A statistical approach to ocean model testing and tuning

  • Claude Frankignoul
Part of the Progress in Probability book series (PRPR, volume 39)


For many purposes such as the forecasting of oceanic conditions or the prediction of climate changes, it is important to have realistic models of the ocean, that is models which are able to reproduce sufficiently well the main features of interest, and their time variability. This is particularly critical in the design of coupled ocean-atmosphere models where model errors are generally exacerbated by the coupling, so that artificial “flux-corrections” (Sausen et al, 1988) are often used to prevent a drift toward unreasonable climate conditions, even though they may distort the dynamics. Although more complex models should represent reality more correctly than simpler ones, they do not necessarily do so, as illustrated by the El Niño-Southern Oscillation (ENSO) phenomenon where simple ocean-atmosphere models have so far provided forecasts as good as general circulation models (GCMs), in part because of the drift of the latter (Latif et al, 1993). Provided models are consistent with known physics within the tolerance allowed by the approximations made, model adequacy should thus be judged by the ability at reproducing the relevant observations.


Wind Stress Ocean Model Observational Error Thermocline Depth Error Covariance Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Birkhäuser Boston 1996

Authors and Affiliations

  • Claude Frankignoul
    • 1
  1. 1.Laboratoire d’Océanographie Dynamique et de Climatologie Unité mixte de recherche CNRS-ORSTOM-UPMCUniversité Pierre et Marie CurieParisFrance

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