Crowdsourcing the Frontier: Advancing Hybrid Physics-ML Climate Simulation via $50,000 Kaggle Competition
Machine learning (ML) parameterizations for climate modeling often suffer from online instability and inconsistent performance when coupled to full-physics climate models. Method: This study leverages a $50,000 Kaggle competition to crowdsource surrogate models for subgrid-scale physical processes, using the ClimSim dataset. Multiple deep learning architectures are designed and rigorously evaluated via online coupling to an interactive climate model featuring comprehensive cloud microphysics. Contribution/Results: All top-performing competition models achieve long-term online stability. Expanding input variables significantly improves predictive accuracy. Several architectures attain state-of-the-art (SOTA) performance on key metrics—including zonal-mean bias and global root-mean-square error—while exhibiting strong offline–online consistency. This work constitutes the first systematic demonstration of the feasibility and reproducibility of the crowdsourcing paradigm for climate ML parameterization. It establishes a viable pathway toward high-resolution, computationally efficient, and reliable long-term climate prediction.