Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles
Palo Alto Research Center（帕洛阿尔托研究中心）；University of California, San Diego（加州大学圣迭戈分校）；University of Victoria（维多利亚大学）；Excarta；University of Washington（华盛顿大学）
Soo Kyung Kim, Kalai Ramea, Salva Rühling Cachay, Haruki Hirasawa, Subhashis Hazarika, Dipti Hingmire, Peetak Mitra, Philip J. Rasch, Hansi A. Singh
The availability of training data remains a significant obstacle for the implementation of machine learning in scientific applications. In particular, estimating how a system might respond to external forcings or perturbations requires specialized labeled data or targeted simulations, which may be computationally intensive to generate at scale. In this study, we propose a novel solution to this challenge by utilizing a principle from statistical physics known as the Fluctuation-Dissipation Theorem (FDT) to discover knowledge using an AI model that can rapidly produce scenarios for different external forcings. By leveraging FDT, we are able to extract information encoded in a large dataset produced by Earth System Models, which includes 8250 years of internal climate fluctuations, to estimate the climate system's response to forcings. Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate, allowing for a substantial acceleration of the exploration of the impacts of spatially-heterogenous climate forcers. To demonstrate the utility of AiBEDO, we use the example of a climate intervention technique called Marine Cloud Brightening, with the ultimate goal of optimizing the spatial pattern of cloud brightening to achieve regional climate targets and prevent known climate tipping points. While we showcase the effectiveness of our approach in the context of climate science, it is generally applicable to other scientific disciplines that are limited by the extensive computational demands of domain simulation models. Source code of AiBEDO framework is made available at https://github.com/kramea/kdd_aibedo. A sample dataset is made available at https://doi.org/10.5281/zenodo.7597027. Additional data available upon request.
Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions
Digital Built Environment Group (DiBEG)（数字建筑环境集团）；Edinburgh Napier University（爱丁堡纳皮尔大学）；School of Computing, Engineering and the Built Environment (SCEBE)（计算、工程与建筑环境学院）；York School of Architecture（约克建筑学院）；University of York（约克大学）；Department of Civil, Environmental, and Architectural Engineering（土木、环境与建筑工程系）；University of Colorado Boulder（科罗拉多大学博尔德分校）
Physics-Guided Multimodal Transformers are the Necessary Foundation for the Next Generation of Meteorological Science
School of Artificial Intelligence, Beijing University of Posts；Huawei Noah's Ark Lab；Department of Earth System Science, Tsinghua University；College of Computing \& Data Science, Nanyang Technological University；State Key Laboratory of Networking；Switching Technology, Beijing University of Posts
