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license: apache-2.0
---
<img src="ACE-logo.png" alt="Logo for the ACE Project" style="width: auto; height: 50px;">
# 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)
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