plt.figure(figsize=(15, 10))
if plot_type == 'lead-lag':
gs = gridspec.GridSpec(2, 2)
#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)
axis=1)
NINO34_SST = (NINO34_SST - np.mean(NINO34_SST)) / np.std(NINO34_SST)
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,
lat_min_tropics, lat_max_tropics = -30, 30
# 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)
try:
# 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)
model_name_list.remove('CanESM5')
model_name_list.append('HadGEM3-GC31-LL')
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')