Conditional multi-step attribution for climate forcings

2024-09-02

AI Summary

Existing climate attribution methods—such as optimal fingerprinting—exhibit limited performance under short temporal scales, low signal-to-noise ratios, or weak forcing–response relationships, and single-step modeling struggles to integrate heterogeneous climate information. This paper proposes a conditional multi-step attribution framework that, for the first time, formalizes the climate forcing–mediator–surface response pathway—as instantiated by stratospheric temperature and radiative flux—as an identifiable causal chain. The method integrates Bayesian inference with multivariate coupled response modeling to quantify forcing intensity. Robustness is enhanced via scalar response feature extraction and joint analysis of mediator variables. Applied to the 1991 Mount Pinatubo eruption, the framework substantially improves attribution confidence over conventional univariate temperature-based approaches, demonstrating its efficacy and novelty in high-noise, weak-signal regimes.

Technology Category

Reasoning under Uncertainty: CausalityMachine Learning: Calibration & Uncertainty QuantificationKnowledge Representation and Reasoning: Nonmonotonic Reasoning

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Attribution of climate impacts to a source forcing is critical to understanding, communicating, and addressing the effects of human influence on the climate. While standard attribution methods, such as optimal fingerprinting, have been successfully applied to long-term, widespread effects such as global surface temperature warming, they often struggle in low signal-to-noise regimes, typical of short-term climate forcings or climate variables which are loosely related to the forcing. Single-step approaches, which directly relate a source forcing and final impact, are unable to utilize additional climate information to improve attribution certainty. To address this shortcoming, this paper presents a novel multi-step attribution approach which is capable of analyzing multiple variables conditionally. A connected series of climate effects are treated as dependent, and relationships found in intermediary steps of a causal pathway are leveraged to better characterize the forcing impact. This enables attribution of the forcing level responsible for the observed impacts, while equivalent single-step approaches fail. Utilizing a scalar feature describing the forcing impact, simple forcing response models, and a conditional Bayesian formulation, this method can incorporate several causal pathways to identify the correct forcing magnitude. As an exemplar of a short-term, high-variance forcing, we demonstrate this method for the 1991 eruption of Mt. Pinatubo. Results indicate that including stratospheric and surface temperature and radiative flux measurements increases attribution certainty compared to analyses derived solely from temperature measurements. This framework has potential to improve climate attribution assessments for both geoengineering projects and long-term climate change, for which standard attribution methods may fail.

Problem

• Attributing climate impacts to natural and anthropogenic forcings
• Improving attribution in low signal-to-noise regimes
• Enhancing certainty using physical pathways and multivariate data

Innovation

• Statistical framework with conditional probabilities
• Incorporates physical pathways for attribution
• Enhances low signal-to-noise scenario confidence