  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.
