Are general circulation models obsolete?

The title of this topic is from a very interesting article by a group of climate modellers asking the question “what now?”

Traditional general circulation models, or GCMs—that is, three-dimensional dynamical models with unresolved terms represented in equations with tunable parameters—have been a mainstay of climate research for several decades, and some of the pioneering studies have recently been recognized by a Nobel prize in Physics. Yet, there is considerable debate around their continuing role in the future. Frequently mentioned as limitations of GCMs are the structural error and uncertainty across models with different representations of unresolved scales and the fact that the models are tuned to reproduce certain aspects of the observed Earth. We consider these shortcomings in the context of a future generation of models that may address these issues through substantially higher resolution and detail, or through the use of machine learning techniques to match them better to observations, theory, and process models. It is our contention that calibration, far from being a weakness of models, is an essential element in the simulation of complex systems, and contributes to our understanding of their inner workings. Models can be calibrated to reveal both fine-scale detail and the global response to external perturbations. New methods enable us to articulate and improve the connections between the different levels of abstract representation of climate processes, and our understanding resides in an entire hierarchy of models where GCMs will continue to play a central role for the foreseeable future.

Julia Slingo and other luminaries: Ambitious partnership needed for reliable climate prediction

Current global climate models struggle to represent precipitation and related extreme events, with serious implications for the physical evidence base to support climate actions. A leap to kilometre-scale models could overcome this shortcoming but requires collaboration on an unprecedented scale.

There was an interesting article on atmospheric prediction for NWP - so sort of related:

Pangu-Weather: Kaifeng Bi et al.,Nov. 2022, arXiv:2211.02556

([2211.02556] Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast) [] ​

There are a lot of comments I could make indicating that this is perhaps not yet quite the triumph of AI over physical modelling (despite the claims in the paper). Never the less it is a big step forward compared to other efforts. The paper has also been causing a degree of existential angst at ECMWF. The optimist in me suggests that this actually opens up a host of new avenues for how we might address things in NWP, and maybe NEWP in general