indirhn = '/data/ericglod/Density_binning/Prod_density_april15/mme_histNat/'
filehn_2d = 'cmip5.multimodel_All.historicalNat.ensm.an.ocn.Omon.density_zon2D.nc'
filehn_1d = 'cmip5.multimodel_All.historicalNat.ensm.an.ocn.Omon.density_zon1D.nc'
fh2d = open_ncfile(indir_rcp85 + filercp85_2d, 'r')
fh1d = open_ncfile(indir_rcp85 + filercp85_1d, 'r')
fhn2d = open_ncfile(indirhn + filehn_2d, 'r')
fhn1d = open_ncfile(indirhn + filehn_1d, 'r')
# ----- Variables ------
# == Read variables ==
lat = fh2d.variables['latitude'][:]
if name == 'Durack & Wijffels':
# -- Choose wich variable to work on
varname = defVarDurack('salinity'); v = 'S'
# varname = defVarDurack('temp'); v = 'T'
density = fh2d.variables['density'][:]
var_mean = varname['var_mean_zonal']
var_change = varname['var_change_zonal']
var_change_er = varname['var_change_zonal_er']
var_attributes = fh2d.variables[var_mean]
var_mean = fh2d.variables[var_mean][:].squeeze()
var_change = fh2d.variables[var_change][:].squeeze()
var_change_er = fh2d.variables[var_change_er][:].squeeze()
# -- Determine field in 1950 and 2000 from mean field
var_1950 = var_mean - var_change/2
var_2000 = var_mean + var_change/2
if lat_choice == 'one':
lat1 = -20
lat1_idx = np.argmin(np.abs(lat - lat1))
else:
latmin = -25
latmax = -15
latmin_idx = np.argmin(np.abs(lat - latmin))
latmax_idx = np.argmin(np.abs(lat - latmax))
weights = np.cos(lat*np.pi/180)
weights = weights[latmin_idx:latmax_idx+1]
#print(np.shape(weights))
#print(weights)
# -- Read and build variables
if name == 'Durack&Wijffels':
varname = defVarDurack(varname)
var_diff = varname['var_change']
if lat_choice == 'one':
var_diff = f.variables[var_diff][:,:,lat1_idx,:].squeeze()
else:
var_diff = f.variables[var_diff][:,:,latmin_idx:latmax_idx+1,:].squeeze()
var_diff = np.ma.average(var_diff, axis=1, weights=weights)
density = f.variables['density'][:]
#Masked values in white
var_diff[var_diff > 4] = 0
elif name == 'mme_hist':
varname = defVarmme(varname)
var = varname['var_global']
if lat_choice == 'one':
var = f.variables[var][88:,:,lat1_idx,:].squeeze()
indir = '/data/ericglod/Density_binning/Obs_Prod_density_april16/'
file = 'DurackandWijffels_GlobalOceanChanges-NeutralDensity_1950-2000_120209_11_46_11_beta.nc'
name = 'Durack & Wijffels'
data = indir + file
f = open_ncfile(data, 'r')
# ----- Variables ------
# Read variables
density = f.variables['density'][:]
lat = f.variables['latitude'][:]
lon = f.variables['longitude'][:]
varname = defVarDurack('temp')
v = 'T'
#varname = defVarDurack('salinity'); v='S'
# -- Look at zonal differences or not
zonal_change = 'No'
#zonal_change = 'global' # Zonal mean is global
#zonal_change = 'basin' # Differentiate the basins when building the zonal mean
# Define variable properties
minmax = varname['minmax']
clevsm = varname['clevsm']
clevsm_bold = varname['clevsm_bold']
legVar = varname['legVar']
unit = varname['unit']
var_change = varname['var_change']
if lat_choice == 'one':
lat1 = -20
lat1_idx = np.argmin(np.abs(lat - lat1))
else:
latmin = -25
latmax = -15
latmin_idx = np.argmin(np.abs(lat - latmin))
latmax_idx = np.argmin(np.abs(lat - latmax))
weights = np.cos(lat * np.pi / 180)
weights = weights[latmin_idx:latmax_idx + 1]
#print(np.shape(weights))
#print(weights)
# -- Read and build variables
if name == 'Durack&Wijffels':
varname = defVarDurack(varname)
var_diff = varname['var_change']
if lat_choice == 'one':
var_diff = f.variables[var_diff][:, :, lat1_idx, :].squeeze()
else:
var_diff = f.variables[var_diff][:, :, latmin_idx:latmax_idx +
1, :].squeeze()
var_diff = np.ma.average(var_diff, axis=1, weights=weights)
density = f.variables['density'][:]
#Masked values in white
var_diff[var_diff > 4] = 0
elif name == 'mme_hist':
varname = defVarmme(varname)
var = varname['var_global']
if lat_choice == 'one':