Tech

UC San Diego and Allen Institute Accelerate Century-Scale Climate Forecasting with Spherical Diffusion Model

April 2, 2026 · From the archive, written in the Professor's voice before our October 2026 redesign from today.ucsd.edu

What happened

Researchers combined generative AI with physics-based data to create Spherical DYffusion, a model projecting 100 years of climate patterns in just 25 hours. This hybrid approach leverages diffusion models on spherical data to drastically speed up long-term climate simulations.

Why it matters

This breakthrough teaches us the power of integrating domain-specific physics with generative AI to overcome computational bottlenecks. It suggests a workflow where AI complements traditional simulations, enabling rapid, large-scale forecasting in environmental science and beyond.

Who's doing it

UC San Diego and the Allen Institute for AI have demonstrated this approach, significantly reducing climate projection times while maintaining accuracy, potentially informing faster policy and adaptation strategies.

Try it

1. Explore diffusion-based generative models specialized for spherical data, starting with research repositories like the Allen Institute’s GitHub (https://github.com/allenai).
2. Gather relevant physics-based climate datasets (e.g., NASA’s Earthdata at https://earthdata.nasa.gov).
3. Combine these with generative AI frameworks such as PyTorch to train or fine-tune a spherical diffusion model, aiming to replicate accelerated climate projections.

Read the original at today.ucsd.edu