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


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.


HYCOM Mesoscale eddies Agulhas system Data assimilation Satellite altimetry 


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 (


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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

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