The climatologists can, at least. Can they scrutinize the esoteric ensemble of weights making up this AI model? And which type of model's going to be easier to update based on changing climate parameters, a climate model or an AI model?

Weather and climate models have their own physics, which at the very least means that the solution is physical for the universe that particular model inhabits. The boundary conditions are parameterized, and those can be tweaked as climate and land use changes.

AI models don’t have any of that, but they are actually more akin to human forecasters, gaining forecast skill from pattern recognition. I think there’s a place for both in weather forecasting, but I’d have zero confidence in an AI climate model, or anything longer than a year. An AI might be very good at seasonal forecasts though, picking up easy to miss signals in the MJO or ENSO.

> Weather and climate models have their own physics, which at the very least means that the solution is physical for the universe that particular model inhabits. The boundary conditions are parameterized, and those can be tweaked as climate and land use changes.

That really isn't true these days. The dynamical cores and physics packages in numerical weather prediction models and general circulation models have more-or-less converged over the past two decades. For instance, you'll find double-moment microphysical schemes in a cross-section of both classes of models, and slightly specialized versions of full-fledged GCMs can be be run within assimilation frameworks to generate true-blooded weather forecasts.

> AI models don’t have any of that, but they are actually more akin to human forecasters, gaining forecast skill from pattern recognition

This grossly sells short what the current crop of AI weather models is capable of, and how they're formulated. It's best to think of them as "emulators" of their physics-based cousins; they're trained to reproduce the state transitions from t=t0 to t=t0+delta_t that an NWP system would generate. It's a bit reductive to call this "pattern matching", especially when we increasingly see that the emulators recover a fair bit of fundamental dynamics (e.g. Greg Hakim's work which reproduces idealized dycore tests on AI-NWP models and clearly demonstrates that they get some things surprisingly correct - even though the setups in these experiments is _far_ from real-world conditions).

> That really isn't true these days. The dynamical cores and physics packages in numerical weather prediction models and general circulation models have more-or-less converged over the past two decades.

Ah, well, I did stop studying GCMs about 20 years ago so perhaps I should shut up and let other people post. I appreciate the detail in your explanation here, and I wouldn’t mind a link to papers explaining the current state of the art.

I'm not sure I can point you to a single reference, but a good starting point would be the UK Met Office's "Unified Model", which provides a framework for configuring model simulations that scale from sub-mesoscale rapid refresh (e.g. the UKV system) to traditional global modeling (e.g. MOGREPS) and beyond into climate (latest versions of the Hadley Centre models, which I think the current production version is HadGEM3).

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Climatologists use climate models to predict the climate. Meteorologists use weather models to predict the weather. They are different time scales and disciplines.