Ensembles of Eddying Ocean Simulations for Climate : Modeling Techniques, Diagnostics, First Results.

Laurent Bessières1, Thierry Penduff2, Stephanie Leroux3, Jean-Marc Molines2, Jean-Michel Brankart2, Bernard Barnier3 and Laurent Terray4, (1)CERFACS-CNRS, Toulouse, France, (2)LGGE - Laboratoire de Glaciologie et Géophysique de l'Environnement, CNRS - Université Grenoble Alpes, Grenoble, France, (3)LGGE Laboratoire de Glaciologie et Géophysique de l’Environnement, Saint Martin d'Hères, France, (4)CERFACS European Centre for Research and Advanced Training in Scientific Computation, Toulouse Cedex 01, France
Abstract:
Unlike laminar Ocean General Circulation Models (OGCMs), eddying OGCMs spontaneously generate a strong chaotic variability, not only at mesoscale but also up to basin- and multidecadal-scales. This component of the variability is largely sensitive to initial states and locally accounts for most of the low-frequency (interannual and slower) variance found in fully-forced oceanic hindcasts. Climate-oriented (multi-decadal) high-resolution ocean simulations therefore require ensemble approaches to disentangle the atmospherically-forced (ensemble mean) and the chaotic (ensemble spread) ocean variability components, and to quantify the uncertainty due to non-linear ocean dynamics.
The OCCIPUT project is currently performing the first 50-member ensemble of 1/4° global ocean/sea-ice hindcasts driven by the same realistic forcing over the last 57 years. The ensemble spread, produced through a stochastic parameterization during the first year, subsequently grows and cascades toward long space and time scales. The NEMO model has been adapted to provide the following features : (1) simultaneous integration of the 50 members as one executable over several thousands of processors, so that ensemble (inter-member) statistics can be computed online as well ; (2) online production of ensemble synthetic observations allowing the use of probabilistic metrics for model assessment ; (3) flexible output archiving strategy through the use of 50 parallelized I/O servers.

We will present our modeling and processing approaches, some probabilistic products derived from this global ensemble run and from its regional (20-year 10-member North Atlantic) version, hence providing insights into the actual imprint of the atmosphere on the ocean variability.