The study summarizes recent advances in our understanding of internal wave–driven turbulent mixing in the ocean interior and introduces new parameterizations for global climate ocean models and their climate impacts.
Ocean turbulence influences the transport of heat, freshwater, dissolved gases such as CO2, pollutants, and other tracers. It is central to understanding ocean energetics and reducing uncertainties in global circulation and simulations from climate models. The dissipation of turbulent energy in stratified water results in irreversible diapycnal (across density surfaces) mixing. Recent work has shown that the spatial and temporal inhomogeneity in diapycnal mixing may play a critical role in a variety of climate phenomena. Hence, a quantitative understanding of the physics that drive the distribution of diapycnal mixing in the ocean interior is fundamental to understanding the ocean’s role in climate.
Diapycnal mixing is very difficult to accurately parameterize in numerical ocean models for two reasons. The first one is due to the discrete representation of tracer advection in directions that are not perfectly aligned with isopycnals, which can result in numerically induced mixing from truncation errors that is larger than observed diapycnal mixing (Griffies et al. 2000; Ilıcak et al. 2012). The second reason is related to the intermittency of turbulence, which is generated by complex and chaotic motions that span a large space–time range. Furthermore, this mixing is driven by a wide range of processes with distinct governing physics that create a rich global geography [see MacKinnon et al. (2013c) for a review]. The difficulty is also related to the relatively sparse direct sampling of ocean mixing, whereby sophisticated ship-based measurements are generally required to accurately characterize ocean mixing processes. Nonetheless, we have sufficient evidence from theory, process models, laboratory experiments, and field measurements to conclude that away from ocean boundaries (atmosphere, ice, or the solid ocean bottom), diapycnal mixing is largely related to the breaking of internal gravity waves, which have a complex dynamical underpinning and associated geography. Consequently, in 2010, a Climate Process Team (CPT), funded by the National Science Foundation (NSF) and the National Atmospheric and Oceanic Administration (NOAA), was convened to consolidate knowledge on internal wave–driven turbulent mixing in the ocean, develop new and more accurate parameterizations suitable for global ocean models, and consider the consequences for global circulation and climate. Here, we report on the major findings and products from this CPT.
Ocean internal gravity waves propagate through the stratified interior of the ocean. They are generated by a variety of mechanisms, with the most important being tidal flow over topography, wind variations at the sea surface, and flow of ocean currents and eddies over topography leading to lee waves (see schematic in Fig. 1). As waves propagate horizontally and vertically away from their generation sites, they interact with each other, producing an internal gravity wave continuum consisting of energy in many frequencies and wavenumbers. The waves with high vertical wavenumbers (small vertical scales) are more likely to break, leading to turbulent mixing. The distribution of diapycnal mixing therefore depends on the entire chain of processes shown in Fig. 1.
Schematic of internal wave mixing processes in the open ocean that are considered as part of this CPT. Tides interact with topographic features to generate high-mode internal waves (e.g., at midocean ridges) and low-mode internal waves (e.g., at tall steep ridges such as the Hawaiian Ridge). Deep currents flowing over topography can generate lee waves (e.g., in the Southern Ocean). Storms cause inertial oscillations in the mixed layer, which can generate both low- and high-mode internal waves (e.g., beneath storm tracks). In the open ocean, these internal waves can scatter off of rough topography and potentially interact with mesoscale fronts and eddies until they ultimately dissipate through wave–wave interactions. Internal waves that reach the shelf and slope can scatter or amplify as they propagate toward shallower water.
Citation: Bulletin of the American Meteorological Society 98, 11; 10.1175/BAMS-D-16-0030.1
A brief history of vertical mixing parameterizations used by ocean models.
In the deep ocean, a prognostic parameterization for internal tide–driven mixing was introduced by St. Laurent et al. (2002), who combined an estimate of internal tide generation over rough topography with an empirical vertical decay scale for the enhanced turbulence (see the section on “Near-field tidal mixing”). State-of-the-art ocean climate simulations prior to the CPT, as represented by the Geophysical Fluid Dynamics Laboratory (GFDL) and National Center for Atmospheric Research (NCAR) phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations (Dunne et al. 2012; Danabasoglu et al. 2012), included a version of Eq. (3) (see the section on “Near-field tidal mixing”), along with parameterizations of mixing in the surface (Large et al. 1994) and bottom boundary layers and/or overflows (Legg et al. 2006; Danabasoglu et al. 2010) and mixing from resolved shear (Large et al. 1994; Jackson et al. 2008). These parameterizations produced spatially and temporally varying diapycnal diffusivities, with bottom enhancement and stratification dependence. However, these simulations did not include an energetically consistent representation of internal tide breaking away from the generation site, explicit representation of mixing from internal waves generated by winds and subinertial flows, nor spatial and temporal variability in the dissipation vertical profile. The work described here has revolved around developing and testing energetically consistent, spatially and temporally variable mixing parameterizations. The resulting parameterizations are based upon internal gravity wave dynamics and the patterns of wave generation, propagation, and dissipation.
Overall strategy and philosophy of the CPT approach.
As with previous CPTs, we have found that parameterizations are most productively developed when there is a broad base of knowledge that is in a state of readiness to be consolidated, implemented, and tested. Much of the basic research described here was published or nearing completion at the time this project started, allowing for a focused effort on parameterization development, model implementation, and global model testing. A key CPT component was the inclusion of four dedicated postdoctoral scholars, who formed “the glue” to bridge the expertise of different principal investigators, promoting projects at the intersection of theory and models, observations, and simulations, while gaining valuable broad training and networking.
