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，成为我们时代的主要科学问题。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。由此产生的预测得到全球曝光并影响全球政策。在预测年际至百年时间尺度的气候方面，海洋是最重要的。动态健全的海洋模型是可靠的气候预测不可或缺的，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几天到几个月，长度尺度为几十到几百公里的海洋现象。中尺度通过其大尺度的反馈已被证明是一个主要的因素，确定内在的海洋变率的年际到十年的时间尺度，其中涵盖了很大一部分的时间谱，在海洋的重要贡献气候。据估计，高达80%的海洋变率是由于这种内在的过程，这给气候预测带来了一个实际问题：中尺度的特征（50公里）与流域尺度（6000公里）相比是很小的，它们在整个地球仪上的直接数值分辨率远远超出了目前的计算机资源。目前用于气候预测的计算资源允许适度但不完整的中尺度分辨率（25公里），需要对剩余的次网格尺度动态进行参数化。可靠的海洋模型将采用基于动力学的参数化。这不是当前的建模实践。目前的做法是使用粘性和混合表示的次网格尺度动态模型，并调整相关参数，以匹配输出到目前的观测结果，证明这种建模认为，大规模的低频风驱动流域尺度环流，随后发展中尺度的不稳定性。然后，通过平衡进入大尺度的能量流与离开大尺度进入中尺度的能量流来设置大尺度环流的幅度。中尺度损失能量的机制没有直接解决，也没有得到很好的理解。模型被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十年尺度变化，并再现海洋环流的基本要素，例如墨西哥湾流与美国东海岸的准确分离。然而，这些参数化没有流动动力学的基础。气候系统的非线性意味着无法保证根据当前条件调整的参数化在模拟变化的气候时会表现良好。同样的困难出现在模拟古气候的基础上的流动结构是远离今天的观察。一个动态为基础的参数化，特别是因为它解决中尺度耗散，需要解决这个问题。我们认为，在全球海洋模型中需要非常高的空间和时间分辨率，以准确地解决海洋边界附近的流动和较低的分辨率，以解决海洋内部的运动之间存在差距。这里提出的形式的动态边界模型可以利用这一差距，允许更准确的模拟，以较低的计算成本，同时增加我们的知识边界混合过程。这直接涉及国家环境资源中心的优先事项，即“根据模型在各种时间和空间尺度上研究海洋中的水循环”。该项目将测试一个关键假设，如果属实，将改变海洋环流的模型。

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.