def prep_SST_index(index_name, nc, times_SST, calendar_SST, t_units_SST,
season):
"""Read in particular SST index and calculate annual mean"""
index_ts = nc.variables[index_name][:]
index_annual_mean, years = calculate_annual_mean(index_ts,
times_SST,
calendar_SST,
t_units_SST,
season=season)
return index_annual_mean, years
def prep_index(nc, index_name, times_SST, calendar_SST, t_units_SST, season):
index_ts = nc.variables[index_name][:]
index_annual_mean, years = calculate_annual_mean(index_ts,
times_SST,
calendar_SST,
t_units_SST,
season=season,
decadal_mean=decadal_mean)
index_annual_mean = (index_annual_mean - np.mean(index_annual_mean)
) / np.std(index_annual_mean)
#index_running_mean = running_mean(index_annual_mean,N)
return index_annual_mean
def prep_index(file_name, ts_name, season):
""" Read in a monthly time series, take the annual mean
and detrend """
nc = Dataset(file_name, 'r')
ts = nc.variables[ts_name][:]
times = nc.variables['times'][:]
calendar = nc.variables['times'].calendar
units = nc.variables['times'].units
ts_am, years = calculate_annual_mean(
ts, times, calendar, units, season=season,
decadal_mean=decadal_mean) # annual mean
# detrend
t = np.arange(years.shape[0])
slope = stats.linregress(t, ts_am)[0]
ts_am = ts_am - slope * t
return ts_am, years
def __init__(self,
index_name,
nc,
times_SST,
calendar_SST,
t_units_SST,
N,
season='JJA'):
""" """
index_ts = nc.variables[index_name][:]
index_annual_mean, years = calculate_annual_mean(index_ts,
times_SST,
calendar_SST,
t_units_SST,
season=season)
index_running_mean = running_mean(index_annual_mean, N)
self.index_running_mean = index_running_mean
self.years = years
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/'
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 + '/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
# 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'
]
# 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)
#time_mask_psl = (times_psl>=earliest_common_time)&(times_psl<=latest_common_time)
#times_SST = times1[time_mask1]
#times2 = times2[time_mask2]
#variable1_data = variable1_data[time_mask1]
#variable2_data = variable2_data[time_mask2]
# calculate annual means of fields
psl_am, years = calculate_annual_mean(psl_data,
times_psl,
calendar_psl,
t_units_psl,
season=season)
pr_am, years = calculate_annual_mean(pr_data,
times_pr,
calendar_pr,
t_units_pr,
season=season)
# perform low pass time filtering on fields
N = 10
halfN = int(N / 2)
psl_rm = running_mean(psl_am, N)
pr_rm = running_mean(pr_am, N)
# prepare SST indices
latest_common_time = np.max(times1)
else:
latest_common_time = np.max(times1)
time_mask1 = (times1 >= earliest_common_time) & (times1 <=
latest_common_time)
time_mask2 = (times2 >= earliest_common_time) & (times2 <=
latest_common_time)
times1 = times1[time_mask1]
times2 = times2[time_mask2]
variable1_data = variable1_data[time_mask1]
variable2_data = variable2_data[time_mask2]
# calculate annual means
variable1_JJA, years = calculate_annual_mean(variable1_data,
times1,
calendar1,
t_units1,
season='JJA')
variable2_JJA, years = calculate_annual_mean(variable2_data,
times2,
calendar2,
t_units2,
season='JJA')
# perform low pass time filtering
variable1_rm = running_mean(variable1_JJA, N)
variable2_rm = running_mean(variable2_JJA, N)
# only use variable in region over East Asia for MCA
lon_mask_EAsia = (lons1 >= lon_min) & (lons1 <= lon_max)
lat_mask_EAsia = (lats1 >= lat_min) & (lats1 <= lat_max)
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,
season=season)
psl_am_ERAI = 0.01 * psl_am_ERAI # convert to hPa
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)
if i % 2 == 0: plt.ylabel('Global mean T (K)', fontsize=20)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
