Ocean Science www.ocean-sci.net 1812-0784 1812-0792 5 4 2009 10.5194/os-5-575-2009 http://www.ocean-sci.net/5/575/2009/ http://www.ocean-sci.net/5/575/2009/os-5-575-2009.html http://www.ocean-sci.net/5/575/2009/os-5-575-2009.pdf 575 589 2009-11-16 Observed and simulated estimates of the meridional overturning circulation at 26.5° N in the Atlantic J. Baehr johanna.baehr@zmaw.de S. Cunnningham H. Haak P. Heimbach T. Kanzow J. Marotzke Massachusetts Institute of Technology, Cambridge, MA, USA National Oceanography Centre, Southampton, UK Max 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 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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