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September 2020 Climate extreme event attribution using multivariate peaks-over-thresholds modeling and counterfactual theory
Anna Kiriliouk, Philippe Naveau
Ann. Appl. Stat. 14(3): 1342-1358 (September 2020). DOI: 10.1214/20-AOAS1355

Abstract

Numerical climate models are complex and combine a large number of physical processes. They are key tools in quantifying the relative contribution of potential anthropogenic causes (e.g., the current increase in greenhouse gases) on high-impact atmospheric variables like heavy rainfall. These so-called climate extreme event attribution problems are particularly challenging in a multivariate context, that is, when the atmospheric variables are measured on a possibly high-dimensional grid.

In this paper we leverage two statistical theories to assess causality in the context of multivariate extreme event attribution. As we consider an event to be extreme when at least one of the components of the vector of interest is large, extreme-value theory justifies, in an asymptotical sense, a multivariate generalized Pareto distribution to model joint extremes. Under this class of distributions, we derive and study probabilities of necessary and sufficient causation as defined by the counterfactual theory of Pearl. To increase causal evidence, we propose a dimension reduction strategy based on the optimal linear projection that maximizes such causation probabilities. Our approach is tested on simulated examples and applied to weekly winter maxima precipitation outputs of the French CNRM from the recent CMIP6 experiment.

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Anna Kiriliouk. Philippe Naveau. "Climate extreme event attribution using multivariate peaks-over-thresholds modeling and counterfactual theory." Ann. Appl. Stat. 14 (3) 1342 - 1358, September 2020. https://doi.org/10.1214/20-AOAS1355

Information

Received: 1 August 2019; Revised: 1 March 2020; Published: September 2020
First available in Project Euclid: 18 September 2020

MathSciNet: MR4152136
Digital Object Identifier: 10.1214/20-AOAS1355

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 3 • September 2020
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