plt.figure()
# for ii, co in zip(range(29), cols):
# plt.plot(hrko_diff[ii, :], levs, linestyle = '-', color = co)
# plt.plot(lrko_diff[ii, :], levs, linestyle = '--', color = co)
# plt.yscale('log')
# plt.ylim(1.e5, 1.e3)
hrko1 = np.mean(hrko_diff[:10, :], axis=0)
hrko2 = np.mean(hrko_diff[10:20, :], axis=0)
hrko3 = np.mean(hrko_diff[20:, :], axis=0)
lrko1 = np.mean(lrko_diff[:10, :], axis=0)
lrko2 = np.mean(lrko_diff[10:20, :], axis=0)
lrko3 = np.mean(lrko_diff[20:, :], axis=0)
colok = [cols[0], cols[15], cols[-1]]
fig = plt.figure()
plt.plot(hrko1, levs, linestyle='-', color=colok[0], label='days 1-10')
cmappa = heatmap
cosoanom = cosolow - presme.values[np.newaxis, ...]
#cbar_range = ctl.get_cbar_range(cosoanom, symmetrical = True)
cbar_range = (-10, 10)
elif var == 'pr':
cmappa = cm.get_cmap('BrBG')
#cosoanom = 100*(cosolow-presme.values[np.newaxis, ...])/presme.values[np.newaxis, ...]
cosoanom = 86400. * (cosolow - presme.values[np.newaxis, ...]
) * 365 # this should be mm per year
# cbar_range = ctl.get_cbar_range(cosoanom, symmetrical = True)
# cbar_range = (cbar_range[0]/10., cbar_range[1]/10.)
cbar_range = (-250, 250)
#cbar_range = (-30, 30)
clevels = np.linspace(cbar_range[0], cbar_range[1], 21)
cset = ctl.color_set(len(anni), bright_thres=0., full_cb_range=True)
cosdue[var] = cosoanom
#####
print('flat global {} {}'.format(var, ssp))
fig, ax = ctl.get_cartopy_fig_ax(visualization='standard',
central_lat_lon=(0, 0),
bounding_lat=None,
figsize=(16, 9),
coast_lw=1)
#tit = plt.title('1850')
# Plotting figure
proj = ccrs.PlateCarree()
plt.legend(fontsize='small', loc=1)
plt.title('Significance of regime structure - ECMWF ensemble')
keytot = key_LR + key_HR
plt.xticks(list(range(len(keytot))), keytot, size='small')
plt.ylabel('Significance')
fig.savefig(cart + 'Significance_ECMWF.pdf')
# print('\n \n \n SOSTITUISCO LA MEDIAAA DI ECMWFFFFFFF\n \n \n')
# sig['ECMWF_LR'] = mean_LR
# sig['ECMWF_HR'] = mean_HR
syms = ['$H$'] * len(models) + ['$L$'] * len(models) + ['D']
labels = ['ECE', 'HadGEM', 'CMCC', 'CNRM', 'MPI', 'ECMWF'
] + 6 * [None] + ['NCEP']
colors = ctl.color_set(len(models) + 1)
wi = 0.3
#plt.ion()
fig = plt.figure()
lab1 = 'LR'
lab2 = 'HR'
for i, mod in enumerate(models):
# plt.scatter(i, sig[mod+'_LR'], color = 'green', s=20, marker = '$L$')
# plt.scatter(i, sig[mod+'_HR'], color = 'orange', s=20, marker = '$H$')
if i > 0:
lab1 = None
lab2 = None
plt.bar(i - wi / 2, sig[mod + '_LR'], width=wi, color='green', label=lab1)
plt.bar(i + wi / 2, sig[mod + '_HR'], width=wi, color='orange', label=lab2)
plt.bar(i + 1 - wi / 2, sig['ERA'], width=wi, color='black', label='ERA')
