Year: 2025
Type: article
Abstract: . Extreme event attribution methodologies have been proposed to estimate the impacts of anthropogenic global warming on observed climatological and meteorological extremes. The classical risk-based approach uses Extreme Value Theory (EVT) to derive changes in the unconditional probabilities of yearly maxima but bears the risk of comparing events with different dynamical mechanisms. The flow analogues method on the other hand is a conditional attribution method which compares events with similar synoptic scale dynamics. Here we propose a procedure for estimating both the intensity change and the probability ratio of observed extreme events with this method. We illustrate the procedure on three recent extreme events in Europe and compare the results obtained to the EVT-based approach. We show that the conditional flow analogues method gives more significant results for these events, which suggests a stronger climate change signal than the one detected with the unconditional approach.
Source: Weather and Climate Dynamics
Institutions: Centre National de la Recherche Scientifique, Université de Versailles Saint-Quentin-en-Yvelines, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, ETH Zurich, Laboratoire des Sciences du Climat et de l'Environnement, Institute for Atmospheric and Climate Science, École Polytechnique, Université Paris Sciences et Lettres, École Normale Supérieure - PSL, Sorbonne Université, Laboratoire de Météorologie Dynamique, London Mathematical Laboratory, Institut Polytechnique de Paris
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4413037321