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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)
2017-12-01
发表期刊JOURNAL OF CLIMATE
ISSN0894-8755
卷号30期号:24页码:9897-9914
产权排序第1完成单位
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.
DOI10.1175/JCLI-D-17-0356.1
项目资助者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)
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收录类别SCI
关键词[WOS]SURFACE AIR-TEMPERATURE ; ARCTIC OSCILLATION ; CLIMATE-CHANGE ; PRECIPITATION EXTREMES ; HYDROLOGICAL SERIES ; MODEL SIMULATIONS ; EASTERN CHINA ; DETECT TREND ; LONG-MEMORY ; ENSO
文章类型Article
语种英语
WOS研究方向Meteorology & Atmospheric Sciences
WOS记录号WOS:000423492500006
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被引频次:1[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
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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.
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