Accurate ocean dynamics modeling is crucial for enhancing understanding of ocean circulation, predicting climate variability, and tackling challenges posed by climate change. Despite improvements in traditional numerical models, predicting global ocean variability over multi-year scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), the first data-driven 3D ocean model for seasonal to decadal prediction of global ocean circulation. ORCA-DL accurately simulates three-dimensional ocean dynamics and outperforms state-of-the-art dynamical models in capturing extreme events, including El Ni\~no-Southern Oscillation and upper ocean heatwaves. This demonstrates the high potential of data-driven models for efficient and accurate global ocean forecasting. Moreover, ORCA-DL stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections.
@article{arxiv.2405.15412,
title = {Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction},
author = {Zijie Guo and Pumeng Lyu and Fenghua Ling and Lei Bai and Jing-Jia Luo and Niklas Boers and Toshio Yamagata and Takeshi Izumo and Sophie Cravatte and Antonietta Capotondi and Wanli Ouyang},
journal= {arXiv preprint arXiv:2405.15412},
year = {2024}
}