School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China.
Key Laboratory of Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China.
Chaos. 2023 Feb;33(2):023105. doi: 10.1063/5.0131133.
Extreme event-based synchronicity is a specific measure of similarity of extreme event-like time series. It is capable to capture the nonlinear interactions between climatic extreme events. In this study, we proposed a modified extreme event-based synchronicity measure that incorporates two types of extreme events (positive and negative) simultaneously in climate anomalies to characterize the synchronization and time delays. Statistical significance of the modified extreme event synchronization measure is tested by Monte-Carlo simulations. The applications of the modified extreme event-based synchronicity measure on synthetic time series verified that it was superior to the traditional event synchronicity measure. Both synchronous and antisynchronous features between climate time series could be captured by the modified measure. It is potentially applied in investigating the interrelationship between climate extremes and climate index or constructing complex networks of climate variables. In addition, this modified extreme event-based synchronicity measure could be easily applied to other types of time series just by identifying the extreme events properly.
基于极端事件的同步性是一种极端事件似时间序列相似度的特定度量方法。它能够捕捉气候极端事件之间的非线性相互作用。在本研究中，我们提出了一种改进的基于极端事件的同步性度量方法，该方法同时将两种类型的极端事件（正和负）纳入气候异常中，以描述同步和时间延迟。通过蒙特卡罗模拟测试了改进的极端事件同步度量的统计显著性。改进的基于极端事件的同步度量在合成时间序列上的应用验证了它优于传统的事件同步度量。改进的度量方法可以捕捉气候时间序列之间的同步和异步特征。它有可能应用于研究气候极值与气候指数之间的相互关系或构建气候变量的复杂网络。此外，这种改进的基于极端事件的同步性度量方法可以很容易地应用于其他类型的时间序列，只需正确识别极端事件即可。
