Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction

• URL: http://arxiv.org/abs/2405.15412v2
• Date: Tue, 29 Oct 2024 06:06:10 GMT
• Title: Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction
• Authors: Zijie Guo, Pumeng Lyu, Fenghua Ling, Lei Bai, Jing-Jia Luo, Niklas Boers, Toshio Yamagata, Takeshi Izumo, Sophie Cravatte, Antonietta Capotondi, Wanli Ouyang,
• Abstract summary: ORCA-DL is the first data-driven 3D ocean model for seasonal to decadal prediction of global ocean circulation. It accurately simulates three-dimensional ocean dynamics and outperforms state-of-the-art dynamical models. It stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections.
• Score: 39.7461632644892
• License:
• Abstract: 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.

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