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  • Data quality control x
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Marina Frants, Gillian M. Damerell, Sarah T. Gille, Karen J. Heywood, Jennifer MacKinnon, and Janet Sprintall

potential to provide mixing estimates over a wide spatial and temporal range, provided the data are of sufficient quality and vertical resolution. The goal of our analysis is first to determine whether either method used with CTD and XCTD profiles can replicate the basic geographic contrast between high and low κ reported in the microstructure data. Our second goal is to provide guidance about the accuracy and performance of finescale methods in the low-stratification environment of the Southern Ocean

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

detailed account of the quality control on the CTD and current meter data is given in Brearley et al. (2013) . Large mooring knockdowns (of up to 800 m) were experienced by all moorings because of instances of intense flow associated with Antarctic Circumpolar Current (ACC) jets and eddies. Consideration was therefore given to the issue of mooring motion correction. Most mooring motion correction schemes for velocity (e.g., Cronin et al. 1992 ) rely on the geostrophic approximation. This is an

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J. Alexander Brearley, Katy L. Sheen, Alberto C. Naveira Garabato, David A. Smeed, and Stephanie Waterman

, both the northeast (NE) and southwest (SW) moorings suffered an implosion of buoyancy, so they only collected usable data until 27 January and 3 February, respectively. In addition, the bottom current meter on the southeast (SE) mooring was lost. On account of this, the analysis in this paper predominantly uses data from the center (C) mooring. After data recovery, a series of processing steps developed initially for the North Atlantic RAPID array was carried out as quality control ( Collins et al

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

. Balwada et al. (2013, unpublished manuscript) give such a descriptive view of the float dispersion in DIMES, including the influence of topography. The second approach concerns how Lagrangian (particle) dispersion is affected by coherent structures, such as manifolds (e.g., Haller and Yuan 2000 ; Wiggins 2005 ). Such studies are usually made with modeled or reconstructed flows with synthetic particles; the application to in situ data is still in its infancy. Third, there is the statistical

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Michael Bates, Ross Tulloch, John Marshall, and Raffaele Ferrari

suppressed mixing along the track of the ACC, even though u rms is at a maximum there. Attempts to quantify the quality of the agreement between “predicted” [using Eq. (6) ] and “estimated” diffusivity [from Abernathey and Marshall (2013) ] enable us to explore the sensitivity of our expression to uncertain parameters that control the degree of suppression, that is, b 1 in Eq. (6) . This suggests that a global value of around 4 is optimum, consistent with prior estimates of Ferrari and Nikurashin

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

and subsequent evolution. The approach taken was as follows: The surface velocity field was first estimated from delayed time satellite altimeter data produced by SSALTO/DUACS, which have been postprocessed and passed through quality control measures. 1 In particular, we used a dataset of weekly sea level anomaly (SLA) merged from two satellites for continuity on a ¼° by ¼° Cartesian grid, from February 2009 to April 2011, in combination with the current mean dynamic topography (MDT) based on

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