Understanding the Factors Controlling MJO Prediction Skill across Events

Abstract

Abstract The prediction skill for individual Madden–Julian oscillation (MJO) events is highly variable, but the key factors behind this remain unclear. Using the latest hindcast results from the subseasonal-to-seasonal (S2S) phase II models, this study attempts to understand the diverse prediction skill for the MJO events with an enhanced convective anomaly over the eastern Indian Ocean (IO) at the forecast start date, by investigating the preference of the prediction skill to the MJO-associated convective anomalies and low-frequency background states (LFBS). Compared to the low-skill MJO events, the high-skill events are characterized by a stronger intraseasonal convection–circulation couplet over the IO before the forecast start date, which could result in a longer zonal propagation range during the forecast period, thereby leading to a higher score for assessing the prediction skill. The difference in intraseasonal fields can further be attributed to the LFBS of IO sea surface temperature (SST) and quasi-biannual oscillation (QBO), with the high-skill (low-skill) events corresponding to a warmer (colder) IO and easterly (westerly) QBO phase. The physical link is that a warm IO could increase the low-level convective instability and thus amplify MJO convection over the IO, whereas an easterly QBO phase could weaken the Maritime Continent barrier effect by weakening the static stability near the tropopause, thus favoring eastward propagation of the MJO. It is also found that the combined effects of IO SST and QBO phases are more effective in influencing MJO prediction skill than individual LFBS. Significance Statement Given the importance of the Madden–Julian oscillation (MJO) in the subseasonal predictability of global weather and climate, how well the MJO itself can be predicted is a matter of concern. This study reveals the critical observational factors that determine the variation in MJO prediction skill across events. Without making any presumptions about what the factors would be, the observed MJO events are separated according to their individual prediction skill, and the difference between the MJO events with higher and lower skill is then investigated. The results show that the low-frequency background states of the Indian Ocean sea surface temperature and the quasi-biannual oscillation are good indicators for MJO prediction skill, for their modulatory role in the MJO propagation range.