Applied Physics Laboratory and School of Oceanography, University of Washington, Seattle, Washington 98105, USA; email:
Department of Mechanical Engineering, University of Washington, Seattle, Washington 98195, USA; email:
Ann Rev Mar Sci. 2018 Jan 3;10:443-473. doi: 10.1146/annurev-marine-121916-063643. Epub 2017 Sep 13.
Mixing efficiency is the ratio of the net change in potential energy to the energy expended in producing the mixing. Parameterizations of efficiency and of related mixing coefficients are needed to estimate diapycnal diffusivity from measurements of the turbulent dissipation rate. Comparing diffusivities from microstructure profiling with those inferred from the thickening rate of four simultaneous tracer releases has verified, within observational accuracy, 0.2 as the mixing coefficient over a 30-fold range of diapycnal diffusivities. Although some mixing coefficients can be estimated from pycnocline measurements, at present mixing efficiency must be obtained from channel flows, laboratory experiments, and numerical simulations. Reviewing the different approaches demonstrates that estimates and parameterizations for mixing efficiency and coefficients are not converging beyond the at-sea comparisons with tracer releases, leading to recommendations for a community approach to address this important issue.
混合效率是势能的净变化与产生混合所消耗的能量之比。需要对效率和相关混合系数进行参数化，以便根据湍能耗散率的测量值估算垂向混合扩散率。通过将微结构剖析得到的扩散率与从四个同时进行的示踪剂释放的增厚率推断出的扩散率进行比较，在观测精度范围内验证了在 30 倍的垂向混合扩散率范围内混合系数为 0.2。尽管可以从等密度面测量中估计某些混合系数，但目前必须从通道流、实验室实验和数值模拟中获得混合效率。回顾不同的方法表明，混合效率和系数的估计值和参数化值在与示踪剂释放的海上比较之外并没有收敛，这导致建议采用一种社区方法来解决这个重要问题。
