The regional features of temperature variation trends over Xinjiang in China by the ensemble empirical mode decomposition method

被引:41
作者
Bai, Ling [1 ]
Xu, Jianhua [1 ]
Chen, Zhongsheng [1 ,2 ]
Li, Weihong [2 ]
Liu, Zuhan [3 ]
Zhao, Benfu [1 ]
Wang, Zujing [1 ]
机构
[1] E China Normal Univ, Educ Minist PRC, Key Lab GISci, Res Ctr East West Cooperat China, Shanghai 200241, Peoples R China
[2] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China
[3] Nanchang Inst Technol, Coll Informat Engn, Nanchang, Peoples R China
关键词
Xinjiang; temperature anomaly; ensemble empirical mode decomposition; intrinsic mode function; regional difference; VARIABILITY;
D O I
10.1002/joc.4202
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Based on a temperature anomaly time series from 16 international exchange stations in Xinjiang from 1957 to 2012, the multi-scale characteristics of temperature variability were analysed using the ensemble empirical mode decomposition (EEMD) method. Regional differences in variation trends and change-points were also preliminarily discussed. The results indicated that in the past 50+ years, the overall temperature in Xinjiang has exhibited a significant nonlinear upward trend, and its changes have clearly exhibited an inter-annual scale (quasi-3 and quasi-6-year) and inter-decadal scale (quasi-10 and quasi-30-year). The variance contribution rates of each component demonstrated that the inter-annual change had a strong influence on the overall temperature change in Xinjiang, and the reconstructed inter-annual variation trend could describe the fluctuation state of the original temperature anomaly during the study period. The reconstructed inter-decadal variability revealed that the climate mode in Xinjiang had a significant transformation before and after 1995, namely the temperature anomaly shift from a negative phase to a positive one. Furthermore, there were regional differences in the nonlinear changes and change-points of temperature. At the same time, the results also suggested that the EEMD method can effectively reveal variations in long-term temperature sequences at different time scales and can be used for the complex diagnosis of nonlinear and non-stationary signal changes.
引用
收藏
页码:3229 / 3237
页数:9
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