| | --- |
| | license: apache-2.0 |
| | library_name: fme |
| | --- |
| | |
| | <img src="ACE-logo.png" alt="Logo for the ACE Project" style="width: auto; height: 50px;"> |
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| | # ACE2-ERA5 |
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| | Ai2 Climate Emulator (ACE) is a family of models designed to simulate atmospheric variability from the time scale of days to centuries. |
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| | **Disclaimer: ACE models are research tools and should not be used for operational climate predictions.** |
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| | 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://www.nature.com/articles/s41612-025-01090-0). As part of that paper, the repository containing training and evaluation scripts and configuration files used for this model is located [here](https://github.com/ai2cm/ace2-paper). |
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| | ### Quick links |
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| | - 📃 [Paper](https://www.nature.com/articles/s41612-025-01090-0) |
| | - 💻 [Code](https://github.com/ai2cm/ace) |
| | - 💬 [Docs](https://ai2-climate-emulator.readthedocs.io/en/stable/) |
| | - 📂 [All Models](https://huggingface.co/collections/allenai/ace-67327d822f0f0d8e0e5e6ca4) |
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| | ### Inference quickstart |
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| | 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. |
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| | 2. Update paths in the `inference_config.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.path`. |
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| | 3. Install code dependencies with `pip install fme`. |
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| | 4. Run inference with `python -m fme.ace.inference inference_config.yaml`. |
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|
| | ### Strengths and weaknesses |
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| | 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 |
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| | 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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