Mathematical Geophysics Research Spotlight

Machine Learning Improves Weather and Climate Models

New research evaluates the performance of generative adversarial networks for stochastic parameterizations.

Source: Journal of Advances in Modeling Earth Systems (JAMES)


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Both weather and climate models have improved drastically in recent years, as advances in one field have tended to benefit the other. But there is still significant uncertainty in model outputs that are not quantified accurately. That’s because the processes that drive climate and weather are chaotic, complex, and interconnected in ways that researchers have yet to describe in the complex equations that power numerical models.

Historically, researchers have used approximations called parameterizations to model the relationships underlying small-scale atmospheric processes and their interactions with large-scale atmospheric processes. Stochastic parameterizations have become increasingly common for representing the uncertainty in subgrid-scale processes, and they are capable of producing fairly accurate weather forecasts and climate projections. But it’s still a mathematically challenging method. Now researchers are turning to machine learning to provide more efficiency to mathematical models.

Here Gagne et al. evaluate the use of a class of machine learning networks known as generative adversarial networks (GANs) with a toy model of the extratropical atmosphere—a model first presented by Edward Lorenz in 1996 and thus known as the L96 system that has been frequently used as a test bed for stochastic parameterization schemes. The researchers trained 20 GANs, with varied noise magnitudes, and identified a set that outperformed a hand-tuned parameterization in L96. The authors found that the success of the GANs in providing accurate weather forecasts was predictive of their performance in climate simulations: The GANs that provided the most accurate weather forecasts also performed best for climate simulations, but they did not perform as well in offline evaluations.

The study provides one of the first practically relevant evaluations for machine learning for uncertain parameterizations. The authors conclude that GANs are a promising approach for the parameterization of small-scale but uncertain processes in weather and climate models. (Journal of Advances in Modeling Earth Systems (JAMES), https://doi.org/10.1029/2019MS001896, 2020)

—Kate Wheeling, Science Writer

Citation: Wheeling, K. (2020), Machine learning improves weather and climate models, Eos, 101, https://doi.org/10.1029/2020EO142422. Published on 07 April 2020.
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