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 numpy as np
import thousandworlds as tw
tw.download_dataset(".")
bundle = tw.load("single-complete", data_dir="dataset")
pred = np.broadcast_to(bundle.Y_train.mean(axis=0), bundle.Y_test.shape)
scores = tw.evaluate.rmse(pred, bundle.Y_test, bundle.field_mask_test, bundle.field_names)
scores["per_variable"]
See the GitHub repository for 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
- Archival mirror: https://doi.org/10.7910/DVN/8IEH6Q (Harvard Dataverse)
- Paper: coming soon!
Citation
If you use ThousandWorlds, please cite the paper:
@misc{thousandworlds2026,
title = {ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets},
author = {{ThousandWorlds authors}},
year = {2026},
note = {Manuscript in preparation}
}
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