Articles | Volume 25, issue 3
Nonlin. Processes Geophys., 25, 605–631, 2018

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 605–631, 2018

Research article 30 Aug 2018

Research article | 30 Aug 2018

Comparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere model

Jonathan Demaeyer and Stéphane Vannitsem

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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Revised manuscript accepted for NPG
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Cited articles

Abramov, R.: A simple stochastic parameterization for reduced models of multiscale dynamics, Fluids, 1,, 2015.
Arnold, H., Moroz, I., and Palmer, T.: Stochastic parametrizations and model uncertainty in the Lorenz'96 system, Philos. T. Roy. Soc. A, 371,, 2013.
Arnold, L.: Hasselmann's program revisited: The analysis of stochasticity in deterministic climate models, in: Stochastic climate models, 141–157, Springer, 2001.
Arnold, L., Imkeller, P., and Wu, Y.: Reduction of deterministic coupled atmosphere–ocean models to stochastic ocean models: a numerical case study of the Lorenz–Maas system, Lect. Notes Math., 18, 295–350, 2003.
Short summary
We investigate the modeling of the effects of the unresolved scales on the large scales of the coupled ocean–atmosphere model MAOOAM. Two different physically based stochastic methods are considered and compared, in various configurations of the model. Both methods show remarkable performances and are able to model fundamental changes in the model dynamics. Ways to improve the parameterizations' implementation are also proposed.