Bred Vector and ENSO Predictability in a Hybrid Coupled Model during the Period 1881–2000

Abstract

In this study, a breeding analysis was conducted for a hybrid coupled El Niño?Southern Oscillation (ENSO) model that assimilated a historic dataset of sea surface temperature (SST) for the 120 yr between 1881 and 2000. Meanwhile, retrospective ENSO forecasts were performed for the same period. For a given initial state, 15 bred vectors (BVs) of both SST and upper-ocean heat content (HC) were derived. It was found that the average structure of the 15 BVs was insensitive to the initial states and independent of season and ENSO phase. The average structure of the BVs shared many features already seen in both the final patterns of leading singular vectors and the ENSO BVs of other models. However, individual BV patterns were quite different from case to case. The BV rate (the average cumulative growth rate of BVs) varied seasonally, and the maximum value appeared at the time when the model ran through the boreal spring and summer. It was also sensitive to the strength of the ENSO signal (i.e., the stronger ENSO signal, the smaller the BV rate). Furthermore, ENSO predictability was explored using BV analysis. Emphasis was placed on the relationship between BVs, which are able to characterize potential predictability without requiring observations, and actual prediction skills, which make use of real observations. The results showed that the relative entropy, defined using breeding vectors, was a good measure of potential predictability. Large relative entropy often leads to a good prediction skill; however, when the relative entropy was small, the prediction skill seemed much more variable. At decadal/interdecadal scales, the variations in prediction skills correlated with relative entropy.