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The regional features of temperature variation trends over Xinjiang in China by the ensemble empirical mode decomposition method
Author(s) -
Bai Ling,
Xu Jianhua,
Chen Zhongsheng,
Li Weihong,
Liu Zuhan,
Zhao Benfu,
Wang Zujing
Publication year - 2015
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4202
Subject(s) - anomaly (physics) , hilbert–huang transform , climatology , mode (computer interface) , scale (ratio) , environmental science , climate change , series (stratigraphy) , variation (astronomy) , nonlinear system , china , mathematics , geology , geography , statistics , energy (signal processing) , physics , computer science , paleontology , oceanography , cartography , archaeology , quantum mechanics , astrophysics , condensed matter physics , operating system
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.