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.
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.
