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The Global Drifter Program deployed a total of 144 Lagrangian drifters drogued at 15 m depth, including 88 equipped with salinity sensors, in support of the first Salinity Processes in the Upper-ocean Regional Study (SPURS-1) in the subtropical North Atlantic Ocean with the goal of measuring salt fluxes associated with surface currents. The quality-controlled data set consists of 996,583 salinity observations collected between August 2012 and April 2014. A comparison of the drifter salinities with Aquarius satellite sea surface salinity (SSS) data shows that the lifespan of the salinity sensor fitted to the drifters is of the order of one year. The salinity and velocity data from the drifters were used to validate salt transport divergence computed with satellite products, with satellite salinity taken from the standard Aquarius v3.0 data set. The results indicate good agreement between the two independent methods, and also demonstrate that the effect of the eddy field combined with SSS variability at the surface dominates the signal. SSS variability within spatial bins as compared to Aquarius-beam footprints measured by drifters can be in excess of 0.1 psu. This result suggests that careful evaluation of the representation error is required when single-point in situ measurements, such as those collected by Argo floats, are used to validate spatially averaged Aquarius salinity data. [ABSTRACT FROM AUTHOR]

We investigated a 100 x 100 km high-salinity region of the North Atlantic subtropical gyre during the Sub-Tropical Atlantic Surface Salinity Experiment/Salinity Processes in the Upper-ocean Regional Study (STRASSE/SPURS) cruise from August 21, 2012, to September 9, 2012. Results showed great variability in sea surface salinity (SSS; over 0.3 psu) in the mesoscale, over 7 cm of total evaporation, and little diapycnal mixing below 36 m depth, the deepest mixed layers encountered. Strong currents in the southwestern part of the domain, and the penetration of freshwater, suggest that advection contributed greatly to salinity evolution. However, it was further observed that a smaller cyclonic structure tucked between the high SSS band and the strongest currents contributed to the transport of high SSS water along a narrow front. Cross-frontal transport by mixing is also a possible cause of summertime reduction of SSS. The observed structure was also responsible for significant southward salt transport over more than 200 km. [ABSTRACT FROM AUTHOR]

Abstract: Argo is an international project that is deploying an array of temperature and salinity profiling floats over the global ocean. Here we use the error formulation derived from Optimal Statistical Interpolation to estimate statistical errors associated with the recovery of the temperature field in the North-East Atlantic ocean. Results indicate that with the present distribution of floats (119 in the considered domain), scales of wavelength larger than 500 km can be recovered with a relative uncertainty (rms error relative to the standard deviation of the field) of about 7% at 50 m, 8% at 200 m and 10% at 1000 m. This corresponds to mean absolute errors of 0.111 °C at 50 m, 0.104 °C at 200 m and 0.073 °C at 1000 m. The splitting of total errors into instrumental and sampling contributions reveals that, in the present scenario, errors are more due to the small number of floats than to instrumental errors, especially at upper levels. For scales larger than 500 km this will hold true until 200–250 floats are deployed (less than 200 for deep levels). In such a simulated scenario, the number of observations and the technology become approximately equally limiting factors for the accuracy of the temperature field mapping, with total relative errors of less than 2% at upper levels and about 3% at 1000 m. [Copyright &y& Elsevier]

Abstract: Linear and non-linear empirical models for salinity (S) are estimated from the Argo temperature (T) and salinity (delayed) data. This study focuses on the reconstruction of salinity in the upper 1200m of the eastern North Atlantic Ocean, a region characterized by the presence of many different water masses. While previous studies have found it necessary to split this region by boxes to fit different polynomial models in each box, a unique model valid for the entire region is fitted here. Argo profiles are randomly distributed on two sets: one for fitting the models and one for testing them. Non-linear regressions are built using neural networks with a single hidden layer and the fitting data set is further divided into two subsets: one for adjusting the coefficients (training data) and one for early stopping of the fitting (validation data). Our results indicate that linear regressions perform better than the climatologic T–S relationship, but that non-linear regressions perform better than the linear ones. Non-linear training using a three-data subsets strategy successfully prevents overfitting even when networks with 90 neurons in the hidden layer are being trained. While the presence of local minima may complicate the generalization of non-linear models to new data, network committees (created by training the same network from different random initial weights) are shown to better reproduce the test data. Several predictors are tested, and the results show that geographical, or surface, information does provide significant information. These results highlight the potential applications of future satellite missions measuring sea-surface salinity to reconstruct, when combined with temperature profiles, vertical salinity profiles. [Copyright &y& Elsevier]

Abstract: The surface salinity response to changes of various external forcings and model parameters is investigated in a regional 1/3° configuration of the NEMO-OPA model implemented over the eastern North-Atlantic subtropical Ocean. Fourteen realistic climatological simulations are run. By default, neither relaxation to climatological surface salinity nor temperature is included. Forcing fields and parameters expected to impact the surface salinity are modified. These include: the wind stress, the surface temperature, wind speed and relative humidity entering the bulk calculation for sensible and latent heat fluxes, the precipitation, the data specified at open boundaries, the lateral viscosity operator, the salt lateral diffusivity, the vertical mixing scheme, the on/off switch of the double diffusion parameterization, the river runoffs and the SSS/SST restoring terms. The SSS standard deviation over this ensemble of model simulations is of the order of 0.1psu, which is also the order of the annual cycle of the surface salinity field in this area. In this experimental framework, the wind stress is found to have the largest impact on the model SSS, both in terms of mean field and annual variability. The sensitivity to the precipitation, atmospheric temperature, open boundary external data, and to the relaxation to climatological SSS and SST, is also significant. Of all the model parameterizations, the lateral salt diffusivity is the one associated to the strongest surface salinity model response. This work allows the identification of the main sources of error for model surface salinity. It paves the way for a more comprehensive investigation of SSS model error statistics, which is needed for future data assimilation experiments motivated by the close launch of the new SSS-observing SMOS and Aquarius satellite missions. [Copyright &y& Elsevier]