Statistical Methods for Extreme Event Attribution in Climate Science

Posted: 12 Mar 2020

See all articles by Philippe Naveau

Philippe Naveau

Laboratoire des Sciences du Climat et de l'Environnement (LSCE)

Alexis Hannart

Ouranos

Aurélien Ribes

Centre National de Recherches Météorologiques

Date Written: March 2020

Abstract

Changes in the Earth's climate have been increasingly observed. Assessing the likelihood that each of these changes has been caused by human influence is important for decision making on mitigation and adaptation policy. Because of their large societal and economic impacts, extreme events have garnered much media attention—have they become more frequent and more intense, and if so, why? To answer such questions, extreme event attribution (EEA) tries to estimate extreme event likelihoods under different scenarios. Over the past decade, statistical methods and experimental designs based on numerical models have been developed, tested, and applied. In this article, we review the basic probability schemes, inference techniques, and statistical hypotheses used in EEA. To implement EEA analysis, the climate community relies on the use of large ensembles of climate model runs. We discuss, from a statistical perspective, how extreme value theory could help to deal with the different modeling uncertainties. In terms of interpretation, we stress that causal counterfactual theory offers an elegant framework that clarifies the design of event attributions. Finally, we pinpoint some remaining statistical challenges, including the choice of the appropriate spatio-temporal scales to enhance attribution power, the modeling of concomitant extreme events in a multivariate context, and the coupling of multi-ensemble and observational uncertainties.

Suggested Citation

Naveau, Philippe and Hannart, Alexis and Ribes, Aurélien, Statistical Methods for Extreme Event Attribution in Climate Science (March 2020). Annual Review of Statistics and Its Application, Vol. 7, Issue 1, pp. 89-110, 2020, Available at SSRN: https://ssrn.com/abstract=3553172 or http://dx.doi.org/10.1146/annurev-statistics-031219-041314

Philippe Naveau (Contact Author)

Laboratoire des Sciences du Climat et de l'Environnement (LSCE) ( email )

Point Courrier 129
Gif-Sur-Yvette, 91191
France

Alexis Hannart

Ouranos

Montréal, H3A 1B9
Canada

Aurélien Ribes

Centre National de Recherches Météorologiques

Toulouse, 31057
France

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