Open AccessThe early warning signals of abrupt climate change in different regions of china
Author(s) -
Hao Wu,
Guolin Feng,
Wei Hou,
Peng Yan
Publication year - 2013
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.62.059202
Subject(s) - climate change , autocorrelation , warning system , abrupt climate change , environmental science , noise (video) , signal (programming language) , china , climatology , global warming , geology , effects of global warming , statistics , computer science , geography , mathematics , telecommunications , oceanography , archaeology , artificial intelligence , programming language , image (mathematics)
In recent years, critical slowing down phenomenon has shown great potentials in disclosing whether a complex dynamic tends toward critical cataclysm. Based on the concept of critical slowing down, the observed data of temperature in different regions in China which have different noises are processed in this article to study the precursory signal of abrupt climate change. First, Mann-Kendall(M-K)method is used to find the locations of the abrupt climate change in different regions, then the autocorrelation coefficient which can characterize critical slowing down is calculated; the appearance-time moments of early warning signals of abrupt climate change under the influence of different noises are also stadied. The results show that for different signal-to-noise ratios, the critical slowing down phenomenon has appeared in the data 5-10 years before the abrupt climate change took place, which indicateds that critical slowing down phenomenon is a possible early warning signal for abrupt climate change and the noise has less influence on the test results for early warning signals of abrupt climate change. Accordingly, it demonstrates the reliability of critical slowing down phenomenon to test the precursory signals of abrupt climate change, which provideds an experimental basis for the wide applications of the present method in real observation data.