Tropical Pacific Ocean model error covariances from Monte Carlo simulations

As a first step towards the development of an Ensemble Kalman Filter (EnKF) for ocean data assimilation in the tropical oceans, this article investigates a novel technique for explicitly perturbing the model error in Monte Carlo simulations. The perturbation technique involves perturbing the surface zonal stress. Estimates of the characteristics of the wind stress errors were obtained from the difference between zonal wind fields from the NCEP and ECMWF re-analyses. In order to create random zonal wind stress perturbations, an EOF analysis was performed on the intraseasonally time-filtered difference between the two re-analysis products. The first 50 EOFs were retained and random wind stress fields for each ensemble member were created by combining random amounts of each EOF. Ensemble runs were performed using a shallow-water model, with both short forecasts and long simulations. Results show covariance patterns characteristic of Kelvin wave and Rossby wave dynamics. There are interesting differences between covariances using short forecasts and those using long simulations. The use of the long simulations produced non-local covariances (e.g. negative covariances between east and west Pacific), whereas short forecasts produced covariances that were localized by the time it takes Kevin and Rossby waves to travel over the forecast period and the scales of spatial covariance in the wind stress errors. The ensembles of short forecasts produced covariances and cross-covariances that can be explained by the dynamics of equatorial Rossby and Kevin waves forced by wind stress errors. The results suggest that the ensemble generation technique to explicitly represent the model error term can be used in an EnKF.