ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions
<|reference_start|>ORCA: A Global Ocean Emulator for Multi-year to Decadal Predictions: Ocean dynamics plays a crucial role in driving global weather and climate patterns. Accurate and efficient modeling of ocean dynamics is essential for improved understanding of complex ocean circulation and processes, for predicting climate variations and their associated teleconnections, and for addressing the challenges of climate change. While great efforts have been made to improve numerical Ocean General Circulation Models (OGCMs), accurate forecasting of global oceanic variations for multi-year remains to be a long-standing challenge. Here, we introduce ORCA (Oceanic Reliable foreCAst), the first data-driven model predicting global ocean circulation from multi-year to decadal time scales. ORCA accurately simulates the three-dimensional circulations and dynamics of the global ocean with high physical consistency. Hindcasts of key oceanic variables demonstrate ORCA's remarkable prediction skills in predicting ocean variations compared with state-of-the-art numerical OGCMs and abilities in capturing occurrences of extreme events at the subsurface ocean and ENSO vertical patterns. These results demonstrate the potential of data-driven ocean models for providing cheap, efficient, and accurate global ocean modeling and prediction. Moreover, ORCA stably and faithfully emulates ocean dynamics at decadal timescales, demonstrating its potential even for climate projections. The model will be available at https://github.com/OpenEarthLab/ORCA.<|reference_end|>
@article{guo2024data-driven,
    title={Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction},
    author={Zijie Guo, Pumeng Lyu, Fenghua Ling, Lei Bai, Jing-Jia Luo, Niklas
  Boers, Toshio Yamagata, Takeshi Izumo, Sophie Cravatte, Antonietta Capotondi,
  Wanli Ouyang},
    journal={arXiv preprint arXiv:2405.15412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15412},
    primaryClass={physics.ao-ph cs.AI cs.LG}
}