Spatially heterogeneous diapycnal mixing in the abyssal ocean: A comparison of two parameterizations to observations

Abstract

[1] The spatial distributions of the diapycnal diffusivity predicted by two abyssal mixing schemes are compared to each other and to observational estimates based on microstructure surveys and large-scale hydrographic inversions. The parameterizations considered are the tidal mixing scheme by Jayne, St. Laurent and co-authors (JSL01) and the Roughness Diffusivity Model (RDM) by Decloedt and Luther. Comparison to microstructure surveys shows that both parameterizations are conservative in estimating the vertical extent to which bottom-intensified mixing penetrates into the stratified water column. In particular, the JSL01 exponential vertical structure function with fixed scale height decays to background values much nearer topography than observed. JSL01 and RDM yield dramatically different horizontal spatial distributions of diapycnal diffusivity, which would lead to quite different circulations in OGCMs, yet they produce similar basin-averaged diffusivity profiles. Both parameterizations are shown to yield smaller basin-mean diffusivity profiles than hydrographic inverse estimates for the major ocean basins, by factors ranging from 3 up to over an order of magnitude. The canonical 10 À4 m 2 s À1 abyssal diffusivity is reached by the parameterizations only at depths below 3 km. Power consumption by diapycnal mixing below 1 km of depth, between roughly 32 S and 48 N, for the RDM and JSL01 parameterizations is 0.40 TW & 0.28 TW, respectively. The results presented here suggest that present-day mixing parameterizations significantly underestimate abyssal mixing. In conjunction with other recently published studies, a plausible interpretation is that parameterizing the dissipation of bottom-generated internal waves is not sufficient to approximate the global spatial distribution of diapycnal mixing in the abyssal ocean.Citation: Decloedt, T., and D. S. Luther (2012), Spatially heterogeneous diapycnal mixing in the abyssal ocean: A comparison of two parameterizations to observations,

Pacific deep circulation and ventilation controlled by tidal mixing away from the sea bottom

“…The DL10 profile is obtained from an empirical relationship between Kv and the roughness of the sea bottom, and becomes broadly similar to that of TideN. This similarity was described in a recent comparison study 13 , which pointed out that both the roughness diffusion model and the parameterization of St Laurent et al 11 appear to underestimate Kv by a factor of B3 compared with the estimates from hydrographic data inversions. The profile of our TideNF is larger than that of TideN and DL10, but agrees well with the above-mentioned observed estimations.…”

“…In our reference simulation (TideN), we set h to 500 m following St Laurent et al 11 A recent study suggested that larger values may be more appropriate for mimicking the observed profiles 13 , and it is also suggested that the choice of h has significant effects on the Pacific THC 33 . Therefore, we also conduct sensitivity simulations (TideN-series), where h is set to be smaller (100 m) or larger (2,000 m) than 500 m, in this study.…”

“…In previous OGCM simulations, the distribution of Kv induced by tidal mixing was derived from a parameterization such as that proposed by St Laurent et al 11 However, these OGCM simulations do not necessarily simulate the THC better than the OGCM simulations that employ the classical Kv profile (which is a function of only depth), particularly for the Pacific Ocean 7,12 . Furthermore, a recent study suggested that the present tidal parameterization fails to reproduce the observed Kv profiles estimated from microstructure surveys and large-scale hydrographic inversions 13 .…”

Influence of Enhanced Abyssal Diapycnal Mixing on Stratification and the Ocean Overturning Circulation

“…4a we plot globally averaged profiles of diapycnal diffusivity from an inverse model based on hydrographic sections (Lumpkin and Speer 2007) from a parameterization of the spatially variable diffusivity based on a large collection of microstructure data from several oceanic regions (Decloedt andLuther 2012, 2010) and from an estimate based on the calculation of internal waves radiated from bottom topography (Nikurashin and Ferrari 2013). In Fig.…”

Overlooked Role of Mesoscale Winds in Powering Ocean Diapycnal Mixing

“…For instance, to the best of our knowledge, the elevated diapycnal mixing observed in the Kuroshio extension region has not been replicated by any state-of-the-art ocean or climate models even though various parameterization schemes have been applied151617181920212223. This might be partly due to inadequacies in the parameterization schemes.…”