Attributing the occurrence and intensity of extreme events with the flow analogue method

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 analogue method is a conditional attribution method which compares events with similar synoptic-scale dynamics. Here we propose a procedure for estimating both the conditional 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 analogue method tends to give more significant results for these events, which suggests a stronger climate change signal than the one detected with the unconditional approach.