Interconnection between the Indian and the East Asian summer monsoon: Spatial synchronization patterns of extreme rainfall events

Abstract

A deeper understanding of the intricate relationship between the two components of the Asian summer monsoon (ASM)-the Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM)-is crucial to improve the subseasonal forecasting of extreme precipitation events. Using an innovative complex network-based approach, we identify two dominant synchronization pathways between ISM and EASM-a southern mode between the Arabian Sea and southeastern China occurring in June, and a northern mode between the core ISM zone and northern China which peaks in July-and their associated large-scale atmospheric circulation patterns. Furthermore, we discover that certain phases of the Madden-Julian oscillation and the lower frequency mode of the boreal summer intraseasonal oscillation (BSISO) seem to favour the overall synchronization of extreme rainfall events between ISM and EASM while the higher-frequency mode of the BSISO is likely to support the shifting between the modes of ISM-EASM connection.

Geographical Trapping of Synchronous Extremes Amidst Increasing Variability of Indian Summer Monsoon Rainfall

“…Previous climate network studies (Boers et al., 2014a, 2014b, 2015, 2019; Gupta et al., 2022; Malik et al., 2012; Stolbova et al., 2014), heat waves (S. Mondal & Mishra, 2021) have extensively used CN distance measures such as “average length of links” or “geographic distance.” The Link Length (LL) L ij between two connected grid locations i and j is calculated by using the formula for spherical earth projected onto the plane as given $$${L}_{ij}=\sqrt{{\left(\delta {\phi }_{ij}\right)}^{2}+{\left(\cos \left({\phi }_{m}\right)\delta {\lambda }_{ij}\right)}^{2}}$$$ where δϕ ij and δλ ij are the differences in latitude and longitude in radians between the grid locations i and j , ϕ m is the mean of the latitudes of i and j , and R is the radius of the earth. The average geographical LL $${L}_{\mathit{avg}}^{i}$$ is the average geographical distance of i th node's links.…”

“…Over the Indian subcontinent, researchers (Malik et al., 2012) applied the CN method to investigate the spatial structure of rainfall extremes during the ISM and identified certain regions that receive rainfall only during the most active phase of ISM. In the study of large‐scale extremes, researchers have used correlation networks and synchrony‐based methods to understand the statistical properties of the underlying networks for extremes in rainfall, temperature, and droughts (Boers et al., 2019; Gupta et al., 2022; Konapala & Mishra, 2017; Malik et al., 2012; S. Mondal et al., 2023; J. Singh et al., 2021; Stolbova et al., 2014). However, little effort has been made to understand the evolution of spatial extent and temporal trends in the properties of the underlying networks.…”

“…Singh et al, 2014). Traditional methods, such as spatial regression analysis and empirical orthogonal functions, do not investigate the spatial characteristics of extreme rainfall co-occurrences (Gupta et al, 2022) and other complex statistical interrelationships of higher order (S. Mondal et al, 2023). Thus, a robust nonlinear approach is necessary to evaluate extreme event time series and comprehend synchronization, particularly when examining the dependence between event time series across different locations.…”

Analysis of spatially coherent forecast error structures

“…Then, measures derived from graph theory are used to characterize the topological features of the complex network so obtained (Donges et al ., 2009). The climate network approach has been used to study patterns of climate variability in different climate variables, such as temperature, pressure, geopotential height, wind, and precipitation, at various scales (Tsonis and Roebber, 2004; Yamasaki et al ., 2008; Donges et al ., 2009a; Ludescher et al ., 2013; Radebach et al ., 2013; Runge et al ., 2015; Gelbrecht et al ., 2017; Boers et al ., 2019; Gupta et al ., 2021; Lu et al ., 2022; Gupta et al ., 2023). The methodology has also been used in previous studies for the purpose of model evaluation in order to identify the underestimation or overestimation of statistical links, and hence teleconnection patterns, by comparing the depiction of climate interactions in the reanalysis data with that in the forecasts (Steinhaeuser and Tsonis, 2014; Boers et al ., 2015; Feldhoff et al ., 2015; Di Capua et al ., 2022; Gregory et al ., 2022; Dalelane et al ., 2023).…”

Glo3DHydroClimEventSet(v1.0): A global‐scale event set of hydroclimatic extremes detected with the 3D DBSCAN‐based workflow (1951–2022)

“…One is a spatiotemporal combination of various extreme events, such as temporally mutual drought‐pluvial transitions (He & Sheffield, 2020), spatial concurrences of dryness and wetness (De Luca et al, 2020), compound droughts and hot extremes (Hao et al, 2022; Liu et al, 2022a; Liu & Zhou, 2021), temporally compound extreme precipitations and heatwaves (Chen et al, 2021; Liu et al, 2022b; Mishra et al, 2022; Sauter et al, 2022). The other is spatial concurrences regarding a specific extreme event (e.g., extreme precipitations (Gupta et al, 2022; Na & Lu, 2023; Wu et al, 2018), heatwaves (Lu et al, 2023) and droughts (Li et al, 2020a, 2021; Mondal et al, 2023)) across the global or continental scales. For instance, in the 2021 early autumn, extreme precipitations concurrently appear over north China and the northwestern part of India (Na & Lu, 2023).…”