def main():
start_year = 1979
end_year = 2000
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
sim_label_to_path = OrderedDict([
(HL_LABEL,
"/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"
),
(NEMO_LABEL,
"/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples"
)
])
var_name_list = ["TT", "PR", "LC", "HR", "HU", "AV", "I5", "AL"]
season_to_months = commons.season_to_months
vname_to_level = {
"TT": 1,
"PR": -1,
"SN": -1,
"LC": -1,
"HR": 1,
"HU": 1,
"AV": -1,
"I5": -1,
"AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID,
"PR": level_kinds.ARBITRARY,
"SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY,
"HR": level_kinds.HYBRID,
"HU": level_kinds.HYBRID,
"AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY,
"AL": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm"
}
# ---> ---->
avg_mask = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
vname = "LC"
common_params = dict(start_year=start_year,
end_year=end_year,
filename_prefix=vname_to_file_prefix[vname],
level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname],
varname=vname,
mask=avg_mask)
hl_icefrac = get_area_avg_timeseries(sim_label_to_path[HL_LABEL],
**common_params)
nemo_icefrac = get_area_avg_timeseries(sim_label_to_path[NEMO_LABEL],
**common_params)
obs_icefrac = GL_obs_timeseries.get_ts_with_real_dates_from_file(
path=
"/RESCUE/skynet3_rech1/huziy/obs_data/Lake_ice_concentration_Great_lakes_timeseries/GLK-30x.TXT",
start_year=start_year - 1,
end_year=end_year - 1)
obs_icefrac /= 100.0
plot_utils.apply_plot_params(font_size=14)
# nemo ice fraction from 1-way coupled
# with MFDataset("/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/NEMO/*grid_T.nc") as ds:
# timevar = ds.variables["time_counter"]
# print(timevar.units)
# times = num2date(timevar[:], timevar.units)
#
# print(times[0], times[-1], type(times[0]))
#
# print(times[:10])
#
# print("===== avg_mask ====")
# print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
#
# print(ds.variables["soicecov"][0, :, :].shape)
# vals = [field.transpose()[20:-20, 20:-20][avg_mask].mean() for field in ds.variables["soicecov"][:]]
#
# assert len(vals) == len(times)
#
# ts_nemo_1way = pd.Series(index=[d for d in times], data=vals)
# ts_nemo_1way.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_5min = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_5min.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/doubled_snowfall/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_double_snow = pd.Series(index=[d for d in times],
data=vals)
ts_nemo_offline_double_snow.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf = pd.Series(index=[d for d in times],
data=vals)
ts_nemo_offline_dt5min_small_ldf.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_30min_eddy_tra10_dyn30_traldf3d/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt30min_small_ldf = pd.Series(index=[d for d in times],
data=vals)
ts_nemo_offline_dt30min_small_ldf.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_3hourly/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc3h = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc3h.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_6hourly_hicrit_0.05m/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc6h = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc6h.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_6hourly_hicrit_0.05m_noicedyn/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc6h_nodyn = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc6h_nodyn.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_5min_noicedyn/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_5min_noicedyn_notime_interpolation_of_forcing/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn_noforc_tint = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn_noforc_tint.sort_index(
inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc5min_lim3_6h/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_lim6h = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_lim6h.sort_index(inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_tke_penetr10m/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_tkepenet_10m = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_tkepenet_10m.sort_index(
inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_tke0.01_penetr10m/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_tke0001penet_10m = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_tke0001penet_10m.sort_index(
inplace=True)
# nemo offline
with MFDataset(
"/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_5min_gls1/GLK_1d_*_grid_T.nc"
) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1 = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1.sort_index(inplace=True)
# nemo offline
data_folder = Path(
"/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/zdf_gls_dt_and_sbc_5min/"
)
with MFDataset([
str(f) for f in sorted(data_folder.iterdir(), key=lambda p: p.name)
if f.name.endswith("_grid_T.nc")
]) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [
field.transpose()[avg_mask].mean()
for field in ds.variables["soicecov"][:]
]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1_5year = pd.Series(
index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1_5year.sort_index(
inplace=True)
fig = plt.figure()
print("hl_icefrac :", hl_icefrac.index[0], hl_icefrac.index[-1],
type(hl_icefrac))
print("nemo_icefrac :", nemo_icefrac.index[0], nemo_icefrac.index[-1])
ax = plt.gca()
# ax = hl_icefrac.plot(lw=2, color="k", label=HL_LABEL)
# nemo_icefrac.plot(lw=2, color="r", ax=ax, label=NEMO_LABEL)
# ts_nemo_1way.plot(lw=2, color="g", label="CRCM5_NEMO_oneway", ax=ax, zorder=5)
# obs_icefrac.plot(lw=2, color="b", ax=ax, label="Obs.")
ax.plot(hl_icefrac.index,
hl_icefrac.values,
lw=2,
color="b",
label=HL_LABEL)
ax.plot(nemo_icefrac.index,
nemo_icefrac.values,
lw=2,
color="r",
label=NEMO_LABEL)
# ax.plot(ts_nemo_1way.index, ts_nemo_1way.values, lw=2, color="g", label="CRCM5_NEMO_oneway")
ax.plot(obs_icefrac.index,
obs_icefrac.values,
lw=2,
color="k",
label="Obs.")
#ax.plot(ts_nemo_offline.index, ts_nemo_offline.values, lw=2, color="#FFA500", label="NEMO-offline")
#ax.plot(ts_nemo_offline_5min.index, ts_nemo_offline_5min.values, lw=2, color="g", label="NEMO-offline-5min")
#ax.plot(ts_nemo_offline_double_snow.index, ts_nemo_offline_double_snow.values, lw=2, color="m", label="NEMO-offline-doublesnow")
ax.plot(ts_nemo_offline_dt5min_small_ldf.index,
ts_nemo_offline_dt5min_small_ldf.values,
lw=2,
color="g",
label="NEMO-offline dt=5min, small ldf")
# ax.plot(ts_nemo_offline_dt30min_small_ldf.index, ts_nemo_offline_dt30min_small_ldf.values, lw=2, color="y", label="NEMO-offline dt=30min,traldf3d, small ldf")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc3h.index, ts_nemo_offline_dt5min_small_ldf_sbc3h.values, lw=2, color="c", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc3h")
ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc6h.index,
ts_nemo_offline_dt5min_small_ldf_sbc6h.values,
lw=2,
color="#6ACC65",
label="NEMO-offline dt=5min,traldf3d, small ldf, sbc6h, hicr5cm")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc6h_nodyn.index, ts_nemo_offline_dt5min_small_ldf_sbc6h_nodyn.values, lw=2, color="#6ACEEE", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc6h, noicedyn")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn.values, lw=2, color="#9ABF12", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc5min, noicedyn")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn_noforc_tint.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn_noforc_tint.values, lw=2, color="#111111", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,noicedyn,noforctint")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_lim6h.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_lim6h.values, lw=2,
# color="m", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc5min, lim 6h")
#
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_tkepenet_10m.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_tkepenet_10m.values, lw=2,
# color="y", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,tke penet 10m")
#
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_tke0001penet_10m.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_tke0001penet_10m.values, lw=2,
# color="#ccceee", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,tke penet0.01 10m")
#
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1.values, lw=2,
# color="#aaa111", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,GLS1m")
ax.plot(
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1_5year.index,
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1_5year.values,
lw=2,
color="#aaa111",
label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,GLS1m,5year")
# ax.set_xlim([date2num(datetime(1979, 10, 1)), date2num(datetime(1980, 12, 31))])
ax.xaxis.set_major_locator(YearLocator())
fig.autofmt_xdate()
ax.legend()
ax.grid()
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
fig.tight_layout()
fig.savefig(os.path.join(
img_folder, "lake_icefr_ts_{}-{}.png".format(start_year, end_year)),
dpi=commons.dpi,
transparent=True,
bbox_inches="tight")
def main():
start_year = 1980
end_year = 2003
months_of_obs = [12, 1, 2, 3, 4, 5]
r_config = RunConfig(
data_path="/RESCUE/skynet3_rech1/huziy/hdf_store/quebec_0.1_crcm5-hcd-rl.hdf5",
start_year=start_year, end_year=end_year, label="ERAI-CRCM5-L"
)
var_name = "LC"
bmp_info = analysis.get_basemap_info(r_config=r_config)
lkid_to_mask = get_lake_masks(bmp_info.lons, bmp_info.lats)
cell_area_m2 = analysis.get_array_from_file(path=r_config.data_path, var_name="cell_area_m2")
# read the model data
lkid_to_ts_model = {}
for lkid, the_mask in lkid_to_mask.items():
lkid_to_ts_model[lkid] = analysis.get_area_mean_timeseries(r_config.data_path, var_name=var_name, the_mask=the_mask * cell_area_m2,
start_year=start_year, end_year=end_year)
df = lkid_to_ts_model[lkid]
# remove the last December
df = df.select(lambda d: not (d.year == end_year and d.month == 12))
# remove the first Jan and Feb
df = df.select(lambda d: not (d.year == start_year and d.month in [1, 2]))
# remove the Feb 29th
df = df.select(lambda d: not (d.month == 2 and d.day == 29))
# select months of interest
df = df.select(lambda d: d.month in months_of_obs)
# calculate the climatology
df = df.groupby(lambda d: datetime(2001 if d.month == 12 else 2002, d.month, d.day)).mean()
df.sort_index(inplace=True)
lkid_to_ts_model[lkid] = df * 100
# read obs data and calculate climatology
lkid_to_ts_obs = {}
for lkid in LAKE_IDS:
lkid_to_ts_obs[lkid] = GL_obs_timeseries.get_ts_from_file(path=os.path.join(OBS_DATA_FOLDER, "{}-30x.TXT".format(lkid)),
start_year=start_year, end_year=end_year - 1)
# get the climatology
dfm = lkid_to_ts_obs[lkid].mean(axis=1)
dfm.index = [datetime(2001, 1, 1) + timedelta(days=int(jd - 1)) for jd in dfm.index]
lkid_to_ts_obs[lkid] = dfm
# plotting
plot_utils.apply_plot_params(font_size=10)
fig = plt.figure()
gs = GridSpec(nrows=len(lkid_to_ts_model), ncols=2)
for row, lkid in enumerate(lkid_to_ts_model):
ax = fig.add_subplot(gs[row, 0])
mod = lkid_to_ts_model[lkid]
obs = lkid_to_ts_obs[lkid]
print(obs.index)
print(obs.values)
ax.plot(mod.index, mod.values, label=r_config.label, color="r", lw=2)
ax.plot(obs.index, obs.values, label="NOAA NIC/CIS", color="k", lw=2)
if row == 0:
ax.legend()
ax.set_title(lkid)
ax.xaxis.set_major_formatter(DateFormatter("%b"))
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "GL_ice-cover-validation.png"), bbox_inches="tight", dpi=common_plot_params.FIG_SAVE_DPI)
def main():
start_year = 1979
end_year = 2000
HL_LABEL = "CRCM5_HL"
NEMO_LABEL = "CRCM5_NEMO"
sim_label_to_path = OrderedDict(
[(HL_LABEL, "/RESCUE/skynet3_rech1/huziy/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/Samples"),
(NEMO_LABEL, "/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl/Samples")]
)
var_name_list = ["TT", "PR", "LC", "HR", "HU", "AV", "I5", "AL"]
season_to_months = commons.season_to_months
vname_to_level = {
"TT": 1, "PR": -1, "SN": -1, "LC": -1, "HR": 1, "HU": 1, "AV": -1, "I5": -1, "AL": -1
}
vname_to_level_kind = {
"TT": level_kinds.HYBRID, "PR": level_kinds.ARBITRARY, "SN": level_kinds.ARBITRARY,
"LC": level_kinds.ARBITRARY, "HR": level_kinds.HYBRID, "HU": level_kinds.HYBRID, "AV": level_kinds.ARBITRARY,
"I5": level_kinds.ARBITRARY, "AL": level_kinds.ARBITRARY
}
vname_to_file_prefix = {
"TT": "dm",
"PR": "pm",
"SN": "pm",
"LC": "pm",
"HR": "dm",
"HU": "dm",
"AV": "pm",
"I5": "pm",
"AL": "pm"
}
# ---> ---->
avg_mask = get_nemo_lakes_mask(samples_dir=sim_label_to_path[NEMO_LABEL])
vname = "LC"
common_params = dict(start_year=start_year, end_year=end_year,
filename_prefix=vname_to_file_prefix[vname], level=vname_to_level[vname],
level_kind=vname_to_level_kind[vname], varname=vname, mask=avg_mask)
hl_icefrac = get_area_avg_timeseries(sim_label_to_path[HL_LABEL], **common_params)
nemo_icefrac = get_area_avg_timeseries(sim_label_to_path[NEMO_LABEL], **common_params)
obs_icefrac = GL_obs_timeseries.get_ts_with_real_dates_from_file(
path="/RESCUE/skynet3_rech1/huziy/obs_data/Lake_ice_concentration_Great_lakes_timeseries/GLK-30x.TXT",
start_year=start_year - 1, end_year=end_year - 1)
obs_icefrac /= 100.0
plot_utils.apply_plot_params(font_size=14)
# nemo ice fraction from 1-way coupled
# with MFDataset("/HOME/huziy/skynet3_rech1/CNRCWP/C5/2016/2-year-runs/coupled-GL+stfl_oneway/NEMO/*grid_T.nc") as ds:
# timevar = ds.variables["time_counter"]
# print(timevar.units)
# times = num2date(timevar[:], timevar.units)
#
# print(times[0], times[-1], type(times[0]))
#
# print(times[:10])
#
# print("===== avg_mask ====")
# print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
#
# print(ds.variables["soicecov"][0, :, :].shape)
# vals = [field.transpose()[20:-20, 20:-20][avg_mask].mean() for field in ds.variables["soicecov"][:]]
#
# assert len(vals) == len(times)
#
# ts_nemo_1way = pd.Series(index=[d for d in times], data=vals)
# ts_nemo_1way.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline.sort_index(inplace=True)
# nemo offline
with MFDataset("/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_5min = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_5min.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/doubled_snowfall/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_double_snow = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_double_snow.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_30min_eddy_tra10_dyn30_traldf3d/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt30min_small_ldf = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt30min_small_ldf.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_3hourly/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc3h = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc3h.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_6hourly_hicrit_0.05m/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc6h = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc6h.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_6hourly_hicrit_0.05m_noicedyn/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc6h_nodyn = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc6h_nodyn.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_5min_noicedyn/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_5min_noicedyn_notime_interpolation_of_forcing/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn_noforc_tint = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn_noforc_tint.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc5min_lim3_6h/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_lim6h = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_lim6h.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_tke_penetr10m/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_tkepenet_10m = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_tkepenet_10m.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_tke0.01_penetr10m/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_tke0001penet_10m = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_tke0001penet_10m.sort_index(inplace=True)
# nemo offline
with MFDataset("/RESCUE/skynet3_rech1/huziy/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/dt_5min_eddy_tra10_dyn30_sbc_forcing_5min_gls1/GLK_1d_*_grid_T.nc") as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1 = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1.sort_index(inplace=True)
# nemo offline
data_folder = Path("/HOME/huziy/skynet3_rech1/NEMO_OFFICIAL/dev_v3_4_STABLE_2012/NEMOGCM/CONFIG/GLK_LIM3/EXP_GLK_LIM3_1980/outputs_nemo_offline/zdf_gls_dt_and_sbc_5min/")
with MFDataset([str(f) for f in sorted(data_folder.iterdir(), key=lambda p: p.name) if f.name.endswith("_grid_T.nc")]) as ds:
timevar = ds.variables["time_counter"]
print(timevar.units)
times = num2date(timevar[:], timevar.units)
print(times[0], times[-1], type(times[0]))
print(times[:10])
print("===== avg_mask ====")
print(avg_mask.min(), avg_mask.max(), avg_mask.shape)
print(ds.variables["soicecov"][0, :, :].shape)
vals = [field.transpose()[avg_mask].mean() for field in ds.variables["soicecov"][:]]
assert len(vals) == len(times)
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1_5year = pd.Series(index=[d for d in times], data=vals)
ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1_5year.sort_index(inplace=True)
fig = plt.figure()
print("hl_icefrac :", hl_icefrac.index[0], hl_icefrac.index[-1], type(hl_icefrac))
print("nemo_icefrac :", nemo_icefrac.index[0], nemo_icefrac.index[-1])
ax = plt.gca()
# ax = hl_icefrac.plot(lw=2, color="k", label=HL_LABEL)
# nemo_icefrac.plot(lw=2, color="r", ax=ax, label=NEMO_LABEL)
# ts_nemo_1way.plot(lw=2, color="g", label="CRCM5_NEMO_oneway", ax=ax, zorder=5)
# obs_icefrac.plot(lw=2, color="b", ax=ax, label="Obs.")
ax.plot(hl_icefrac.index, hl_icefrac.values, lw=2, color="b", label=HL_LABEL)
ax.plot(nemo_icefrac.index, nemo_icefrac.values, lw=2, color="r", label=NEMO_LABEL)
# ax.plot(ts_nemo_1way.index, ts_nemo_1way.values, lw=2, color="g", label="CRCM5_NEMO_oneway")
ax.plot(obs_icefrac.index, obs_icefrac.values, lw=2, color="k", label="Obs.")
#ax.plot(ts_nemo_offline.index, ts_nemo_offline.values, lw=2, color="#FFA500", label="NEMO-offline")
#ax.plot(ts_nemo_offline_5min.index, ts_nemo_offline_5min.values, lw=2, color="g", label="NEMO-offline-5min")
#ax.plot(ts_nemo_offline_double_snow.index, ts_nemo_offline_double_snow.values, lw=2, color="m", label="NEMO-offline-doublesnow")
ax.plot(ts_nemo_offline_dt5min_small_ldf.index, ts_nemo_offline_dt5min_small_ldf.values, lw=2, color="g", label="NEMO-offline dt=5min, small ldf")
# ax.plot(ts_nemo_offline_dt30min_small_ldf.index, ts_nemo_offline_dt30min_small_ldf.values, lw=2, color="y", label="NEMO-offline dt=30min,traldf3d, small ldf")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc3h.index, ts_nemo_offline_dt5min_small_ldf_sbc3h.values, lw=2, color="c", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc3h")
ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc6h.index, ts_nemo_offline_dt5min_small_ldf_sbc6h.values, lw=2, color="#6ACC65", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc6h, hicr5cm")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc6h_nodyn.index, ts_nemo_offline_dt5min_small_ldf_sbc6h_nodyn.values, lw=2, color="#6ACEEE", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc6h, noicedyn")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn.values, lw=2, color="#9ABF12", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc5min, noicedyn")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn_noforc_tint.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_nodyn_noforc_tint.values, lw=2, color="#111111", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,noicedyn,noforctint")
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_lim6h.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_lim6h.values, lw=2,
# color="m", label="NEMO-offline dt=5min,traldf3d, small ldf, sbc5min, lim 6h")
#
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_tkepenet_10m.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_tkepenet_10m.values, lw=2,
# color="y", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,tke penet 10m")
#
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_tke0001penet_10m.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_tke0001penet_10m.values, lw=2,
# color="#ccceee", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,tke penet0.01 10m")
#
# ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1.values, lw=2,
# color="#aaa111", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,GLS1m")
ax.plot(ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1_5year.index, ts_nemo_offline_dt5min_small_ldf_sbc5min_gls1_5year.values, lw=2,
color="#aaa111", label="NEMO-offline dt=5min,traldf3d,small ldf,sbc5min,GLS1m,5year")
# ax.set_xlim([date2num(datetime(1979, 10, 1)), date2num(datetime(1980, 12, 31))])
ax.xaxis.set_major_locator(YearLocator())
fig.autofmt_xdate()
ax.legend()
ax.grid()
if not os.path.isdir(img_folder):
os.mkdir(img_folder)
fig.tight_layout()
fig.savefig(os.path.join(img_folder, "lake_icefr_ts_{}-{}.png".format(start_year, end_year)), dpi=commons.dpi, transparent=True, bbox_inches="tight")
