Sea Surface Salinity and Temperature Budgets in the North Atlantic Subtropical Gyre during SPURS Experiment: August 2012-August 2013

Sommer, Anna; Reverdin, Gilles; Kolodziejczyk, Nicolas; Boutin, Jacqueline

doi:10.3389/fmars.2015.00107

Cited by 11 publications

(15 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By discovering SST/SSS decoupling in the frontal regions (Kolodziejczyk et al, 2015;Kao and Lagerloef, 2018), we are redefining the role of salinity in density variability, thermohaline circulation, and in the energy balance of the upper ocean. Satellite SSS observations also improved the ability to study the kinetic energy variability of ocean circulation (Gordon and Giulivi, 2014;Reul et al, 2014a;Sommer et al, 2015;Busecke et al, 2017;Melnichenko et al, 2017), elevating the role of eddy transport in the ocean freshwater balance, even in the interiors of the subtropical gyres where eddies have historically been thought to have a negligible effect.…”

Section: New Opportunities In Mesoscale Oceanographymentioning

confidence: 99%

Satellite Salinity Observing System: Recent Discoveries and the Way Forward

et al. 2019

Self Cite

View full text Add to dashboard Cite

Advances in L-band microwave satellite radiometry in the past decade, pioneered by ESA's SMOS and NASA's Aquarius and SMAP missions, have demonstrated an unprecedented capability to observe global sea surface salinity (SSS) from space. Measurements from these missions are the only means to probe the very-near surface salinity (top cm), providing a unique monitoring capability for the interfacial exchanges of water between the atmosphere and the upper-ocean, and delivering a wealth of information on various salinity processes in the ocean, linkages with the climate and water cycle, including land-sea connections, and providing constraints for ocean prediction models. The satellite SSS data are complimentary to the existing in situ systems such as Argo that provide accurate depiction of large-scale salinity variability in the open ocean but under-sample mesoscale variability, coastal oceans and marginal seas, and energetic regions such as boundary currents and fronts. In particular, salinity remote sensing has proven valuable to systematically monitor the open oceans as well as coastal regions up to approximately 40 km from the coasts. This is critical to addressing societally relevant topics, such as land-sea linkages, coastal-open ocean exchanges, research in the carbon cycle, near-surface mixing, and air-sea exchange of gas and mass. In this paper, we provide a community perspective on the major achievements of satellite SSS for the aforementioned topics, the unique capability of satellite salinity observing system and its complementarity with other platforms, uncertainty characteristics of satellite SSS, and measurement versus sampling errors in relation to in situ salinity measurements. We also discuss the need for technological innovations to improve the accuracy, resolution, and coverage of satellite SSS, and the way forward to both continue and enhance salinity remote sensing as part of the integrated Earth Observing System in order to address societal needs.

show abstract

Section: New Opportunities In Mesoscale Oceanographymentioning

confidence: 99%

Satellite Salinity Observing System: Recent Discoveries and the Way Forward

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…We focus on the subtropical region (50°W-20°W; 15°N-40°N) of the north Atlantic in 2013. This region is chosen because it is very well covered by regular ship tracks spaced by approximately one month, it is strongly impacted by the seasonally-varying latitudinal biases, it is characterized by mesoscale variability that is not resolved by the ISAS analysis (Kolodziejczyk et al 2015;Sommer et al 2015), and it is not used for choosing the reference dwell lines of the seasonal latitudinal correction. We analyze below the density spectra (Figure 11, top) and the squared coherence (Figure 11, bottom) of ISAS, of 10-day L3P and L3Q, of 18-day CEC with ship SSS.…”

Section: Comparison To Ship Sssmentioning

confidence: 99%

“…The spatial resolution and spatio-temporal coverage of the SMOS mission (50 km resolution; global coverage every 3 to 5 days) also allow the unprecedented detection of SSS mesoscale features associated with the transport across frontal regions (e.g. Reul et al, 2014b;Kolodziejczyk et al, 2015), very hardly accessible from Aquarius measurement (100-150 km resolution; global coverage every 8 days).…”

Section: Introductionmentioning

confidence: 99%

New SMOS Sea Surface Salinity with reduced systematic errors and improved variability

Boutin

Vergely

Marchand

et al. 2018

Remote Sensing of Environment

147

118

View full text Add to dashboard Cite

Salinity observing satellites have the potential to monitor river freshwater plumes mesoscale spatiotemporal variations better than any other observing system. In the case of the Soil Moisture and Ocean Salinity (SMOS) satellite mission, this capacity was hampered due to the contamination of SMOS data processing by strong land-sea emissivity contrasts. Kolodziejczyk et al. (2016) (hereafter K2016) developed a methodology to mitigate SMOS systematic errors in the vicinity of continents, that greatly improved the quality of the SMOS Sea Surface Salinity (SSS). Here, we find that SSS variability, however, often remained underestimated, such as near major river mouths. We revise the K2016 methodology with: a) a less stringent filtering of measurements in regions with high SSS natural variability (inferred from SMOS measurements) and b) a correction for seasonally-varying latitudinal systematic errors. With this new mitigation, SMOS SSS becomes more consistent with the independent SMAP SSS close to land, for instance capturing consistent spatio-temporal variations of low salinity waters in the Bay of Bengal and Gulf of Mexico. The standard deviation of the differences between SMOS and SMAP weekly SSS is <0.3 pss in most of the open ocean. The standard deviation of the differences between 18-day SMOS SSS and 100-km averaged ship SSS is 0.20 pss (0.24 pss before correction) in the open ocean. Even if this standard deviation of the differences increases closer to land, the larger SSS variability yields a more favorable signal-to-noise ratio, with r2 between SMOS and SMAP SSS larger than 0.8. The correction also reduces systematic biases associated with man-made Radio Frequency Interferences (RFI), although SMOS SSS remains more impacted by RFI than SMAP SSS. This newly-processed dataset will allow the analysis of SSS variability over a larger than 8 years period in regions previously heavily influenced by land-sea contamination, such as the Bay of Bengal or the Gulf of Mexico. Highlights ► Improved SMOS salinity systematic error correction from Kolodziejczyk et al. (2016) ► Refined variability of sea surface salinity near e.g. major river mouths ► Consistent mesoscale patterns observed by SMOS and SMAP satellite missions

show abstract

{\overset{w}{true}}_{- \bar{h}}

), most of the studies use the Ekman pumping velocity as an approximation or even disregard the third term in ENTR (e.g., Ren et al, ; Ren & Riser, ; Sommer et al, ; Yu, ).…”

Section: Processes Causing Mls Changesmentioning

confidence: 99%

“…It is assumed that horizontal velocities are depth independent in the mixed layer, thus u, v are taken from the observational OSCAR data set for the calculation of the mean horizontal advection and of the lateral advection across the sloping mixed layer. Because no measurements exists of vertical velocity at the mixed-layer base ( w 2 h ), most of the studies use the Ekman pumping velocity as an approximation or even disregard the third term in ENTR (e.g., Ren et al, 2011;Ren & Riser, 2009;Sommer et al, 2015;Yu, 2011). Figure 5 is the average vertical velocity at the mixed-layer base taken from the model (Figure 5a) and the vertical velocity calculated as the divergence of the model mixed-layer averaged horizontal velocity fields (Figure 5b).…”

Section: Mixed-layer Salinity Balance Equationmentioning

confidence: 99%

Mechanisms of Mixed‐Layer Salinity Seasonal Variability in the Indian Ocean

Kohler¹,

Serra²,

Bryan

et al. 2018

JGR Oceans

View full text Add to dashboard Cite

Based on a joint analysis of an ensemble mean of satellite sea surface salinity retrievals and the output of a high‐resolution numerical ocean circulation simulation, physical processes are identified that control seasonal variations of mixed‐layer salinity (MLS) in the Indian Ocean, a basin where salinity changes dominate changes in density. In the northern and near‐equatorial Indian Ocean, annual salinity changes are mainly driven by respective changes of the horizontal advection. South of the equatorial region, between 45°E and 90°E, where evaporation minus precipitation has a strong seasonal cycle, surface freshwater fluxes control the seasonal MLS changes. The influence of entrainment on the salinity variance is enhanced in mid‐ocean upwelling regions but remains small. The model and observational results reveal that vertical diffusion plays a major role in precipitation and river runoff dominated regions balancing the surface freshwater flux. Vertical diffusion is important as well in regions where the advection of low salinity leads to strong gradients across the mixed‐layer base. There, vertical diffusion explains a large percentage of annual MLS variance. The simulation further reveals that (1) high‐frequency small‐scale eddy processes primarily determine the salinity tendency in coastal regions (in particular in the Bay of Bengal) and (2) shear horizontal advection, brought about by changes in the vertical structure of the mixed layer, acts against mean horizontal advection in the equatorial salinity frontal regions. Observing those latter features with the existing observational components remains a future challenge.

show abstract

Sea Surface Salinity and Temperature Budgets in the North Atlantic Subtropical Gyre during SPURS Experiment: August 2012-August 2013

Abstract: International audienc

Cited by 11 publications

References 39 publications

Satellite Salinity Observing System: Recent Discoveries and the Way Forward

Satellite Salinity Observing System: Recent Discoveries and the Way Forward

New SMOS Sea Surface Salinity with reduced systematic errors and improved variability

Mechanisms of Mixed‐Layer Salinity Seasonal Variability in the Indian Ocean

Contact Info

Product

Resources

About