Submesoscale Instabilities and Turbulence Across Oceans: Connecting Theory and Observations

由于地球的自转，相对较大规模的海洋流动通常超过几十英里，大多局限在水平面内。在较小的次中尺度上，这种限制大大减少，允许更剧烈的垂直运动，这在调节上层海洋热量含量和海气交换方面发挥了关键作用。因此，改进这些过程在数值模式中的表现对于准确预报从ENSO到热带季风到北冰洋海冰融化速度等广泛的直接社会相关现象十分重要。由于其固有的短横向尺度(Km量级)和快时间尺度(惯性周期量级或更快)，亚中尺度流动在海洋中很难直接观测到。另一方面，数值和分析研究在描述理想化的亚中尺度流动的动力学方面取得了实质性进展。对于不同的亚中尺度不稳定性的开始，已经制定了标准。在全球海洋模式中，这些不稳定因素没有得到解决，已经进行了参数化来解释这些不稳定因素。然而，从这些研究中获得的知识几乎没有得到观察的证实或与之比较，这使得我们很难有信心地向前推进。该项目将通过开发和应用一个全面的线性稳定性模型和分析现有的几个重要的海洋数据集来弥补这一差距。这个项目主要由一位早期职业科学家撰写，并将为其提供资金。由早期职业科学家开发的模型是新颖的，因为它是由观察到的密度和速度剖面强迫的，不像过去使用的许多理想化的平均状态。初步工作表明，它有能力复制各种亚中尺度不稳定，从混合层斜压不稳定到对称不稳定，再到朗缪尔环流，以及介于两者之间的一切。与以前的线性稳定性模型不同，它可以从一个不稳定类平稳过渡到另一个不稳定类；即，它从一个平均流态输入产生广泛的不稳定谱。该模型将受到最近几次海洋探险积累的数据集的影响，然后与之进行比较。其中两组数据显示了亚中尺度活动的有力证据，既有轶事例子，也有系统的统计数据。模型技能将使用其中几个示例中的配置文件进行开发和测试。反过来，预计模型结果将有助于阐明观测中存在的动力学不稳定性的性质；由于该模型是用真实的梯度轮廓初始化的，并允许广泛范围的不稳定性的完全复杂叠加，因此它唯一适合于在宽带海洋中解缠复杂的观测。将模式预测与跨越多个大洋流域和多个季节的现有观测进行比较，将有助于加深对次中尺度变率的广泛模式的理解，这种模式可用于验证新的全球参数化发展。

Ocean flows at relatively large scales, typically more than tens of miles, are mostly confined to horizontal plane due to the rotation of the Earth. At the smaller submesoscale, this constraint is much reduced, allowing more vigorous vertical motion, which plays a crucial role in mediating upper ocean heat content and air-sea exchange. Improving the representation of these processes in numerical models is therefore important for accurate forecasts of a wide range of directly societally relevant phenomena, from ENSO to tropical monsoons to the rate of Arctic sea ice melt. Due to their intrinsic short lateral scales (order of km) and fast time scales (order of inertial period or faster), submesoscale flows have been difficult to observe directly in the ocean. Numerical and analytical studies, on the other hand, have made substantial progress in describing the dynamics of idealized submesoscale flows. Criteria have been developed for the onset of different submesoscale instabilities. Parametrizations have been made to account for these instabilities in global ocean models where they are not resolved. Yet little of the knowledge gained from these studies has been confirmed by or compared with observations, which makes it difficult to move forward with confidence. This project will address this gap by combining development and application of a comprehensive linear stability model with analysis of several substantial oceanographic datasets already in hand. This project largely written by and will fund an early career scientist.The model, developed by the early career scientist, is novel in that it is forced by observed profiles of density and velocity, unlike many idealized mean states that have been used in the past. Preliminary work demonstrates its capacity to reproduce a vast range of types of submesoscale instabilities, from mixed layer baroclinic instability to symmetric instability to Langmuir circulation, and everything in between. Unlike linear stability models before, it can smoothly transition from one instability class to another; i.e. it produces a broad spectrum of instabilities from a single mean flow state input. The model will be forced by and then compared with amassed datasets from several recent oceanographic expeditions. Two of the data-sets have shown strong evidence of submesoscale activity, both with anecdotal examples and systematic statistics. Model skill will be developed and tested using profiles from several of these examples. In turn, it is anticipated that the model results will help illuminate the nature of the dynamical instabilities present in the observations; because the model is initialized with real gradient profiles and allows a fully complex superposition of a wide range of instabilities, it is uniquely suited for detangling complex observations in a broadband ocean. Comparison of model predictions with available observations that span multiple ocean basins and multiple seasons will help develop understanding of broad patterns of submesoscale variability, the sorts of patterns that can be used to validate new global parametrization development.