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Moist Energy Balance Model (mebm) Guide

License: GPL v3

Installation

First, get the repository on your computer with

git clone https://github.com/hgpeterson/mebm

and

cd mebm

so that you're in the package. Next, install the Python packages you'll need using

pip install -r requirements.txt

which will install matplotlib, numpy, scipy, climt, and sympl if you don't already have them. climt (and its dependency, sympl) are only used for the more complicated radiative transfer schemes. Now, to install the mebm package, type

pip install -e .

where the -e flag allows you to edit the mebm source if you wish. Now you can use the mebm package in any Python script by putting

...
import mebm
...

at the top.

Quick Guide

For an informative example, see the file in this repository under example/example.py, the contents of which are

import mebm 

# Instantiate `model` object
model = mebm.MoistEnergyBalanceModel(N_pts=2**9+1, max_iters=1e3, tol=1e-4, 
            diffusivity="constant", control_file="default")

# Set its initial temperature, insolation type, albedo, and outgoing longwave
model.set_init_temp(init_temp_type="legendre", low=250, high=300)
model.set_insol(insol_type="perturbation", perturb_center=15, 
            perturb_spread=4.94, perturb_intensity=10)
model.set_albedo(al_feedback=True, alb_ice=0.6, alb_water=0.2)
model.set_olr(olr_type="full_radiation", A=None, B=None, emissivity=None)

# Solve model equation
model.solve(numerical_method="multigrid", frames=100)

# Save model data to `simulation.npz`
model.save_data(control=False)

# Log energy flux equator information to `itcz.log`
model.log_efe(fname_efe="itcz.log")

# Calculate climate feedback transports 
model.calculate_feedbacks()

# Create some plots based on model solution
model.save_plots()

Let's go through each line. First, we instantiate a model object with

model = mebm.MoistEnergyBalanceModel(N_pts=2**9+1, max_iters=1e3, tol=1e-4, 
            diffusivity="constant", control_file="default")

This creates a model with 513 evenly spaced gridpoints (in x = sin(phi) coordinates). The solve method will use at most 1000 multigrid V-cycle iterations or terminate when changes are less than 0.0001 K. This particular model run will use a constant eddy diffusivity (2.6e-4 kg m2 s-1 from Hwange and Frierson 2010) and comparer the results to the default control file, though you can supply your own after using control=True in the method save_data which will output a ctrl.npz file.

TBC...

References

Clark, S.K., Y. Ming, I.M. Held, P.J. Phillipps, 2018: The Role of the Water Vapor Feedback in the ITCZ Response to Hemispherically Asymmetric Forcings. Journal of Climate. 31, 3659–3678.

Hwang, Y.T. and D.M.W. Frierson, 2010: Increasing Atmospheric Poleward Energy Transport with Global Warming. Geo. Res. Lett.. 37, 1–5.

Peterson, H. and W. Boos, 2020: Feedbacks and Eddy Diffusivity in an Energy Balance Model of Tropical Rainfall Shifts. npj Climate and Atmospheric Science. 3, 11.