 # Reference: "How Model Complexity Influences Sea Ice Stability",

# T.J.W. Wagner & I. Eisenman, J Clim (2015)

#

# WE15_EBM_simple.m:

# This code describes the EBM as discussed in Sec. 2b of the article above,

# hereafter WE15. Here we use central difference spatial integration and

# time stepping with MATLAB's ode45.

#

# The code WE15_EBM_fast.m, on the other hand, uses a faster, but more

# complicated formulation of the diffusion operator and Implicit Euler time

# stepping.

#

# Parameters are as described in WE15, table 1. Note that we do not include

# ocean heat flux convergence or a seasonal cylce in the forcing

# (equivalent to S_1 = F_b = 0 in WE15). This code uses an ice albedo when

# T<0 (WE15 instead uses the condition E<0, which is appropriate for the

# inclusion of a seasonal cycle in ice thickness). In this code, we define

# T = Ts - Tm, where Ts is the surface temperature and Tm the melting point

# (WE15, by contrast, defines T = Ts).

#

# Till Wagner & Ian Eisenman, Mar 15

##-------------------------------------------------------------------------

import numpy as np

from scipy.integrate import odeint

import matplotlib.pyplot as plt

##Model parameters (WE15, Table 1 and Section 2d) -------------------------

D = 0.6 # diffusivity for heat transport (W m^-2 K^-1)

A = 193 # OLR when T = 0 (W m^-2)

B = 2.1 # OLR temperature dependence (W m^-2 K^-1)

cw = 9.8 # ocean mixed layer heat capacity (W yr m^-2 K^-1)

S0 = 420 # insolation at equator (W m^-2)

S2 = 240 # insolation spatial dependence (W m^-2)

a0 = 0.7 # ice-free co-albedo at equator

a2 = 0.1 # ice=free co-albedo spatial dependence

ai = 0.4 # co-albedo where there is sea ice

F = 0 # radiative forcing (W m^-2)

# -------------------------------------------------------------------------

n = 50 # grid resolution (number of points between equator and pole)

x = np.linspace(0,1,n)

dx = 1.0/(n-1)

S = S0-S2*x**2 # insolation [WE15 eq. (3) with S_1 = 0]

aw = a0-a2*x**2 # open water albedo

# ODE with spatial finite differencing-------------------------------------

def odefunc(T,t):

alpha = aw*(T>0)+ai*(T<0)

C = alpha*S-A+F

Tdot = np.zeros(x.shape)

# solve c_wdT/dt = D(1-x^2)d^

for i in range(1,n-1):

Tdot[i]=(D/dx**2)*(1-x[i]**2)*(T[i+1]-2*T[i]+T[i-1])-(D*x[i]/dx)*(T[i+1]-T[i-1])

# solve c_wdT/dt = D(1-x^2)d^2T/dx^2 - 2xDdT/dx + C - BT [cf. WE15, eq.(2)]

# use central difference

Tdot = D*2*(T-T)/dx**2

Tdot[-1] = -D*2*x[-1]*(T[-1]-T[-2])/dx

f = (Tdot+C-B*T)/cw

return f

T0 = 10*np.ones(x.shape) # initial condition (constant temp. 10C everywhere)

time = np.linspace(0.0,30.0,1000) # time span in years

sol = odeint(odefunc,T0,time) # solve

fig = plt.figure(1)

fig.suptitle('EBM_simple_WE15')

plt.subplot(121)

plt.plot(time,sol)

plt.xlabel('t (years)')

plt.ylabel('T (in \$^\circ\$C)')

plt.subplot(122)

plt.plot(x,sol[-1,:])

plt.xlabel('x')

plt.show()