This repo contains code accompanying four papers describing ACE models:
- "ACE: A fast, skillful learned global atmospheric model for climate prediction" (link)
- "Application of the Ai2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity" (link)
- "ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses" (link)
- "ACE2-SOM: Coupling to a slab ocean and learning the sensitivity of climate to changes in CO2" (link)
pip install fme
See complete documentation here and a quickstart guide here.
Pretrained model checkpoints are available in the ACE Hugging Face collection.
Two versions of the complete dataset described in arxiv:2310.02074 are available on a requester pays Google Cloud Storage bucket:
gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/data/repeating-climSST-1deg-zarrs
gs://ai2cm-public-requester-pays/2023-11-29-ai2-climate-emulator-v1/data/repeating-climSST-1deg-netCDFs
The zarr format is convenient for ad-hoc analysis. The netCDF version contains our
train/validation split which was used for training and inference.
The datasets used in the ACE2 paper are available at:
gs://ai2cm-public-requester-pays/2024-11-13-ai2-climate-emulator-v2-amip/data/c96-1deg-shield/
gs://ai2cm-public-requester-pays/2024-11-13-ai2-climate-emulator-v2-amip/data/era5-1deg-1940-2022.zarr/
The dataset used in the ACE2-SOM paper is available at:
gs://ai2cm-public-requester-pays/2024-12-05-ai2-climate-emulator-v2-som/SHiELD-SOM-C96