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Abstract:
Extreme temperature events (ETEs), such as recent severe heat waves, are one of the consequences of climate change and global warming. To better understand their complex behavior and forecast corresponding hydrologic dynamics, investigating their spatial and temporal patterns are essential. We, therefore, analyzed ETEs over the Contiguous United States (CONUS) for both summer and winter seasons using complex network theory. For this purpose, the daily maximum and minimum temperature data were collected from the Climate Prediction Center (CPC) database. To determine the level of similarity between two geographic nodes, we employed the event synchronization (ES) method and then constructed the network of ETEs. The constructed networks were next corrected for boundary effects. Network measures i.e., degree centrality (DC), mean geographic distance (MGD), clustering coefficient (CC), betweenness centrality (BC) and long-ranged directedness (LD) were determined to analyze complex patterns within each network. Based on the network measures, we found that the ETEs are more synchronized in winters than those in summers. In addition, the BC and LD revealed that California plays an important role in the large-scale propagation of ETEs during summers, while Texas and the eastern region of New Mexico during winters. Furthermore, the evolution of ETEs from 1979 to 2022 uncovered increasing and decreasing trends for the summer and winter seasons, respectively. We also detected teleconnections near the geographic distance of 3500 km and above over the CONUS. By applying the Louvain method, we detected different communities in the network of ETEs for the summer and winter seasons. We also investigated the effect of time lag, , in the ES method on the complex network results by varying its value from 0 to 10 days. Statistical analyses including the Kolmogorov-Smirnov and paired t-test demonstrated significant differences in the results.
