Mixing in equatorial oceans

Moum, James N.; Natarov, Andrei; Richards, Kelvin J; Smyth, William D.

doi:10.1016/b978-0-12-821512-8.00017-7

Cited by 1 publication

(2 citation statements)

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“…The pronounced overestimation of K T can also be understood from a physical oceanographic perspective. Physically, subject to intense deep-cycle turbulence [ 3 , 6 , 30 ], ocean tracers and momentum are well mixed above the EUC core, leading to a weak stratification and a weak shear in the regions where the training data are measured. In contrast, vertical diffusion is very weak in the ocean interior.…”

Section: Resultsmentioning

confidence: 99%

“…The Pacific equatorial cold tongue is a key region whose sea surface temperature (SST) variations impact worldwide through atmospheric teleconnections. It is widely accepted that oceanic turbulent mixing plays a major role in the equatorial heat budget [ 3 , 4 , 30 ]. However, great uncertainties still exist in the parameterizations of oceanic turbulent mixing [ 7 , 8 , 12 , 33–35 ], which has been a primary source for model biases in ocean and climate modeling.…”

Section: Discussionmentioning

confidence: 99%

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Physics-informed deep-learning parameterization of ocean vertical mixing improves climate simulations

Zhu

Moum

Wang

et al. 2022

National Science Review

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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 poorly in the tropics. Recent advances in deep learning method and new availability of long-term turbulence measurements provide an opportunity to explore data-driven approaches to parameterize 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 that employs turbulence measurements, 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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Section: Resultsmentioning

confidence: 99%