Identifying climate memory impacts on climate network analysis using fractional integral techniques

Climate network (CN) analysis has demonstrated significant potential and is widely applied in climate research. However, revealing the underlying mechanisms behind the results obtained from CN analysis remains challenging. One possible reason for this difficulty lies in the method used to determine the links between nodes in the climate network. The commonly used Pearson correlation analysis may not be able to fully capture the complex dynamics of the climate system. In particular, the multi-scale interactions among multiple processes may induce scaling behaviors in the climate system, which further lead to long-term climate memory. The presence of such memory may influence CN analysis outcomes. In this work, we aim to identify the climate memory impacts on the CN analysis. Combining with the Fractional Integral Statistical Model (FISM), we proposed a new approach named as CN-FISM. The FISM model allows for the extraction of the climate memory component, enabling the modification of time series to preserve a specified length of memory. By conducting CN analysis on these adjusted series, one thus can quantify the impacts of climate memory. This approach has been successfully employed to a recent CN analysis on the Pacific Decadal Oscillation (PDO) phase change. Compared with the current Pearson correlation-based CN approach, the CN-FISM may enhance the interpretability of CN results.