--- pretty_name: ThousandWorlds license: cc-by-4.0 size_categories: - 1K 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. [![Code](https://img.shields.io/badge/code-GitHub-181717.svg?logo=github)](https://github.com/edstevenson/ThousandWorlds) 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. ![ThousandWorlds dataset schematic](imgs/OVERVIEW.png) ## Quickstart The easiest way to use the benchmark is through the [Python code](https://github.com/edstevenson/ThousandWorlds): ```bash git clone https://github.com/edstevenson/ThousandWorlds.git cd ThousandWorlds pip install -e . ``` ```python 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: ```bibtex @misc{thousandworlds2026, title = {ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets}, author = {{ThousandWorlds authors}}, year = {2026}, note = {Manuscript in preparation} } ```