Open AccessGlobal Sea Surface Temperature Prediction Using a Multimodel Ensemble
Author(s) -
JongSeong Kug,
JuneYi Lee,
InSik Kang
Publication year - 2007
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr3458.1
Subject(s) - el niño southern oscillation , ensemble forecasting , sea surface temperature , forecast skill , climatology , simple (philosophy) , ensemble learning , meteorology , environmental science , computer science , machine learning , geology , geography , philosophy , epistemology
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.