Intercomparison of heating rates generated by global climate model longwave radiation codes
F. Baer, N. Arsky,1 J. J. Charney,2 and R. G. Ellingson
Department of Meteorology, University of Maryland College Park
Abstract. Longwave radiative heating, which has a pronounced
impact on climate prediction, is represented in Global Climate
Models (GCMs) by an algorithm which converts model input parameters
to heating rates. Since each GCM has a unique longwave radiative
heating parameterization, an intercomparison of seven frequently
used algorithms designed to assess their variability to input
data was performed. The algorithms' heating rate calculation,
which is perhaps the most important aspect of the parameterization
in that it is a principal part which the GCM actually incorporates
into its climate prediction, was evaluated by subjecting each
to identical input parameters and comparing the resulting output.
It should be noted that the overall shape of a given heating
rate profile depends strongly on the depth of the model layers
over which the average conditions were determined. But since
GCMs ultimately see the heating rates only at model levels, this
aspect of heating rate calculations is transparent to the models
themselves. For clear sky conditions, the algorithms were tested
with a diverse range of input data taken from different geographic
locations and seasons and with various distributions of vertical
levels. Analysis of the results from these clear sky experiments
indicated that heating rate profiles generated by the algorithms
were similar, with maximum variations of the order of 0.5°K/d.
The differences in algorithm output became substantially more
pronounced when clouds at one or more levels with varying thickness
were introduced into the input conditions, particularly if the
clouds were thicker than one model level. Indeed, for some cloud
configurations the resulting profiles of heating rates appear
to have no correspondence whatsoever to one another. How important
these differences are to ultimate GCM climate predictions is currently
under study.