DOI: 10.1002/joc.5867
Scopus记录号: 2-s2.0-85054583920
论文题名: Non-stationary peaks-over-threshold analysis of extreme precipitation events in Finland, 1961–2016
作者: Pedretti D. ; Irannezhad M.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2019
卷: 39, 期: 2 起始页码: 1128
结束页码: 1143
语种: 英语
英文关键词: climate change
; extreme precipitation
; extreme value analysis
; Generalized Pareto
; non-stationarity
; Poisson distribution
Scopus关键词: Atmospheric pressure
; Decision making
; Poisson distribution
; Precipitation (meteorology)
; Uncertainty analysis
; Atmospheric circulation patterns
; Decision making process
; Extreme precipitation
; Extreme precipitation events
; Extreme value analysis
; Generalized Pareto
; Non-stationarities
; North Atlantic oscillations
; Climate change
; Arctic Oscillation
; autumn
; climate change
; climate effect
; climate variation
; estimation method
; extreme event
; North Atlantic Oscillation
; precipitation intensity
; summer
; threshold
; trend analysis
; weather forecasting
; Atlantic Ocean
; Baltic Sea
; Bothnian Sea
; Finland
; Gulf of Bothnia
; Lappi [Finland]
英文摘要: There is an urgent need to understand and predict how extreme precipitation events (EPEs) will change at high latitudes, both for local climate change adaptation plans and risk mitigation and as a potential proxy “anticipating” the impact of climate change elsewhere in the world. This paper illustrates that a combination of non-stationary modelling approaches can be adopted to evaluate trends in EPEs under uncertainty. A large database of daily rainfall events from 281 sparsely distributed weather stations in Finland between 1961 and 2016 was analysed. Among the tested methods, Poisson distributions provided a powerful method to evaluate the impacts of multiple physical covariates, including temperature and atmospheric circulation patterns (ACPs), on the resulting trends. The analysis demonstrates that non-stationarity is statistically valid for the majority of observations, independently of their location in the country and the season of the year. However, subsampling can severely hinder the statistical validity of the trends, which can be easily confused with random noise and therefore complicate the decision-making processes regarding long-term planning. Scaling effects have a strong impact on the estimates of non-stationary parameters, as homogenizing the data in space and time reduces the statistical validity of the trends. Trends in EPE statistics (mean, 90 and 99% percentiles) and best-fitted Generalized Pareto parameters in the tails of the distributions appear to be stronger when approaching the Polar region (Lapland) than away from it, consistent with the Arctic amplification of climate change. ACPs are key covariates in physically explaining these trends. In particular, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) can explain statistically significant increases in extreme precipitation in Lapland, Bothnian and South regions of Finland, particularly during summer and fall seasons. © 2018 Royal Meteorological Society Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/116562
Appears in Collections: 全球变化的国际研究计划
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作者单位: Geological Survey of Finland (GTK), Espoo, Finland; School of Environmental Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China; Water Resources and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland
Recommended Citation:
Pedretti D.,Irannezhad M.. Non-stationary peaks-over-threshold analysis of extreme precipitation events in Finland, 1961–2016[J]. International Journal of Climatology,2019-01-01,39(2)