Preconfigured Energy Balance Models

In this document the basic use of climlab's preconfigured EBM class is shown.

Contents are how to

setup an EBM model
show and access subprocesses
integrate the model
access and plot various model variables
calculate the global mean of the temperature



In [1]:

    
# import header

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import climlab
from climlab import constants as const
from climlab.domain.field import global_mean

Model Creation

The regular path for the EBM class is climlab.model.ebm.EBM but it can also be accessed through climlab.EBM

An EBM model instance is created through



In [2]:

    
# model creation
ebm_model = climlab.EBM()

By default many parameters are set during initialization:

num_lat=90, S0=const.S0, A=210., B=2., D=0.55, water_depth=10., Tf=-10, a0=0.3, a2=0.078, ai=0.62, timestep=const.seconds_per_year/90., T0=12., T2=-40

For further details see the climlab documentation.

Many of the input parameters are stored in the following dictionary:



In [3]:

    
# print model parameters
ebm_model.param









    Out[3]:





{'A': 210.0,
 'B': 2.0,
 'D': 0.555,
 'S0': 1365.2,
 'Tf': -10.0,
 'a0': 0.3,
 'a2': 0.078,
 'ai': 0.62,
 'timestep': 350632.51200000005,
 'water_depth': 10.0}

The model consists of one state variable (surface temperature) and a couple of defined subprocesses.



In [4]:

    
# print model states and suprocesses
print ebm_model









    



climlab Process of type <class 'climlab.model.ebm.EBM'>. 
State variables and domain shapes: 
  Ts: (90, 1) 
The subprocess tree: 
top: <class 'climlab.model.ebm.EBM'>
   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>
   LW: <class 'climlab.radiation.AplusBT.AplusBT'>
   albedo: <class 'climlab.surface.albedo.StepFunctionAlbedo'>
      iceline: <class 'climlab.surface.albedo.Iceline'>
      cold_albedo: <class 'climlab.surface.albedo.ConstantAlbedo'>
      warm_albedo: <class 'climlab.surface.albedo.P2Albedo'>
   insolation: <class 'climlab.radiation.insolation.P2Insolation'>

Model subprocesses

The subprocesses are stored in a dictionary and can be accessed through



In [5]:

    
# access model subprocesses
ebm_model.subprocess.keys()









    Out[5]:





['diffusion', 'LW', 'albedo', 'insolation']

So to access the time type of the Longwave Radiation subprocess for example, type:



In [6]:

    
# access specific subprocess through dictionary
ebm_model.subprocess['LW'].time_type









    Out[6]:





'explicit'

Model integration

The model time dictionary shows information about all the time related content and quantities.



In [7]:

    
# accessing the model time dictionary
ebm_model.time









    Out[7]:





{'day_of_year_index': 0,
 'days_elapsed': 0,
 'days_of_year': array([   0.        ,    4.05824667,    8.11649333,   12.17474   ,
          16.23298667,   20.29123333,   24.34948   ,   28.40772667,
          32.46597333,   36.52422   ,   40.58246667,   44.64071333,
          48.69896   ,   52.75720667,   56.81545333,   60.8737    ,
          64.93194667,   68.99019333,   73.04844   ,   77.10668667,
          81.16493333,   85.22318   ,   89.28142667,   93.33967333,
          97.39792   ,  101.45616667,  105.51441333,  109.57266   ,
         113.63090667,  117.68915333,  121.7474    ,  125.80564667,
         129.86389333,  133.92214   ,  137.98038667,  142.03863333,
         146.09688   ,  150.15512667,  154.21337333,  158.27162   ,
         162.32986667,  166.38811333,  170.44636   ,  174.50460667,
         178.56285333,  182.6211    ,  186.67934667,  190.73759333,
         194.79584   ,  198.85408667,  202.91233333,  206.97058   ,
         211.02882667,  215.08707333,  219.14532   ,  223.20356667,
         227.26181333,  231.32006   ,  235.37830667,  239.43655333,
         243.4948    ,  247.55304667,  251.61129333,  255.66954   ,
         259.72778667,  263.78603333,  267.84428   ,  271.90252667,
         275.96077333,  280.01902   ,  284.07726667,  288.13551333,
         292.19376   ,  296.25200667,  300.31025333,  304.3685    ,
         308.42674667,  312.48499333,  316.54324   ,  320.60148667,
         324.65973333,  328.71798   ,  332.77622667,  336.83447333,
         340.89272   ,  344.95096667,  349.00921333,  353.06746   ,
         357.12570667,  361.18395333]),
 'num_steps_per_year': 90.0,
 'steps': 0,
 'timestep': 350632.51200000005,
 'years_elapsed': 0}

To integrate the model forward in time different methods are availible:



In [8]:

    
# integrate model for a single timestep
ebm_model.step_forward()

The model time step has increased from 0 to 1:



In [9]:

    
ebm_model.time['steps']









    Out[9]:





1



In [10]:

    
# integrate model for a 50 days
ebm_model.integrate_days(50.)









    



Integrating for 12 steps, 50.0 days, or 0.136895462792 years.
Total elapsed time is 0.144444444444 years.



In [11]:

    
# integrate model for two years
ebm_model.integrate_years(1.)









    



Integrating for 90 steps, 365.2422 days, or 1.0 years.
Total elapsed time is 1.14444444444 years.



In [12]:

    
# integrate model until solution converges
ebm_model.integrate_converge()









    



Total elapsed time is 9.14444444444 years.

Plotting model variables

A couple of interesting model variables are stored in a dictionary named diagnostics. It has following entries:



In [13]:

    
ebm_model.diagnostics.keys()









    Out[13]:





['ASR', 'OLR', 'icelat', 'net_radiation', 'albedo', 'insolation']

They can be accessed respecively to the keys through ebm_model.diagnostics['ASR']. Most of them can be also accessed directly as model attributes like:



In [14]:

    
ebm_model.icelat









    Out[14]:





array([-70.,  70.])

The following code does the plotting for some model variables.



In [15]:

    
# creating plot figure
fig = plt.figure(figsize=(15,10))

# Temperature plot
ax1 = fig.add_subplot(221)
ax1.plot(ebm_model.lat,ebm_model.Ts)

ax1.set_xticks([-90,-60,-30,0,30,60,90])
ax1.set_xlim([-90,90])
ax1.set_xlabel('latitude')
ax1.set_ylabel('surface temperature (degC)', fontsize=12)
ax1.grid()

# Albedo plot
ax2 = fig.add_subplot(223, sharex = ax1)
ax2.plot(ebm_model.lat,ebm_model.albedo)

ax2.set_ylabel('albedo', fontsize=12)
ax2.set_ylim([0,1])
ax2.grid()

# Net Radiation plot
ax3 = fig.add_subplot(222, sharex = ax1)
ax3.plot(ebm_model.lat, ebm_model.OLR, label='OLR',
                                         color='cyan')
ax3.plot(ebm_model.lat, ebm_model.ASR, label='ASR',
                                         color='magenta')
ax3.plot(ebm_model.lat, ebm_model.ASR-ebm_model.OLR, 
                                         label='net radiation',
                                         color='red')

ax3.set_ylabel('radiation (W/m$^2$)', fontsize=12)
ax3.legend(loc='best')
ax3.grid()


# Energy Balance plot
net_rad = np.squeeze(ebm_model.net_radiation)
transport = ebm_model.heat_transport_convergence()

ax4 = fig.add_subplot(224, sharex = ax1)
ax4.plot(ebm_model.lat, net_rad, label='net radiation', 
                                             color='red')
ax4.plot(ebm_model.lat, transport, label='heat transport', 
                                             color='blue')
ax4.plot(ebm_model.lat, net_rad+transport, label='balance',
                                             color='black')

ax4.set_ylabel('energy (W/m$^2$)', fontsize=12)
ax4.legend(loc='best')
ax4.grid()


plt.show()

Global mean temperature

The model's state dictionary has following entries:



In [16]:

    
ebm_model.state.keys()









    Out[16]:





['Ts']

So the surface temperature can usually be accessed through ebm_model.state['Ts'] but is also availible as a model attribute: ebm_model.Ts

The global mean of the model's surface temperature can be calculated through



In [17]:

    
# calculate global mean temperature
global_mean(ebm_model.Ts)









    Out[17]:





Field(14.288135944994657)

Note that in the header the global_mean method has been imported!



In [18]:

    
print 'The global mean temperature is %s degC with a model ice edge at %s deg.' \
       %(np.round(global_mean(ebm_model.Ts),2), np.max(ebm_model.icelat))









    



The global mean temperature is 14.29 degC with a model ice edge at 70.0 deg.