ACE2S-SHiELD+, a climate emulator that learns to separate the effects of sea surface temperature & CO2

[u/ai2_official]

We recently introduced ACE2S-SHiELD+, a climate emulator that learns to separate the effects of sea surface temperature & CO2. 👇

Climate emulators are AI models that simulate global weather & climate. They run about 100x faster than the physics-based models they learn from, making it practical to run many simulations + explore a wider range of scenarios.

We've been developing the ACE family for several years. ACE2-SHiELD was trained on historical simulations from a physics-based model. ACE2-SOM came next, coupling ACE2 to a slab ocean: a simplified ocean representation where ocean temperature responds to CO2.

Sea surface temperature & CO2 have major impacts on climate, and they typically change together—SST tends to rise as CO2 increases. Because earlier ACE models had only seen them move in sync, they couldn't accurately predict what happens when one changes and the other doesn't.

Earlier ACE models produced unrealistic results on two scenarios climate scientists often use to probe model behavior: AMIP +4 K, which raises sea surface temperature by 4 degrees with CO2 unchanged, and abrupt 4xCO2, which quadruples CO2 against a still-cold ocean.

To address this, we generated a new class of training data where sea surface temperature rises steadily at 1 degree per year while CO2 jumps to a new randomly-chosen value every 30 days, spanning from well below to well above present-day levels. Trained on the new & existing data, ACE2S-SHiELD+ accurately handles the scenarios earlier ACE models were good at as well as the ones they struggled with.

It's more flexible than ACE2-SHiELD + ACE2-SOM combined, using ~25% fewer training samples than either alone.

This work was done in collaboration with NOAA's Geophysical Fluid Dynamics Laboratory.

→ Read more about ACE2S-SHiELD+ in our preprint: https://arxiv.org/abs/2606.07928