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Multimodel Ensembling in Seasonal Climate Forecasting at IRI

Anthony G. Barnston
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Simon J. Mason
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Lisa Goddard
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David G. DeWitt
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Stephen E. Zebiak
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The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM forecasts may render them more sufficient in their own right.

International Research Institute for Climate Prediction, Columbia University, Palisades, New York

CORRESPONDING AUTHOR: Anthony G. Barnston, International Research Institute for Climate Prediction, Columbia University, 61 Route 9W, Monell Rm. 227, Palisades, NY 10964, E-mail: tonyb@iri.columbia.edu

The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM forecasts may render them more sufficient in their own right.

International Research Institute for Climate Prediction, Columbia University, Palisades, New York

CORRESPONDING AUTHOR: Anthony G. Barnston, International Research Institute for Climate Prediction, Columbia University, 61 Route 9W, Monell Rm. 227, Palisades, NY 10964, E-mail: tonyb@iri.columbia.edu
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