Changes of extreme precipitation and nonlinear influence of climate variables over monsoon region in China
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AbstractThe El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO) and Pacific decadal oscillation (PDO) are well understood to be major drivers for the variability of precipitation extremes over monsoon regions in China (MRC). However, research on monsoon extremes in China and their associations with climate variables is limited. In this study, we examine the space-time variations of extreme precipitation across the MRC, and assess the time-varying influences of the climate drivers using Bayesian dynamic linear regression and their combined nonlinear effects through fitting generalized additive models. Results suggest that the central-east and south China is dominated by less frequent but more intense precipitation. Extreme rainfalls show significant positive trends, coupled with a significant decline of dry spells, indicating an increasing chance of occurrence of flood-induced disasters in the MRC during 1960–2014. Majority of the regional indices display some abrupt shifts during the 1990s. The influences of climate variables on monsoon extremes exhibit distinct interannual or interdecadal variations. IOD, ENSO and AMO have strong impacts on monsoon and extreme precipitation, especially during the 1990s, which is generally consistent with the abrupt shifts in precipitation regimes around this period. Moreover, ENSO mainly affects moderate rainfalls and dry spells, while IOD has a more significant impact on precipitation extremes. These findings could be helpful for improving the forecasting of monsoon extremes in China and the evaluations of climate models.
CitationGao T, Wang HJ, Zhou T (2017) Changes of extreme precipitation and nonlinear influence of climate variables over monsoon region in China. Atmospheric Research 197: 379–389. Available: http://dx.doi.org/10.1016/j.atmosres.2017.07.017.
SponsorsThis study is jointly supported by National Natural Science Foundation of China (Key Program) (No. 41330423), R&D Special Fund for Public Welfare Industry (meteorology) (No. GYHY201506012), the KAUSTOSR-2015-CRG4-2582 project, National Science FoundationDMS-1149355, Natural Science Foundation and Sci-tech development project of Shandong Province (No. ZR2015DQ004; J15LH10), Project funded by China Postdoctoral Science Foundation (No. 1191005830), and the Young Academic Backbone in Heze University (No. XY14BS05). We are grateful to the constructive comments and suggestions from the editor and anonymous reviewers, which have greatly helped improve the quality of this work.