Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season

Abstract

Abstract. Investigating the synchrony and interdependency of heavy rainfall occurrences is crucial to understand the underlying physical mechanisms and reduce physical and economic damages by improved forecasting strategies. In this context, studies utilizing functional network representations have recently contributed to significant advances in the understanding and prediction of extreme weather events. To thoroughly expand on previous works employing the latter framework to the East Asian summer monsoon (EASM) system, we focus here on changes in the spatial organization of synchronous heavy precipitation events across the monsoon season (April to August) by studying the temporal evolution of corresponding network characteristics in terms of a sliding window approach. Specifically, we utilize functional climate networks together with event coincidence analysis for identifying and characterizing synchronous activity from daily rainfall estimates between 1998 and 2018. Our results demonstrate that the formation of the Baiu front as a main feature of the EASM is reflected by a double-band structure of synchronous heavy rainfall with two centers north and south of the front. Although the two separated bands are strongly related to either low- or high-level winds, which are commonly assumed to be independent, we provide evidence that it is rather their mutual interconnectivity that changes during the different phases of the EASM season in a characteristic way. Our findings shed some new light on the interplay between tropical and extratropical factors controlling the EASM intraseasonal evolution, which could potentially help to improve future forecasts of the Baiu onset in different regions of East Asia.

Coupled network analysis revealing global monthly scale co-variability patterns between sea-surface temperatures and precipitation in dependence on the ENSO state

“…Communities are groups of nodes within a network that are relatively tightly connected with each other while being only weakly connected to the rest of the network [47]. This general feature can be characterized by different more specific properties (for example, the corresponding network modularity [47]) and has recently found its first applications in the context of functional climate network analysis [48][49][50][51]. Together with the practical relevance of communities as serving as meaningful subsystems that are solely determined by the overall connectivity structure [52], the broadness of this concept has made community detection an active area of research within complex network science [53,54].…”

“…In the context of functional climate networks, such small communities are more likely to represent physically meaningful spatial co-variability patterns because they are more strongly connected internally. Moreover, recent applications of Infomap in a climate network context have demonstrated that it provides certain climatologically meaningful features like the spatial coherency and connectedness of the identified communities [50,51].…”

“…Note that the presented cross-degree patterns do not distinguish between the destinations of links emanating from the SST field. For the latter purpose, our analysis calls for further indepth investigations by either utilizing measures that capture spatially explicit information, like link distance or directionality related network properties [50,51,65], or systematically identifying pairs of source and target regions of different types of links (representing positive and negative correlations, respectively) [51,66]. Such more detailed analyses are, however, beyond the scope of the present work.…”

Spatial organization of connectivity in functional climate networks describing event synchrony of heavy precipitation

“…In addition to the ISM, some authors have also investigated other branches of the Asian monsoon system such as the East Asian monsoon system (EASM) [50]. In particular, the Baiu front as a key part of the EASM system has been characterized by quantifying its spatial pattern [51] and temporal evolution [52] in terms of complex network characteristics.…”

Evolving climate network perspectives on global surface air temperature effects of ENSO and strong volcanic eruptions

“…One rather fundamental property of large networks is their (possibly hierarchical) organization in terms of communities -an aspect that has also been addressed recently in the context of key patterns in climate data [14,33,34]. Here, a community is a subset of densely connected nodes which exhibit only few interactions with the rest of the network [35,36].…”