Based on the complex network method, this study reveals the temporal and spatial characteristics of the synchronization of extreme rainfall events in southwest China (SWC) during the main rainfall season (May–September). Our results show the significant synchronization pattern of extreme rainfall events in SWC, which is closely associated with the convergence of warm‐wet air and cold‐dry air and the corresponding occurrence of extreme rainfall in SWC. The network divergence indicates a dominated direction of the net influence of extreme rainfall from the Sichuan Basin to the Yunnan–Guizhou Plateau, implying the extreme rainfall synchronization moving from north to south in SWC is more frequent than the opposite direction. The development of the low vortex and the north wind system usually shows a movement from north to south, resulting in the evolution of extreme rainfall events more tend to present a southward movement. The low‐reinforced latitude cyclonic circulation leads to the strengthened transportation of water vapour from the tropical area to the north and converges with the anticyclonic circulation in the mid‐latitudes to form the movement of heavy rainfall events from the Yunnan–Guizhou Plateau to the Sichuan Basin. Therefore, the synchronization direction of extreme rainfall events in SWC has obvious asymmetry spatial feature due to the more frequent and stronger circulation system moving from north to south than the opposite.
Droughts and floods have frequently occurred in Southwest China (SWC) during the past several decades. Yet, the understanding of the mechanism of precipitation in SWC is still a challenge, since the East Asian monsoon and Indian monsoon potentially influence the rainfall in this region. Thus, the prediction of precipitation in SWC has become a difficult and critical topic in climatology. We develop a novel multi-variable network-based method to delineate the relations between the global sea surface temperature anomalies (SSTA) and the precipitation anomalies (PA) in SWC. Our results show that the out-degree patterns in the Pacific, Atlantic and Indian Ocean significantly influence the PA in SWC. In particular, we find that such patterns dominated by extreme precipitation change with the seasons. Furthermore, we uncover that the teleconnections between the global SSTA and rainfall can be described by the in-degree patterns, which dominated by several vital nodes within SWC. Based on the characteristics of these nodes, we find that the key SSTA areas affect the pattern of the nodes in SWC with some specific time delays that could be helpful to improve the long-term prediction of precipitation in SWC.
No abstract
Particulate matter (PM2.5 and PM10) and ozone (O3) are the two major air pollutants in China in recent years. The fluctuations of PM2.5, PM10 and O3 strongly depend on the weather processes and anthropogenic emission. These processes may lead to the existence of short- and long-term memory behaviors in air pollutants. Hence, here we use the autoregressive parameter a of the first-order autoregressive process [AR (1)] to characterize the short-term memory effects of pollutants. We estimate the scaling exponent α using detrended fluctuation analysis (DFA) for the long-term memory effects of air pollutants (PM2.5, PM10, and O3) in summer and winter for different cities in China. Our results show that PM2.5, PM10, and O3 have strong short-term and long-term memory characteristics both in summer and winter. Furthermore, both the short- and long-term memory effects are stronger in winter than summer for most cities associated with stronger and longer persistent weather systems in winter. In general, the scaling exponent α of PM2.5 and PM10 are smaller for northern cities than those of southern cities in China. The long-term memory patterns of O3 are stronger in northern cities and weaker in southern cities in relative to those of PM2.5 and PM10 in winter. Our results show that the short- and long-term memory behaviors of air pollutions are dominated by the weather systems with different time scales.
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