def trend_pic(MIP,
VAR,
ModelList,
order,
year_min,
year_max,
conv_pic_hist,
gap,
rmv_disc,
verbose=False):
'''Compute zos or zostoga trend over the pre-industrial control model simulations.
Use rmv_disc=True to remove discontinuities in time. Only works for time
series (zostoga) not for 3D data (zos)'''
EXP = 'piControl'
tot_year = year_max - year_min + 1
if MIP == 'cmip5':
files = loc.select_cmip5_files('piControl', VAR, ModelList)
elif MIP == 'cmip6':
files = loc.select_cmip6_files('piControl', VAR, ModelList)
if verbose:
print("#### Using following files: ####")
[print(str(x)) for x in files]
ds = xr.open_mfdataset(files, combine='by_coords', use_cftime=True)
y_ds = loc.yearly_mean(ds)
if ModelList.Model == 'BCC-CSM2-MR' and VAR == 'zos':
y_ds = y_ds.rename({'lat': 'rlat', 'lon': 'rlon'})
new_year = np.array(y_ds.time) + conv_pic_hist
overlap_years = len(
np.where((new_year >= year_min) & (new_year <= year_max))[0])
print(f'Number of overlapping years : {overlap_years}')
# Require that at least 90% of the years are available
if overlap_years >= tot_year * 0.9:
print('Using branching time')
branching_method = 'unsing_branching_time'
y_ds = y_ds.assign_coords(time=(y_ds.time + conv_pic_hist))
else:
print('Not using branching time for piControl')
branching_method = 'not_unsing_branching_time'
# Assumes piControl simulation starts in 1850
y_ds = y_ds.assign_coords(time=(y_ds.time - y_ds.time[0] + 1850.5))
VAR1 = y_ds[VAR].squeeze()
VAR1 = VAR1.sel(time=slice(year_min, year_max))
if rmv_disc:
VAR1 = loc.remove_discontinuities(VAR1, gap)
VAR1_coeff = VAR1.polyfit(dim='time', deg=order)
return VAR1_coeff.polyfit_coefficients, branching_method
try:
print('#### Using the following files: ####')
[print(str(x)) for x in hist_files]
except:
sys.exit('ERROR: No file available at that location')
hist_ds = open_files(hist_files)
if EXP=='historical':
all_ds = hist_ds
else:
sce_ds = open_files(sce_files)
all_ds = xr.concat([hist_ds,sce_ds],'time')
VAR1 = all_ds[VAR].squeeze()
VAR1a = loc.yearly_mean(VAR1)
# Remove discontinuites in some time series
try:
VAR1a = loc.remove_discontinuities(VAR1a, gap)
except ValueError as err:
print(err.args)
continue
# Compute the trend from the piControl simulations and save trend
if verbose:
pic.info_branching(hist_ds.attrs)
try:
# Convert the year from piControl to historical run
if MIP == 'cmip5':
sce_ds = xr.open_mfdataset(sce_files,
combine='by_coords',
use_cftime=True)
except:
print(f'!!!!!!!!! Could not open data from {Model[i]}!!!!!!!!!!!!!!!')
print(
'Try the function open_mfdataset with the option combine="nested" '
)
continue
if EXP != 'historical':
ds = xr.concat([hist_ds, sce_ds], 'time')
else:
ds = hist_ds
y_ds = loc.yearly_mean(ds)
if Model[i] == 'BCC-CSM2-MR':
y_ds = y_ds.rename({'lat': 'rlat', 'lon': 'rlon'})
if 'latitude' and 'longitude' in y_ds.coords:
y_ds = y_ds.rename({'latitude': 'lat', 'longitude': 'lon'})
elif 'nav_lat' and 'nav_lon' in y_ds.coords:
y_ds = y_ds.rename({'nav_lat': 'lat', 'nav_lon': 'lon'})
# Build regridder with xESMF
try:
reg_method = 'bilinear'
regridder = xe.Regridder(
y_ds,
ds_out,