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Climate Dynamics

, Volume 50, Issue 5–6, pp 2007–2021 | Cite as

Extended-range forecasting of Chinese summer surface air temperature and heat waves

  • Zhiwei Zhu
  • Tim Li
Article

Abstract

Because of growing demand from agricultural planning, power management and activity scheduling, extended-range (5–30-day lead) forecasting of summer surface air temperature (SAT) and heat waves over China is carried out in the present study via spatial–temporal projection models (STPMs). Based on the training data during 1960–1999, the predictability sources are found to propagate from Europe, Northeast Asia, and the tropical Pacific, to influence the intraseasonal 10–80 day SAT over China. STPMs are therefore constructed using the projection domains, which are determined by these previous predictability sources. For the independent forecast period (2000–2013), the STPMs can reproduce EOF-filtered 30–80 day SAT at all lead times of 5–30 days over most part of China, and observed 30–80 and 10–80 day SAT at 25–30 days over eastern China. Significant pattern correlation coefficients account for more than 50% of total forecasts at all 5–30-day lead times against EOF-filtered and observed 30–80 day SAT, and at a 20-day lead time against observed 10–80 day SAT. The STPMs perform poorly in reproducing 10–30 day SAT. Forecasting for the first two modes of 10–30 day SAT only shows useful skill within a 15-day lead time. Forecasting for the third mode of 10–30 day SAT is useless after a 10-day lead time. The forecasted heat waves over China are determined by the reconstructed SAT which is the summation of the forecasted 10–80 day SAT and the lower frequency (longer than 80-day) climatological SAT. Over a large part of China, the STPMs can forecast more than 30% of heat waves within a 15-day lead time. In general, the STPMs demonstrate the promising skill for extended-range forecasting of Chinese summer SAT and heat waves.

Keywords

Spatial–temporal projection model Summer surface air temperature over China Heat waves Extended-range forecast 

Notes

Acknowledgements

This work was supported by NSFC Project 41630423, 973 Project 2015CB453200, NSF AGS-1643297 and AGS-1565653, NSFC 41475084, NRL Grant N00173-161G906, Jiangsu NSF Project BK20150062, Jiangsu Shuang-Chuang Team (R2014SCT001) and the Key Laboratory of Meteorological Disaster of Ministry of Education (KLME1407). This is SOEST Contribution Number 10015, IPRC Contribution Number 1254, and ESMC Contribution Number 163.

Supplementary material

382_2017_3733_MOESM1_ESM.pdf (8.1 mb)
Supplementary material 1 (PDF 8267 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

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 Sciences, SOESTUniversity of Hawaii at ManoaHonoluluUSA

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