Collaborative Research: Global Estimation of Lagrangian Characteristics of the Ocean Circulation

合作研究：海洋环流拉格朗日特征的全球估计

• 批准号: 1658302
• 负责人: Harper Simmons
• 金额: $ 48.65万
• 依托单位: University of Alaska Fairbanks Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1658302&HistoricalAwards=false
• 关键词: Collaborative; Research; Global; Estimation; Lagrangian

The ocean is a complex turbulent fluid that can be studied in the traditional fixed (Eulerian)coordinate system or a moving (Lagrangian) reference frame that follow the major ocean currents. Four key quantities that may be measured from Lagrangian data are the diffusivity, the Lagrangian integral timescale, the spin parameter and the spectral slope or (equivalently) the fractal dimension. The first three are of active interest to the oceanographic community due to their relevance for increasing the fidelity of the ocean circulation in large-scale ocean and climate models. The fourth quantity, the spectral slope, is potentially of equal importance, yet both its values and it meaning are largely unexplored, and it has yet to be examined on global scale. These Lagrangian characteristics are central to a number of important hypotheses; yet the difficulties in estimating them from data are well known and lead to outstanding uncertainties. As shown herein, these four quantities are tightly connected because they describe the four most important features of the frequency spectrum of Lagrangian velocities - a fact which suggests a new and unified approach to their analysis, by directly investigating the details of the spectrum itself. The proposed study will apply rigorous techniques from Big Data to estimate all four Lagrangian characteristics simultaneously from all available Lagrangian data. The result will be the highest resolution maps yet made of the Lagrangian characteristics, both at surface and at depth. The overarching goal of increasing the realism of the ocean circulation in climate models is a topic of great societal interest, because it would bolster climate variability adaptation and mitigation efforts. More immediately, this project will contribute to the maintenance, improvement, and broader distribution of the only active archive of acoustically tracked float data, one of the most valuable in situ windows into the ocean circulation. Innovative analysis algorithms developed or refined throughout this project will be openly shared with the community, contributing to the software infrastructure that supports scientific research. A new, highly optimized implementation of idealized numerical models for geophysical fluid dynamics will similarly be further developed, and distributed to community, during this project. The application of Big Data techniques to model output, allowing very large datasets to be reduced to much smaller numbers of parameters, will be particularly useful in future model/data intercomparisons. Finally, this project will support a graduate student, who will be trained in the application of Big Data techniques to analyzing numerical model output, as well as an early-career scientist.The approach will build on previous work in several important ways: (i) by making best use of available statistical information, thereby increasing the effective spatial resolution, perhaps dramatically; (ii) by avoiding potentially serious estimation errors arising from interactions of the four parameters; (iii) by allowing quantification of uncertainty; and (iv) by permitting the formal and systematic testing of a number of important physical hypothesis. A parallel analysis of a vastly larger ensemble of trajectories from a realistic model will allow quantification of uncertainties arising from data sparsity, and will enable the model's skill at reproducing observed Lagrangian features to be closely scrutinized. Finally, idealized numerical modeling and theory will provide the bridge to directly connect the observable features of Lagrangian trajectories with the underlying physics. The main intellectual contribution will be to answer a number of important questions, framed in detail herein, such as: Can the influence of surface quasigeostrophy, interior quasigeostrophy, and other processes be distinguished on the basis of their Lagrangian spectra? What does the Lagrangian spectral slope tell us about the nature of ocean turbulence? When and where is anisotropy necessary to effectively describe diffusivity? Does the spin parameter accurately capture the effect of coherent eddies on the background spectrum? These and other questions can be answered with the first global study of Lagrangian velocity spectra, with careful attention to quantifying errors and to establishing the correct physical interpretations of the controlling parameters in different regimes.

海洋是一种复杂的湍流，可以在传统的固定(欧拉)坐标系或跟随主要洋流的移动(拉格朗日)参照系中进行研究。从拉格朗日数据可以测量的四个关键量是扩散系数、拉格朗日积分时间标度、自旋参数和光谱斜率或(相当于)分维。前三项引起海洋学界的积极兴趣，因为它们与在大规模海洋和气候模式中提高海洋环流的保真度有关。第四个量，光谱斜率，可能具有同样的重要性，但它的价值和意义在很大程度上还没有被探索，它还有待于在全球范围内进行检验。这些拉格朗日特征是许多重要假设的核心；然而，从数据中估计它们的困难是众所周知的，并导致了突出的不确定性。如本文所示，这四个量紧密相连，因为它们描述了拉格朗日速度频谱的四个最重要的特征--这一事实表明了一种新的统一的分析方法，通过直接调查频谱本身的细节。这项拟议的研究将应用来自大数据的严格技术，从所有可用的拉格朗日数据中同时估计所有四个拉格朗日特征。其结果将是迄今为止最高分辨率的拉格朗日特征地图，无论是在表面还是在深度。在气候模型中增加海洋环流的真实性是一个极具社会意义的主题，因为这将支持气候多变性的适应和缓解努力。更直接的是，该项目将有助于维护、改进和更广泛地分发唯一活跃的声学跟踪浮标数据档案，这是了解海洋环流的最有价值的现场窗口之一。在整个项目中开发或改进的创新分析算法将与社区公开分享，为支持科学研究的软件基础设施做出贡献。在该项目期间，还将进一步开发一种新的、高度优化的地球物理流体动力学理想化数值模型的实现，并将其分发给社区。将大数据技术应用于模型输出，允许将非常大的数据集减少到更少的参数数量，这将在未来的模型/数据相互比较中特别有用。最后，这个项目将支持一名研究生和一名初出茅庐的科学家，他们将接受应用大数据技术分析数值模式输出的培训。该方法将在几个重要方面建立在以前工作的基础上：(I)通过最佳利用现有的统计信息，从而显著提高有效空间分辨率；(Ii)通过避免由于四个参数的相互作用而产生的潜在的严重估计误差；(Iii)允许量化不确定性；以及(Iv)允许对一些重要的物理假设进行正式和系统的测试。对现实模型中更大的轨迹集合进行并行分析，将允许量化数据稀疏引起的不确定性，并将使模型在再现观察到的拉格朗日特征方面的技能得到密切关注。最后，理想化的数值模拟和理论将提供直接连接拉格朗日轨迹的可观测特征和基本物理的桥梁。主要的智力贡献将是回答一些在这里详细描述的重要问题，例如：表面准旋转、内部准旋转和其他过程的影响能根据它们的拉格朗日谱来区分吗？关于海洋湍流的性质，拉格朗日光谱斜率告诉了我们什么？何时何地需要各向异性才能有效地描述扩散系数？自旋参数是否准确地捕捉到了相干涡旋对背景光谱的影响？通过对拉格朗日速度谱的第一次全球研究，仔细注意量化误差和建立不同区域中控制参数的正确物理解释，这些问题和其他问题都可以得到解答。

项目成果

Estimates of Near-Inertial Wind Power Input Using Novel In Situ Wind Measurements from Minimet Surface Drifters in the Iceland Basin

使用冰岛盆地小型表面漂流器的新型现场风测量估算近惯性风功率输入

• DOI: 10.1175/jpo-d-21-0283.1
• 发表时间: 2022
• 期刊: Journal of Physical Oceanography
• 影响因子: 3.5
• 作者: Klenz, Thilo;Simmons, Harper L.;Centurioni, Luca;Lilly, Jonathan M.;Early, Jeffrey J.;Hormann, Verena
• 通讯作者: Hormann, Verena