The climatic interdependence of extreme-rainfall events around the globe

TL;DR

This paper uses a complex-network-based clustering approach to identify and analyze global patterns of extreme rainfall event interdependence, revealing key monsoon-related structures and their atmospheric connections during boreal summer.

Contribution

It introduces a robust, dataset-agnostic clustering workflow combining consensus clustering and mutual correspondences to study ERE interdependence patterns globally.

Findings

Identified consistent synchronized structures of EREs globally during boreal summer.

Uncovered primary monsoon system components and their variability.

Verified atmospheric connection patterns through a case study on the Asian summer monsoon.

Abstract

The identification of regions of similar climatological behavior can be utilized for the discovery of spatial relationships over long-range scales, including teleconnections. In this regard, the global picture of the interdependence patterns of extreme rainfall events (EREs) still needs to be further explored. To this end, we propose a top-down complex-network-based clustering workflow, with the combination of consensus clustering and mutual correspondences. Consensus clustering provides a reliable community structure under each dataset, while mutual correspondences build a matching relationship between different community structures obtained from different datasets. This approach ensures the robustness of the identified structures when multiple datasets are available. By applying it simultaneously to two satellite-derived precipitation datasets, we identify consistent synchronized structures of EREs around the globe, during boreal summer. Two of them show independent spatiotemporal characteristics, uncovering the primary compositions of different monsoon systems. They explicitly manifest the primary intraseasonal variability in the context of the global monsoon, in particular the `monsoon jump' over both East Asia and West Africa and the mid-summer drought over Central America and southern Mexico. Through a case study related to the Asian summer monsoon (ASM), we verify that the intraseasonal changes of upper-level atmospheric conditions are preserved by significant connections within the global synchronization structure. Our work advances network-based clustering methodology for (i) decoding the spatiotemporal configuration of interdependence patterns of natural variability and for (ii) the intercomparison of these patterns, especially regarding their spatial distributions over different datasets.