def calc_syn_SICEXT_TS(run_type, ref_start, ref_end, monthly=True):
out_name = "./" + run_type + "_sicext_ts.nc"
dates = numpy.array([1899 + float(x)/12 for x in range(0, 2424)], 'f')
lats = numpy.array([90-x for x in range(0,180)], 'f')
d_lon = 1.0
mv = -1e30
if not os.path.exists(out_name):
ptiles = numpy.array([10,25,50,75,90], 'f')
samples = numpy.array([0,4,9,14,19], 'f')
samples_2 = numpy.arange(0, len(ptiles)*len(samples), dtype='i')
print len(samples_2), samples_2.dtype
ens_ts = numpy.zeros([2, len(ptiles)*len(samples), dates.shape[0]], 'f')
# get the synthetic SIC filename
#
c = 0
for ptile in ptiles:
for a in samples:
syn_sst_fname = get_syn_sst_filename(run_type,ref_start,ref_end,neofs,eof_year,int(ptile),ivm,monthly)
syn_sst_fname = syn_sst_fname[:-3] + "_s" + str(int(a)) + ".nc"
syn_sic_fname = syn_sst_fname.replace("ssts", "sic").replace("sst", "sic")
sic_data = load_sst_data(syn_sic_fname, "sic")
sic_ext_nh, sic_ext_sh = calc_sea_ice_extent(sic_data, lats, d_lon, mv)
print sic_ext_nh.shape, ens_ts.shape
ens_ts[0,c] = sic_ext_nh
ens_ts[1,c] = sic_ext_sh
c += 1
# save the timeseries
save_ens_ts_file(out_name, ens_ts, samples_2, dates, "sic")
else:
ens_ts = load_data(out_name, "sic")*1.11
return ens_ts, dates
def calc_syn_GMSST_TS(run_type, ref_start, ref_end, monthly=True):
out_name = "./" + run_type + "_gmsst_ts.nc"
dates = numpy.array([1899 + float(x)/12 for x in range(0, 2424)], 'f')
if not os.path.exists(out_name):
# get the synthetic SST filename
#
ptiles = numpy.array([10,25,50,75,90], 'f')
samples = numpy.array([0,4,9,14,19], 'f')
samples_2 = numpy.arange(0, len(ptiles)*len(samples), dtype='i')
print len(samples_2), samples_2.dtype
ens_ts = numpy.zeros([2, len(ptiles)*len(samples), dates.shape[0]], 'f')
c = 0
for ptile in ptiles:
for a in samples:
syn_sst_fname = get_syn_sst_filename(run_type,ref_start,ref_end,neofs,eof_year,int(ptile),ivm,monthly)
syn_sst_fname = syn_sst_fname[:-3] + "_s" + str(int(a)) + ".nc"
sst_data = load_sst_data(syn_sst_fname, "sst")
gmsst_nh = calc_GMSST(sst_data[:,:90,:],1)
gmsst_sh = calc_GMSST(sst_data[:,90:,:],2)
ens_ts[0,c] = gmsst_nh
ens_ts[1,c] = gmsst_sh
c += 1
# save the timeseries
save_ens_ts_file(out_name, ens_ts, samples_2, dates, "gmsst")
else:
ens_ts = load_data(out_name, "gmsst")
return ens_ts, dates
def create_cmip5_long_term_mean_timeseries(run_type,
ref_start,
ref_end,
monthly=True,
run_n=400):
# create the cmip5 timeseries of the cmip5 ensemble mean
# This consists of the ens mean of the cmip5 anomalies from the reference
# plus the HadISST reference
# check which run type we should actually load.
# Likely is rcp45 + adjustment
if run_type == "likely":
load_run_type = "rcp45"
else:
load_run_type = run_type
# get the dates
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
hadisst_ey = 2010
# load the ensemble mean of the anomalies
cmip5_ens_mean_anoms_fname = get_concat_anom_sst_ens_mean_smooth_fname(
load_run_type, ref_start, ref_end, monthly=monthly)
cmip5_ens_mean_anoms = load_sst_data(cmip5_ens_mean_anoms_fname, "sst")
# if we have the likely scenario then fit the period between 2016 and 2035 to the likely
# scenario from AR5 Ch11
if run_type == "likely":
cmip5_ens_mean_anoms = fit_mean_to_likely(cmip5_ens_mean_anoms,
monthly)
# add it onto the ensemble mean anomalies
cmip5_ens_mean = cmip5_ens_mean_anoms
return cmip5_ens_mean
def calc_HadISST_GMSST_TS():
in_fname = get_HadISST_input_filename(400)
hadisst = load_sst_data(in_fname, "sst")
dates = numpy.array([1850 + float(x)/12 for x in range(0, hadisst.shape[0])], 'f')
gmsst_hadisst_nh = calc_GMSST(hadisst[:,:90,:],1)
gmsst_hadisst_sh = calc_GMSST(hadisst[:,90:,:],2)
return gmsst_hadisst_nh, gmsst_hadisst_sh, dates
def correct_cmip5_long_term_mean_timeseries(cmip5_ts, monthly=True, run_n=400):
# correct the ensemble mean of the CMIP5 ensemble, this is achieved by subtracting the
# difference between the 5 year mean of HadISST and the 5 year mean of the CMIP5 ensemble
# mean for 2006->2010 (the overlap period) from the CMIP5 ensemble mean
# need to load in the hadisst values to enable the correction
# get the dates
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
hadisst_ey = 2010
if monthly:
hadisst_smoothed_fname = get_HadISST_month_smooth_filename(histo_sy, hadisst_ey, run_n)
else:
hadisst_smoothed_fname = get_HadISST_smooth_fname(histo_sy, hadisst_ey, run_n)
hadisst_sst = load_sst_data(hadisst_smoothed_fname, "sst")
# if monthly calculate the overlap indices in terms of months by multiplying by 12
if monthly:
# correct each month individually
ovl_idx = (hadisst_ey - histo_sy) * 12 # start offset
mon_correct = numpy.zeros([12, cmip5_ts.shape[1], cmip5_ts.shape[2]], 'f')
for m in range(0, 12):
cmip5_ens_mean_monmean = numpy.mean(cmip5_ts[ovl_idx-5+m:ovl_idx+m:12], axis=0)
hadisst_monmean = numpy.mean(hadisst_sst[ovl_idx-5+m::12], axis=0)
mon_correct[m] = hadisst_monmean - cmip5_ens_mean_monmean
# tile and subtract from cmip5 timeseries
n_repeats = cmip5_ts.shape[0] / 12
cmip5_ens_mean_correction = numpy.tile(mon_correct, [n_repeats,1,1])
else:
ovl_idx = hadisst_ey - histo_sy
cmip5_ens_mean_timmean = numpy.mean(cmip5_ts[ovl_idx-5:ovl_idx], axis=0)
hadisst_timmean = numpy.mean(hadisst_sst[ovl_idx-5:], axis=0)
cmip5_ens_mean_correction = hadisst_timmean - cmip5_ens_mean_timmean
return cmip5_ts + cmip5_ens_mean_correction
def create_cmip5_long_term_mean_timeseries(run_type, ref_start, ref_end, monthly=True, run_n=400):
# create the cmip5 timeseries of the cmip5 ensemble mean
# This consists of the ens mean of the cmip5 anomalies from the reference
# plus the HadISST reference
# check which run type we should actually load.
# Likely is rcp45 + adjustment
if run_type == "likely":
load_run_type = "rcp45"
else:
load_run_type = run_type
# get the dates
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
hadisst_ey = 2010
# load the ensemble mean of the anomalies
cmip5_ens_mean_anoms_fname = get_concat_anom_sst_ens_mean_smooth_fname(load_run_type, ref_start, ref_end, monthly=monthly)
cmip5_ens_mean_anoms = load_sst_data(cmip5_ens_mean_anoms_fname, "sst")
# if we have the likely scenario then fit the period between 2016 and 2035 to the likely
# scenario from AR5 Ch11
if run_type == "likely":
cmip5_ens_mean_anoms = fit_mean_to_likely(cmip5_ens_mean_anoms, monthly)
# add it onto the ensemble mean anomalies
cmip5_ens_mean = cmip5_ens_mean_anoms
return cmip5_ens_mean
def create_syn_SST_PCs(run_type, ref_start, ref_end, eof_year, neofs, nsamps, model_mean=False, monthly=False):
# load the PCs, EOFs for this year
pcs_fname = get_cmip5_PC_filename(run_type, ref_start, ref_end, eof_year, model_mean, monthly)
pcs = load_data(pcs_fname, "sst")
eof_fname = get_cmip5_EOF_filename(run_type, ref_start, ref_end, eof_year, model_mean, monthly)
eofs = load_data(eof_fname, "sst")
# load the smoothed ensemble mean
ens_mean_fname = get_concat_anom_sst_ens_mean_smooth_fname(run_type, ref_start, ref_end, monthly)
ens_mean = load_sst_data(ens_mean_fname, "sst")
# we only need one ensemble mean - calculate decadal mean
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
ens_mean = ens_mean[eof_year-histo_sy]
# transform pc data to R compatible format
pcs = pcs.byteswap().newbyteorder()
# create the return storage
select_PCs = numpy.zeros([pcs.shape[0], nsamps, neofs], 'f')
# percentile ranges
ptiles = [0.10, 0.25, 0.50, 0.75, 0.90]
select_PCs = numpy.random.random(select_PCs.shape)
# now loop through each month pcs - if yearly mean then there will only be one
for m in range(0, pcs.shape[0]):
# fit a copula to the principle components
pc_mvdc = fit_mvdc(pcs[m], neofs)
# generate a large sample of GMSSTs and their corresponding PCs
sst_means_and_PCs = generate_large_sample_of_SSTs(pc_mvdc, eofs[m], ens_mean, neofs)
# now sample the distribution to get nsamps number of PCs which
# represent the distribution of GMSSTs
select_PCs[m] = sample_SSTs(sst_means_and_PCs, neofs, nsamps, ptiles)
# sort the pcs based on the first pc for each of the percentiles
sorted_select_PCs = numpy.zeros([pcs.shape[0], nsamps, neofs], 'f')
pts_per_pc = int(nsamps/len(ptiles))
for m in range(0, pcs.shape[0]):
for p in range(0, len(ptiles)):
s = p*pts_per_pc
e = (p+1)*pts_per_pc
# get the first pc for this ptile
pc0 = select_PCs[m,s:e,0]
# sort it and get the indices
pc0_sort = numpy.argsort(pc0)
# sort all the pcs so that the corresponding pc0 is ascending
for f in range(0, neofs):
pc1 = select_PCs[m,s:e,f]
sorted_select_PCs[m,s:e,f] = pc1[pc0_sort]
# save
out_fname = get_syn_SST_PCs_filename(run_type, ref_start, ref_end, eof_year, monthly)
out_fname = out_fname[:-3] + "_new.nc"
# fix the missing value meta data
out_attrs = {"missing_value" : 2e20}
# save the selected PCAs
save_pcs(out_fname, sorted_select_PCs, out_attrs)
print out_fname
def calc_HadISST_GMSST_SICEXT():
in_fname = get_HadISST_input_filename(400)
hadisst = load_sst_data(in_fname, "sic")
dates = numpy.array([1850 + float(x)/12 for x in range(0, hadisst.shape[0])], 'f')
lats = numpy.array([90-x for x in range(0,180)], 'f')
d_lon = 1.0
mv = -1.0e30
sicext_hadisst_nh, sicext_hadisst_sh = calc_sea_ice_extent(hadisst,lats, d_lon,mv)
return sicext_hadisst_nh, sicext_hadisst_sh, dates
def create_Ma_syn_SST_PCs(run_type, ref_start, ref_end, eof_year, neofs, ptile, model_mean=False, monthly=False):
# load the PCs, EOFs for this year
pcs_fname = get_cmip5_PC_filename(run_type, ref_start, ref_end, eof_year, model_mean, monthly)
pcs = load_data(pcs_fname, "sst")
eof_fname = get_cmip5_EOF_filename(run_type, ref_start, ref_end, eof_year, model_mean, monthly)
eofs = load_data(eof_fname, "sst")
# load the smoothed ensemble mean
ens_mean_fname = get_concat_anom_sst_ens_mean_smooth_fname(run_type, ref_start, ref_end, monthly)
ens_mean = load_sst_data(ens_mean_fname, "sst")
# we only need one ensemble mean - calculate decadal mean
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
ens_mean = ens_mean[eof_year-histo_sy]
# transform pc data to R compatible format
pcs = pcs.byteswap().newbyteorder()
nsamps = 100
nmons = pcs.shape[0]
# create the return storage
select_PCs = numpy.zeros([pcs.shape[0], nsamps, neofs+2], 'f')
# now loop through each month pcs - if yearly mean then there will only be one
for m in range(0, nmons):
# fit a copula to the principle components
pc_mvdc = fit_mvdc(pcs[m], neofs)
# generate a large sample of GMSSTs and their corresponding PCs
sst_means_and_PCs = generate_Ma_large_sample_of_SSTs(pc_mvdc, eofs[m], ens_mean, neofs)
# now sample the distribution to get nsamps number of PCs which
# represent the distribution of GMSSTs
select_PCs[m] = sample_Ma_SSTs(sst_means_and_PCs, neofs, nsamps, ptile)
# sort the pcs based on the first pc for each of the percentiles
sorted_select_PCs = numpy.zeros([nmons, 2, neofs], 'f')
for m in range(0, nmons):
# get the NA indices for this month
na_idxs = select_PCs[m,:,1]
# sort it and get the indices
na_idxs_sort = numpy.argsort(na_idxs)
# get the first and last in the list sorted by NA indices
# - i.e. where the North Atlantic index is the most different
# we just want the PCs now
for e in range(0, neofs):
sorted_select_PCs[m,0,e] = select_PCs[m,:,2+e][na_idxs_sort[0]]
sorted_select_PCs[m,1,e] = select_PCs[m,:,2+e][na_idxs_sort[-1]]
# we now have two sets of PCs - one at each end of the distribution of NA SST gradient for the desired percentile
# save
out_fname = get_Ma_syn_SST_PCs_filename(run_type, ref_start, ref_end, eof_year, ptile, monthly)
# fix the missing value meta data
out_attrs = {"missing_value" : 2e20}
# save the selected PCAs
save_pcs(out_fname, sorted_select_PCs, out_attrs)
print out_fname
def create_hadisst_monthly_reference(run_type, ref_start, ref_end, n_repeats, run_n=400):
# create the annual cycle from hadisst, repeating it a number of times
# so as to add it onto the CMIP5 timeseries
# get the dates
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
hadisst_ey = 2010
# load in the monthly smoothed reference
mon_smooth_name = get_HadISST_monthly_reference_fname(histo_sy, hadisst_ey, ref_start, ref_end, run_n)
mon_smooth_ref = load_sst_data(mon_smooth_name, "sst")
mon_smooth_ref_tile = numpy.tile(mon_smooth_ref, [n_repeats,1,1])
return mon_smooth_ref_tile
def create_hadisst_long_term_timeseries(monthly=True, run_n=400):
# get the dates
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
hadisst_ey = 2010
# create the long term trend timeseries from the monthly smoothed hadisst data
if monthly:
hadisst_smoothed_fname = get_HadISST_month_smooth_filename(histo_sy, hadisst_ey, run_n)
else:
hadisst_smoothed_fname = get_HadISST_smooth_fname(histo_sy, hadisst_ey, run_n)
hadisst_sst = load_sst_data(hadisst_smoothed_fname, "sst")
return hadisst_sst
def plot_test_residuals(histo_sy, histo_ey, ref_start, ref_end, run_n):
# load the yearly eofs and pcs
yr_eof_fname = get_HadISST_residual_EOFs_fname(histo_sy, histo_ey, run_n)
yr_eofs = load_sst_data(yr_eof_fname, "sst")
yr_pcs_fname = get_HadISST_residual_PCs_fname(histo_sy, histo_ey, run_n)
yr_pcs = load_data(yr_pcs_fname)
# load the monthly eofs and pcs
mn_eof_fname = get_HadISST_monthly_residual_EOFs_fname(histo_sy, histo_ey, run_n)
mn_eofs = load_sst_data(mn_eof_fname, "sst")
mn_pcs_fname = get_HadISST_monthly_residual_PCs_fname(histo_sy, histo_ey, run_n)
mn_pcs = load_data(mn_pcs_fname)
# load the smoothed hadisst data
# smooth_fname = get_HadISST_smooth_fname(histo_sy, histo_ey, run_n)
# smooth_hadisst = load_sst_data(smooth_fname, "sst")
# smooth_gmsst = calc_GMSST(smooth_hadisst)
# smooth_gmsst = smooth_gmsst - numpy.mean(smooth_gmsst[1986-1899:2006-1899])
# reconstruct the fields
yr_resids = reconstruct_field(yr_pcs, yr_eofs, 20)
mn_resids = reconstruct_field(mn_pcs, mn_eofs, 20)
# calculate the gmsst
yr_gmsst = calc_GMSST(yr_resids)
mn_gmsst = calc_GMSST(mn_resids)
# plot them
yr_t = numpy.arange(1899,2011,1)
mn_t = numpy.arange(1899,2011,1.0/12)
sp = plt.subplot(111)
sp.plot(yr_t, yr_gmsst, 'r', zorder=1)
sp.plot(mn_t, mn_gmsst, 'k', zorder=0)
sp.plot(yr_t, smooth_gmsst[:-1], 'b', lw=2.0)
plt.savefig("hadisst_resids.pdf")
def create_hadisst_long_term_timeseries(monthly=True, run_n=400):
# get the dates
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
hadisst_ey = 2010
# create the long term trend timeseries from the monthly smoothed hadisst data
if monthly:
hadisst_smoothed_fname = get_HadISST_month_smooth_filename(
histo_sy, hadisst_ey, run_n)
else:
hadisst_smoothed_fname = get_HadISST_smooth_fname(
histo_sy, hadisst_ey, run_n)
hadisst_sst = load_sst_data(hadisst_smoothed_fname, "sst")
return hadisst_sst
def create_hadisst_monthly_reference(run_type,
ref_start,
ref_end,
n_repeats,
run_n=400):
# create the annual cycle from hadisst, repeating it a number of times
# so as to add it onto the CMIP5 timeseries
# get the dates
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
hadisst_ey = 2010
# load in the monthly smoothed reference
mon_smooth_name = get_HadISST_monthly_reference_fname(
histo_sy, hadisst_ey, ref_start, ref_end, run_n)
mon_smooth_ref = load_sst_data(mon_smooth_name, "sst")
mon_smooth_ref_tile = numpy.tile(mon_smooth_ref, [n_repeats, 1, 1])
return mon_smooth_ref_tile
def correct_cmip5_long_term_mean_timeseries(cmip5_ts, monthly=True, run_n=400):
# correct the ensemble mean of the CMIP5 ensemble, this is achieved by subtracting the
# difference between the 5 year mean of HadISST and the 5 year mean of the CMIP5 ensemble
# mean for 2006->2010 (the overlap period) from the CMIP5 ensemble mean
# need to load in the hadisst values to enable the correction
# get the dates
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
hadisst_ey = 2010
if monthly:
hadisst_smoothed_fname = get_HadISST_month_smooth_filename(
histo_sy, hadisst_ey, run_n)
else:
hadisst_smoothed_fname = get_HadISST_smooth_fname(
histo_sy, hadisst_ey, run_n)
hadisst_sst = load_sst_data(hadisst_smoothed_fname, "sst")
# if monthly calculate the overlap indices in terms of months by multiplying by 12
if monthly:
# correct each month individually
ovl_idx = (hadisst_ey - histo_sy) * 12 # start offset
mon_correct = numpy.zeros([12, cmip5_ts.shape[1], cmip5_ts.shape[2]],
'f')
for m in range(0, 12):
cmip5_ens_mean_monmean = numpy.mean(cmip5_ts[ovl_idx - 5 +
m:ovl_idx + m:12],
axis=0)
hadisst_monmean = numpy.mean(hadisst_sst[ovl_idx - 5 + m::12],
axis=0)
mon_correct[m] = hadisst_monmean - cmip5_ens_mean_monmean
# tile and subtract from cmip5 timeseries
n_repeats = cmip5_ts.shape[0] / 12
cmip5_ens_mean_correction = numpy.tile(mon_correct, [n_repeats, 1, 1])
else:
ovl_idx = hadisst_ey - histo_sy
cmip5_ens_mean_timmean = numpy.mean(cmip5_ts[ovl_idx - 5:ovl_idx],
axis=0)
hadisst_timmean = numpy.mean(hadisst_sst[ovl_idx - 5:], axis=0)
cmip5_ens_mean_correction = hadisst_timmean - cmip5_ens_mean_timmean
return cmip5_ts + cmip5_ens_mean_correction
def create_cmip5_rcp_anomalies(run_type, ref_start, ref_end, eof_year, percentile, monthly=True):
# create the time series of anomalies from the mean of the various
# samples in the CMIP5 ensemble
# This spans the uncertainty of the GMT response to GHG forcing in CMIP5
if run_type == "likely":
load_run_type = "rcp45"
else:
load_run_type = run_type
# load the eof patterns in the eof_year
eof_fname = get_cmip5_EOF_filename(load_run_type, ref_start, ref_end, eof_year, monthly=monthly)
eofs = load_sst_data(eof_fname, "sst")
# load the principle components for the eof_year
syn_pc_fname = get_syn_SST_PCs_filename(load_run_type, ref_start, ref_end, eof_year, monthly=monthly)
syn_pc_fname_new = syn_pc_fname[:-3] + "_new.nc"
syn_pc = load_data(syn_pc_fname_new, "sst")
# load the timeseries of scalings and offsets to the pcs over the CMIP5 period
proj_pc_scale_fname = get_cmip5_proj_PC_scale_filename(load_run_type, ref_start, ref_end, eof_year, monthly=monthly)
proj_pc_scale = load_data(proj_pc_scale_fname, "sst_scale")
proj_pc_offset = load_data(proj_pc_scale_fname, "sst_offset")
# corresponding weights that we supplied to the EOF function
coslat = numpy.cos(numpy.deg2rad(numpy.arange(89.5, -90.5,-1.0))).clip(0., 1.)
wgts = numpy.sqrt(coslat)[..., numpy.newaxis]
# create the timeseries of reconstructed SSTs for just this sample
# recreate the field - monthy by month if necessary
if monthly:
syn_sst_rcp = numpy.ma.zeros([proj_pc_scale.shape[0], eofs.shape[2], eofs.shape[3]], 'f')
for m in range(0, 12):
pc_ts = syn_pc[m,percentile,:neofs] * proj_pc_scale[m::12,:neofs] + proj_pc_offset[m::12,:neofs]
syn_sst_rcp[m::12] = reconstruct_field(pc_ts, eofs[m], neofs, wgts)
else:
pc_ts = syn_pc[0,percentile,:neofs] * proj_pc_scale[:,:neofs] + proj_pc_offset[:,:neofs]
syn_sst_rcp = reconstruct_field(pc_ts, eofs[0], neofs, wgts)
return syn_sst_rcp
def create_syn_SST_PCs(run_type,
ref_start,
ref_end,
eof_year,
neofs,
nsamps,
model_mean=False,
monthly=False):
# load the PCs, EOFs for this year
pcs_fname = get_cmip5_PC_filename(run_type, ref_start, ref_end, eof_year,
model_mean, monthly)
pcs = load_data(pcs_fname, "sst")
eof_fname = get_cmip5_EOF_filename(run_type, ref_start, ref_end, eof_year,
model_mean, monthly)
eofs = load_data(eof_fname, "sst")
# load the smoothed ensemble mean
ens_mean_fname = get_concat_anom_sst_ens_mean_smooth_fname(
run_type, ref_start, ref_end, monthly)
ens_mean = load_sst_data(ens_mean_fname, "sst")
# we only need one ensemble mean - calculate decadal mean
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
ens_mean = ens_mean[eof_year - histo_sy]
# transform pc data to R compatible format
pcs = pcs.byteswap().newbyteorder()
# create the return storage
select_PCs = numpy.zeros([pcs.shape[0], nsamps, neofs], 'f')
# percentile ranges
ptiles = [0.10, 0.25, 0.50, 0.75, 0.90]
select_PCs = numpy.random.random(select_PCs.shape)
# now loop through each month pcs - if yearly mean then there will only be one
for m in range(0, pcs.shape[0]):
# fit a copula to the principle components
pc_mvdc = fit_mvdc(pcs[m], neofs)
# generate a large sample of GMSSTs and their corresponding PCs
sst_means_and_PCs = generate_large_sample_of_SSTs(
pc_mvdc, eofs[m], ens_mean, neofs)
# now sample the distribution to get nsamps number of PCs which
# represent the distribution of GMSSTs
select_PCs[m] = sample_SSTs(sst_means_and_PCs, neofs, nsamps, ptiles)
# sort the pcs based on the first pc for each of the percentiles
sorted_select_PCs = numpy.zeros([pcs.shape[0], nsamps, neofs], 'f')
pts_per_pc = int(nsamps / len(ptiles))
for m in range(0, pcs.shape[0]):
for p in range(0, len(ptiles)):
s = p * pts_per_pc
e = (p + 1) * pts_per_pc
# get the first pc for this ptile
pc0 = select_PCs[m, s:e, 0]
# sort it and get the indices
pc0_sort = numpy.argsort(pc0)
# sort all the pcs so that the corresponding pc0 is ascending
for f in range(0, neofs):
pc1 = select_PCs[m, s:e, f]
sorted_select_PCs[m, s:e, f] = pc1[pc0_sort]
# save
out_fname = get_syn_SST_PCs_filename(run_type, ref_start, ref_end,
eof_year, monthly)
out_fname = out_fname[:-3] + "_new.nc"
# fix the missing value meta data
out_attrs = {"missing_value": 2e20}
# save the selected PCAs
save_pcs(out_fname, sorted_select_PCs, out_attrs)
print out_fname
def create_Ma_syn_SSTs(run_type, ref_start, ref_end, sy, ey, eof_year, neofs,
ptile, monthly):
# determine which hadisst ensemble member to use
hadisst_ens_members = [
1059, 115, 1169, 1194, 1346, 137, 1466, 396, 400, 69
]
run_n = hadisst_ens_members[numpy.random.randint(0,
len(hadisst_ens_members))]
# load the CMIP5 ensemble mean timeseries
# load the ensemble mean of the anomalies
cmip5_ens_mean_anoms_fname = get_concat_anom_sst_ens_mean_smooth_fname(
run_type, ref_start, ref_end, monthly)
cmip5_ens_mean_anoms = load_sst_data(cmip5_ens_mean_anoms_fname, "sst")
# load the eof patterns in the eof_year
eof_fname = get_cmip5_EOF_filename(run_type,
ref_start,
ref_end,
eof_year,
monthly=True)
eofs = load_sst_data(eof_fname, "sst")
# load the principle components for the eof_year
syn_pc_fname = get_Ma_syn_SST_PCs_filename(run_type,
ref_start,
ref_end,
eof_year,
ptile,
monthly=True)
syn_pc = load_data(syn_pc_fname, "sst")
# load the timeseries of scalings and offsets to the pcs over the CMIP5 period
proj_pc_scale_fname = get_cmip5_proj_PC_scale_filename(run_type,
ref_start,
ref_end,
eof_year,
monthly=True)
proj_pc_scale = load_data(proj_pc_scale_fname, "sst_scale")
proj_pc_offset = load_data(proj_pc_scale_fname, "sst_offset")
# corresponding weights that we supplied to the EOF function
coslat = numpy.cos(numpy.deg2rad(numpy.arange(89.5, -90.5,
-1.0))).clip(0., 1.)
wgts = numpy.sqrt(coslat)[..., numpy.newaxis]
# create the timeseries of reconstructed SSTs for just this sample
# recreate the field - monthy by month
# pattern number
pn = 0
nmons = 12
# sub set the mean anomalies and the proj_pc_scale and offset
cmip5_sy = 1899
si = (sy - cmip5_sy) * 12
ei = (ey - cmip5_sy) * 12
cmip5_ens_mean_anoms = cmip5_ens_mean_anoms[si:ei]
if ey == 2101:
# create 2101
S = cmip5_ens_mean_anoms.shape
cmip5_ens_mean_anoms2 = numpy.zeros([S[0] + 12, S[1], S[2]], 'f')
cmip5_ens_mean_anoms2[:S[0]] = cmip5_ens_mean_anoms
cmip5_ens_mean_anoms2[-12:] = cmip5_ens_mean_anoms[-12:]
cmip5_ens_mean_anoms = cmip5_ens_mean_anoms2
proj_pc_scale = proj_pc_scale[si - 12:ei]
proj_pc_offset = proj_pc_offset[si - 12:ei]
syn_sst_rcp = numpy.ma.zeros(
[proj_pc_scale.shape[0], eofs.shape[2], eofs.shape[3]], 'f')
#
for pn in range(0, 2): # two patterns per percentile
for m in range(0, nmons):
pc_ts = syn_pc[m, pn, :neofs] * proj_pc_scale[
m::12, :neofs] + proj_pc_offset[m::12, :neofs]
syn_sst_rcp[m::12] = reconstruct_field(pc_ts, eofs[m], neofs, wgts)
# load the hadisst reference
n_repeats = cmip5_ens_mean_anoms.shape[
0] / 12 # number of repeats = number of years
hadisst_ac = create_hadisst_monthly_reference(run_type, ref_start,
ref_end, n_repeats,
run_n)
# load the internal variability - we are only interested in the 30 year observed ones
resid_fname = get_HadISST_monthly_residuals_fname(1899, 2010, 400)
intvar = load_data(resid_fname, "sst")
intvar = intvar[(1973 - 1899) * 12:(2007 - 1899) * 12]
print "cmip5_ens_mean_anoms ", cmip5_ens_mean_anoms.shape
print "syn_sst_rcp ", syn_sst_rcp.shape
print "hadisst_ac ", hadisst_ac.shape
print "intvar ", intvar.shape
out_data = cmip5_ens_mean_anoms + syn_sst_rcp + hadisst_ac + intvar
# save the synthetic ssts
save_Ma_syn_SSTs(out_data, run_type, ref_start, ref_end, sy, ey, ptile,
pn)
def create_Ma_syn_SST_PCs(run_type,
ref_start,
ref_end,
eof_year,
neofs,
ptile,
model_mean=False,
monthly=False):
# load the PCs, EOFs for this year
pcs_fname = get_cmip5_PC_filename(run_type, ref_start, ref_end, eof_year,
model_mean, monthly)
pcs = load_data(pcs_fname, "sst")
eof_fname = get_cmip5_EOF_filename(run_type, ref_start, ref_end, eof_year,
model_mean, monthly)
eofs = load_data(eof_fname, "sst")
# load the smoothed ensemble mean
ens_mean_fname = get_concat_anom_sst_ens_mean_smooth_fname(
run_type, ref_start, ref_end, monthly)
ens_mean = load_sst_data(ens_mean_fname, "sst")
# we only need one ensemble mean - calculate decadal mean
histo_sy, histo_ey, rcp_sy, rcp_ey = get_start_end_periods()
ens_mean = ens_mean[eof_year - histo_sy]
# transform pc data to R compatible format
pcs = pcs.byteswap().newbyteorder()
nsamps = 100
nmons = pcs.shape[0]
# create the return storage
select_PCs = numpy.zeros([pcs.shape[0], nsamps, neofs + 2], 'f')
# now loop through each month pcs - if yearly mean then there will only be one
for m in range(0, nmons):
# fit a copula to the principle components
pc_mvdc = fit_mvdc(pcs[m], neofs)
# generate a large sample of GMSSTs and their corresponding PCs
sst_means_and_PCs = generate_Ma_large_sample_of_SSTs(
pc_mvdc, eofs[m], ens_mean, neofs)
# now sample the distribution to get nsamps number of PCs which
# represent the distribution of GMSSTs
select_PCs[m] = sample_Ma_SSTs(sst_means_and_PCs, neofs, nsamps, ptile)
# sort the pcs based on the first pc for each of the percentiles
sorted_select_PCs = numpy.zeros([nmons, 2, neofs], 'f')
for m in range(0, nmons):
# get the NA indices for this month
na_idxs = select_PCs[m, :, 1]
# sort it and get the indices
na_idxs_sort = numpy.argsort(na_idxs)
# get the first and last in the list sorted by NA indices
# - i.e. where the North Atlantic index is the most different
# we just want the PCs now
for e in range(0, neofs):
sorted_select_PCs[m, 0, e] = select_PCs[m, :,
2 + e][na_idxs_sort[0]]
sorted_select_PCs[m, 1, e] = select_PCs[m, :,
2 + e][na_idxs_sort[-1]]
# we now have two sets of PCs - one at each end of the distribution of NA SST gradient for the desired percentile
# save
out_fname = get_Ma_syn_SST_PCs_filename(run_type, ref_start, ref_end,
eof_year, ptile, monthly)
# fix the missing value meta data
out_attrs = {"missing_value": 2e20}
# save the selected PCAs
save_pcs(out_fname, sorted_select_PCs, out_attrs)
print out_fname
def create_Ma_syn_SSTs(run_type, ref_start, ref_end, sy, ey, eof_year, neofs, ptile, monthly):
# determine which hadisst ensemble member to use
hadisst_ens_members = [1059, 115, 1169, 1194, 1346, 137, 1466, 396, 400, 69]
run_n = hadisst_ens_members[numpy.random.randint(0, len(hadisst_ens_members))]
# load the CMIP5 ensemble mean timeseries
# load the ensemble mean of the anomalies
cmip5_ens_mean_anoms_fname = get_concat_anom_sst_ens_mean_smooth_fname(run_type, ref_start, ref_end, monthly)
cmip5_ens_mean_anoms = load_sst_data(cmip5_ens_mean_anoms_fname, "sst")
# load the eof patterns in the eof_year
eof_fname = get_cmip5_EOF_filename(run_type, ref_start, ref_end, eof_year, monthly=True)
eofs = load_sst_data(eof_fname, "sst")
# load the principle components for the eof_year
syn_pc_fname = get_Ma_syn_SST_PCs_filename(run_type, ref_start, ref_end, eof_year, ptile, monthly=True)
syn_pc = load_data(syn_pc_fname, "sst")
# load the timeseries of scalings and offsets to the pcs over the CMIP5 period
proj_pc_scale_fname = get_cmip5_proj_PC_scale_filename(run_type, ref_start, ref_end, eof_year, monthly=True)
proj_pc_scale = load_data(proj_pc_scale_fname, "sst_scale")
proj_pc_offset = load_data(proj_pc_scale_fname, "sst_offset")
# corresponding weights that we supplied to the EOF function
coslat = numpy.cos(numpy.deg2rad(numpy.arange(89.5, -90.5,-1.0))).clip(0., 1.)
wgts = numpy.sqrt(coslat)[..., numpy.newaxis]
# create the timeseries of reconstructed SSTs for just this sample
# recreate the field - monthy by month
# pattern number
pn = 0
nmons=12
# sub set the mean anomalies and the proj_pc_scale and offset
cmip5_sy = 1899
si = (sy-cmip5_sy)*12
ei = (ey-cmip5_sy)*12
cmip5_ens_mean_anoms = cmip5_ens_mean_anoms[si:ei]
if ey == 2101:
# create 2101
S = cmip5_ens_mean_anoms.shape
cmip5_ens_mean_anoms2 = numpy.zeros([S[0]+12, S[1], S[2]], 'f')
cmip5_ens_mean_anoms2[:S[0]] = cmip5_ens_mean_anoms
cmip5_ens_mean_anoms2[-12:] = cmip5_ens_mean_anoms[-12:]
cmip5_ens_mean_anoms = cmip5_ens_mean_anoms2
proj_pc_scale = proj_pc_scale[si-12:ei]
proj_pc_offset = proj_pc_offset[si-12:ei]
syn_sst_rcp = numpy.ma.zeros([proj_pc_scale.shape[0], eofs.shape[2], eofs.shape[3]], 'f')
#
for pn in range(0, 2): # two patterns per percentile
for m in range(0, nmons):
pc_ts = syn_pc[m,pn,:neofs] * proj_pc_scale[m::12,:neofs] + proj_pc_offset[m::12,:neofs]
syn_sst_rcp[m::12] = reconstruct_field(pc_ts, eofs[m], neofs, wgts)
# load the hadisst reference
n_repeats = cmip5_ens_mean_anoms.shape[0] / 12 # number of repeats = number of years
hadisst_ac = create_hadisst_monthly_reference(run_type, ref_start, ref_end, n_repeats, run_n)
# load the internal variability - we are only interested in the 30 year observed ones
resid_fname = get_HadISST_monthly_residuals_fname(1899, 2010, 400)
intvar = load_data(resid_fname, "sst")
intvar = intvar[(1973-1899)*12:(2007-1899)*12]
print "cmip5_ens_mean_anoms ", cmip5_ens_mean_anoms.shape
print "syn_sst_rcp ", syn_sst_rcp.shape
print "hadisst_ac ", hadisst_ac.shape
print "intvar ", intvar.shape
out_data = cmip5_ens_mean_anoms + syn_sst_rcp + hadisst_ac + intvar
# save the synthetic ssts
save_Ma_syn_SSTs(out_data, run_type, ref_start, ref_end, sy, ey, ptile, pn)
def create_cmip5_rcp_anomalies(run_type,
ref_start,
ref_end,
eof_year,
percentile,
monthly=True):
# create the time series of anomalies from the mean of the various
# samples in the CMIP5 ensemble
# This spans the uncertainty of the GMT response to GHG forcing in CMIP5
if run_type == "likely":
load_run_type = "rcp45"
else:
load_run_type = run_type
# load the eof patterns in the eof_year
eof_fname = get_cmip5_EOF_filename(load_run_type,
ref_start,
ref_end,
eof_year,
monthly=monthly)
eofs = load_sst_data(eof_fname, "sst")
# load the principle components for the eof_year
syn_pc_fname = get_syn_SST_PCs_filename(load_run_type,
ref_start,
ref_end,
eof_year,
monthly=monthly)
syn_pc_fname_new = syn_pc_fname[:-3] + "_new.nc"
syn_pc = load_data(syn_pc_fname_new, "sst")
# load the timeseries of scalings and offsets to the pcs over the CMIP5 period
proj_pc_scale_fname = get_cmip5_proj_PC_scale_filename(load_run_type,
ref_start,
ref_end,
eof_year,
monthly=monthly)
proj_pc_scale = load_data(proj_pc_scale_fname, "sst_scale")
proj_pc_offset = load_data(proj_pc_scale_fname, "sst_offset")
# corresponding weights that we supplied to the EOF function
coslat = numpy.cos(numpy.deg2rad(numpy.arange(89.5, -90.5,
-1.0))).clip(0., 1.)
wgts = numpy.sqrt(coslat)[..., numpy.newaxis]
# create the timeseries of reconstructed SSTs for just this sample
# recreate the field - monthy by month if necessary
if monthly:
syn_sst_rcp = numpy.ma.zeros(
[proj_pc_scale.shape[0], eofs.shape[2], eofs.shape[3]], 'f')
for m in range(0, 12):
pc_ts = syn_pc[m, percentile, :neofs] * proj_pc_scale[
m::12, :neofs] + proj_pc_offset[m::12, :neofs]
syn_sst_rcp[m::12] = reconstruct_field(pc_ts, eofs[m], neofs, wgts)
else:
pc_ts = syn_pc[
0, percentile, :
neofs] * proj_pc_scale[:, :neofs] + proj_pc_offset[:, :neofs]
syn_sst_rcp = reconstruct_field(pc_ts, eofs[0], neofs, wgts)
return syn_sst_rcp