Even if perfect observations and models were available, the time interval for which weather forecasts can be accurate is limited. This limit is related to fundamental physical characteristics of the earth's atmosphere, which make small errors grow very fast and spread out, a feature known as the butterfly effect.

The “butterfly effect” refers to a well-known and unfortunate property of the atmospheric circulation: Tiny uncertainties or errors in the initial conditions are rapidly amplified, creating a fundamental, intrinsic predictability limit for weather forecasting that cannot be overcome

We find, that in contrast to standard weather forecasting models, the initial difference grow only slowly in the AI-based model and there is no indication of a butterfly effect at all. This provides an example of how machine learning models can fail to reproduce a fundamental physical principle, even though they can accurately mimic many observed behaviors.

We find that the AI-based model completely fails to reproduce the rapid initial growth rates and hence would incorrectly suggest an unlimited predictability of the atmosphere.

Selz, T., & Craig, G. C. (2023). Can Artificial Intelligence‐Based Weather Prediction Models Simulate the Butterfly Effect?. Geophysical Research Letters, 50(20), e2023GL105747.

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