AI Can Generate Climate Models 25 Times Faster Than Standard Methods

The algorithms behind generative AI tools like DallE, when combined with physics-based data, can be used to develop better ways to model the Earth’s climate. Computer scientists in Seattle and San Diego have now used this combination to create a model that is capable of predicting climate patterns over 100 years 25 times faster than the state of the art.

Specifically, the model, called Spherical DYffusion, can project 100 years of climate patterns in 25 hours–a simulation that would take weeks for other models. In addition, existing state-of-the-art models need to run on supercomputers. This model can run on GPU clusters in a research lab.

“Data-driven deep learning models are on the verge of transforming global weather and climate modeling,” the researchers from the University of California San Diego and the Allen Institute for AI, write.

The research team is presenting their work at the NeurIPS conference 2024, Dec. 9 to 15 in Vancouver, Canada.

There are some limitations to the model that researchers aim to overcome in its next iterations, such as including more elements in their simulations. Next steps include simulating how the atmosphere responds to CO2.

“We emulated the atmosphere, which is one of the most important elements in a climate model,” said Rose Yu, a faculty member in the UC San Diego Department of Computer Science and Engineering and one of the paper’s senior authors.

The work stems from an internship that one of Yu’s Ph.D. students, Salva Ruhling Cachay, did at the Allen Institute for AI (Ai2).

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