Add model, config, and README

README.md

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 ---
-license: cc-by-4.0
+license: apache-2.0
+library_name: fme
 ---
+
+<img src="ACE-logo.png" alt="Logo for the ACE Project" style="width: auto; height: 50px;">
+
+# ACE2-SHiELD
+
+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-SHiELD is trained on output from [SHiELD](https://www.gfdl.noaa.gov/shield/), NOAA GFDL's physics-based atmospheric model, 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).
+
+### Quick links
+
+- 📃 [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)
+
+### Inference quickstart
+
+1. Download this repository for the model checkpoint. Download the forcing data and initial conditions from the [ACE2S-SHiELD+](https://huggingface.co/allenai/ACE2S-SHiELD-plus) repository — specifically the `forcing_data/amip/` directory (covering 1979–2021) and `initial_conditions/amip/ic.nc` (a single initial condition for 1979-01-01).
+
+2. Update paths in the `inference_config.yaml`. Specifically, update `experiment_dir`, `checkpoint_path`, `initial_condition.path` and `forcing_loader.dataset.data_path`. Optionally, configure `data_writer.names` to select which output variables to save.
+
+3. Install code dependencies with `pip install fme`.
+
+4. Run inference with `python -m fme.ace.inference inference_config.yaml`.
+
+See the [ACE docs](https://ai2-climate-emulator.readthedocs.io/en/stable/) for full details on configuring inference and output data.
+
+### Data availability
+
+**Forcing and initial condition data** for AMIP-style simulations are available in the [ACE2S-SHiELD+](https://huggingface.co/allenai/ACE2S-SHiELD-plus) repository. Forcing data covers 1979–2021 (`forcing_data/amip/`). A single initial condition for 1979-01-01 is available (`initial_conditions/amip/ic.nc`).
+
+**Training and validation data** are not hosted on Hugging Face, but are available in a requester-pays Google Cloud Storage bucket at:
+```
+gs://ai2cm-public-requester-pays/2024-11-13-ai2-climate-emulator-v2-amip/data/c96-1deg-shield
+```
+
+### Strengths and weaknesses
+
+The behavior of ACE2-SHiELD is similar to that of [ACE2-ERA5](https://huggingface.co/allenai/ACE2-ERA5) as described in the [ACE2 paper](https://www.nature.com/articles/s41612-025-01090-0). Please refer to that model card and paper for a detailed discussion of strengths and weaknesses.

ace2_shield_ckpt.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2debfee21a4cee81f34631c81539a7f312b9ea67a73cebd0f9b00cbe3cc23482
+size 1823643243

inference_config.yaml

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+experiment_dir: /output_directory
+n_forward_steps: 400
+forward_steps_in_memory: 50
+checkpoint_path: /ace2_shield_ckpt.tar
+logging:
+  log_to_screen: true
+  log_to_wandb: false
+  log_to_file: true
+  project: ace
+initial_condition:
+  path: /initial_conditions/amip/ic.nc
+  start_indices:
+    times:
+      - "1979-01-01T00:00:00"
+forcing_loader:
+  dataset:
+    data_path: /forcing_data/amip
+  num_data_workers: 4
+data_writer:
+  save_prediction_files: true
+  save_monthly_files: false
+  names: ["TMP2m", "VGRD10m", "PRATEsfc"]