气候变化领域集成服务门户

With the acceleration of global warming, the frequent occurrence of extreme climate events has inflicted great socio-economies losses and casualties; therefore, it is particularly important to explore the characteristics of extreme climate events. Nine extreme climate indices defined by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI) were constructed from a long term (1960-2014) continuous dataset of 70 meteorological stations in Northeast China (NEC). We detected the current and future spatial variation characteristics using the Sen's slope estimator and R/S analysis method, respectively, and periodicities of individual extreme climate indices were calculated by Morlet continuous wavelet transform methods. Furthermore, a variety of marginal distribution functions was adopted to construct trivariate copula functions in order to calculate the joint probabilities and return periods. The results show that there were different spatiotemporal variation patterns of extreme climate indices: the extreme cold temperature index frost days (FDO) had a significant decreasing trend, summer days (SU2S), tropical nights (TR20), and warm days (TX90) had a significant increasing trend. However, no significant changes in the extreme precipitation indices were detected consistently; only a few stations scattered over NEC showed significant changes in these indices. Most of the extreme temperature indices showed significant periods of about 1.5, 2, and 3 years, whereas extreme cold and precipitation indices had approximate periods of 3, 3.5, and 4 years. Southeastern Jilin and northeastern Liaoning Provinces had colder winters and higher annual precipitation than other regions. The probabilities of high temperatures and heavy precipitation were relatively high in western Liaoning, Jilin, and Heilongjiang Provinces.