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Sub‐seasonal prediction of rainfall over the South China Sea and its surrounding areas during spring–summer transitional season
Author(s) -
Li Qingquan,
Wang Juanhuai,
Yang Song,
Wang Fang,
Wu Jie,
Hu Yamin
Publication year - 2020
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.6456
Subject(s) - climatology , empirical orthogonal functions , environmental science , monsoon , precipitation , bay , forcing (mathematics) , sea surface temperature , china , teleconnection , bengal , geography , el niño southern oscillation , oceanography , meteorology , geology , archaeology
The sub‐seasonal characteristics and prediction of rainfall over the South China Sea and surrounding areas during spring–summer transitional season (April–May–June) are investigated using a full set of hindcasts generated by the Dynamic Extended Range Forecast operational system version 2.0 (DERF2.0) of Beijing Climate Center, China Meteorological Administration. The onset and development of Asian summer monsoon and the seasonal migration of rain belt over East Asia can be well depicted by the model hindcasts at various leads. However, there exist considerable differences between model results and observations, and model biases depend not only on the lead time, but also on the stage of monsoon evolution. In general, forecast skill drops with increasing lead time but rises again after lead time becomes longer than 30 days, possibly associated with the effect of slowly‐varying forcing or atmospheric variability. An abrupt turning point of bias development appears around mid‐May when bias growths of wind and precipitation exhibit significant changes over the northwestern Pacific and South Asia, especially over the Bay of Bengal and the South China Sea. This abrupt bias change is reasonably captured by the first two modes of multivariate empirical orthogonal function analysis, which reveals several important features associated with the bias change. This analysis may provide useful information for further improving model performance in sub‐seasonal rainfall prediction.

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