Impact of ensemble‐based hybrid background‐error covariances in ECMWF's next‐generation ocean reanalysis system

Abstract An ensemble of data assimilations (EDA) can provide valuable information on analysis and short‐range forecast uncertainties. The present European Centre for Medium‐Range Weather Forecasts (ECMWF) operational ocean analysis and reanalysis system, called the Ocean Reanalysis System 5 (ORAS5), produces an ensemble but does not exploit it for the specification of the background‐error covariance matrix , a key component of the data assimilation system. In this article, we describe EDA developments for the ocean, which take advantage of the short‐range forecast ensemble for specifying, in two distinct ways, parameters of a covariance model representation of . First, we generate a climatological ensemble over an extended period to produce seasonally varying climatological estimates of background‐error variances and horizontal correlation length‐scales. Second, in each assimilation cycle, we diagnose flow‐dependent variances from the ensemble and blend them with the climatological estimates to form hybrid variances. We also use the ensemble to diagnose flow‐dependent vertical correlation length‐scales. We demonstrate for the Argo‐rich period that this new, ensemble‐based hybrid formulation of results in a significant reduction of background errors compared with the parameterized formulation of used in ORAS5, with the globally averaged reduction ranging between 5% and 15% for subsurface temperature and salinity and up to and for sea‐surface temperature and sea‐surface height, respectively. The new ocean EDA system will be employed in ORAS6, ECMWF's next‐generation ocean reanalysis system.