  Predictability of flow is examined in a barotropic vorticity model that
admits low frequency regime transitions between zonal and dipolar states. Such
transitions in the model were first studied by Bouchet and Simonnet (2009) and
are reminiscent of regime change phenomena in the weather and climate systems
wherein extreme and abrupt qualitative changes occur, seemingly randomly, after
long periods of apparent stability. Mechanisms underlying regime transitions in
the model are not well understood yet. From the point of view of atmospheric
and oceanic dynamics, a novel aspect of the model is the lack of any source of
background gradient of potential-vorticity such as topography or planetary
gradient of rotation rate (e.g., as in Charney & DeVore '79).
  We consider perturbations that are embedded onto the system's chaotic
attractor under the full nonlinear dynamics as bred vectors---nonlinear
generalizations of the leading (backward) Lyapunov vector. We find that
ensemble predictions that use bred vector perturbations are more robust in
terms of error-spread relationship than those that use Lyapunov vector
perturbations. In particular, when bred vector perturbations are used in
conjunction with a simple data assimilation scheme (nudging to truth), we find
that at least some of the evolved perturbations align to identify
low-dimensional subspaces associated with regions of large forecast error in
the control (unperturbed, data-assimilating) run; this happens less often in
ensemble predictions that use Lyapunov vector perturbations. Nevertheless, in
the inertial regime we consider, we find that (a) the system is more
predictable when it is in the zonal regime, and that (b) the horizon of
predictability is far too short compared to characteristic time scales
associated with processes that lead to regime transitions, thus precluding the
possibility of predicting such transitions.
