Multiple serial correlations in global air temperature anomaly time series

doi:10.1371/journal.pone.0306694

	Multiple serial correlations in global air temperature anomaly time series
	Gao, Meng1 ; Fang, Xiaoyu 1; Ge, Ruijun 1; Fan, You-ping 1; Wang, Yueqi 2
发表期刊	PLOS ONE
ISSN	1932-6203
	2024-07-09
卷号	19 期号:7 页码:20
DOI	10.1371/journal.pone.0306694
通讯作者	Gao, Meng([email protected])
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.
资助机构	Key Program of Shandong Natural Science Foundation ; National Natural Science Foundation of China
收录类别	SCI
语种	英语
关键词[WOS]	LONG-RANGE DEPENDENCE ; TERM-MEMORY ; SEASONALITY
研究领域[WOS]	Science & Technology - Other Topics
WOS记录号	WOS:001270730100001
引用统计
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/35667
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
通讯作者	Gao, Meng
作者单位	1.Yantai Univ, Sch Math & Informat Sci, Yantai, Peoples R China 2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, 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.