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.

混合是一个重要的控制如何海洋功能，但我们的理解海洋混合是显着限制了复杂的变化混合率和过程，并通过混合测量的缺乏。该研究计划的长期目标是解决测量不足问题，并促进我们对海洋混合率，时空分布，机制，影响和海洋模型中的代表性的理解。该计划的旗舰创新是使用仪器化水下机器人，提供对海洋湍流和对海洋物理，化学和生物学重要的其他水特性的近乎连续，持续的测量，从而提供前所未有的海洋混合环境视图。我们还将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变化性，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，以改善下一代海洋模型的混合表示。在这个为期5年的计划中，我们将重点关注加拿大的两个不同地区：北冰洋的博福特海和不列颠哥伦比亚省海岸。我们的目标是：1.利用机器人观测海洋湍流，以获得对海洋混合的新见解; 2.通过将混合参数化应用于历史观测来绘制混合地理图; 3.通过对北冰洋模式的实验，确定混合变率对北冰洋状态的影响及其在气候系统中的作用; 4.利用理论和理想化建模研究，制定海洋混合参数化框架。预期成果包括对我们重点区域的海洋混合率、变异性、机制和影响有新的了解，以及更好地认识到目前模式混合规定的局限性和可以改进的方式。这些努力还将建设加拿大海洋机器人海洋观测的能力，连接观测和建模界，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理解以及我们赖以预测未来环境变化的这些系统的数值模型至关重要。对该领域的好处包括提供新颖的，高分辨率和多学科的数据集，以及更好地了解混合速率和机制，可应用于海洋科学多学科的紧迫研究问题。对加拿大的惠益包括提供对加拿大沿海沃茨的可持续发展和气候变化适应至关重要的信息和系统理解，以及加强加拿大在国际科学界的地位，因为加拿大站在海洋观测技术及其促成的科学的前沿。