Synchronization of extreme rainfall during the Australian summer monsoon: Complex network perspectives

Abstract

Monsoon rains are an important fresh water supply for agricultural activity, while extreme rainfalls during a monsoon season frequently cause flash floods. In this study, a nonlinear causation measure of event synchronization is used to set complex networks of extreme rainfall during the Australian summer monsoon (ASM) development between 1st November and 1st March. We adopted Tropical Rainfall Measuring Mission-based satellite rain rate estimates from 1998 to 2015. Examining several standard network centrality measures, such as degree and local clustering, we revealed the multiscale nature of ASM development, which previously was only studied by weather analysis methods. The land-sea contrast in surface heating critical for ASM is depicted clearly by the degree centrality. In addition, both the clustering coefficient and the community structure show critical change in spatial pattern matching with the climatological average onset time of the ASM during late December. The former is likely related to the interaction between synoptic forcing and mesoscale convection during monsoon onset, resulting in characteristic changes in the rainfall field. One of the network communities also extends spatially during the onset, revealing critical information from the near-equatorial region to ASM and would be applicable to monitor monsoon development. Results from this study further support that network measures as defined by a single parameter of rainfall have enormous potential for monsoon onset prediction.

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

“…The spatiotemporal dynamics of heavy rainfall associated with typhoons has been recently studied by Ozturk et al (2018Ozturk et al ( , 2019 with methods originating from the field of complex networks. Similar functional network representations of climate variability (Tsonis and Roebber, 2004;Donges et al, 2009;Donner et al, 2017;Dijkstra et al, 2019) have already been successfully used in past works to identify regions where heavy rainfall coherently occurs over considerably large spatial regions, e.g., in the context of the ISM (Malik et al, 2010(Malik et al, , 2012Stolbova et al, 2014), the South American Monsoon System (SAMS) (Boers et al, 2013(Boers et al, , 2014bGelbrecht et al, 2018), or the Australian summer monsoon (Cheung and Ozturk, 2020). For the EASM, an initial study employing a network-based analysis (He et al, 2014) has only recently been complemented by further research, revealing a distinct precipitation pattern for the Baiu and typhoon seasons over Japan (Ozturk et al, 2019).…”

“…The front retreats are associated with the convective activity over the northwestern Pacific and the northward shift of the NPSH from late July onward (Ninomiya and Muraki, 1986;Ueda and Yasunari, 1995;Okada and Yamazaki, 2012). The onset and strength of Baiu vary on an interannual basis (Zhu et al, 2013), which reduces its predictability via repetitive atmospheric circulation systems associated with large-scale teleconnection patterns like the Antarctic Oscillation, South Asian Anticyclone (SAA), and El Niño-Southern Oscillation (Zhu et al, 2008;Choi et al, 2012;Li et al, 2018).…”

Complex Network Approach for Detecting Tropical Cyclones

“…Different network measures are used to characterize the structural properties of the network over many spatial scales (i.e., the network topology), ranging from local properties such as the number of first neighbours of a node v (degree centrality), to global network measures such as the clustering coefficient or the average path length. For instance, the local clustering coefficient, a measure of similarity based on network topology, can be associated with spatial homogeneity of a rainfall field (Cheung and Ozturk, 2020;Boers et al, 2013) while the regions of high betweenness centrality reveal flow of energy and information that can be related to transport phenomena such as global surface ocean currents and winds (Donges et al, 2010;Boers et al, 2013). Climate networks have proved to be a very promising approach in the study of global patterns of extreme-rainfall teleconnections (Boers et al, 2019), and have aided in the improved prediction of climate phenomena such as the El Niño (Ludescher et al, 2014(Ludescher et al, , 2013Meng et al, 2017), the Indian Summer Monsoon (Malik et al, 2012;Stolbova et al, 2014) and the South American Monsoon (Ciemer et al, 2018), which occur over seasonal or annual time scales.…”

“…They exhibit rich dynamics, such as, structure formation and evolving collective behaviour between some of the ele-125 ments. Time-evolving complex networks have been used to investigate failure propagation in power-grids (Albert et al, 2004;Li et al, 2018), hierarchical structures in the brain (Strogatz, 2001;Zhou et al, 2006;Lehnertz et al, 2014), structural differences in the interconnec-130 tivity of the climate system between El Niño and La Niña conditions (Marwan et al, 2010), early-warning of El Niño events (Ludescher et al, 2014(Ludescher et al, , 2013Meng et al, 2017), transition of regional connectivity during the South American Monsoon onset (Ciemer et al, 2018), 135 and the multiscale nature of Australian Summer Monsoon (ASM) development (Cheung and Ozturk, 2020). In our work, we use networks constructed over overlapping short-length sliding time windows to compare the spatial patterns of the various topological properties, 140 such as degree and clustering coefficient, and the spatial scales involved, for short time frames around the occurrence of individual TCs.…”

Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

“…under global warming [8][9][10], while little research has been conducted on the regionality and the persistence of extreme precipitation events [11]. Extreme precipitation events are often more destructive if the intensity and frequency are relatively high within a certain spatial scope and temporal range [12,13].…”