Collaborative Research: A global census of submesoscale energetics using in-situ drifter observations and a high resolution ocean model

海洋运动在很宽的尺度范围内都是湍流的，但观测上这种湍流主要是在最小（1公里）和最大（100公里）尺度上进行研究的。由于观测的局限性，海洋流场在中尺度下的许多特性仍然相对难以捉摸：大多数高分辨率现场观测测量的空间范围有限，而基于卫星的速度估计的空间分辨率有限。此外，利用简化的物理学假设（地转性）估计卫星表面速度，该假设在中尺度以下分解。总的来说，目前还没有关于动能作为次中尺度范围内长度尺度函数的全球观点。该项目的主要目标是i）估计亚中尺度的动能分布和转移，ii）了解平衡和波浪状流分量在设置这些亚中尺度能量中的作用，以及iii）量化这些亚中尺度流特性如何并评估其在全球范围内变化的原因。这项研究将量化全球次中尺度动能含量，其动力学特征，并转移作为空间尺度的函数，使用现有的表面漂移观测NOAA。在这些尺度上对动能的全球观测以前从未进行过研究：这项工作是一个独特的机会，可以描述海洋中尺度流动的空间格局和季节变化，并评估混合层深度和表面强迫对这些尺度上能量含量的影响。这些估计数将提供第一个全球观测基线，以便与今后的观测和高分辨率模拟进行比较，本工作将进行一次这样的比较。此外，这些观测将通过量化不同尺度的能量转移来阐明全球海洋的能量收支。在粗分辨率气候模式中，次网格尺度参数化代表了次中尺度运动的影响：这项工作提供的基线将有助于改善这些参数化在未来。该分析将提供有用的地面实况，以验证和校准未来的卫星观测，并量化高分辨率海洋模型中的偏差。提高对海洋能量学的理解也与为海洋和气候模型制定更好的次网格尺度参数化直接相关。此外，该项目将生成有文档记录的开源Python代码，用于处理观测和合成拉格朗日数据，以供未来的海洋能量学研究使用。在劳动力培训方面，该项目将支持一名研究生，他们将学习如何使用Python中的并行处理工具分析高分辨率模型数据。两名本科生将进行适当的研究项目，并将通过在两个不同的机构本科生计划的研究经验进行指导。为实现其目标，本研究将借助两点空间统计分析来自全球漂流者方案漂流表面浮标的位置和速度数据。具体而言，它将使用二阶结构函数来量化动能如何作为尺度的函数分布，并使用三阶结构函数来量化动能如何跨尺度转移。此外，它将利用拉格朗日滤波来量化平衡运动和波浪运动之间的统计特性和相互作用。这些观测数据分析将得到高分辨率全球海洋模拟分析的支持，这将允许量化拉格朗日采样造成的偏差。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Oceanic motions are turbulent over a wide range of scales, but observationally this turbulence has primarily been investigated at the smallest ( 1km) and at the largest ( 100km) scales. Many of the properties of the oceanic flow field at intermediate scales, referred to as the submesoscales, have remained relatively elusive because of observational limitations: most high-resolution in-situ observational measurements have limited spatial range while satellite-based velocity estimates have limited spatial resolution. In addition, satellite-based surface velocities are estimated using the simplified physics assumptions (geostrophy), which breaks down at the submesoscales. Overall, a global view of kinetic energy as a function of length scale in the submesoscale range is not currently available. The primary goals of this project are i) to estimate kinetic energy distribution and transfers at submesoscales, ii) to understand the role of balanced and wave-like flow components in setting these submesoscale energetics, and iii) to quantify how and assess why these submesoscale flow properties vary globally. This research will quantify global submesoscale kinetic energy content, its dynamical characteristics, and transfers as a function of spatial-scale, using existing surface drifter observations from NOAA. Global observations of kinetic energy at these scales have never been examined before: this work is a unique opportunity to characterize the spatial patterns and seasonal variability of ocean submesoscale flows, and to assess the effects of mixed layer depth and surface forcing on energy content at these scales. These estimates will provide the first global observational baseline to compare against future observations and high-resolution simulations, and one such comparison will be performed in this work. In addition, these observations will elucidate the energy budget of the global ocean by quantifying energy transfers across scales. In coarse resolution climate models, subgrid-scale parameterizations represent the effects of submesoscale motions: the baseline provided by this work will help improve these parameterizations in the future. The analysis will provide a useful ground truth to validate and calibrate future satellite observations, and to quantify biases in high resolution ocean models. Improved understanding of ocean energetics also has direct relevance for the development of better subgrid scale parameterizations for ocean and climate models. Additionally, this project will generate documented and open-source Python code for processing observational and synthetic Lagrangian data for future studies of ocean energetics. In terms of workforce training, this project will support one graduate student, who will learn how to analyze high-resolution model data using parallel processing tools in Python. Two undergraduate students will conduct suitable research projects and will be mentored through the Research Experiences for Undergraduates program at two different institutions. Two early-career scientists will be supported by this project.To achieve its goals, this study will analyze the position and velocity data from the drifting surface buoys of the Global Drifter Program, with the help of two-point spatial statistics. Specifically, it will use the second order structure function to quantify how kinetic energy is distributed as a function of scale, and the third order structure function to quantify how kinetic energy is transferred across scales. Additionally, it will make use of Lagrangian filtering to quantify the statistical properties of and interactions between balanced and wave-like motions. These observational data analyses will be supported by the analysis of a high-resolution global ocean simulation, which will allow for the quantification of the biases caused by Lagrangian sampling.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.