def sweep_down(model, dq, years, verbose=False):
q = q_from_S0(model.subprocess['insolation'].S0)
if verbose:
print 'Starting sweep down from initial q = {}'.format(q)
snowball = False
last_stable_ice_edge = None
previous_stable_ice_edge = None
qlist = []
ice_area_list = []
while not snowball:
qlist.append(q)
model.subprocess['insolation'].S0 = S0_from_q(q)
model.integrate_years(years, verbose=False)
model.integrate_years(1., verbose=False)
ice_area_list.append(model.timeave['ice_area'])
if verbose:
print 'q = {}'.format(q)
print 'ice_area = {}'.format(model.timeave['ice_area'])
if (model.timeave['ice_area'] == 1.):
snowball = True
else:
try:
previous_stable_ice_edge = climlab.process_like(
last_stable_ice_edge)
except:
pass
last_stable_ice_edge = climlab.process_like(model)
q -= dq
if previous_stable_ice_edge is None:
return ice_area_list, qlist, last_stable_ice_edge
else:
return ice_area_list, qlist, previous_stable_ice_edge
def test_radiative_forcing():
'''Run a single-column radiative-convective model with RRTMG radiation
out to equilibrium. Clone the model, double CO2 and measure the instantaneous
change in TOA flux. It should be positive net downward flux.'''
# State variables (Air and surface temperature)
state = climlab.column_state(num_lev=30, water_depth=1.)
# Fixed relative humidity
h2o = climlab.radiation.ManabeWaterVapor(name='WaterVapor', state=state)
# Couple water vapor to radiation
# Set icld=0 for clear-sky only (no need to call cloud overlap routine)
rad = climlab.radiation.RRTMG(name='Radiation',
state=state,
specific_humidity=h2o.q,
icld=0)
# Convective adjustment
conv = climlab.convection.ConvectiveAdjustment(name='Convection',
state=state,
adj_lapse_rate=6.5)
# Couple everything together
rcm = climlab.couple([rad, h2o, conv], name='Radiative-Convective Model')
rcm.integrate_years(5.)
assert np.abs(rcm.ASR - rcm.OLR) < 0.1 # close to energy balance
rcm2 = climlab.process_like(rcm)
rcm2.subprocess['Radiation'].absorber_vmr['CO2'] *= 2.
rcm2.compute_diagnostics()
assert (rcm2.ASR - rcm2.OLR) > 1. # positive radiative forcing
# Test the xarray interface
to_xarray(rcm2)
def test_radiative_forcing():
'''Run a single-column radiative-convective model with CAM3 radiation
out to equilibrium. Clone the model, double CO2 and measure the instantaneous
change in TOA flux. It should be positive net downward flux.'''
# State variables (Air and surface temperature)
state = climlab.column_state(num_lev=30, water_depth=1.)
# Parent model process
rcm = climlab.TimeDependentProcess(state=state)
# Fixed relative humidity
h2o = climlab.radiation.ManabeWaterVapor(state=state)
# Couple water vapor to radiation
rad = climlab.radiation.RRTMG(state=state, h2ovmr=h2o.q)
# Convective adjustment
conv = climlab.convection.ConvectiveAdjustment(state=state,
adj_lapse_rate=6.5)
# Couple everything together
rcm.add_subprocess('Radiation', rad)
rcm.add_subprocess('WaterVapor', h2o)
rcm.add_subprocess('Convection', conv)
rcm.integrate_years(5.)
#assert np.abs(rcm.ASR - rcm.OLR) < 0.1 # close to energy balance
# There is currently a problem with energy conservation in the RRTM module, need to look into this.
rcm2 = climlab.process_like(rcm)
rcm2.subprocess['Radiation'].co2vmr *= 2.
rcm2.compute_diagnostics()
def test_radiative_forcing():
'''Run a single-column radiative-convective model with RRTMG radiation
out to equilibrium. Clone the model, double CO2 and measure the instantaneous
change in TOA flux. It should be positive net downward flux.'''
# State variables (Air and surface temperature)
state = climlab.column_state(num_lev=30, water_depth=1.)
# Fixed relative humidity
h2o = climlab.radiation.ManabeWaterVapor(name='WaterVapor', state=state)
# Couple water vapor to radiation
# Set icld=0 for clear-sky only (no need to call cloud overlap routine)
rad = climlab.radiation.RRTMG(name='Radiation',
state=state,
specific_humidity=h2o.q,
icld=0)
# Convective adjustment
conv = climlab.convection.ConvectiveAdjustment(name='Convection',
state=state,
adj_lapse_rate=6.5)
# Couple everything together
rcm = climlab.couple([rad,h2o,conv], name='Radiative-Convective Model')
rcm.integrate_years(5.)
assert np.abs(rcm.ASR - rcm.OLR) < 0.1 # close to energy balance
rcm2 = climlab.process_like(rcm)
rcm2.subprocess['Radiation'].absorber_vmr['CO2'] *= 2.
rcm2.compute_diagnostics()
assert (rcm2.ASR - rcm2.OLR) > 1. # positive radiative forcing
# Test the xarray interface
to_xarray(rcm2)
def test_re_radiative_forcing():
state = climlab.column_state(num_lev=num_lev)
rad = climlab.radiation.CAM3(state=state)
rad.integrate_years(2)
assert np.abs(rad.ASR - rad.OLR) < 0.1 # close to energy balance
rad2 = climlab.process_like(rad)
rad2.absorber_vmr['CO2'] *= 2.
rad2.compute_diagnostics()
assert (rad2.ASR - rad2.OLR) > 1. # positive radiative forcing
def diffmodel(rcmodel):
diffmodel = climlab.process_like(rcmodel)
# meridional diffusivity in m**2/s
K = 0.05 / diffmodel.Tatm.domain.heat_capacity[0] * climlab.constants.a**2
d = climlab.dynamics.MeridionalDiffusion(K=K,
state={'Tatm': diffmodel.state['Tatm']},
**diffmodel.param)
diffmodel.add_subprocess('diffusion', d)
return diffmodel
def test_re_radiative_forcing():
state = climlab.column_state(num_lev=num_lev)
rad = climlab.radiation.CAM3(state=state)
rad.integrate_years(2)
assert np.abs(rad.ASR - rad.OLR) < 0.1 # close to energy balance
rad2 = climlab.process_like(rad)
rad2.absorber_vmr['CO2'] *= 2.
rad2.compute_diagnostics()
assert (rad2.ASR - rad2.OLR) > 1. # positive radiative forcing
def test_rce_radiative_forcing(rcm):
'''Run a single-column radiative-convective model with CAM3 radiation
out to equilibrium. Clone the model, double CO2 and measure the instantaneous
change in TOA flux. It should be positive net downward flux.'''
rcm.integrate_years(5.)
assert np.abs(rcm.ASR - rcm.OLR) < 0.1 # close to energy balance
rcm2 = climlab.process_like(rcm)
rcm2.subprocess['Radiation'].absorber_vmr['CO2'] *= 2.
rcm2.compute_diagnostics()
assert (rcm2.ASR - rcm2.OLR) > 1. # positive radiative forcing
def diffmodel(rcmodel):
diffmodel = climlab.process_like(rcmodel)
# thermal diffusivity in W/m**2/degC
D = 0.05
# meridional diffusivity in 1/s
K = D / diffmodel.Tatm.domain.heat_capacity[0]
d = climlab.dynamics.diffusion.MeridionalDiffusion(K=K,
state={'Tatm': diffmodel.state['Tatm']}, **diffmodel.param)
diffmodel.add_subprocess('diffusion', d)
return diffmodel
def test_rce_radiative_forcing(rcm):
'''Run a single-column radiative-convective model with CAM3 radiation
out to equilibrium. Clone the model, double CO2 and measure the instantaneous
change in TOA flux. It should be positive net downward flux.'''
rcm.integrate_years(5.)
assert np.abs(rcm.ASR - rcm.OLR) < 0.1 # close to energy balance
rcm2 = climlab.process_like(rcm)
rcm2.subprocess['Radiation'].absorber_vmr['CO2'] *= 2.
rcm2.compute_diagnostics()
assert (rcm2.ASR - rcm2.OLR) > 1. # positive radiative forcing
def diffmodel(rcmodel):
diffmodel = climlab.process_like(rcmodel)
# thermal diffusivity in W/m**2/degC
D = 0.05
# meridional diffusivity in 1/s
K = D / diffmodel.Tatm.domain.heat_capacity[0]
d = climlab.dynamics.MeridionalDiffusion(
K=K, state={'Tatm': diffmodel.state['Tatm']}, **diffmodel.param)
diffmodel.add_subprocess('diffusion', d)
return diffmodel
def test_external_tendency():
"""Check that we can add an externally defined tendency to a
radiative-convective model."""
model = climlab.GreyRadiationModel(num_lev=30)
model2 = climlab.process_like(model)
model.step_forward()
ext = climlab.process.ExternalForcing(state=model2.state)
temp_tend = 1E-5 # K/s
ext.forcing_tendencies['Tatm'][:] = temp_tend
model2.add_subprocess('External', ext)
model2.step_forward()
assert model.tendencies['Tatm'] + temp_tend == pytest.approx(model2.tendencies['Tatm'])
def diffmodel_surfflux(diffmodel):
diffmodel_surfflux = climlab.process_like(diffmodel)
# process models for surface heat fluxes
shf = SensibleHeatFlux(state=diffmodel_surfflux.state, Cd=0.5E-3)
lhf = LatentHeatFlux(state=diffmodel_surfflux, Cd=0.5E-3)
# set the water vapor input field for LHF
lhf.q = diffmodel_surfflux.q
diffmodel_surfflux.add_subprocess('SHF', shf)
diffmodel_surfflux.add_subprocess('LHF', lhf)
# Convective adjustment for atmosphere only
diffmodel_surfflux.remove_subprocess('convective adjustment')
conv = ConvectiveAdjustment(state={'Tatm':diffmodel_surfflux.state['Tatm']},
**diffmodel_surfflux.param)
diffmodel_surfflux.add_subprocess('convective adjustment', conv)
return diffmodel_surfflux
def diffmodel_surfflux(diffmodel):
'''Explicit surface sensible and latent heat fluxes.'''
diffmodel_surfflux = climlab.process_like(diffmodel)
# process models for surface heat fluxes
shf = climlab.surface.SensibleHeatFlux(state=diffmodel_surfflux.state, Cd=0.5E-3)
lhf = climlab.surface.LatentHeatFlux(state=diffmodel_surfflux.state, Cd=0.5E-3)
# set the water vapor input field for LHF
lhf.q = diffmodel_surfflux.q
diffmodel_surfflux.add_subprocess('SHF', shf)
diffmodel_surfflux.add_subprocess('LHF', lhf)
# Convective adjustment for atmosphere only
diffmodel_surfflux.remove_subprocess('convective adjustment')
conv = climlab.convection.ConvectiveAdjustment(state={'Tatm':diffmodel_surfflux.state['Tatm']},
**diffmodel_surfflux.param)
diffmodel_surfflux.add_subprocess('convective adjustment', conv)
return diffmodel_surfflux
col = climlab.GreyRadiationModel()
lev = col.lev
Tinterp = np.flipud(
np.interp(np.flipud(lev), np.flipud(level), np.flipud(Tglobal)))
col.Ts[:] = Tglobal[0]
col.Tatm[:] = Tinterp
epsarray = np.linspace(0.01, 0.1, 100)
OLRarray = np.zeros_like(epsarray)
# col está em equilibro térmico
eps = brentq(OLRanom, 0.01, 0.1)
col.subprocess['LW'].absorptivity = eps
col2 = climlab.process_like(col)
def log(a):
return math.log(a)
absorptivityChangeRate = 1.0005
years = 90
# Aumentando a taxa de absorção de radiação com amplo comprimento de onda
for i in range(years):
col2.subprocess['LW'].absorptivity *= absorptivityChangeRate
col2.integrate_years(1.)
col2.compute_diagnostics()
absorptivityChangeRate += -(1 /
(log(1 / (absorptivityChangeRate - 1)) *
