Exploring a new methodology to quantify natural variability in conditional extreme event attribution

Anthropogenic climate change (ACC) is one of the most demanding challenges facing our society. The intensification and increased frequency of many extreme events due to ACC are among its most impactful consequences, threatening human health, infrastructure, and ecosystems. In this context, raising the awareness of the general public of the relationship between ACC, extremes, and associated impacts becomes a crucial task.

This work is grounded in attribution science and focuses on quantifying and understanding the influence of internal climate variability on extreme events. Among the many tools available for attribution, we use ClimaMeter [Faranda et al. 2023], a rapid framework designed to provide context for extreme events in relation to ACC. ClimaMeter’s approach emphasizes the dynamics associated with extreme events and identifies weather conditions similar to those characterizing the event of interest, leveraging the analogues methodology for conditional attribution [Yiou, 2014]. The analysis provided by such a framework enables the evaluation of significant changes over time of the event’s dynamics and associated meteorological hazards and links them to ACC.

An essential part of ClimaMeter’s methodology is quantifying the influence of natural variability relative to ACC in explaining the changes associated with the event. Specifically, three modes of Sea Surface Temperature variability are taken into account: the El Niño-Southern Oscillation, the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation. These three modes are considered with equal weight and changes not explained by them are assumed to be due to ACC [Faranda et al., 2023]. While the methodology is rapid and easy to communicate, it also has some limitations. In this work, we investigate the implications of this approach. First, we test it on a pre-industrial simulation of the IPSL climate model to evaluate its performance under stationary climate conditions. Additionally, we explore a generalization of the current methodology, aiming to refine the quantification of natural variability by weighing the three modes based on the event region and associated hazard. This generalized approach has the potential to expand ClimaMeter’s methodology and provide new insights into the complex mechanisms linking natural variability and extremes.