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（Eulerian）坐标系或遵循主要洋流的移动（拉格朗日）参考框架中进行研究。可以从拉格朗日数据测量的四个关键量是扩散率，拉格朗日积分时间尺度，旋转参数和光谱斜率或（等效地）分形维度。前三个是海洋学界积极关注的，因为它们与大规模海洋和气候模型中海洋循环的忠诚相关。第四次数量（光谱斜率）可能具有同等的重要性，但其值和含义在很大程度上尚未探索，并且尚未在全球范围内进行检查。这些拉格朗日特征对于许多重要的假设至关重要。然而，从数据中估算它们的困难是众所周知的，并导致出色的不确定性。如本文所示，这四个数量紧密相连，因为它们描述了拉格朗日速度频率频谱的四个最重要特征 - 这一事实通过直接研究频谱本身的细节，提出了一种新的和统一的分析方法。拟议的研究将应用来自大数据的严格技术来同时从所有可用的拉格朗日数据中估算所有四个拉格朗日特征。结果将是在地表和深度上对拉格朗日特征的最高分辨率图。在气候模型中增加海洋循环现实主义的总体目标是一个极大的社会兴趣的话题，因为它将增强气候变化适应和缓解措施。更立即，该项目将有助于唯一的声音跟踪浮点数据的活跃档案的维护，改进和更广泛的分布，这是通向海洋循环的最有价值的原位窗户之一。在整个项目中开发或完善的创新分析算法将与社区公开共享，从而为支持科学研究的软件基础架构做出了贡献。在此项目期间，将进一步开发出一种新的，高度优化的用于地球物理动力学的数值模型，并分发给社区。大数据技术在建模输出中的应用，允许将非常大的数据集降低到少量的参数，将在将来的模型/数据对立面中特别有用。最后，该项目将支持一名研究生，该研究生将接受大数据技术的应用来分析数值模型输出以及早期职业科学家。该方法将以几种重要的方式以先前的方式为基础：（i）通过充分利用可用的统计信息，从而增加了有效的空间分辨率，也许会大大增加； （ii）避免了四个参数相互作用引起的潜在严重估计误差； （iii）允许量化不确定性； （iv）通过允许对许多重要的物理假设进行正式和系统的测试。对来自现实模型的大量轨迹集合的平行分析将允许量化来自数据稀疏性引起的不确定性，并使模型能够重现观察到的Lagrangian特征的技能。最后，理想化的数值建模和理论将提供桥梁，以将拉格朗日轨迹的可观察到的特征与基础物理联系起来。主要的智力贡献将是回答许多重要的问题，在此详细介绍，例如：表面quasigeostrophy，内部准杂质的影响以及其他过程可以根据其Lagrangian Spectra区分？拉格朗日频谱斜坡告诉我们有关海洋湍流的性质？有效地描述扩散率的何时何地需要何时何地？旋转参数是否会准确捕获相干涡流对背景光谱的影响？这些问题和其他问题可以通过对拉格朗日速度光谱的首次全球研究来回答，并仔细注意量化错误并在不同制度中建立对控制参数的正确物理解释。

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