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

基本信息

批准号：
1658564
负责人：
Jeffrey Early
金额：
$ 65.88万
依托单位：
NorthWest Research Associates, Incorporated
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 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1658564&HistoricalAwards=false
关键词：
Collaborative Research Global Estimation Lagrangian

项目摘要

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流体，可以在传统的固定（欧拉）坐标系或遵循主要洋流的移动（拉格朗日）参考系中进行研究。可以从拉格朗日数据测量的四个关键量是扩散率、拉格朗日积分时标、自旋参数和谱斜率或（等效地）分形维数。前三个模型因其与提高大规模海洋和气候模型中海洋环流的保真度相关而受到海洋学界的积极关注。第四个量，即光谱斜率，可能同样重要，但其值和含义在很大程度上尚未被探索，并且尚未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，根据数据估算它们的困难是众所周知的，并导致显着的不确定性。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，通过直接研究频谱本身的细节，可以采用一种新的统一的分析方法。拟议的研究将应用大数据的严格技术，从所有可用的拉格朗日数据同时估计所有四个拉格朗日特征。结果将是迄今为止由拉格朗日特征制成的最高分辨率的地图，无论是在表面还是在深度。提高气候模型中海洋环流的真实性的总体目标是一个引起社会极大关注的话题，因为它将支持气候变化适应和缓解努力。更直接的是，该项目将有助于维护、改进和更广泛地分发声学跟踪浮标数据的唯一活跃档案，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完善的创新分析算法将与社区公开共享，为支持科学研究的软件基础设施做出贡献。在该项目期间，将类似地进一步开发新的、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。大数据技术在模型输出中的应用，允许将非常大的数据集减少到更少的参数数量，这在未来的模型/数据比较中将特别有用。最后，该项目将支持一名研究生，以及一名早期职业科学家，他们将接受大数据技术应用分析数值模型输出的培训。该方法将以几个重要的方式建立在以前的工作基础上：（i ）通过充分利用现有的统计信息，从而提高有效的空间分辨率，也许是显着的； (ii) 避免四个参数相互作用所产生的潜在严重估计误差； (iii) 允许量化不确定性； (iv) 允许对一些重要的物理假设进行正式和系统的测试。对来自现实模型的更大的轨迹集合进行并行分析将允许量化由数据稀疏性引起的不确定性，并使模型再现观察到的拉格朗日特征的技能得到仔细审查。最后，理想化的数值模型和理论将提供直接连接拉格朗日轨迹的可观测特征与基础物理的桥梁。主要的智力贡献将是回答本文详细阐述的许多重要问题，例如：表面准地转、内部准地转和其他过程的影响能否根据拉格朗日光谱来区分？拉格朗日谱斜率告诉我们有关海洋湍流本质的什么信息？何时何地需要各向异性才能有效描述扩散率？自旋参数能否准确捕捉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拉格朗日速度谱的首次全球研究来回答，并仔细注意量化误差并建立不同状态下控制参数的正确物理解释。