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Innovation

Improving Climate Model Accuracy

For scenarios that require a rapid response

Published: Tuesday, June 21, 2022 - 12:00

(ORNL: Oak Ridge, Tennessee) -- A study led by Oak Ridge National Laboratory researchers promises to help sharpen accuracy for climate-change models and enable more reliable predictions of extreme weather.

The team’s results outline an invertible neural network—a type of artificial intelligence that mimics the human brain to improve calibration for models that attempt to predict the pace and results of climate change based on existing climate data. Tests found that the network improved models’ accuracy and consistency at a speed as much as 30 times faster than other methods.

“This network holds the potential to fundamentally change how we approach calibration and simulation in traditional earth-system modeling,” says ORNL’s Dan Lu,  the study’s lead author. “The network is efficient enough to solve problems within seconds after being trained, and thus can be used to make quick, accurate predictions in scenarios that require a rapid response.”

The model will be regularly updated to ensure further improvements.

First published June 2, 2022, on Oak Ridge National Laboratory News.

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About The Authors

Oak Ridge National Laboratory’s picture

Oak Ridge National Laboratory

Oak Ridge National Laboratory is a multiprogram science and technology laboratory managed for the U.S. Department of Energy by UT-Battelle, LLC. Scientists and engineers at ORNL conduct basic and applied research and development to create scientific knowledge and technological solutions that strengthen the nation's leadership in key areas of science; increase the availability of clean, abundant energy; restore and protect the environment; and contribute to national security.

Matt Lakin’s picture

Matt Lakin

Science writer Matt Lakin has nearly 20 years of experience in newspaper and multimedia journalism in the Southeast. Before joining ORNL in 2019, Matt worked as a reporter and editor for newspapers in Knoxville, Tennessee; Bristol, Virginia; and Dalton, Georgia. He was named Journalist of the Year by the Tennessee Associated Press in 2017.