Estimating Causal Attribution of Anthropogenic Forcing on High-Temperature Extremes Using a Latent Gaussian Spatial Model

2026-04-25

AI Summary

This study quantifies the causal contribution of anthropogenic forcing to the probability of extreme high-temperature events by constructing a causal inference framework based on comparisons between factual and counterfactual scenarios, assessing impacts through shifts in the return periods of annual maximum temperatures. Methodologically, it innovatively integrates a bivariate generalized extreme value distribution with a latent Gaussian spatial model, incorporating a multivariate intrinsic conditional autoregressive structure and an efficient “Max-and-smooth” Bayesian inference approach to enable precise estimation and identification of spatially heterogeneous causal effects. Applied to the contiguous United States, the framework yields statistically significant posterior estimates of causal effects, spatiotemporal trends, and credible hotspot regions.

Technology Category

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

Application Category

• 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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Climate change has become a significant global concern due to its capacity to cause substantial disruption to daily life by increasing the frequency and intensity of extreme weather events. Given the rising trend of human interventions in the climate system over recent decades, this study aims to quantify the relative contribution of anthropogenic forcing to the increasing likelihood of climate extremes, with a particular emphasis on high-temperature extremes. Our analysis focuses on annual temperature maxima from the IPSL-CM6A model in the CMIP6 experiment. We propose a novel causal inference framework that focuses on differences in return levels derived from annual temperature maxima between the factual and counterfactual worlds. While jointly modeling the annual maxima from the two worlds using a bivariate generalized extreme value distribution, we model the spatially-varying coefficients using a latent Gaussian framework. Specifically, given that the data are available over a $1^\circ \times 1^\circ$ grid, we employ the multivariate intrinsic conditional autoregressive model for the latent layer in the proposed hierarchical model, ensuring proper posterior distributions. We implement a recently developed highly-efficient approximate Bayesian inference technique, `Max-and-smooth', that uses a Laplace approximation of the likelihood and then performs Gibbs sampling based on the approximate posterior. The results include posterior estimates of the causal effect of anthropogenic forcing on high-temperature extremes, along with the trends in this effect, over the factual world. Furthermore, we estimate credible regions for a significant causal effect to facilitate hotspot detection across the mainland United States.

Problem

• causal attribution
• anthropogenic forcing
• high-temperature extremes
• climate extremes
• extreme value analysis

Innovation

• causal inference
• latent Gaussian spatial model
• generalized extreme value distribution
• Max-and-smooth
• anthropogenic forcing