The Climate Analytics Lab aims to reduce uncertainty in the role of anthropogenic aerosol on the climate by exploiting recent advances in machine learning to enable improved projections and gain new insights from observations.
Project Highlights
JAX-GCM
A fully differentiable climate model enabling ML-enhanced climate science and gradient-based optimization.
GAIA Initiative
Harnessing AI to understand, forecast, and steward Earth’s complex systems through interdisciplinary collaboration.
News
- 26/06 - 🎉 It’s official! Our paper on JAX-GCM, unlocking true differentiability for the future of climate modeling, has been accepted! Read about the project here
- 25/12 - UC San Diego launches GAIA, a new initiative combining AI with Earth and ocean science, co-led by Duncan. UC San Diego Today
- 25/10 - New papers: Jorge Baño-Medina recently published three excellent papers building large ensembles of AI weather models, their use for attribution, and how well they represent atmospheric physics. It was a pleasure working with him on them!
- 25/04 - Our new paper “Integrating Top‐Down Energetic Constraints With Bottom‐Up Process‐Based Constraints for More Accurate Projections of Future Warming” has been published in Geophysical Research Letters.
- 25/03 - Congratulations to Zoe Ludena, Eric Pham, and Ylesia Wu for winning the best Capstone Project award at the 2025 UCSD Data Science Showcase! Read more about their ‘SeeRise’ project here and interact with their app here. Well done!! 🎉