NSFGEO-NERC Stirring at the Walls - A dynamical boundary model for the ocean

气候变化由于其巨大的社会、经济和政治后果，成为我们这个时代最主要的科学问题。研究未来气候的主要科学工具是耦合数值模式，其中气候系统的各个组成部分相互作用，产生对未来气候状态的估计。由此产生的预测受到全球关注，并影响全球政策。在年际到百年时间尺度的气候预测中，最重要的是海洋。动态健全的海洋模式是可靠的气候预报不可或缺的一部分，但由于我们对海洋功能的科学理解存在差距，它们存在根本性的弱点。海洋中的大部分动能存在于所谓的“中尺度”，这一术语指的是时间尺度为日至月，长度尺度为10至100公里的海洋现象。中尺度通过其大尺度反馈已被证明是决定年际至年代际时间尺度上海洋内在变化的主要因素，这涵盖了海洋对气候有重要贡献的时间谱的很大一部分。据估计，高达80%的海洋变率是由这种内在过程造成的。这就给气候预报带来了一个实际问题：中尺度由与流域尺度（6000公里）相比较小（50公里）的特征组成，而在气候模拟所需的时间内，它们在全球范围内的直接数值分辨率远远超出了当前的计算机资源。目前的气候预测计算资源允许适度但不完整的中尺度分辨率（25公里），需要对剩余的亚网格尺度动力学进行参数化。可靠的海洋模式将采用基于动力学的参数化。这不是当前的建模实践。目前的实践使用粘性和混合表征对亚网格尺度动力学进行建模，并调整相关参数以使输出与当前观测结果相匹配，通过论证大尺度低频风驱动盆地尺度环流，从而通过不稳定性发展中尺度来证明这种建模的合理性。然后通过平衡进入大尺度的能量流和从大尺度进入中尺度的能量流来确定大尺度环流的振幅。中尺度损失能量的机制没有得到直接解决，也没有得到很好的理解。模式调整到具有代表性的中尺度能量水平，因此它们表现出合理的年代际变化，并重现海洋环流的基本要素，例如墨西哥湾流与美国东海岸的精确分离。然而，这些参数化在流体动力学中没有基础。气候系统的非线性意味着，在模拟气候变化时，不能保证调整到当前条件的参数化将表现良好。在古气候的模拟中也出现了同样的困难，因为古气候下的水流结构与现在的观测结果相距甚远。为了解决这个问题，需要一个动态的参数化，特别是当它处理中尺度耗散时。我们认为，在全球海洋模式中，精确解决海洋边界附近流动所需的极高空间和时间分辨率与解决海洋内部运动所需的较低分辨率之间存在差距。本文提出的一种动态边界模型可以利用这种差距，以更低的计算成本进行更精确的模拟，同时增加我们对边界混合过程的了解。这直接解决了NERC“基于模型的各种时空尺度的海洋水循环研究”的优先事项。这个项目将检验一个关键假设，如果这个假设成立，将改变海洋环流模型。

Climate change, because of its enormous social, economic and political consequences, reigns as the leading scientific problem of our times. The primary scientific tool in the study of future climate is the coupled numerical model, in which the various components of the climate system interact, producing an estimate of a future climate state. The resulting projections receive global exposure and impact global policy. Of primary importance in the prediction of climate on interannual to centennial time scales is the ocean. Dynamically sound ocean models are integral to reliable climate forecasting, yet due to gaps in our scientific understanding of ocean function, they suffer from a fundamental weakness. Most of the kinetic energy in the ocean resides in the so-called `mesoscale', a term referring to ocean phenomenon with time scales of days to months and length scales of 10's to 100's of km. The mesoscale through its large scale feedbacks has been shown to be a major factor determining intrinsic ocean variability on interannual to decadal timescales, which covers a significant fraction of the temporal spectrum over which the ocean contributes importantly to climate. It has been estimated that up to 80% of ocean variability is due to such intrinsic processes.This presents climate forecasting with a practical problem: the mesoscale consists of features that are small (50 km)compared to the basin scale (6000 km) and their direct numerical resolution over the entire globe for times required in climate simulations is far beyond current computer resources. Present computational resources for climate projection allow for modest but incomplete mesoscale resolution (25 km), necessitating the parametrisation of the remaining sub-grid scale dynamics. Reliable ocean models will employ parametrisations based on dynamics. This is not current modelling practice. Current practice models sub-grid scale dynamics using viscous and mixing representations and tunes the related parameters to match output to present observations, justifying this modelling by arguing that large scale low frequency winds drive the basin scale circulation that subsequently develops the mesoscale by instabilities. The amplitude of the large scale circulation is then set by balancing the energy flow into the large scale with the energy flow out of the large scale into the mesoscale. The mechanisms by which the mesoscale loses energy are not addressed directly and are not as well understood. Models are tuned to representative mesoscale energy levels so they exhibit reasonable decadal scale variability and to reproduce essential elements of the ocean circulation, such as accurate separation of the Gulf Stream from the east coast of the US. These parametrisations have, however, no basis in the dynamics of the flow. The nonlinearity of the climate system means that there is no assurance that a parametrisation tuned to present conditions will perform well when modelling a changing climate. The same difficulty arises in the modelling of palaeoclimates where the underlying flow structure is far from present day observations. A dynamically based parametrisation, especially as it addresses mesoscale dissipation, is needed to address this issue. We argue that there is a gap between the very high spatial and temporal resolution required in global ocean models to accurately resolve the flow near ocean boundaries and the lower resolution required to resolve the motion of the ocean interior. A dynamical boundary model of the form proposed here can exploit this gap allowing more accurate simulations at lower computational cost while simultaneously increasing our knowledge of boundary mixing processes. This addresses directly the NERC priority of ``studies of water circulation in seas and oceans on a variety of temporal and spatial scales based on modelling''. This project will test a key hypothesis that, if true, will change the modelling of ocean circulation.