Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913566664163328 |
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| author | Guo, Zijie Lyu, Pumeng Ling, Fenghua Bai, Lei Luo, Jing-Jia Boers, Niklas Yamagata, Toshio Izumo, Takeshi Cravatte, Sophie Capotondi, Antonietta Ouyang, Wanli |
| author_facet | Guo, Zijie Lyu, Pumeng Ling, Fenghua Bai, Lei Luo, Jing-Jia Boers, Niklas Yamagata, Toshio Izumo, Takeshi Cravatte, Sophie Capotondi, Antonietta Ouyang, Wanli |
| contents | 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 Niño-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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15412 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction Guo, Zijie Lyu, Pumeng Ling, Fenghua Bai, Lei Luo, Jing-Jia Boers, Niklas Yamagata, Toshio Izumo, Takeshi Cravatte, Sophie Capotondi, Antonietta Ouyang, Wanli Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning 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 Niño-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. |
| title | Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.15412 |