# Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model

arXiv: https://arxiv.org/abs/2406.08632 · HTML: https://mlsanity.com/paper/2406.08632/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://mlsanity.com/paper/2306.03838/) (2023)
- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023)
- [Forecasting Global Weather with Graph Neural Networks](https://mlsanity.com/paper/2202.07575/) (2022)
- [Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models](https://mlsanity.com/paper/2102.05107/) (2021)
- [ACE: A fast, skillful learned global atmospheric model for climate prediction](https://mlsanity.com/paper/2310.02074/) (2023)
- [Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh](https://mlsanity.com/paper/2311.06253/) (2023)
- [AI-GOMS: Large AI-Driven Global Ocean Modeling System](https://mlsanity.com/paper/2308.03152/) (2023)
- [A Practical Probabilistic Benchmark for AI Weather Models](https://mlsanity.com/paper/2401.15305/) (2024)
- [Dynamical Tests of a Deep-Learning Weather Prediction Model](https://mlsanity.com/paper/2309.10867/) (2023)
- [OceanNet: A principled neural operator-based digital twin for regional oceans](https://mlsanity.com/paper/2310.00813/) (2023)

## Built on by (most cited first, 21 of 21)

- [Samudra: An AI Global Ocean Emulator for Climate](https://mlsanity.com/paper/2412.03795/) (2024)
- [SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators](https://mlsanity.com/paper/2509.12490/) (2025)
- [The impact of internal variability on benchmarking deep learning climate emulators](https://mlsanity.com/paper/2408.05288/) (2024)
- [FuXi-2.0: Advancing machine learning weather forecasting model for practical applications](https://mlsanity.com/paper/2409.07188/) (2024)
- [Robustness of AI-based weather forecasts in a changing climate](https://mlsanity.com/paper/2409.18529/) (2024)
- [FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere](https://mlsanity.com/paper/2411.10191/) (2024)
- [Generative assimilation and prediction for weather and climate](https://mlsanity.com/paper/2503.03038/) (2025)
- [Causal Climate Emulation with Bayesian Filtering](https://mlsanity.com/paper/2506.09891/) (2025)
- [Regional Ocean Forecasting with Hierarchical Graph Neural Networks](https://mlsanity.com/paper/2410.11807/) (2024)
- [TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State](https://mlsanity.com/paper/2504.09940/) (2025)
- [LUCIE-3D: A three-dimensional climate emulator for forced responses](https://mlsanity.com/paper/2509.02061/) (2025)
- [Learning Coupled Earth System Dynamics with GraphDOP](https://mlsanity.com/paper/2510.20416/) (2025)
- [PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting](https://mlsanity.com/paper/2605.08935/) (2026)
- [Regularization of ML models for Earth systems by using longer model timesteps](https://mlsanity.com/paper/2503.18023/) (2025)
- [Exploring Design Choices for Autoregressive Deep Learning Climate Models](https://mlsanity.com/paper/2505.02506/) (2025)
- [A machine learning model for skillful climate system prediction](https://mlsanity.com/paper/2505.06269/) (2025)
- [Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting](https://mlsanity.com/paper/2605.15470/) (2026)
- [Data-driven global ocean model resolving ocean-atmosphere coupling dynamics](https://mlsanity.com/paper/2508.10908/) (2025)
- [Hierarchical Testing of a Hybrid Machine Learning-Physics Global Atmosphere Model](https://mlsanity.com/paper/2602.11313/) (2026)
- [FuXiWeather2: Learning accurate atmospheric state estimation for operational global weather forecasting](https://mlsanity.com/paper/2603.15358/) (2026)
- [Emulating the Forced Response of Climate Models with Generative Machine Learning](https://mlsanity.com/paper/2605.16929/) (2026)

## Abstract (arXiv)

Seasonal climate forecasts are socioeconomically important for managing the impacts of extreme weather events and for planning in sectors like agriculture and energy. Climate predictability on seasonal timescales is tied to boundary effects of the ocean on the atmosphere and coupled interactions in the ocean-atmosphere system. We present the Ocean-linked-atmosphere (Ola) model, a high-resolution (0.25deg) Artificial Intelligence/ Machine Learning (AI/ML) coupled earth-system model which separately models the ocean and atmosphere dynamics using an autoregressive Spherical Fourier Neural Operator architecture, with a view towards enabling fast, accurate, large ensemble forecasts on the seasonal timescale. We find that Ola exhibits learned characteristics of ocean-atmosphere coupled dynamics including tropical oceanic waves with appropriate phase speeds, and an internally generated El Ni~no/Southern Oscillation (ENSO) having realistic amplitude, geographic structure, and vertical structure within the ocean mixed layer. We present initial evidence of skill in forecasting the ENSO which compares favorably to the SPEAR model of the Geophysical Fluid Dynamics Laboratory.