Collaborative Proposal: Harnessing simulation data to characterize transition layer mixing rates and mechanisms

该奖项由《2021年美国救援计划法案》(公法117-2)提供全部或部分资金。该项目将研究海洋过渡层(TL)中的卷吸过程，TL是湍流近地表混合层下部和稳定分层的海洋内部之间的垂直区域。该项目将使用一种新的方法组合，包括通过上层对现有的高分辨率剖面数据进行进一步分析，对过渡层中的夹带和内波过程进行高分辨率建模，以及机器学习技术。这些过程有助于确定海洋混合层的深度和温度，并最终调节全球海洋的海-气交换效应，但它们在数值模式参数化中仍然缺乏理解或表达。结果将与改进物理和生物地球化学海洋模型直接相关。此外，所开发的方法将适用于解释和分析来自各种地球物理流动的数据，并将公开提供分析脚本。这项工作将支持一名早期职业调查员、一名研究生的培训、对当地外展和教育项目的贡献，并将成为世界卫生组织地球物理流体动力学暑期项目研究生项目的基础。将产生一套高分辨率的直接数值模拟，涵盖一系列预期的热释光机制，包括开尔文-亥姆霍兹和Holmboe不稳定性和界面波。使用标准的流体动力学分析，包括线性不稳定性的表征和流动能量学的详细分析，这个全面的流场资料库将被用来确定给定的分层混合事件在多大程度上可以从有限的测量中得到表征。然后，这些模拟将被用作基于神经网络的流动分类方法的训练数据，从而允许根据潜在的波或不稳定性对输入的温度和速度分布进行分类。在使用模拟数据验证分类方法后，它将应用于观测，从而能够识别驱动TL中夹带的相关机制。因此，与每种机制相关的混合知识可以用来描述观测记录中的混合效率和湍流通量。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project will examine entrainment processes in the ocean transition layer (TL), the vertical region between the lower part of the turbulent near-surface mixed layer and the stably stratified ocean interior. The project will use a novel combination of approaches, including further analysis of existing high-resolution profile data through the upper ocean layers, high-resolution modeling of entrainment and internal wave processes in the transition layer, and machine learning techniques. These processes help determine ocean mixed layer depth and temperature and ultimately mediate air-sea exchange effects on the global ocean, yet they remain poorly understood or represented in numerical model parameterizations. Outcomes will be directly relevant to improving physical and biogeochemical ocean models. Additionally, the methods developed will be applicable to interpreting and analyzing data from a variety of geophysical flows, and the analysis scripts will be made publicly available. The work will support an early career investigator, the training of a graduate student, contributions to local outreach and educational programs, and will form the basis for a project for a graduate student in the WHOI summer program in Geophysical Fluid Dynamics. A suite of high-resolution direct numerical simulations will be generated covering a range of expected TL mechanisms, including Kelvin-Helmholtz and Holmboe instabilities and interfacial waves. Using standard fluid dynamical analyses, including characterization of the linear instabilities and a detailed analysis of the flow energetics, this comprehensive library of flow fields will be used to determine how well a given stratified mixing event can be characterized from limited measurements. The simulations will then be used as training data for a neural network-based flow classification method, allowing for input profiles of temperature and velocity to be classified in terms of the underlying waves or instabilities. After the classification method is validated using simulation data, it will be applied to the observations, allowing for identification of the relevant mechanisms driving entrainment in the TL. Knowledge of the mixing associated with each mechanism can thus be used to describe the mixing efficiency and turbulent fluxes in the observational record.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.