Ensemble quantification of short-term predictability of the ocean fine-scale dynamics

• Main Authors: Leroux, Stéphanie, Brankart, Jean-Michel, Albert, Aurélie, Molines, Jean-Marc, Brodeau, Laurent, Le Sommer, Julien, Penduff, Thierry, Brasseur, Pierre
• Other Authors: Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Observatoire des Sciences de l'Univers de Grenoble (Fédération OSUG)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP), Université Grenoble Alpes (UGA)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2021
• Subjects: [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography; North Atlantic
• Online Access: https://hal.science/hal-04549971; https://doi.org/10.5194/egusphere-egu21-12001

International audience In this contribution, we investigate the predictability properties of the ocean dynamics using an ensemble of medium range numerical forecasts. This question is particularly relevant for ocean dynamics at small scales (< 30 km), where sub-mesoscale dynamics is responsible for the fast evolution of ocean properties. Relatively little is known about the predictability properties of a high resolution model, and hence about the accuracy and resolution that is needed from the observation system used to generate the initial conditions.A kilometric-scale regional configuration of NEMO for the Western Mediterranean (MEDWEST60, at 1/60º horizontal resolution) has been developed, using boundary conditions from a larger North Atlantic configuration at same resolution (eNATL60). This deterministic model has then been transformed into a probabilistic model by introducing innovative stochastic parameterizations of model uncertainties resulting from unresolved processes. The purpose is here primarily to generate ensembles of model states to initialize predictability experiments. The stochastic parameterization is also applied to assess the possible impact of irreducible model uncertainties on the skill of the forecast. A set of three ensemble experiments (20 members and 2 months ) are performed, one with the deterministic model initiated with perturbed initial conditions, and two with the stochastic model, for two different amplitudes of model uncertainty. In all three experiments, the spread of the ensemble is shown to emerge from the small scales (10 km wavelength) and progressively upscales to the largest structures. After two months, the ensemble variance saturates over most of the spectrum (except in the largest scales), whereas the small scales (< 30 km) are fully decorrelated between the different members. These ensemble simulations are thus appropriate to provide a statistical description of the dependence between initial accuracy and forecast accuracy over the full range of ...