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

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

#

# WE15_EBM_fast.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

# Implicit Euler time stepping.

#

# The code WE15_EBM_simple.m, on the other hand, uses a simpler formulation

# of the diffusion operator and time stepping with Matlab's ode45.

#

# 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

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)

nt = .5

dur = 100

dt = 1/nt

# Spatial Grid ---------------------------------------------------------

dx = 1.0/n # grid box width

x = np.arange(dx/2,1+dx/2,dx) #native grid

xb = np.arange(dx,1,dx)

# Diffusion Operator (WE15, Appendix A) -----------------------------------

lam = D/dx**2*(1-xb**2)

L1=np.append(0, -lam)

L2=np.append(-lam, 0)

L3=-L1-L2

diffop = - np.diag(L3) - np.diag(L2[:n-1],1) - np.diag(L1[1:n],-1);

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

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

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

allT = np.zeros([dur*nt,n])

t = np.linspace(0,dur,dur*nt)

I = np.identity(n)

invMat = np.linalg.inv(I+dt/cw*(B*I-diffop))

# integration over time using implicit difference and

# over x using central difference (through diffop)

for i in range(0,int(dur*nt)):

a = aw*(T>0)+ai*(T<0) # WE15, eq.4

C = a*S-A+F

T0 = T+dt/cw*C

# Governing equation [cf. WE15, eq. (2)]:

# T(n+1) = T(n) + dt*(dT(n+1)/dt), with c_w*dT/dt=(C-B*T+diffop*T)

# -> T(n+1) = T(n) + dt/cw*[C-B*T(n+1)+diff_op*T(n+1)]

# -> T(n+1) = inv[1+dt/cw*(1+B-diff_op)]*(T(n)+dt/cw*C)

T = np.dot(invMat,T0)

allT[i,:]=T

fig = plt.figure(1)

fig.suptitle('EBM_fast_WE15')

plt.subplot(121)

plt.plot(t,allT)

plt.xlabel('t (years)')

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

plt.subplot(122)

plt.plot(x,T)

plt.xlabel('x')

plt.show()

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