calculate_significance=False,
title='Difference')
cs = contour_plot(
2,
2,
np.mean(data_am[NINO34_plus & IOBM_plus, :, :], axis=0) -
np.mean(data_am[NINO34_plus & IOBM_minus, :, :], axis=0),
clevs,
lats_var,
lons_var,
None,
mask=None,
calculate_significance=False,
title='Difference')
ax = plt.subplot(gs[3, :])
make_plots.colorbar(ax, cs, orientation='horizontal')
#save figure
if decadal_mean == True:
save_file_name = figure_dir + '/IOBM_NINO34_composites_' + var_name + '_decadal_' + model_name + '_' + season + '.png'
else:
save_file_name = figure_dir + '/IOBM_NINO34_composites_' + var_name + '_' + model_name + '_' + season + '.png'
print('saving to %s' % (save_file_name))
plt.savefig(save_file_name, bbox_inches='tight')
elif average_over_models == True:
# add composites to arrays
if i == 0:
standard_lats = np.arange(-88, 88.1, 2)
standard_lons = np.arange(0, 360.1, 2)
n_lats, n_lons = standard_lats.shape[0], standard_lons.shape[0]
if decadal_mean == False:
lats_psl,
lons_psl,
title=index_name + ' psl',
extent=[60, 300, -30, 60])
ax = plt.subplot(
gs[j, 2], projection=ccrs.PlateCarree(central_longitude=180.))
cs_pr = contour_plot(86400 * regress_coeffs_pr,
clevs_pr,
lats_pr,
lons_pr,
title=index_name + ' pr',
cmap='RdBu',
extent=[60, 150, 20, 50])
ax = plt.subplot(gs[j + 1, 0])
make_plots.colorbar(ax, cs_SST)
ax = plt.subplot(gs[j + 1, 1])
make_plots.colorbar(ax, cs_psl)
ax = plt.subplot(gs[j + 1, 2])
make_plots.colorbar(ax, cs_pr)
# save
#plt.subplots_adjust(hspace=0.4)
save_file_name = figure_dir + '/regress_SST_indices_circulation_' + model_name + '_' + season + '.png'
print('saving to %s' % (save_file_name))
plt.savefig(save_file_name, bbox_inches='tight')
except:
message = 'Error, skipping model ' + model_name
print(message)