TITLE

Global Sea Surface Temperature Prediction Using a Multimodel Ensemble

AUTHOR(S)
Kug, Jong-Seong; Lee, June-Yi; Kang, In-Sik
PUB. DATE
September 2007
SOURCE
Monthly Weather Review;Sep2007, Vol. 135 Issue 9, p3239
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
In a tier-two seasonal prediction system, prior to AGCM integration, global SSTs should first be predicted as a boundary condition to the AGCM. In this study, a global SST prediction system has been developed as a part of the tier-two seasonal prediction system. This system uses predictions from four models—one dynamic, two statistical, and persistence—and a simple composite ensemble method is applied to these models. The simple composite ensemble prediction system has predictive skill over most of the global oceans for up to a 6-month forecast lead time. The simple ensemble method is also compared with other more sophisticated ensemble methods. The simple composite method has forecast skill comparable to the other ensemble methods over the ENSO region and significantly better skill outside the ENSO region.
ACCESSION #
26534180

 

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