  An Ensemble of Data Assimilations (EDA) can provide valuable information on
the analysis and short-range forecast uncertainties. The present ECMWF
operational ocean analysis and reanalysis system, called ORAS5, produces an
ensemble but does not exploit it for the specification of the background-error
covariance matrix $\mathbf{B}$, 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 $\mathbf{B}$. 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, on 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, hybrid formulation of $\mathbf{B}$ results
in a significant reduction of background errors compared to the parameterized
formulation of $\mathbf{B}$ used in ORAS5. The new ocean EDA system will be
employed in ORAS6, ECMWF's next generation ocean reanalysis system.
