### Alott time series
yearmin = 2006
yearmax = 2080
years = np.arange(yearmin, yearmax + 1, 1)
months = [
r'Jan', r'Feb', r'Mar', r'Apr', r'May', r'Jun', r'Jul', r'Aug', r'Sep',
r'Oct', r'Nov', r'Dec'
]
yearslens = np.arange(1920, 2080 + 1, 1)
yearsclimo = np.arange(1981, 2010 + 1, 1)
ense = ['02','03','04','05','06','07','08','09'] + \
map(str,np.arange(10,36,1)) + map(str,np.arange(101,106,1))
### Read in functions
sitqq, lat2, lon2 = lens.readLENSEnsemble(directorydatal, 0.15, 'rcp85')
lats = np.unique(lat2)
lons = np.unique(lon2)
trendd = True
if trendd == True:
def deTrend(y):
x = np.arange(y.shape[0])
slopes = np.empty((y.shape[1], y.shape[2]))
intercepts = np.empty((y.shape[1], y.shape[2]))
for i in xrange(y.shape[1]):
for j in xrange(y.shape[2]):
mask = np.isfinite(y[:, i, j])
yy = y[:, i, j]
### Alott time series
yearmin = 1979
yearmax = 2015
years = np.arange(yearmin, yearmax + 1, 1)
months = [
r'Jan', r'Feb', r'Mar', r'Apr', r'May', r'Jun', r'Jul', r'Aug', r'Sep',
r'Oct', r'Nov', r'Dec'
]
yearslens = np.arange(1920, 2080 + 1, 1)
yearsclimo = np.arange(1981, 2010 + 1, 1)
ense = ['02','03','04','05','06','07','08','09'] + \
map(str,np.arange(10,36,1)) + map(str,np.arange(101,106,1))
### Read in functions
sithq, lat2, lon2 = lens.readLENSEnsemble(directorydatal, 0.15, 'historical')
sitf, lat2, lon2 = lens.readLENSEnsemble(directorydatal, 0.15, 'rcp85')
lats = np.unique(lat2)
lons = np.unique(lon2)
sitalln = np.append(sithq, sitf, axis=1)
yearsq = np.where((yearslens >= 1979) & (yearslens <= 2015))[0]
sitallq = sitalln[:, yearsq, :, :, :]
def readPIOMAS(directorydata, threshold):
files = 'piomas_regrid_sit_LENS_19792015.nc'
filename = directorydata + files
data = Dataset(filename)
titletime = currentmn + '/' + currentdy + '/' + currentyr
print '\n' '----Plot LENS Correlations - %s----' % titletime
### Alott time series
year1 = 1920
year2 = 2080
years = np.arange(year1, year2 + 1, 1)
months = [
r'Jan', r'Feb', r'Mar', r'Apr', r'May', r'Jun', r'Jul', r'Aug', r'Sep',
r'Oct', r'Nov', r'Dec'
]
ense = ['02','03','04','05','06','07','08','09'] + \
map(str,np.arange(10,36,1)) + map(str,np.arange(101,106,1))
#### Read in functions
sith, lats, lons = lens.readLENSEnsemble(directorydataSIT, 0.15, 'historical')
sitf, lats, lons = lens.readLENSEnsemble(directorydataSIT, 0.15, 'rcp85')
### Read T2M
data = Dataset(directorydataN + 'lens_regrid_T2M_19202080.nc')
tasall = data.variables['T2M'][:]
data.close()
### Combine SIT periods
sitall = np.append(sith, sitf, axis=1)
#### 2D lat/lon arrays
lons, lats = np.meshgrid(lons, lats)
### Plot figure
plt.rc('text', usetex=True)
map(str,np.arange(10,36,1)) + map(str,np.arange(101,106,1))
### Alott time series
year1 = 1920
year2 = 2080
months = [r'Jan',r'Feb',r'Mar',r'Apr',r'May',r'Jun',r'Jul',r'Aug',
r'Sep',r'Oct',r'Nov',r'Dec']
yearslens = np.arange(year1,year2+1,1)
yearsclimo = np.arange(1981,2010+1,1)
### Select variable
variable = 'SLP'
### Read in functions
var,lats1,lons1 = LV.readLENSEnsemble(directorydataL,variable)
sith,lats2,lons2 = lens.readLENSEnsemble(directorydataSIT,0.15,'historical')
### 2D lat/lon arrays
lons2,lats2 = np.meshgrid(lons2,lats2)
lons1,lats1 = np.meshgrid(lons1,lats1)
###########################################################################
###########################################################################
###########################################################################
### Regrid
def regrid(lat1,lon1,lat2,lon2,var,years):
"""
Interpolated on selected grid.
[year,month,lat,lon]
"""
### Mask out threshold values
if threshold == 'None':
sitp[np.where(sitp < 0)] = np.nan
sitp[np.where(sitp > 12)] = np.nan
else:
sitp[np.where(sitp < threshold)] = np.nan
sitp[np.where(sitp < 0)] = np.nan
sitp[np.where(sitp > 12)] = np.nan
print 'Completed: Read PIOMAS SIT!'
return sitp
#### Call functions
sit, lats, lons = lens.readLENSEnsemble(directorydatal, 0.15, 'rcp85')
lons, lats = np.meshgrid(lons, lats)
sitp = readPIOMAS(directorydatap, 0.15)
def weightThick(var, lats, types):
"""
Area weights sit array 5d [ens,year,month,lat,lon] into [ens,year,month]
"""
if types == 'lens':
sityr = np.empty((var.shape[0], var.shape[1], var.shape[2]))
for ens in xrange(var.shape[0]):
for i in xrange(var.shape[1]):
for j in xrange(var.shape[2]):
