Hyperbolic map unravels eight regions in temperature volatility regionalization of Mainland China

Abstract

Abstract Abrupt temperature volatility has detrimental effects on daily activities, macroeconomic growth, and human health. Predicting abrupt temperature volatility and thus diminishing its negative impacts can be achieved by exploring homogeneous regions of temperature volatility and analyzing the driving factors. To investigate the regionalization of temperature volatility in Mainland China, a network constructed by the cosine similarity of temperature volatility series from Mainland China was embedded in hyperbolic space. Subsequently, we partitioned the network on the hyperbolic map using the critical gap method and then found eight regions in all. Ultimately, a network of communities was constructed while the interaction among communities was quantified. This yields a perspective of temperature volatility regionalization that can accurately reflect factors including altitude, climate type, and the geographic location of mountains. Further analysis demonstrates that the regionalization in the hyperbolic map is distinct from provinces and has a realistic basis: communities in southwest China show strong correlations due to the temperature sensitivity to altitude, and communities in northern China show a convergence in the area of Dingxi, Gansu, mainly owing to the strong temperature sensitivity to climate types. As a consequence, node distributions and community divisions in the hyperbolic map can offer new insights into the regionalization of temperature volatility in Mainland China. The results demonstrate the potential of hyperbolic embedding of complex networks in forecasting future node associations in real-world data.