The authors present inferences of diapycnal diffusivity from a compilation of over 5200 microstructure profiles. As microstructure observations are sparse, these are supplemented with indirect measurements of mixing obtained from (i) Thorpe-scale overturns from moored profilers, a finescale parameterization applied to (ii) shipboard observations of upper-ocean shear, (iii) strain as measured by profiling floats, and (iv) shear and strain from full-depth lowered acoustic Doppler current profilers (LADCP) and CTD profiles. Vertical profiles of the turbulent dissipation rate are bottom enhanced over rough topography and abrupt, isolated ridges. The geography of depth-integrated dissipation rate shows spatial variability related to internal wave generation, suggesting one direct energy pathway to turbulence. The global-averaged diapycnal diffusivity below 1000-m depth is O(10 . The compiled microstructure observations sample a wide range of internal wave power inputs and topographic roughness, providing a dataset with which to estimate a representative global-averaged dissipation rate and diffusivity. However, there is strong regional variability in the ratio between local internal wave generation and local dissipation. In some regions, the depthintegrated dissipation rate is comparable to the estimated power input into the local internal wave field. In a few cases, more internal wave power is dissipated than locally generated, suggesting remote internal wave sources. However, at most locations the total power lost through turbulent dissipation is less than the input into the local internal wave field. This suggests dissipation elsewhere, such as continental margins.

[1] The influence of turbulent ocean mixing transcends its inherently small scales to affect large scale ocean processes including water-mass transformation, stratification maintenance, and the overturning circulation. However, the distribution of ocean mixing is not well described by sparse ship-based observations since this mixing is both spatially patchy and temporally intermittent. We use strain information from Argo float profiles in the upper 2,000 m of the ocean to generate over 400,000 estimates of the energy dissipation rate, indicative of ocean mixing. These estimates rely on numerous assumptions, and do not take the place of direct measurement methods. Temporally averaged estimates reveal clear spatial patterns in the parameterized dissipation rate and diffusivity distribution across all the oceans. They corroborate previous observations linking elevated dissipation rates to regions of rough topography. We also observe heightened estimated dissipation rates in areas of high eddy kinetic energy, as well as heightened diffusivity in high latitudes where stratification is weak. The seasonal dependence of mixing is observed in the Northwest Pacific, suggesting a wind-forced response in the upper ocean.

O cean turbulence influences the transport of heat, freshwater, dissolved gases such as CO 2 , 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. 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.

4Finescale methods are currently being applied to estimate the mean turbulent dissipation rate 5 and diffusivity on regional and global scales. We evaluate finescale estimates derived from 6 isopycnal strain by comparing them with average microstructure profiles from six diverse 7 environments including the equator, above ridges, near seamounts, and in strong currents. 8 The finescale strain estimates are derived from at least ten nearby Argo profiles (generally 9 <60 km distant) with no temporal restrictions including measurements separated by seasons 10 or decades. The absence of temporal limits is reasonable in these cases since we find the 11 dissipation rate is steady over seasonal timescales at the latitudes we are considering (0 • -30 • 12 and 40 • -50 • ). In contrast, a seasonal cycle of a factor of 2-5 in the upper 1000 m is found 13 under storm tracks (30 • -40 • ) in both hemispheres. Agreement between the mean dissipation 14 rate calculated using Argo profiles and mean from microstructure profiles is within a factor of 15 2-3 for 96% of the comparisons. This both is congruous with the physical scaling underlying 16 the finescale parameterization and indicates that the method is effective for estimating the 17 regional mean dissipation rates in the open ocean.18 1 1983). This allows for an expression of the down-spectrum energy cascade in terms of the 45 shear and/or strain spectra as explained in Polzin et al. (2014). Assuming that this internal 46 wave energy cascade sets the energy dissipation rate (Assumption B) allows for calculation 47 of the dissipation rate from the shear or strain spectra. 48A key point is that finescale estimates of the dissipation rate are measuring distinctly 49 different quantities than microstructure measurements. Both the length and time scales are 50 larger for the finescale estimates than for the microstructure. It may be helpful to think of 51 the microstructure profiles as a snapshot of the turbulence at an instant, while the finestruc-52 ture profiles are estimates of the average dissipation rate expected over several wave periods. 53 Therefore, comparing equivalent quantities requires averaging multiple microstructure pro-54 files in time. 55 A set of parameterizations using finescale shear profiles has been tested in a variety 56 of contexts, showing good agreement with microstructure in open-ocean conditions (Gregg 57 1989; Polzin et al. 1995; Winkel et al. 2002; Polzin et al. 2014). Implementation of shear 58 parameterizations have revealed reasonable patterns of diapycnal mixing (Polzin et al. 1997; 59 Kunze et al. 2006; Huussen et al. 2012). The shear-based parameterization is known not to 60 be effective in regions where the underlying assumptions behind the parameterization do not 61 apply (Polzin et al. 2014), such as on continental shelves (MacKinnon and Gregg 2003), and 62 in strong geostrophic flow over rough topography (Waterman et al. 2014). Studies have also 63 uncovered discrepancies in the presence of very large overturning internal waves (Klyma...