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  4. 中科院海岸带环境过程与生态修复重点实验室
Multiple serial correlations in global air temperature anomaly time series

文献类型：期刊论文

作者Gao, Meng2; Fang, Xiaoyu2; Ge, Ruijun2; Fan, You-ping2; Wang, Yueqi1
刊名PLOS ONE
出版日期2024-07-09
卷号19期号:7页码:20
ISSN号1932-6203
DOI10.1371/journal.pone.0306694
通讯作者Gao, Meng(gaomeng03@hotmail.com)
英文摘要Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth's climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Ni & ntilde;o-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Delta sigma, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.
WOS关键词LONG-RANGE DEPENDENCE ; TERM-MEMORY ; SEASONALITY
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001270730100001
资助机构Key Program of Shandong Natural Science Foundation ; National Natural Science Foundation of China
源URL[http://ir.yic.ac.cn/handle/133337/35666]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
烟台海岸带研究所_海岸带信息集成与综合管理实验室
通讯作者Gao, Meng
作者单位1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Peoples R China
2.Yantai Univ, Sch Math & Informat Sci, Yantai, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Meng,Fang, Xiaoyu,Ge, Ruijun,et al. Multiple serial correlations in global air temperature anomaly time series[J]. PLOS ONE,2024,19(7):20.
APA Gao, Meng,Fang, Xiaoyu,Ge, Ruijun,Fan, You-ping,&Wang, Yueqi.(2024).Multiple serial correlations in global air temperature anomaly time series.PLOS ONE,19(7),20.
MLA Gao, Meng,et al."Multiple serial correlations in global air temperature anomaly time series".PLOS ONE 19.7(2024):20.

入库方式： OAI收割

来源：烟台海岸带研究所

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