def _compute_fixed(self):
'''Recompute any fixed quantities after a change in parameters'''
phi = np.deg2rad(self.lat)
try:
albedo = self.a0 + self.a2 * P2(np.sin(phi))
except:
albedo = np.zeros_like(phi)
# make sure that the diagnostic has the correct field dimensions.
dom = self.domains['default']
self.albedo = Field(albedo, domain=dom)
def _compute_fixed(self):
phi = np.deg2rad(self.lat)
# Why is there a silent fail here? Should get rid of this.
try:
insolation = self.S0 / 4 * (1. + self.s2 * P2(np.sin(phi)))
# make sure that the diagnostic has the correct field dimensions.
dom = self.domains['default']
#self.insolation = Field(insolation, domain=dom)
self.insolation[:] = Field(insolation, domain=dom)
except:
pass
def _compute_fixed(self):
#lat = self.domains['default'].axes['lat'].points
lat = self.lat
phi = np.deg2rad(lat)
try:
insolation = self.S0 / 4 * (1. + self.s2 * P2(np.sin(phi)))
# make sure that the diagnostic has the correct field dimensions.
dom = self.domains['default']
self.diagnostics['insolation'] = Field(insolation, domain=dom)
except:
pass
def _compute_fixed(self):
'''Recompute any fixed quantities after a change in parameters'''
try:
lon, lat = np.meshgrid(self.lon, self.lat)
except:
lat = self.lat
phi = np.deg2rad(lat)
try:
albedo = self.a0 + self.a2 * P2(np.sin(phi))
except:
albedo = np.zeros_like(phi)
# make sure that the diagnostic has the correct field dimensions.
#dom = self.domains['default']
# this is a more robust way to get the single value from dictionary:
dom = next(iter(self.domains.values()))
self.albedo = Field(albedo, domain=dom)
def _compute_fixed(self):
phi = np.deg2rad(self.lat)
# Why is there a silent fail here? Should get rid of this.
try:
insolation = self.S0 / 4 * (1. + self.s2 * P2(np.sin(phi)))
dom = self.domains['default']
try:
insolation = to_latlon(insolation, domain=dom)
self.insolation[:] = insolation
except:
self.insolation[:] = Field(insolation, domain=dom)
# make sure that the diagnostic has the correct field dimensions.
#self.insolation = Field(insolation, domain=dom)
self.insolation[:] = Field(insolation, domain=dom)
self.coszen[:] = self._coszen_from_insolation()
# Silent fail only for attribute error: _s2 is not an attribute of self
# but s2 parameter is being stored in self._s2
except AttributeError:
pass
def test_analytical():
'''Check to see if the the numerical solution converges to the analytical
steady-state solution of the simple EBM with constant albedo'''
param = {
'a0': 0.3,
'a2': 0.,
'ai': 0.3,
's2': -0.48,
'S0': 1360.,
'A': 210.,
'B': 2.,
'D': 0.55,
'Tf': -1000., # effectively makes albedo constant
}
m = climlab.EBM(**param)
m.integrate_years(5)
Tnumerical = np.squeeze(m.Ts)
delta = param['D'] / param['B']
x = np.sin(np.deg2rad(m.lat))
Tanalytical = ((1 - param['a0']) * param['S0'] / 4 *
(1 + param['s2'] * P2(x) /
(1 + 6 * delta)) - param['A']) / param['B']
assert Tnumerical == pytest.approx(Tanalytical, abs=2E-2)
from climlab import domain
from climlab.domain import field
from climlab.utils.legendre import P2
import numpy as np
import matplotlib.pyplot as plt
# create domain
sfc = domain.zonal_mean_surface(num_lat=36)
lat = sfc.lat.points
lat_rad = np.deg2rad(lat)
# define initial temperature distribution
T0 = 15.
T2 = -20.
Ts = field.Field(T0 + T2 * P2(np.sin(lat_rad)), domain=sfc)
# create budyko transport process
budyko_transp = BudykoTransport(b=4., state=Ts)
### Integrate & Plot ###
fig = plt.figure()
ax = fig.add_subplot(111)
for i in np.arange(0, 3, 1):
ax.plot(lat, budyko_transp.default, label='day %s' % (i * 40))
budyko_transp.integrate_days(40.)
ax.set_title('Standalone Budyko Transport')
ax.set_xlabel('latitude')