allenai/ACE2S-SHiELD-plus

ACE2S-SHiELD+

Ai2 Climate Emulator (ACE) is a family of models designed to simulate atmospheric variability from the time scale of days to centuries.

Disclaimer: ACE models are research tools and should not be used for operational climate predictions.

ACE2S-SHiELD+ is an emulator of GFDL's physics-based SHiELD model, trained on a combination of AMIP, equilibrium-climate, and ramped-SST-random-CO₂ data. It has comparable skill to ACE2-SHiELD in AMIP inference and ACE2-SOM in slab-ocean-coupled equilibrium-climate inference, but also can accurately emulate scenarios with independent perturbations to the SST or CO₂, like AMIP +4 K or slab-ocean-coupled abrupt 4xCO₂. It is a stochastic model, which facilitates running large ensembles of simulations from the same initial conditions, has an improved representation of the spherical power spectrum of its predicted variables, and also includes a new constraint to conserve global atmospheric total energy.

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Briefly, the strengths of ACE2S-SHiELD+ are:

• It can accurately perform historical AMIP simulations, slab-ocean-coupled simulations with constant or steadily increasing CO₂ between 1x and 4x the concentration of the present day.
• Unlike prior models, it can additionally accurately separate the independent effects of SST and CO₂ on climate, including in AMIP +4 K and abrupt 4xCO₂ inference, notably emulating the correct radiative sensitivity to changes in CO₂ including the implicit response of clouds.
• In addition to conserving dry air mass and water like prior models, it also is constrained to conserve global atmospheric total energy within the same average residual of SHiELD.

Some known weaknesses are:

• It is trained to emulate a physics-based model and therefore inherits the biases relative to observations thereof.
• As in the case of ACE2-SOM and SHiELD, in slab-ocean mode, sea-ice coverage, ocean heat transport, and ocean mixed layer depth are prescribed based on a present-day climatologies and therefore do not respond to changes in CO₂. Prescribed sea-ice means projections with ACE2S-SHiELD+ are missing an important feedback mechanism known to amplify warming in the polar regions. Due to the narrow spread in sea ice coverage in samples seen during training, generalization ability to unseen sea ice coverage is limited.
• Similar to ACE2-SOM, abrupt regime shifts in stratospheric temperature and moisture can occur in inference runs, which sometimes can affect predictions of other variables.

Inference speed:

• Note that due to architectural and hyperparameter changes, inference speed with ACE2S-SHiELD+ is roughly half that of ACE2-SHiELD or ACE2-SOM, but it is still 68x faster than SHiELD when each are run on typical hardware.

License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2’s Responsible Use Guidelines.