Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-157-2023
https://doi.org/10.5194/gmd-16-157-2023
Development and technical paper
 | 
04 Jan 2023
Development and technical paper |  | 04 Jan 2023

How does 4DVar data assimilation affect the vertical representation of mesoscale eddies? A case study with observing system simulation experiments (OSSEs) using ROMS v3.9

David E. Gwyther, Shane R. Keating, Colette Kerry, and Moninya Roughan

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Cited articles

Abernathey, R. and Haller, G.: Transport by Lagrangian Vortices in the Eastern Pacific, J. Phys. Oceanogr., 48, 667–685, https://doi.org/10.1175/jpo-d-17-0102.1, 2018. a
Ballabrera-Poy, J., Hackert, E., Murtugudde, R., and Busalacchi, A. J.: An Observing System Simulation Experiment for an Optimal Moored Instrument Array in the Tropical Indian Ocean, J. Climate, 20, 3284–3299, https://doi.org/10.1175/jcli4149.1, 2007. a
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. Roy. Meteor. Soc., 134, 1951–1970, https://doi.org/10.1002/qj.339, 2008a. a, b
Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics, Q. J. Roy. Meteor. Soc., 134, 1971–1996, https://doi.org/10.1002/qj.340, 2008b. a
Bonavita, M., Raynaud, L., and Isaksen, L.: Estimating background-error variances with the ECMWF Ensemble of Data Assimilations system: some effects of ensemble size and day-to-day variability, Q. J. Roy. Meteor. Soc., 137, 423–434, https://doi.org/10.1002/qj.756, 2011. a, b
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Short summary
Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.