PDF《of炉he tropical oceans》

DOI 10.

1007/s00382-015-2569-2 Clim Dyn Warm season heavy rainfall events over?the Huaihe River Valley and?their linkage with?wintertime thermal condition of?the tropical oceans Laifang?Li1,5 ?・ Wenhong?Li1 ?・ Qiuhong?Tang2 ?・ Pengfei?Zhang3,4 ?・ Yimin?Liu3 ? Received:

13 September

2014 / Accepted:

16 March

2015 ? Springer-Verlag Berlin Heidelberg

2015 Furthermore, the interannual variation of summer pre- cipitation is attributable to the variation of heavy rainfall frequency over the HRV. The heavy rainfall frequency, in turn, is influenced by sea surface temperature anomalies (SSTAs) over the north Indian Ocean, equatorial western Pacific, and the tropical Atlantic. The tropical SSTAs mod- ulate the HRV heavy rainfall events by influencing atmos- pheric circulation favorable for the onset and maintenance of heavy rainfall events. Occurring 5?months prior to the summer season, these tropical SSTAs provide potential sources of prediction skill for heavy rainfall events over the HRV. Using these preceding SSTA signals, we show that the support vector machine algorithm can predict HRV heavy rainfall satisfactorily. The improved prediction skill has important implication for the nation'

s disaster early warning system. Keywords? Heavy rainfall events?・ Seasonal climate prediction?・ Bayesian inference on precipitation?・ Huaihe River Valley?・ Normal mixture model?・ Support vector machine 1?Introduction The Huaihe River Valley (HRV) is a key agricultural area in China to sustain food supplies to whole nation, where summertime flooding events are a primary breaker of local agriculture (Li et?al. 2011). Historical records showed that devastating flooding events over this region are tightly associated with heavy rainfall events. For example, in the late June and early July of 2003, consecutive heavy rain- fall events brought about 450?mm precipitation into the HRV, causing severe flooding, loss of life and billions of dollars in agricultural and economic damage (Zhang and Abstract? Warm season heavy rainfall events over the Huaihe River Valley (HRV) of China are amongst the top causes of agriculture and economic loss in this region. Thus, there is a pressing need for accurate seasonal predic- tion of HRV heavy rainfall events. This study improves the seasonal prediction of HRV heavy rainfall by implementing a novel rainfall framework, which overcomes the limitation of traditional probability models and advances the statis- tical inference on HRV heavy rainfall events. The frame- work is built on a three-cluster Normal mixture model, whose distribution parameters are sampled using Bayes- ian inference and Markov Chain Monte Carlo algorithm. The three rainfall clusters reflect probability behaviors of light, moderate, and heavy rainfall, respectively. Our anal- ysis indicates that heavy rainfall events make the largest contribution to the total amount of seasonal precipitation. * Wenhong Li wenhong.li@duke.edu

1 Earth and?Ocean Sciences, Nicholas School of?the Environment and?Earth Sciences, Duke University, 321C Old Chem. Bldg, P.O. Box 90227, Durham, NC 27708, USA

2 Key Laboratory of?Water Cycle and?Related Land Surface Processes, Institute of?Geographic Sciences and?Natural Resources Research, Chinese Academy of?Sciences, Beijing, People'

s Republic of?China

3 State Key Laboratory of?Numerical Modeling for?Atmospheric Sciences and?Geophysical Fluid Dynamics, Institute of?Atmospheric Physics, Chinese Academy of?Sciences, Beijing, People'

s Republic of?China

4 University of?Chinese Academy of?Sciences, Beijing, People'