May 14, 2025
Conference Paper

ClimGen: Learning the Forcing-Response Relationship in Climate System

Abstract

Solar Radiation Management (SRM) is emerging as a potential geoengineering strategy to address the anthropogenic impact on climate, but its effective implementation requires an iterative and large ensemble of highly accurate and efficient climate projections. Traditional climate projections rely on executing computationally demanding and time-consuming numerical climate models. Recent advances in machine learning (ML) aim to enhance these approaches by emulating traditional methods. In this work, we propose a novel framework for directly learning the relationship between solar radiation flux at the top of the atmosphere and the corresponding surface temperature response. To evaluate the feasibility of this direct ML-based projection, we developed a dataset using an intermediate complexity model, incorporating a comprehensive suite of different forcing patterns and evaluation metrics to rigorously assess the ML model’s performance. We introduce a Conditional Denoising Diffusion Probabilistic Model (cDDPM) for this task, which demonstrates encouraging skill in representing climate statistics under previously unseen forcing patterns. This approach provides a promising pathway for direct climate projections by accurately learning the forcing-response relationship, with a wide range of applications in impact mitigation, emissions policy design, and SRM strategies.

Published: May 14, 2025

Citation

Chen T., P. Kooloth, J. Lu, and Z. Hou. 2025. ClimGen: Learning the Forcing-Response Relationship in Climate System. In ICLR 2025 Workshop: Tackling Climate Change with Machine Learning, April 24-28. 2025, Singapore, Paper No. 52. Pittsburgh, Pennsylvania:Climate Change AI. PNNL-SA-211021.

Research topics