Using the Flexible Global Ocean-Atmosphere-Land System model (FGOALS) version g1.11, a group of seasonal hindcasting experiments were carried out. In order to investigate the potential predictability of sea surface temperature (SST), singular value decomposition (SVD) analyses were applied to extract dominant coupled modes between observed and predicated SST from the hindcasting experiments in this study. The fields discussed are sea surface temperature anomalies over the tropical Pacific basin (20~0S-20~0N...
【英文摘要】
Using the Flexible Global Ocean–Atmosphere–Land System model (FGOALS) version g1.11, a group of seasonal hindcasting experiments were carried out. In order to investigate the potential predictability of sea surface temperature (SST), singular value decomposition (SVD) analyses were applied to extract dominant coupled modes between observed and predicated SST from the hindcasting experiments in this study. The fields discussed are sea surface temperature anomalies over the tropical Pacific basin (20~0S–20~0N...
【基金】
supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 40975065 and 40821092);
the National Basic Research Program (NBRP) “Ocean–atmosphere interaction over the joining area of Asia and the Indian-Pacific Ocean (AIPO) and its impact on the short-term climate variation in China” project(2006CB403605)
【更新日期】
2010-08-06
【分类号】
P731.11;P434
【正文快照】
1. Introduction There has been an increasing interest in seasonal forecasting in recent years, of which the main develop- ment direction has been dynamical prediction (Palmer et al., 2004; Wang et al., 2009). However, the predic- tive ability of dynamical
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