Abstract
The seasonal prediction skill of the Asian summer monsoon is assessed using retrospective predictions (1982-2009) from the ECMWF System 4 (SYS4) and NCEP CFS version 2 (CFSv2) seasonal prediction systems. In both SYS4 and CFSv2, a cold bias of sea-surface temperature (SST) is found over the equatorial Pacific, North Atlantic, Indian Oceans and over a broad region in the Southern Hemisphere relative to observations. In contrast, a warm bias is found over the northern part of North Pacific and North Atlantic. Excessive precipitation is found along the ITCZ, equatorial Atlantic, equatorial Indian Ocean and the maritime continent. The southwest monsoon flow and the Somali Jet are stronger in SYS4, while the south-easterly trade winds over the tropical Indian Ocean, the Somali Jet and the subtropical northwestern Pacific high are weaker in CFSv2 relative to the reanalysis. In both systems, the prediction of SST, precipitation and low-level zonal wind has greatest skill in the tropical belt, especially over the central and eastern Pacific where the influence of El Nino-Southern Oscillation (ENSO) is dominant. Both modeling systems capture the global monsoon and the large-scale monsoon wind variability well, while at the same time performing poorly in simulating monsoon precipitation. The Asian monsoon prediction skill increases with the ENSO amplitude, although the models simulate an overly strong impact of ENSO on the monsoon. Overall, the monsoon predictive skill is lower than the ENSO skill in both modeling systems but both systems show greater predictive skill compared to persistence.
Original language | English |
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Pages (from-to) | 2975-2991 |
Number of pages | 17 |
Journal | Climate Dynamics |
Volume | 39 |
Issue number | 12 |
DOIs | |
State | Published - Nov 2012 |
Bibliographical note
Funding Information:We would like to thank the reviewers for thoughtful and helpful comments. The ECMWF System 4 reforecasts were obtained by the authors through a commercial agreement with ECMWF. The Climate Dynamics Division of the National Science Foundation under grant NSF-AGS 0965610 provided funding support for this research.