Articles | Volume 8, issue 10
https://doi.org/10.5194/gmd-8-3119-2015
https://doi.org/10.5194/gmd-8-3119-2015
Development and technical paper
 | 
06 Oct 2015
Development and technical paper |  | 06 Oct 2015

Increasing vertical mixing to reduce Southern Ocean deep convection in NEMO3.4

C. Heuzé, J. K. Ridley, D. Calvert, D. P. Stevens, and K. J. Heywood

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Cited articles

Adcroft, A.: Representation of topography by porous barriers and objective interpolation of topographic data, Ocean Model., 67, 13–27, https://doi.org/10.1016/j.ocemod.2013.03.002, 2013.
Axell, L. B.: Wind-driven internal waves and Langmuir circulations in a numerical ocean model of the southern Baltic Sea, J. Geophys. Res., 107, 3204, https://doi.org/10.1029/2001JC000922, 2002.
Azaneu, M., Kerr, R., and Mata, M. M.: Assessment of the representation of Antarctic Bottom Water properties in the ECCO2 reanalysis, Ocean Sci., 10, 923–946, https://doi.org/10.5194/os-10-923-2014, 2014.
Bates, M. L., Griffies, S. M., and England, M. H.: A dynamic, embedded Lagrangian model for ocean climate models. Part I: Theory and implementation, Ocean Model., 59, 51–59, https://doi.org/10.5194/os-10-923-2014, 2012.
Briegleb, B. P., Danabasoglu, G., and Large, W.: An overflow parameterization for the ocean component of the community climate system model, Tech. rep, National Center for Atmospheric Research, Boulder, Colorado, 2010.
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Short summary
Most ocean models, including NEMO, have unrealistic Southern Ocean deep convection. That is, through extensive areas of the Southern Ocean, they exhibit convection from the surface of the ocean to the sea floor. We find this convection to be an issue as it impacts the whole ocean circulation, notably strengthening the Antarctic Circumpolar Current. Using sensitivity experiments, we show that counter-intuitively the vertical mixing needs to be enhanced to reduce this spurious convection.
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