Satellite‐Based Sea Surface Salinity Designed for Ocean and Climate Studies

Boutin, Jacqueline; Reul, Nicolás; Koehler, J.; Martin, Adrien; Catany, Rafael; Guimbard, Sébastien; Rouffi, F.; Vergely, Jean‐Luc; Arias, Manuel; Chakroun, Meriem; Corato, Giovanni; Estella-Perez, Victor; Hasson, Audrey; Josey, Simon A.; Khvorostyanov, Dimitry; Kolodziejczyk, Nicolas; Mignot, Juliette; Olivier, Léa; Reverdin, Gilles; Stammer, Detlef; Supply, Alexandre; Thouvenin-Masson, Clovis; Turiel, Antonio; Vialard, Jérôme; Cipollini, Paolo; Donlon, Craig; Sabia, Roberto; Mecklenburg, Susanne

doi:10.1029/2021jc017676

Cited by 42 publications

(24 citation statements)

References 103 publications

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“…The CCI + SSS satellite SSS retrievals used here have been globally validated and their accuracy was stated to be better than 0.15 in most areas [25]. However, uncertainties in the satellite-derived salinity data remain high near coastlines due to land contamination of the signal there [24].…”

Section: Comparison Of Near Coastal Satellite Salinity Data With In-s...mentioning

confidence: 88%

“…ESA CCI sea-surface salinity: The sea-surface salinity (SSS) data are a composite of the bias-corrected sea-surface salinity from the European satellite mission Soil Moisture and Ocean Salinity SMOS (from 2010 to 2019) and the NASA missions Aquarius (from 2012 to 2015) and SMAP (from 2015 to present). The quality of the satellite-derived salinity data is described in Reference [24], and the data were provided by the ESA Climate Change Initiative (CCI) [25], available at https://data.ceda.ac.uk/neodc/esacci/ sea_surface_salinity/data/v03.21/7days (accessed on 11 November 2021).…”

Section: Input Datasets 211 Satellite Datamentioning

confidence: 99%

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Interannual Variability of the Congo River Plume-Induced Sea Surface Salinity

Martins

Stammer

2022

Remote Sensing

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Based on satellite surface salinity (SSS) observations from the SMOS, Aquarius and SMAP missions, we investigate the interannual SSS variability during the period from 2010 to 2020 in the Gulf of Guinea, impacted by the Congo River run-off. Combined with in situ data, the available 11 years of satellite salinity data suggest that the plume of Congo run-off primarily spreads into western directions, leading to reduced SSS. A fraction of it also shows a coastal southward extent subject to interannual variability influenced by coastal trapped waves. The low-salinity water is associated with high values of net primary production, confirming the riverine origin of the nutrient rich plume. No correlation can be found between the plume patterns and the different upwelling strengths in the subsequent upwelling months, nor could a correlation be found with the occurrence of the Benguela Niños. Linking the occurrence of a barrier layer to the occurrence of low-salinity plumes remains difficult, mainly because of the sparseness of in situ data. However, the influence of the low-salinity layer is evident in its stronger stratification and an increased available potential energy.

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Section: Comparison Of Near Coastal Satellite Salinity Data With In-s...mentioning

confidence: 88%

Section: Input Datasets 211 Satellite Datamentioning

confidence: 99%

Interannual Variability of the Congo River Plume-Induced Sea Surface Salinity

Martins

Stammer

2022

Remote Sensing

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“…A1). The authors of the SSS-CCI dataset (Boutin et al, 2021) also noticed larger seasonal biases in the SSS in respect to Argo salinities before mid-2015, over the global ocean. The largest differences relevant for our region are observed at high latitudes cold waters and boreal winter above 47 • N. After 2015 the integration of a new satellite (SMAP) and a change in the calibration mode of the satellite used over the period 2010-2019 (SMOS) in November 2014 improved the quality of the seasonal signal (Boutin et al, 2021).…”

Section: Appendix B: Delay In the Sss-cci Seasonal Variationsmentioning

confidence: 94%

“…We validate our results against other observational and synthetic datasets. The Sea Surface Salinity (SSS) CCI dataset is distributed at 0.25 • x 0.25 • horizontal grid resolution from 2010 to 2019 (Boutin et al, 2021). We do not use SSS as an input variable for several reasons.…”

Section: Additional Datasetsmentioning

confidence: 99%

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Four-dimensional temperature, salinity and mixed layer depth in the Gulf Stream, reconstructed from remote sensing and in situ observations with neural networks

Pauthenet

Bachelot

Balem

et al. 2022

Preprint

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Abstract. Despite the ever-growing amount of ocean’s data, the interior of the ocean remains under sampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T-S) profiles down to 1000 m and the Mixed Layer Depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in-situ profiles in the Gulf Stream region. To achieve vertical coherence of the profiles, the MLD prediction is used to adjust a posteriori the vertical gradients of predicted T-S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4◦ daily grid, producing four-dimensional fields of temperature and salinity, with their associated confidence interval issued from the bootstrap. OSnet profiles have root mean square error comparable with the observation-based Armor3D weekly product and the physics-based ocean reanalysis Glorys12. The maximum of uncertainty is located north of the Gulf Stream, between the shelf and the current, where the thermohaline variability is large. The OSnet reconstructed field is coherent even in the pre-ARGO years, demonstrating the good generalization properties of the network. It reproduces the warming trend of surface temperature, the seasonal cycle of surface salinity and mesoscale structures of temperature, salinity and MLD. While OSnet delivers an accurate interpolation of the ocean’s stratification, it is also a tool to study how the interior of the ocean’s behaviour reflects on surface data. We can compute the relative importance of each input for each T-S prediction and analyse how the network learns which surface feature influences most which property and at which depth. Our results are promising and demonstrate the power of machine learning methods to improve the prediction of ocean interior properties from observations of the ocean surface.

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