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  • The Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES) x
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Ru Chen, Sarah T. Gille, Julie L. McClean, Glenn R. Flierl, and Alexa Griesel

, the cross-stream diffusivity from the MW theory in section 2 is consistent with the single-wavenumber formula for cross-stream diffusivities from Ferrari and Nikurashin (2010) (F–N theory). a. Review of the F–N theory Following Flierl and McGillicuddy (2002) , Ferrari and Nikurashin (2010) assumed that eddies are forced by stochastic time-varying forcing with a single wavenumber. They employed a surface quasigeostrophic model and defined the mean velocity U and buoyancy B as where N

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Alberto C. Naveira Garabato, Kurt L. Polzin, Raffaele Ferrari, Jan D. Zika, and Alexander Forryan

have regularly focused on understanding how the region is forced by wind and air–sea exchanges of buoyancy. Appealing to residual-mean theory, the circulation ensuing from these forcings is conceptually expressed as an interplay between an Eulerian-mean, wind-driven Ekman overturning cell that acts to overturn isoneutral surfaces and a mesoscale eddy-induced cell that acts to flatten those surfaces. In this paradigm, the Southern Ocean overturning is portrayed to consist of upwelling of Circumpolar

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F. Sévellec, A. C. Naveira Garabato, J. A. Brearley, and K. L. Sheen

from the work of Bryden (1980) to include ageostrophic and time-varying forcings. We show that, in this area, time-mean vertical motion in the deep ocean is primarily determined by the properties of the time-mean horizontal flow, which exhibits a spatial structure consistent with the occurrence of a stratified Taylor column over a topographic obstacle encompassed by the moorings. Unlike in classical Taylor column theory for a uniform fluid, which induces a vertically uniform horizontal deviation

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L. St. Laurent, A. C. Naveira Garabato, J. R. Ledwell, A. M. Thurnherr, J. M. Toole, and A. J. Watson

forcing. Of primary interest to DIMES is the diapycnal mixing acting within the deep water masses. We focus here on the density surface targeted by the on-going tracer release experiment ( Ledwell et al. 2011 ; A. Watson et al. 2012, unpublished manuscript). This surface, the γ n = 27.9 kg m −3 neutral density contour, varies in depth from 600 to 2000 m (south to north) across Drake Passage with a mean depth of ~1500 m (as shown in Figs. 4 and 5 ). Diapycnal diffusivities were calculated using

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Dhruv Balwada, Kevin G. Speer, Joseph H. LaCasce, W. Brechner Owens, John Marshall, and Raffaele Ferrari

deviation around 50°–55° for almost all bins. The EB nature of the ACC was discussed in a dynamical setting by Hughes and Killworth (1995) . They showed that for a linear geostrophic flow in the interior (away from influence of wind stress) the turning of the velocity vector with depth took the form where N 2 is the usual Brunt–Väisälä frequency, w is the vertical velocity, f is the Coriolis force, | u | is the flow speed, and ϕ z is the variation of angle with depth. This formula holds on

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J. H. LaCasce, R. Ferrari, J. Marshall, R. Tulloch, D. Balwada, and K. Speer

layers of unequal thickness such that the top 70 layers, which span the upper 1900 m, are all less than 35 m thick. For further details on model forcing and initial conditions, see Tulloch et al. (2013, manuscript submitted to J. Phys. Oceanogr. ). Particles were released where the selected DIMES floats were released every 10 days during the 2-yr simulation and on 20 different γ surfaces. Additional sets of particles were released at 60°S, between 110° and 100°W, for additional calculations

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Emma J. D. Boland, Emily Shuckburgh, Peter H. Haynes, James R. Ledwell, Marie-José Messias, and Andrew J. Watson

–diffusion equation to generate a set of model chemical distributions for different assumed diffusivities. The velocity fields have been taken from large-scale meteorological datasets. The chemical fields have in some cases been initialized from satellite observations and in some cases driven by a hypothesized large-scale forcing. The approach has sometimes been to try to simulate specific features in the observed chemical distributions and sometimes to simulate the generic spatial structure of the chemical

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