In Corazza et al, 2002, we showed that the use of the standard 3D-Var error covariance matrix augmented with global bred vectors reduced by about 20% the analysis errors. However, when we added random errors (as if they were observation errors) to the bred vector at the beginning of each integration, the analysis errors were much smaller, more than 40% smaller than the regular 3D-Var. We conjecture that this is because, in the standard breeding approach (Toth and Kalnay, 1993, 1997), the bred vectors tend to collapse into a subspace which is too small.