Global Sea Surface Temperature Prediction Using a Multimodel Ensemble

Jong-Seong Kug Climate Environment System Research Center, Seoul National University, Seoul, South Korea

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June-Yi Lee International Pacific Research Center, SOEST, University of Hawaii at Manoa, Honolulu, Hawaii

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In-Sik Kang Climate Environment System Research Center, Seoul National University, Seoul, South Korea

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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.

Corresponding author address: Dr. In-Sik Kang, School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, South Korea. Email: kang@climate.snu.ac.kr

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.

Corresponding author address: Dr. In-Sik Kang, School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, South Korea. Email: kang@climate.snu.ac.kr

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