通过破碎波数据驱动的气泡速度参数化
Data Driven Air Entrainment Velocity Parameterization by Breaking Waves
AI总结：
本文提出了一种基于机器学习的全球参数化方法，用于改进气泡介导的CO2转移速度和海盐气溶胶排放，通过利用破碎波数据减少模型误差。
摘要：
波破碎将湍流和气泡注入上层海洋，调节气-海交换的动量、热量、气体和海雾气溶胶。这些通量依赖于海况的非线性关系，但在耦合大气-波-海洋模型中仍缺乏良好表征，其中空气吸入速度通常仅用风速或显著波高进行参数化。我们开发了一种全球机器学习参数化方法，该方法基于43年的WAVEWATCH III模拟，该模拟解析了破碎前沿分布及其相关能量学。一个具有七个物理驱动预测因子（风速、波高、波龄、陡度、方向和深度）的多层感知机能够高技能地重现谱参考Va。该模型减少了长期存在的整体公式偏差，特别是在以 swell 为主的低纬度地区过度估计，在风暴路径上低估。应用于全球范围，它提高了由气泡介导的CO2转移速度和海盐气溶胶排放，将误差减少了一个数量级。对独立HiWinGS观测的验证支持了在深水中的稳健性能。
英文摘要：
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.
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