Data Driven Air Entrainment Velocity Parameterization by Breaking Waves

TL;DR

This paper presents a machine learning-based parameterization of air entrainment velocity in the ocean, improving the representation of wave breaking effects on air-sea exchanges in climate models.

Contribution

It introduces a global machine learning model trained on 43 years of simulation data to accurately predict air entrainment velocity using physically relevant predictors.

Findings

Reduces biases in existing bulk formulas.

Improves estimates of CO2 transfer velocity and sea-salt aerosol emissions.

Validated against independent ocean observations.

Abstract

Wave breaking injects turbulence and bubbles into the upper ocean, modulating air-sea exchange of momentum, heat, gases, and sea-spray aerosols. These fluxes depend nonlinearly on sea state but remain poorly represented in coupled atmosphere-wave-ocean models, where air-entrainment velocity is often parameterized using wind speed or significant wave height alone. We develop a global machine-learning parameterization of Va trained on a 43-year WAVEWATCH III simulation that resolves the breaker-front distribution and associated energetics. A multilayer perceptron with seven physically motivated predictors (wind speed, wave height, wave age, steepness, direction, and depth) reproduces spectral-reference Va with high skill. The model reduces longstanding biases in bulk formulas, notably overestimation in swell-dominated low latitudes and underestimation in storm tracks. Applied globally, it improves bubble-mediated CO2 transfer velocity and sea-salt aerosol emission, reducing errors by an order of magnitude. Validation against independent HiWinGS observations supports robust deep-water performance.