AERIS Earth Systems Model Pushes AI for Science to New Heights

From hurricanes to heatwaves, our ability to predict the weather helps guide critical decisions in energy, agriculture, public safety and beyond. However, producing reliable forecasts, both quickly and far into the future, has long pushed the limits of science and computing.

Now, researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory introduce AERIS — the Argonne Earth Systems Model for Reliable and Skillful Predictions. AERIS is a breakthrough AI system that learns from decades of Earth systems data to deliver fast, high-resolution forecasts from hours to months into the future. In early tests, AERIS produced medium-range forecasts in about 30 seconds and was able to predict extreme events with high accuracy.

AERIS is one of the largest AI models for science created to date. It was built and trained on Aurora, one of the world’s fastest supercomputers, located at the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility. Developed in partnership with Intel and Hewlett Packard Enterprise, Aurora is an exascale system capable of performing more than a quintillion — that is, a billion billion — calculations per second and ranks among the top-performing machines globally for AI workloads.

The combined scale of Aurora and the model itself, trained on high-resolution data and billions of parameters, enabled AERIS to push beyond the 10-day limit typical of many AI Earth system models. Traditional models begin to lose accuracy around 10 days because the atmosphere and ocean are chaotic and sensitive to small changes.

The team’s work has been recognized as a finalist for the Association for Computing Machinery’s prestigious 2025 Gordon Bell Special Prize for Climate Modeling. Running on nearly the full Aurora system — 10,624 nodes and 63,744 graphics processing units (GPUs) — AERIS sustained 10.21 exaflops and peaked at 11.21 exaflops. That means AERIS achieved over 10 quintillion operations per second, far beyond Aurora’s 1 exaflop capacity, because AI workloads use smaller data types that enable the machine to handle many more tasks simultaneously.

A Different Kind of Earth Systems Model

AERIS takes a different path from traditional weather models. Numerical prediction systems solve complex equations to simulate atmospheric physics, which limits how fast they can run and how well they can use large archives of observations. AERIS is purely data-driven, learning patterns directly from decades of observations. The team trained AERIS on a massive dataset of high-resolution images of global weather conditions from 1979 to 2018, totaling 16 terabytes ¬— equivalent to roughly 4 million photos stored on a smartphone.

AERIS uses a modern AI technique called a diffusion model, best known for generating images, to create many plausible forecasts and estimate their uncertainty. It is the first billion-parameter diffusion model of its kind, designed to generate ensembles rather than a single forecast to better capture uncertainty.

It also reads the data pixel by pixel, keeping fine details that other methods often blur. This produces sharper, more realistic forecasts, but requires a lot of computing power.

Meeting those demands required both Aurora’s scale and a new way to divide the work. To run AERIS effectively on Aurora, the team developed a method called Sequence Window Parallelism (SWiPe). This approach efficiently distributes the model’s compute tasks and data across Aurora’s more than 60,000 GPUs while reducing communication between them.

The team also tested AERIS on the LUMI supercomputer at the IT Center for Science in Kajaani, Finland, showing the approach works well on different kinds of computer systems.

Early Results, Practical Impact

In tests, AERIS outperformed a leading European forecasting system for predictions up to 10 days. It also stayed stable out to 90 days, capturing long term ocean-atmosphere patterns and realistic tropical wave behavior at high resolution.

At the same time, the researchers note that AERIS is still early in its development and faces practical limits. Experiments with new configurations, higher resolutions, or added data can require enormous computing time, with some runs taking a week or more even on an exascale system. AERIS was designed so it could later be “distilled” into a smaller version that could run on less powerful machines, including laptops. They also see their approach as a blueprint that could help researchers build large-scale AI models for other areas of science.

The study, “AERIS: Argonne Earth Systems Model for Reliable and Skillful Predictions,” was authored by Väinö Hatanpää, Jason Stock, Eugene Ku, Murali Emani, Sam Foreman, Chunyong Jung, Sandeep Madireddy, Varuni Sastry, Sam Wheeler, Huihuo Zheng, Troy Arcomano, Venkatram Vishwanath, and Rao Kotamarthi from Argonne National Laboratory; Ray A. O. Sinurat from the University of Chicago; and Tung Nguyen from the University of California, Los Angeles.

Cover Image: Joseph Insley, ALCF Visualization and Data Analytics Team 

Notices and Disclaimers

Performance varies by use, configuration, and other factors. Learn more on the Performance Index site.
Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available ​updates. See backup for configuration details. No product or component can be absolutely secure.
Your costs and results may vary.
Intel technologies may require enabled hardware, software, or service activation.
© Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.