A dynamical adjustment perspective on extreme event attribution

Abstract

Abstract. Here we demonstrate that dynamical adjustment allows a straightforward\napproach to extreme event attribution within a conditional framework. We\nillustrate the potential of the approach with two iconic extreme events that\noccurred in 2010: the early winter European cold spell and the Russian\nsummer heat wave. We use a dynamical adjustment approach based on\nconstructed atmospheric circulation analogues to isolate the various\ncontributions to these two extreme events using only observational and\nreanalysis datasets. Dynamical adjustment results confirm previous findings\nregarding the role of atmospheric circulation in the two extreme events and\nprovide a quantitative estimate of the various dynamic and thermodynamic\ncontributions to the event amplitude. Furthermore, the approach is also used\nto identify the drivers of the recent 1979–2018 trends in summer extreme\nmaximum and minimum temperature changes over western Europe and western\nAsia. The results suggest a significant role of the dynamic component in\nexplaining temperature extreme changes in different regions, including\nregions around the Black and Caspian seas as well as central Europe and the\ncoasts of western Europe. Finally, dynamical adjustment offers a simple and\ncomplementary storyline approach to extreme event attribution with the\nadvantage that no climate model simulations are needed, making it a\npromising candidate for the fast-track component of any real-time extreme\nevent attribution system.\n