https://phys.org/news/2025-08-ai-simulates-years-current-climate.html
by Gillian Dohrn, University of Washington
Traditional weather forecasting models run on energy-hogging supercomputers that are typically housed at large research institutions. In the past five years, artificial intelligence has emerged as a powerful tool for cheaper, faster forecasting, but most AI-powered models can
In a new study published in AGU Advances, University of Washington researchers used AI to simulate Earth’s current climate and interannual variability for up to 1,000 years. The model runs on a single processor and takes just 12 hours to generate a forecast. On a state-of-the-art supercomputer, the same simulation would take approximately 90 days.
“We are developing a tool that examines the variability in our current climate to help answer this lingering question: Is a given event the kind of thing that happens naturally, or not?” said Dale Durran, a UW professor of atmospheric and climate science.
Durran was one of the first to introduce AI into weather forecasting more than five years ago when he and former UW graduate student Jonathan Weyn partnered with Microsoft Research. Durran also holds a joint position as a researcher with California-based Nvidia.
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“To train an AI model, you have to give it tons of data,” Durran said. “But if you break up the available historical data by season, you don’t get very many chunks.”
The most accurate global datasets for the daily weather go back to roughly 1979. Although there are plenty of days between then and now that can be used to train a daily weather forecast model, the same period contains fewer seasons. This lack of historical data was perceived as a barrier to using AI for seasonal forecasting.
Counterintuitively, the Durran group’s latest contribution to forecasting, Deep Learning Earth SYstem Model, or DLESyM , was trained for one-day forecasts, but still learned how to capture seasonal variability.
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Neither the CMIP6 models nor DLESyM are 100% accurate, but the fact that the AI-based approach was competitive while using so much less power is significant.
“Not only does the model have a much lower carbon footprint, but anyone can download it from our website and run complex experiments, even if they don’t have supercomputer access,” Durran said. “This puts the technology within reach for many other researchers.”
