Collaborative Research: Quantifying regional variability in abyssal mixing from Ship-based Chi-pod measurements

合作研究：通过船基 Chi-pod 测量量化深海混合的区域变化

基本信息

批准号：
2023289
负责人：
Sarah Purkey
金额：
$ 30.78万
依托单位：
University of California-San Diego Scripps Inst of Oceanography
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2023289&HistoricalAwards=false
关键词：
Collaborative Research Quantifying regional variability

项目摘要

Despite its large climatic importance, the dynamics controlling the strength of the meridional overturning circulation remain poorly understood due to limited observations. In particular, the heterogeneity of deep mixing and the cause of spatial variability is still poorly understood due to a lack of direct observations. This project will conduct a global analysis of full ocean-depth microstructure measurements to explore the magnitude, patterns, and physical processes underlying turbulent mixing rates across different physical and dynamical regimes. The work will capitalize on over 1000 new microstructure full-depth profiles collected on nine GO-SHIP repeat hydrography cruises, including zonal and meridional sections across the Indian, Pacific and Atlantic oceans. The work will build on existing robust processing techniques for this novel type of microstructure data, making all results available to the oceanographic research community.This project will use the new GO-SHIP chi-pod data set, which offers the largest comprehensive dataset of deep turbulence measurements to date, to advance understanding of deep ocean mixing. The project will evaluate the application of fine scale parameterizations in the deep ocean compared to microstructure; explore the magnitude, patterns, and physical processes underlying observed turbulent mixing rates across different physical and dynamical regimes; and calculate mean basin and global rates of diffusivity from measurements of mixing from the chi-pods and finescale parameterizations. The analysis will lead to a better understanding of the limits and applicability of widely used finescale parameterizations which are based on assumptions of turbulence driven by internal wave-wave interactions. The results will improve understanding of the processes underlying observed patterns of mixing. Finally, the project will investigate the role of processes that set deep turbulent mixing rates for which the finescale technique does not apply, including internal-wave mean flow interaction and double diffusion.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.

尽管气候的重要性很大，但由于观察到的观察力有限，控制子午倾覆循环强度的动力学仍然很少理解。特别是，由于缺乏直接观察，深层混合的异质性和空间变异性的原因仍然很差。该项目将对完整的海洋深度微观结构测量结果进行全球分析，以探讨不同物理和动力学制度的湍流混合速率的大小，模式和物理过程。这项工作将利用1000多个新的微观结构全深度概况，这些轮廓在九艘重复的水文巡航中收集，包括印度，太平洋和大西洋的Zonal和子午线部分。这项工作将基于这种新型微观结构数据的现有强大处理技术，使海洋学研究社区的所有结果都可以使用。本项目将使用新的Go-Ship Chi-Pod数据集，该数据集提供了迄今为止最大的深度湍流测量数据集，以促进对深海混合的了解。该项目将评估与微观结构相比，在深海中进行精细的参数化的应用；探索在不同的物理和动态状态下观察到的湍流混合速率的大小，模式和物理过程；并计算出从Chi-Pods和罚款参数化的混合测量值的平均盆地和全局扩散率的速率。该分析将更好地理解广泛使用的罚款参数化的限制和适用性，这些参数基于内部波波相互作用驱动的湍流假设。结果将改善对观察到的混合模式的过程的理解。最后，该项目将调查设定罚款技术不适用深度动荡混合速率的过程的作用，包括内部波平均流动互动和双重扩散。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优点和更广泛影响的审查标准来通过评估来支持的。