Bred vectors and forecast error in the NASA coupled general circulation model

Breeding experiments are performed in the operational CGCM of the NASA seasonal to interannual prediction project (NSIPP). The system uses the optimal interpolation to update the oceanic initial state with the real temperature observations. In this study, we examine the characteristics of the bred vectors and the one-month forecast error, including their relationship to each other and to the background anomalous variations. We show that bred vectors have features similar to the one-month forecast error in both temporal and spatial structure and both of them are very sensitive to the background ENSO variations. Our results indicate that one-month forecast error of temperature in NSIPP CGCM is dominated by dynamical errors whose shape can be captured by bred vectors, especially when the BV growth rate is large. The results suggest that the bred vector can be used to detect the structure of the forecast error. This can benefit the ensemble forecasting in providing the shape of the dominan uncertainties in the seasonal-to-interannual related variabilities.