Abstract's details

Learning from large-ensemble ocean simulations to better interpret satellite and in-situ ocean data. - The Occiput large-ensemble dataset and some applications -

Stéphanie Leroux (Ocean next, France)


Thierry Penduff (IGE, Grenoble, France); jean-Marc Molines (IGE, Grenoble, France); Laurent Brodeau (Ocean Next, Grenoble, France); Jacques Verron (Ocean Next, Grenoble, France); Julien Le Sommer (IGE, Grenoble, France)

Event: 2017 Ocean Surface Topography Science Team Meeting

Session: Science II: Large Scale Ocean Circulation Variability and Change

Presentation type: Type Poster

Contribution: not provided


Over the last decades, altimeter and other satellite and in-situ ocean observations have provided crucial information to increase our knowledge of the global oceanic state, its variability, and long-term changes.
Comparing observations to ocean numerical simulations is a routinely-used approach to either validate models, calibrate new observation systems, or inverstigate physical processes and mechanisms. But such comparison requires some knowledge of the different types of uncertainties attached to the compared datasets.
Given the chaotic, non-linear nature of the ocean system, ocean models in the turbulent regime are highly sensitive to initial conditions and spontaneously generate a chaotic intrinsic variability that has recently been shown to be significant even on low-frequency (interannual and longer periods) and on basin scale (e.g. Penduff et al, 2011, Serazin et al, 2015, Leroux et al 2017).
Performing ensemble simulations is a way to take into account this intrinsic uncertainty, inherent to the ocean circulation, by sampling a range of possible trajectories of equal likelihood.
In other words, it means that the most accurate collection of satellite/in-situ observations can only fit a model simulation up to a certain point, as the observations describe the one time-evolution that the ocean state has followed in reality, randomly picked among an ensemble of possible evolutions seen as equally-likely by an ocean model.

At Ocean Next, in partnership with the MEOM group within several projects (e.g. ANR OCCIPUT and PIRATE-OSTST), we develop such probabilistic approaches, based on large-ensemble eddy-permitting ocean simulations. Our goal is to better quantify and characterize the model uncertainty related to the intrinsic variabity of the ocean, and to provide useful information to better interpret satellite and in-situ ocean data. It includes a quantification of the chaotic variability and a better characterization of the locations, depth, temporal and spatial scales that are the most affected by a chaotic behaviour in models, and wich are thus affected by the largest uncertainty in any comparison with satellite or in-situ observations.

This poster will present the ensemble version of the ocean model NEMO adapted in the group to perform large ensemble simulations in one single executable for N ensemble members run in parallel, hence allowing for communication between the members during the integration. The OCCIPUT global 1/4º large-ensemble (N=50) simulation will be introduced, along with the associated sythetic observation dataset which was produced online. This synthetic observation dataset provides 50 synthetic versions of along-track Jason-2 altimeter data and ENACT–ENSEMBLES temperature and salinity profile data, generated online using the NEMO observation operator, used within each of the ensemble member. We will then present some examples of results and discuss applications to valorize from this novel dataset.

Poster show times:

Room Start Date End Date
Concerto Ballroom Thu, Oct 26 2017,14:00 Thu, Oct 26 2017,18:00
Stéphanie Leroux
Ocean next