Skip to main content

Advertisement

Log in

A Comparison of Satellite-Derived Sea Surface Salinity and Salt Fluxes in the Southern Ocean

  • Original Paper
  • Published:
Remote Sensing in Earth Systems Sciences Aims and scope Submit manuscript

Abstract

Sea surface salinity (SSS) derived from the multi-satellite missions, NASA’s Aquarius/SAC–D and Soil Moisture Active and Passive (SMAP), and ESA’s Soil Moisture and Ocean Salinity (SMOS) are compared and used to estimate horizontal advective salt fluxes in the Southern Ocean (SO). In comparison with an Argo product, all three satellites estimate similar SSS in the Southern Hemisphere mid-latitudes (30° S–45° S) with low variability among the products. At high latitudes, there are temporal patterns of bias (relative to Argo) in Aquarius during Austral summer and in SMOS during Austral winter. Differences in the satellite products and Argo exist along coastal boundaries, low temperatures, and strong currents. Satellite-derived salinity indicates low temporal–mean standard deviations with Aquarius (0.215) and moderate standard deviations with SMOS (0.294) and SMAP (0.325) against Argo in the SO. Differences in satellite-derived zonal and meridional SSS gradients are large; standard deviation values are 2.52 and 1.49 × 10−6 psu m−1, respectively, and similarly located within the sub-tropical salinity maxima, Antarctic Circumpolar Current, and coastal zones. Differences in the horizontal advective fluxes are on average small, but large variability greater than 275 mm month−1 indicates errors of similar magnitude to the estimated Argo flux. Based on these results, the use of satellite-derived salinity may prove to be a useful resource for observing salinity and horizontal salt fluxes, outside the inaccuracies associated with the high latitudes and coastal currents between the various remotely sensed products, and could significantly influence the results depending on the product.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Gille ST (2002) Warming of the Southern Ocean since the 1950’s. Science 295:1275–1277. https://doi.org/10.1126/science.1065863.

    Article  Google Scholar 

  2. Dong S, Sprintall J, Gille ST, Talley L (2008) Southern Ocean mixed–layer depth from Argo float profiles. J Geophys Res 113:C06013. https://doi.org/10.1029/2006JC004051.

    Article  Google Scholar 

  3. Liu J, Curry JA (2010) Accelerated warming of the Southern Ocean and its impacts on the hydrological cycle and sea ice. PNAS 107:14987–14992. https://doi.org/10.1073/pnas.1003336107

    Article  Google Scholar 

  4. McNeil BI, Matear RJ (2008) Southern Ocean acidification: a tipping point at 450–ppm atmospheric CO2. PNAS 105:18860–18864. https://doi.org/10.1073/pnas.0806318105

    Article  Google Scholar 

  5. Montes-Hugo M, Doney SC, Ducklow HW, Fraser W, Martinson D, Stammerjohn SE, Schofield O (2009) Recent changes in phytoplankton communities associated with rapid regional climate change along the western Antarctic Peninsula. Science 323:1470–1473. https://doi.org/10.1126/science.1164533

    Article  Google Scholar 

  6. Sarmiento JL, Hughes TMC, Stouffer RJ, Manabe S (1998) Simulated response of the ocean carbon cycle to anthropogenic climate warming. Nature 393:245–249. https://doi.org/10.1038/30455

    Article  Google Scholar 

  7. Toggweiler JR, Samuels BL (1995) Effect of sea ice on the salinity of Antarctic bottom waters. J Phys Oceanogr 25:1980–1997. https://doi.org/10.1175/15200485(1995)025<1980:EOSIOT>2.0.CO,2

    Article  Google Scholar 

  8. Meyer A, Sloyan BM, Polzin KL, Phillips HE, Bindoff NL (2015) Mixing variability in the Southern Ocean. J Phys Oceanogr 45(4):966–987. https://doi.org/10.1175/JPO-D-14-0110.1

    Article  Google Scholar 

  9. Rintoul, S. R., Naveira Garabato, A. C. (2013) Dynamics of the Southern Ocean circulation, ocean circulation and climate, Gerold Siedler, Stephen M. Griffies, John Gould and John A. Church, Elsevier Ltd, 103:471–492

  10. Sheen KL, Naveira Garabato AC, Brearley JA, Meredith MP, Polzin KL, Smeed DA, Forryan A, King BA, Sallée JB, Laurent LS, Thurnherr AM (2014) Eddy–induced variability in Southern Ocean abyssal mixing on climatic timescales. Nat Geosci 7(8):577–582. https://doi.org/10.1038/ngeo2200.

    Article  Google Scholar 

  11. Lyman JM, Johnson GC (2008) Estimating annual global upper–ocean heat content anomalies despite irregular in-situ ocean sampling. J Clim 21(21):5629–5641. https://doi.org/10.1175/2008JCLI2259.1

    Article  Google Scholar 

  12. Moon J-H, Song YT (2014) Seasonal salinity stratifications in the near–surface layer from Aquarius, Argo, and an ocean model: focusing on the tropical Atlantic/Indian oceans. J Geophys Res Oceans 119:6066–6077. https://doi.org/10.1002/2014JC009969

    Article  Google Scholar 

  13. Song YT, Lee T, Moon J–H, Qu T, Yueh S (2015) Modeling skin–layer salinity with an extended surface–salinity layer. J Geophys Res Oceans 120:1079–1095. https://doi.org/10.1002/2014JC010346

    Article  Google Scholar 

  14. Boutin J et al (2016) Satellite and in-situ salinity: understanding near–surface stratification and subfootprint variability. Bull Amer Meteor Soc 97:1391–1407. https://doi.org/10.1175/BAMS-D-15-00032.1.

    Article  Google Scholar 

  15. Boutin J, Martin N, Reberdin G, Yin X, Gaillard F (2013) Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain. Ocean Sci 9:183–192. https://doi.org/10.5194/os-9-183-2013

    Article  Google Scholar 

  16. Drucker R, Riser SC (2014) Validation of Aquarius sea surface salinity with Argo: analysis of error due to depth of measurement and vertical salinity stratification. J Geophys Res Oceans 119:4626–4637. https://doi.org/10.1002/2014JC010045

    Article  Google Scholar 

  17. Henocq C, Boutin J, Petitcolin F, Reverdin G, Arnault S, Lattes P (2010) Vertical variability of near–surface salinity in the tropics: consequences for L–band radiometer calibration and validation. J Atmos Ocean Technol 27:192–209. https://doi.org/10.1175/2009jtecho670.1.

    Article  Google Scholar 

  18. Dinnat EP, Boutin J, Yin X, Le Vine DM (2014) Inter–comparison of SMOS and Aquarius sea surface salinity: effects of the dielectric constant and vicarious calibration. IEEE Microrad 55–60:14528317. https://doi.org/10.1109/MicroRad.2014.6878907

    Article  Google Scholar 

  19. Le Vine DM, Dinnat EP, Meissner T, Yueh SH, Wentz FJ, Torrusio SE, Lagerloef G (2015) Status of Aquarius/SAC–D and Aquarius salinity retrievals. IEEE J Sel Topics Appl Earth Observ Remote Sens 8(12):5401–5415. https://doi.org/10.1109/JSTARS.2015.2427159

    Article  Google Scholar 

  20. Reul N, Tenerelli J, Boutin J, Chapron B, Paul F, Brion E, Gaillard F, Archer O (2012) Overview of the first SMOS sea surface salinity products. Part I: quality assessment for the second half of 2010. IEEE Trans Geosci Remote Sens 50(5):1636–1647. https://doi.org/10.1109/LGRS.2012.2207943.

    Article  Google Scholar 

  21. Yin X, Boutin J, Martin N, Spurgeon P, Vergely J–L, Gaillard F (2014) Errors in SMOS sea surface salinity and their dependency on priori wind speed. Remote Sens Environ 146:159–171. https://doi.org/10.1016/j.rse.2013.09.008

    Article  Google Scholar 

  22. Johnson ES, Lagerloef GS, Gunn JT, Bonjean F (2002) Surface salinity advection in the tropical oceans compared with atmospheric freshwater forcing: a trial balance. J Geophys Res Oceans 107(C12). https://doi.org/10.1029/2001JC001122

    Article  Google Scholar 

  23. Yu L (2011) A global relationship between the ocean water cycle and near-surface salinity. J Geophys Res Oceans 116(C10). https://doi.org/10.1029/2010JC006937

  24. Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., Ponte, R. M. (2017) ECCO version 4 release 3. http://hdl.handle.net/1721.1/110380

  25. Hackert E, Busalacchi AJ, Ballabrera-Poy J (2014) Impact of Aquarius sea surface salinity observations on coupled forecasts for the tropical Indo–Pacific Ocean. J Geophys Res Oceans 119:4045–4067. https://doi.org/10.1002/2013JC009697

    Article  Google Scholar 

  26. Köhl A, Martins MS, Stammer D (2014) Impact of assimilating surface salinity from SMOS on ocean circulation estimates. J Geophys Res Oceans 119:5449–5464. https://doi.org/10.1002/2014JC010040

    Article  Google Scholar 

  27. Toyoda T, Fujii Y, Kuragano T, Matthews JP, Abe H, Ebuchi N, Usui N, Ogawa K, Kamachi M (2015) Improvements to a global ocean data assimilation system through the incorporation of Aquarius surface salinity data. Q J R Meteorol Soc 141(692):2750–2759. https://doi.org/10.1002/qj.2561

    Article  Google Scholar 

  28. Vinogradova NT, Ponte RM, Fukumori I, Wang O (2014) Estimating satellite salinity errors for assimilation of Aquarius SMOS data into climate models. J Geophys Res Oceans 119:4732–4744. https://doi.org/10.1002/2014JC009906

    Article  Google Scholar 

  29. Olmedo E, Martínez J, Umbert M, Hoareau N, Portabella M, Ballabrera J, Turiel A (2016) Improving time and space resolution of SMOS salinity maps using multifractal fusion. Remote Sens Environ 180:246–263. https://doi.org/10.1016/j.rse.2016.02.038

    Article  Google Scholar 

  30. Kao, H.-Y., Lagerloef, G., Lee, T., Melnichenko, O., Hacker, P. (2017) Aquarius salinity validation analysis, data version 5.0. Aquarius Science Team Technology Document, http://podaac.jpl.nasa.gov/aquarius.

  31. Lagerloef, G., Kao, H.-Y., Meissner, T., Vazquez, J. (2015) Aquarius salinity validation analysis, data version 4.0. Aquarius Science Team Technology Document, http://podaac.jpl.nasa.gov/aquarius.

  32. Meissner, T., Wentz, F. J. (2016) Remote Sensing Systems SMAP ocean surface salinities [level 2C, level 3 running 8–day, level 3 monthly], Version 2.0 validated release. Remote Sensing Systems, Santa Rosa, CA., USA. https://doi.org/10.5067/SMP20-3SMCS

  33. Wentz, F., Le Vine, D.M. (2012) Aquarius salinity retrieval algorithm, ATBD. RSS Technical Report 082912, ftp://podaac-ftp.jpl.nasa.gov/allData/aquarius/docs.

  34. Wentz, F., Ricciardulli, L., Gentemann, C., Meissner, T., Hilburn, K. A., Scott, J. (2013) Remote Sensing Systems Coriolis WindSat [Daily] Environmental Suite on 0.25 deg grid, Version 7.0.1, [WSPD_LF]. RSS Technical Report 091316 19, Remote Sensing Systems, Santa Rosa, CA, USA. www.remss.com/missions/windsat.

  35. Meissner, T., Wentz, F., Scott, J. (2015) Remote Sensing Systems SMAP level 3 ocean surface salinities [running 8–day, monthly] on 0.25 deg grid, version 1.0 (BETA). Technical report, Remote Sensing Systems, Santa Rosa, CA., USA. (http://www.remss.com/missions/smap)

  36. Ren L, Speer K, Chassignet EP (2011) The mixed layer salinity budget and sea ice in the Southern Ocean. J Geophys Res 116:C08031. https://doi.org/10.1029/2010JC006634.

    Article  Google Scholar 

  37. Bonjean F, Lagerloef GSE (2002) Diagnostic model and analysis of the surface currents in the tropical Pacific Ocean. J Phys Oceanogr 32:2938–2954. https://doi.org/10.1175/1520-0485(2002)032<2938:DMAAOT>2.0.CO;2

    Article  Google Scholar 

  38. Lee T (2016) Consistency of Aquarius sea surface salinity with Argo products on various spatial and temporal scales. Geophys Res Lett 43(8):3857–3864. https://doi.org/10.1002/2016GL068822

    Article  Google Scholar 

  39. Boutin J, Martin N, Yin X, Font J, Reul N, Spurgeon P (2012) First assessment of SMOS data over open ocean: part II—sea surface salinity. IEEE Trans Geosci Remote Sens 50(5):1662–1675. https://doi.org/10.1109/TGRS.2012.2184546

    Article  Google Scholar 

Download references

Acknowledgments

Brady S. Ferster is supported by the NASA/South Carolina Space Grant Graduate Fellowship. Aquarius version 5.0 L3 (ftp://podaac-ftp.jpl.nasa.gov/allData/aquarius/L3/mapped/V5/monthly/SCI/) and OSCAR (https://doi.org/10.1175/1520-0485(2002)032<2938:DMAAOT>2.0.CO;2) datasets are obtained from the NASA’s JPL Physical Oceanography Distributed Active Archive Center (PO.DAAC). The SMOS unbiased binned data used for this study is the L3 Operational version 2.0 provided by the ESA obtained from the SMOS Barcelona Expert Center Data distribution and visualization services (https://doi.org/10.1016/j.rse.2016.02.038). The SMAP data are produced by Remote Sensing Systems, Santa Rosa, CA, and version 2.0 level 3 is obtained from NASA JPL PO.DAAC (ftp://podaac-ftp.jpl.nasa.gov/allData/smap/L3/RSS/V2/monthly/SCI/). Argo data (Argo DOI: https://doi.org/10.17882/42182) is obtained from the Asia-Pacific Data Research Center (APDRC) of the International Pacific Research Centre (IPRC), the 1° gridded on standard levels product. The authors would like to thank the anonymous reviewers and the editor, whose comments significantly contributed to the improvement of this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Brady S. Ferster.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ferster, B.S., Subrahmanyam, B. A Comparison of Satellite-Derived Sea Surface Salinity and Salt Fluxes in the Southern Ocean. Remote Sens Earth Syst Sci 1, 1–13 (2018). https://doi.org/10.1007/s41976-018-0001-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41976-018-0001-5

Keywords

Navigation