#cov_thfRnet = st2.cov(-thf,Q_net_surf) #cov_QrRnet = st2.cov(Qr, Q_net_surf) # #cov_thfthf = st2.cov(-thf,-thf) #cov_Qrthf = st2.cov(Qr,-thf) # #cov_QrQr = st2.cov(Qr,Qr) #cov_QrRnet = st2.cov(Qr,Q_net_surf) #cov_QsRnet = st2.cov(Q_s,Q_net_surf) #cov_QrQs = st2.cov(Qr,Q_s) #cov_ssttend = st2.cov(tendsst,tendsst) # Compute lagged sst autocorrelations r1corrs = st2.cor(sst, sst, lagx=1) r2corrs = st2.cor(sst, sst, lagx=2) # Scaling factor (to convert from units of W*K/(s*m^2) to K^2) fac = (2 * dt**2 / (Cbar * (1 - r2corrs))) #fac=(dt**2)/(2*Cbar*(1-r1corrs)) G = fac / Cbar # Compute observed SST variance T_var = sst.var(dim='time') H_var = (Cbar * tendsst).var(dim='time') # Compute contributions to SST variance
#timeslice = slice(0,nt)
#timeslice = slice(int(Tn),nt-int(Tn))
# Q_s = Q_s.isel(time=timeslice)
# Q_r = Q_r.isel(time=timeslice)
# tendsst = tendsst.isel(time=timeslice)
# sst = sst.isel(time=timeslice)
order = 5
fs = 1 # sample rate, (cycles per month)
Tn = 4. * 12.
cutoff = 1 / Tn # desired cutoff frequency of the filter (cycles per month)
if lowpass:
sst_lp = st.butter_lowpass_filter_xr(sst, cutoff, fs, order)
r2corrs = st2.cor(sst_lp, sst_lp, lagx=2)
else:
r2corrs = st2.cor(sst, sst, lagx=2)
#Niño 3.4 (5N-5S, 170W-120W
order = 5
fs = 1 # sample rate, (cycles per month)
Tn_enso = 6.
cutoff_enso = 1 / Tn # desired cutoff frequency of the filter (cycles per month)
enso = st2.spatial_ave_xr(sst.sel(lon=slice(190, 240)),
lats=lats.sel(lat=slice(-5, 5)))
enso = st.butter_lowpass_filter_xr(enso, cutoff_enso, fs, order)
if not (lowpass):
field = Q_s
fieldname = 'Qs'
fieldtitle = r'$Q_s$'
if corr:
units = ''
else:
units = r'W/m$^2$'
for ll, lag in enumerate(lags):
print('lag',lag)
print
if corr:
lagcorrs[ll,:] = st2.cor(field,sst,lagx=lag)
else:
# Standardize SST
sst = (sst - sst.mean(dim='time'))/sst.std(dim='time')
lagcorrs[ll,:], intercept = st2.reg(field,sst,lagx=lag)
# Plotting
bnds = [np.round(lonbounds[0]-359), np.round(lonbounds[1]-361), latbounds[0], latbounds[1]]
cent = (bnds[0]+bnds[1])/2.
prj = cart.crs.PlateCarree(central_longitude=cent)
pardiff = 30.
merdiff = 60.
if lonbounds[1] - lonbounds[0] <= 180:
x1 = lonbounds[0]
x2 = lonbounds[1]
y1 = latbounds[0]
y2 = latbounds[1]
#Q_s = a1*Q_o + residual
#Q_s = a1*Q_o + Q_{s,r}
a1, b = st2.reg(Qr, Q_s)
Q_s_o = a1 * Qr
Q_s_r = Q_s - Q_s_o
Q_s_o = Q_s_o.transpose('time', 'lat', 'lon')
a1_test = st2.cor(Q_s_o, Q_s_r)
lats_ave = lats.sel(lat=slice(latbounds[0], latbounds[1]))
Qs_sa = st2.spatial_ave_xr(
Q_s.sel(lat=slice(latbounds[0], latbounds[1]),
lon=slice(lonbounds[0], lonbounds[1])), lats_ave)
Qo_sa = st2.spatial_ave_xr(
Qr.sel(lat=slice(latbounds[0], latbounds[1]),
lon=slice(lonbounds[0], lonbounds[1])), lats_ave)
tendsst_sa = st2.spatial_ave_xr(
tendsst.sel(lat=slice(latbounds[0], latbounds[1]),
lon=slice(lonbounds[0], lonbounds[1])), lats_ave)
Cbar_sa = st2.spatial_ave_xr(
Cbar.sel(lat=slice(latbounds[0], latbounds[1]),
lon=slice(lonbounds[0], lonbounds[1])), lats_ave)
fieldname = 'Qs'
fieldtitle = r'$Q_s$'
color='C0'
if corr:
units = ''
else:
units = r'W/m$^2$'
for ll, lag in enumerate(lags):
#print('lag',lag)
print
if corr:
lagcorrs[ll,:] = st2.cor(sst,field,lagx=-lag)
else:
# Standardize SST
sst = (sst - sst.mean(dim='time'))/sst.std(dim='time')
lagcorrs[ll,:], intercept = st2.reg(sst,field,lagx=-lag)
avelagcorrs[jj,:] = st2.spatial_ave(np.ma.array(lagcorrs, mask = np.isnan(lagcorrs)), lats)
# Plotting
vmin=-0.6
vmax=0.6
cbstep=0.2
if not(corr):
vmin=-15.0
