Machine learning for alien climates: Introducing the ThousandWorlds benchmark

ThousandWorlds is a comprehensive benchmark designed to aid the machine learning and exoplanet communities in studying alien climates by providing a dataset of 1,760 simulations from five global climate models (GCMs), supplemented with additional bespoke runs. These simulations cover a range of planetary conditions, from icy to sauna-like environments, and are used to perform parameter-to-field regression, predicting a planet's 3D climate based on eight parameters. The ThousandWorlds dataset addresses the challenge of limited data, parameter-to-field prediction, and varying fidelity across multiple simulators, offering an opportunity for the ML community to explore and benchmark methods in an area of scientific research that lacks dominant deep learning solutions. The dataset's design considers spherical geometry and structured missingness, providing a platform to test and improve models, such as Gaussian processes and deep learning approaches, for more accurate climate predictions on exoplanets.