Diagnosing multiscale entrainment in density-driven flows in the ocean

众所周知，发生在海洋中的密度驱动流动中的小尺度混合过程对全球海洋环流(从而对气候)至关重要，但混合的规模远远小于全球海洋环流模式中的一个网格单元(其中单元的数量受到计算机能力的限制)。这些过程包括风暴和海洋表面结冰(如在拉布拉多海发生)导致稠密(冷/咸)水迅速下沉，以及稠水在海底陡峭的斜坡上流动。这个问题的解决方案是应用这些过程的物理知识，以便它们的影响可以在全球模型中规定(这称为参数化)。必须准确预测的一个关键数量是进入这些流动的夹带。如果考虑从系数烟囱中上升的热空气，则可以根据热空气的密度与周围环境的较低密度之间的差异来预测空气上升的高度和速度。当空气上升时，流动变得湍流，来自周围的空气混合到热空气中，改变了密度：这被称为卷吸。在海洋中有许多小尺度混合过程的情况下，很难预测夹带(因为地球自转的影响很重要，而且动力学相当复杂)。这个项目的目的是通过研究这些流动的理想化模型的数据，建立这些密度驱动的流动中小尺度混合的完整图景。我们将使用从帝国理工学院海洋模型(ICOM)获得的数据，该模型有一个动态变化的网格，非常适合研究这些问题。关键概念将是拉格朗日粒子轨迹：流体粒子随着流动移动时所走的路径。沿着这些路径，揭示了来自周围的水是如何混合到水流中的。该项目的第一部分将是建立一个计算工具，用于在从海洋模型获得的大数据集上计算拉格朗日粒子轨迹。主要的挑战是生成一种可以在具有许多处理器的计算机上运行的代码，尽管这方面的基本策略已经开发得很好，因此可以利用现有的软件来处理并行方面。该项目的第二部分是应用一系列技术来研究颗粒轨迹，以确定密度驱动流动中的关键混合过程。混合的完整图片将被制作出来，然后可以用来开发用于全球海洋模型的参数。

It is well-known in ocean modelling that small scale mixing processes in density-driven flows that take place in the ocean are crucial for the global ocean circulation (and hence climate), but the scale of mixing is much smaller than one grid cell in global ocean circulation models (where the number of cells is constrained by the limits of computer power). These processes include the rapid sinking of dense (cold/salty) water due to storms and ice formation at the ocean surface (such as takes place in the Labrador Sea), and the flow of dense water over steep slopes in the ocean floor. The solution to this is to apply physical knowledge of these processes so that their effects can be prescribed in the global models (this is called parameterisation). A key quantity which must be accurately predicted is the entrainment into these flows. If one considers hot air rising from a factor chimney, then it is possible to predict how high and how fast the air will rise based on the difference between the density of the hot air and the lower density of the surroundings. As the air rises, the flow becomes turbulent, and air from the surroundings is mixing into the hot air, changing the density: this is called entrainment. In the case of many small scale mixing processes in the ocean, it is hard to predict the entrainment (because the effect of the rotation of the Earth is important, and the dynamics is rather complicated). The aim of this project is to build up a complete picture of small scale mixing in these density-driven flows by studying data from idealised models of these flows. We will use data obtained from the Imperial College Ocean Model (ICOM), which has a dynamically changing grid that is very suitable for studying these problems. The key concept will be Lagrangian particle trajectories: the paths that fluid particles take as they move with the flow. Following these paths reveals how water from the surroundings is mixed into the flow. The first part of the project will be to build a computational tool for computing Lagrangian particle trajectories on large datasets obtained from ocean models. The main challenge is to produce a code which can be run on a computer with many processors, although the basic strategy for this is well-developed and so one can make use of existing software to take care of the parallel aspects. The second part of the project is to apply a range of techniques to studying the particle trajectories to identify the key mixing processes in the density-driven flows. A complete picture of the mixing will be produced which can then be used to develop parameterisations for use in global ocean models.