In climate simulations, small-scale processes shape ocean dynamics but remain computationally expensive to resolve directly. For this reason, their contributions are commonly approximated using empirical parameterizations, which lead to significant errors in long-term projections. In this work, we develop parameterizations based on Fourier Neural Operators, showcasing their accuracy and generalizability in comparison to other approaches. Finally, we discuss the potential and limitations of neural networks operating in the frequency domain, paving the way for future investigation.

翻译：在气候模拟中，小尺度过程塑造了海洋动力学特性，但直接解析这些过程计算成本高昂。因此，通常采用经验参数化方法近似其贡献，但这会导致长期预测出现显著误差。本研究基于傅里叶神经算子开发参数化方案，展示了该方案相较其他方法的准确性与泛化能力。最后，我们讨论了频域神经网络的应用潜力与局限性，为后续研究奠定基础。