Event synchrony measures for functional climate network analysis: A case study on South American rainfall dynamics.

Area of Science:

• Climatology
• Network Science
• Extreme Weather Events

Background:

• Understanding spatiotemporal patterns of climate extremes is crucial due to ongoing climate change.
• Data-driven methods, like functional climate networks, aid in predicting extreme events and their interrelations.
• Existing methods like event synchronization (ES) have limitations in handling clustered extreme events.

Purpose of the Study:

• To compare the functional climate network representations of South American heavy precipitation events using ES and event coincidence analysis (ECA).
• To investigate the impact of temporal event clustering correction on these network structures.
• To combine complementary information from ES and ECA to better understand the spatiotemporal organization of extreme events during the South American Monsoon season.

Main Methods:

• Application of event synchronization (ES) and event coincidence analysis (ECA) to South American heavy precipitation data.
• Comparison of network features derived from ES and ECA, with and without correction for temporal event clustering.
• Analysis of spatiotemporal organization of extreme events during the South American Monsoon season.

Main Results:

• Both ES and ECA exhibit distinct biases impacting the resulting network structures.
• Corrected ES captures multiple time scales of heavy rainfall cascades simultaneously.
• ECA effectively disentangles these time scales, enabling explicit tracing of spatiotemporal propagation.

Conclusions:

• ES and ECA provide complementary insights into the spatiotemporal dynamics of extreme precipitation.
• Corrected ES and ECA together offer a more comprehensive understanding of extreme event organization.
• The findings advance the analysis of climate extremes and their propagation patterns.