ThousandWorlds

ThousandWorlds is a benchmark for emulating exoplanet climates: 1760 simulations across 5 GCMs, 8 planet parameters, and atmospheric variables on a 32 x 64 x 10 latitude-longitude-pressure grid. It includes three nested benchmark subsets, two evaluation protocols, and eight released baseline methods.

Inputs are 8 continuous planet parameters plus the source GCM label. Outputs are time-averaged climate fields on a 32 x 64 latitude-longitude grid: three-dimensional variables are stored as pressure-level channels, and two-dimensional variables are stored as single-level fields.

Quickstart

The easiest way to use the benchmark is through the Python code:

git clone https://github.com/edstevenson/ThousandWorlds.git
cd ThousandWorlds
pip install -e .

import thousandworlds as tw

tw.download_dataset()
bundle = tw.load("single-complete", data_dir="dataset")

See the GitHub repository for the full quickstart, notebooks, baseline code, evaluation utilities, and reproducing paper results.

Files

The release includes:

• archives/dataset.tar.gz: the ThousandWorlds dataset.
• archives/results-baselines-*.tar.gz: baseline predictions for the 3 subsets.
• croissant.json: Croissant metadata.
• archives/*.sha256: checksum sidecars.

Dataset Contents

The dataset contains gridded fields (NumPy), input metadata (CSV), predefined train/test splits, normalization statistics, and spherical harmonic coefficients plus inverse-SHT weights for spectral methods.

Subsets

The dataset is organized into three subsets of increasing complexity and realism:

Subset	Simulations	Fields	Description
single-complete	256	48	Smaller subset; simulations from a single GCM, complete observations only.
multi-complete	1659	48	All 5 GCMs, still with no missing fields.
multi-partial	1760	53	Full dataset; all 5 GCMs, with missing fields represented as NaNs.

The subset split files contain:

File	single-complete	multi-complete	multi-partial
train.csv	206	1538	1626
test.csv	50	90	100
test_shared_planets_only.csv	-	58	60
held_out_aux.csv	-	31	34

held_out_aux.csv is excluded from train and test to prevent train-test leakage (it contains simulations from auxiliary GCMs that correspond to identical planets present in the test set).

Inputs

Each simulation has one row in dataset/inputs.csv, keyed by simulation_id. The public model inputs are stellar temperature, stellar flux, radius, gravity, rotation period, surface pressure, CO2, CH4, and gcm_label. The metadata also includes is_target_gcm, in_target_physical_domain, planet_id, and source.

Parameter	Range
Radius (Earth radii)	[0.7, 1.4]
Surface gravity (m s^-2)	[6.0, 16.0]
Rotation period (days)	[0.1, 1000.0]
Surface pressure (bar)	[0.5, 5]
CO2 volume fraction (%)	[0, 100]
CH4 volume fraction (%)	[0, 5]
Incident stellar flux (W m^-2)	[500, 1500]
Stellar temperature (K)	[2500, 5800]

Outputs

Target fields include surface temperature, 3D temperature, specific humidity, cloud fraction, east-west wind, north-south wind, absorbed shortwave radiation, and outgoing longwave radiation. Gridded targets are provided on a 32 x 64 latitude-longitude grid, with vertical fields stored on relative pressure levels.

Variable	Dimensionality	Unit
Surface temperature	2D	K
Temperature	3D	K
Specific humidity	3D	dex
Cloud fraction	3D	1
East-west wind	3D	m s^-1
North-south wind	3D	m s^-1
Absorbed shortwave radiation	2D	W m^-2
Outgoing longwave radiation	2D	W m^-2

The gridded field archives are:

File	Shape	Contents
dataset/fields/all-obs.npz	(1760, 53, 32, 64)	Field archive covering all 5 GCMs with structured whole-field missingness.
dataset/fields/complete-obs-only.npz	(1659, 48, 32, 64)	Complete-observation field archive.

Spectral Coefficients: The spectral coefficient archives mirror those field archives with T21 spherical harmonic coefficients: dataset/coefficients/*.npz stores coefficients with 484 coefficients per field and a field_mask for missing fields. Whole-field missingness is represented as all-NaN gridded channels and as false entries in the spectral field_mask.

Evaluation

The package includes loaders and metrics for two benchmark protocols:

• Standard: the main test protocol, ideal for ML model comparison.
• Shared-planets: evaluate on planets shared across target and auxiliary GCMs; used to assess performance relative to inter-GCM error, i.e. how close a model gets to the epistemic uncertainty floor of the problem.

Released baselines include train mean, kNN, PCA ridge, PCA-MLP, Coord-MLP, Coord-DeepONet, PPCA-ICM, and GPLFR. Baseline artifacts include predictions, resolved configs, and metrics JSON files.

Links

• DOI: https://doi.org/10.57967/hf/8695
• Code: https://github.com/edstevenson/ThousandWorlds
• Paper: https://arxiv.org/abs/2606.18338

Citation

If you use ThousandWorlds, please cite the paper:

@article{thousandworlds2026,
  title = {ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets},
  author = {Stevenson, Edward T. and Mak, Mei Ting and Wolf, Eric and Sergeev, Denis E. and Hammond, Tobi and Mayne, N. J. and Cranmer, Miles},
  year = {2026},
  eprint = {2606.18338},
  archivePrefix = {arXiv},
  doi = {10.48550/arXiv.2606.18338}
}