Understanding extreme heat: Causes and time scales revealed by Rényi information transfer

Abstract

Recently, Paluš et al. (2024) demonstrated that information-theoretic generalization of Granger causality – based on conditional mutual information/transfer entropy – when reformulated in terms of Rényi entropy, provides a time-series analysis tool suitable for identifying the causes of extreme values in affected variables. Investigating the causes of warm summer surface air temperature extremes in Europe, Rényi information transfer highlights the role of blocking events among large-scale circulation patterns and modes of variability. Soil moisture interacts with air temperature on a daily scale, exhibiting bidirectional causal effects on the mean, whereas its influence on temperature extremes emerges over longer time scales, from a fortnight to a month. In contrast, the causal effect of blocking on temperature extremes is primarily observed at the daily scale. Using tools from Rényi information theory, we aim to disentangle this complex, multicausal, multiscale phenomenon and identify the regions in Europe where these factors modulate the probability of extreme summer heat. * This research was supported by the Johannes Amos Comenius Programme (P JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks; and by the Czech Science Foundation, Project No. 25-18105S. * Paluš, M., Chvosteková, M., & Manshour, P. (2024). Causes of extreme events revealed by Rényi information transfer. Science Advances, 10(30), eadn1721.