Spatial and temporal variability of global ocean mixing inferred from Argo profiles

Abstract

[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.

On the observability of turbulent transport rates by Argo: supporting evidence from an inversion experiment

“…This geography qualitatively agrees with the fine structure observed by Argo (Fig. 1 in Whalen et al, 2012) and theoretical predictions of intensified parametric subharmonic instability (e.g., MacKinnon and Winters, 2005). However, interleaving of weak and strong mixing layers is a common feature of the K d inverse estimate.…”

“…2, 9; regions in green) likely denote large uncertainties reflecting that available data constraints are insufficiently sensitive to prompt sizable parameter adjustments (as noted by Liu et al, 2012). The relatively weak values of K d − K d in the ACC (as compared with, e.g., Whalen et al, 2012, but not with Liu et al, 2012) may be one example. Similarly, the fact that K d − K d is generally muted in the abyss (albeit with notable exceptions in the Southern Ocean) is not surprising and does not imply that K d is a precise first guess.…”

“…However, the vast collection of in situ profiles provided by Argo (Roemmich et al, 1999(Roemmich et al, , 2009) may offer new opportunities to infer turbulent transport rates from the sea surface to 2000 m depth. Inferences of diffusivities is possible through the analysis of Argo variance fields combined with conceptual turbulence models (Wu et al, 2011;Whalen et al, 2012;Cole et al, 2015). The extensive observation of the broad-scale hydrography characteristics by Argo may also provide a basis for the inversion of turbulent transport rates.…”

Penetration depth of diapycnal mixing generated by wind stress and flow over topography in the northwestern Pacific

“…Whalen et al [2012] have found the insensitivity to the choice of the segment length from 200 to 240 m in the calculation of the turbulent mixing. The surface 300 m layer was removed to avoid the overestimation in the presence of sharp pycnoclines , and also the analysis of the surface mixed layer where the assumption that turbulent mixing is primarily a result of wave breaking resulting from the downscale cascade is not valid.…”

On the Benefit of Current and Future ALPS Data for Improving Arctic Coupled Ocean-Sea Ice State Estimation

“…For vertical turbulent and molecular mixing, measurements of T, S, and velocities at vertical spatial scales of centimeters are required throughout the entire water column and at horizontal spacing spanning the entire ocean, a suite of measurements not easy to collect (Killworth, 1998). In the absence of direct observations, mixing fields are often inferred from indirect observations and theories (e.g., Kunze et al, 2006;Whalen et al, 2012;Cole et al, 2015). At high latitudes, lack of knowledge in the three-dimensional distribution of these mixing fields is one of the primary reasons the Arctic Ocean's mean horizontal and vertical hydrographic structure is not well reproduced in numerical models (Holloway et al, 2007;Nguyen et al, 2011;Ilicak et al, 2016).…”