ArticleNational science review2022
Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations.
Article in National science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.BMC anesthesiology · 2023Trial
- Fusing physics and AI enhances accuracy and diagnostic capabilities in air quality forecasting.National science review · 2026Article
- AI for atmosphere-ocean sciences: advancements, challenges and ways forward.National science review · 2026Review
- Projection of ENSO using observation-informed deep learning.Nature communications · 2025Article
- Reconstructing historical climate fields with deep learning.Science advances · 2025Article
- Machine learning to optimize additive manufacturing for visible photonics.Nanophotonics (Berlin, Germany) · 2023Article
- A self-attention-based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions.Science advances · 2023Article
- Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsisFrontiers in genetics · 2023Article
- Commentary on 'Physics-informed deep learning parameterization of ocean vertical mixing improves climate simulations' by ZhuNational science review · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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