When AI models miss the butterfly effect

I keep hearing "the model is learning the physics" from machine learning people talking about AI weather models. What they usually mean is a specific kind of model: the generative, transformer-based emulators trained end-to-end on reanalysis data, no dynamical core, no equations, just a network fit to the data. GraphCast, Pangu, GenCast, and the generative-data-assimilation models coming out of places like NVIDIA (Manshausen et al.'s model is one example, not the point) all fall in that bucket. That's the question I actually want to answer for myself, and it's narrower than "do AI weather models learn physics" as a blanket claim, because some AI weather models explicitly aren't in that bucket. NeuralGCM (Kochkov et al. 2024) wires an actual differentiable dynamical core into the model and only learns the subgrid closure. Caltech, MIT, and JPL's CliMA earth-system model, still in development, is built the same way: physics as far as it can go, learning only where it can't. Those get to…

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