#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_enso = 12.*2
cutoff_enso = 1/Tn_enso # 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)
sste = st2.regressout_x(enso, sste)
Q_se = st2.regressout_x(enso, Q_se)
Q_re = st2.regressout_x(enso, Q_re)
Q_tote = st2.regressout_x(enso, Q_tote)
#lambdaQ_totlag0 = st.cov(sste, Q_tote, lagx=0)/st.cov(sste,sste, lagx=0)
lambdaQ_totlag1 = st.cov(sste, Q_tote, lagx=-1)/st.cov(sste,sste, lagx=-1)
#lambdaQ_totlag2 = st.cov(sste, Q_tote, lagx=-2)/st.cov(sste,sste, lagx=-2)
#lambdaQ_totlag3 = st.cov(sste, Q_tote, lagx=-3)/st.cov(sste,sste, lagx=-3)
#lambdaQ_tot = (lambdaQ_totlag1 + lambdaQ_totlag2 + lambdaQ_totlag3)/3.
lambdaQ_tot = lambdaQ_totlag1
#lambdaQ_tot = lambdaQ_totlag3
# Filter requirements.
order = 5
fs = 1 # sample rate, (cycles per month)
Tn = 12. * 3
cutoff = 1 / Tn # desired cutoff frequency of the filter (cycles per month)
if not (lowpass):
Tn = 0.
# Butterworth low-pass filter
if lowpass:
#sst = xr.DataArray(st.butter_lowpass_filter(sst, cutoff, fs, order),dims=sst.dims,coords=sst.coords)
#thf = xr.DataArray(st.butter_lowpass_filter(thf, cutoff, fs, order),dims=thf.dims,coords=thf.coords)
#Q_net_surf = xr.DataArray(st.butter_lowpass_filter(Q_net_surf, cutoff, fs, order),dims=Q_net_surf.dims,coords=Q_net_surf.coords)
sst = st.butter_lowpass_filter_xr(sst, cutoff, fs, order)
thf = st.butter_lowpass_filter_xr(thf, cutoff, fs, order)
Q_net_surf = st.butter_lowpass_filter_xr(Q_net_surf, cutoff, fs, order)
Q_ek = st.butter_lowpass_filter_xr(Q_ek, cutoff, fs, order)
h = st.butter_lowpass_filter_xr(h, cutoff, fs, order)
#Q_ek_f = st.butter_lowpass_filter_xr(Q_ek_f, cutoff, fs, order)
#Q_ek = butter_lowpass_filter(Q_ek, cutoff, fs, order)
#Q_g = butter_lowpass_filter(Q_g, cutoff, fs, order)
# Mask zero values (continents)
ocean_points1 = ~(sst == 0)
ocean_points2 = ~(xr.ufuncs.isnan(sst))
ocean_points = xr.ufuncs.logical_or(ocean_points1, ocean_points2)
sst = sst.where(ocean_points)
thf = thf.where(ocean_points)
Q_net_surf = Q_net_surf.where(ocean_points)
# Filter requirements.
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 not(lowpass):
Tn = 0.
# Butterworth low-pass filter
if lowpass:
#sst = xr.DataArray(st.butter_lowpass_filter(sst, cutoff, fs, order),dims=sst.dims,coords=sst.coords)
#thf = xr.DataArray(st.butter_lowpass_filter(thf, cutoff, fs, order),dims=thf.dims,coords=thf.coords)
#Q_net_surf = xr.DataArray(st.butter_lowpass_filter(Q_net_surf, cutoff, fs, order),dims=Q_net_surf.dims,coords=Q_net_surf.coords)
sst = st.butter_lowpass_filter_xr(sst, cutoff, fs, order)
#thf = st.butter_lowpass_filter_xr(thf, cutoff, fs, order)
Qs = st.butter_lowpass_filter_xr(Qs, cutoff, fs, order)
#Q_ek = st.butter_lowpass_filter_xr(Q_ek, cutoff, fs, order)
# sst_ECCO = st.butter_lowpass_filter_xr(sst_ECCO, cutoff, fs, order)
# Tmxl_ECCO = st.butter_lowpass_filter_xr(Tmxl_ECCO, cutoff, fs, order)
#Q_ek_f = st.butter_lowpass_filter_xr(Q_ek_f, cutoff, fs, order)
#Q_ek = butter_lowpass_filter(Q_ek, cutoff, fs, order)
#Q_g = butter_lowpass_filter(Q_g, cutoff, fs, order)
# Mask zero values (continents)
ocean_points1 = ~(sst==0)
ocean_points2 = ~(xr.ufuncs.isnan(sst))
ocean_points = xr.ufuncs.logical_or(ocean_points1, ocean_points2)
sst = sst.where(ocean_points)
Qs = Qs.where(ocean_points)
nt = sst.shape[0]
#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)
# Filter requirements.
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 not (lowpass):
Tn = 0.
# Butterworth low-pass filter
if lowpass:
#sst = xr.DataArray(st.butter_lowpass_filter(sst, cutoff, fs, order),dims=sst.dims,coords=sst.coords)
#thf = xr.DataArray(st.butter_lowpass_filter(thf, cutoff, fs, order),dims=thf.dims,coords=thf.coords)
#Q_net_surf = xr.DataArray(st.butter_lowpass_filter(Q_net_surf, cutoff, fs, order),dims=Q_net_surf.dims,coords=Q_net_surf.coords)
sst = st.butter_lowpass_filter_xr(sst, cutoff, fs, order)
thf = st.butter_lowpass_filter_xr(thf, cutoff, fs, order)
#Q_net_surf = st.butter_lowpass_filter_xr(Q_net_surf, cutoff, fs, order)
#Q_ek = st.butter_lowpass_filter_xr(Q_ek, cutoff, fs, order)
#Q_ek_f = st.butter_lowpass_filter_xr(Q_ek_f, cutoff, fs, order)
#Q_ek = butter_lowpass_filter(Q_ek, cutoff, fs, order)
#Q_g = butter_lowpass_filter(Q_g, cutoff, fs, order)
# Mask zero values (continents)
ocean_points1 = ~(sst == 0)
ocean_points2 = ~(xr.ufuncs.isnan(sst))
ocean_points = xr.ufuncs.logical_or(ocean_points1, ocean_points2)
sst = sst.where(ocean_points)
thf = thf.where(ocean_points)
#Q_net_surf = Q_net_surf.where(ocean_points)
#Q_ek = Q_ek.where(ocean_points)
# Scale the SST tendency by the ratio of MXL temp tendency / SST tendency from ECCO (as an esimate for a more physical result)
sst_raw = sst
Qs_raw = Qs
tendT_mxl_raw = tendT_mxl
#Q_net_surf_raw = Q_net_surf
#Q_ek_raw = Q_ek
for jj, Tn in enumerate(Tns):
cutoff = 1 / Tn
print('Tn', Tn)
if Tn > 0:
sst = st.butter_lowpass_filter_xr(sst_raw, cutoff, fs, order)
#thf = st.butter_lowpass_filter_xr(thf_raw, cutoff, fs, order)
Qs = st.butter_lowpass_filter_xr(Qs_raw, cutoff, fs, order)
tendT_mxl = st.butter_lowpass_filter_xr(tendT_mxl_raw, cutoff, fs,
order)
#Q_net_surf = st.butter_lowpass_filter_xr(Q_net_surf_raw, cutoff, fs, order)
#Q_ek = st.butter_lowpass_filter_xr(Q_ek_raw, cutoff, fs, order)
#sst_ECCO = st.butter_lowpass_filter_xr(sst_ECCO, cutoff, fs, order)
#Tmxl_ECCO = st.butter_lowpass_filter_xr(Tmxl_ECCO, cutoff, fs, order)
#Q_ek = butter_lowpass_filter(Q_ek, cutoff, fs, order)
#Q_g = butter_lowpass_filter(Q_g, cutoff, fs, order)
#Tmxl_var_ECCO = Tmxl_ECCO.var(dim='time')
#sst_var_ECCO = sst_ECCO.var(dim='time')
# Scale the SST tendency by the ratio of MXL temp tendency / SST tendency from ECCO (as an esimate for a more physical result)
sst_raw = sst
thf_raw = thf
Q_net_surf_raw = Q_net_surf
sst_ECCO_raw = sst_ECCO
Tmxl_ECCO_raw = Tmxl_ECCO
#Q_ek_raw = Q_ek
for jj, Tn in enumerate(Tns):
cutoff = 1 / Tn
print('Tn', Tn)
if Tn > 0:
sst = st.butter_lowpass_filter_xr(sst_raw, cutoff, fs, order)
thf = st.butter_lowpass_filter_xr(thf_raw, cutoff, fs, order)
Q_net_surf = st.butter_lowpass_filter_xr(Q_net_surf_raw, cutoff, fs,
order)
#Q_ek = st.butter_lowpass_filter_xr(Q_ek_raw, cutoff, fs, order)
#sst_ECCO = st.butter_lowpass_filter_xr(sst_ECCO, cutoff, fs, order)
#Tmxl_ECCO = st.butter_lowpass_filter_xr(Tmxl_ECCO, cutoff, fs, order)
#Q_ek = butter_lowpass_filter(Q_ek, cutoff, fs, order)
#Q_g = butter_lowpass_filter(Q_g, cutoff, fs, order)
# Tmxl_var_ECCO = Tmxl_ECCO.var(dim='time')
# sst_var_ECCO = sst_ECCO.var(dim='time')
# Scale the SST tendency by the ratio of MXL temp tendency / SST tendency from ECCO (as an esimate for a more physical result)
# Tmxlfrac = Tmxl_var_ECCO/sst_var_ECCO
# sst = sst*np.sqrt(Tmxlfrac)
# Filter requirements.
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 not (lowpass):
Tn = 0.
# Butterworth low-pass filter
if lowpass:
#sst = xr.DataArray(st.butter_lowpass_filter(sst, cutoff, fs, order),dims=sst.dims,coords=sst.coords)
#thf = xr.DataArray(st.butter_lowpass_filter(thf, cutoff, fs, order),dims=thf.dims,coords=thf.coords)
#Q_net_surf = xr.DataArray(st.butter_lowpass_filter(Q_net_surf, cutoff, fs, order),dims=Q_net_surf.dims,coords=Q_net_surf.coords)
sst = st.butter_lowpass_filter_xr(sst, cutoff, fs, order)
thf = st.butter_lowpass_filter_xr(thf, cutoff, fs, order)
Q_net_surf = st.butter_lowpass_filter_xr(Q_net_surf, cutoff, fs, order)
#Q_ek = st.butter_lowpass_filter_xr(Q_ek, cutoff, fs, order)
sst_ECCO = st.butter_lowpass_filter_xr(sst_ECCO, cutoff, fs, order)
Tmxl_ECCO = st.butter_lowpass_filter_xr(Tmxl_ECCO, cutoff, fs, order)
#Q_ek_f = st.butter_lowpass_filter_xr(Q_ek_f, cutoff, fs, order)
#Q_ek = butter_lowpass_filter(Q_ek, cutoff, fs, order)
#Q_g = butter_lowpass_filter(Q_g, cutoff, fs, order)
# Mask zero values (continents)
ocean_points1 = ~(sst == 0)
ocean_points2 = ~(xr.ufuncs.isnan(sst))
ocean_points = xr.ufuncs.logical_or(ocean_points1, ocean_points2)
sst = sst.where(ocean_points)
thf = thf.where(ocean_points)
sst_raw = sst
thf_raw = thf
Q_net_surf_raw = Q_net_surf
sst_ECCO_raw = sst_ECCO
Tmxl_ECCO_raw = Tmxl_ECCO
#Q_ek_raw = Q_ek
for jj, Tn in enumerate(Tns):
cutoff = 1 / Tn
print('Tn', Tn)
if Tn > 0:
sst = st.butter_lowpass_filter_xr(sst_raw, cutoff, fs, order)
thf = st.butter_lowpass_filter_xr(thf_raw, cutoff, fs, order)
Q_net_surf = st.butter_lowpass_filter_xr(Q_net_surf_raw, cutoff, fs,
order)
err_sst = st.butter_lowpass_filter_xr(err_sst, cutoff, fs, order)
err_thf = st.butter_lowpass_filter_xr(err_thf, cutoff, fs, order)
sst_ECCO = st.butter_lowpass_filter_xr(sst_ECCO_raw, cutoff, fs, order)
Tmxl_ECCO = st.butter_lowpass_filter_xr(Tmxl_ECCO_raw, cutoff, fs,
order)
#Q_ek = butter_lowpass_filter(Q_ek, cutoff, fs, order)
#Q_g = butter_lowpass_filter(Q_g, cutoff, fs, order)
Tmxl_var_ECCO = Tmxl_ECCO.var(dim='time')
sst_var_ECCO = sst_ECCO.var(dim='time')
# Scale the SST tendency by the ratio of MXL temp tendency / SST tendency from ECCO (as an esimate for a more physical result)
