A dynamical adjustment perspective on extreme event attribution

Abstract

Abstract. Here we demonstrate that dynamical adjustment allows a straightforward approach to extreme event attribution within a conditional framework. We illustrate the potential of the approach with two iconic extreme events that occurred in 2010: the early winter European cold spell and the Russian summer heat wave. We use a dynamical adjustment approach based on constructed atmospheric circulation analogues to isolate the various contributions to these two extreme events using only observational and reanalysis datasets. Dynamical adjustment results confirm previous findings regarding the role of atmospheric circulation in the two extreme events and provide a quantitative estimate of the various dynamic and thermodynamic contributions to the event amplitude. Furthermore, the approach is also used to identify the drivers of the recent 1979–2018 trends in summer extreme maximum and minimum temperature changes over western Europe and western Asia. The results suggest a significant role of the dynamic component in explaining temperature extreme changes in different regions, including regions around the Black and Caspian seas as well as central Europe and the coasts of western Europe. Finally, dynamical adjustment offers a simple and complementary storyline approach to extreme event attribution with the advantage that no climate model simulations are needed, making it a promising candidate for the fast-track component of any real-time extreme event attribution system.