colors_wERA = colors + ['black']
color_main.append('black')
#############################################################################################
#############################################################################################
mean_field_all = dict()
lowfrvar = dict()
highfrvar = dict()
stat_eddy_all = dict()
file_in = '/data-hobbes/fabiano/OBS/ERA/ERA40+Int_daily_1957-2018_zg500_remap25_meters.nc'
#var, coords, aux_info = ctl.read_iris_nc(file_in, extract_level_hPa = 500)
var, coords, aux_info = ctl.readxDncfield(file_in, extract_level=500)
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
mean_field_all['ERA'], lowfrvar['ERA'], highfrvar['ERA'], stat_eddy_all[
'ERA'] = ctl.variability_lowhi(lat,
lon,
var,
dates,
season,
area=area,
dates_range=ctl.range_years(1957, 2014))
cart_in = '/data-hobbes/fabiano/PRIMAVERA/incoming/hist-1950/'
flist = cart_in + 'lista_filez_v7.dat'
Rearth = 6371.0e3 # mean radius
#####################################
L = 2501000.0
Rearth = 6371.0e3
##
cart_in = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/'
file_list = cart_in + 'ERAInterim_6hrs_1988_vatazgq.nc'
file_ps = cart_in + 'ERAInterim_mon_1988_ps.nc'
cart_out = '/home/fabiano/Research/lavori/SPHINX_for_lisboa/heat_flux/ERA_ref_6hrs/'
# Loading ERA reference
era_fi = 'prova_heatflux_1988_MM.nc'
vars, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(
cart_in + era_fi)
print(vars.keys())
fi = 'northward_water_flux.nc'
var2, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(
cart_in + fi)
print(var2.keys())
vars[list(var2.keys())[0]] = L * var2[list(var2.keys())[0]]
era_zonal_factor = 2 * np.pi * Rearth * np.cos(np.deg2rad(lat))
era_fluxes_maps = dict()
era_fluxes_zonal = dict()
seasons = ['Feb', 'DJF', 'JJA']
fluxnames = ['tot', 'SH', 'PE', 'LH']
lon_limits=(0., 359.3),
lat_limits=(-89.46, 89.46))
periods = [(1900, 2005), (2005, 2050), (2050, 2090), (2090, 2110),
(2110, 2160)]
allvars = ['tas', 'hcc', 'mcc', 'lcc', 'rlut', 'rsus']
#deg_periods = [(0., 1.), (1.,2.), (2.,3.), (3.,4.), (4., 5.)]
# da fare:
# diff plots hcc, mcc, lcc vs tas con colore dato dall'anno. Qui posso usare gli annual. Separare tropici?
# ovmoller lat/temp ?
# trova warming pattern per grado di temp
varnam = 'tas'
tas, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(
cart + filename.format(varnam))
# periodi
# globalT = dict()
# for ens in ens_mem:
# globalT[ens] = ctl.global_mean(tas[ens], lat)
#
# globalT['base'] = np.mean([globalT[ens] for ens in ens_mem if 'lcb' in ens], axis = 0)
# globalT['stoc'] = np.mean([globalT[ens] for ens in ens_mem if 'lcs' in ens], axis = 0)
# globalT['base_std'] = np.std([globalT[ens] for ens in ens_mem if 'lcb' in ens], axis = 0)
# globalT['stoc_std'] = np.std([globalT[ens] for ens in ens_mem if 'lcs' in ens], axis = 0)
#
# trenddict = dict()
#
# for varnam in allvars:
filogen = cart + 'out_prima_coup_v7_DJF_EAT_4clus_4pcs_1957-2014_refEOF.p'
results_refEOF, results_ref = pickle.load(open(filogen, 'rb'))
results_refEOF['ERA'] = results_ref
results_refEOF.pop('HadGEM3-GC31-LL_r1i1p2f1')
results_refEOF.pop('EC-Earth3P_r1i1p1f1')
results_refEOF.pop('EC-Earth3P-HR_r1i1p1f1')
allresmembers = list(results_refEOF.keys())
# mod = model_names[5]
# mem = ens_mems[5]
# print(mod, mem)
mod = 'ERA'
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,
wind_low[:, ila, ilo] = np.convolve(lanc20,
wind_area[:, ila, ilo],
mode='same')
#wind_low = ctl.running_mean(wind_area, 10)
wind_low_djf, dates = ctl.sel_season(wind_low, coords['dates'], 'DJF')
return wind_low_djf, dates
area = [-60., 0., 20., 70.]
cart_out = '/home/fabiano/Research/lavori/Jet_latitude/'
#orogfi = '/data-hobbes/reference/ERAInterim/geopot_vegcover_25.nc'
orogfi = '/data-hobbes/reference/ERAInterim/geopot_vegcover.nc'
orog, coords, aux_info = ctl.readxDncfield(orogfi, select_var='z')
#orog = orog/9.80665
orogmask = orog > 1300.0
orogarea, _, _ = ctl.sel_area(coords['lat'], coords['lon'], orogmask, area)
orogarea = orogarea[0]
#ctl.plot_map_contour(orogmask, plot_type = 'pcolormesh')
cart_in = '/nas/reference/ERA40/daily/u/'
file_in = 'u_Aday_ERA40_{}01_{}12.nc'
yea = 1957
file_in_57 = 'u_Aday_ERA40_195709_195712.nc'
winds = []
alldates = []
wind, coords, aux_info = ctl.readxDncfield(cart_in + file_in_57,
#filename = '1850-2160-{}-monthly.nc'
filename = '1850-2160-{}-monthly_remap25_rechunk.nc'
ens_mem = ['lcb0', 'lcs0', 'lcb1', 'lcs1', 'lcb2', 'lcs2']
figures = []
lat, lon = ctl.genlatlon(73, 144)
fields = dict()
for varnam in varlist:
print(varnam)
ifile = cart + filename.format(varnam)
if varnam == 'tas':
ifile = cart + '1950-2160-tas-monthly_remap25_rechunk.nc'
var, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(
ifile)
print(var.keys())
weights = abs(np.cos(np.deg2rad(lat)))
# var, coords, aux_info = ctl.read_iris_nc(ifile)
# lat = coords['lat']
# lon = coords['lon']
# dates = coords['dates']
for ens in ens_mem:
# zonal mean
for season in ['year', 'DJF', 'JJA']:
var_pre, dates_pre = ctl.sel_time_range(
var[ens], dates, ctl.range_years(2085, 2095))
var_pre_seas, _ = ctl.seasonal_climatology(var_pre, dates_pre,
season)
listafils.append(
'/data-hobbes/fabiano/OBS/ERA/ERA40+Int_daily_1957-2018_zg500_remap25_meters.nc'
)
model_names.append('ERA')
nya = 30 # number of years for running mean
clm_fullperiod = dict()
datesall = dict()
climate_mean_dict = dict()
allnums = [5, 10, 20]
for ifile, name in zip(listafils, model_names):
print(name)
var, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(
ifile, extract_level=500)
# var, coords, aux_info = ctl.read_iris_nc(ifile)
# lat = coords['lat']
# lon = coords['lon']
# dates = coords['dates']
var, dates = ctl.sel_time_range(var, dates, ctl.range_years(1957, 2014))
for num in allnums:
clm, dtclm, _ = ctl.daily_climatology(var, dates, num)
clmarea, lat_area, lon_area = ctl.sel_area(lat, lon, clm, 'EAT')
clm_fullperiod[(name, num)] = clmarea
dtclmpd = pd.to_datetime(dtclm)
datesall[name] = dtclmpd
climate_mean, dates_climate_mean = ctl.trend_daily_climat(var,
filogen = cart + 'out_panosjet_DJF_EAT_4clus_4pcs_1957-2014_refCLUS.p'
results_refCLUS, results_ref = pickle.load(open(filogen, 'rb'))
results_refCLUS['ERA'] = results_ref
cart = '/home/fabiano/Research/lavori/WeatherRegimes/panosjet/'
filogen = cart + 'out_panosjet_DJF_EAT_4clus_4pcs_1957-2014_refEOF.p'
results_refEOF, results_ref = pickle.load(open(filogen, 'rb'))
results_refEOF['ERA'] = results_ref
##################################################################
cart_sst = '/data-woodstock/PRIMAVERA/stream1/merged/tos/'
filsst = 'tos-{}-1950-2014-{}-remap.nc'
#cart_sst = '/nas/PRIMAVERA/Stream1/hist-1950/{}/{}/tos/'
filera = '/nas/reference/ERA40+Int/sst_Amon_ERA40_195701-201812_1deg.nc'
sstera, datacoords, _ = ctl.readxDncfield(filera)
datesera = datacoords['dates']
lat = datacoords['lat']
lon = datacoords['lon']
sstera = sstera - 273.15
sstera_mean, sstera_sd = ctl.seasonal_climatology(sstera, datesera, 'DJF', dates_range = ctl.range_years(1957, 2014))
area_box = (-80, 10, 20, 80)
sstera_mean_area, latsel, lonsel = ctl.sel_area(lat, lon, sstera_mean, area_box)
okpoera = (sstera_mean_area < -100) | (sstera_mean_area > 500)
allcose = dict()
allrms = dict()
allpatcor = dict()
import climtools_lib as ctl
import cartopy.crs as ccrs
import cartopy.util as cartopy_util
import cartopy.feature as cfeature
from matplotlib.colors import LightSource
import seaborn as sns
##############################################
############ INPUTS #########
elev_file = '/home/fabiano/Research/temp/elev.0.25-deg.nc'
elev, lat, lon, var_units = ctl.readxDncfield(elev_file)
elev = elev.squeeze()
# map = ctl.plot_map_contour(elev, lat, lon, visualization = 'Robinson')
### Trying hillshades
proj = ccrs.Robinson(central_longitude=0.)
# Plotting figure
fig = plt.figure(figsize=(32, 24))
ax = plt.subplot(projection=proj)
cmappa = cm.get_cmap('gist_earth')
cbar_range = None
clevels = [
daynum = np.arange(len(results_ssp['EC-Earth3_r1i1p1f1']['dates']))
m, c, err_m, err_c = ctl.linear_regre_witherr(
daynum, results_ssp['EC-Earth3_r1i1p1f1']['pcs'])
cartmon_hist = '/data-hobbes/fabiano/CMIP6/historical_mon_zg/'
cartmon_ssp = '/data-hobbes/fabiano/CMIP6/ssp585_mon_zg/'
filssp = 'zg_Amon_ssp585_{}_{}_2015-2100.nc'
filhist = 'zg_{}_mon.nc'
season = 'NDJFM'
cose = dict()
for modmem in okmods:
mod, mem = modmem.split('_')
zgssp, coords, _ = ctl.readxDncfield(cartmon_ssp + filssp.format(mod, mem))
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
#trendssp, errtrendssp = ctl.local_lineartrend_climate(lat, lon, zgssp, dates, season)
zg_noglob, coeffs, var_regional, dates_seas = ctl.remove_global_polytrend(
lat, lon, zgssp, dates, season, deg=3, area='NML')
trend, errtrend, _, _ = ctl.local_lineartrend_climate(
lat, lon, zg_noglob, dates, None)
zontrend = ctl.zonal_mean(trend)
zonerrtrend = ctl.zonal_mean(errtrend)
zg_noglob_zonme = ctl.zonal_mean(zg_noglob)
zg_se = zg_noglob - zg_noglob_zonme[..., np.newaxis]
se_trend, se_errtrend, _, _ = ctl.local_lineartrend_climate(
from matplotlib.colors import LogNorm
from datetime import datetime
from scipy import stats
import pandas as pd
#############################
cart_in = '/nas/reference/ERAInterim/daily/'
filnam = cart_in + '{}/{}_Aday_ERAInterim_201801_201812.nc'
cart_out = '/home/fabiano/Research/lavori/dynamics_exe/'
# 29 Dec 2018
u, ucoords, _ = ctl.readxDncfield(filnam.format('u', 'u'))
v, vcoords, _ = ctl.readxDncfield(filnam.format('v', 'v'))
ta, tacoords, _ = ctl.readxDncfield(filnam.format('ta', 'ta'))
zg, zgcoords, _ = ctl.readxDncfield(filnam.format('zg', 'zg'))
zg = 9.80665 * zg
pv, pvcoords, _ = ctl.readxDncfield(filnam.format('pv', 'pv'))
#u.shape (365, 5, 241, 480)
lat_orig = ucoords['lat']
lon_orig = ucoords['lon']
levs = np.array([850, 500, 200, 50, 30])
area = (-180, 180, 10, 85)
#for da in np.arange(365):
da = -3
###############################################
L = 2501000.0
Rearth = 6371.0e3
##
cart_in = '/data-hobbes/fabiano/SPHINX/heat_flux/fluxes_row/'
cart_in_ref = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/'
file_list = cart_in_ref+'ERAInterim_6hrs_1988_vatazgq.nc'
file_ps = cart_in_ref+'ERAInterim_mon_1988_ps.nc'
cart_out = '/home/fabiano/Research/lavori/SPHINX_for_lisboa/heat_flux/'
# Loading ERA reference
era_fi = 'prova_heatflux_1988_MM.nc'
vars, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(cart_in_ref+era_fi)
print(vars.keys())
fi = 'northward_water_flux.nc'
var2, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(cart_in_ref+fi)
print(var2.keys())
vars[list(var2.keys())[0]] = L*var2[list(var2.keys())[0]]
era_zonal_factor = 2*np.pi*Rearth*np.cos(np.deg2rad(lat))
era_fluxes_maps = dict()
era_fluxes_zonal = dict()
seasons = ['Feb','DJF', 'JJA']
fluxnames = ['tot', 'SH', 'PE', 'LH']
eraname = {'tot': 'p76.162', 'SH': 'p70.162', 'PE': 'p74.162', 'LH': 'p72.162'}
for mod in okmods_mo:
print(mod)
if mod not in all_mods:
print('NO data for {}'.format(mod))
continue
climmeans = dict()
climmeans['EAT'] = []
climmeans['PNA'] = []
for mem in all_mems[mod]:
okfil = [fi for fi in filli if mod in fi and mem in fi][0]
try:
#var, coords, aux_info = ctl.read_cmip6_data(fieldnam, 'day', 'historical', mod, sel_member = mem, extract_level_hPa = levok, regrid_to_reference_file = ref_file, sel_yr_range = (1964, 2014), select_season_first = True, season = 'ONDJFMA', select_area_first = True, area = 'NML')
var, coords, aux_info = ctl.readxDncfield(cart_data + okfil)
except Exception as exp:
print('Unable to read data for {}, going on with next model..'.format(mod + '_' + mem))
print(exp)
continue
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
var, dates = ctl.sel_time_range(var, dates, ctl.range_years(1964, 2014))
zg_dtr, coeffs, var_regional, dates_seas = ctl.remove_global_polytrend(lat, lon, var, dates, 'NDJFM', deg = 1, area = 'NML', print_trend = True)
for area in ['EAT', 'PNA']:
zg_ok, _, _ = ctl.sel_area(lat, lon, zg_dtr, area)