Abstract
Abstract A restricted statistical correction (RSC) approach is introduced to assess the sources of error in general circulation models (GCMs). RSC models short-term forecast error by considering linear transformations of the GCM's forcing terms, which produce a “best” model in a restricted least squares sense. The results of RSC provide 1) a partitioning of the systematic error among the various GCM's forcing terms, and 2) a consistent partitioning of the nonsystematic error among the GCM forcing terms, which maximize the explained variance. In practice, RSC requires a substantial reduction in the dimensionality of the resulting regression problem: the approach described here projects the fields on the eigenvectors of the error covariance matrix. An example of RSC is presented for the Goddard Earth Observing System (GEOS) GCM's vertically integrated moisture equation over the continental United States during spring. The results are based on the history of analysis increments (“errors”) from a multiyear da...
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