def plot_scatter_STD(ds_dict, varn, year_start, year_end, fut_year_start, fut_year_end):
color_dict = dict(
CESM='r',
MPI='g',
CanESM='y',
ERA20C='k',
BEST='k',
CMIP5='b')
for key in ds_dict.keys():
if key in ['ERA20C']:
plt.axvline(
cos_lat_weighted_mean(ut.linear_detrend(
ds_dict[key][varn].sel(year=slice(year_start, year_end)),axis='year').std('year')).data,
color=color_dict[key],
linestyle='solid',
linewidth=1,
label=key)
if key in ['BEST']:
plt.axvline(
cos_lat_weighted_mean(ut.linear_detrend(
ds_dict[key][varn].sel(year=slice(year_start, year_end)),axis='year').std('year')).data,
color=color_dict[key],
linestyle='dashed',
linewidth=1,
label=key)
if key in ['CESM','MPI','CanESM','CMIP5']:
plt.scatter(
cos_lat_weighted_mean(ut.linear_detrend(
ds_dict[key][varn].sel(year=slice(year_start, year_end)),axis='year').std('year')).data,
cos_lat_weighted_mean(ut.linear_detrend(
ds_dict[key][varn].sel(year=slice(fut_year_start, fut_year_end)),axis='year').std('year')).data,
s=5,
color=color_dict[key],
label=key)
def plot_fit_STD(ds_dict,
varn,
year_start,
year_end,
fut_year_start,
fut_year_end,
keys=None,
**kwargs):
if keys is None:
keys = ['CESM', 'MPI', 'CanESM', 'CMIP5']
elif isinstance(keys, str):
keys = [keys]
xx, yy = [], []
for key in keys:
xx += list(
cos_lat_weighted_mean(
ut.linear_detrend(
ds_dict[key][varn].sel(year=slice(year_start, year_end)),
axis='year').std('year')).data)
yy += list(
cos_lat_weighted_mean(
ut.linear_detrend(ds_dict[key][varn].sel(
year=slice(fut_year_start, fut_year_end)),
axis='year').std('year')).data)
a, b, r_value, p_value, std_err = linregress(xx, yy)
f = lambda x: a * x + b
x = np.array([np.min(xx), np.max(xx)])
plt.plot(x, f(x), **kwargs)
return r_value