--- license: apache-2.0 --- Logo for the ACE Project # ACE2-ERA5 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.** ACE2-ERA5 is trained on the [ERA5 dataset](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803) and is described in [ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses](https://arxiv.org/abs/2411.11268). ### Quick links - 📃 [Paper](https://arxiv.org/abs/2411.11268) - 💻 [Code](https://github.com/ai2cm/ace) - 💬 [Docs](https://ai2-climate-emulator.readthedocs.io/en/stable/) - 📂 [All Models](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4) ### Inference quickstart 1. Download this repository. Optionally, you can just download a subset of the `forcing_data` and `initial_conditions` for the period you are interested in. 2. Update paths in the `inference_config.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.path`. 3. Install code dependencies with `pip install fme`. 4. Run inference with `python -m fme.ace.inference inference_config.yaml`. ### Strengths and weaknesses Briefly, the strengths of ACE2-ERA5 are: - accurate atmospheric warming response to combined increase of sea surface temperature and CO2 over last 80 years - highly accurate atmospheric response to El Niño sea surface temperature variability - good representation of the geographic distribution of tropical cyclones - accurate Madden Julian Oscillation variability - realistic stratospheric polar vortex strength and variability - exact conservation of global dry air mass and moisture Some known weaknesses are: - the individual sensitivities to changing sea surface temperature and CO2 are not entirely realistic - the medium-range (3-10 day) weather forecast skill is not state of the art - not expected to generalize accurately for large perturbations of certain inputs (e.g. doubling of CO2)