The impact of initial condition uncertainty on numerical simulations of blocking

The impact of initial condition uncertainty (ICU) on the onset and maintenance of eastern North Pacific blocking is examined within the framework of a general circulation model (GCM) and the perfect model assumption. Comparisons are made with the contrasting zonal flow regime. Twenty-member ensembles of perturbed simulations are run out to 15 days for the zonal flow, and for blocking at lead times of 8, 4, 2, and 0 days. Blocking occurs in 95% of the 0-day lead simulations and declines monotonically to 65% for the 8-day lead simulations. The uncertainty in the exact time of onset among those simulations that form blocks also increases with lead time. The synoptic-scale features in both the blocking and zonal ensembles saturate, relative to climatological variance, and decorrelate (anomaly correlation coefficient < 0.5) by 6 days. The planetary-scale features, however, maintain skill relative to climatology beyond 10 days. The zonal simulations are generally the first to saturate and decorrelate, followed by simulations of blocking maintenance (0-day lead) and onset (2-, 4-, and 8-day lead), respectively. Thus, initial flows that project negatively (zonal flows) on the GCM's Pacific-North American teleconnectivity pattern are more sensitive to ICU, and thus are less predictable than positive (blocking flows) projections. While the results for this study demonstrate that error growth due to ICU ultimately imposes limits on the predictability of blocking, they also suggest that skillful ensemble predictions of transitions to a blocked state are possible at long lead times if the model error is held to a minimum. The majority of the perturbed simulations make the transition into a blocked state with an associated sustenance of skill even after the loss of skill in the synoptic-scale waves. The results are consistent with the hypothesis that the planetary-scale waves may need to be preconditioned toward the formation of blocking events. They also may, in part, help explain the poor performance of operational models in forecasts of blocking onset.