Time of emergence of compound events: contribution of univariate and dependence properties

Abstract

Abstract. Many climate-related disasters often result from a combination of several climate phenomena, also referred to as “compound events’’ (CEs). By interacting with each other, these phenomena can lead to huge environmental and societal impacts, at a scale potentially far greater than any of these climate events could have caused separately. Marginal and dependence properties of the climate phenomena forming the CEs are key statistical properties characterising their probabilities of occurrence. In this study, we propose a new methodology to assess the time of emergence of CE probabilities, which is critical for mitigation strategies and adaptation planning. Using copula theory, we separate and quantify the contribution of marginal and dependence properties to the overall probability changes of multivariate hazards leading to CEs. It provides a better understanding of how the statistical properties of variables leading to CEs evolve and contribute to the change in their occurrences. For illustrative purposes, the methodology is applied over a 13-member multi-model ensemble (CMIP6) to two case studies: compound wind and precipitation extremes over the region of Brittany (France), and frost events occurring during the growing season preconditioned by warm temperatures (growing-period frost) over central France. For compound wind and precipitation extremes, results show that probabilities emerge before the end of the 21st century for six models of the CMIP6 ensemble considered. For growing-period frosts, significant changes of probability are detected for 11 models. Yet, the contribution of marginal and dependence properties to these changes in probabilities can be very different from one climate hazard to another, and from one model to another. Depending on the CE, some models place strong importance on both marginal properties and dependence properties for probability changes. These results highlight the importance of considering changes in both marginal and dependence properties, as well as their inter-model variability, for future risk assessments related to CEs.