def readHeatFlux():
### Call arguments
varnames = 'RNET'
experiments = [r'\textbf{FIT--HIT}', r'\textbf{FICT--HIT}']
### Call function for rnet data from reach run
lat, lon, time, lev, tashit = MO.readExperiAll('%s' % varnames, 'HIT',
'surface')
lat, lon, time, lev, tasfict = MO.readExperiAll('%s' % varnames, 'FICT',
'surface')
### Create 2d array of latitude and longitude
lon2, lat2 = np.meshgrid(lon, lat)
### Calculate Oct-Mar
tashitmo = np.append(tashit[:, 9:, :, :], tashit[:, :3, :, :], axis=1)
tasfictmo = np.append(tasfict[:, 9:, :, :], tasfict[:, :3, :, :], axis=1)
### Take difference
ficthit = np.nanmean(tasfictmo - tashitmo, axis=0)
### Statistical Test
stat_FICTHIT, pvalue_FICTHIT = UT.calc_indttest(tasfictmo, tashitmo)
return ficthit[:3], lat, lon, pvalue_FICTHIT[:3]
### Alott time series
year1 = 1900
year2 = 2000
years = np.arange(year1,year2+1,1)
### Call arguments
varnames = ['Z500','Z30','SLP','T2M','U10','U500','U300','SWE','THICK','P',
'EGR','WAFZ850','WAFZ150','WAFY850','WAFY150']
runnames = [r'HIT',r'FIT',r'FICT']
experiments = [r'\textbf{FIT--HIT}',r'\textbf{FIT--HIT}',r'\textbf{FIT--HIT}',
r'\textbf{FICT--HIT}',r'\textbf{FICT--HIT}',r'\textbf{FICT--HIT}']
qbophase = ['pos','non','neg']
period = 'F'
for v in range(len(varnames)):
### Call function for surface temperature data from reach run
lat,lon,time,lev,tashit = MO.readExperiAll('%s' % varnames[v],'HIT',
'surface')
lat,lon,time,lev,tasfit = MO.readExperiAll('%s' % varnames[v],'FIT',
'surface')
lat,lon,time,lev,tasfict = MO.readExperiAll('%s' % varnames[v],'FICT',
'surface')
### Create 2d array of latitude and longitude
lon2,lat2 = np.meshgrid(lon,lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
titletime = currentmn + '/' + currentdy + '/' + currentyr
print('\n' '----Plotting QBO profile regional comparisons - %s----' % titletime)
### Alott time series
year1 = 1900
year2 = 2000
years = np.arange(year1,year2+1,1)
runnames = [r'CIT',r'FSUB',r'FPOL']
experiments = [r'\textbf{FSUB--CIT}',r'\textbf{FSUB--CIT}',r'\textbf{FSUB--CIT}',
r'\textbf{FPOL--CIT}',r'\textbf{FPOL--CIT}',r'\textbf{FPOL--CIT}']
qbophase = ['pos','non','neg']
period = 'N'
### Call function for vertical temperature data
lat,lon,time,lev,epy_h = MO.readExperiAll('EPY','CIT','profile')
lat,lon,time,lev,epz_h = MO.readExperiAll('EPZ','CIT','profile')
lat,lon,time,lev,div_h = MO.readExperiAll('DEPF','CIT','profile')
lat,lon,time,lev,epy_f = MOR.readExperiAllRegional('EPY','FSUB','profile')
lat,lon,time,lev,epz_f = MOR.readExperiAllRegional('EPZ','FSUB','profile')
lat,lon,time,lev,div_f = MOR.readExperiAllRegional('DEPF','FSUB','profile')
lat,lon,time,lev,epy_fpol = MOR.readExperiAllRegional('EPY','FPOL','profile')
lat,lon,time,lev,epz_fpol = MOR.readExperiAllRegional('EPZ','FPOL','profile')
lat,lon,time,lev,div_fpol = MOR.readExperiAllRegional('DEPF','FPOL','profile')
### Separate per month
if period == 'N':
epy_moh = epy_h[:,-2,:,:]
epz_moh = epz_h[:,-2,:,:]
def readVariables(varnames, period, location):
### Call function for surface temperature data from reach run
lat, lon, time, lev, tashit = MO.readExperiAll('%s' % varnames, 'HIT',
'surface')
lat, lon, time, lev, tasfict = MO.readExperiAll('%s' % varnames, 'FICT',
'surface')
### Create 2d array of latitude and longitude
lon2, lat2 = np.meshgrid(lon, lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
pos_hit = np.append(
np.genfromtxt(filenamehitp, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitp2, unpack=True, usecols=[0], dtype='int') +
101)
non_hit = np.append(
np.genfromtxt(filenamehitno, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitno2, unpack=True, usecols=[0], dtype='int') +
101)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int') +
101)
filenamefictp = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictno = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
1]
filenamefictn = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
filenamefictp2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
0]
filenamefictno2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
1]
filenamefictn2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
2]
pos_fict = np.append(
np.genfromtxt(filenamefictp, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictp2, unpack=True, usecols=[0], dtype='int') +
101)
non_fict = np.append(
np.genfromtxt(filenamefictno, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictno2, unpack=True, usecols=[0], dtype='int') +
101)
neg_fict = np.append(
np.genfromtxt(filenamefictn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictn2, unpack=True, usecols=[0], dtype='int') +
101)
### Concatonate runs
runs = [tashit, tasfict]
### Separate per periods (ON,DJ,FM)
if period == 'ON':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, 9:11, :, :], axis=1)
elif period == 'DJ':
tas_mo = np.empty(
(3, tashit.shape[0] - 1, tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i], tas_mo[i] = UT.calcDecJan(runs[i], runs[i], lat, lon,
'surface', 1)
elif period == 'FM':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, 1:3, :, :], axis=1)
elif period == 'DJF':
tas_mo = np.empty(
(3, tashit.shape[0] - 1, tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i], tas_mo[i] = UT.calcDecJanFeb(runs[i], runs[i], lat, lon,
'surface', 1)
elif period == 'M':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, 2, :, :]
elif period == 'D':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, -1, :, :]
elif period == 'N':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, -2, :, :]
elif period == 'ND':
tas_mo = np.empty(
(3, tashit.shape[0], tashit.shape[2], tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, -2:, :, :], axis=1)
else:
ValueError('Wrong period selected! (ON,DJ,FM)')
### Composite by QBO phase
tas_mohitpos = tas_mo[0][pos_hit, :, :]
tas_mofictpos = tas_mo[1][pos_fict, :, :]
tas_mohitnon = tas_mo[0][non_hit, :, :]
tas_mofictnon = tas_mo[1][non_fict, :, :]
tas_mohitneg = tas_mo[0][neg_hit, :, :]
tas_mofictneg = tas_mo[1][neg_fict, :, :]
### Compute comparisons for months - select region
if varnames == 'SLP':
lonq = np.where((lon >= 80) & (lon <= 120))[0]
ficthitpos = tas_mofictpos[:, :, lonq]
ficthitnon = tas_mofictnon[:, :, lonq]
ficthitneg = tas_mofictneg[:, :, lonq]
latq = np.where((lat >= 40) & (lat <= 65))[0]
ficthitpos = ficthitpos[:, latq]
ficthitnon = ficthitnon[:, latq]
ficthitneg = ficthitneg[:, latq]
lat2sq = lat2[latq, :]
lat2s = lat2sq[:, lonq]
ficthitpos = UT.calc_weightedAve(ficthitpos, lat2s)
ficthitnon = UT.calc_weightedAve(ficthitnon, lat2s)
ficthitneg = UT.calc_weightedAve(ficthitneg, lat2s)
diffruns = [
ficthitpos.squeeze(),
ficthitnon.squeeze(),
ficthitneg.squeeze()
]
return diffruns, lat, lon, lev
def calcVarResp(varnames,period,qbophase):
### Call function for surface temperature data from reach run
lat,lon,time,lev,tashit = MO.readExperiAll('%s' % varnames,'HIT',
'surface')
lat,lon,time,lev,tasfict = MO.readExperiAll('%s' % varnames,'FICT',
'surface')
### Create 2d array of latitude and longitude
lon2,lat2 = np.meshgrid(lon,lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
pos_hit = np.append(np.genfromtxt(filenamehitp,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitp2,unpack=True,usecols=[0],dtype='int')+101)
non_hit = np.append(np.genfromtxt(filenamehitno,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitno2,unpack=True,usecols=[0],dtype='int')+101)
neg_hit = np.append(np.genfromtxt(filenamehitn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitn2,unpack=True,usecols=[0],dtype='int')+101)
filenamefictp = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictno = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[1]
filenamefictn = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
filenamefictp2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictno2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[1]
filenamefictn2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
pos_fict = np.append(np.genfromtxt(filenamefictp,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictp2,unpack=True,usecols=[0],dtype='int')+101)
non_fict = np.append(np.genfromtxt(filenamefictno,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictno2,unpack=True,usecols=[0],dtype='int')+101)
neg_fict = np.append(np.genfromtxt(filenamefictn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictn2,unpack=True,usecols=[0],dtype='int')+101)
### Concatonate runs
runs = [tashit,tasfict]
### Separate per periods (ON,DJ,FM)
if period == 'ON':
tas_mo = np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,9:11,:,:],axis=1)
elif period == 'DJ':
tas_mo = np.empty((3,tashit.shape[0]-1,tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i],tas_mo[i] = UT.calcDecJan(runs[i],runs[i],lat,
lon,'surface',1)
elif period == 'FM':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,1:3,:,:],axis=1)
elif period == 'DJF':
tas_mo= np.empty((3,tashit.shape[0]-1,tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i],tas_mo[i] = UT.calcDecJanFeb(runs[i],runs[i],lat,
lon,'surface',1)
elif period == 'M':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,2,:,:]
elif period == 'D':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,-1,:,:]
elif period == 'N':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,-2,:,:]
elif period == 'ND':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,-2:,:,:],axis=1)
else:
ValueError('Wrong period selected! (ON,DJ,FM)')
### Composite by QBO phase
tas_mohitpos = tas_mo[0][pos_hit,:,:]
tas_mofictpos = tas_mo[1][pos_fict,:,:]
tas_mohitneg = tas_mo[0][neg_hit,:,:]
tas_mofictneg = tas_mo[1][neg_fict,:,:]
### Compute climatology
climohitpos = np.nanmean(tas_mohitpos,axis=0)
climohitneg = np.nanmean(tas_mohitneg,axis=0)
climo = [climohitpos,climohitneg]
### Compute comparisons for months - taken ensemble average
ficthitpos = np.nanmean(tas_mofictpos - tas_mohitpos,axis=0)
ficthitneg = np.nanmean(tas_mofictneg - tas_mohitneg,axis=0)
diffruns = [ficthitpos,ficthitneg]
### Calculate significance for ND
stat_FICTHITpos,pvalue_FICTHITpos = UT.calc_indttest(tas_mo[1][pos_fict,:,:],
tas_mo[0][pos_hit,:,:])
stat_FICTHITnon,pvalue_FICTHITnon = UT.calc_indttest(tas_mo[1][non_fict,:,:],
tas_mo[0][non_hit,:,:])
stat_FICTHITneg,pvalue_FICTHITneg = UT.calc_indttest(tas_mo[1][neg_fict,:,:],
tas_mo[0][neg_hit,:,:])
pruns = [pvalue_FICTHITpos,pvalue_FICTHITneg]
return diffruns,pruns,climo,lat,lon
titletime = currentmn + '/' + currentdy + '/' + currentyr
print('\n' '----Plotting linear interference - %s----' % titletime)
### Alott time series
year1 = 1800
year2 = 2000
years = np.arange(year1,year2+1,1)
historicalforced = []
futureforced = []
lonss = []
varnames = ['GEOPxwave_all','GEOPxwave1','GEOPxwave2']
period = 'D'
for v in range(len(varnames)):
### Call function for wave data from reach run
lat,lon1,time,lev,varcit = MO.readExperiAll('%s' % varnames[v],
'CIT','profile')
lat,lon1,time,lev,varfpol = MOR.readExperiAllRegional('%s' % varnames[v],
'FPOL','profile')
### Missing data
varcit[np.where(varcit<-100000)]=np.nan
### Modify lons
lon1[-1] = 360
### Create 2d array of latitude and longitude
lon2,lat2 = np.meshgrid(lon1,lat)
### Concatonate runs
runnames = [r'CIT',r'FPOL']
experiments = [r'\textbf{FPOL--CIT}']
year2 = 2000
years = np.arange(year1, year2 + 1, 1)
historicalforcedpos = []
futureforcedpos = []
historicalforcednon = []
futureforcednon = []
historicalforcedneg = []
futureforcedneg = []
lonssq = []
varnames = ['GEOPxwave_all', 'GEOPxwave1', 'GEOPxwave2']
qbophase = ['pos', 'non', 'neg']
period = 'N'
for v in range(len(varnames)):
### Call function for surface temperature data from reach run
lat, lon1, time, lev, tashit = MO.readExperiAll('%s' % varnames[v], 'HIT',
'profile')
lat, lon1, time, lev, tasfict = MO.readExperiAll('%s' % varnames[v],
'FICT', 'profile')
### Modify lons
lon1[-1] = 360
### Create 2d array of latitude and longitude
lon2, lat2 = np.meshgrid(lon1, lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
def readWAFy(varnames, qbophase):
lat, lon, time, lev, tashit = MO.readExperiAll('%s' % varnames[0], 'HIT',
'profile')
lat, lon, time, lev, tasfict = MO.readExperiAll('%s' % varnames[0], 'FICT',
'profile')
### Create 2d array of latitude and longitude
lon2, lat2 = np.meshgrid(lon, lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitno2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[1]
filenamehitn2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
pos_hit = np.append(
np.genfromtxt(filenamehitp, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitp2, unpack=True, usecols=[0], dtype='int') +
101)
non_hit = np.append(
np.genfromtxt(filenamehitno, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitno2, unpack=True, usecols=[0], dtype='int') +
101)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int') +
101)
filenamefictp = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictno = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
1]
filenamefictn = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
filenamefictp2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
0]
filenamefictno2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
1]
filenamefictn2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
2]
pos_fict = np.append(
np.genfromtxt(filenamefictp, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictp2, unpack=True, usecols=[0], dtype='int') +
101)
non_fict = np.append(
np.genfromtxt(filenamefictno, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictno2, unpack=True, usecols=[0], dtype='int') +
101)
neg_fict = np.append(
np.genfromtxt(filenamefictn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictn2, unpack=True, usecols=[0], dtype='int') +
101)
### Concatonate runs
runs = [tashit, tasfict]
### Separate per periods (ON,DJ,FM)
if period == 'ON':
tas_mo = np.empty((3, tashit.shape[0], tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, 9:11, :, :, :], axis=1)
elif period == 'DJ':
tas_mo = np.empty((3, tashit.shape[0] - 1, tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i], tas_mo[i] = UT.calcDecJan(runs[i], runs[i], lat, lon,
'profile', 1)
elif period == 'FM':
tas_mo = np.empty((3, tashit.shape[0], tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, 1:3, :, :, :], axis=1)
elif period == 'DJF':
tas_mo = np.empty((3, tashit.shape[0] - 1, tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i], tas_mo[i] = UT.calcDecJanFeb(runs[i], runs[i], lat, lon,
'profile', 1)
elif period == 'M':
tas_mo = np.empty((3, tashit.shape[0], tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, 2, :, :, :]
elif period == 'D':
tas_mo = np.empty((3, tashit.shape[0], tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, -1, :, :, :]
elif period == 'N':
tas_mo = np.empty((3, tashit.shape[0], tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:, -2, :, :, :]
elif period == 'ND':
tas_mo = np.empty((3, tashit.shape[0], tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, -2:, :, :, :], axis=1)
else:
ValueError('Wrong period selected! (ON,DJ,FM)')
### Composite by QBO phase
tas_mohitposq = tas_mo[0][pos_hit, :, :]
tas_mofictposq = tas_mo[1][pos_fict, :, :]
tas_mohitnonq = tas_mo[0][non_hit, :, :]
tas_mofictnonq = tas_mo[1][non_fict, :, :]
tas_mohitnegq = tas_mo[0][neg_hit, :, :]
tas_mofictnegq = tas_mo[1][neg_fict, :, :]
### Take zonal average
tas_mohitpos = np.nanmean(tas_mohitposq, axis=3)
tas_mofictpos = np.nanmean(tas_mofictposq, axis=3)
tas_mohitnon = np.nanmean(tas_mohitnonq, axis=3)
tas_mofictnon = np.nanmean(tas_mofictnonq, axis=3)
tas_mohitneg = np.nanmean(tas_mohitnegq, axis=3)
tas_mofictneg = np.nanmean(tas_mofictnegq, axis=3)
ficthitpos = np.nanmean(tas_mofictpos - tas_mohitpos, axis=0) / np.nanstd(
tas_mofictpos, axis=0)
ficthitnon = np.nanmean(tas_mofictnon - tas_mohitnon, axis=0) / np.nanstd(
tas_mofictnon, axis=0)
ficthitneg = np.nanmean(tas_mofictneg - tas_mohitneg, axis=0) / np.nanstd(
tas_mofictneg, axis=0)
diffruns_mo = [ficthitneg, ficthitpos, ficthitneg - ficthitpos]
### Calculate significance
stat_FICTHITpos, pvalue_FICTHITpos = UT.calc_indttest(
tas_mohitpos, tas_mofictpos)
stat_FICTHITnon, pvalue_FICTHITnon = UT.calc_indttest(
tas_mohitnon, tas_mofictnon)
stat_FICTHITneg, pvalue_FICTHITneg = UT.calc_indttest(
tas_mohitneg, tas_mofictneg)
pruns_mo = [pvalue_FICTHITneg, pvalue_FICTHITpos, pvalue_FICTHITneg]
return diffruns_mo, pruns_mo, lat, lon, lev
def calcVarResp(varnames,period,qbophase):
### Call function for surface temperature data from reach run
lat,lon,time,lev,tashit = MO.readExperiAll('%s' % varnames,'HIT',
'surface')
lat,lon,time,lev,tasfict = MO.readExperiAll('%s' % varnames,'FICT',
'surface')
lat,lon,time,lev,tasfit = MO.readExperiAll('%s' % varnames,'FIT',
'surface')
### Create 2d array of latitude and longitude
lon2,lat2 = np.meshgrid(lon,lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitn2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
pos_hit = np.append(np.genfromtxt(filenamehitp,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitp2,unpack=True,usecols=[0],dtype='int')+101)
neg_hit = np.append(np.genfromtxt(filenamehitn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitn2,unpack=True,usecols=[0],dtype='int')+101)
filenamefictp = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictn = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
filenamefictp2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictn2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
pos_fict = np.append(np.genfromtxt(filenamefictp,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictp2,unpack=True,usecols=[0],dtype='int')+101)
neg_fict = np.append(np.genfromtxt(filenamefictn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictn2,unpack=True,usecols=[0],dtype='int')+101)
filenamefitp = directorydata + 'FIT/monthly/QBO_%s_FIT.txt' % qbophase[0]
filenamefitn = directorydata + 'FIT/monthly/QBO_%s_FIT.txt' % qbophase[2]
filenamefitp2 = directorydata2 + 'FIT/monthly/QBO_%s_FIT.txt' % qbophase[0]
filenamefitn2 = directorydata2 + 'FIT/monthly/QBO_%s_FIT.txt' % qbophase[2]
pos_fit = np.append(np.genfromtxt(filenamefitp,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefitp2,unpack=True,usecols=[0],dtype='int')+101)
neg_fit = np.append(np.genfromtxt(filenamefitn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefitn2,unpack=True,usecols=[0],dtype='int')+101)
### Concatonate runs
runs = [tashit,tasfict,tasfit]
### Separate per periods (ON,DJ,FM)
if period == 'ON':
tas_mo = np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,9:11,:,:],axis=1)
elif period == 'DJ':
tas_mo = np.empty((3,tashit.shape[0]-1,tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i],tas_mo[i] = UT.calcDecJan(runs[i],runs[i],lat,
lon,'surface',1)
elif period == 'FM':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,1:3,:,:],axis=1)
elif period == 'DJF':
tas_mo= np.empty((3,tashit.shape[0]-1,tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i],tas_mo[i] = UT.calcDecJanFeb(runs[i],runs[i],lat,
lon,'surface',1)
elif period == 'M':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,2,:,:]
elif period == 'D':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,-1,:,:]
elif period == 'N':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,-2,:,:]
elif period == 'ND':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,-2:,:,:],axis=1)
else:
ValueError('Wrong period selected! (ON,DJ,FM)')
### Composite by QBO phase
tas_mohitpos = tas_mo[0][pos_hit,:,:]
tas_mofictpos = tas_mo[1][pos_fict,:,:]
tas_mofitpos = tas_mo[2][pos_fit,:,:]
tas_mohitneg = tas_mo[0][neg_hit,:,:]
tas_mofictneg = tas_mo[1][neg_fict,:,:]
tas_mofitneg = tas_mo[2][neg_fit,:,:]
### Slice U10 at 65N
latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]
lat = lat[latq].squeeze()
tas_mohitpos = tas_mohitpos[:,latq,:].squeeze()
tas_mohitneg = tas_mohitneg[:,latq,:].squeeze()
tas_mofictpos = tas_mofictpos[:,latq,:].squeeze()
tas_mofictneg = tas_mofictneg[:,latq,:].squeeze()
tas_mofitpos = tas_mofitpos[:,latq,:].squeeze()
tas_mofitneg = tas_mofitneg[:,latq,:].squeeze()
### Take zonal mean
hitpos = np.nanmean(tas_mohitpos,axis=1)
hitneg = np.nanmean(tas_mohitneg,axis=1)
fictpos = np.nanmean(tas_mofictpos,axis=1)
fictneg = np.nanmean(tas_mofictneg,axis=1)
fitpos = np.nanmean(tas_mofitpos,axis=1)
fitneg = np.nanmean(tas_mofitneg,axis=1)
runs = [hitpos,hitneg,fictpos,fictneg,fitpos,fitneg]
return runs,lat,lon
def procData(varnames, period, qbophase):
"""
Function reads in zonal wind data for listed experiments. It then
post-processes the data for calculating the zero-wind line
Parameters
----------
varnames : list of strings
list of variables to download
period : list of strings
month(s) to calculate
qbophase : list of strings
list of qbo phases
Returns
-------
avez : list of arrays
arrays for post-processed data for each experiment
lat : 1d array
latitude
Usage
-----
avez,lat = procData(varnames,period,qbophase)
"""
print('\n>>> Using procData function!')
### Read in data
for v in range(len(varnames)):
### Call function for surface temperature data from reach run
lat, lon, time, lev, tashit = MO.readExperiAll('%s' % varnames[v],
'HIT', 'profile')
lat, lon, time, lev, tasfit = MO.readExperiAll('%s' % varnames[v],
'FIT', 'profile')
lat, lon, time, lev, tasfict = MO.readExperiAll(
'%s' % varnames[v], 'FICT', 'profile')
### Create 2d array of latitude and longitude
lon2, lat2 = np.meshgrid(lon, lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[
0]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[
2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[
0]
filenamehitn2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[
2]
pos_hit = np.append(
np.genfromtxt(filenamehitp, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitp2, unpack=True, usecols=[0], dtype='int')
+ 101)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int')
+ 101)
filenamefitp = directorydata + 'FIT/monthly/QBO_%s_FIT.txt' % qbophase[
0]
filenamefitn = directorydata + 'FIT/monthly/QBO_%s_FIT.txt' % qbophase[
2]
filenamefitp2 = directorydata2 + 'FIT/monthly/QBO_%s_FIT.txt' % qbophase[
0]
filenamefitn2 = directorydata2 + 'FIT/monthly/QBO_%s_FIT.txt' % qbophase[
2]
pos_fit = np.append(
np.genfromtxt(filenamefitp, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefitp2, unpack=True, usecols=[0], dtype='int')
+ 101)
neg_fit = np.append(
np.genfromtxt(filenamefitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefitn2, unpack=True, usecols=[0], dtype='int')
+ 101)
filenamefictp = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
0]
filenamefictn = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
2]
filenamefictp2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
0]
filenamefictn2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[
2]
pos_fict = np.append(
np.genfromtxt(filenamefictp, unpack=True, usecols=[0],
dtype='int'),
np.genfromtxt(
filenamefictp2, unpack=True, usecols=[0], dtype='int') + 101)
neg_fict = np.append(
np.genfromtxt(filenamefictn, unpack=True, usecols=[0],
dtype='int'),
np.genfromtxt(
filenamefictn2, unpack=True, usecols=[0], dtype='int') + 101)
### Concatonate runs
runs = [tashit, tasfit, tasfict]
### Separate per periods (ON,DJ,FM)
if period == 'DJF':
tas_mo = np.empty((3, tashit.shape[0] - 1, tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i], tas_mo[i] = UT.calcDecJanFeb(
runs[i], runs[i], lat, lon, 'profile', 17)
elif period == 'ND':
tas_mo = np.empty((3, tashit.shape[0], tashit.shape[2],
tashit.shape[3], tashit.shape[4]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:, -2:, :, :], axis=1)
else:
ValueError('Wrong period selected! (DJF)')
### Concatonate runs with selected level
levq = np.where(lev == 30)[0] # selected at 10 hPa
tas_mo = [
tas_mo[0][:, levq, :, :], tas_mo[1][:, levq, :, :],
tas_mo[2][:, levq, :, :]
]
### Composite by QBO phase
tas_mofitpos = tas_mo[1][pos_fit, :, :].squeeze()
tas_mohitpos = tas_mo[0][pos_hit, :, :].squeeze()
tas_mofictpos = tas_mo[2][pos_fict, :, :].squeeze()
tas_mofitneg = tas_mo[1][neg_fit, :, :].squeeze()
tas_mohitneg = tas_mo[0][neg_hit, :, :].squeeze()
tas_mofictneg = tas_mo[2][neg_fict, :, :].squeeze()
### Compute meridional average
tas_mofitposz = np.nanmean(tas_mofitpos, axis=2)
tas_mohitposz = np.nanmean(tas_mohitpos, axis=2)
tas_mofictposz = np.nanmean(tas_mofictpos, axis=2)
tas_mofitnegz = np.nanmean(tas_mofitneg, axis=2)
tas_mohitnegz = np.nanmean(tas_mohitneg, axis=2)
tas_mofictnegz = np.nanmean(tas_mofictneg, axis=2)
avez = [
tas_mofitposz, tas_mohitposz, tas_mofictposz, tas_mofitnegz,
tas_mohitnegz, tas_mofictnegz
]
print('\n*Completed: Finished procData function!')
return avez, lat
currenttime = currentmn + '_' + currentdy + '_' + currentyr
titletime = currentmn + '/' + currentdy + '/' + currentyr
print('\n' '----Plotting QBO profile - %s----' % titletime)
### Alott time series
year1 = 1900
year2 = 2000
years = np.arange(year1, year2 + 1, 1)
### Call arguments
varnames = ['U']
runnames = [r'HIT', 'CTLQ']
qbophase = ['pos', 'non', 'neg']
v = 0
### HIT -- Call function for surface temperature data from reach run
lat, lon, time, lev, uhitq = MO.readExperiAll('%s' % varnames[v], 'HIT',
'profile')
uhitq = np.asarray(uhitq)
uhitq[np.where(uhitq < -999)] = np.nan
### Create 2d array of latitude and longitude
monthq = [0, 1, 2, 8, 9, 10, 11]
latq = np.where((lat >= -5) & (lat <= 5))[0]
uhit = np.nanmean(uhitq[:, :, :, latq, :], axis=3)
uhitzonal = np.nanmean(uhit[:, :, :, :], axis=3)
### Calculate anomalies
meanu = np.nanmean(uhitzonal, axis=0)
anom = uhitzonal - meanu
ustack = np.reshape(
years = np.arange(year1, year2 + 1, 1)
### Call arguments
varnames = ['Z300']
runnames = [r'CIT', r'FSUB', r'FPOL']
experiments = [
r'\textbf{FSUB--CIT}', r'\textbf{FSUB--CIT}', r'\textbf{FSUB-CIT}',
r'\textbf{FPOL--CIT}', r'\textbf{FPOL--CIT}', r'\textbf{FPOL--CIT}'
]
qbophase = ['pos', 'non', 'neg']
period = 'N'
wv = 'Z300xwave2'
MASK = False
for v in range(len(varnames)):
### Call function for data from reach run
lat, lon, time, lev, tascit = MO.readExperiAll('%s' % varnames[v], 'CIT',
'surface')
lat, lon, time, lev, tasfsub = MOR.readExperiAllRegional(
'%s' % varnames[v], 'FSUB', 'surface')
lat, lon, time, lev, tasfpol = MOR.readExperiAllRegional(
'%s' % varnames[v], 'FPOL', 'surface')
### Create 2d array of latitude and longitude
lon2, lat2 = np.meshgrid(lon, lat)
### Read in QBO phases
filenamecitp = directorydata + 'CIT/monthly/QBO_%s_CIT.txt' % qbophase[0]
filenamecitno = directorydata + 'CIT/monthly/QBO_%s_CIT.txt' % qbophase[1]
filenamecitn = directorydata + 'CIT/monthly/QBO_%s_CIT.txt' % qbophase[2]
filenamecitp2 = directorydata2 + 'CIT/monthly/QBO_%s_CIT.txt' % qbophase[0]
filenamecitno2 = directorydata2 + 'CIT/monthly/QBO_%s_CIT.txt' % qbophase[1]
filenamecitn2 = directorydata2 + 'CIT/monthly/QBO_%s_CIT.txt' % qbophase[2]
titletime = currentmn + '/' + currentdy + '/' + currentyr
print('\n' '----Plotting QBO profile comparisons - %s----' % titletime)
### Alott time series
year1 = 1900
year2 = 2000
years = np.arange(year1,year2+1,1)
runnames = [r'HIT',r'FIT',r'FICT']
experiments = [r'\textbf{FIT--HIT}',r'\textbf{FIT--HIT}',r'\textbf{FIT--HIT}',
r'\textbf{FICT--HIT}',r'\textbf{FICT--HIT}',r'\textbf{FICT--HIT}']
qbophase = ['pos','non','neg']
period = 'N'
### Call function for vertical temperature data
lat,lon,time,lev,epy_h = MO.readExperiAll('EPY','HIT','profile')
lat,lon,time,lev,epz_h = MO.readExperiAll('EPZ','HIT','profile')
lat,lon,time,lev,div_h = MO.readExperiAll('DEPF','HIT','profile')
lat,lon,time,lev,epy_f = MO.readExperiAll('EPY','FIT','profile')
lat,lon,time,lev,epz_f = MO.readExperiAll('EPZ','FIT','profile')
lat,lon,time,lev,div_f = MO.readExperiAll('DEPF','FIT','profile')
lat,lon,time,lev,epy_fict = MO.readExperiAll('EPY','FICT','profile')
lat,lon,time,lev,epz_fict = MO.readExperiAll('EPZ','FICT','profile')
lat,lon,time,lev,div_fict = MO.readExperiAll('DEPF','FICT','profile')
### Separate per month
if period == 'N':
epy_moh = epy_h[:,-2,:,:]
epz_moh = epz_h[:,-2,:,:]
currenttime = currentmn + '_' + currentdy + '_' + currentyr
titletime = currentmn + '/' + currentdy + '/' + currentyr
print('\n' '----Plotting Heat Flux Comparison Regional - %s----' % titletime)
### Alott time series
year1 = 1800
year2 = 2000
years = np.arange(year1, year2 + 1, 1)
### Call arguments
varnames = 'RNET'
runnames = [r'CIT', r'FSUB', r'FPOL']
experiments = [r'\textbf{FSUB--CIT}', r'\textbf{FPOL--CIT}']
### Call function for rnet data from reach run
lat, lon, time, lev, tascit = MO.readExperiAll('%s' % varnames, 'CIT',
'surface')
lat, lon, time, lev, tasfsub = MOR.readExperiAllRegional(
'%s' % varnames, 'FSUB', 'surface')
lat, lon, time, lev, tasfpol = MOR.readExperiAllRegional(
'%s' % varnames, 'FPOL', 'surface')
### Create 2d array of latitude and longitude
lon2, lat2 = np.meshgrid(lon, lat)
### Calculate Oct-Mar
tascitmo = np.append(tascit[:, 9:, :, :], tascit[:, :3, :, :], axis=1)
tasfsubmo = np.append(tasfsub[:, 9:, :, :], tasfsub[:, :3, :, :], axis=1)
tasfpolmo = np.append(tasfpol[:, 9:, :, :], tasfpol[:, :3, :, :], axis=1)
### Take difference
fsubcit = np.nanmean(tasfsubmo - tascitmo, axis=0)
def readVar(varnames,runnames,qbophase,period):
for v in range(len(varnames)):
### Call function for surface temperature data from reach run
lat,lon,time,lev,tashit = MO.readExperiAll('%s' % varnames[v],'HIT',
'surface')
lat,lon,time,lev,tasfit = MO.readExperiAll('%s' % varnames[v],'FIT',
'surface')
lat,lon,time,lev,tasfict = MO.readExperiAll('%s' % varnames[v],'FIT',
'surface')
### Create 2d array of latitude and longitude
lon2,lat2 = np.meshgrid(lon,lat)
### Read in QBO phases
filenamehitp = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitn = directorydata + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
filenamehitp2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[0]
filenamehitn2 = directorydata2 + 'HIT/monthly/QBO_%s_HIT.txt' % qbophase[2]
pos_hit = np.append(np.genfromtxt(filenamehitp,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitp2,unpack=True,usecols=[0],dtype='int')+101)
neg_hit = np.append(np.genfromtxt(filenamehitn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitn2,unpack=True,usecols=[0],dtype='int')+101)
filenamefictp = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictn = directorydata + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
filenamefictp2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[0]
filenamefictn2 = directorydata2 + 'FICT/monthly/QBO_%s_FICT.txt' % qbophase[2]
pos_fict = np.append(np.genfromtxt(filenamefictp,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictp2,unpack=True,usecols=[0],dtype='int')+101)
neg_fict = np.append(np.genfromtxt(filenamefictn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictn2,unpack=True,usecols=[0],dtype='int')+101)
### Concatonate runs
runs = [tashit,tasfit,tasfict]
### Separate per periods (ON,DJ,FM)
if period == 'ON':
tas_mo = np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,9:11,:,:],axis=1)
elif period == 'DJ':
tas_mo = np.empty((3,tashit.shape[0]-1,tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i],tas_mo[i] = UT.calcDecJan(runs[i],runs[i],lat,
lon,'surface',1)
elif period == 'FM':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,1:3,:,:],axis=1)
elif period == 'DJF':
tas_mo= np.empty((3,tashit.shape[0]-1,tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i],tas_mo[i] = UT.calcDecJanFeb(runs[i],runs[i],lat,
lon,'surface',1)
elif period == 'M':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,2,:,:]
elif period == 'D':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,-1,:,:]
elif period == 'N':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,-2,:,:]
elif period == 'ND':
tas_mo= np.empty((3,tashit.shape[0],tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = np.nanmean(runs[i][:,-2:,:,:],axis=1)
else:
print(ValueError('Wrong period selected!'))
### Composite by QBO phase
vhitpos = tas_mo[0][pos_hit,:,:]
vfictpos = tas_mo[2][pos_fict,:,:]
vhitneg = tas_mo[0][neg_hit,:,:]
vfictneg = tas_mo[2][neg_fict,:,:]
### Compute comparisons for months - taken ensemble average
ficthitpos = np.nanmean(vfictpos - vhitpos,axis=0)
ficthitneg = np.nanmean(vfictneg - vhitneg,axis=0)
return vhitpos,vfictpos,vhitneg,vfictneg,ficthitpos,ficthitneg,lat,lon
