资源环境科技发展态势分析平台

Despite recent progress in seasonal forecast systems, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic-dynamical model (SDM) to predict the IOD using seasonally modulated El Nino-Southern Oscillation (ENSO) forcing together with a seasonally modulated Indian Ocean coupled ocean-atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System version 2 and the Scale Interaction Experiment-Frontier system. The improvements mainly originate from better prediction of ENSO-dependent IOD events and from reducing false alarms. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal-to-noise ratio of the system. Therefore, potential future ENSO improvements in models should translate to more skillful IOD predictions.

Plain Language Summary The Indian Ocean Dipole (IOD) is a prominent climate phenomenon occurring in the tropical Indian Ocean. Since IOD events have large socioeconomic and environmental impacts globally, predicting them has become a scientific challenge of considerable importance. The current predictive skill for the IOD exhibited by operational seasonal forecast models remains poor compared to that for the El Nino-Southern Oscillation (ENSO), partly due to the failure of models to realistically simulate the observed ENSO-IOD relationship. To this end, we have developed a simple stochastic-dynamical model (SDM) to predict the IOD using the observed IOD-ENSO relationship together with operational ENSO forecast information. The SDM demonstrates considerably improved IOD forecast skill compared to current operational models.