Evaluating the present-day simulation of clouds, precipitation,
and radiation in climate models
Robert Pincus, Crispian P. Batstone, Robert J. Patrick Hofmann, Karl E. Taylor and Peter J. Glecker
Journal of Geophysical Research, July 2008. doi:10.1029/2007JD009334.
This paper describes a set of metrics for evaluating the simulation of clouds, radiation, and precipitation in the present-day climate. As with the skill scores used to measure the accuracy of short-term weather forecasts, these metrics are low-order statistical measures of agreement with relevant, well-observed physical quantities. The metrics encompass five statistical summaries computed for five physical quantities (longwave, shortwave, and net cloud radiative effect, projected cloud fraction, and surface precipitation rate) over the global climatological annual cycle. Agreement is measured against two independent observational datasets.
The metrics are computed for the models that participated in the Coupled Model Intercomparison Project phase 3, which formed the basis for the Fourth Assessment of the IPCC. Model skill does not depend strongly on the dataset used for verification, indicating that observational uncertainty does not limit the ability to assess model simulations of these fields. No individual model excels in all scores though the "IPCC mean model," constructed by averaging the fields produced by all the CMIP models, performs particularly well across the board. This skill is due primarily to the individual model errors being distributed on both sides of the observations, and to a lesser degree to the models having greater skill at simulating large-scale features than those near the grid scale. No measure of model skill considered here is a good predictor of the strength of cloud feedbacks under climate change.
The model climatologies, observational data sets, and metric scores are available on-line.
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