Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution

This paper presents the technical implementation of a new, probabilistic version of the NEMO ocean–sea-ice modelling system. Ensemble simulations with N members running simultaneously within a single executable, and interacting mutually if needed, are made possible through an enhanced message-passin...

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Bibliographic Details
Published in:Geoscientific Model Development 2017-03, Vol.10 (3), p.1091-1106
Main Authors: Bessières, Laurent, Leroux, Stéphanie, Brankart, Jean-Michel, Molines, Jean-Marc, Moine, Marie-Pierre, Bouttier, Pierre-Antoine, Penduff, Thierry, Terray, Laurent, Barnier, Bernard, Sérazin, Guillaume
Format: Article
Language:English
Subjects:
Atmosphere
Chaos
Climate models
Computer simulation
Data assimilation
Dimensions
Eddies (Fluid dynamics)
Mesoscale turbulence
Message passing
Modelling
Ocean circulation
Ocean models
Ocean, Atmosphere
Oceanic turbulence
Oceans
Partial differential equations
Probability distribution
Resolution
Sciences of the Universe
Sea ice
Simulation
Turbulence
Variability
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Summary:This paper presents the technical implementation of a new, probabilistic version of the NEMO ocean–sea-ice modelling system. Ensemble simulations with N members running simultaneously within a single executable, and interacting mutually if needed, are made possible through an enhanced message-passing interface (MPI) strategy including a double parallelization in the spatial and ensemble dimensions. An example application is then given to illustrate the implementation, performances, and potential use of this novel probabilistic modelling tool. A large ensemble of 50 global ocean–sea-ice hindcasts has been performed over the period 1960–2015 at eddy-permitting resolution (1∕4°) for the OCCIPUT (oceanic chaos – impacts, structure, predictability) project. This application aims to simultaneously simulate the intrinsic/chaotic and the atmospherically forced contributions to the ocean variability, from mesoscale turbulence to interannual-to-multidecadal timescales. Such an ensemble indeed provides a unique way to disentangle and study both contributions, as the forced variability may be estimated through the ensemble mean, and the intrinsic chaotic variability may be estimated through the ensemble spread.
ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
DOI:10.5194/gmd-10-1091-2017