def readMHF100():
### Call functions for MHF data at 100 hPa
lat, lon, time, lev, varhit = DO.readMeanExperiAll('MHF100', 'HIT',
'profile')
lat, lon, time, lev, varfit = DO.readMeanExperiAll('MHF100', 'FIT',
'profile')
lat, lon, time, lev, varFIT = DO.readMeanExperiAll('MHF100', 'FIT',
'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') +
100)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int') +
100)
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') +
100)
neg_FIT = np.append(
np.genfromtxt(filenameFITn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenameFITn2, unpack=True, usecols=[0], dtype='int') +
100)
### Concatonate runs
var_mo = [varhit, varfit, varFIT]
### Composite by QBO phase and take zonal mean
hitpos = var_mo[0][pos_hit, :].squeeze()
FITpos = var_mo[2][pos_FIT, :].squeeze()
hitneg = var_mo[0][neg_hit, :].squeeze()
FITneg = var_mo[2][neg_FIT, :].squeeze()
### Take zonal mean
hitpos = np.nanmean(hitpos, axis=2)
FITpos = np.nanmean(FITpos, axis=2)
hitneg = np.nanmean(hitneg, axis=2)
FITneg = np.nanmean(FITneg, axis=2)
### Store data in a list
diffruns = [FITpos, FITneg, hitpos, hitneg]
return diffruns
def readZ30(varnames):
lat,lon,time,lev,tashit = DO.readMeanExperiAll('%s' % varnames,
'HIT','surface')
lat,lon,time,lev,tasfict = DO.readMeanExperiAll('%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]
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')+100)
neg_hit = np.append(np.genfromtxt(filenamehitn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitn2,unpack=True,usecols=[0],dtype='int')+100)
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')+100)
neg_fict = np.append(np.genfromtxt(filenamefictn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictn2,unpack=True,usecols=[0],dtype='int')+100)
tas_mo = [tashit,tasfict]
### 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 comparisons for months - select region
if varnames == 'Z30':
latq = np.where((lat >=65) & (lat <=90))[0]
fictpos = tas_mofictpos
fictneg = tas_mofictneg
fictpos = fictpos[:,:,latq]
fictneg = fictneg[:,:,latq]
lat2s = lat2[latq,:]
fictpos = UT.calc_weightedAve(fictpos,lat2s)
fictneg = UT.calc_weightedAve(fictneg,lat2s)
hitpos = tas_mohitpos
hitneg = tas_mohitneg
hitpos = hitpos[:,:,latq]
hitneg = hitneg[:,:,latq]
hitpos = UT.calc_weightedAve(hitpos,lat2s)
hitneg = UT.calc_weightedAve(hitneg,lat2s)
diffruns = [fictpos.squeeze(),fictneg.squeeze(),hitpos.squeeze(),hitneg.squeeze()]
return diffruns
year1 = 1800
year2 = 2000
years = np.arange(year1,year2+1,1)
### Add parameters
MASK = False
N=30 # days
varnames = ['U200']
runnames = [r'HIT',r'FIT',r'FICT']
qbophase = ['pos','non','neg']
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}']
### Call functions for variable profile data for polar cap
for v in range(len(varnames)):
lat,lon,time,lev,varhit = DO.readMeanExperiAll('%s' % varnames[v],
'HIT','surface')
lat,lon,time,lev,varfit = DO.readMeanExperiAll('%s' % varnames[v],
'FIT','surface')
lat,lon,time,lev,varfict = DO.readMeanExperiAll('%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]
lev = data.variables['level'][:]
lat = data.variables['latitude'][:]
lon = data.variables['longitude'][:]
var[i, :, :] = data.variables['%s' % varid][:]
data.close()
else:
print(ValueError('Wrong height - (surface or profile!)!'))
print('Completed: Read data for *%s%s* : %s!' %
(experi[:4], i + 1, varid))
print('Completed: Read members 1-200!')
print('\n*Completed: Finished readCTLQ function!')
return lat, lon, time, lev, var
### Call functions for variable profile data for polar cap
lat, lon, time, lev, tashit = DO.readMeanExperiAll('%s' % varnames, 'HIT',
'profile')
lat, lon, time, lev, tasfit = DO.readMeanExperiAll('%s' % varnames, 'FIT',
'profile')
lat, lon, time, lev, tasfict = DO.readMeanExperiAll('%s' % varnames, 'FICT',
'profile')
lat, lon, time, lev, tasfic = DO.readMeanExperiAll('%s' % varnames, 'FIC',
'profile')
lat, lon, time, lev, tascit = DO.readMeanExperiAll('%s' % varnames, 'CIT',
'profile')
lat, lon, time, lev, tasfsub = DOR.readMeanExperiAllRegional(
'%s' % varnames, 'FSUB', 'profile')
lat, lon, time, lev, tasfpol = DOR.readMeanExperiAllRegional(
'%s' % varnames, 'FPOL', 'profile')
lat, lon, time, lev, tasctlq = readCTLQ('%s' % varnames, 'CTLQ', 'profile')
### Create 2d array of latitude and longitude
def readWAF(varnames, runnames, experiments, qbophase):
"""
Function reads in WAF data for listed experiments
Parameters
----------
varnames : string
variable to download
runnames : list of strings
model experiments to read in
experiments : list of strings
model simulations to compare
qbophase : list of strings
list of qbo phases
Returns
-------
diffruns : list of arrays
arrays for each experiment variable
pruns : list of arrays
arrays of p-values for each experiment variable
lev : 1d array
leves
Usage
-----
diffruns,pruns,lev = readWAF(varnames,runnames,experiments,qbophase)
"""
print('\n>>> Using readWAF function!')
### Call functions for variable profile data for polar cap
lat, lon, time, lev, varhit = DO.readMeanExperiAll('%s' % varnames, 'HIT',
'profile3')
lat, lon, time, lev, varfict = DO.readMeanExperiAll(
'%s' % varnames, 'FICT', 'profile3')
### 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') +
100)
non_hit = np.append(
np.genfromtxt(filenamehitno, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitno2, unpack=True, usecols=[0], dtype='int') +
100)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int') +
100)
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') +
100)
non_fict = np.append(
np.genfromtxt(filenamefictno, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictno2, unpack=True, usecols=[0], dtype='int') +
100)
neg_fict = np.append(
np.genfromtxt(filenamefictn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictn2, unpack=True, usecols=[0], dtype='int') +
100)
### Concatonate runs
var_mo = [varhit, varfict]
### Save memory
del varhit
del varfict
### Composite by QBO phase
var_mohitpos = var_mo[0][pos_hit, :]
var_mofictpos = var_mo[1][pos_fict, :]
var_mohitnon = var_mo[0][non_hit, :]
var_mofictnon = var_mo[1][non_fict, :]
var_mohitneg = var_mo[0][neg_hit, :]
var_mofictneg = var_mo[1][neg_fict, :]
### Compute comparisons for months - taken ensemble average
ficthitpos = np.nanmean(var_mofictpos - var_mohitpos, axis=0)
ficthitnon = np.nanmean(var_mofictnon - var_mohitnon, axis=0)
ficthitneg = np.nanmean(var_mofictneg - var_mohitneg, axis=0)
ficthitposa = UT.calc_weightedAve(ficthitpos, lat2)
ficthitnona = UT.calc_weightedAve(ficthitnon, lat2)
ficthitnega = UT.calc_weightedAve(ficthitneg, lat2)
diffruns = [ficthitposa, ficthitnona, ficthitnega]
### Calculate significance for days
stat_FICTHITpos, pvalue_FICTHITpos = UT.calc_indttest(
var_mo[1][pos_fict, :], var_mo[0][pos_hit, :])
stat_FICTHITnon, pvalue_FICTHITnon = UT.calc_indttest(
var_mo[1][non_fict, :], var_mo[0][non_hit, :])
stat_FICTHITneg, pvalue_FICTHITneg = UT.calc_indttest(
var_mo[1][neg_fict, :], var_mo[0][neg_hit, :])
pvalue_FICTHITposa = UT.calc_weightedAve(pvalue_FICTHITpos, lat2)
pvalue_FICTHITnona = UT.calc_weightedAve(pvalue_FICTHITnon, lat2)
pvalue_FICTHITnega = UT.calc_weightedAve(pvalue_FICTHITneg, lat2)
pruns = [pvalue_FICTHITposa, pvalue_FICTHITnona, pvalue_FICTHITnega]
print('\n*Completed: Finished readWAF function!')
return diffruns, pruns, lev
def readVariables(varnames,period,region):
lat,lon,time,lev,tashit = DO.readMeanExperiAll('%s' % varnames,
'HIT','surface')
lat,lon,time,lev,tasfict = DO.readMeanExperiAll('%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')+100)
non_hit = np.append(np.genfromtxt(filenamehitno,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitno2,unpack=True,usecols=[0],dtype='int')+100)
neg_hit = np.append(np.genfromtxt(filenamehitn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitn2,unpack=True,usecols=[0],dtype='int')+100)
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')+100)
non_fict = np.append(np.genfromtxt(filenamefictno,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictno2,unpack=True,usecols=[0],dtype='int')+100)
neg_fict = np.append(np.genfromtxt(filenamefictn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictn2,unpack=True,usecols=[0],dtype='int')+100)
### Concatonate runs
runs = [tashit,tasfict]
### Separate per periods (ON,DJ,FM)
if period == 'D':
tas_mo= np.empty((2,tashit.shape[0],90,tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,60:150,:,:]
# tas_mo[i] = np.nanmean(runs[i][:,-2:,:,:],axis=1)
else:
ValueError('Wrong period selected!')
### 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 region == 'Atlantic':
lonq = np.append(np.where((lon >=310) & (lon <=360))[0],
np.where((lon >=0) & (lon <=20))[0],axis=0)
elif region == 'Pacific':
lonq = np.where((lon >= 120) & (lon <= 170))[0]
ficthitpos = tas_mofictpos[:,:,:,lonq]
ficthitnon = tas_mofictnon[:,:,:,lonq]
ficthitneg = tas_mofictneg[:,:,:,lonq]
### Calculate upper lats
latqu = np.where((lat >=60) & (lat <=90))[0]
ficthitposu = ficthitpos[:,:,latqu]
ficthitnonu = ficthitnon[:,:,latqu]
ficthitnegu = ficthitneg[:,:,latqu]
lat2squ = lat2[latqu,:]
lat2su = lat2squ[:,lonq]
ficthitposuu = UT.calc_weightedAve(ficthitposu,lat2su)
ficthitnonuu = UT.calc_weightedAve(ficthitnonu,lat2su)
ficthitneguu = UT.calc_weightedAve(ficthitnegu,lat2su)
### Calculate lower lats
latql = np.where((lat >=20) & (lat <=50))[0]
ficthitposl = ficthitpos[:,:,latql]
ficthitnonl = ficthitnon[:,:,latql]
ficthitnegl = ficthitneg[:,:,latql]
lat2sql = lat2[latql,:]
lat2sl = lat2sql[:,lonq]
ficthitposll = UT.calc_weightedAve(ficthitposl,lat2sl)
ficthitnonll = UT.calc_weightedAve(ficthitnonl,lat2sl)
ficthitnegll = UT.calc_weightedAve(ficthitnegl,lat2sl)
### Calculate Zonal Index (Z500u - Z500l)
zdiffpos = ficthitposll - ficthitposuu
zdiffnon = ficthitnonll - ficthitnonuu
zdiffneg = ficthitnegll - ficthitneguu
diffruns_fict = [zdiffpos,zdiffnon,zdiffneg]
###########################################################################
### Calculate for HIT
hitpos = tas_mohitpos[:,:,:,lonq]
hitnon = tas_mohitnon[:,:,:,lonq]
hitneg = tas_mohitneg[:,:,:,lonq]
### Calculate upper lats
latqu = np.where((lat >=60) & (lat <=90))[0]
hitposu = hitpos[:,:,latqu]
hitnonu = hitnon[:,:,latqu]
hitnegu = hitneg[:,:,latqu]
lat2squ = lat2[latqu,:]
lat2su = lat2squ[:,lonq]
hitposuu = UT.calc_weightedAve(hitposu,lat2su)
hitnonuu = UT.calc_weightedAve(hitnonu,lat2su)
hitneguu = UT.calc_weightedAve(hitnegu,lat2su)
### Calculate lower lats
latql = np.where((lat >=20) & (lat <=50))[0]
hitposl = hitpos[:,:,latql]
hitnonl = hitnon[:,:,latql]
hitnegl = hitneg[:,:,latql]
lat2sql = lat2[latql,:]
lat2sl = lat2sql[:,lonq]
hitposll = UT.calc_weightedAve(hitposl,lat2sl)
hitnonll = UT.calc_weightedAve(hitnonl,lat2sl)
hitnegll = UT.calc_weightedAve(hitnegl,lat2sl)
### Calculate Zonal Index (Z500u - Z500l)
zdiffposh = hitposll - hitposuu
zdiffnonh = hitnonll - hitnonuu
zdiffnegh = hitnegll - hitneguu
diffruns_hit = [zdiffposh,zdiffnonh,zdiffnegh]
return diffruns_fict,diffruns_hit,lat,lon
years = np.arange(year1, year2 + 1, 1)
### Add parameters
N = 7 # days
MASK = True
varnames = ['GEOP', 'TEMP', 'U', 'V']
runnames = [r'CIT', r'FSUB', r'FPOL']
qbophase = ['pos', 'non', 'neg']
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}'
]
### Call functions for variable profile data for polar cap
for v in range(len(varnames)):
lat, lon, time, lev, varcit = DO.readMeanExperiAll('%s' % varnames[v],
'CIT', 'profile')
lat, lon, time, lev, varfsub = DOR.readMeanExperiAllRegional(
'%s' % varnames[v], 'FSUB', 'profile')
lat, lon, time, lev, varfpol = DOR.readMeanExperiAllRegional(
'%s' % varnames[v], 'FPOL', 'profile')
### 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]
def readDataGEOP(varnames):
lat, lon, time, lev, varhit = DO.readMeanExperiAll('%s' % varnames, 'HIT',
'profileregional')
lat, lon, time, lev, varfict = DO.readMeanExperiAll(
'%s' % varnames, 'FICT', 'profileregional')
### 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') +
100)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int') +
100)
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') +
100)
neg_fict = np.append(
np.genfromtxt(filenamefictn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamefictn2, unpack=True, usecols=[0], dtype='int') +
100)
### Concatonate runs
var_mo = [varhit, varfict]
### Composite by QBO phase
var_mohitpos = var_mo[0][pos_hit, :]
var_mofictpos = var_mo[1][pos_fict, :]
var_mohitneg = var_mo[0][neg_hit, :]
var_mofictneg = var_mo[1][neg_fict, :]
### Compute comparisons for months - taken ensemble average
ficthitpos = np.nanmean(var_mofictpos - var_mohitpos, axis=0)
ficthitneg = np.nanmean(var_mofictneg - var_mohitneg, axis=0)
diffruns = [ficthitpos, ficthitneg]
### Calculate significance
stat_FICTHITpos, pvalue_FICTHITpos = UT.calc_indttest(
var_mo[1][pos_fict, :], var_mo[0][pos_hit, :])
stat_FICTHITneg, pvalue_FICTHITneg = UT.calc_indttest(
var_mo[1][neg_fict, :], var_mo[0][neg_hit, :])
pruns = [pvalue_FICTHITpos, pvalue_FICTHITneg]
return diffruns, pruns, lev
def readVariables(varnames,period,location):
lat,lon,time,lev,tashit = DO.readMeanExperiAll('%s' % varnames,
'HIT','surface')
lat,lon,time,lev,tasfict = DO.readMeanExperiAll('%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')+100)
non_hit = np.append(np.genfromtxt(filenamehitno,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitno2,unpack=True,usecols=[0],dtype='int')+100)
neg_hit = np.append(np.genfromtxt(filenamehitn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamehitn2,unpack=True,usecols=[0],dtype='int')+100)
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')+100)
non_fict = np.append(np.genfromtxt(filenamefictno,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictno2,unpack=True,usecols=[0],dtype='int')+100)
neg_fict = np.append(np.genfromtxt(filenamefictn,unpack=True,usecols=[0],dtype='int'),
np.genfromtxt(filenamefictn2,unpack=True,usecols=[0],dtype='int')+100)
### Concatonate runs
runs = [tashit,tasfict]
### Separate per periods (ON,DJ,FM)
if period == 'D':
tas_mo= np.empty((2,tashit.shape[0],31,tashit.shape[2],tashit.shape[3]))
for i in range(len(runs)):
tas_mo[i] = runs[i][:,90:121,:,:]
# 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!')
### 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 readJet(varnames, qbophase, location):
### Call functions for variable profile data for polar cap
for v in range(len(varnames)):
lat, lon, time, lev, varhit = DO.readMeanExperiAll(
'%s' % varnames[v], 'HIT', 'surface')
lat, lon, time, lev, varfict = DO.readMeanExperiAll(
'%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]
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')
+ 100)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int')
+ 100)
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') + 100)
neg_fict = np.append(
np.genfromtxt(filenamefictn, unpack=True, usecols=[0],
dtype='int'),
np.genfromtxt(
filenamefictn2, unpack=True, usecols=[0], dtype='int') + 100)
### Concatonate runs
var_mo = [varhit, varfict]
### Composite by QBO phase
var_mohitpos = var_mo[0][pos_hit, :]
var_mofictpos = var_mo[1][pos_fict, :]
var_mohitneg = var_mo[0][neg_hit, :]
var_mofictneg = var_mo[1][neg_fict, :]
### Average over longitude
if location == 'Atlantic':
lonq = np.append(np.where((lon >= 310) & (lon <= 360))[0],
np.where((lon >= 0) & (lon <= 20))[0],
axis=0)
if location == 'Pacific':
lonq = np.where((lon >= 120) & (lon <= 220))[0]
var_mohitposz = np.nanmean(var_mohitpos[:, :, :, lonq], axis=3)
var_mofictposz = np.nanmean(var_mofictpos[:, :, :, lonq], axis=3)
var_mohitnegz = np.nanmean(var_mohitneg[:, :, :, lonq], axis=3)
var_mofictnegz = np.nanmean(var_mofictneg[:, :, :, lonq], axis=3)
### Save climatologies
climo = [var_mohitnegz, var_mohitposz, np.zeros(var_mohitposz.shape)]
### Compute comparisons for months - taken ensemble average
ficthitpos = np.nanmean(var_mofictposz - var_mohitposz, axis=0)
ficthitneg = np.nanmean(var_mofictnegz - var_mohitnegz, axis=0)
diffruns = [ficthitneg, ficthitpos, ficthitneg - ficthitpos]
### Smoothing for statistics
varstat = [
var_mohitnegz, var_mohitposz, var_mofictnegz, var_mofictposz
]
stats = []
for yr in range(len(varstat)):
varqstat = np.empty((varstat[yr].shape[0], 212 - (N - 1), 96))
for ph in range(var_mohitnegz.shape[0]):
for s in range(var_mohitnegz.shape[2]):
varqstat[ph, :, s] = np.convolve(varstat[yr][ph, :, s],
np.ones((N, )) / N,
mode='valid')
empty = np.empty(
(varstat[yr].shape[0], N - 1, varstat[yr].shape[2]))
empty[:] = np.nan
varnstat = np.append(np.asarray(varqstat), empty, axis=1)
stats.append(varnstat)
### Calculate significance
stat_FICTHITpos, pvalue_FICTHITpos = UT.calc_indttest(
stats[3], stats[1])
stat_FICTHITneg, pvalue_FICTHITneg = UT.calc_indttest(
stats[2], stats[0])
pruns = [
pvalue_FICTHITneg, pvalue_FICTHITpos,
np.zeros(pvalue_FICTHITneg.shape)
]
### Delete files for memory
del var_mo
del varhit
del varfict
return lat, lon, diffruns, climo, pruns
titletime = currentmn + '/' + currentdy + '/' + currentyr
print('\n' '----Calculating Vorticity for SeaIceQBO - %s----' % titletime)
#### Alott time series
year1 = 1800
year2 = 2000
years = np.arange(year1, year2 + 1, 1)
### Add parameters
varnames = ['U', 'V']
runnames = [r'FICT']
qbophase = ['pos', 'non', 'neg']
experiments = [r'\textbf{FICT--HIT}']
### Call functions for variable profile data for polar cap
lat, lon, time, lev, varuu = DO.readMeanExperiAll('U', 'FICT', 'profile2')
lat, lon, time, lev, varvv = DO.readMeanExperiAll('V', 'FICT', 'profile2')
lat, lon, time, lev, hituu = DO.readMeanExperiAll('U', 'HIT', 'profile2')
lat, lon, time, lev, hitvv = DO.readMeanExperiAll('V', 'HIT', 'profile2')
### 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]
def readVariables(varnames):
lat, lon, time, lev, tashit = DO.readMeanExperiAll('%s' % varnames, 'HIT',
'surface')
lat, lon, time, lev, tasFIT = DO.readMeanExperiAll('%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') +
100)
neg_hit = np.append(
np.genfromtxt(filenamehitn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenamehitn2, unpack=True, usecols=[0], dtype='int') +
100)
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') +
100)
neg_FIT = np.append(
np.genfromtxt(filenameFITn, unpack=True, usecols=[0], dtype='int'),
np.genfromtxt(filenameFITn2, unpack=True, usecols=[0], dtype='int') +
100)
tas_mo = [tashit, tasFIT]
### Composite by QBO phase
tas_mohitpos = tas_mo[0][pos_hit, :, :, :]
tas_moFITpos = tas_mo[1][pos_FIT, :, :, :]
tas_mohitneg = tas_mo[0][neg_hit, :, :, :]
tas_moFITneg = tas_mo[1][neg_FIT, :, :, :]
### Compute comparisons for months - select region
if varnames == 'SLP':
lonq = np.where((lon >= 80) & (lon <= 120))[0]
FITpos = tas_moFITpos[:, :, :, lonq]
FITneg = tas_moFITneg[:, :, :, lonq]
latq = np.where((lat >= 40) & (lat <= 65))[0]
FITpos = FITpos[:, :, latq]
FITneg = FITneg[:, :, latq]
lat2sq = lat2[latq, :]
lat2s = lat2sq[:, lonq]
FITpos = UT.calc_weightedAve(FITpos, lat2s)
FITneg = UT.calc_weightedAve(FITneg, lat2s)
hitpos = tas_mohitpos[:, :, :, lonq]
hitneg = tas_mohitneg[:, :, :, lonq]
hitpos = hitpos[:, :, latq]
hitneg = hitneg[:, :, latq]
hitpos = UT.calc_weightedAve(hitpos, lat2s)
hitneg = UT.calc_weightedAve(hitneg, lat2s)
elif varnames == 'T1000':
lonq = np.where((lon >= 70) & (lon <= 140))[0]
FITpos = tas_moFITpos[:, :, :, lonq]
FITneg = tas_moFITneg[:, :, :, lonq]
latq = np.where((lat >= 35) & (lat <= 60))[0]
FITpos = FITpos[:, :, latq]
FITneg = FITneg[:, :, latq]
lat2sq = lat2[latq, :]
lat2s = lat2sq[:, lonq]
FITpos = UT.calc_weightedAve(FITpos, lat2s) - 273.15
FITneg = UT.calc_weightedAve(FITneg, lat2s) - 273.15
hitpos = tas_mohitpos[:, :, :, lonq]
hitneg = tas_mohitneg[:, :, :, lonq]
hitpos = hitpos[:, :, latq]
hitneg = hitneg[:, :, latq]
hitpos = UT.calc_weightedAve(hitpos, lat2s) - 273.15
hitneg = UT.calc_weightedAve(hitneg, lat2s) - 273.15
diffruns = [
FITpos.squeeze(),
FITneg.squeeze(),
hitpos.squeeze(),
hitneg.squeeze()
]
return diffruns
year2 = 2000
years = np.arange(year1, year2 + 1, 1)
### Add parameters
MASK = False
varnames = ['MHF100']
runnames = [r'HIT', r'FIT', r'FICT']
qbophase = ['pos', 'non', 'neg']
phases = ['QBO-W', 'QBO-N', 'QBO-E']
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}'
]
### Call functions for MHF data at 100 hPa
lat, lon, time, lev, varhit = DO.readMeanExperiAll('MHF100', 'HIT', 'profile')
lat, lon, time, lev, varfit = DO.readMeanExperiAll('MHF100', 'FIT', 'profile')
lat, lon, time, lev, varfict = DO.readMeanExperiAll('MHF100', '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(
