Local versus regional impact changes for storms like Boris (2024): insights from high-resolution pseudo global warming simulations

The severity of the impacts of (convective) rainfall extremes in the past year alone, e.g., storm Boris and the flooding in middle Europe, or the flooding in the Valencia region, is mind blowing. With several hundreds of millimeters of rain falling in often fewer than 48 hours, the flooding was locally very disruptive, or even catastrophic. While often embedded in large-scale and reasonably well predictable (but anomalous) flow conditions, the level of small-scale detail and the role of smaller-scale (convective) processes that ultimately determine whether the situation "gets out of hand" - or not - is challenging both observation networks, and the NWP and climate-modelling centers. 

In this presentation we take the example of storm Boris that caused widespread flooding in Middle Europe in September 2024 to illustrate that only by simulating the event at very high resolution the true changes in the impacts are revealed. Using a pseudo-global warming (PGW) framework in which the event is placed in historic and possible future climate conditions, we show that on a local scale the response strongly exceeds the regional response. By subsequently matching the patterns to underlying population densities an impression is obtained of how this leads to a greatly elevated impact on society.

Different frameworks have been developed to analyse, attribute and project extreme events often immediately after, or even prior to the event. The regional PGW framework we are adopting here is but one of the several existing approaches based on analysing 'counterfactuals', i.e., simulating the event in a different climate. Another framework is that of dynamic analogues which relies on deriving paste-to-present or present-to-future changes, by selecting and comparing similar (observed or modelled) events based on large-scale flow similarity. In this approach therefore, the event is also captured. Structural similarity in terms of flow conditions is not required by the approach of world-weather attribution (WWA). The WWA-approach examines changing frequency and intensity of local or regional extremes using non-stationary extreme-value analysis of observational and model data, and blends these two lines of information. All methods have their advantages and disadvantages. At best, these methods give overlapping results, but in practice they highlight different aspects of the (past or future) changes. This forces one to think how to combine or merge the output from the different methodologies to provide society with the most relevant information and to better anticipate on the future changes.