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

doi:10.1175/JCLI-D-17-0356.1

YIC-IR > 中国科学院海岸带环境过程与生态修复重点实验室 > 海岸带信息集成与战略规划研究中心

	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)
发表期刊	JOURNAL OF CLIMATE
ISSN	0894-8755
	2017-12-01
卷号	30 期号:24 页码:9897-9914
关键词	SURFACE AIR-TEMPERATURE ARCTIC OSCILLATION CLIMATE-CHANGE PRECIPITATION EXTREMES HYDROLOGICAL SERIES MODEL SIMULATIONS EASTERN CHINA DETECT TREND LONG-MEMORY ENSO
研究领域	Meteorology & Atmospheric Sciences
DOI	10.1175/JCLI-D-17-0356.1
产权排序	第1完成单位
作者部门	海岸带信息集成与综合管理实验室
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.
文章类型	Article
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)
收录类别	SCI
语种	英语
关键词[WOS]	SURFACE AIR-TEMPERATURE ; ARCTIC OSCILLATION ; CLIMATE-CHANGE ; PRECIPITATION EXTREMES ; HYDROLOGICAL SERIES ; MODEL SIMULATIONS ; EASTERN CHINA ; DETECT TREND ; LONG-MEMORY ; ENSO
研究领域[WOS]	Meteorology & Atmospheric Sciences
WOS记录号	WOS:000423492500006
引用统计	被引频次：39[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/23569
专题	中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
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.