Advertisement

Ocean Dynamics

, Volume 68, Issue 9, pp 1071–1091 | Cite as

Using an eddy-tracking algorithm to understand the impact of assimilating altimetry data on the eddy characteristics of the Agulhas system

  • Marc de Vos
  • Björn Backeberg
  • François Counillon
Article

Abstract

A complex and highly dynamical ocean region, the Agulhas Current System plays an important role in the transfer of energy, nutrients and organic material from the Indian to the Atlantic Ocean. Its dynamics are not only important locally, but affect the global ocean-atmosphere system. In working towards improved ocean reanalysis and forecasting capabilities, it is important that numerical models simulate mesoscale variability accurately—especially given the scarcity of coherent observational platforms in the region. Data assimilation makes use of scarce observations, a dynamical model and their respective error statistics to estimate a new, improved model state that minimises the distance to the observations whilst preserving dynamical consistency. Qualitatively, it is unclear whether this minimisation directly translates to an improved representation of mesoscale dynamics. In this study, the impact of assimilating along-track sea-level anomaly (SLA) data into a regional Hybrid Coordinate Ocean Model (HYCOM) is investigated with regard to the simulation of mesoscale eddy characteristics. We use an eddy-tracking algorithm and compare the derived eddy characteristics of an assimilated (ASSIM) and an unassimilated (FREE) simulation experiment in HYCOM with gridded satellite altimetry-derived SLA data. Using an eddy tracking algorithm, we are able to quantitatively evaluate whether assimilation updates the model state estimate such that simulated mesoscale eddy characteristics are improved. Additionally, the analysis revealed limitations in the dynamical model and the data assimilation scheme, as well as artefacts introduced from the eddy tracking scheme. With some exceptions, ASSIM yields improvements over FREE in eddy density distribution and dynamics. Notably, it was found that FREE significantly underestimates the number of eddies south of Madagascar compared to gridded altimetry, with only slight improvements introduced through assimilation, highlighting the models’ limitation in sustaining mesoscale activity in this region. Interestingly, it was found that the threshold for the maximum eddy propagation velocity in the eddy detection scheme is often exceeded when data assimilation relocates an eddy, causing the algorithm to interpret the discontinuity as eddy genesis, which directly influences the eddy count, lifetime and propagation velocity, and indirectly influences other metrics such as non-linearity. Finally, the analysis allowed us to separate eddy kinetic energy into contributions from detected mesoscale eddies and meandering currents, revealing that the assimilation of SLA has a greater impact on mesoscale eddies than on meandering currents.

Keywords

HYCOM Mesoscale eddies Agulhas system Data assimilation Satellite altimetry 

Notes

Funding information

This work has been jointly supported by the Nansen-Tutu Centre for Marine Environmental Research, Cape Town, South Africa, the National Research Foundation of South Africa (Grant Number: 112105), the Nansen Environmental and Remote Sensing Center, Bergen, Norway and through South Africa-Norway Research Co-operation on Climate Change, the Environment and Clean Energy project “Seasonal to Decadal Changes Affecting Marine Productivity: An Interdisciplinary Investigation (SCAMPI)”. This work has also received a grant for computer time from the Norwegian Program for supercomputing (NOTUR project number nn2993k). The altimeter products used in this study were produced by Ssalto/Duacs and distributed by Aviso, with the support from CNES (http://www.aviso.oceanobs.com/duacs/).

References

  1. Antonov J, Locarnini R, Boyer T, Mishonov A, Garcia H (2006) World Ocean Atlas 2005, Volume 1: Salinity. p. 182 pp. NOAA Atlas NESDIS 61. 182. U.S. Government Printing Office, Washington D.C.Google Scholar
  2. Backeberg B, Johannessen J, Bertino L, Reason C (2008) The greater Agulhas current system: an integrated study of its mesoscale variability. J Oper Oceanogr 1(1):29–44CrossRefGoogle Scholar
  3. Backeberg BC, Bertino L, Johannessen JA (2009) Evaluating two numerical advection schemes in HYCOM for eddy-resolving modelling of the Agulhas Current. Ocean Sci 5:173–190CrossRefGoogle Scholar
  4. Backeberg B, Counillon F, Johannessen J, Pujol M (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
  5. Beal L, de Ruijter W, Biastoch A, Zahn R (2011) On the role of the Agulhas system in ocean circulation and climate. Nature 472(7344):429–436CrossRefGoogle Scholar
  6. Biastoch A, Lutjeharms J, Böning C, Scheinert M (2008) Mesoscale peturbations control inter-ocean exchange south of Africa. Geophys Res Lett 35(20)Google Scholar
  7. Bleck R (2002) An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates. Ocean Model 4(1):55–58CrossRefGoogle Scholar
  8. Braby L, Backeberg B, Ansorge I, Roberts M, Krug M, Reason C (2016) Observed eddy dissipation in the Agulhas Current. Geophys Res Lett 43(15):8143–8150CrossRefGoogle Scholar
  9. Brankart J, Ubelmann C, Testut C, Cosme E, Brasseur P, Verron J (2009) Efficient parameterization of the observation error covariance matrix for square root or ensemble Kalman filters: application to ocean altimetry. Mon Weather Rev 136(6):1908–1927CrossRefGoogle Scholar
  10. Chassignet EP, Hurlburt HE, Smedstad OM, Halliwell GR, Hogan PJ, Wallcraft AJ, Baraille R, Bleck R (2007) The HYCOM (hybrid coordinate ocean model) data assimilative system. J Mar Syst 65(1):60–83CrossRefGoogle Scholar
  11. Chelton D, Schlax M, Samelson R, de Szoeke R (2007) Global observations of large oceanic eddies. Geophys Res Lett 34(15)Google Scholar
  12. Chelton D, Schlax M, Samelson R (2011) Global observations of nonlinear mesoscale eddies. Prog Oceanogr 61(2):167–216CrossRefGoogle Scholar
  13. Counillon F, Bertino L (2009a) Ensemble optimal interpolation: multivariate properties in the Gulf of Mexico. Tellus A 61(2):296–308CrossRefGoogle Scholar
  14. Counillon F, Bertino L (2009b) High-resolution ensemble forecasting for the Gulf of Mexico eddies and fronts. Ocean Dyn 59(1):83–95CrossRefGoogle Scholar
  15. Danielson R, Johannessen J, Rio M, Quartly G, Collard F, Chapron B, Donlon C (in prep) Exploitation of error correlation in a large analysis and validation study: the GlobCurrent case. To be submitted to the Special Issue on Advances in surface current in remote Sensing of EnvironmentGoogle Scholar
  16. Dee D, Uppala S, Simmons A, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M, Balsamo G, Bauer P, Bechtold P, Beljaars A, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer A, Haimberger L, Healy S, Hersbach H, Hólm E, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally A, Monge-Sanz B, Morcrette J, Park B, Peubey C, de Rosnay P, Tavolato C, Thpaut J, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597CrossRefGoogle Scholar
  17. Ducet N, Le Traon P, Reverdin G (2000) Global high-resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and -2. J Geophys Res 105(C8):19477–19498CrossRefGoogle Scholar
  18. Escudier R, Renault L, Pascual A, Brasseur P, Chelton D, Beuvier J (2016) Eddy properties in the western Mediterranean Sea from satellite altimetry and a numerical simulation. J Geophys Res Oceans 27(3):564–579Google Scholar
  19. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367CrossRefGoogle Scholar
  20. Faghmous JH, Frenger I, Yao Y, Warmka R, Lindell A, Kumar V (2015) A daily global mesoscale ocean eddy dataset from satellite altimetry. Scientific Data 2:150028CrossRefGoogle Scholar
  21. Frenger I, Gruber N, Knutti R, Münnich M (2013) Imprint of Southern Ocean eddies on winds, clouds and rainfall. Nat Geosci 6(8):608–612CrossRefGoogle Scholar
  22. Frenger I, Münnich M, Gruber N, Knutti R (2015) Southern Ocean eddy phenomenology. J Geophys Res Oceans 120(11):7413–7449CrossRefGoogle Scholar
  23. Gaspari G, Cohn S (1999) Construction of correlation functions in two and three dimensions. Q J R Meteorol Soc 125(554):723–757CrossRefGoogle Scholar
  24. George M, Bertino L, Johannessen O, Samuelsen A (2010) Validation of a hybrid coordinate ocean model for the Indian Ocean. J Oper Oceanogr 3(2):25–38CrossRefGoogle Scholar
  25. Griffies SM, Bryan FO, Chassignet EP, Gerdes R, Hasumi H, Webb D (2000) Developments in ocean climate modelling. Ocean Model 2(3):123–192CrossRefGoogle Scholar
  26. Halo I, Backeberg B, Penven P, Ansorge I, Reason C, Ullgren J (2014a) Eddy properties in the Mozambique Channel: a comparison between observations and two numerical circulation models. Deep Sea Res Part 2 Top Stud Oceanogr 100:38–53CrossRefGoogle Scholar
  27. Halo I, Penven P, Backeberg B, Ansorge I, Shillinton F, Roman R (2014b) Mesoscale eddy variability in the southern extension of the East Madagascar Current: seasonal cycle, energy conversion terms, and eddy mean properties. J Geophys Res 119(10):7324–7356CrossRefGoogle Scholar
  28. Henson S, Thomas A (2008) A census of oceanic anticyclonic eddies in the Gulf of Alaska. Deep-Sea Res I Oceanogr Res Pap 55(2):163–176CrossRefGoogle Scholar
  29. Isern-Fontanet J, García-Ladona E, Font J (2006) Vortices of the Mediterranean Sea: an altimetric perspective. J Phys Oceanogr 36(1):87–103CrossRefGoogle Scholar
  30. Lathuiliere C, Levy M, Echevin V (2010) Impact of eddy-driven vertical fluxes on phytoplankton abundance in the euphotic layer. J Plankton Res 33(5):827–831CrossRefGoogle Scholar
  31. Locarnini R, Mishonov A, Antonov JI, Boyer TP, Garcia HE (2006) World Ocean Atlas 2005, Volume 1: Temperature. U.S. Government Printing Office, Washington D.C., p 182Google Scholar
  32. Loveday B, Penven P, Reason C (2015) Southern annular mode and westerly-wind driven changes in Indian-Atlantic exchange mechanisms. Geophys Res Lett 42(12):4912–4921CrossRefGoogle Scholar
  33. Lutjeharms J, Cooper J (1996) Interbasin leakage through Agulhas Current filaments. Deep-Sea Res I Oceanogr Res Pap 43(2):213217–215238Google Scholar
  34. Lutjeharms J, Van Ballegooyen R (1988) The retroflection of the Agulhas Current. J Phys Oceanogr 18(11):1570–1583CrossRefGoogle Scholar
  35. Mason E, Pascual A, McWilliams J (2014) A new sea surface height–based code for oceanic mesoscale eddy tracking. J Atmos Ocean Technol 31(5):1181–1188CrossRefGoogle Scholar
  36. Miyoshi T, Kondo K (2013) A multi-scale localization approach to an ensemble Kalan filter. SOLA 9:170–173CrossRefGoogle Scholar
  37. Nencioli F, Dong C, Dickey T, Washburn L, McWilliams J (2010) A vector geometry–based eddy detection algorithm and its application to a high-resolution numerical model product and high-frequency radar surface velocities in the Southern California Bight. J Atmos Ocean Technol 27(3):564–579CrossRefGoogle Scholar
  38. Oke P, Allen J, Miller R, Egbert G, Kosro P (2002) Assimilation of surface velocity data into a primitive equation coastal ocean model. J Geophys Res 107(C9):1–5CrossRefGoogle Scholar
  39. Oke P, Schiller A, Griffin D, Brassington G (2005) Ensemble data assimilation for an eddy-resolving ocean model of the Australian region. Q J R Meteorol Soc 131(613):3301–3311CrossRefGoogle Scholar
  40. Oke P, Sakov P, Corney S (2007) Impacts of localisation in the EnKF and EnOI: experiments with a small model. Ocean Dyn 57(1):32–45CrossRefGoogle Scholar
  41. Oke P, Brassington G, Griffin D, Schiller A (2010) Ocean data assimilation: a case for ensemble optimal interpolation. Aust Meteorol Oceanogr J 59(Sp. Iss):67–76CrossRefGoogle Scholar
  42. Okubo A (1970) Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences. Deep-Sea Res Oceanogr Abstr 17(3):445–454CrossRefGoogle Scholar
  43. Olson BD, Evans RH (1986) Rings of the Agulhas Current. Deep Sea Res Part 1 Oceanogr Res Pap 33(1):27–42Google Scholar
  44. Omta A, Llido J, Garcon V, Kooijman S, Dijkstra H (2009) The interpretation of satellite chlorophyll observations: the case of the Mozambique Channel. Deep-Sea Res I Oceanogr Res Pap 56(6):974–988CrossRefGoogle Scholar
  45. Pasquero C, Provenzale A, Babiano A (2001) Parameterization of dispersion in two-dimensional turbulence. J Fluid Mech 439:279–303CrossRefGoogle Scholar
  46. Penven P (2005) Average circulation, seasonal cycle, and mesoscale dynamics of the Peru Current System: a modeling approach. J Geophys Res 110(C10)Google Scholar
  47. Pilo G, Mata M, Azevedo J (2015) Eddy surface properties and propagation at Southern Hemisphere western boundary current systems. Ocean Sci Discuss 12(1):135–160Google Scholar
  48. Pujol MI, Faugère Y, Taburet G, Dupuy S, Pelloquin C, Ablain M, Picot N (2016) DUACS DT2014: the new multi-mission altimeter data set reprocessed over 20 years. Ocean Sci 12(5):1067–1090CrossRefGoogle Scholar
  49. Rainwater S, Bishop CH, Campbell WF (2015) The benefits of correlated observation errors for small scales. Q J R Meteorol Soc 141(693):3439–3445CrossRefGoogle Scholar
  50. Renault L, Molemaker MJ, McWilliams JC, Shchepetkin AF, Lemarié F, Chelton D, Illig S, Hall A (2016) Modulation of wind work by oceanic current interaction with the atmosphere. J Phys Oceanogr 66(6):1685–1704CrossRefGoogle Scholar
  51. Ridderinkhof W, Le Bars D, Heydt A, de Ruijter W (2013) Dipoles of the south East Madagascar Current. Geophys Res Lett 40(3):558–562CrossRefGoogle Scholar
  52. Rouault M, Penven P (2011) New perspectives on Natal Pulses from satellite observations. J Geophys Res Oceans 116(C7)Google Scholar
  53. Sakov P, Bertino L (2011) Relation between two common localisation methods for the EnKF. Comput Geosci 15(2):225–237CrossRefGoogle Scholar
  54. Schouten M, de Ruijter W, van Leeuwen P (2002) Upstream control of Agulhas ring shedding. J Geophys Res 107(C8)Google Scholar
  55. Souza J, De Boyer Montegut C, Le Traon P (2011) Comparison between three implementations of automatic identification algorithms for the quantification and characterization of mesoscale eddies in the South Atlantic Ocean. Ocean Sci 7(3):317–334CrossRefGoogle Scholar
  56. Srinivasan A, Chassignet EP, Bertino L, Brankart JM, Brasseur P, Chin TM, Counillon F, Cummings JA, Mariano AJ, Smedstad OM, Thacker WC (2011) A comparison of sequential assimilation schemes for ocean prediction with the Hybrid Coordinate Ocean Model (HYCOM): twin experiments with static forecast error covariances. Ocean Model 37(3):85–111CrossRefGoogle Scholar
  57. Swart NC, Lutjeharms JRE, Ridderinkhof H, De Ruijter WPM (2010) Observed characteristics of Mozambique Channel eddies. J Geophys Res: Oceans 115:C09006/1–C09006/14Google Scholar
  58. Weiss J (1991) The dynamics of enstrophy transfer in two-dimensional hydrodynamics. Physica D 48(2):273–294CrossRefGoogle Scholar
  59. Xie J, Counillon F, Zhu J, Bertino L (2011) An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI. Ocean Sci 7(5):609–627CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Marine Research Unit, South African Weather ServiceCape TownSouth Africa
  2. 2.Nansen-Tutu Centre for Marine Environmental Research, Oceanography DepartmentUniversity of Cape TownCape TownSouth Africa
  3. 3.Coastal Systems Research Group, Council for Scientific and Industrial ResearchStellenboschSouth Africa
  4. 4.Nansen Environmental and Remote Sensing CenterBergenNorway
  5. 5.Bjerknes Centre for Climate ResearchBergenNorway

Personalised recommendations