Quantile Regression-Based Spatiotemporal Analysis of Extreme Temperature Change in China

doi:10.1175/JCLI-D-17-0356.1

CORC > 烟台海岸带研究所 > 中国科学院烟台海岸带研究所 > 中科院海岸带环境过程与生态修复重点实验室 > 海岸带信息集成与综合管理实验室

	Quantile Regression-Based Spatiotemporal Analysis of Extreme Temperature Change in China
	Gao, Meng1 ; Franzke, Christian L. E.2,3; Gao, Meng(Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China)
刊名	JOURNAL OF CLIMATE
	2017-12-01
卷号	30 期号:24 页码:9897-9914
ISSN号	0894-8755
DOI	10.1175/JCLI-D-17-0356.1
产权排序	第1完成单位
文献子类	Article
英文摘要	In this study, temporal trends and spatial patterns of extreme temperature change are investigated at 352 meteorological stations in China over the period 1956-2013. The temperature series are first examined for evidence of long-range dependence at daily and monthly time scales. At most stations there is evidence of significant long-range dependence. Noncrossing quantile regression has been used for trend analysis of temperature series. For low quantiles of daily mean temperature and monthly minimum value of daily minimum temperature (TNn) in January, there is an increasing trend at most stations. A decrease is also observed in a zone ranging from northeastern China to central China for higher quantiles of daily mean temperature and monthly maximum value of daily maximum temperature (TXx) in July. Changes of the large-scale atmospheric circulation partly explain the trends of temperature extremes. To reveal the spatial pattern of temperature changes, a density-based spatial clustering algorithm is used to cluster the quantile trends of daily temperature series for 19 quantile levels (0.05, 0.1,..., 0.95). Spatial cluster analysis identifies a few large clusters showing different warming patterns in different parts of China. Finally, quantile regression reveals the connections between temperature extremes and two large-scale climate patterns: El Nino-Southern Oscillation (ENSO) and the Arctic Oscillation (AO). The influence of ENSO on cold extremes is significant at most stations, but its influence on warm extremes is only weakly significant. The AO not only affects the cold extremes in northern and eastern China, but also affects warm extremes in northeastern and southern China.
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WOS关键词	SURFACE AIR-TEMPERATURE ; ARCTIC OSCILLATION ; CLIMATE-CHANGE ; PRECIPITATION EXTREMES ; HYDROLOGICAL SERIES ; MODEL SIMULATIONS ; EASTERN CHINA ; DETECT TREND ; LONG-MEMORY ; ENSO
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
WOS记录号	WOS:000423492500006
资助机构	Youth Innovation Promotion Association of CAS(2016195) ; CAS Knowledge Innovation Project(KZCX2-EW-QN209) ; National Natural Science Foundation of China(31570423) ; German Research Foundation through the collaborative research center "Energy transfers in atmosphere and ocean" at the University of Hamburg(TRR181)
内容类型	期刊论文
源URL	[http://ir.yic.ac.cn/handle/133337/23569]
专题	烟台海岸带研究所_海岸带信息集成与综合管理实验室
通讯作者	Gao, Meng; Gao, Meng(Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China)
作者单位	1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China 2.Univ Hamburg, Inst Meteorol, Hamburg, Germany 3.Univ Hamburg, Ctr Earth Syst Res & Sustainabil, Hamburg, Germany
推荐引用方式 GB/T 7714	Gao, Meng,Franzke, Christian L. E.,Gao, Meng. Quantile Regression-Based Spatiotemporal Analysis of Extreme Temperature Change in China[J]. JOURNAL OF CLIMATE,2017,30(24):9897-9914.
APA	Gao, Meng,Franzke, Christian L. E.,&Gao, Meng.(2017).Quantile Regression-Based Spatiotemporal Analysis of Extreme Temperature Change in China.JOURNAL OF CLIMATE,30(24),9897-9914.
MLA	Gao, Meng,et al."Quantile Regression-Based Spatiotemporal Analysis of Extreme Temperature Change in China".JOURNAL OF CLIMATE 30.24(2017):9897-9914.