期刊
CLIMATE DYNAMICS
卷 49, 期 11-12, 页码 4281-4292出版社
SPRINGER
DOI: 10.1007/s00382-017-3586-0
关键词
Nonstationarity; Extreme precipitation event; Climate change; ENSO
资金
- National Natural Science Foundation of China [41622101, 91547118]
- State Key Laboratory of Earth Surface Processes and Resource Ecology
The El Nio-Southern Oscillation (ENSO) and local temperature are important drivers of extreme precipitation. Understanding the impact of ENSO and temperature on the risk of extreme precipitation over global land will provide a foundation for risk assessment and climate-adaptive design of infrastructure in a changing climate. In this study, nonstationary generalized extreme value distributions were used to model extreme precipitation over global land for the period 1979-2015, with ENSO indicator and temperature as covariates. Risk factors were estimated to quantify the contrast between the influence of different ENSO phases and temperature. The results show that extreme precipitation is dominated by ENSO over 22% of global land and by temperature over 26% of global land. With a warming climate, the risk of high-intensity daily extreme precipitation increases at high latitudes but decreases in tropical regions. For ENSO, large parts of North America, southern South America, and southeastern and northeastern China are shown to suffer greater risk in El Nio years, with more than double the chance of intense extreme precipitation in El Nio years compared with La Nia years. Moreover, regions with more intense precipitation are more sensitive to ENSO. Global climate models were used to investigate the changing relationship between extreme precipitation and the covariates. The risk of extreme, high-intensity precipitation increases across high latitudes of the Northern Hemisphere but decreases in middle and lower latitudes under a warming climate scenario, and will likely trigger increases in severe flooding and droughts across the globe. However, there is some uncertainties associated with the influence of ENSO on predictions of future extreme precipitation, with the spatial extent and risk varying among the different models.
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