IsoSim: A Long-term Benchmark Dataset for Water Isotope Emulation in Global Climate Models

Isotopic ratios of hydrogen and oxygen in water serve as powerful tracers of the Earth's hydrological cycle, offering insights into the origins of water vapor, large-scale atmospheric circulation, and moisture transport dynamics. However, integrating water isotopes into fully coupled global climate models (GCMs) is both scientifically and technically challenging due to the complex interactions between water isotopes and the atmosphere, hydrosphere, and cryosphere, as well as the extensive modifications to model physics and dynamics. As a result, most GCMs lack support for isotopes and even the few existing isotope-enabled GCMs still remain highly expensive to run, significantly limiting their usability. Machine learning (ML) offers promising opportunities to emulate the complex process as powerful mathematical approximators. The water isotope fields from the emulators bring potential for applications in isotope-unenabled GCMs. However, the absence of a publicly available ML-ready dataset has hindered the development of robust ML-based emulators. To address this gap, we introduce IsoSim, the first ML-ready benchmark dataset designed to facilitate the development of ML emulators for water isotopes in GCMs. This dataset includes global climate variables and water isotope fields across three spatial dimensions (latitude, longitude, and height) from isotope-enabled GCM simulations, spanning 500 years at a monthly resolution. We also include different climatic scenarios and a diverse set of learning-based emulators to carry out extensive evaluations and build the benchmarks. The dataset and results serve as reference points to compare machine learning models' ability in approximating complex physical relationships.