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

合作研究：利用原位漂流者观测和高分辨率海洋模型进行全球亚尺度能量普查

• 批准号: 2242109
• 负责人: C Spencer Jones
• 金额: $ 22.92万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242109&HistoricalAwards=false
• 关键词: Collaborative; Research; global; census; submesoscale

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.

海洋运动在各种尺度上都是湍流的，但是从观察上讲，这种湍流主要是在最小（1公里）和最大（100公里）的尺度上研究的。由于观察局限性的局限性，在中间尺度上的海洋流场的许多特性仍然相对难以捉摸：大多数高分辨率的现场观测测量值有限，而基于卫星的速度估计值有限。此外，使用简化的物理假设（地球植物）估算了基于卫星的表面速度，该假设在submissoscales上分解。总体而言，目前尚不可用的，在子尺度范围内将动能作为长度尺度的函数的全球视图。该项目的主要目标是i）估计子计算中的动能分布和转移，ii）了解平衡和波浪状流量成分在设置这些子阶级能量学方面的作用，以及iiii），以量化如何和评估这些集群尺度尺度流量的方式以及为什么全球范围不同。这项研究将使用NOAA的现有表面漂移器观察结果来量化全球尺度动力学能量含量，其动力学特征，并随空间规模的函数量化。以前从未对这些量表的动能观察到全球观察：这项工作是表征海洋尺度尺度流的空间模式和季节性变化的独特机会，并评估混合层深度和表面强迫对这些量表的能量含量的影响。这些估计将为与未来的观察和高分辨率模拟进行比较提供第一个全球观察基线，并将在这项工作中进行一种比较。此外，这些观察结果将通过量化范围的能源传输来阐明全球海洋的能源预算。在粗分辨率的气候模型中，亚网格规模的参数化代表了子尺度动作的效果：这项工作提供的基线将有助于改善这些参数化。该分析将提供有用的基础真理，以验证和校准未来的卫星观测值，并量化高分辨率海洋模型中的偏见。对海洋能量学的理解的提高也可以直接相关，以开发海洋和气候模型的更好的亚网格量表参数化。此外，该项目将生成记录和开源的Python代码，用于处理观察性和合成拉格朗日数据，以实现对海洋能量学的未来研究。在劳动力培训方面，该项目将支持一位研究生，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。两名本科生将进行合适的研究项目，并通过在两个不同机构的本科生计划的研究经验进行指导。该项目将支持两名早期职业科学家。为了实现其目标，本研究将在两点空间统计数据的帮助下分析来自全球漂流者计划的漂移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的分布方式，以及三阶结构函数以量化如何在尺度上传递动能。此外，它将利用拉格朗日过滤来量化平衡和类似波的运动之间的统计特性和相互作用。这些观察数据分析将通过对高分辨率全球海洋仿真的分析来支持，这将允许量化由拉格朗日抽样引起的偏见。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查审查标准来通过评估来通过评估来支持的。