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 and is described in ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses.

Quick links

• 📃 Paper
• 💻 Code
• 💬 Docs
• 📂 All Models

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)