Sensitivity of El Nino diversity prediction to parameters in an intermediate coupled model

There are large uncertainties that exist in El Nino-Southern Oscillation (ENSO) predictions. Errors in model parameters are one of the factors limiting ENSO prediction skill. In this study, we look for the model parameters that can induce the largest least-square changes in the sea surface temperature predictions for four El Nino events that were optimized to fit observations in the tropical Pacific: 1982 and 1987 eastern Pacific (EP) events and 1990 and 1994 central Pacific (CP) events. The ENSO model is an intermediate coupled model used at the Institute of Oceanology, Chinese Academy of Sciences (IOCAS ICM). The sensitivity of the prediction skill for each type of El Nino to the model parameters is analyzed using an optimization parameter sensitivity analysis (OPSA) method. Perturbation experiments allow us to identify three important model parameters that play a key role in ENSO dynamics from nine selected parameters. These three, alpha(Tea), alpha(tau), and alpha(HF), are related to the thermocline feedback, wind stress, and heat flux, respectively. alpha(Tea) and alpha(tau) are important for both types of El Nino events, while alpha(HF) is another important parameter that impacts CP events. Our results show that ENSO predictions can be improved by accurate estimates of the sensitive parameters identified here. In particular, it is reasonable that accurate estimates of alpha(HF) make more sense for improving CP El Nino prediction than for improving EP El Nino prediction. Increasing alpha(Tea) or alpha(tau) or decreasing alpha(HF) tends to increase the amplitudes of these warm events. The two types of El Nino events have different sensitivities to the parameter alpha(HF), which is attributed to the role played by the vertical mixing process in the eastern equatorial Pacific. These parameter sensitivity analyses provide an improved understanding of ENSO predictability, and highlight the special importance of alpha(HF) for enhancing the prediction skill for the CP El Nino.