Climate Dynamics

, Volume 27, Issue 7–8, pp 851–879 | Cite as

Transient climate change scenario simulation of the Mediterranean Sea for the twenty-first century using a high-resolution ocean circulation model

  • S. SomotEmail author
  • F. Sevault
  • M. Déqué


A scenario of the Mediterranean Sea is performed for the twenty-first century based on an ocean modelling approach. A climate change IPCC-A2 scenario run with an atmosphere regional climate model is used to force a Mediterranean Sea high-resolution ocean model over the 1960–2099 period. For comparison, a control simulation as long as the scenario has also been carried out under present climate fluxes. This control run shows air–sea fluxes in agreement with observations, stable temperature and salinity characteristics and a realistic thermohaline circulation simulating the different intermediate and deep water masses described in the literature. During the scenario, warming and saltening are simulated for the surface (+3.1°C and + 0.48 psu for the Mediterranean Sea at the end of the twenty-first century) and for the deeper layers (+1.5°C and + 0.23 psu on average). These simulated trends are in agreement with observed trends for the Mediterranean Sea over the last decades. In addition, the Mediterranean thermohaline circulation (MTHC) is strongly weakened at the end of the twenty-first century. This behaviour is mainly due to the decrease in surface density and so the decrease in winter deep-water formation. At the end of the twenty-first century, the MTHC weakening can be evaluated as −40% for the intermediate waters and −80% for the deep circulation with respect to present-climate conditions. The characteristics of the Mediterranean Outflow Waters flowing into the Atlantic Ocean are also strongly influenced during the scenario.


Mixed Layer Depth Buoyancy Flux Deep Water Formation Mediterranean Outflow Waters Levantine Basin 
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.



This work was supported by the European Union Program Energy, Environment and Sustainable Development under contract EVK2–2001–00156 (PRUDENCE) and by the GICC-MedWater program of the Ministère de l’Ecologie et du Développement Durable (French Environment Ministry). We also thank Dr. Michel Crépon, Dr Bernard Barnier and Dr Gurvan Madec for helpful comments and suggestions.


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© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Météo-FranceCentre National de Recherches MétéorologiquesToulouse cedex 1France

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