Papers › ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the...
ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO₂
Spencer K. Clark, Oliver Watt-Meyer, Anna Kwa, Jeremy McGibbon, Brian Henn, W. Andre Perkins, Elynn Wu, Lucas M. Harris, Christopher S. Bretherton
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While autoregressive machine-learning-based emulators have been trained to produce stable and accurate rollouts in the climate of the present-day and recent past, none so far have been trained to emulate the sensitivity of climate to substantial changes in CO₂ or other greenhouse gases. As an initial step we couple the Ai2 Climate Emulator version 2 to a slab ocean model (hereafter ACE2-SOM) and train it on output from a collection of equilibrium-climate physics-based reference simulations with varying levels of CO₂. We test it in equilibrium and non-equilibrium climate scenarios with CO₂ concentrations seen and unseen in training. ACE2-SOM performs well in equilibrium-climate inference with both in-sample and out-of-sample CO₂ concentrations, accurately reproducing the emergent time-mean spatial patterns of surface temperature and precipitation change with CO₂ doubling, tripling, or quadrupling. In addition, the vertical profile of atmospheric warming and change in extreme precipitation rates up to the 99.9999th percentile closely agree with the reference model. Non-equilibrium-climate inference is more challenging. With CO₂ increasing gradually at a rate of 2% year⁻¹, ACE2-SOM can accurately emulate the global annual mean trends of surface and lower-to-middle atmosphere fields but produces unphysical jumps in stratospheric fields. With an abrupt quadrupling of CO₂, ML-controlled fields transition unrealistically quickly to the 4xCO₂ regime. In doing so they violate global energy conservation and exhibit unphysical sensitivities of and surface and top of atmosphere radiative fluxes to instantaneous changes in CO₂. Future emulator development needed to address these issues should improve its generalizability to diverse climate change scenarios.
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