Ocean mixing rates, distributions, mechanisms, impacts and representations from theoretical and observational perspectives

从理论和观测角度看海洋混合率、分布、机制、影响和表征

• 批准号: RGPIN-2020-05799
• 负责人: Waterman, Stephanie
• 金额: $ 5.15万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751318
• 关键词: Ocean; mixing; rates; distributions; mechanisms

Mixing is an important control on how the ocean functions; yet our understanding of ocean mixing is significantly limited by complex variability in mixing rates and processes and by a paucity of mixing measurements. The long-term objectives of this research program are to address measurement scarcity and advance our understanding of ocean mixing rates, space-time distributions, mechanisms, impacts and representations in ocean models. The flagship innovation of this program is the use of an instrumented underwater robot that provides near-continuous, sustained measurements of ocean turbulence and other water properties important to ocean physics, chemistry and biology, thus delivering unprecedented views of the ocean mixing environment. We also will apply turbulence parameterizations to mined data to understand the variability of ocean mixing more broadly, perform experiments with realistic ocean models to understand the impacts of mixing space-time geography on ocean state and functioning, and conduct theoretical studies to improve representations of mixing for next-generation ocean models. In this 5-year program, we will focus on 2 distinct regions of importance to Canada: the Beaufort Sea in the Arctic Ocean and the British Columbia coast. Our objectives are to: 1. exploit robotic observations of ocean turbulence to gain new insights into ocean mixing; 2. map mixing geography by applying mixing parameterizations to historical observations; 3. target the impact of mixing variability on Arctic Ocean state and its role in the climate system via experiments with Arctic Ocean models; and 4. develop frameworks for the parametrization of ocean mixing using theory and idealized modelling studies. Anticipated outcomes include new knowledge of ocean mixing rates, variability, mechanisms and impacts in our regions of focus, and a better appreciation of the limitations of current model mixing prescriptions and ways that they could be improved. These efforts will also build capacity for robotic ocean observing in Canada's oceans, connect the observational and modelling communities, and train 14 HQP in an environment committed to inclusion. This research is critical and urgent to improve our understanding of how our marine systems function, as well as the numerical models of these systems that we rely on to make predictions of future environmental change. Benefits to the field include the provision of novel, highly-resolved and multidisciplinary datasets, and an improved understanding of mixing rates and mechanisms that can be applied to urgent research questions in multiple disciplines of ocean science. Benefits to Canada include the provision of information and system understanding critical for sustainable development and climate change adaptation in Canada's coastal waters, as well as the strengthening of Canada's standing in the international science community by being at the forefront of ocean observing technology and the science it enables.

混合是对海洋功能的重要控制；然而，我们对海洋混合的理解受到混合速度和过程的复杂可变性以及混合测量的缺乏的严重限制。这项研究计划的长期目标是解决测量稀缺的问题，并促进我们对海洋混合率、时空分布、机制、影响和海洋模式表示的理解。该项目的旗舰创新是使用仪表化水下机器人，提供对海洋湍流和其他对海洋物理、化学和生物重要的水性质的近连续、持续的测量，从而提供对海洋混合环境的前所未有的看法。我们还将把湍流参数化应用于挖掘的数据，以更广泛地了解海洋混合的可变性，用现实的海洋模型进行实验，以了解混合时空地理对海洋状态和功能的影响，并进行理论研究，以改进下一代海洋模型的混合表示。在这个为期5年的计划中，我们将重点关注对加拿大具有重要意义的两个不同地区：北冰洋的波弗特海和不列颠哥伦比亚省海岸。我们的目标是：1.利用对海洋湍流的机器人观测来获得对海洋混合的新见解；2.通过将混合参数应用于历史观测来绘制混合地形图；3.通过使用北冰洋模式的实验来确定混合可变性对北冰洋状态的影响及其在气候系统中的作用；以及4.使用理论和理想化的模拟研究来开发海洋混合的参数化框架。预期成果包括对海洋混合率、可变性、机制和在我们重点区域的影响的新知识，以及更好地认识到当前模式混合处方的局限性和可以改进的方式。这些努力还将建设在加拿大海洋中进行机器人海洋观测的能力，将观测和建模社区联系起来，并在致力于包容的环境中培训14名HQP。这项研究对于提高我们对海洋系统如何运作的了解，以及我们用来预测未来环境变化的这些系统的数值模型，是至关重要和紧迫的。对该领域的好处包括提供新的、高分辨率和多学科的数据集，以及更好地了解可应用于海洋科学多个学科的紧急研究问题的混合速率和机制。对加拿大的好处包括提供对加拿大沿海水域的可持续发展和适应气候变化至关重要的信息和系统了解，以及通过站在海洋观测技术及其带来的科学的前沿，加强加拿大在国际科学界的地位。