On eddy transport in the ocean. Part I: The diffusion tensor
On eddy transport in the ocean. Part I: The diffusion tensor
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DOI：
10.1016/j.ocemod.2021.101831
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发表时间：
2021-08
期刊：
影响因子：
3.2
通讯作者：
M. Haigh;Luolin Sun;J. McWilliams;P. Berloff
中科院分区：
文献类型：
--
作者：
M. Haigh;Luolin Sun;J. McWilliams;P. Berloff
这项研究解释了双涡旋涡旋分辨大洋环流中质量示踪剂和被动示踪剂的等重涡输运。本文着重于涡旋示踪通量的传输/扩散张量表示，另一篇文章将着重于平流涡旋诱导的示踪和质量传输。我们使用空间滤波器来分离大尺度和小尺度，这导致了不同于通过时间雷诺涡旋分解获得的结果。为了实现参数化，我们通过传输张量K将涡旋示踪通量与大尺度示踪梯度联系起来。K的对称部分是扩散张量S，它将扩散通量参数化，其混合性质由其本征值的符号决定。S的特征值是强健的反号(极)的，因此通过上下梯度通量来量化示踪剂的丝状化。鉴于极地特征值的普遍存在--这也是雷诺涡旋通量的特征值--代表其相关影响，应成为今后关闭涡流示踪剂运输的目标。考虑到涡激输运固有的不均匀性和各向异性，我们认为完全输运张量比标量系数或对角张量更适合于这一任务。表示优先混合方向的扩散轴倾向于与大尺度速度矢量以及大尺度相对涡度和层厚度的等高线相一致。强大的剪力可以抑制这种排列。我们表明，大尺度速度梯度矩阵可能适合于传输张量的参数化，特别是在深度。此外，由于K和S的输入在一定的大尺度流动特征的条件下呈现概率分布，我们认为涡旋输送的随机闭合是最合适的。
This study provides an interpretation of isopycnal eddy transport for mass and passive tracers in double-gyre eddy-resolving oceanic circulation. This paper focuses on a transport/diffusion tensor representation of the eddy tracer flux, and a companion paper will focus on advective eddy-induced tracer and mass transports. We use a spatial filter to separate the large and small scales, which leads to results distinct from those obtained via a temporal Reynolds eddy decomposition. To work towards a parameterisation, we relate the eddy tracer flux to the large-scale tracer gradient via the transport tensor K. The symmetric part of K is the diffusion tensor, S, which parameterises diffusive fluxes and whose mixing properties are determined by the signs of its eigenvalues. The eigenvalues of S are robustly of opposite sign (polar) and thus quantify filamentation of the tracer via both up-and down-gradient fluxes. Given the prevalence of polar eigenvalues–which are also obtained for Reynolds eddy fluxes–representing their associated effects should be a target of future eddy tracer transport closures. Given the inherent inhomogeneity and anisotropy of the eddy-induced transport, we argue that a full transport tensor is better suited to this task than scalar coefficients or diagonal tensors. The diffusion axis, which represents the direction of preferential mixing, tends to align with the large-scale velocity vector and contours of large-scale relative vorticity and layer thickness. Strong shears can inhibit this alignment. We show that the large-scale velocity gradient matrix may be suitable for parameterising the transport tensor, in particular at depth. Furthermore, since entries of K and S exhibit probabilistic distributions when conditioned on certain large-scale flow features, we suggest that a stochastic closure for the eddy transport would be most suitable.