def get_first_guess_surface_h(data_logger):
"""TODO: Function to conduct spinup for first guess surface_h"""
fl = data_logger.flowline_init
if 'surface_h' in list(data_logger.spinup_options.keys()):
mb_options = data_logger.spinup_options['surface_h']['mb_model']
if mb_options['type'] == 'constant':
yr_start_run = mb_options['years'][0]
yr_end_run = mb_options['years'][1]
halfsize = (yr_end_run - yr_start_run) / 2
mb_spinup = MultipleFlowlineMassBalance(
data_logger.gdir,
fls=[fl],
mb_model_class=ConstantMassBalance,
filename='climate_historical',
input_filesuffix='',
y0=yr_start_run + halfsize,
halfsize=halfsize)
mb_spinup.temp_bias = mb_options['t_bias']
model = FluxBasedModel(copy.deepcopy([fl]),
mb_spinup,
y0=yr_start_run)
model.run_until(yr_end_run)
return model.fls[0].surface_h
else:
raise NotImplementedError(
f'mb type {mb_options["type"]} not implemented!')
else:
raise NotImplementedError(
'The provided spinup option is not implemented!')
def create_spinup_glacier(gdir, rgi_id_to_name, yr_start_run, yr_end_run,
yr_spinup, used_mb_models):
"""TODO
"""
# now creat spinup glacier with consensus flowline starting from ice free,
# try to match length at rgi_date for 'realistic' experiment setting
fl_consensus = gdir.read_pickle('model_flowlines',
filesuffix='_consensus')[0]
length_m_ref = fl_consensus.length_m
fls_spinup = copy.deepcopy([fl_consensus])
fls_spinup[0].thick = np.zeros(len(fls_spinup[0].thick))
halfsize = (yr_start_run - yr_spinup) / 2
yr_rgi = gdir.rgi_date
mb_spinup = MultipleFlowlineMassBalance(gdir,
fls=fls_spinup,
mb_model_class=ConstantMassBalance,
filename='climate_historical',
input_filesuffix='',
y0=yr_spinup + halfsize,
halfsize=halfsize)
mb_historical = MultipleFlowlineMassBalance(gdir,
fls=fls_spinup,
mb_model_class=PastMassBalance,
filename='climate_historical',
input_filesuffix='')
def spinup_run(t_bias):
# with t_bias the glacier state after spinup is changed between iterations
mb_spinup.temp_bias = t_bias
# run the spinup
model_spinup = FluxBasedModel(copy.deepcopy(fls_spinup),
mb_spinup,
y0=0)
model_spinup.run_until_equilibrium(max_ite=1000)
# Now conduct the actual model run to the rgi date
model_historical = FluxBasedModel(model_spinup.fls,
mb_historical,
y0=yr_spinup)
model_historical.run_until(yr_rgi)
cost = (model_historical.length_m - length_m_ref) / length_m_ref * 100
return cost
glacier_name = rgi_id_to_name[gdir.rgi_id]
if experiment_glaciers[glacier_name]['t_bias_spinup_limits'] is None:
print(
'returned spinup_run function for searching for the t_bias_limits!'
)
return gdir, spinup_run
else:
t_bias_spinup_limits = \
experiment_glaciers[glacier_name]['t_bias_spinup_limits']
if experiment_glaciers[glacier_name]['t_bias_spinup'] is None:
# search for it by giving back
t_bias_spinup = brentq(spinup_run,
t_bias_spinup_limits[0],
t_bias_spinup_limits[1],
maxiter=20,
disp=False)
else:
t_bias_spinup = experiment_glaciers[glacier_name]['t_bias_spinup']
print(
f'{gdir.name} spinup tbias: {t_bias_spinup} with L mismatch at rgi_date:'
f' {spinup_run(t_bias_spinup)} m')
mb_spinup.temp_bias = t_bias_spinup
model_spinup = FluxBasedModel(copy.deepcopy(fls_spinup), mb_spinup, y0=0)
model_spinup.run_until_equilibrium(max_ite=1000)
gdir.write_pickle(model_spinup.fls,
'model_flowlines',
filesuffix='_spinup')