def readVar(varnames):
### Call function to read in ERA-Interim
lat, lon, time, lev, era = MOR.readDataR('SNC', 'surface', False, True)
### Select October-November for index
eraq = np.nanmean(era[:, 9:11, :, :], axis=1)
# eraq = era[:,9:10,:,:].squeeze() # Octobers
return eraq, lat, lon, lev
def readVar(varnames,monthperiod):
### Call function to read in ERA-Interim
lat,lon,time,lev,era = MOR.readDataR(varnames,'surface',False,True)
### Call functions to read in WACCM data
models = np.empty((len(runnamesm),ensembles,era.shape[0],era.shape[1],
era.shape[2],era.shape[3]))
for i in range(len(runnamesm)):
lat,lon,time,lev,models[i] = MOM.readDataM(varnames,runnamesm[i],
'surface',False,True)
### Retrieve time period of interest
if monthperiod == 'DJF':
modq = np.empty((len(runnamesm),ensembles,era.shape[0]-1,era.shape[2],
era.shape[3]))
for i in range(len(runnamesm)):
for j in range(ensembles):
modq[i,j,:,:,:] = UT.calcDecJanFeb(models[i,j,:,:,:],
lat,lon,'surface',1)
eraq = UT.calcDecJanFeb(era,lat,lon,'surface',1)
elif monthperiod == 'Annual':
modq = np.nanmean(models[:,:,:,:,:,:],axis=3)
eraq = np.nanmean(era[:,:,:,:],axis=1)
### Take ensemble mean
modmean = np.nanmean(modq,axis=1)
### Slice over Arctic polar cap
latslicemin = 40
latslicemax = 65
lonslicemin = 50
lonslicemax = 130
latq = np.where((lat >= latslicemin) & (lat <= latslicemax))[0]
lat = lat[latq]
lonq = np.where((lon >= lonslicemin) & (lon <= lonslicemax))[0]
lon = lon[lonq]
eraq = eraq[:,latq,:]
eraq = eraq[:,:,lonq]
modmean = modmean[:,:,latq,:]
modmean = modmean[:,:,:,lonq]
### Meshgrid for lat/lon
lon2,lat2 = np.meshgrid(lon,lat)
eramean = UT.calc_weightedAve(eraq,lat2)
mmean = UT.calc_weightedAve(modmean,lat2)
### Create climo over time series
eraiave = np.nanmean(eramean)
modelave = np.nanmean(mmean,axis=1)
### Calculate anomalies
eraanom = eramean - eraiave
modelanom = (mmean.transpose() - modelave).transpose()
return eraanom, modelanom
def readVar(varnames, runnamesm, period):
### Call function to read in ERA-Interim (detrended)
lat, lon, time, lev, era = MOR.readDataR(varnames, 'surface', True, True)
### Call functions to read in WACCM data (detrended)
models = np.empty((len(runnamesm), ensembles, era.shape[0], era.shape[1],
era.shape[2], era.shape[3]))
for i in range(len(runnamesm)):
lat, lon, time, lev, models[i] = MOM.readDataM(varnames, runnamesm[i],
'surface', True, True)
### Retrieve time period of interest
if period == 'ON':
modq = np.nanmean(models[:, :, :, 9:11, :, :], axis=3)
eraq = np.nanmean(era[:, 9:11, :, :], axis=1)
elif period == 'OND':
modq = np.nanmean(models[:, :, :, -3:, :, :], axis=3)
eraq = np.nanmean(era[:, -3:, :, :], axis=1)
elif period == 'ND':
modq = np.nanmean(models[:, :, :, -2:, :, :], axis=3)
eraq = np.nanmean(era[:, -2:, :, :], axis=1)
elif period == 'N':
modq = models[:, :, :, -2, :, :].squeeze()
eraq = era[:, -2, :, :].squeeze()
elif period == 'D':
modq = models[:, :, :, -1:, :, :].squeeze()
eraq = era[:, -1:, :, :].squeeze()
elif period == 'JJA':
modq = np.nanmean(models[:, :, :, 5:8, :, :], axis=3)
eraq = np.nanmean(era[:, 5:8, :, :], axis=1)
elif period == 'Annual':
modq = np.nanmean(models[:, :, :, :, :, :], axis=3)
eraq = np.nanmean(era[:, :, :, :], axis=1)
elif period == 'DJF':
modq = np.empty((len(runnamesm), ensembles, era.shape[0] - 1,
era.shape[2], era.shape[3]))
for i in range(len(runnamesm)):
for j in range(ensembles):
modq[i,
j, :, :, :] = UT.calcDecJanFeb(models[i, j, :, :, :, :],
lat, lon, 'surface', 1)
eraq = UT.calcDecJanFeb(era, lat, lon, 'surface', 1)
### Slice U10 at 65N
latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]
lat = lat[latq].squeeze()
eraq = eraq[:, latq, :].squeeze()
modq = modq[:, :, :, latq, :].squeeze()
### Take zonal mean
eraq = np.nanmean(eraq, axis=1)
modq = np.nanmean(modq, axis=3)
return modq, eraq, lat, lon
def readVar(varnames, runnamesm, period):
if varnames == 'SST':
world = False
else:
world = True
### Call function to read in ERA-Interim (detrended)
lat, lon, time, lev, era = MOR.readDataR(varnames, 'surface', True, world)
### Call functions to read in WACCM data (detrended)
models = np.empty((len(runnamesm), ensembles, era.shape[0], era.shape[1],
era.shape[2], era.shape[3]))
for i in range(len(runnamesm)):
lat, lon, time, lev, models[i] = MOM.readDataM(varnames, runnamesm[i],
'surface', True, world)
return models, era, lat, lon
def readVar(varnames,runnamesm):
### Call function to read in ERA-Interim
lat,lon,time,lev,era = MOR.readDataR('T2M','surface',False,True)
### Call functions to read in WACCM data
models = np.empty((len(runnamesm),ensembles,era.shape[0],era.shape[1],
era.shape[2],era.shape[3]))
for i in range(len(runnamesm)):
lat,lon,time,lev,models[i] = MOM.readDataM(varnames,runnamesm[i],
'surface',False,True)
### Select October-November for index
modq = np.nanmean(models[:,:,:,9:11,:,:],axis=3)
### Calculate ensemble mean
modmean = np.nanmean(modq[:,:,:,:,:],axis=1)
return modmean,lat,lon,lev
def readVar(varnames):
### Call function to read in ERA-Interim (detrended)
lat, lon, time, lev, era = MOR.readDataR(varnames, 'surface', True, True)
### Call functions to read in WACCM data (detrended)
models = np.empty((len(runnamesm), ensembles, era.shape[0], era.shape[1],
era.shape[2], era.shape[3]))
for i in range(len(runnamesm)):
lat, lon, time, lev, models[i] = MOM.readDataM(varnames, runnamesm[i],
'surface', True, True)
### Retrieve time period of interest
modq = np.empty((len(runnamesm), ensembles, era.shape[0] - 1, era.shape[2],
era.shape[3]))
for i in range(len(runnamesm)):
for j in range(ensembles):
modq[i, j, :, :, :] = UT.calcDecJanFeb(models[i, j, :, :, :], lat,
lon, 'surface', 1)
eraq = UT.calcDecJanFeb(era, lat, lon, 'surface', 1)
return modq, eraq, lat, lon
def readDataTrends(varnames, month, years, yearmn, yearmx, region):
### Call function to read in ERA-Interim
lat, lon, time, lev, era = MOR.readDataR(varnames, 'surface', False, True)
### Call functions to read in WACCM data
models = np.empty((len(runnamesm), ensembles, era.shape[0], era.shape[1],
era.shape[2], era.shape[3]))
for i in range(len(runnamesm)):
lat, lon, time, lev, models[i] = MOM.readDataM(varnames, runnamesm[i],
'surface', False, True)
### Retrieve time period of interest
if month == 'DJF':
modq = np.empty((len(runnamesm), ensembles, era.shape[0] - 1,
era.shape[2], era.shape[3]))
for i in range(len(runnamesm)):
for j in range(ensembles):
modq[i, j, :, :, :] = UT.calcDecJanFeb(models[i, j, :, :, :],
lat, lon, 'surface', 1)
eraq = UT.calcDecJanFeb(era, lat, lon, 'surface', 1)
if region == 'Siberia':
latslicemin = 40
latslicemax = 65
lonslicemin = 50
lonslicemax = 130
latq = np.where((lat >= latslicemin) & (lat <= latslicemax))[0]
lat = lat[latq]
lonq = np.where((lon >= lonslicemin) & (lon <= lonslicemax))[0]
lon = lon[lonq]
eraq = eraq[:, latq, :]
eraq = eraq[:, :, lonq]
modq = modq[:, :, :, latq, :]
modq = modq[:, :, :, :, lonq]
elif region == 'NA':
latslicemin = 35
latslicemax = 50
lonslicemin = 260
lonslicemax = 290
latq = np.where((lat >= latslicemin) & (lat <= latslicemax))[0]
lat = lat[latq]
lonq = np.where((lon >= lonslicemin) & (lon <= lonslicemax))[0]
lon = lon[lonq]
eraq = eraq[:, latq, :]
eraq = eraq[:, :, lonq]
modq = modq[:, :, :, latq, :]
modq = modq[:, :, :, :, lonq]
elif region == 'Arctic':
latq = np.where(lat >= 65)[0]
lat = lat[latq]
eraq = eraq[:, latq, :]
modq = modq[:, :, :, latq, :]
elif region == 'NH':
eraq = eraq
modmean = modq
### Calculate the trend for WACCM
modtrend = np.empty(
(len(runnamesm), ensembles, modq.shape[3], modq.shape[4]))
for i in range(len(runnamesm)):
modtrend[i, :, :, :] = UT.detrendData(modq[i], years, 'surface',
yearmn, yearmx)
print('Completed: Simulation --> %s!' % runnamesm[i])
### Calculate decadal trend
dectrend = modtrend * 10.
### Calculate the trend for ERA-Interim
retrend, std_err = UT.detrendDataR(eraq, years, 'surface', yearmn, yearmx)
#### Calculate decadal trend
redectrend = retrend * 10.
std_err = std_err * 10.
return lat, lon, dectrend, redectrend, std_err
years = np.arange(year1, year2 + 1, 1)
### Add parameters
ensembles = 10
su = [0, 1, 2, 3, 5, 6, 7]
period = 'JFM'
varnames = ['T2M', 'SLP', 'Z500', 'Z50', 'U200', 'U10', 'THICK']
runnames = [r'ERA-I', r'CSST', r'CSIC', r'AMIP', r'AMQ', r'AMS', r'AMQS']
runnamesm = [r'CSST', r'CSIC', r'AMIP', r'AMQ', r'AMS', r'AMQS']
### Define directories
directoryfigure = '/home/zlabe/Desktop/SNR/%s/' % period
for v in range(len(varnames)):
### Call function to read in ERA-Interim
lat, lon, time, lev, era = MOR.readDataR(varnames[v], 'surface', False,
True)
### Call functions to read in WACCM data
models = np.empty((len(runnamesm), ensembles, era.shape[0], era.shape[1],
era.shape[2], era.shape[3]))
for i in range(len(runnamesm)):
lat, lon, time, lev, models[i] = MOM.readDataM(varnames[v],
runnamesm[i], 'surface',
False, True)
### Retrieve time period of interest
if period == 'DJF':
modq = np.empty((len(runnamesm), ensembles, era.shape[0] - 1,
era.shape[2], era.shape[3]))
for i in range(len(runnamesm)):
for j in range(ensembles):
currenttime = currentmn + '_' + currentdy + '_' + currentyr
titletime = currentmn + '/' + currentdy + '/' + currentyr
print('\n' '----Plotting WACC T2M Trends - %s----' % titletime)
#### Alott time series
year1 = 1978
year2 = 2016
years = np.arange(year1, year2 + 1, 1)
### Add parameters
ensembles = 10
varnames = ['T2M']
runnames = [r'CSST', r'CSIC', r'AMIP', r'AMQ', r'AMS', r'AMQS']
### Call function to read in ERA-Interim
lat, lon, time, lev, era = MOR.readDataR('T2M', 'surface', False, True)
### Call functions to read in WACCM data
models = np.empty((len(runnames), ensembles, era.shape[0], era.shape[1],
era.shape[2], era.shape[3]))
for i in range(len(runnames)):
lat, lon, time, lev, models[i] = MOM.readDataM('T2M', runnames[i],
'surface', False, True)
### Retrieve time period of interest
modq = np.empty(
(len(runnames), ensembles, era.shape[0] - 1, era.shape[2], era.shape[3]))
for i in range(len(runnames)):
for j in range(ensembles):
modq[i, j, :, :, :] = UT.calcDecJanFeb(models[i, j, :, :, :], lat, lon,
'surface', 1)
