Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations Article Swipe
YOU?
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· 2022
· Open Access
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· DOI: https://doi.org/10.1093/nsr/nwac044
Uncertainties in ocean-mixing parameterizations are primary sources for ocean and climate modeling biases. Due to lack of process understanding, traditional physics-driven parameterizations perform unsatisfactorily in the tropics. Recent advances in the deep-learning method and the new availability of long-term turbulence measurements provide an opportunity to explore data-driven approaches to parameterizing oceanic vertical-mixing processes. Here, we describe a novel parameterization based on an artificial neural network trained using a decadal-long time record of hydrographic and turbulence observations in the tropical Pacific. This data-driven parameterization achieves higher accuracy than current parameterizations, demonstrating good generalization ability under physical constraints. When integrated into an ocean model, our parameterization facilitates improved simulations in both ocean-only and coupled modeling. As a novel application of machine learning to the geophysical fluid, these results show the feasibility of using limited observations and well-understood physical constraints to construct a physics-informed deep-learning parameterization for improved climate simulations.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/nsr/nwac044
- OA Status
- gold
- Cited By
- 91
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214812787
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214812787Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/nsr/nwac044Digital Object Identifier
- Title
-
Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulationsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-04Full publication date if available
- Authors
-
Yuchao Zhu, Rong‐Hua Zhang, James N. Moum, Fan Wang, Xiaofeng Li, Delei LiList of authors in order
- Landing page
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https://doi.org/10.1093/nsr/nwac044Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1093/nsr/nwac044Direct OA link when available
- Concepts
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Mixing (physics), Generalization, Climate model, Artificial neural network, Ocean observations, Hydrography, Deep sea, Climatology, Meteorology, Computer science, Turbulence, Environmental science, Oceanography, Climate change, Artificial intelligence, Geology, Physics, Mathematics, Quantum mechanics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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91Total citation count in OpenAlex
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2025: 31, 2024: 31, 2023: 25, 2022: 4Per-year citation counts (last 5 years)
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44Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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