A compound event framework for understanding extreme impacts

TLDR

Climate variables such as rainfall, temperature, and pressure signal hazards like cyclones, floods, and fires, and extreme impacts often arise from combinations of non‑extreme variables—a concept termed a compound event that depends on variable type, scale, dependence, and stakeholder perspective. The authors propose using influence diagrams to define, map, analyze, model, and communicate compound event risk. Influence diagrams are employed to represent relationships among variables, scales, dependencies, and stakeholder perspectives in compound event modeling. Ultimately, a greater appreciation of compound events will lead to further insight and a changed perspective on how impact risks are associated with climate‑related hazards. WIREs Clim Change 2014, 5:113–128, doi:10.1002/wcc.252.

Abstract

Climate and weather variables such as rainfall, temperature, and pressure are indicators for hazards such as tropical cyclones, floods, and fires. The impact of these events can be due to a single variable being in an extreme state, but more often it is the result of a combination of variables not all of which are necessarily extreme. Here, the combination of variables or events that lead to an extreme impact is referred to as a compound event. Any given compound event will depend upon the nature and number of physical variables, the range of spatial and temporal scales, the strength of dependence between processes, and the perspective of the stakeholder who defines the impact. Modeling compound events is a large, complex, and interdisciplinary undertaking. To facilitate this task we propose the use of influence diagrams for defining, mapping, analyzing, modeling, and communicating the risk of the compound event. Ultimately, a greater appreciation of compound events will lead to further insight and a changed perspective on how impact risks are associated with climate‐related hazards. WIREs Clim Change 2014, 5:113–128. doi: 10.1002/wcc.252 This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Assessing Impacts of Climate Change > Representing Uncertainty