Ocean Dynamics

, Volume 62, Issue 7, pp 1091–1109 | Cite as

Estimates of surface drifter trajectories in the equatorial Atlantic: a multi-model ensemble approach

  • Robert Bruce Scott
  • Nicolas Ferry
  • Marie Drévillon
  • Charlie N. Barron
  • Nicolas C. Jourdain
  • Jean-Michel Lellouche
  • Edward Joseph Metzger
  • Marie-Hélène Rio
  • Ole Martin Smedstad
Article
Part of the following topical collections:
  1. Topical Collection on Advances in Search and Rescue at Sea

Abstract

We compared the estimates of surface drifter trajectories from 1 to 7 days in the equatorial Atlantic over an 18-month period with five eddying ocean general circulation model (OGCM) reanalyses and one observational product. The cumulative distribution of trajectory error was estimated using over 7,000 days of drifter trajectories. The observational product had smaller errors than any of the individual OGCM reanalyses. Three strategies for improving trajectory estimates using the ensemble of five operational ocean analysis and forecasting products were explored: two methods using a multi-model ensemble estimate and also spatial low-pass filtering. The results were insensitive to the method used to create the ensemble estimates, and by most measures, the results were better than the observational product. Comparison of relative skill of the various OGCM reanalyses suggested promising avenues for exploration for further improvements: forcing with higher frequency wind stress and quality control of input data. One of the lowest horizontal resolution OGCMs, with 1/4° longitude horizontal resolution, made the best trajectory estimates. The individual OGCMs were dominated by errors at spatial scales smaller than about 100 to 200 km, i.e., less than the local deformation radius. But buried in those errors were valuable signals that could be retrieved by combining all the OGCM velocity fields to produce a multi-model ensemble-based estimate. This estimate had skill down to spatial scales about 75 km. Results from this study are consistent with previous work showing that ensemble-mean forecast skill is superior to individual forecasts.

Keywords

Ocean prediction Surface drifters Data assimilation Eddying OGCM Model intercomparison Model–data comparison Multi-model ensemble prediction 

Notes

Acknowledgements

Eric Greiner suggested to combine model ensemble velocity fields (personal communication, 2010). The null hypothesis that the model ensemble velocity fields have more skill only because they were smoother came from Carl Wunsch (personal communication, 2010). The presentation of the manuscript benefitted substantially from two anonymous reviews. RBS thanks the National Oceanography Centre, Southampton (NOCS) for hosting an extended visit. RBS was supported by National Science Foundation grants OCE-0526412 and OCE-0851457 and NASA subcontract through Boston University and NOCS. This is UTIG contribution #2481.

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Robert Bruce Scott
    • 1
    • 2
  • Nicolas Ferry
    • 4
  • Marie Drévillon
    • 4
  • Charlie N. Barron
    • 3
  • Nicolas C. Jourdain
    • 5
  • Jean-Michel Lellouche
    • 4
  • Edward Joseph Metzger
    • 3
  • Marie-Hélène Rio
    • 6
  • Ole Martin Smedstad
    • 7
  1. 1.Institute for Geophysics, Jackson School of GeosciencesThe University of Texas at AustinAustinUSA
  2. 2.Laboratoire de Physique des Océans UMR6523 (CNRS, UBO, IFREMER, IRD)BrestFrance
  3. 3.Naval Research Laboratory, Stennis Space CenterHancock CountyUSA
  4. 4.Mercator-OcéanToulouseFrance
  5. 5.Laboratoire des écoulements géophysiques et industrielsGrenobleFrance
  6. 6.Collecte Localisation SatellitesRamonville St-AgneFrance
  7. 7.QinetiQ North America, Stennis Space CenterHancock CountyUSA

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