Towards an impact-based approach to the detection of analogues: the case study of Emilia-Romagna floods in May 2023

The framework of weather analogues is a powerful methodology for the detection of the climate change fingerprint on weather extremes, that has been widely used in several contexts. The procedure has several advantages compared to standard model-based attribution exercises, being fast and not computationally expensive. Here we address whether the detection of analogs based on impacts (e.g., environmental, socio-economic) of a severe weather event can provide added value on the attribution of the event intensity or likelihood to climate change.

As a case study, we analyse the twin Emilia-Romagna flood event of May 2023. It caused a sizable amount of casualties, widespread destruction and substantial economic damage. We detect analogues of the river runoff as an impact-based observable of interest, addressing it in an univariate context, but also jointly with other observables (i.e., in a multivariate framework), such as mean sea-level pressure, total precipitation, and 850 hPa vorticity. We therefore detect the optimal set of variables for performing multivariate analysis and the appropriate analysis domain. We suggest that by combining river runoff with other observables by carefully selecting the spatial domain, we obtain a clearer view of the role played by anthropogenic climate change for this event, also including the additional vulnerability linked to the environmental impact of human activities, such as land-use change and freshwater diversion.