Accurate Subseasonal-to-Seasonal (S2S) ocean simulation is critically important for marine research, yet remains challenging due to its substantial thermal inertia and extended time delay. Machine learning (ML)-based models have demonstrated significant advancements in simulation accuracy and computational efficiency compared to traditional numerical methods. Nevertheless, a significant limitation of current ML models for S2S ocean simulation is their inadequate incorporation of physical consistency and the slow-changing properties of the ocean system. In this work, we propose a neural ocean model (NeuralOM) for S2S ocean simulation with a multi-scale interactive graph neural network to emulate diverse physical phenomena associated with ocean systems effectively. Specifically, we propose a multi-stage framework tailored to model the ocean's slowly changing nature. Additionally, we introduce a multi-scale interactive messaging module to capture complex dynamical behaviors, such as gradient changes and multiplicative coupling relationships inherent in ocean dynamics. Extensive experimental evaluations confirm that our proposed NeuralOM outperforms state-of-the-art models in S2S and extreme event simulation. The codes are available atthis https URL.
View on arXiv@article{gao2025_2505.21020,
title={ NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation },
author={ Yuan Gao and Ruiqi Shu and Hao Wu and Fan Xu and Yanfei Xiang and Ruijian Gou and Qingsong Wen and Xian Wu and Xiaomeng Huang },
journal={arXiv preprint arXiv:2505.21020},
year={ 2025 }
}