气候变化领域集成服务门户(CCPortal): Copula-based non-stationarity detection of the precipitation-temperature dependency structure dynamics and possible driving mechanism

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DOI	10.1016/j.atmosres.2020.105280
	Copula-based non-stationarity detection of the precipitation-temperature dependency structure dynamics and possible driving mechanism
	Dong H.; Huang S.; Fang W.; Leng G.; Wang H.; Ren K.; Zhao J.; Ma C.
发表日期	2021
ISSN	0169-8095
卷号	249
英文摘要	In the context of global warming, precipitation (P) and temperature (T) are the most important climate indicators playing important roles in the hydrological cycle. Nevertheless, the response of their dependency structures to the changing environment is not clearly revealed on a regional or global scale. To this end, the non-stationarity of the precipitation-temperature (P-T) dependency structure was identified via the Copula-based Likelihood-ratio (CLR) method, which further verified through the frequently used double mass curve method. Furthermore, local meteorological factors (e.g. wind speed (WS), sunshine duration (SD), relative humidity (RH) and vapour pressure (VP)) and teleconnection factors (e.g. the Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO) and Sunspots) were selected to explore possible driving forces and mechanism of the P-T dependency structure dynamics. The Datong River Basin (DRB), located in the Qinghai-Tibet Plateau, one of the climate change-sensitive and eco-sensitive areas worldwide, was selected as a case study. Results showed that: (1) the CLR method simultaneously capturing bivariate linear and nonlinear information is more superior than the double mass curve in detecting the non-stationarity of bivariate dependency structure; (2) change points of P-T dependency structure were identified at Qilian and Minhe stations, indicating that its non-stationarity occurred in the DRB; (3) in terms of local meteorological factors, the P-T dependency structure dynamics were directly driven by the VP, which was closely associated with the Clausius-Clapeyron (CC) equation where P and T would be theoretically linked by the atmospheric moisture; (4) in terms of teleconnection factors, the impacts of AO and PDO on local meteorological (VP, WS, and RH) are dominant, which further leads to the change in the P-T dependency structure dynamics. Generally, this study provides important insights into the response of the P-T dependency structure dynamics to a changing environment, where the proposed research framework could be extended to any other watershed and any bivariate hydro-meteorological elements. © 2020 Elsevier B.V.
英文关键词	Copula-based Likelihood-ratio method; Double mass curve; Driving mechanism; Non-stationarity; Precipitation-temperature dependency structure; Teleconnection factors
语种	英语
scopus关键词	Atmospheric humidity; Atmospheric pressure; Dynamics; Global warming; Nonlinear equations; Wind; Dependency structures; Meteorological elements; Meteorological factors; Nonlinear information; Pacific decadal oscillation; Precipitation temperature; Qinghai Tibet plateau; Southern oscillation; Atmospheric structure; air temperature; atmospheric dynamics; detection method; meteorology; precipitation assessment; river basin; teleconnection; China; Datong Basin; Qinghai-Xizang Plateau; Shanxi
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141649
作者单位	State Key Laboratory of Eco-Hydraulic in Northwest Arid Region of China, Xi'an University of Technology, Xi'an, 710048, China; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; China Institute of Water Resources and Hydropower Research, State Key Lab Simulat & Regulat Water Cycle River, Beijing, 100038, China
推荐引用方式 GB/T 7714	Dong H.,Huang S.,Fang W.,et al. Copula-based non-stationarity detection of the precipitation-temperature dependency structure dynamics and possible driving mechanism[J],2021,249.
APA	Dong H..,Huang S..,Fang W..,Leng G..,Wang H..,...&Ma C..(2021).Copula-based non-stationarity detection of the precipitation-temperature dependency structure dynamics and possible driving mechanism.Atmospheric Research,249.
MLA	Dong H.,et al."Copula-based non-stationarity detection of the precipitation-temperature dependency structure dynamics and possible driving mechanism".Atmospheric Research 249(2021).