Formación y empleo: datos para el debate

Generally the vertical velocities were significantly smaller than horizontal velocities due to vertical stratification, which creates a barrier to vertical motion. Their location and magnitude in the ocean are thought to play an important role in the distribution of trac- ers, such as heat and nutrients (Strass, 1992). Using simple scaling arguments mesoscale velocities can range from 10 - 100 m d−1(Allen et al., 2001), with the submesoscale being larger.

Currently vertical velocities are difficult to measure in the ocean, partly because they are small, but also because most studies require high resolution spatial data taken over a relatively short time period. There are several methods used to estimate vertical velocities. The omega equation uses density measurements to obtain vertical velocities from geostrophic flow and requires high resolution ship surveys (Allen et al., 2002). These mesoscale surveys should have about 3-4 km along track spatial resolution and be synoptic, i.e. completed in a time shorter than the time taken for the feature to propagate or for properties to significantly alter. Using this method it has been shown that vertical velocities in the mesoscale range from 5 - 100 m d−1 (Allen & Smeed, 1996; Allen et al., 2005; Pidcock et al., 2013). This encompasses the range of vertical velocities seen in this study, with the mean absolute velocity as 5 m d−1 and the range from negligible to 35 m d−1. Most of these studies target areas with known high vertical velocities, such as strong ocean fronts, geostrophic jets and strong eddy features and therefore in general vertical velocities in the ocean are likely to be smaller. On the other hand, significant smoothing in the Omega method may occur due to sparse sampling leading to an underestimate of vertical velocities by up to 55% (Allen et al., 2001). Vertical velocities have also been calculated from gliders using the glider flight model and minimising for flight parameters, lift, drag, compressibility, thermal expansion and glider volume (Merckelbach et al., 2010; Frajka-Williams et al., 2011). I used the glider flight model to estimate vertical velocities, using the method of Frajka-Williams et al. (2011). However confidence in the results was low and therefore it was decided not to use these in the flux calculations. The vertical velocities calculated were very large (on the order of 1000 m d−1) and did not compare well with the velocities calculated from the moorings. There were indications that the parameters (volume, lift, drag, thermal expansion, compressibility) are poorly constrained, and problems with the glider flight in general, as there are considerable differences between the climb and dive velocities.

6.2.3 The Nitrate Budget

The main findings of the nitrate fluxes that:

• there is strong variability in the diffusive and vertical advective nitrate fluxes • the vertical advective flux is the largest contributor of nitrate supply to the eu-

photic zone

• the summed fluxes support between 75 and 102% of annual primary production.

There has been much debate in the literature about closing the nutrient budget. Global geochemical estimates of new production are significantly higher than estimates of nutri- ent supply from winter convection by a factor of two and nutrient fluxes from mesoscale eddies account for only 20 - 30% of the annual budget (McGillicuddy et al., 2003, 2007). Much of this discrepancy is thought to be a lack of observations at high spatial resolution, to capture the submesoscale (Klein & Lapeyre, 2009). I have shown here that vertical nitrate fluxes into the euphotic zone are in balance with primary production (Chapter 5). The fluxes at 50 m from convection, mesoscale vertical advection and diffusion, can support all the observed primary production (75 - 102%). The submesoscale fluxes could contribute an upper estimate of an extra 24% of the total primary production at 50 m, if the proportion of mesoscale to submesoscale is the same at 50 m as at 120 m (Section 5.4.6). Some of the fluxes may include recycled nitrate that has already been used in production and has sunk out of the euphotic zone, been regenerated and brought back up by ocean movement. It is also likely that the upper estimate used here is an overestimate as nitrate could be subducted back below the euphotic zone before it has been fully consumed by phytoplankton. The assumption of the Redfield ratio would also have an impact on the influence of the fluxes on the phytoplankton primary production (see section 5.4.8). Furthermore there is a potentially significant error associated with the nitrate-density relationship, which is particularly apparent on the vertical advective fluxes (Table 5.1).

The vertical advective flux was the most important flux of nitrate into the euphotic zone, followed by the diffusive flux and winter convective mixing, which both supplied similar amounts (Table 5.1). A modelling study by McGillicuddy et al. (2003) shows that the most important flux at the North Atlantic Bloom Experiment site was the convective flux, which differs from the observation here. They found that the vertical advective flux was negative, but was mostly controlled by strong negative fluxes during the restratification of the water column in winter. As there are no flux measurements during winter I cannot explicitly say that this would not lead to an annual downward

flux with the method used here. With more measurements at 120 m the minimum overall nitrate flux is negative. Large negative fluxes are seen at the beginning of May in the minimum estimate of the mesoscale fluxes at 120 m (Figure 5.9) at the same time as the water column begins to stratify (Figure 4.3). This could be an indication that nitrate is subducted out of the euphotic zone during restratification. However if assuming that all the nitrate is consumed, as for the maximum estimate, the flux would still be positive. This may be evidence that assuming total consumption is an overestimate, especially during certain times of year. However this is not seen in the submesoscale fluxes at 120 m, as the mixed layer shoals above 120 m in May the submesoscale fluxes are positive (Figure 5.10). This study effectively demonstrates the difference between the mesoscale and submesoscale fluxes (Figures 5.9 and 5.10).

The most important component of the maximum vertical advective flux, at depths of 50 and 120 m, for both the mesoscale and submesoscale, was the time varying component for the maximum flux estimates, suggesting that the fluxes were dominated by internal waves. Whereas for the minimum estimates the timevarying component largely cancelled out, which is unsurprising for internal waves. Therefore for the minimum estimate at 50 m the divergent component was more important indicating that eddies propagating through the site were also important (S´evellec et al., 2015). The minimum estimates at 120 m had negative fluxes for the divergent component, which leads to the conclusion that eddies contribute to the subduction of nitrate at this depth found by (McGillicuddy et al., 2003).

The modelling study by McGillicuddy et al. (2003) found that the diffusive flux at the North Atlantic Bloom Experiment contributed to 20% of the modelled production, very similar to the result here (25%). On the other hand, observations by Martin et al. (2010a) showed that convective mixing supplied 40-fold more nitrate to the euphotic zone than turbulent diffusion at the Porcupine Abyssal Plain-Sustained Observatory (PAP-SO) site. However, as the fluxes by Martin et al. were taken over just 2 weeks (between June and July), it is possible that over the whole year spikes in turbulent diffusivity were missed as they are sparse. The diffusive flux calculated here showed low values during the same time of year, with the highest fluxes in Autumn and May, demonstrating that it may be difficult to extrapolate short surveys to a yearly budget. There is less error associated with the nitrate-density relationship on the diffusive fluxes as it uses the nitrate gradient rather than absolute values. There will be additional error associated with the diffusivity measurements, but that is beyond this study to quantify.