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(SAF) to the north. High rates of diapycnal mixing for the Southern Ocean as a whole have been suggested by box inverse studies of the circulation ( Heywood et al. 2002 ; Lumpkin and Speer 2007 ; Zika et al. 2009 ). However, these estimates are indirect, being based mainly on mass balance derived from geostrophic flow estimates, and are subject to large uncertainty. Other studies have utilized finescale parameterizations for estimation of the mixing rates as part of larger-scale Southern Ocean
(SAF) to the north. High rates of diapycnal mixing for the Southern Ocean as a whole have been suggested by box inverse studies of the circulation ( Heywood et al. 2002 ; Lumpkin and Speer 2007 ; Zika et al. 2009 ). However, these estimates are indirect, being based mainly on mass balance derived from geostrophic flow estimates, and are subject to large uncertainty. Other studies have utilized finescale parameterizations for estimation of the mixing rates as part of larger-scale Southern Ocean
1. Introduction The global ocean circulation is often divided into a nearly horizontal, or approximately isopycnal, component and an overturning component that is more tightly linked to diabatic processes in the interior or at the polar extremes. The polar extremes of dense water formation create water masses that spread and fill the global ocean, but this spreading depends on the topography of ocean basins. The cold deep water formed in the northern polar regions of the Atlantic Ocean, North
1. Introduction The global ocean circulation is often divided into a nearly horizontal, or approximately isopycnal, component and an overturning component that is more tightly linked to diabatic processes in the interior or at the polar extremes. The polar extremes of dense water formation create water masses that spread and fill the global ocean, but this spreading depends on the topography of ocean basins. The cold deep water formed in the northern polar regions of the Atlantic Ocean, North
have been employed to estimate diffusivities in the tropical North Atlantic (e.g., Banyte et al. 2013 ), western boundary currents (e.g., Chen et al. 2014 ), the Southern Ocean (e.g., LaCasce et al. 2014 ; Tulloch et al. 2014 ), and the global surface (e.g., Abernathey and Marshall 2013 ). Understanding the estimated diffusivity patterns is a necessary step toward improving eddy parameterization schemes. A common approach is to interpret the mixing length L mix instead of the diffusivity
have been employed to estimate diffusivities in the tropical North Atlantic (e.g., Banyte et al. 2013 ), western boundary currents (e.g., Chen et al. 2014 ), the Southern Ocean (e.g., LaCasce et al. 2014 ; Tulloch et al. 2014 ), and the global surface (e.g., Abernathey and Marshall 2013 ). Understanding the estimated diffusivity patterns is a necessary step toward improving eddy parameterization schemes. A common approach is to interpret the mixing length L mix instead of the diffusivity
North America. Alford (2001) , Watanabe and Hibiya (2002) , and Alford (2003) expanded the use of the slab model to estimate the global flux using long-term global reanalysis surface winds. Since then, a number of studies have investigated the sensitivity of the global wind work calculation to the properties of the input fields and mixed layer climatology ( Jiang et al. 2005 ; Rimac et al. 2013 ). Direct connections between observations and simulated currents have remained elusive ( Alford et
North America. Alford (2001) , Watanabe and Hibiya (2002) , and Alford (2003) expanded the use of the slab model to estimate the global flux using long-term global reanalysis surface winds. Since then, a number of studies have investigated the sensitivity of the global wind work calculation to the properties of the input fields and mixed layer climatology ( Jiang et al. 2005 ; Rimac et al. 2013 ). Direct connections between observations and simulated currents have remained elusive ( Alford et
