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SEASONAL PREDICTIONS FOR SPRING AND AUTUMN SURFACE AIR TEMPERATURES OVER SOUTHERN CHINA BY THE NCEP CFSV2
  Revised:August 15, 2019
KeyWords:southern China temperature  NCEP CFSv2  prediction  spring  autumn
Fund:
Author NameAffiliationE-mail
LI Chan-zhu 1. School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510080 China
2. Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-Sen University, Guangzhou 510080 China
3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000 China
4. Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, Guangzhou 510080 China
5. Thai Meteorological Department, Bangna, Bangkok 10260 Thailand 
 
YANG Song  yangsong3@mail.sysu.edu.cn 
LI Chun-hui   
Charoon LAOHALERTCHAI   
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Abstract:
      In this study, we investigate the variations of spring and autumn air temperatures in southern China (SC) and associated atmospheric circulation patterns. During the boreal spring, the SC air temperature is mainly influenced by tropical sea surface temperature anomalies (SSTAs). On the one hand, the El Ni?o SSTA pattern may induce a stronger-than-normal western Pacific subtropical high, which leads to warming in SC. On the other hand, the warm SSTAs in the tropical Indian Ocean may trigger anomalous Rossby wave trains, which propagate northeastward and result in anomalously high temperature in SC. During the boreal autumn, however, the SC temperature is more likely affected by mid-latitude atmospheric circulation, such as the wave trains forced by the North Atlantic SSTAs. The NCEP Climate Forecast System version 2 (CFSv2) is able to capture the climatology of SC air temperatures during both spring and autumn. For interannual variation, the CFSv2 shows a good skill for predicting the SC temperature in spring, due to the model’s good performance in capturing the associated atmospheric circulation anomalies as responses to tropical SSTAs, in spite of the overestimated relationship with the El Ni?o–Southern Oscillation (ENSO). However, the model has a poor skill for predicting the SC temperature in autumn, primarily due to the unrealistic prediction of its relationship with the ENSO.
DOI:10.16555/j.1006-8775.2019.04.003
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