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The robustness of the skewness as an early warning signal for abrupt climate change
Author(s) -
Xie XiaoQiang,
He WenPing,
Gu Bin,
Mei Ying,
Wang Jinsong
Publication year - 2019
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.6179
Subject(s) - skewness , warning system , robustness (evolution) , noise (video) , econometrics , climate change , statistics , environmental noise , kurtosis , environmental science , computer science , mathematics , physics , geology , artificial intelligence , telecommunications , sound (geography) , biochemistry , chemistry , oceanography , acoustics , image (mathematics) , gene
As a dynamical system approaches its critical threshold, the probability density distribution of the system will change significantly. Therefore, it is possible to present an early warning signal based on the changing skewness before reaching the critical threshold. Based on a zero‐dimensional climate model and several typical fold models, this paper systematically studies the influence of noise and missing data on the performance of the skewness coefficient as an early warning signal of an abrupt climate change. The results in three types of fold models show that the skewness coefficient has anti‐noise ability to some extent, but strong noise will significantly reduce the magnitude of the skewness coefficient and the time for early warning will also be shortened. In some cases, strong noise even will lead to the result that the skewness does not work in warning an impending abrupt change. However, the influence of strong noise on skewness is insignificant in the zero‐dimensional climate model. Therefore, the influence of strong noise needs to be considered in the practical application of the skewness coefficient as an early warning signal of an abrupt change. In addition, the results of all the models also indicate that different degrees of the missing data have no statistically significant effect on the warning performance of the skewness coefficient, even when the length of the missing data is up to 20% of the total sample size used in the present paper.