nt = sst.shape[0]
lons[0] = 0
nlat = len(lats)
nlon = len(lons)
t = sst.getTime().asRelativeTime("months since 1980")
t = np.array([x.value for x in t])
t = 1980 + t/12.
tyears = np.arange(np.ceil(t[0]), np.round(t[-1]))
#initial/final years for base period
baseti = 0
basetf = 10
sst_an = an_ave(sst)
#CRE_surf_an = an_ave(thf)
#ps_an = an_ave(ps)
field_an = an_ave(field)
if fsave == 'qvdiffCC':
dqstardt = np.repeat(dqstardt[np.newaxis,...],sst_an.shape[0],axis=0)
meanRH10m = np.repeat(meanRH10m[np.newaxis,...],sst_an.shape[0],axis=0)
field_an = field_an - np.multiply(meanRH10m/100., an_ave(t10m))
field_an = 1e3*np.multiply(dqstardt, field_an)
#detrend annual fields instead of monthly? prevents thf from blowing up for some reason...
#if detr:
# sst_an, params = detrend(sst_an)
grid = cdms2.createGenericGrid(lats, lons)
#horizontally interpolate SST to coarser 3D field grid
sst = sst.regrid(grid, regridTool="esmf", regridMethod="linear")
t = sst.getTime().asRelativeTime("months since 1980")
t = np.array([x.value for x in t])
t = 1980 + t / 12.
tyears = np.arange(np.ceil(t[0]), np.round(t[-1]))
#initial/final years for base period
baseti = 0
basetf = 10
sst = an_ave(sst)
#CRE_surf_an = an_ave(thf)
#ps_an = an_ave(ps)
field = an_ave(field)
nt = sst.shape[0]
#detrend annual fields instead of monthly? prevents thf from blowing up for some reason...
if detr:
sst, params = detrend_separate(sst)
#ps_an, params = detrend_separate(ps_an)
field, params = detrend_separate(field)
sst_globe_an = spatial_ave(sst, lats)
field_globe_an = spatial_ave(field, lats)
grid = cdms2.createGenericGrid(lats, lons)
#horizontally interpolate SST to coarser 3D field grid
sst = sst.regrid(grid, regridTool="esmf", regridMethod="linear")
t = sst.getTime().asRelativeTime("months since 1980")
t = np.array([x.value for x in t])
t = 1980 + t / 12.
tyears = np.arange(np.ceil(t[0]), np.round(t[-1]))
#initial/final years for base period
baseti = 0
basetf = 10
sst = an_ave(sst)
#CRE_surf_an = an_ave(thf)
#ps_an = an_ave(ps)
field = an_ave(field)
ctfield = an_ave(ctfield)
nt = sst.shape[0]
#detrend annual fields instead of monthly? prevents thf from blowing up for some reason...
if detr:
sst, params = detrend_separate(sst)
#ps_an, params = detrend_separate(ps_an)
field, params = detrend_separate(field)
ctfield, params, detrend_separate(ctfield)
sst_globe_an = spatial_ave(sst, lats)
nt = sst.shape[0]
lons[0] = 0
nlat = len(lats)
nlon = len(lons)
t = sst.getTime().asRelativeTime("months since 1980")
t = np.array([x.value for x in t])
t = 1980 + t/12.
tyears = np.arange(np.ceil(t[0]), np.round(t[-1]))
#initial/final years for base period
baseti = 0
basetf = 10
sst_an = an_ave(sst)
#CRE_surf_an = an_ave(thf)
#ps_an = an_ave(ps)
field_an = an_ave(field)
#detrend annual fields instead of monthly? prevents thf from blowing up for some reason...
if detr:
sst_an, params = detrend_separate(sst_an)
#ps_an, params = detrend_separate(ps_an)
field_an, params = detrend_separate(field_an)
#CHANGE THIS TO MODIFY LAG FOR CALCULATING CORRELATIONS
ltlag = 8
stlag = 1
lagmax=11
nlon = len(lons)
t = sst.getTime().asRelativeTime("months since 1980")
t = np.array([x.value for x in t])
t = 1980 + t / 12.
tyears = np.arange(np.ceil(t[0]), np.round(t[-1]))
#bounds for AMO (AMOmid in O'Reilly 2016 is defined between 40 N and 60 N, Gulev. et al. 2013 defined between 30 N and 50 N)
latbounds = [0, 60]
#initial/final indices for base period
baseti = 0
basetf = 10
sst_an = an_ave(sst)
thf_an = an_ave(thf)
ps_an = an_ave(ps)
#detrend annual fields instead of monthly? prevents thf from blowing up for some reason...
if detr:
sst_an, params = detrend_separate(sst_an)
ps_an, params = detrend_separate(ps_an)
thf_an, params = detrend_separate(thf_an)
AMO, sstanom_globe_an, sstanom_na_an = calc_NA_globeanom(
sst_an, latbounds, lats, lons, baseti, basetf)
NAthf2, thfanom_globe_an, thfanom_na_an = calc_NA_globeanom(
thf_an, latbounds, lats, lons, baseti, basetf)
#thf blows up after detrending, need to mask values. Why doesn't this work for plotting later?
fnames = glob.glob(fin + 'era_*new.nc')
for fname in fnames:
fthf = cdms2.open(fname)
lhf = fthf('slhf')
lhf = lhf / (12 * 3600)
shf = fthf('sshf')
#sshf is accumulated
shf = shf / (12 * 3600)
thf = shf + lhf
thf = thf.subRegion(latitude=(minlat, maxlat), longitude=(minlon, maxlon))
lats = thf.getLatitude()[:]
thf_globe_ave = spatial_ave(thf, lats)
t = lhf.getTime().asRelativeTime("hours since 1900")
t = np.array([x.value for x in t])
t = 1900 + t / (24 * 365)
ts = np.concatenate([ts, t])
thfs = np.concatenate([thfs, thf_globe_ave])
thfs_an = an_ave(thfs)
tyears = np.arange(ts[0], ts[-1])
plt.plot(tyears, -thfs_an)
plt.show()
N = 11
ci = (N - 1) / 2
AMO_smooth = running_mean(AMO, N)
AMO_std = (AMO - np.ma.mean(AMO)) / np.ma.std(AMO)
AMO_test_std = (AMO_WARM_REMOVED -
np.ma.mean(AMO_WARM_REMOVED) / np.ma.std(AMO_WARM_REMOVED))
sst_na = sst.subRegion(latitude=(latbounds[0], latbounds[1]),
longitude=(280, 360))
nalats = sst_na.getLatitude()[:]
#sst_na_ave = cdutil.averager(sst_na, axis='xy', weights='weighted')
sst_na_an = an_ave(sst_na)
#sst_na_an_detr = signal.detrend(sst_na_an, axis=0)
#sst_globe = sst.subRegion(latitude=(-60,60))
sst_an = an_ave(sst)
#sst_globe_an = an_ave(sst_globe)
#sst_globe_an_detr = signal.detrend(sst_globe_an, axis=0)
#sst_globe_ave = cdutil.averager(sst_globe, axis='xy', weights='weighted')
#sst_globe_ave_an = an_ave(sst_globe_ave)
#sstbase_globe = MV.average(sst_globe_an[ti:tf], axis=0).getValue()
#sst_globeanom_an = (sst_globe_an.T - sst_globe_ave_an).T
#sstglobeanom_na_an_ave = spatial_ave(sstglobeanom_na_an, nalats)
#lhf_annave = an_ave(lhf)