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Ocean Dynamics

, Volume 65, Issue 11, pp 1441–1460 | Cite as

Potential of space-borne GNSS reflectometry to constrain simulations of the ocean circulation

A case study for the South African current system
  • Jan SaynischEmail author
  • Maximilian Semmling
  • Jens Wickert
  • Maik Thomas
Article

Abstract

The Agulhas current system transports warm and salty water masses from the Indian Ocean into the Southern Ocean and into the Atlantic. The transports impact past, present, and future climate on local and global scales. The size and variability, however, of the respective transports are still much debated. In this study, an idealized model based twin experiment is used to study whether sea surface height (SSH) anomalies estimated from reflected signals of the Global Navigation Satellite System reflectometry (GNSS-R) can be used to determine the internal water mass properties and transports of the Agulhas region. A space-borne GNSS-R detector on the International Space Station (ISS) is assumed and simulated. The detector is able to observe daily SSH fields with a spatial resolution of 1–5. Depending on reflection geometry, the precision of a single SSH observation is estimated to reach 3 cm (20 cm) when the carrier phase (code delay) information of the reflected GNSS signal is used. The average precision over the Agulhas region is 7 cm (42 cm). The proposed GNSS-R measurements surpass the radar-based satellite altimetry missions in temporal and spatial resolution but are less precise. Using the estimated GNSS-R characteristics, measurements of SSH are generated by sampling a regional nested general circulation model of the South African oceans. The artificial observations are subsequently assimilated with a 4DVAR adjoint data assimilation method into the same ocean model but with a different initial state and forcing. The assimilated and the original, i.e., the sampled model state, are compared to systematically identify improvements and degradations in the model variables that arise due to the assimilation of GNSS-R based SSH observations. We show that SSH and the independent, i.e., not assimilated model variables velocity, temperature, and salinity improve by the assimilation of GNSS-R based SSH observations. After the assimilation of 90 days of SSH observations, improvements in the independent variables cover the whole water column. Locally, up to 39 % of the original model state are recovered. Shorter assimilation windows result in enhanced reproduction of the observed and assimilated SSH but are accompanied by an insufficient or wrong recovery of sub-surface water properties. The assimilation of real GNSS-R observations, when available, and consequently the estimation of Agulhas water mass properties and the leakage of heat and salt into the Atlantic will benefit from this model-based study.

Keywords

Agulhas current Data assimilation GNSS reflectometry 

References

  1. Albertella A, Savcenko R, Janji T, Rummel R, Bosch W, Schröter J (2012) High resolution dynamic ocean topography in the Southern Ocean from GOCE. Geophys J Int 190:922–930CrossRefGoogle Scholar
  2. Backeberg BC, Counillon F, Johannessen JA, Pujol M-I (2014) Assimilating along-track SLA data using the EnOI in an eddy resolving model of the Agulhas system. Ocean Dyn 64(8):1121–1136CrossRefGoogle Scholar
  3. Beal LM, De Ruijter WPM, Biastoch A, Zahn R (2011) On the role of the agulhas system in ocean circulation and climate. Nature 472(7344):429–436CrossRefGoogle Scholar
  4. Bosch W, Savcenko R, Dettmering D, Schwatke C (2012) A two-decade time series of Eddy-resolving Dynamic Ocean Topography (iDOT). In: Ouwehand L (ed) Proceedings of “20 Years of Progress in Radar Altimetry”. ESA/ESTECGoogle Scholar
  5. Cardellach E, Rius A (2008) A new technique to sense non-Gaussian features of the sea surface from L-band Bi-static GNSS reflections. Rem Sens Environ 112:2927–2937CrossRefGoogle Scholar
  6. Carreno-Luengo H, Park H, Camps A, Fabra F, Rius A. (2013) GNSS-R derived centimetric sea topography: an airborne experiment demonstration. IEEE Sel T Appl Earth Obs Remote Sens 6(3):1468–1478CrossRefGoogle Scholar
  7. Evensen G (1994) Inverse methods and data assimilation in nonlinear ocean models. Physica D 77(1–3):108–129CrossRefGoogle Scholar
  8. Ivchenko VO, Sidorenko D, Danilov S, Losch M, Schröter J (2011) Can sea surface height be used to estimate oceanic transport variability? Geophys Res Lett 38Google Scholar
  9. Koutsodendris A, Pross J, Zahn R (2014) Exceptional agulhas leakage prolonged interglacial warmth during mis 11c in europe. Paleoceanography:1–32Google Scholar
  10. Kuhlmann J, Dobslaw H, Petrick C, Thomas M (2013) Ocean bottom pressure signals around southern Africa from in situ measurements, satellite data, and modeling. J Geophys Res 118(10):4889–4898CrossRefGoogle Scholar
  11. Le Bars D, Durgadoo JV, Dijkstra HA, Biastoch A, De Ruijter WPM (2014) An observed 20-year time series of Agulhas leakage. Ocean Sci 10(4):601–609CrossRefGoogle Scholar
  12. Le Dimet FX, Talagrand O (1986) Variational algorithms for analysis and assimilation of meteorological observations—theoretical aspects. Tellus Ser A-Dyn Meteorol Oceanol 38(2):97–110CrossRefGoogle Scholar
  13. Lowe ST, Zuffada C, Chao P, Kroger Y, Young LE, LaBrecque JL (2002) 5-cm-precision aircraft ocean altimetry using GPS reflections. Geophys Res Lett 29(10):1375–1378CrossRefGoogle Scholar
  14. Martin-Neira M (1993) A passive reflectometry and interferometry system (PARIS): application to ocean altimetry. ESA Journal 17:331–355Google Scholar
  15. Moore AM, Arango HG, Broquet G, Powell BS, Weaver AT, Zavala-Garay J (2011a) The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation. Prog Oceanogr 91(1):34–49CrossRefGoogle Scholar
  16. Moore AM, Arango HG, Broquet G, Edwards C, Veneziani M, Powell B, Foley D, Doyle JD, Costa D, Robinson P (2011b) The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part II—performance and application to the California Current System. Prog Oceanogr 91(1):50–73CrossRefGoogle Scholar
  17. Moore AM, Arango HG, Broquet G, Edwards C, Veneziani M, Powell B, Foley D, Doyle JD, Costa D, Robinson P (2011c) The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part III—observation impact and observation sensitivity in the California Current System. Prog Oceanogr 91(1):74–94CrossRefGoogle Scholar
  18. Pilo GS, Mata MM, Azevedo JLL (2015) Eddy surface properties and propagation at southern hemisphere western boundary current systems. Ocean Sci Discuss 12(1):135–160CrossRefGoogle Scholar
  19. Pujol M-I, Dibarboure G, Le Traon P-Y, Klein P (2012) Using high-resolution altimetry to observe mesoscale signals. J Atmos Ocean Technol 29(9):1409–1416CrossRefGoogle Scholar
  20. Rius A, Cardellach E, Martin-Neira M (2010) Altimetric analysis of the sea surface GPS reflected signals. IEEE Trans Geosci Remote Sens 48:2119–2127CrossRefGoogle Scholar
  21. Ruehs S, Durgadoo JV, Behrens E, Biastoch A (2013) Advective timescales and pathways of agulhas leakage. Geophys Res Lett 40(15):3997–4000CrossRefGoogle Scholar
  22. Ruffini G, Soulat F, Caparrini M, Germain O, Martin-Neira M (2004) The eddy experiment: accurate GNSS-R ocean altimetry from low altitude aircraft. Geophys Res Lett 31(L1230):1–4Google Scholar
  23. Sebille VE, Beal LM, Biastoch A (2010) Sea surface slope as a proxy for Agulhas Current strength. Geophys Res Lett 37Google Scholar
  24. Semmling AM, Beckheinrich J, Wickert J, Beyerle G, Schön S, Fabra F, Pflug H, He K, Schwabe J, Scheinert M (2014) Sea surface topography retrieved from GNSS reflectometry phase data of the GEOHALO flight mission. Geophys Res Lett 41:954– 960CrossRefGoogle Scholar
  25. Semmling M (2012) Altimetric Monitoring of Disko Bay using Interferometric GNSS Observations on L1 and L2, vol. Scientific Technical Report STR12/04. Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences. doi: 10.2312/GFZ.b103-12049
  26. Shchepetkin AF, McWilliams JC (2003) A method for computing horizontal pressure-gradient force in an oceanic model with a nonaligned vertical coordinate. J Geophys Res-Oceans 108 (C3)Google Scholar
  27. Shchepetkin AF, McWilliams JC (2005) The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Model 9(4):347– 404CrossRefGoogle Scholar
  28. Thomas M, Sündermann J, Maier-Reimer E (2001) Consideration of ocean tides in an OGCM and impacts on subseasonal to decadal polar motion excitation. Geophys Res Lett 28(12):2457– 2460CrossRefGoogle Scholar
  29. Uppala S, Dee D, Kobayashi S, Berrisford P, Simmons A (2008) Toward a climate data assimilation system: Status update of ERA Interim, Technical report, ECMWF NewslGoogle Scholar
  30. Weijer W, van Sebille E (2014) Impact of agulhas leakage on the Atlantic overturning circulation in the ccsm4. J Climate 27(1):101–110CrossRefGoogle Scholar
  31. Wickert J, Michalak G, Schmidt T, Beyerle G, Cheng CZ, Healy SB, Heise CY, Huang S, Jakowski N, Köhler W, Mayer C, Offiler D, Ozawa E, Pavelyev AG, Rothacher M, Tapley B, Arras C (2009) GPS radio occultation: results from CHAMP, GRACE and FORMOSAT-3/COSMIC. Terr Atmos Ocean Sci 20:35– 50CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Jan Saynisch
    • 1
    Email author
  • Maximilian Semmling
    • 1
  • Jens Wickert
    • 1
  • Maik Thomas
    • 1
    • 2
  1. 1.Helmholtz Centre Potsdam GFZ German Research CentrePotsdamGermany
  2. 2.Freie Universität Berlin Institute of MeteorologyBerlinGermany

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