Climate Dynamics

, Volume 48, Issue 5–6, pp 1633–1645 | Cite as

Empirical prediction of the onset dates of South China Sea summer monsoon

  • Zhiwei Zhu
  • Tim Li


The onset of South China Sea summer monsoon (SCSSM) signifies the commencement of the wet season over East Asia. Predicting the SCSSM onset date is of significant importance. In this study, we establish two different statistical models, namely the physical–empirical model (PEM) and the spatial–temporal projection model (STPM) to predict the SCSSM onset. The PEM is constructed from the seasonal prediction perspective. Observational diagnoses reveal that the early onset of the SCSSM is preceded by (a) a warming tendency in middle and lower troposphere (850–500 hPa) over central Siberia from January to March, (b) a La Niña-like zonal dipole sea surface temperature pattern over the tropical Pacific in March, and (c) a dipole sea level pressure pattern with negative center in subtropics and positive center over high latitude of Southern Hemisphere in January. The PEM built on these predictors achieves a cross-validated reforecast temporal correlation coefficient (TCC) skill of 0.84 for the period of 1979–2004, and an independent forecast TCC skill of 0.72 for the period 2005–2014. The STPM is built on the extended-range forecast perspective. Pentad data are used to predict a zonal wind index over the South China Sea region. Similar to PEM, the STPM is constructed using 1979–2004 data. Based on the forecasted zonal wind index, the independent forecast of the SCSSM onset dates achieves a TCC skill of 0.90 for 2005–2014. The STPM provides more detailed information for the intraseasonal evolution during the period of the SCSSM onset (pentad 25–35). The two models proposed herein are expected to facilitate the real-time prediction of the SCSSM onset.


The onset dates of South China Sea summer monsoon Seasonal prediction Extended-range forecast Physical–empirical model Spatial–temporal projection model 



The authors would like to thank two anonymous reviewers for their constructive comments and suggestions. We also thank NCC and ITMM for providing the onset dates of SCSSM. This work was supported by China National 973 project 2015CB453200, ONR grant N00014-16-12260, NSFC grant 41475084, Jiangsu Natural Science Foundation Key project (BK20150062), Jiangsu Shuang-Chuang Team (R2014SCT001), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). This is SOEST contribution number 9626, IPRC contribution Number 1189, and ESMC Number 106.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)Nanjing University of Information Science and TechnologyNanjingChina
  2. 2.International Pacific Research Center and Department of Atmospheric SciencesUniversity of Hawaii at ManoaHonoluluUSA

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