The surface freshwater and heat fluxes lead to water-mass transformation rates in Sver- drups (1Sv = 106m3s−1). MultiplyingADS
A by the inverse solution ofDresults in water- mass transformation due to salt diffusion by small-scale mixing processes in (SA,Θ)
coordinates. A similar calculation can be done for the horizontal and isopycnal eddy diffusion and for the heat terms. The resulting water-mass transformation for surface and diffusive fluxes show blue colours when volume transports are directed towards lower salinities or temperatures (freshening and cooling) and red colours when directed towards higher salinities or temperatures (salinification or warming) (Fig. 5.4).
Figure 5.4: The water-mass transformation rates (Sv) in (SA,Θ) coordinates due to
salt fluxes (upper panels a-d) and heat fluxes (lower panels (e-h). Separated into surface forcing (a,e), small-scale diffusion (b,f), horizontal eddy diffusion (c,g) and isopycnal eddy diffusion (d,h). Positive (negative) values indicate a volume transport directed towards higher (lower)SAand Θ values. Black contours represent the surface referenced
potential density anomaly values.
Surface Forcing
The distribution of the surface salt fluxes show three areas of freshening and one area of salinification (Fig. 5.4a). For Θ > 20oC, an area which we relate with the (sub) tropical mixed layer, already fresh water is freshened even more and already salty water is salinified even more. The freshening at lower temperatures (Θ < 15oC) is also act- ing upon already relative fresh water, associated with higher latitudes in the Southern Hemisphere (SA≈34 g kg−1) and in the North Pacific (SA≈32.7 g kg−1) (Fig. 5.4a).
The water-mass transformation due to surface freshwater fluxes acts to increase salinity gradients. Note the lack of freshwater fluxes for the σ0 = 27−28 kg m−3, which may
be because for example Brine rejection is not included in these fluxes.
The distribution of the surface heat flux shows a general tendency for warming of already warm waters (Θ > 20oC, (Fig. 5.4a)). Then there is a sloping band of heating and
cooling aligned next to each other forSA= [34−36] g kg−1 and Θ = [5−25]oC. This
band is a result of seasonal cycles at mid latitudes. When considering a constantSA, the
heating is at a higher temperature than the cooling. This water-mass transformation due to surface heat fluxes acts to increase temperature gradients. This is also valid for
the strong warming/cooling dipole at Θ = [−2−5] and SA ≈ 34.1 g kg−1, related to
heat fluxes at high latitudes.
Small-scale diffusion
Water-mass transformation due to small scale mixing processes is larger than that due to eddy mixing processes and is abundant in (SA,Θ) coordinates (Fig. 5.4b and 5.4f).
As mixing acts to destroy tracer gradients, the small-scale mixing results in an opposite structure to that of surface forcing (Fig. 5.4a and 5.4e), especially for Θ >20oC. This places strong constraints on the inverse estimate of D= 6.52±0.04 x 10−5 m2 s−1 for
CARS (Table (5.2), slightly lower than the original value of DBL = 1 x 10−4 m2 s−1,
used by Bryan and Lewis (1979)). Recall that this value of D= 6.52±0.04 x 10−5 m2 s−1 multiplies the shape functionfD(z) (Eq. 5.3).
By multiplyingDwith the average offBL(z) over a particular depth, we obtain (global)
averages for D. We obtain Dz>−2000m = 2.2 x 10−5 m2 s−1 for the upper 2000m and
Dz≤−2000m = 7.5 x 10−5 m2 s−1 for depths greater than 2000m. We used 2000m to
separate interior from upper ocean, as this is the depth to which small-scale mixing is required to bring up bottom waters, before isopycnal upwelling becomes relevant, reduc- ing the required mixing to close the circulation (Toggweiler and Samuels, 1998, Sloyan and Rintoul, 2001). The upper ocean estimate corresponds very well with the observed values by Waterhouse et al. (2014) and the inverse estimates of Lumpkin and Speer (2007) and Zika et al. (2010b), even though the latter was a local estimate. The deep value corresponds to that of Lumpkin and Speer (2007), but is an order of magnitude lower than that of Waterhouse et al. (2014). Our global average (i.e full depth structure average) gives Dglobal = 5.54 x 10−5 m2 s−1, lower than DMunk = 1.3 x 10−4 (Munk,
1966), and an order of magnitude lower than Ganachaud and Wunsch (2000) and Wa- terhouse et al. (2014). The small-scale mixing results for LR-CARS can be found in Table 5.2 and are not discussed as they are similar to that of CARS.
Horizontal eddy diffusion
Although confined to a smaller area in (SA,Θ) coordinates, the water-mass transforma-
tion rates due to horizontal eddy diffusion in the upper 400m of the ocean, have signif- icant magnitudes and are abundant (Fig. 5.4c and 5.4g). We obtain KH = 1634±50
m2 s−1. This value is a maximum value that multiplies the structure function and corresponds to a resolution of 0.5ox0.5o (CARS). The global average value of KH,
KH,global = 123±4 m2 s−1. For LR-CARS KH is about 1.5 times larger. Climate
Estimate CARS LR-CARS D 6.52 ±0.04 x 10−5 m2 s−1 6.81±0.04 x 10−5 m2 s−1 Dglobal 5.54 ±0.03 x 10−5 m2 s−1 5.79±0.03 x 10−5 m2 s−1 Dz>−2000m 2.2 x 10−5 m2 s−1 2.3 x 10−5 m2 s−1 Dz≤−2000m 7.5 x 10−5 m2 s−1 7.8 x 10−5 m2 s−1 KH 1634±50 m2 s−1 2461±118 m2 s−1 KH,global 123±4 m2 s−1 185 m2 s−1 KI 80 ±8 m2 s−1 667±56 m2 s−1 KI,global 2.4± 0.2 m2 s−1 20 ±2 m2 s−1 KH,global Dglobal 2.2 x 10 6 3.2 x 106 KH,global KI,global 50 9.25
Table 5.2: The inverse estimate of the diffusion coefficients and their global or depth
averaged equivalents.
the quasi-Stokes (GM) coefficient, being of the order of 1000 m2 s−1, much larger than our estimate.
Isopycnal eddy diffusion
By definition the isopycnal eddy diffusion only effects the interior circulation of the ocean, reducing its spread in (SA,Θ) coordinates (Fig. 5.4d and 5.4h). We obtain a maximum
of KI = 80±8 m2 s−1 that multiplies the structure function and KI,global = 2.4±0.2
m2 s−1. For LR-CARS we find that KI = 667±56 m2 s−1, is a factor 8 larger than
CARS KI and compares well with the values obtained by Zika et al. (2010b). Do note
that KI,global = 20±2 m2 s−1 for LR-CARS, which is about a factor 50 smaller than
that typically used in models.
As a result of the strong constraints on D and KH, the uncertainty embedded in the
inverse estimate may reduce the constraints on KI, increasing the uncertainty on the
estimate of KI. In Appendix C it is shown that changing prior estimates and column
weighting mainly impacts KI and to a lesser extend KH or D. However, it turns out
that the solution obtained from the sensitivity test is similar to what we present here, but with a slightly larger standard deviation (Table C.1).
Combining the water-mass transformation rates
Clearly the global sum of the water-mass transformation rates due to horizontal and isopycnal eddy diffusion is smaller than that due to small-scale mixing processes and surface forcing, especially for higher temperatures related to near-surface dynamics. Hieronymus et al. (2014) obtained a similar result from a model (their Fig. 6), in
agreement with Bates et al. (2014) who showed eddy diffusivities are suppressed near the equator. Note however, that the smallness of the globally averaged contribution of eddy diffusion to water-mass transformations still allows their contribution to be significant locally, both in Cartesian and (SA,Θ) coordinates (Fig. 5.4c,d,g and 5.4h).
For Θ>25oC, small-scale mixing is the only compensation to the surface fluxes, clearly illustrating that the water-mass transformations due to salt fluxes (A-D) and heat fluxes (E-H) do not add up to zero. The residual creates a net water-mass transformation that is expressed as a diathermohaline streamfunction difference, used to estimate ΨdiaS
AΘ.