Insights from very-large-ensemble data assimilation experiments with a high-resolution general circulation model of the Red Sea

Ensemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from an ensemble of samples of ocean states, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long-range correlations resulting from the limited-size ensembles imposed by computational burden constraints. Such ad-hoc methods were found to be not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of one-year-long ensemble experiments with a fully realistic EnKF-DA system in the Red Sea using tens -to thousands of ensemble members. The system assimilates satellite and in-situ observations and accounts for model uncertainties by integrating a 4-km-resolution ocean model with European Center for Medium Range Weather Forecast (ECMWF) atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. OceanOur results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensembles leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by: (i) amplified spurious long-range correlations produced by the low-rank nature of the ECMWF atmospheric forcing ensemble; and (ii) non-Gaussianity generated by the perturbed internal physical parameterization schemes. Large-ensemble forcing fields and non-Gaussian DA methods might be needed to get full benefits from large ensembles in ocean DA.