#model_name_list.remove('AWI-CM-1-1-MR')
for i, model_name in enumerate(model_name_list):
try:
# read in SSTs
if model_name != 'ERA20C':
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/tos_regridded/'
full_path = data_path0 + ML.model_institute[
model_name] + '/' + model_name + '/tos/'
list_of_files = files_in_directory(
full_path,
concat_directory=True,
include_files_with=ML.ensemble_id[model_name])
tos_data, lats, lons, levs, times, calendar, t_units = read_in_variable(
list_of_files[:], 'tos')
else:
file_name = [
'/network/group/aopp/met_data/MET003_ERA20C/data/tos/mon/tos_mon_ERA20C_2.5x2.5_189002-201012.nc'
]
tos_data, lats, lons, levs, times, calendar, t_units = read_in_variable(
file_name, 'sst')
SST_am, years_SST = calculate_annual_mean(tos_data,
times,
calendar,
t_units,
season=season)
# read in THF
if model_name != 'ERA20C':
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/piControl/'
season)
# read in variable
#data_path0 = '/network/group/aopp/predict/AWH007_BEFORT_CMIP6/piControl/'
#data_path1 = '/piControl/Amon/'+var_name+'/gn/latest'
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/piControl/ua/'
data_path1 = '/' + var_name + '/'
full_path = data_path0 + model_institute[
model_name] + '/' + model_name + data_path1
#list_of_files = files_in_directory(full_path,concat_directory=True,exclude_files_with='AER')
list_of_files = [
full_path + var_name + '_Amon_' + model_name + '_piControl_' +
ensemble_id[model_name] + '_' + season + '.nc'
]
toc1 = time.perf_counter()
data_am, lats_var, lons_var, levs_var, times_var, calendar_var, t_units_var = read_in_variable(
list_of_files[:], var_name, chosen_level=chosen_level)
toc2 = time.perf_counter()
if decadal_mean == True:
data_am, years = take_decadal_mean(data_am, times_var,
calendar_var, t_units_var)
#data_am, years = calculate_annual_mean(var_data,times_var,calendar_var,t_units_var,season=season,decadal_mean=decadal_mean)
print(toc2 - toc1)
print(data_am.shape, IOBM.shape)
climatology_model = np.mean(data_am, axis=0)
data_am = data_am - np.mean(data_am, axis=0).reshape(
1, lats_var.shape[0], lons_var.shape[0]) # calculate anomalies
if var_name == 'psl': data_am = 0.01 * data_am # convert to hPa
elif var_name == 'pr': data_am = 86400 * data_am # convert to mm/day
# create composite masks
NINO34_plus = NINO34 > 1
return NINO34_SST
# read in data
id = 'r102i1p1f1' #'r101i1p1f1' #'r1i1p2f1' #'r2i1p1f1' #'r1i1p5f1' #'r1i1p3f1' #'r1i1p1f3' #'r1i2p1f1' # 'r1i1p1f2' #'r1i1p1f1' # ensemble member
for i, model_name in enumerate(model_name_list):
try:
if model_name is not 'ERA20C':
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/tos_regridded/'
full_path = data_path0 + model_institute[
model_name] + '/' + model_name + '/tos/'
list_of_files = files_in_directory(full_path,
concat_directory=True,
include_files_with=id)
tos_data, lats, lons, levs, times, calendar, t_units = read_in_variable(
list_of_files[:], 'tos')
else:
list_of_files = [
'/network/group/aopp/met_data/MET003_ERA20C/data/tos/mon/tos_mon_ERA20C_2.5x2.5_189002-201012.nc'
]
tos_data, lats, lons, levs, times, calendar, t_units = read_in_variable(
list_of_files[:], 'sst')
PDO = calculate_PDO(tos_data, lats, lons, times, t_units, calendar)
IPO = calculate_IPO(tos_data, lats, lons, times, t_units, calendar)
IOBM = calculate_IOBM(tos_data, lats, lons, times, t_units, calendar)
IOBM2 = calculate_IOBM2(tos_data, lats, lons, times, t_units, calendar)
IOD = calculate_IOD(tos_data, lats, lons, times, t_units, calendar)
NINO34 = calculate_NINO34(tos_data, lats, lons, times, t_units,
calendar)
# calculate regression onto original time series
a = np.arange(0.1, 0.81, 0.1)
clevs = np.append(-a[::-1], a)
for i, model_name in enumerate(model_name_list):
try:
# read in SST and THF perform correlation analysis
if model_name != 'ERA20C':
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/tos_regridded/'
full_path = data_path0 + ML.model_institute[
model_name] + '/' + model_name + '/tos/'
list_of_files = files_in_directory(
full_path,
concat_directory=True,
include_files_with=ML.ensemble_id[model_name])
tos_data, lats_SST, lons_SST, levs_SST, times_SST, calendar_SST, t_units_SST = read_in_variable(
list_of_files[:], 'tos')
else:
file_name = [
'/network/group/aopp/met_data/MET003_ERA20C/data/tos/mon/tos_mon_ERA20C_2.5x2.5_189002-201012.nc'
]
tos_data, lats_SST, lons_SST, levs_SST, times, calendar, t_units = read_in_variable(
file_name, 'sst')
SST_am, years_SST = calculate_annual_mean(tos_data,
times_SST,
calendar_SST,
t_units_SST,
season=season)
# set all unreasonably large values to NaN
SST_am[np.abs(SST_am) > 1e3] = np.nan
NINO34, years = prep_SST_index('NINO34',nc_SST,times_SST,calendar_SST,t_units_SST,season)
IOBM2, years = prep_SST_index('IOBM2',nc_SST,times_SST,calendar_SST,t_units_SST,season)
if apply_running_mean is not None:
NINO34 = running_mean(NINO34,apply_running_mean)
IOBM2 = running_mean(IOBM2,apply_running_mean)
# detrend SST
NINO34 = detrend(NINO34)
IOBM2 = detrend(IOBM2)
# read in THF
if model_name != 'ERA20C':
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/piControl/'
full_path = data_path0 + 'hfls/'+ ML.model_institute[model_name] + '/' + model_name + '/hfls/'
list_of_files = [full_path+'hfls_Amon_'+model_name+'_piControl_'+ML.ensemble_id[model_name]+'_'+season+'.nc']
hfls,lats_var,lons_var,levs_var,times_var,calendar_var,t_units_var = read_in_variable(list_of_files[:],'hfls')
full_path = data_path0 + 'hfss/' + ML.model_institute[model_name] + '/' + model_name + '/hfss/'
list_of_files = [full_path+'hfss_Amon_'+model_name+'_piControl_'+ML.ensemble_id[model_name]+'_'+season+'.nc']
hfss,lats_THF,lons_THF,levs_THF,times_THF,calendar_THF,t_units_THF = read_in_variable(list_of_files[:],'hfss')
THF_am = hfls + hfss
# create THF index
THF_NINO34 = create_THF_index(THF_am,lats_THF,lons_THF,-5,5,190,240)
THF_IOBM2 = create_THF_index(THF_am,lats_THF,lons_THF,-20,20,40,110)
if apply_running_mean is not None:
THF_NINO34 = running_mean(THF_NINO34,apply_running_mean)
THF_IOBM2 = running_mean(THF_IOBM2,apply_running_mean)
# read in zonal wind
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/piControl/ua/'
full_path = data_path0 + ML.model_institute[model_name] + '/' + model_name + '/ua/'
NINO34 = running_mean(NINO34, apply_running_mean)
# regress out other index
IOBM2_no_NINO34 = regress_out_index(IOBM2, NINO34)
NINO34_no_IOBM2 = regress_out_index(NINO34, IOBM2)
# read in circulation indices
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/piControl/ua/'
data_path1 = '/' + var_name + '/'
full_path = data_path0 + model_institute[
model_name] + '/' + model_name + data_path1
list_of_files = [
full_path + var_name + '_Amon_' + model_name + '_piControl_' +
ensemble_id[model_name] + '_' + season + '.nc'
]
ua200, lats_var, lons_var, levs_var, times_var, calendar_var, t_units_var = read_in_variable(
list_of_files[:], var_name, chosen_level=20000)
ua850, lats_var, lons_var, levs_var, times_var, calendar_var, t_units_var = read_in_variable(
list_of_files[:], var_name, chosen_level=85000)
if season == 'DJF':
ua200 = ua200[::-1][1:][::-1]
ua850 = ua850[::-1][1:][::-1]
if apply_running_mean is not None:
ua200 = running_mean(ua200, apply_running_mean)
ua850 = running_mean(ua850, apply_running_mean)
#nHalf = int((apply_running_mean)/2)
#ua200 = ua200[nHalf:][::-1][nHalf:][::-1]
#ua850 = ua850[nHalf:][::-1][nHalf:][::-1]
# calculate regressions
# also calculate pvalues and show regcoeffs < 0.05
regress_coeff_IOBM2_ua200 = do_reg(IOBM2, ua200)
# read in SST file and times
SST_index_file_name = loading_dir + '/SST_indices_' + model_name + '.nc'
nc_SST = Dataset(SST_index_file_name, 'r')
times_SST = nc_SST.variables['times'][:]
calendar_SST = nc_SST.variables['times'].calendar
t_units_SST = nc_SST.variables['times'].units
# read in precipitation, zonal wind, slp
data_path0 = '/network/group/aopp/predict/AWH007_BEFORT_CMIP6/piControl/'
# precip
data_path_pr = '/piControl/Amon/pr/gn/latest'
full_path_pr = data_path0 + model_institute[
model_name] + '/' + model_name + data_path_pr
list_of_files_pr = files_in_directory(full_path_pr,
concat_directory=True)
pr_data, lats_pr, lons_pr, levs_pr, times_pr, calendar_pr, t_units_pr = read_in_variable(
list_of_files_pr[:], 'pr')
# slp
data_path_psl = '/piControl/Amon/psl/gn/latest'
full_path_psl = data_path0 + model_institute[
model_name] + '/' + model_name + data_path_psl
list_of_files_psl = files_in_directory(full_path_psl,
concat_directory=True)
psl_data, lats_psl, lons_psl, levs_psl, times_psl, calendar_psl, t_units_psl = read_in_variable(
list_of_files_psl[:], 'psl')
# truncate times so that only a common time period is used
#earliest_common_time = np.min(times_SST)
#if np.min(times_SST) != np.min(times_psl) & (np.min(times_psl) > np.min(times_SST)): earliest_common_time = np.min(times_psl)
#latest_common_time = np.max(times_SST)
#if np.max(times_SST) != np.max(times_psl) & (np.max(times_psl) < np.max(times_SST)): latest_common_time = np.max(times_psl)
#time_mask_SST = (times_SST>=earliest_common_time)&(times_SST<=latest_common_time)
# regress out other index
IOBM2_no_NINO34 = regress_out_index(IOBM2, NINO34)
IOD_no_NINO34 = regress_out_index(IOD, NINO34)
NINO34_no_IOBM2 = regress_out_index(NINO34, IOBM2)
# read in circulation indices
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/piControl/ua/'
data_path1 = '/' + var_name + '/'
full_path = data_path0 + ML.model_institute[
model_name] + '/' + model_name + data_path1
list_of_files = [
full_path + var_name + '_Amon_' + model_name + '_piControl_' +
ML.ensemble_id[model_name] + '_' + season + '.nc'
]
ua200, lats_ua, lons_ua, levs_ua, times_ua, calendar_ua, t_units_ua = read_in_variable(
list_of_files[:], var_name, chosen_level=20000)
U_climatology = np.mean(ua200, axis=0)
if season == 'DJF':
ua200 = ua200[::-1][1:][::-1]
if apply_running_mean is not None:
ua200 = running_mean(ua200, apply_running_mean)
# calculate regressions
regress_coeff_IOBM2_ua200 = do_reg(IOBM2, ua200)
regress_coeff_IOBM2_no_NINO34_ua200 = do_reg(
IOBM2_no_NINO34, ua200)
regress_coeff_IOD_ua200 = do_reg(IOD, ua200)
regress_coeff_IOD_no_NINO34_ua200 = do_reg(IOD_no_NINO34, ua200)
regress_coeff_NINO34_ua200 = do_reg(NINO34, ua200)
# running mean
N = 10
halfN = int(N / 2)
for i, model_name in enumerate(model_name_list):
try:
data_path0 = '/network/group/aopp/predict/AWH007_BEFORT_CMIP6/piControl/'
# variable 1
data_path1 = '/piControl/Amon/' + variable1_name + '/gn/latest'
full_path = data_path0 + model_institute[
model_name] + '/' + model_name + data_path1
list_of_files1 = files_in_directory(full_path, concat_directory=True)
variable1_data, lats1, lons1, levs1, times1, calendar1, t_units1 = read_in_variable(
list_of_files1[:], variable1_name, chosen_level=chosen_level1)
# variable 2
data_path2 = '/piControl/Amon/' + variable2_name + '/gn/latest'
full_path = data_path0 + model_institute[
model_name] + '/' + model_name + data_path2
list_of_files2 = files_in_directory(full_path, concat_directory=True)
variable2_data, lats2, lons2, levs2, times2, calendar2, t_units2 = read_in_variable(
list_of_files2[:], variable2_name, chosen_level=chosen_level2)
# truncate time series so that only common time periods are included
if np.min(times1) != np.min(times2):
if np.min(times1) > np.min(times2):
earliest_common_time = np.min(times1)
else:
earliest_common_time = np.min(times2)
cs = m.add_filled_contours(lons, lats, field1, clevs)
m.add_contours(lons, lats, field2, clevs2, contour_labels=False)
make_plots.add_lat_lon(ax)
m.geography(ax, extent=[10, 220, 0, 70])
make_plots.plot_box(lon_min=110, lon_max=180, lat_min=20, lat_max=50)
ax.set_aspect(2)
return cs
# select season
season = 'JJA'
# Read in HadGEM3 data and calculate annual means
full_path_psl = '/network/group/aopp/predict/AWH007_BEFORT_CMIP6/piControl/MOHC/HadGEM3-GC31-LL/piControl/Amon/psl/gn/latest/'
list_of_files_psl = files_in_directory(full_path_psl, concat_directory=True)
psl_HadGEM, lats_HadGEM, lons_HadGEM, levs_HadGEM, times_HadGEM, calendar_HadGEM, t_units_HadGEM = read_in_variable(
list_of_files_psl[:], 'psl')
psl_am_HadGEM, years = calculate_annual_mean(psl_HadGEM,
times_HadGEM,
calendar_HadGEM,
t_units_HadGEM,
season=season)
psl_am_HadGEM = 0.01 * psl_am_HadGEM # convert to hPa
# Read in ERA-Interim and calculate annual means
file_name = '/network/aopp/preds0/pred/data/Obs/Reanalysis/psl/mon/psl_mon_ERAInterim_1x1_197901-201512.nc'
psl_ERAI, lats_ERAI, lons_ERAI, levs_ERAI, times_ERAI, calendar_ERAI, t_units_ERAI = read_in_variable(
[file_name], 'msl')
psl_am_ERAI, years = calculate_annual_mean(psl_ERAI,
times_ERAI,
calendar_ERAI,
t_units_ERAI,
model_name_list.append('CESM2')
plt.figure(figsize=(30, 35))
gs = gridspec.GridSpec(8, 2)
for i, model_name in enumerate(model_name_list):
try:
#data_path0 = '/network/aopp/hera/mad/patterson/iCMIP6/data/piControl/ts/'
data_path0 = '/network/group/aopp/predict/AWH007_BEFORT_CMIP6/piControl/' #AWI/AWI-CM-1-1-MR/piControl/Amon/tas/gn/latest
full_path = data_path0 + ML.model_institute[
model_name] + '/' + model_name + '/piControl/Amon/tas/gn/latest/'
list_of_files = files_in_directory(full_path,
concat_directory=True,
exclude_files_with='ImonAnt')
ts, lats_ts, lons_ts, levs_ts, times_ts, calendar_ts, t_units_ts = read_in_variable(
list_of_files[:], 'tas')
ts_am, years = calculate_annual_mean(ts,
times_ts,
calendar_ts,
t_units_ts,
season=season)
ts_mean = global_mean(ts_am, lats_ts)
ax = plt.subplot(gs[i])
plt.title(model_name, fontsize=20)
plt.plot(ts_mean, color='r')
plt.xlim(0, 1000)
if i >= 14: plt.xlabel('Time (years)', fontsize=20)
# otherwise read in SST and zonal wind files and perform regression, saving these
except:
print(
'Cannot find regression file so reading in files to create one.'
)
# read in SST data
if model_name != 'ERA20C':
data_path0 = '/network/aopp/hera/mad/patterson/CMIP6/data/tos_regridded/'
full_path = data_path0 + ML.model_institute[
model_name] + '/' + model_name + '/tos/'
list_of_files = files_in_directory(
full_path,
concat_directory=True,
include_files_with=ML.ensemble_id[model_name])
tos_data, lats_SST, lons_SST, levs_SST, times_SST, calendar_SST, t_units_SST = read_in_variable(
list_of_files[:], 'tos')
else:
file_name = [
'/network/group/aopp/met_data/MET003_ERA20C/data/tos/mon/tos_mon_ERA20C_2.5x2.5_189002-201012.nc'
]
tos_data, lats_SST, lons_SST, levs_SST, times, calendar, t_units = read_in_variable(
file_name, 'sst')
SST_am, years_SST = calculate_annual_mean(tos_data,
times_SST,
calendar_SST,
t_units_SST,
season=season)
# set all unreasonably large values to NaN
SST_am[np.abs(SST_am) > 1e3] = np.nan
