17 Feb 2022
17 Feb 2022
Status: this preprint is currently under review for the journal OS.

Ensemble quantification of short-term predictability of the ocean dynamics at kilometric-scale resolution: A Western Mediterranean test-case

Stephanie Leroux1,2, Jean-Michel Brankart1, Aurélie Albert1,2, Laurent Brodeau1,2, Jean-Marc Molines1, Quentin Jamet1, Julien Le Sommer1, Thierry Penduff1, and Pierre Brasseur1 Stephanie Leroux et al.
  • 1Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France
  • 2Ocean Next, Grenoble, France

Abstract. We investigate the predictability properties of the ocean dynamics using an ensemble of short-term numerical regional ocean forecasts forced by prescribed atmospheric conditions. In that purpose, we developed a kilometric-scale, regional model for the Western Mediterranean sea (MEDWEST60, at 1/60º horizontal resolution). A probabilistic approach is then followed, where a stochastic parameterization of model uncertainties is introduced in this model to initialize ensemble predictability experiments. 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 stochastic model perturbations. 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, and the small scales (< 100 km) have become fully decorrelated across the ensemble members. These ensemble simulations are thus appropriate to provide a statistical description of the dependence between initial accuracy and forecast accuracy for time-lags between 1 and 20 days.

The predictability properties are statistically assessed using a cross-validation algorithm (i.e. using alternatively each ensemble member as the reference truth and the remaining 19 members as the ensemble forecast) together with a given score to characterize the initial and forecast accuracy. From the joint distribution of initial and final scores, it is then possible to quantify the probability distribution of the forecast score given the initial score, or reciprocally to derive conditions on the initial accuracy to obtain a target forecast skill.The misfit between ensemble members is quantified in terms of overall accuracy (CRPS score), geographical position of the ocean structures (location score), and spatial spectral decorrelation of the Sea Surface Height 2-D fields (decorrelation score). With this approach, we estimate for example that, in the region and period of interest, the initial location accuracy required (necessary condition) with a perfect model (no model uncertainty) to obtain a location accuracy of the forecast of 10 km with a 95 % confidence is about 8 km for a 1-day forecast, 4 km for a 5-day forecast, 1.5 km for a 10-day forecast, and this requirement cannot be met with a 15-day or longer forecast.

Stephanie Leroux et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on os-2022-11', Anonymous Referee #1, 13 May 2022
    • AC1: 'Reply on RC1', Stéphanie Leroux, 29 Jul 2022
  • RC2: 'Too much focus on the diagnostics, too little on the perturbation method.', Anonymous Referee #2, 23 May 2022
    • AC2: 'Reply on RC2', Stéphanie Leroux, 29 Jul 2022

Stephanie Leroux et al.

Stephanie Leroux et al.


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Short summary
The goal of the study is to evaluate the predictability of the ocean circulation at kilometric scale, in order to anticipate the requirements of the future operational forecasting systems. For that purpose, ensemble experiments have been performed with a regional model for the Western Mediterranean (at 1/60º horizontal resolution). From these ensemble experiments, we show that it is possible to compute targeted predictability scores, which depend on initial and model uncertainties.