Ocean modeling is a powerful tool for simulating the physical, chemical, and biological processes of the ocean, which is the foundation for marine science research and operational oceanography. Modern numerical ocean modeling mainly consists of governing equations and numerical algorithms. Nonlinear instability, computational expense, low reusability efficiency and high coupling costs have gradually become the main bottlenecks for the further development of numerical ocean modeling. Recently, artificial intelligence-based modeling in scientific computing has shown revolutionary potential for digital twins and scientific simulations, but the bottlenecks of numerical ocean modeling have not been further solved. Here, we present AI-GOMS, a large AI-driven global ocean modeling system, for accurate and efficient global ocean daily prediction. AI-GOMS consists of a backbone model with the Fourier-based Masked Autoencoder structure for basic ocean variable prediction and lightweight fine-tuning models incorporating regional downscaling, wave decoding, and biochemistry coupling modules. AI-GOMS has achieved the best performance in 30 days of prediction for the global ocean basic variables with 15 depth layers at 1/4{\deg} spatial resolution. Beyond the good performance in statistical metrics, AI-GOMS realizes the simulation of mesoscale eddies in the Kuroshio region at 1/12{\deg} spatial resolution and ocean stratification in the tropical Pacific Ocean. AI-GOMS provides a new backbone-downstream paradigm for Earth system modeling, which makes the system transferable, scalable and reusable.

翻译：海洋模拟是模拟海洋物理、化学和生物过程的强大工具，是海洋科学研究和业务化海洋学的基础。现代数值海洋模拟主要由控制方程和数值算法构成。非线性不稳定性、计算成本高昂、复用效率低下以及耦合成本高，已逐渐成为数值海洋模拟进一步发展的主要瓶颈。近年来，基于人工智能的科学计算建模在数字孪生和科学模拟方面展现出革命性潜力，但数值海洋模拟的瓶颈尚未得到进一步解决。在此，我们提出AI-GOMS，一个大型人工智能驱动的全球海洋模拟系统，用于实现精确高效的全球海洋逐日预测。AI-GOMS由一个基于傅里叶掩码自编码器结构的基础模型（用于基本海洋变量预测）以及多个轻量级微调模型（包含区域降尺度、波浪解码和生物化学耦合模块）组成。AI-GOMS在1/4度空间分辨率、15个深度层的全球海洋基本变量30天预测中取得了最佳性能。除统计指标表现优异外，AI-GOMS还实现了黑潮区域1/12度空间分辨率下的中尺度涡旋模拟以及热带太平洋海洋层结的模拟。AI-GOMS为地球系统建模提供了一种新的“基础模型-下游任务”范式，使系统具备可迁移性、可扩展性和可复用性。