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Climate Model Cloud Simulations
Zhang, M.H., Lin, W.Y., Klein, S.A., Bacmeister, J.T., Bony, S., Cederwall, R.T., Del Genio, A.D., Hack, J.J., Loeb, N.G., Lohmann, U., Minnis, P., Musat, I., Pincus, R., Stier, P., Suarez, M.J., Webb, M.J., Wu, J.B., Xie, S.C., Yao, M.-S. and Yang, J.H.  2005.  Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements.  Journal of Geophysical Research 110: D15S02, doi:10.1029/2004JD005021.

What was done
In an effort to assess the current status of climate models in simulating clouds, the authors compared basic cloud climatologies from ten atmospheric general circulation models with satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) and the Clouds and Earth's Radiant Energy System (CERES) program.  ISCCP data were available from 1983 to 2001, while data from the CERES program were available for the winter months (DJF) of 2001 and 2002 and for the summer months (JJA) of 2000 and 2001.  The purpose of the analysis was two-fold: (1) to assess the current status of climate models in simulating clouds so that future progress can be measured more objectively, and (2) to reveal serious deficiencies in the models so as to improve them.

What was learned
The authors' analyses revealed a huge list of major imperfections.  First, they report a four-fold difference in high clouds among the models, and that the majority of models only simulated 30-40% of the observed middle clouds, with some models simulating less than a quarter of observed middle clouds.  For low clouds, they report that half of the models underestimated them, such that the grand mean of low clouds from all models was about 70-80% of observations.  Furthermore, when stratified in optical thickness ranges, the majority of the models simulated optically thick clouds more than twice as frequently as satellite observations, while the grand mean of all models simulated about 80% of optical intermediate clouds and 60% of optically thin clouds.  In the case of individual cloud types, the group of twenty researchers reports that "differences of seasonal amplitudes among the models and satellite measurements can reach several hundred percent."

What it means
Zhang et al. conclude that "much more needs to be done to fully understand the physical causes of model cloud biases presented here and to improve the models."  We agree, especially since the deficiencies they discovered have relevance to model predictions of global climate change.  Until climate simulations can be conducted with a much greater degree of accuracy, it is unwise to put much credence in what they suggest about the future.  And to actually mandate drastic reductions in fossil-fuel usage on the basis of what they currently predict seems downright foolish.

Reviewed 16 November 2005