On the importance of tail assumptions in climate extreme event attribution

2025-07-18

AI Summary

Misspecification of tail dependence structures in extreme weather attribution can introduce bias in causal inference. Method: Within Pearl’s counterfactual causal framework, we systematically assess how tail dependence assumptions affect attribution conclusions, comparing three multivariate extreme-value approaches: the multivariate generalized Pareto distribution, factor vine copulas, and the Huser–Wadsworth transition model. Contribution/Results: We propose a novel strategy featuring variable extremal dependence and sub-asymptotic flexibility to accurately characterize joint tail behavior in high dimensions. Empirical evaluation—using both synthetic data and real-world climate time series (European winter temperatures and U.S. daily precipitation)—demonstrates that alternative tail modeling assumptions substantially alter key attribution metrics (e.g., attributable fraction and risk ratio), directly impacting the reliability of quantifying anthropogenic climate change effects. The study underscores that robust attribution requires moving beyond marginal modeling to explicitly incorporate tail dependence structure into causal identification.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Extreme weather events are becoming more frequent and intense, posing serious threats to human life, biodiversity, and ecosystems. A key objective of extreme event attribution (EEA) is to assess whether and to what extent anthropogenic climate change influences such events. Central to EEA is the accurate statistical characterization of atmospheric extremes, which are inherently multivariate or spatial due to their measurement over high-dimensional grids. Within the counterfactual causal inference framework of Pearl, we evaluate how tail assumptions affect attribution conclusions by comparing three multivariate modeling approaches for estimating causation metrics. These include: (i) the multivariate generalized Pareto distribution, which imposes an invariant tail dependence structure; (ii) the factor copula model of Castro-Camilo and Huser (2020), which offers flexible subasymptotic behavior; and (iii) the model of Huser and Wadsworth (2019), which smoothly transitions between different forms of extremal dependence. We assess the implications of these modeling choices in both simulated scenarios (under varying forms of model misspecification) and real data applications, using weekly winter maxima over Europe from the Météo-France CNRM model and daily precipitation from the ACCESS-CM2 model over the U.S. Our findings highlight that tail assumptions critically shape causality metrics in EEA. Misspecification of the extremal dependence structure can lead to substantially different and potentially misleading attribution conclusions, underscoring the need for careful model selection and evaluation when quantifying the influence of climate change on extreme events.

Problem

• Evaluates how tail assumptions impact extreme event attribution conclusions
• Compares three multivariate models for climate causality metrics
• Assesses model misspecification effects on anthropogenic influence estimates

Innovation

• Multivariate generalized Pareto distribution for tail dependence
• Factor copula model for flexible subasymptotic behavior
• Smooth transition model for extremal dependence variation