Vertical mixing is important in the ocean for maintaining its stratification, redistributing temperature and salinity, distributing nutrients and pollutants, and the energy cascade. It plays a key role in ocean energy transport, climate change, and marine ecosystems. Getting the mixing right in ocean circulation and climate models is critical in reproducing ocean and climate physics. Ocean models, like the Regional Ocean Modeling System (Rutgers ROMS 3.4), provide several options for determining vertical mixing through the vertical mixing parameterization schemes. To evaluate which of these methods best reproduces realistic vertical mixing by internal tides, simulations of baroclinic tides generated by a seamount were performed using seven different vertical mixing parameterizations: Mellor-Yamada 2.5 (MY), Large-McWilliams-Doney’s Kpp (LMD), Nakanishi-Niino’s modification of Mellor-Yamada (NN), and four versions of Generic Length Scale (GLS). The GLS versions in ROMS 3.4 severely overmixed the water column within a day and were not considered realistic. We suspect that a coding error has been introduced for it. We focused on the performance of the MY, LMD, and NN vertical mixing parameterizations. LMD was found to overmix the water column. The performance of MY and NN were nearly equivalent and both well reproduced the observed velocity and diffusivity fields. NN performed slightly better by having a lower rms for M2 and K1, less benthic mixing, more mid-water column mixing, less overmixing, and fewer extremely high diffusivities (> 1 m2 s−1).

中文翻译：

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区域海洋建模系统（ROMS）中用于潮汐混合的垂直混合参数化性能的评估

垂直混合在海洋中对于保持其分层，重新分配温度和盐度，分配养分和污染物以及能量级联非常重要。它在海洋能源运输，气候变化和海洋生态系统中发挥着关键作用。在海洋环流和气候模型中实现正确的混合对于复制海洋和气候物理学至关重要。海洋模型（如区域海洋建模系统（Rutgers ROMS 3.4））为通过垂直混合参数化方案确定垂直混合提供了几种选择。为了评估哪种方法最能通过内部潮汐再现真实的垂直混合，使用七个不同的垂直混合参数设置对海山产生的斜压潮进行了模拟：Mellor-Yamada 2.5（MY），Large-McWilliams-Doney's Kpp（LMD），Nakanishi-Niino修改了Mellor-Yamada（NN），以及四个版本的通用长度刻度（GLS）。ROMS 3.4中的GLS版本在一天之内严重混合了水柱，因此不现实。我们怀疑已为此引入了编码错误。我们专注于MY，LMD和NN垂直混合参数化的性能。发现LMD使水柱过度混合。MY和NN的性能几乎相等，并且都很好地再现了观察到的速度和扩散场。通过使M2和K1的均方根值较低，底栖混合较少，中水柱混合较多，过混合较少以及极高的扩散率（> 1 m2 s-1）较少，NN的性能稍好。4在一天之内严重混合了水柱，因此不现实。我们怀疑已为此引入了编码错误。我们专注于MY，LMD和NN垂直混合参数化的性能。发现LMD使水柱过度混合。MY和NN的性能几乎相等，并且都很好地再现了观察到的速度和扩散场。通过使M2和K1的均方根值较低，底栖混合较少，中水柱混合较多，过混合较少以及极高的扩散率（> 1 m2 s-1）较少，NN的性能稍好。4在一天之内严重混合了水柱，因此不现实。我们怀疑已为此引入了编码错误。我们专注于MY，LMD和NN垂直混合参数化的性能。发现LMD使水柱过度混合。MY和NN的性能几乎相等，并且都很好地再现了观察到的速度和扩散场。通过使M2和K1的均方根值较低，底栖混合较少，中水柱混合较多，过混合较少以及极高的扩散率（> 1 m2 s-1）较少，NN的性能稍好。MY和NN的性能几乎相等，并且都很好地再现了观察到的速度和扩散场。通过使M2和K1的均方根值较低，底栖混合较少，中水柱混合较多，过混合较少以及极高的扩散率（> 1 m2 s-1）较少，NN的性能稍好。MY和NN的性能几乎相等，并且都很好地再现了观察到的速度和扩散场。通过使M2和K1的均方根值较低，底栖混合较少，中水柱混合较多，过混合较少以及极高的扩散率（> 1 m2 s-1）较少，NN的性能稍好。