ClimGen: Learning the Forcing-Response Relationship in Climate System
Keywords: GenAI, c-DDPM, Climate, Forcing-Response relationship
TL;DR: A conditional denoising diffusion probabilistic model was trained to project surface temperature responses conditioned on top of the atmosphere solar radiation forcing.
Abstract: Solar Radiation Management (SRM) is emerging as a viable geoengineering strategy to address the climate change crisis, but its effective implementation requires iterative and large ensemble of highly accurate and efficient climate projections. Traditional climate projections rely on executing computational 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 benchmark 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 (c-DDPM) for this task, which demonstrates superior performance 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 wide range of applications in climate change mitigation, emissions policy design, and SRM strategies.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 1362
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