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Séverine Fournier, Nicolas Reul , Bertrand Chapron Laboratoire Océanographie Spatiale, IFREMER

Large Tropical River Plume Monitoring with SMOS to better estimate Land- Sea Freshwater Fluxes. Séverine Fournier, Nicolas Reul , Bertrand Chapron Laboratoire Océanographie Spatiale, IFREMER Joe Salisbury, Doug Vandemark University of New Hampshire, Durham, NH, USA

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Séverine Fournier, Nicolas Reul , Bertrand Chapron Laboratoire Océanographie Spatiale, IFREMER

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  1. Large Tropical River Plume Monitoring with SMOS to betterestimate Land-SeaFreshwater Fluxes Séverine Fournier, Nicolas Reul, Bertrand Chapron Laboratoire Océanographie Spatiale, IFREMER Joe Salisbury, Doug Vandemark University of New Hampshire, Durham, NH, USA ESA-EGU-SOLAS – Air-SeaGas Flux Climatology ; Progress and Future Prospect 24th-27thSeptember 2013 – Ifremer, Brest, France

  2. Rivers, important variables in oceanography • Surface freshwateris important to Air-Sea interactions by modifying : • open ocean SSS (density) • buoyancy of the surface layer & vertical stratification Rivers : important factors of the Air/Sea interactions • Sources of organic & inorganicmaterialswhich have a keyrole in manybiological, physical & chemicalprocesses. Rivers representkeyhydrologic components of freshwater Land/Sea exchanges • Particularly the Amazon River plume : the world’slargest river in terms of dischargelevels

  3. Conservative Mixing in Rivers’ plume • SSS/opticalproperties conservative mixing • A wellknown inverse correlation SSS/light absorption and SSS/light attenuation (Hu et al. 2004, Del Vecchio & Subramaniam 2004, Molleri et al. 2010, Salisbury et al., 2010) Bio-optical parameter salinity river mouth S = 0 open sea S = 36

  4. Deviationsfrom the conservative mixing : • Physicalprocesses • Bio-optical & bio-chemicalprocesses primary production rivers evaporation Bio-optical parameter precipitations removalprocesses (photobleaching, flocculation…) river mouth S = 0 salinity open sea S = 36

  5. Up to now, the monitoring of the Amazon River plume and of the conservative mixingwerelimited due to a lack of joint SSS/opticalproperties observations Molleri et al, 2010 Del Vecchio and Subramaniam, 2004

  6. Since 2010, spacebornemeasurements of SSS are available for the first time from SMOS & Aquarius missions unprecedent spatial & temporal resolution In situ SSS – April 2010 SMOS SSS – April 2010

  7. Objectives • Illustrate the new monitoring capabilities for the oceanicfreshwater pool generated by the Amazon discharge • Study the quasi-linear seasonnally varying conservative mixing derived from the satellite SSS and Ocean Color properties • Investigate non conservative behaviours of the conservative mixing • Estimate the SSS at high spatial resolution (4 km) from Ocean Color data

  8. Data • SMOS SSS: 10-daydaily running mean, 0.25 degreeresolution (CATDS CEC products) • MERIS/MODIS/SeaWIFS CDM absorption: 10-daydaily running mean, 4-km resolution (GlobColour ACRI-ST) • In situ SSS (Coriolis, IRD, variousresearchcampaigns) • ORE HYBAM Amazon & OrinocodischargesatObidos & Bolivar gauges • 8-dayCarbonbased Production Model (CbPM) Net PrimaryProductivityfromOceanProductivity

  9. Amazon Plume - Local OceanCurrents

  10. SMOS SSS - 2010 GlobColourAcdm - 2010 New MonitoringCapabilities of the Amazon River Plume : Combination of SMOS SSS + OceanColor data + River discharge data

  11. SMOS plume monitoring capabilities SMOS SSS August 2010 Amazon discharge Amazon plume extension = Amazon discharge proxy 35 psu contour Plume area (35 psu contour) 3 monthslag

  12. The Conservative Mixingseen by SMOS and OceanColorSensors Annualrelationships Acdm (/m) K490 (/ m) SSS (psu) SSS (psu)

  13. Observedseasonal and interannualvariabilities in the SSS/ACDM relationship Sources of thesevariabilities have to beexplored in terms of: • River cycle (endmember variations, Amazon tributaries) • Biogeochemicalprocesses (photobleaching, primary production) • Physicalprocesses (advection, wind, rain)

  14. Observedseasonal and interannualvariabilities in the SSS/ACDM relationship • Amazon discharge in phase with the endmember of the SSS/Acdmrelationship discharge = main source of the conservative mixingseasonal cycle Endmember value Acdm river mouth S = 0 open sea S = 36 SSS

  15. Deviationsfrom the conservative mixing Estimation of Acdmfrom SMOS SSS and the monthly 2010-2012 SSS/Acdmrelationship June 2010-2012 June 2010 – SMOS SSS

  16. Deviationsfrom the conservative mixingJune 2010 SyntheticAcdm GlobColourAcdm Acdm (/m) ObservedAcdm Primary productivity Anomaly < 0 SyntheticAcdm Anomaly > 0 ObservedAcdm Net PrimaryProductivity Salinity (psu)

  17. High resolution SSS fromOceanColor • OceanColorsensors : 4 km – SMOS : 25 km distinguish structures not well-resolved by microwave SSS sensors • Data availablefrom2002 • Coastal observations 25-km SMOS SSS 4-km Acdm April 2011 October 2010 April 2011 October 2010

  18. High resolution SSS from Ocean Color June 2010 4-km reconstructed SSS June 2010 4-km GlobcolourAcdm Colibri TSG transect - June 2010

  19. High resolution SSS Validation SSS (psu) SSS (psu) In situ SSS (psu)

  20. April 2011 4-km Acdm April 2011 4-km SSS April 2011 25-km SMOS SSS

  21. October 2010 4-km Acdm October 2010 4-km SSS October 2010 25-km SMOS SSS

  22. Conclusions • Consistencybetween SMOS SSS (microwave instrument) & OceanColor (optical instrument) • New approach of the SSS/Acdmrelationshipthanks to remotesensing : largelyimprovedspatio-temporal monitoring • For the first time, the seasonal and interannualvariabilities of the conservative mixing are highlighted • Study of the deviationsfrom the conservative mixing • High Resolution SSS estimatescanberetrieved

  23. Thankyou for your attention • Séverine Fournier • severine.fournier@ifremer.fr

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