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Volume 5, issue 4 | Copyright
Ocean Sci., 5, 575-589, 2009
https://doi.org/10.5194/os-5-575-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 Nov 2009

16 Nov 2009

Observed and simulated estimates of the meridional overturning circulation at 26.5° N in the Atlantic

J. Baehr1,*, S. Cunnningham2, H. Haak3, P. Heimbach1, T. Kanzow**,2, and J. Marotzke3 J. Baehr et al.
  • 1Massachusetts Institute of Technology, Cambridge, MA, USA
  • 2National Oceanography Centre, Southampton, UK
  • 3Max Planck Institute for Meteorology, Hamburg, Germany
  • *now at: Institute of Oceanography, KlimaCampus, University of Hamburg, Grindelberg 5, 20144 Hamburg, Germany
  • **now at: IFM-GEOMAR, Kiel, Germany

Abstract. Daily timeseries of the meridional overturning circulation (MOC) estimated from the UK/US RAPID/MOCHA array at 26.5° N in the Atlantic are used to evaluate the MOC as simulated in two global circulation models: (I) an 8-member ensemble of the coupled climate model ECHAM5/MPI-OM, and (II) the ECCO-GODAE state estimate. In ECHAM5/MPI-OM, we find that the observed and simulated MOC have a similar variability and time-mean within the 99% confidence interval. In ECCO-GODAE, we find that the observed and simulated MOC show a significant correlation within the 99% confidence interval. To investigate the contribution of the different transport components, the MOC is decomposed into Florida Current, Ekman and mid-ocean transports. In both models, the mid-ocean transport is closely approximated by the residual of the MOC minus Florida Current and Ekman transports. As the models conserve volume by definition, future comparisons of the RAPID/MOCHA mid-ocean transport should be done against the residual transport in the models. The similarity in the variance and the correlation between the RAPID/MOCHA, and respectively ECHAM5/MPI-OM and ECCO-GODAE MOC estimates at 26.5° N is encouraging in the context of estimating (natural) variability in climate simulations and its use in climate change signal-to-noise detection analyses. Enhanced confidence in simulated hydrographic and transport variability will require longer observational time series.

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