Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles

In this study, we propose a solution to estimating system responses to external forcings or perturbations. We utilize the Fluctuation-Dissipation Theorem (FDT) from statistical physics to extract knowledge using an AI model that can rapidly produce scenarios for different external forcings by leveraging FDT and analyzing a large dataset from Earth System Models. Our model, AiBEDO, accurately captures the complex effects of radiation perturbations on global and regional surface climate, enabling faster exploration of the impacts of spatially-heterogenous climate forcings. We demonstrate its effectiveness by applying AiBEDO to Marine Cloud Brightening, a climate intervention technique, aiming to optimize cloud brightening patterns for regional climate targets and prevent climate tipping points. Our approach has broader applicability to other scientific disciplines with computationally demanding simulation models. Source code of AiBEDO framework is made available at https://github.com/kramea/cikm_aibedo. A sample dataset is made available at https://doi.org/10.5281/zenodo.7597027. Additional data available upon request.