A modified extreme event-based synchronicity measure for climate time series
Meng Gao1, Ying Zhao1, Zhen Wang1
1School of Mathematics and Information Sciences, Yantai University, Yantai 264005, China.
Chaos (Woodbury, N.Y.)
|March 1, 2023
Summary
This study introduces a new measure for extreme event synchronicity in climate data, improving the analysis of nonlinear interactions. The modified method effectively captures both positive and negative extreme events, offering superior insights into climate variability.
Area of Science:
- Climatology
- Time Series Analysis
- Network Science
Background:
- Extreme events in climate time series exhibit complex nonlinear interactions.
- Existing synchronicity measures may not fully capture the nuances of these interactions.
- Understanding interrelationships between climate extremes is crucial for climate modeling and prediction.
Purpose of the Study:
- To develop a modified extreme event-based synchronicity measure.
- To incorporate both positive and negative extreme events simultaneously.
- To characterize synchronization and time delays in climate anomalies.
Main Methods:
- Proposed a modified extreme event-based synchronicity measure.
- Utilized Monte-Carlo simulations to test statistical significance.
- Applied the measure to synthetic time series for validation.
Main Results:
- The modified measure effectively captures nonlinear interactions between extreme climatic events.
- It successfully identifies both synchronous and antisynchronous features in climate time series.
- Demonstrated superiority over traditional event synchronicity measures.
Conclusions:
- The modified extreme event-based synchronicity measure offers enhanced capabilities for analyzing climate extremes.
- It has potential applications in investigating climate index interrelationships and constructing climate variable networks.
- The method is adaptable to other time series by proper extreme event identification.
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