# ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation

arXiv: https://arxiv.org/abs/2306.08754 · HTML: https://mlsanity.com/paper/2306.08754/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [PDEBENCH: An Extensive Benchmark for Scientific Machine Learning](https://mlsanity.com/paper/2210.07182/) (2022)
- [ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models](https://mlsanity.com/paper/2111.14671/) (2021)
- [Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks](https://mlsanity.com/paper/2302.07260/) (2023)
- [ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events](https://mlsanity.com/paper/1612.02095/) (2016)

## Built on by (most cited first, 10 of 10)

- [AI Foundation Models for Weather and Climate: Applications, Design, and Implementation](https://mlsanity.com/paper/2309.10808/) (2023)
- [The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications](https://mlsanity.com/paper/2412.01953/) (2024)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Projected Neural Differential Equations for Learning Constrained Dynamics](https://mlsanity.com/paper/2410.23667/) (2024)
- [Learning Stochastic Multiscale Models](https://mlsanity.com/paper/2506.22655/) (2025)
- [Setting the Standard: Recommended Practices for Data Preprocessing in Data-Driven Climate Prediction](https://mlsanity.com/paper/2508.07062/) (2025)
- [Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies](https://mlsanity.com/paper/2510.03305/) (2025)
- [Data-driven stochastic reduced-order modeling of parametrized dynamical systems](https://mlsanity.com/paper/2601.10690/) (2026)
- [The Rise of AI in Weather and Climate Information and its Impact on Global Inequality](https://mlsanity.com/paper/2603.05710/) (2026)
- [ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets](https://mlsanity.com/paper/2606.18338/) (2026)

## Abstract (arXiv)

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining physics with machine learning (ML) offer faster, higher fidelity climate simulations by outsourcing compute-hungry, high-resolution simulations to ML emulators. However, these hybrid ML-physics simulations require domain-specific data and workflows that have been inaccessible to many ML experts. As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators. The ClimSim dataset includes 5.7 billion pairs of multivariate input/output vectors, capturing the influence of high-resolution, high-fidelity physics on a host climate simulator's macro-scale state. The dataset is global and spans ten years at a high sampling frequency. We provide a cross-platform, containerized pipeline to integrate ML models into operational climate simulators for hybrid testing. We also implement various ML baselines, alongside a hybrid baseline simulator, to highlight the ML challenges of building stable, skillful emulators. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim and https://github.com/leap-stc/climsim-online) are publicly released to support the development of hybrid ML-physics and high-fidelity climate simulations.