ACE2S-SHiELD+ reference data

This bucket contains reference data used in training and evaluating models described in the ACE2S-SHiELD+ manuscript. The reference data is derived from data generated from simulations with GFDL's SHiELD model. All simulations were configured identically, with the exception of the initial conditions, ocean boundary conditions, and the concentration of CO₂. We refer readers to the ACE2, ACE2-SOM, and ACE2S-SHiELD+ papers for more specific details regarding these runs, but a high-level summary of this bucket's contents is provided in the table below.

For how these datasets were used in training and/or evaluation in the ACE2S-SHiELD+ manuscript, see the configurations in the ACE-experiments directory of the ace2s-shield-plus-paper repository on GitHub.

Opening data directly with xarray

With the huggingface_hub Python package installed in your environment, you can open and interact with these datasets directly with xarray. There are no egress fees and therefore no credentials are required. Opening a dataset is as simple as:

>>> import xarray as xr
>>> STORE = "hf://buckets/allenai/ACE2S-SHiELD-plus-reference-data/random-CO2-ensemble/ramped-sst-1xCO2-random-perturbation-ic_0001.zarr"
>>> ds = xr.open_zarr(STORE, chunks={"time": 360})

Here chunks={"time": 360} is specified to override the native inner chunk size along the "time" dimension, which is 1 for optimal performance during training. For analyses that depend on aggregations across the "time" dimension it is significantly more performant to use the shard size, in this case 360, as the dask chunk size. If your use-case more resembles the training regime, then you should likely omit it.

¹ Note ic_0001.zarr omits its spin-up period.
² Spin-up data is only provided for select runs and is split off from the main segments in the separate spin-up subdirectory.