  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.
