A statistical framework for conditional extreme event attribution

Abstract

Abstract. The goal of the attribution of individual events is to estimate whether and to what extent the probability of an extreme climate event evolves when external conditions (e.g., due to anthropogenic forcings) change. Many types of climate extremes are linked to the variability of the large-scale atmospheric circulation. It is hence essential to decipher the roles of atmospheric variability and increasing mean temperature in the change of probabilities of extremes. It is also crucial to define a background state (or counterfactual) to which recent observations are compared. In this paper we present a statistical framework to determine the dynamical (linked to the atmospheric circulation) and thermodynamical (linked to slow forcings) contributions to the probability of extreme climate events. We illustrate this methodology on a record precipitation event that hit southern United Kingdom in January 2014. We compare possibilities for the creation of two states (or "worlds") in which probability change is determined. These two worlds are defined in a large ensemble of atmospheric model simulations (Weather@Home factual and counterfactual simulations) and separate periods (new: 1951-2014, and old: 1900-1950) in reanalyses and observations. We discuss how the atmospheric circulation conditioning can affect the interpretation of extreme event attribution. We eventually show the qualitative coherence of results between the choice of worlds (factual/counterfactual vs. new/old).

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

“…To do so, applying a high-dimensional R 2 D 2 (and other methods) to various CMIP5 (and upcoming CMIP6) GCM simulations or to various CORDEX RCM runs would generate useful large datasets of multivariate bias corrected climate simulations. From the purely climatic point of view, those datasets would provide a corrected ensemble to conduct climate change studies, such as related to detection and attribution questions (e.g., Yiou et al, 2017), to the evolution in risks of compound events (e.g., Bevacqua et al, 2017) or more generally related to understanding of climate changes. From the societal and/or environmental point of view, those ensembles of multivariate corrected simulations would allow us to investigate how the correction of the dependence structures might modify the impacts of climate change.…”

Trends of atmospheric circulation during singular hot days in Europe

“…Shepherd (2016) highlighted that it is possible to study the dynamic and thermodynamic contributions separately. Few papers have studied the influence of climate change on the dynamics applied to a singular event (Vautard et al 2016, Yiou et al 2017. Both of those articles calculate the dynamical difference between two worlds (with and without climate change).…”

Conditional and residual trends of singular hot days in Europe

“…Understanding the evolution of the processes leading to extreme temperatures is however partially hindered by the differences between available climate model simulations (Fischer and Schär 2008), which can be caused by both internal variability and different model parameterizations of physical processes. One way to move forward is to disentangle the dynamical and nondynamical contributions to an extreme event, and to study the evolution of one or both of these contributions (Trenberth et al 2015, Shepherd 2016, Vautard et al 2016, Yiou et al 2017.…”