Advancing research on compound weather and climate events via large ensemble model simulations

Abstract

Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. For example, compound hot-dry events frequently cause damage to human and natural systems, often exceeding separate impacts from heatwaves and droughts. Focussing on four event types arising from different combinations of climate variables across space and time, we illustrate that robust analyses of compound events – such as frequency and uncertainty analysis under present-day and future conditions, event attribution, and exploration of low-probability-high-impact events – require very large sample sizes. In particular, the required sample is much larger than that needed for routinely considered univariate extremes. We demonstrate how large ensemble simulations from multiple climate models are crucial for advancing our assessments of compound events and for constructing robust model projections. For example, among the case studies, we focus on compound hot-dry events and show that large ensemble model simulations allow for identifying plausible extremely dry climates that, if occurring in a warmer world, would be associated with high risk from compound hot-dry events. Overall, combining large ensemble simulations with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.