Probablistic Emulation of a Global Climate Model with Spherical DYffusion
Keywords: deep learning, climate modeling, climate science, ai for science, climate change
TL;DR: We present the first conditional generative model for efficient, probabilistic emulation of a realistic global climate model, which beats relevant baselines and nearly reaches a gold standard for successful climate model emulation.
Abstract: Data-driven deep learning models are transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where the complexity of the data and long inference rollouts pose significant challenges. Here, we present the first conditional generative model that produces accurate and physically consistent global climate ensemble simulations by emulating a coarse version of the United States' primary operational global forecast model, FV3GFS.
Our model integrates the dynamics-informed diffusion framework (DYffusion) with the Spherical Fourier Neural Operator (SFNO) architecture, enabling stable 100-year simulations at 6-hourly timesteps while maintaining low computational overhead compared to single-step deterministic baselines.
The model achieves near gold-standard performance for climate model emulation, outperforming existing approaches and demonstrating promising ensemble skill.
This work represents a significant advance towards efficient, data-driven climate simulations that can enhance our understanding of the climate system and inform adaptation strategies. Code is available at [https://github.com/Rose-STL-Lab/spherical-dyffusion](https://github.com/Rose-STL-Lab/spherical-dyffusion).
Primary Area: Machine learning for physical sciences (for example: climate, physics)
Submission Number: 11626
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