def get_sic_ens_mean_fname(run_type, model, anoms=False):
out_dir = get_output_directory(run_type, 1986, 2005)
fng = out_dir+"/concat_sic_anoms/atlas_sic_OImon_"+model+"_"+run_type+"_ens_mean_??????-??????_1x1"
fng += "_anoms.nc"
print fng
fname = glob.glob(fng)[0]
return fname
def get_siex_anom_ts_fname(run_type, ref_start, ref_end, monthly=False):
out_dir = get_output_directory(run_type, ref_start, ref_end)
out_name = out_dir + "/" + out_dir.split("/")[-1] + "_siex_anom_ts"
if monthly:
out_name += "_mon"
out_name += ".nc"
return out_name
def get_Ma_syn_SST_PCs_filename(run_type, ref_start, ref_end, eof_year, ptile, monthly):
out_dir = get_output_directory(run_type, ref_start, ref_end, eof_year)
fname = out_dir+"/MaSST_EOF_" + get_file_suffix(run_type, ref_start, ref_end, eof_year) + "_p"+str(ptile)+"_synth_pc"
if monthly:
fname += "_mon"
fname += ".nc"
return fname
def get_siex_anom_ts_fname(run_type, ref_start, ref_end, monthly=False):
out_dir = get_output_directory(run_type, ref_start, ref_end)
out_name = out_dir + "/" + out_dir.split("/")[-1] + "_siex_anom_ts"
if monthly:
out_name += "_mon"
out_name += ".nc"
return out_name
def get_sic_ens_mean_fname(run_type, model, anoms=False):
out_dir = get_output_directory(run_type, 1986, 2005)
fng = out_dir + "/concat_sic_anoms/atlas_sic_OImon_" + model + "_" + run_type + "_ens_mean_??????-??????_1x1"
fng += "_anoms.nc"
print fng
fname = glob.glob(fng)[0]
return fname
def plot_tas_ar5(run_type, ref_start, ref_end):
sp = plt.subplot(111)
Y0 = 2009.0
Y1 = 2025.5
Y2 = 2035
C = 0.16
grad0 = (0.3-C)/(Y1 - Y0)
grad1 = (0.7-C)/(Y1 - Y0)
ym0 = grad0*(2016-Y0)+C - 0.1
yx0 = grad1*(2016-Y0)+C + 0.1
ym1 = grad0*(2035-Y0)+C - 0.1
yx1 = grad1*(2035-Y0)+C + 0.1
sp.plot([2016,2035,2035,2016,2016],[ym0,ym1,yx1,yx0,ym0], 'k', lw=2.0, zorder=3)
t_var = numpy.arange(1899,2100+1)
print t_var.shape
for rcp in ["rcp26", "rcp45", "rcp85"]:
if rcp == "rcp26":
col = '#888888'
if rcp == "rcp45":
col = 'r'
if rcp == "rcp85":
col = 'c'
out_dir = get_output_directory(rcp, ref_start, ref_end)
out_name = out_dir + "/" + out_dir.split("/")[1] + "_tos_tas_GM_ts.nc"
fh = netcdf_file(out_name, 'r')
tas = numpy.array(fh.variables["tas"][:])
smoothed_tas = numpy.zeros(tas.shape, 'f')
n_ens = tas.shape[0]
X = numpy.arange(1899,2101)
c = 0
for e in range(0, n_ens):
if tas[e,0] < 1000:
tas_e = tas[e].byteswap().newbyteorder()
TAS = running_gradient_3D(tas_e.reshape(tas_e.shape[0],1,1), 10)
smoothed_tas[c] = TAS.flatten()
c += 1
tas_min = numpy.min(smoothed_tas[:c], axis=0)
tas_max = numpy.max(smoothed_tas[:c], axis=0)
tas_5 = numpy.percentile(smoothed_tas[:c], 5, axis=0)
tas_95 = numpy.percentile(smoothed_tas[:c], 95, axis=0)
tas_50 = numpy.percentile(smoothed_tas[:c], 50, axis=0)
sp.plot(t_var, tas_min, '-', c=col, lw=2.0, zorder=0, alpha=0.5)
sp.plot(t_var, tas_max, '-', c=col, lw=2.0, zorder=0, alpha=0.5)
sp.plot(t_var, tas_50, '-', c=col, lw=2.0, zorder=2, alpha=0.5)
sp.fill_between(t_var, tas_5, tas_95, edgecolor=col, facecolor=col, zorder=1, alpha=0.5)
plt.gca().set_ylim([-0.5,2.5])
plt.gca().set_xlim([1986,2050])
f = plt.gcf()
f.set_size_inches(10.5, 5.0)
plt.savefig("ar5_ch11_fig25.pdf")
def get_gmt_gmsst_anom_ts_fname(run_type, ref_start, ref_end, monthly=False, lat=-1.0, lon=-1.0):
out_dir = get_output_directory(run_type, ref_start, ref_end)
out_name = out_dir + "/" + out_dir.split("/")[-1] + "_tos_tas_GM_ts"
if monthly:
out_name += "_mon"
if lat != -1.0 and lon != -1.0:
out_name += "_ll"+str(lat)+"N_"+str(lon)+"E"
out_name += ".nc"
return out_name
def get_Ma_syn_SST_PCs_filename(run_type, ref_start, ref_end, eof_year, ptile,
monthly):
out_dir = get_output_directory(run_type, ref_start, ref_end, eof_year)
fname = out_dir + "/MaSST_EOF_" + get_file_suffix(
run_type, ref_start, ref_end,
eof_year) + "_p" + str(ptile) + "_synth_pc"
if monthly:
fname += "_mon"
fname += ".nc"
return fname
def get_cmip5_proj_PC_filename(run_type, ref_sy, ref_ey, year, model_mean=False, monthly=False):
if model_mean:
suffix = "_mm"
else:
suffix = ""
out_dir = get_output_directory(run_type, ref_sy, ref_ey, year)
fname = out_dir+"/cmip5_EOF_" + get_file_suffix(run_type, ref_sy, ref_ey, year) + suffix + "_proj_pc"
if monthly:
fname += "_mon"
fname += ".nc"
return fname
def get_cmip5_proj_PC_scale_filename(run_type, ref_start, ref_end, eof_year, model_mean=False, monthly=False):
out_dir = get_output_directory(run_type, ref_start, ref_end, eof_year)
if model_mean:
suffix = "_mm"
else:
suffix = ""
fname = out_dir+"/cmip5_EOF_" + get_file_suffix(run_type, ref_start, ref_end, eof_year) + suffix + "_proj_pc_scale"
if monthly:
fname += "_mon"
fname += ".nc"
return fname
def get_gmt_gmsst_anom_ts_fname(run_type,
ref_start,
ref_end,
monthly=False,
lat=-1.0,
lon=-1.0):
out_dir = get_output_directory(run_type, ref_start, ref_end)
out_name = out_dir + "/" + out_dir.split("/")[-1] + "_tos_tas_GM_ts"
if monthly:
out_name += "_mon"
if lat != -1.0 and lon != -1.0:
out_name += "_ll" + str(lat) + "N_" + str(lon) + "E"
out_name += ".nc"
return out_name
def remap(model_name, var):
sy = 2006
ey = 2100
if var == "tos":
ens_fnames = get_ensemble_fnames_tos(run_type, model)
elif var == "sic":
ens_fnames = get_ensemble_fnames_sic(run_type, model)
ens_mean_string = get_ensemble_mean_string(ens_fnames, sy, ey)
out_dir = get_output_directory(run_type, 1986, 2005)
run_n = ens_fnames[0].split("/")[9]
out_ens = out_dir + "/" + ens_fnames[0].split("/")[10].replace(run_n, "ens_mean")
cdo.ensmean(input=ens_mean_string, output=out_ens)
remap_string, lsm_grid = get_remap_string(var)
out_remap = out_ens[:-3] + "_1x1.nc"
cdo.remapbil(lsm_grid, input=out_ens, output=out_remap)
print out_remap
os.remove(out_ens)
def remap(model_name, var):
sy = 2006
ey = 2100
if var == "tos":
ens_fnames = get_ensemble_fnames_tos(run_type, model)
elif var == "sic":
ens_fnames = get_ensemble_fnames_sic(run_type, model)
ens_mean_string = get_ensemble_mean_string(ens_fnames, sy, ey)
out_dir = get_output_directory(run_type, 1986, 2005)
run_n = ens_fnames[0].split("/")[9]
out_ens = out_dir + "/" + ens_fnames[0].split("/")[10].replace(
run_n, "ens_mean")
cdo.ensmean(input=ens_mean_string, output=out_ens)
remap_string, lsm_grid = get_remap_string(var)
out_remap = out_ens[:-3] + "_1x1.nc"
cdo.remapbil(lsm_grid, input=out_ens, output=out_remap)
print out_remap
os.remove(out_ens)
def get_concat_sic_output_path(run_type, ref_start, ref_end):
out_path = get_output_directory(run_type, ref_start,
ref_end) + "/concat_sic_anoms/"
if not os.path.exists(out_path):
os.mkdir(out_path)
return out_path
def get_cmip5_model_mean_index_filename(run_type, ref_start, ref_end):
fname = get_output_directory(run_type, ref_start, ref_end, None)+\
"/cmip5_" + run_type + "_" + str(ref_start) + "_" +\
str(ref_end) + "_mm_index_mapping.txt"
return fname
def get_concat_output_path(run_type, ref_start, ref_end):
cmip5_out_path = get_output_directory(run_type, ref_start, ref_end)
out_path = cmip5_out_path + "/concat_sst_anoms/"
return out_path
def get_concat_output_path(run_type, ref_start, ref_end):
cmip5_out_path = get_output_directory(run_type, ref_start, ref_end)
out_path = cmip5_out_path+"/concat_sst_anoms/"
return out_path
def get_concat_sic_output_path(run_type, ref_start, ref_end):
out_path = get_output_directory(run_type, ref_start, ref_end)+"/concat_sic_anoms/"
if not os.path.exists(out_path):
os.mkdir(out_path)
return out_path
def get_cmip5_model_mean_index_filename(run_type, ref_start, ref_end):
fname = get_output_directory(run_type, ref_start, ref_end, None)+\
"/cmip5_" + run_type + "_" + str(ref_start) + "_" +\
str(ref_end) + "_mm_index_mapping.txt"
return fname