Weak Mixing in the Eastern North Atlantic: An ... - AMS Journals

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Weak Mixing in the Eastern North Atlantic: An Application of the Tracer-Contour Inverse Method JAN D. ZIKA Climate Change Research Centre, University of New South Wales, Sydney, and CSIRO Wealth from Oceans National Research Flagship, Hobart, Australia

TREVOR J. MCDOUGALL AND BERNADETTE M. SLOYAN Centre for Australian Weather and Climate Research, CSIRO and the Bureau of Meteorology, and CSIRO Wealth from Oceans National Research Flagship, Hobart, Australia (Manuscript received 9 September 2009, in final form 16 March 2010) ABSTRACT The tracer-contour inverse method is used to infer mixing and circulation in the eastern North Atlantic. Solutions for the vertical mixing coefficient D, the along-isopycnal mixing coefficient K, and a geostrophic streamfunction C are all direct outputs of the method. The method predicts a vertical mixing coefficient O(1025 m2 s21) in the upper 1000 m of the water column, consistent with in situ observations. The method predicts a depth-dependent along-isopycnal mixing coefficient that decreases from O(1000 m2 s21) close to the mixed layer to O(100 m2 s21) in the interior, which is also consistent with observations and previous hypotheses. The robustness of the result is tested with a rigorous sensitivity analysis including the use of two independently constructed datasets. This study confirms the utility of the tracer-contour inverse method. The results presented support the hypothesis that vertical mixing is small in the thermocline of the subtropical Atlantic Ocean. A strong depth dependence of the along-isopycnal mixing coefficient is also demonstrated, supporting recent parameterizations for coarse-resolution ocean models.

1. Introduction The vertical mixing coefficient D controls the diapycnal component of the meridional overturning circulation (Munk and Wunsch 1998). The lack of knowledge of vertical mixing, its magnitude and spatial variation, leads to large uncertainties in our estimates of the volume, heat, freshwater, nutrient, and tracer transport and the role of the ocean in climate sensitivity and climate feedbacks (Manabe and Stouffer 1988). The along-isopycnal mixing coefficient K is used for mixing tracers such as temperature and salinity or potential vorticity along isopycnal surfaces. The value of K, like D, is thought to strongly control the overturning circulation and climate sensitivity (Gnanadesikan 1999; Sijp et al. 2006).

Corresponding author address: Jan Zika, Laboratoire des ećoulements geóphysiques et industriels (LEGI), BP 53-38041 Grenoble, CEDEX 9, France. E-mail: [email protected] DOI: 10.1175/2010JPO4360.1 Ó 2010 American Meteorological Society

Munk (1966) pointed out the role of vertical mixing in the global overturning circulation using simple theoretical arguments. He discusses the balance between upwelling and vertical mixing in the thermocline. Given 25 Sv (Sv [ 106 m3 s21) of diapycnal upwelling, Munk showed that the globally averaged vertical mixing coefficient D must be approximately 1024 m2 s21. Zika et al. (2009), similar to the analysis of Munk (1966), explore both the along-isopycnal and diapycnal components of the Southern Ocean meridional overturning circulation. The along-isopycnal component of the overturning is ‘‘upwelled’’ along isopycnal layers to the surface boundary layer of the Southern Ocean. Zika et al. found that, to upwell 20–50 Sv of dense water, the along-isopycnal mixing coefficient K must be 150– 450 m2 s21 and the vertical mixing coefficient must be 0.5–1.5 3 1024 m2 s21 below the thermocline at latitudes of the Antarctic Circumpolar Current. What fraction of the meridional overturning circulation is upwelled through isopycnals below the thermocline or upwelled along isopycnals in the Southern Ocean is unknown.

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Most observations of D have been much smaller than those predicted by Munk (1966). From the distribution of the tracer SF6, released below the thermocline in the eastern North Atlantic during the North Atlantic Tracer Release Experiment (NATRE), Ledwell et al. (1993) estimates D to be O(1025 m2 s21). Many studies have used microstructure to estimate vertical mixing (e.g., Gregg 1987). These have tended to reveal weak mixing O(1025 m2 s21) in most regions. Other in situ measurement approaches, while confirming weak mixing over much of the World Ocean, have identified stronger mixing, O(1024 to 1023 m2 s21), close to rough topography and in energetic regions such as the Southern Ocean (Naveira-Garabato et al. 2004; Kunze et al. 2006; Sloyan 2005). Since direct estimates of diapycnal mixing are sparse and infrequent, it is not clear whether vertical mixing in these energetic regions is sufficient to induce global diapycnal upwelling on the order of 25 Sv. Few direct estimates exist for K in the oceans. Ledwell et al. (1998) estimated that the SF6 tracer, released in the eastern North Atlantic at an approximate depth of 300 m, mixed horizontally at a rate on the order of 1000 m2 s21 over the 30 months of their experiment. Horizontal mixing at the sea surface has been estimated to be equal to or greater than that determined by Ledwell (i.e., 1000–10 000 m2 s21) (Zhurbas and Oh 2004; Marshall et al. 2006; Salleé et al. 2008). A depth dependence of the mixing coefficient, particularly that which mixes potential vorticity or interface height [commonly referred to as k in the Eulerian coordinate parameterization of Gent et al. (1995), with interface height being the depth of isopycnals], is reinforced by adjoint inversions (Ferraira et al. 2005) and eddy resolving models (Eden and Greatbatch 2008). It should be noted that calculations of K, in eddy resolving simulations, are not trivial, even with complete knowledge of a model’s full velocity and density fields (Eden et al. 2007). Inverse methods are tools used to diagnose the ocean circulation from hydrographic data. Such methods seldom give insight into mixing processes. The most well known method is the box inverse method (Wunsch 1978). In the box method, oceanic sections bound regions of the oceans or entire ocean basins. In each region mass, heat, salt, and other properties are conserved in isopycnal layers. Inverse box models simultaneously solve a set of equations for various unknowns, including reference level velocities and diapycnal fluxes of properties or the vertical mixing coefficient D. Box inversions are always underdetermined, and the final solution depends largely on the choice of model and model variance. This sensitivity is particularly strong when mixing coefficients are considered as unknowns in inverse studies (Tziperman

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1988). Inverse models have tended to perform better when individual diapycnal property fluxes are considered (Sloyan and Rintoul 2000) or when observed mixing coefficients are included as ‘‘knowns’’ (St. Laurent et al. 2001). Recent adjoint and gridded methods (Wunsch and Heimbach 2007; Herbei et al. 2008) have shown promise but have, so far, been unable to resolve the spatial structure of D and K. Zika et al. (2010, hereafter ZMS10) present a new inverse method called the tracer-contour inverse method. This inverse method is less sensitive to error than existing methods and has been validated against the output of a numerical model. With these advances and the presence of new observations that give a better representation of the upper 2000 m of the ocean, it may now be possible to accurately infer the ocean circulation and rates of along-isopycnal and vertical mixing directly from hydrographic data. The purpose of this study is twofold: first, to demonstrate the utility of this new inverse method by applying it to ocean observations in the North Atlantic (Fig. 1) where direct estimates of vertical and along-isopycnal mixing exist and, second, to reveal the depth dependence of the mixing coefficients in that region. This article is structured as follows: In section 2, we briefly describe the tracer-contour inverse method and the ocean climatology used. In section 3, we present the results of the tracer-contour inverse method, as applied to a region of the North Atlantic. Conclusions are given in section 4. In appendix B, we test the sensitivity of the method to the adjustment of various parameters and differing data sources.

2. The tracer-contour inverse method and hydrographic data Here we describe the tracer-contour inverse method of ZMS10 and the data used in this study. We then explain how the inverse method is applied in the eastern North Atlantic.

a. The tracer-contour inverse method The components of the mean velocity both across tracer contours on isopycnals and through isopycnals may be related to mixing of temperature, salinity, and potential vorticity through advective–diffusive balances. These balances may be derived from the conservation equations of heat and salt alone (e.g., McDougall 1984; Zika et al. 2009). The following advective–diffusive balances are used in this study and are those derived in ZMS10: v n 5 K/l? 1 $g K n 1 D/lg 1 KPV /lh Qt jg /j$g Qj (1)

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FIG. 1. Pressure on the g rf 5 26.525 kg m23 (s0 ’ 26.75 kg m23) surface from the climatology of DW10. Also marked is the region considered in this study (solid black rectangle), the site of mooring C from the Subduction Experiment (black cross, Joyce et al. 1998), and the NATRE study regions (Ledwell et al. 1998) showing the approximate SF6 tracer extent during fall 1992 (dark gray rectangle), spring 1993 (medium gray rectangle), and November 1994 (light gray rectangle).

and wg 5 D/hg 1 Dz 1 K/h? ,

(2)

where v is the mean lateral velocity ([u, y, 0]), wg is the diapycnal velocity component, S is salinity, and Q is conservative temperature. All variables are temporal and thickness-weighted means, averaged on neutral density surfaces (gn), except v, which is split into a temporal mean on isopycnals [lhs of Eq. (1)] and a bolus term [KPV/lh, the rhs of (1)]. The direction of the along-isopycnal temperature (and salinity) gradient is n (n 5 $gQ/j$gQj). The scale lengths lh, l?, lg and scale heights hg and h? are discussed below and are given in complete form in appendix A. These scale lengths and heights represent the balance between advection and either vertical mixing (denoted with the superscript g), along-isopycnal mixing of tracers (denoted with the superscript ?), and alongisopycnal mixing of layer thickness (denoted with the superscript h). The density variable used is neutral density (McDougall 1987), and the temperature variable used is conservative temperature (McDougall 2003). The previous two points are not necessarily critical to the analysis presented here and the variables potential temperature u and potential density, s1 or s2, could be used instead. Equations (1) and (2) are derived and discussed in Zika et al. (2009) and ZMS10. For completeness, we briefly describe them here. In (1), if there is a component of the mean velocity down the along-isopycnal temperature gradient (i.e., the lhs is nonzero), some combination

of the following processes must be at play: along-isopycnal mixing works to compensate the anomaly introduced by the downgradient advection (1/l? ’ =2gQ/j$gQj and $gK n); vertical mixing acts to restore the anomaly by mixing on the Q 2 S curvature 1/lg 5 where

Rr Q2z d2 S , j$g Sj (Rr 1) dQ2

$g SQz ; Rr 5 $g QSz

there is a compensating advection due to the bolus velocity (1/lh 5 $gh n/h); or the temperature contour simply moves in space (Qtjg /j$gQj). That is, some or all of the terms on the rhs of (1) must be nonzero to balance the lhs. Nonlinear effects owing to cabbeling and thermobaricity are included in the 1/l? term in (1). Equation (1) is used by Zika et al. (2009) to infer the Southern Ocean overturning circulation as a function of K and D. In (2), if there is mean advection across a density surface, wg, there must be a compensating effect of vertical mixing (1/hg ’ gzz/gz and/or Dz) or nonlinear effects such as cabbeling and thermobaricity (1/hg). Equation (1) differs from (2) in that the vertical coordinate moves with the isopycnal surfaces, whereas the lateral coordinate is fixed in space; hence, there is no gt/gz term in (1) as there would be if depth z was the vertical coordinate. Equation (2) is similar to that used by Munk

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(1966), as it relates diapycnal upwelling to vertical mixing but also includes nonlinear processes (i.e., the K term on the right-hand side). For illustrative purposes, (1) and (2) are rederived in appendix A in the special case of a steady flow with a linear equation of state and uniform mixing coefficients. In such a case, wg 5 Dg zz/gz and g2z d2 Q 2 v n 5 K=g Q/j$g Qj 1 D , j$g Qj dg2 x,y

[Cp0 ]xx2 ,,yy2 1 1

ðx

2 ,y2 ,g

x1 ,y1 ,g

?

g

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revealing the essential flavor of (1) and (2). In the tracer-contour inverse method, the pointwise advective–diffusive balance equations are integrated along tracer contours on isopycnals using (1) and across isopycnals using (2). Here we consider contours of conservative temperature, which are equivalent, on isopycnals, to contours of constant salinity. As such, mixing is related to the large-scale circulation that is represented by a geostrophic streamfunction. The equations used in the tracer-contour inverse method are

h

f (K/l 1 $g K n 1 D/l 1 KPV /l Qt jg /j$g Qj) dxQ 5

"ð

#x2 ,y2

p p0

dcontour (p9) dp9

(3) x1 ,y1

and ð u þ ð u þ þ þ p0 g (v ) mhC dl 1 w C dA KPV $g h mC dl K$g C mh dl DCz dA 5 (vp0 v) mhC dl. l

Equation (3) is the tracer-contour equation and relates mixing on an isopycnal g to the flow on a reference pressure p0. In (3), Cp0 is the geostrophic streamfunction on p0 (Fig. 2). The Coriolis frequency is f. The coordinate xQ is that Ð running perpendicular to n on the isopycnal; that is, dxQ is the integral along a temperature contour on an isopycnal. The tracer-contour specific volume anomaly is dcontour(p) 5 1/r(S, Q, p) 2 1/r(Scontour, Qcontour, p), where Scontour and Qcontour are the conservative temperature and salinity values of the contour between (x2, y2) and (x1, y1); that is, dcontour is redefined for each contour. Equation (4) is the layer conservation equation for volume and any conservative tracer C and, like (3), relates mixing on g to flow at p0 (Fig. 2). For conservation of volume C 5 1, for conservation of temperature anomaly C 5 Q Q, and for conservation of salinity anomaly C 5 S S, where Q and S are layer averages. In (4), vp0 is the lateral velocity on the reference pressure and m is the direction normal to the lateral boundaries of the isopycnal layer. The thickness of the isopycnal layer is h 5 Dgn/gnz for some arbitrary Dgn about the surface g, where []ul is the difference between the upper and lower interfaces of that layer (Fig. 2). The diapycnal advection wg is explicitly related to mixing using (2). We explicitly relate the lateral advection terms in (4) (i.e., the first term on the lhs and the rhs) to the geostrophic streamfunction Cp0 using a form of the depth-integrated thermal wind equation, as shown in appendix C. The tracer-contour inverse method combines aspects of the three major inverse modeling concepts of modern oceanography, the box, beta spiral, and Bernoulli

(4)

l

methods, and the advective–diffusive balance concept is motivated by Munk (1966). By assuming the mixing coefficients are constant on each layer or have some spatial form, the tracer-contour inverse method is overdetermined. There is a direct relationship between lateral advection and diffusion, and the diffusion terms are leading order. In the method, there is a linear relationship between advection and vertical mixing through the Q 2 S curvature (d2Q/dS2jx,y, a quantity less susceptible to noise due to heave than Qzz and Szz are individually). Down-temperature and down-salinity gradient transports along isopycnals are explicitly considered in the tracer-contour inverse method, and it is these transports that constitute the along-isopycnal component of the thermohaline overturning circulation. In the tracercontour inverse method, a streamfunction variable is solved for, along ‘‘sections,’’ allowing for direct integration with the box inverse method and the computation of section transports for comparison with, or constraint by, shipboard observations.

b. Data and setup of the inversion The climatology of Durack and Wijffels (2010, hereafter DW10) is used in this study. Hydrographic data from shipboard observations and Argo floats, up to and including 2008, have been used in their analysis. Annual and interannual cycles as well as a linear trend have been locally fit to the data, minimizing temporal aliasing. The data was averaged by DW10 on approximate neutral density surfaces (grf, Jackett and McDougall 2005), spaced by 0.025 kg m23 on a 18 latitude 3 28 longitude grid. The tracer-contour inverse method has also been

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FIG. 2. Schematic showing how the tracer-contour inverse method is implemented. Mixing at points along a tracer contour between (x1, y1) and (x2, y2) on the isopycnal surface g is related to a geostrophic streamfunction on the reference pressure p0 using (3), shown in green. The unit vector n is normal to the contour on the isopycnal. The tracer C, typically volume, conservative temperature anomaly, or salinity anomaly, is conserved in an isopycnal layer bounded by sections using (4). Part of an isopycnal layer is shown in light blue, bound by surfaces with tracer properties Cu and Cl. In practice, the layer is a complete rectangle bound laterally by all casts (from i 5 1 to i 5 L). Properties are advected or mixed through the upper and lower bounding surfaces of the layer and across each section. The velocity across a section is related to the streamfunction on the reference surface using a form of the thermal wind equation (appendix C). The unit vector m is normal to the section. In this study, Cp0 is solved for each cast i and the mixing coefficients K and D are solved for on each layer.

applied to the climatology of Gouretski and Koltermann (2004, hereafter GK04), and these results are discussed in appendix B. The inversion is carried out for the NATRE region in the North Atlantic (208–308N, 408– 258W) shown in Fig. 1. Regularly spaced points on the boundary of the domain (between 208 and 308N, 408 and 258W), given by the grid of the climatology, are referred to as ‘‘casts,’’ and the eastern, northern, western, and southern boundaries of the domain are referred to as ‘‘sections.’’ There is a total of 34 casts. Tracer contours (contours of constant Q and S in this study) are defined on grf surfaces between 200 and 1800 m. As in ZMS10, contours are defined, on each surface, by the temperature and salinity at the cast locations; that is, there is about one tracer contour per cast per surface. At each point on a contour the flow across the contour, in the direction n, is related to along-isopycnal and diapycnal mixing through the scale lengths lh, l?, and lg (1). So, integrating f/lh,

f/l?, and f/lg along each contour (green line in Fig. 2) gives the difference in geostrophic streamfunction, between the ends of the contour, as a function of mixing (3). Maps of the lateral mixing coefficient at the sea surface from Zhurbas and Oh (2004) suggest gradients of the mixing coefficient are very small in the latitude bands considered here. For this reason, we choose to find solutions where K does not vary laterally and hence ignore the $gK n term in (1). It is likely that at the boundary between different mixing regimes this term will become important and the sensitivity of the method to this choice is left to future work. By default we assume a statistically steady state, hence assuming the unsteady term Qtjg/j$gQj is negligible. This choice, and sensitivity to it, are discussed in appendix B. Volume, conservative temperature anomaly, and salinity anomaly are conserved on 28 isopycnal layers between 200 and 1800 m (4). Each ‘‘layer’’ in which properties are conserved may encompass more than one of

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FIG. 3. Vertical mixing coefficient D, as determined using the tracer-contour inverse method (solid line) and the standard error of the inversion (light gray shading). Also shown are the estimates of D from Ledwell et al. (1993, 1998) and Ferrari and Polzin (2005) and their reported uncertainties.

the ‘‘surfaces’’ on which tracer contours are defined. The density range of each layer is chosen such that at least 40 tracer contours are within each layer, allowing K and D to be well resolved within each layer. The diapycnal fluxes of properties due to advection wg are related to mixing through (2). The geostrophic advection across a section is v m, where m is the unit vector normal to the section (Fig. 2). This advection is related to the geostrophic streamfunction along the section. The difference in geostrophic streamfunction is related, at each pair of casts, to the reference level streamfunction Cp0 at 1000 db (C2). A streamfunction is solved for, on the reference pressure, at each of the 34 cast locations. The diffusivities K and D are solved for on each density layer. Contours with very small along-isopycnal gradients ($gQ , 2 3 1028 K m21) are excluded. The results are generally insensitive to minor changes of the choice of reference level, depth range, minimum number of contours per layer, and the minimum along-isopycnal temperature gradient. The inversion is more sensitive to the factors considered in appendix B. The 28 isopycnal layers give 28 D variables and 28 K variables. There are 34 reference level streamfunction variables and 1608 tracer-contour equations. All equations (contour and box equations) are scaled to be O(1). The contour equations are additionally scaled by the normalized mean isopycnal gradient of the contour,

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FIG. 4. Along-isopycnal mixing coefficient K, as determined using the tracer-contour inverse method (solid line) and the standard error of the inversion (light gray shading). Also shown are the estimates of K from Joyce et al. (1998), Ledwell et al. (1998), Jenkins (1998), Armi and Stommel (1983), Spall et al. (1993), and Zika and McDougall (2008) and their reported uncertainties.

ð j$g Qj dxQ dxQ ð ð dxQ max j$g Qj dxQ ð

in which max() is over all tracer contours, as (1) becomes singular for very small $gQ. As in ZMS10, the relative weighting of the tracer-contour and box equations [i.e. the weight given to (3) relative to (4)] is determined by minimizing the condition number of the matrix A in the linear system, Ax 5 b. Sensitivity analysis of the changes to the equation weighting are given in appendix B.

3. Results a. Vertical mixing The tracer-contour inverse method gives direct estimates of the mixing coefficients K and D (Figs. 3 and 4). The vertical mixing coefficient D in the upper 200– 1000 m of the water column is O(1025 m2 s21). There is a modest increase in D toward the deepest layers at 1800 m. The deepest value of D is 2.6 6 0.8 3 1025 at 1800 m. A SF6 tracer has been released in the eastern North Atlantic at an approximate depth of 300 m, and its dispersion used to infer a vertical mixing coefficient (Ledwell

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et al. 1993, 1998). Two observation of the effective mixing coefficient for that tracer were made: 1.2(60.1) 3 1025 m2 s21 in the spring of 1992 and 1.7(60.1) 3 1025 m2 s21 in the fall of 1993 (Fig. 3). As there are only two measurements, a suitable error range for the ‘‘long term mean’’ D is not possible. Promisingly, the error range of D in the upper 200–400-m depth range, as estimated in this study, almost overlaps with the spring 1992 measurements of Ledwell et al. (1993). Microstructure measurements of a vertical mixing coefficient in the NATRE region at depths of 200– 400 m are generally O(1025 m2 s21). For example, St. Laurent and Schmitt (1999) observed mixing of 0.1 2 1.3 3 1025 6 1025 m2 s21 considering diffusivities for density (Dr 5 0.1 3 1025 6 0.3 3 1025 m2 s21), potential temperature (Du 5 0.8 3 1025 6 0.1 3 1025 m2 s21), and salinity (DS 5 1.3 3 1025 6 0.1 3 1025 m2 s21). Unlike St. Laurent and Schmitt, we do not consider double-diffusive convection since we have taken the vertical mixing coefficient D to be the same for both Q and S. Ferrari and Polzin (2005) find an equally low turbulent diffusivity close to 0.7 3 1025 m2 s21. There are large uncertainties in microstructure estimates of D owing to the assumption of a turbulent mixing efficiency. Despite this uncertainty, the estimates of D presented here agree very strongly with the microstructure estimates of D of Ferrari and Polzin (2005) and St. Laurent and Schmitt (1999). The increase with depth of D toward 1800 m, observed in this study, is also consistent with Ferrari and Polzin (2005). Microstructure estimates of D are near instantaneous, representing turbulent motions evolving over minutes and hours. The estimates of D by Ledwell et al. (1993, 1998) represent mixing of a tracer integrated over a 6–18-month time period. The estimate of D presented here represents the long-term mean effective mixing of heat and salt across isopycnals. That the three approaches (microstructure, tracer release, and tracer-contour inverse method)—each with their respective time scales, spatial scale, and uncertainties—agree on a value of D O(1025 m2 s21) is encouraging. Our results support the hypothesis that, below the thermocline in the eastern North Atlantic, the D estimated by microstructure studies is equivalent to the long-term mean D and is thus an appropriate value for use in ocean circulation models. This canonical ‘‘background’’ value is consistent with the hypothesis that mixing is low away from boundary layers and is unable to sustain the upwelling required in the balance of Munk (1966).

b. Along-isopycnal mixing The along-isopycnal mixing coefficient K is estimated to be O(1000 m2 s21) within the upper 200–400 m of the

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water column, consistent with the North Atlantic Tracer Release Experiment studies for the same region (Ledwell et al. 1998; Sundermeyer and Price 1998). The mixing coefficient smoothly decreases with depth. Between 500and 1000-m depth, K reduces to around 0–200 m2 s21 (Fig. 4). These values are broadly consistent with nearby estimates from float trajectories (Joyce et al. 1998; Spall et al. 1993); 3He, 3H, and tritium tracer studies (Jenkins 1987, 1998); and other inverse studies (Armi and Stommel 1983; Zika and McDougall 2008). Ferrari and Polzin (2005) are able to derive a vertical profile of the alongisopycnal mixing coefficient using a mixing length argument and combining CTD and mooring data from NATRE and the Subduction Experiment (Joyce et al. 1998). There is uncertainty in the mixing efficiency that Ferrari and Polzin (2005) choose; thus, they are able to adjust the overall magnitude of K to fit the observations shown in Fig. 4. Our vertical profile, which cannot be adjusted and is a direct output of the inverse method, is very similar to that of Ferrari and Polzin (2005). There are no scaling parameters used in this study. The vertical profile of K presented here is consistent with the findings of Ferraira et al. (2005), which suggested a depth dependence to the mixing coefficient K that is mostly O(1000 m2 s21) in the upper 200–500 m of the water column, decreasing to O(100 m2 s21) at 1000– 2000-m depth. However, in the NATRE region, the estimates of Ferraira et al. (2005) are mostly negative. Recent parameterization efforts have focused on the need for a depth dependence of the along-isopycnal mixing coefficient (Eden and Greatbatch 2008; Danabasoglu and Marshall 2008), motivated largely by inferences from eddy resolving models and theoretical arguments. This study represents one of the few demonstrations of a strongly depth-dependent along-isopycnal mixing coefficient, inferred directly from observational data.

c. Geostrophic flow Because the geostrophic streamfunction at the reference level is an output of the tracer-contour inverse method, the full depth geostrophic velocity and transport are easily inferred (Fig. 5). The mean geostrophic velocity is to the southwest throughout most of the study region. The velocity is intensified in the upper 400 m of the water column with velocities of up to 2–3 cm s21. The flow is generally stronger in the zonal direction and intensifies in the southwest corner of the domain. This pattern is consistent with the largely westward migration of the SF6 tracer throughout the NATRE study of approximately 1.3 cm s21 (Ledwell et al. 1998, see their Table 1). The flow is also consistent with the southeastward flowing limb of the wind-driven North Atlantic subtropical gyre in Sverdrup balance (Sverdrup 1947). As

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FIG. 5. (top) Cross-sectional velocity v m (cm s21) on grf 5 26.525 kg m23 (s0 ’ 26.75 kg m23). Length of arrows represents magnitude of v m, whereas the direction indicates whether the flow is into or out of the study region. Each section has a corresponding velocity figure starting from (left) western section moving clockwise to (right) the southern section. The gray box indicates the study region. Ledwell et al. (1998) release a SF6 tracer at s0 5 26.75 kg m23, which is advected to the southeast, consistent with the flow field observed here. (bottom) Cross section of velocity on neutral density surfaces v m (positive values are directed out of the study region). As in (top), each section has a corresponding velocity figure.

deep velocities are estimated to be small, O(0.2 cm s21), the inferred circulation is consistent with studies that use atlas data and a deep level of no motion (e.g., Reid 1978). The typical columnar nature of the velocity field, seen in conventional inverse calculations using one-time hydrographic surveys, is not seen here partly because individual eddies are not present in the climatology. The deep flow is in no way minimized or assumed small in this analysis. Despite this, the solution for the geostrophic velocity at 1800 m is small, O(0.2 cm s21), relative to the upper 500 m O(2 cm s21). However, the inferred contribution to the section transport between 1800 m and the sea floor across each section is significant, O(2–3 Sv), demonstrating the need for an appropriate estimate of the deep velocity. Some structure to deep currents is revealed, including subsurface zonal currents.

4. Conclusions The tracer-contour inverse method has been applied to the mean hydrography of the eastern North Atlantic. Vertical profiles of the vertical mixing coefficient D and along-isopycnal mixing coefficient K have been diagnosed, as well as the mean geostrophic circulation.

The mixing coefficients and circulation patterns are consistent with in situ measurements. Vertical mixing is found to be weak, O(1025 m2 s21), throughout most of the water column. These low mixing values are consistent with the hypothesis that abyssal mixing is small in the subtropical oceans and is insufficient, on its own, to close the meridional overturning circulation. It is likely that the methods demonstrated in this study will be able to identify regions of intense mixing and quantify the global spatial structure of the mixing coefficients. In the NATRE region, along-isopycnal mixing is found to be strong, O(1000 m2 s21), near the thermocline and reduces with depth to background values O(100 m2 s21) below 500-m depth. Our results confirm that alongisopycnal mixing varies with depth as inferred by Ferrari and Polzin (2005) and Ferraira et al. (2005), further motivating parameterizations for this effect in coarseresolution ocean models (Eden and Greatbatch 2008; Danabasoglu and Marshall 2008). Uniquely, we reveal a depth dependence of the along-isopycnal mixing coefficient from mean hydrographic data alone. The eastern North Atlantic is thought to be a region of low eddy kinetic energy and small diapycnal mixing and,

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as such, is a suitable region for determining the background K and D. The tracer-contour inverse method accurately reproduces in situ observations there, suggesting that the method can be used to identify regions of both weak and intense vertical and along-isopycnal mixing in the world’s oceans. Acknowledgments. We thank Paul Durack and Dr. Susan Wijffels for the use of their climatology. We are grateful to Andrew Meijers, Frank Colberg, and two anonymous reviewers for their helpful comments. This work contributes to the CSIRO Climate Change Research Program and has been partially supported by the CSIRO Wealth from Oceans Flagship and the Australian Research Council Centre of Excellence for Mathematics and Statistics of Complex Systems.

APPENDIX A Derivation of the Advective–Diffusive Balance Equations with a Linear Equation of State For illustrative purposes, we rederive the advective– diffusive balance equations (1) and (2) in the specific case of flow with a linear equation of state (g } bS 2 aQ), spatially uniform along-isopycnal K and vertical D mixing coefficients, and an absence of sources and sinks (i.e., the tracers are conservative). In an isopycnal framework with a thickness-weighted lateral velocity vector vh/h and diapycnal velocity wg, the conservation equations for heat Q and salt S are then vh/h $g Q 1 wg Qz 5 K=2g Q 1 DQzz

(A1)

and vh/h $g S 1 wg Sz 5 K=2g S 1 DSzz .

Hence, where n 5 $gQ/j$gQj and exploiting the equality A3z(d2B/dA2)jx,y 5 AzBzz 2 BzAzz, (A5) becomes =2g Q g2z d2 Q (A6) 1D vh/h n 5 K . j$g Qj j$g Qj dg2 x,y In fact, the following advective–diffusive balance equation can be derived for any tracer C [ZMS10, Eq. (C9)]: =2g C g2z d2 C (A7) 1D vh/h n 5 K . j$g Cj j$g Cj dg2 x,y So, for any tracer, the component of the flow across contours on isopycnals is related to along-isopycnal mixing through the isopycnal curvature of the tracer (=2gC/j$gCj) and to vertical mixing through the curvature of the tracer with respect to density d2C/dg2jx,y. Note that the curvature of Q or S with respect to g is proportional to the curvature of Q with respect to S. In this study we take the fully nonlinear, unsteady case and separate the mean velocity v (not thickness weighted) and the eddy-induced velocity v9h9/h (the difference between the thickness- and not thickness-weighted velocities). We consider only conservative tracers and do not consider tracers other than Q and S. Hence, for our purposes the advective diffusive balance equations are, following ZMS10, v n 5 K/l? 1 $g K n 1 D/lg 1 K PV /lh Qt jg /j$g Qj

(A2)

Since we are in an isopycnal coordinate with a linear equation of state, the following apply: a$gQ 5 b$gS, $ga [ $gb 5 0, g z 5 bSz 2 aQz, and g zz 5 bSzz 2 aQzz. Hence, (A2) may be written as a a 1 g vh/h $g Q 1 w Qz 1 gz b b b a a 1 2 . (A3) 5 K=g Q 1 D Qzz 1 gzz b b b Taking (A1) multiplied by a minus (A3) multiplied by b, all terms involving Q cancel, giving wg 5 D(g zz /gz ).

discussed by Munk (1966; in Munk’s case, the heights were Qzz/Qz and Szz/Sz). Equation (A4) reveals the basic flavor of (2). Now, substituting (A4) into (A1), we have ! Q z . (A5) vh/h $g Q 5 K=2g Q 1 D Qzz gzz gz

(A4)

In this special case, the diapycnal velocity is related to the vertical mixing coefficient by a ‘‘mixing height,’’ as

(A8) and wg 5 D/hg 1 Dz 1 K/h?

(A9)

with the following ‘‘scale lengths,’’ dependent on the mean hydrography: 1/l? 5 1/lg 5

Qz $g (h$g S) Sz $g (h$g Q) h(Qz j$g Sj Sz j$g Qj) Szz Qz Qzz Sz , Qz j$g Sj Sz j$g Qj

1/lh 5 $g log(h) n, 1/h? 5

=2g Qj$g Sj =2g Sj$g Qj j$g SjQz j$g QjSz

,

,

1890


and

1/hg 5

Qzz j$g Sj Szz j$g Qj j$g SjQz j$g QjSz

.

See ZMS10 for the equivalent form of (A8) and the lengths l for the case of a general tracer C.

APPENDIX B Uncertainties and Sensitivity Analysis Mixing parameters and diapycnal fluxes determined using inverse methods are known to be highly sensitive to the model details and the data sources used (Tziperman 1988; Lux et al. 2001; St. Laurent et al. 2001). This sensitivity is not evident in standard statistical parameters [i.e., the singular values of the SVD solution] and is best explored through sensitivity tests. It is thus pertinent to test whether the results presented here, for the vertical and along-isopycnal mixing coefficients, are robust in terms of their sensitivity to changes to key assumptions and parameterizations, equation weighting, and data sources. To test the robustness of our results, we conduct a series of sensitivity tests, in each case, assessing the quantitative and qualitative changes made to the vertical and along-isopycnal mixing rates inferred. We show that the choice of equation weighting, parameterizations, and data source has a largely negligible effect, the last being the most influential. In this study, the bolus velocity v* is represented as a downgradient diffusion of the thickness of isopycnal layers h. The rate of diffusion of thickness is controlled by the mixing coefficient KPV such that v*h 5 2KPV$g(h). In the analysis presented in section 3, we assume that the coefficient for thickness is the same as that for temperature and salinity (i.e., KPV 5 K). These choices are arbitrary, and one could equally mix potential vorticity f/h instead of h and/or allow K and KPV to vary independently. To test the influence of this parameter in the eastern North Atlantic, we remove the thickness term from the inverse method. The effect on the mixing coefficients and their standard error is negligible (dotted line, Figs. B1a,b). This suggests that the choice of parameterization is inconsequential in this region and the method would be unable to diagnose KPV independently here. However, the bolus velocity is likely to be of leading order importance in the Southern Ocean and regions of steeply sloping isopycnals (Gent et al. 1995; McDougall and McIntosh 2001). Here a statistically steady balance is assumed such that the trend in Q and S, on isopycnal surfaces, is negligible

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when compared to the mean advection, along-isopycnal mixing, and diapycnal mixing terms (i.e., we have assumed Qtjg 5 0). In the climatology of DW10, trends have been quantified and are discussed in DW10. We test the importance of the trend term by retaining the Qtjg/$gQ term in Eq. (1). This term effectively represents the gradual movement of temperature contours on isopycnals. The effect on the inverse calculation in the eastern North Atlantic is negligible (dashed line, Figs. B1a,b), suggesting a steady-state balance is a valid assumption in the region considered. This assumption may break down in regions where strong changes are detected on isopycnals and/or along isopycnal gradients are particularly low. In ZMS10 and here, the relative weighting of the contour equation (3) versus the large-scale conservation, or box equation (4), is chosen by minimizing the condition number of the matrix A in the system Ax 5 b. It is conventional, in box inverse modeling studies, to weight equations by a prior error estimate. As there is significant uncertainty in the estimation of prior error, the choice is still largely arbitrary. To test the sensitivity of our mixing estimates to the weighting of contour versus box equations, we simply multiply and divide by 2, the weighting given by the minimum condition number criterion. This change in the weights gives a negligible impact on the along-isopycnal mixing coefficient profile in the upper 2000 m and a small impact on the vertical mixing profile in the upper 1000 m (Figs. B1c,d). There is a nonnegligible effect on the vertical mixing coefficient D in the 1000–2000-m depth range, although they show an increase with depth for all choices of weights. There is a small amount of sensitivity to the choice of reference level, partly because this choice affects the magnitude of b, which in turn affects the relative weights of each equation. The scale lengths, lg and l?, and scale heights, hg and h?, are functions of the curvatures and gradients of temperature and salinity determined from the climatology. It is likely that the way in which a climatology is constructed and the amount and quality of data that goes into it will have a bearing on the length scales and hence the resultant mixing coefficients determined by the tracer-contour inverse method. To test the sensitivity of our results to these differences, we redo the inversion using the climatology of GK04. The climatology of GK04 is constructed using data from the World Ocean Circulation Experiment (WOCE) and pre-WOCE periods and hence contains no data from Argo floats. Both GK04 and DW10 are averaged isopycnally, although using different approaches. We have mapped the GK04 climatology onto neutral density surfaces (gn, Jackett and McDougall 1997). We

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FIG. B1. (left) Vertical mixing coefficient D and (right) along-isopycnal mixing coefficient K, as determined using the tracer-contour inverse method. Black lines represent the estimated value, whereas the area between the gray lines represent the standard error. (a),(b) For the standard settings (solid line), no bolus velocity (dotted line) and including an unsteady term (dashed line); (c),(d) for the standard settings (solid line), increasing the weight on the contour equations (dashed line) and increasing the weight on the box equations (dotted line); and (e),(f) as in (c),(d), but using the climatology of GK04.

interpolate onto the same grid as DW10 so that a clear comparison can be made between the two. The velocity and mixing coefficients generated by the tracer-contour inverse method are generally more noisy when using the GK04 climatology. When using GK04, the results are more sensitive to the proximity of the analysis to the mixed layer. For this reason, we avoid using data shallower than 250 m. The vertical mixing coefficient, inferred

using GK04, is weak, O(1025 m2 s21), throughout all of the water column, increasing somewhat in the 1000– 2000-m depth range (Fig. B1e). The along-isopycnal mixing coefficient K, inferred using GK04, decreases from highs O(1000 m2 s21) in the upper 500 m of the water column to O(100 m2 s21) below 1000 m (Fig. B1f). These results are very similar to those derived using DW10 and earlier estimates summarized in Figs. 3 and 4.

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APPENDIX C Equivalent Form of the Thermal Wind Equation The following equivalent form of the thermal wind equation, in finite difference form and in terms of a reference level streamfunction, is used in this study: ð i11 (vp0 v) m dx 5 f i11/2 [Cp0 Cg ]i11 i i

" 5f i11/2 fp pi11/2 gd( p)

#i11

ðp d(p9) dp9 p0

.

(C1)

i

In (C1) i and i 1 1 are closely spaced ‘‘casts’’ moving clockwise (outward to the left) along a bounding ‘‘section’’ of the domain (Fig. 2). Values at the midpoint between cast i and i 1 1 on the isopycnal g are given the subscript i 1 ½: for example, the Coriolis frequency fi11/2 and pressure pi11/2. The specific volume anomaly is d( p), with reference values 08C and 35 psu. In (3) dcontour( p) is used, where the reference values are the conservative temperature and salinity of each contour on each isopycnal. Some improved accuracy in (C1) may be possible with the use of a more locally referenced form of d and additional terms (McDougall and Klocker 2010), although the difference is negligible in this study as isopycnals are not steeply sloped. For L casts, the lateral advection terms in (4) are then þ

L p0

(v v) mhC dx 5

å f i11/2 hi11/2 Ci11/2 i51

"

3 fp pi11/2 gd( p)

#i11

ðp d(p9) dp9 p0

(C2) i

and þ

L p0

(v ) mhC dx 5

å f i11/2 hi11/2 Ci11/2 [Cp ]i11 i . i51 0

(C3)

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