# AI-GOMS: Large AI-Driven Global Ocean Modeling System

arXiv: https://arxiv.org/abs/2308.03152 · HTML: https://mlsanity.com/paper/2308.03152/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers](https://mlsanity.com/paper/2111.13587/) (2021)
- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023)
- [Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast](https://mlsanity.com/paper/2211.02556/) (2022)
- [Bridging observation, theory and numerical simulation of the ocean using Machine Learning](https://mlsanity.com/paper/2104.12506/) (2021)

## Built on by (most cited first, 27 of 27)

- [Samudra: An AI Global Ocean Emulator for Climate](https://mlsanity.com/paper/2412.03795/) (2024)
- [Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model](https://mlsanity.com/paper/2406.08632/) (2024)
- [SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators](https://mlsanity.com/paper/2509.12490/) (2025)
- [NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation](https://mlsanity.com/paper/2505.21020/) (2025)
- [LangYa: Revolutionizing Cross-Spatiotemporal Ocean Forecasting](https://mlsanity.com/paper/2412.18097/) (2024)
- [Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance](https://mlsanity.com/paper/2403.13850/) (2024)
- [FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution](https://mlsanity.com/paper/2506.03210/) (2025)
- [Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2312.06261/) (2023)
- [OceanForecastBench: A Benchmark Dataset for Data-Driven Global Ocean Forecasting](https://mlsanity.com/paper/2511.18732/) (2025)
- [MedFormer: a data-driven model for forecasting the Mediterranean Sea](https://mlsanity.com/paper/2509.00015/) (2025)
- [PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting](https://mlsanity.com/paper/2605.08935/) (2026)
- [Ocean Wave Forecasting with Deep Learning as Alternative to Conventional Models](https://mlsanity.com/paper/2406.03848/) (2024)
- [Advancing Ocean State Estimation with efficient and scalable AI](https://mlsanity.com/paper/2511.06041/) (2025)
- [Eddy-Resolving Global Ocean Forecasting with Multi-Scale Graph Neural Networks](https://mlsanity.com/paper/2601.12775/) (2026)
- [Improved Forecasts of Global Extreme Marine Heatwaves Through a Physics-guided Data-driven Approach](https://mlsanity.com/paper/2412.15532/) (2024)
- [KunPeng: A Global Ocean Environmental Model](https://mlsanity.com/paper/2504.04766/) (2025)
- [How to systematically develop an effective AI-based bias correction model?](https://mlsanity.com/paper/2504.15322/) (2025)
- [LanTu: Dynamics-Enhanced Deep Learning for Eddy-Resolving Ocean Forecasting](https://mlsanity.com/paper/2505.10191/) (2025)
- [Data-driven global ocean model resolving ocean-atmosphere coupling dynamics](https://mlsanity.com/paper/2508.10908/) (2025)
- [Principled Operator Learning in Ocean Dynamics: The Role of Temporal Structure](https://mlsanity.com/paper/2510.09792/) (2025)
- [Deploying Atmospheric and Oceanic AI Models on Chinese Hardware and Framework: Migration Strategies, Performance Optimization and Analysis](https://mlsanity.com/paper/2510.17852/) (2025)
- [SSTODE: Ocean-Atmosphere Physics-Informed Neural ODEs for Sea Surface Temperature Prediction](https://mlsanity.com/paper/2511.05629/) (2025)
- [Neural ocean forecasting from sparse satellite-derived observations: a case-study for SSH dynamics and altimetry data](https://mlsanity.com/paper/2512.22152/) (2025)
- [Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation](https://mlsanity.com/paper/2608.10277/) (2026)
- [Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models](https://mlsanity.com/paper/2609.14821/) (2026)
- [HClimRep-Ocean: A Global Ocean Emulator on an Unstructured Mesh](https://mlsanity.com/paper/2609.28601/) (2026)
- [OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling](https://mlsanity.com/paper/2610.15518/) (2026)

## Abstract (arXiv)

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 1/4deg spatial resolution. Beyond the good performance in statistical metrics, AI-GOMS realizes the simulation of mesoscale eddies in the Kuroshio region at 1/12deg 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.