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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/nhess-2019-290
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-290
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Sep 2019

30 Sep 2019

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This preprint was under review for the journal NHESS but the revision was not accepted.

Complex Networks Reveal Teleconnections between the Global SST and Rainfall in Southwest China

Panjie Qiao1, Wenqi Liu1, Yongwen Zhang1,2, and Zhiqiang Gong3 Panjie Qiao et al.
  • 1Data Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China
  • 2Department of Physics, Bar-Ilan University, Ramat Gan 52900, Israel
  • 3Laboratory for Climate Studies, National Climate Research Center CMA, Beijing 100081, China

Abstract. Extreme drought events have frequently occurred in Southwest China (SWC) in this century. The rainfall of SWC could be related to several climate systems such as the East Asia monsoon, Indian monsoon and El Nino-southern Oscillation. Also it tightly depends on the variety of landforms and the complex terrain of SWC. Therefore the mechanism and prediction of rainfall in the area have became a difficult and central issue in climatology. Here we develop a novel multi-variable network method to delineate the relation between the global sea surface temperature anomalies (SSTA) and the rainfall of SWC. Our results show the important degree patterns in the Western, Eastern Pacific Ocean and Indian Ocean, which significantly influence the rainfall in SWC. Particularly the patterns will change with season and connect to some specific subareas within SWC. The strongest teleconnection is observed for the spring rainfall. The underlying mechanism of our observed teleconnection could be related to the large-scale ocean–atmosphere circulations. Moreover, we can identify the time-lag of the teleconnection links that can potentially improve the prediction of rainfall in SWC.

Panjie Qiao et al.

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Panjie Qiao et al.

Panjie Qiao et al.

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