Ensemble hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific are studied using an intermediate coupled model (ICM), in which an ensemble Kalman filter (EnKF) data assimilation system is implemented to provide the initial ensemble. A linear, first‐order Markov stochastic model is adopted to represent model errors. Parameters in the stochastic model are estimated by comparing observation‐minus‐forecast values over 30 years. Twelve‐month, 120 ensemble hindcasts are performed over the period 1995–2004, each with 100 ensemble members. This ensemble technique provides a simple method of extending the standard ICM forecasts to the probabilistic domain. The results show that the prediction skill of the ensemble mean is better than that of one single deterministic forecast using the same ICM. For the probabilistic perspective, those ensemble forecasts have their ensembles following observed SST anomaly variations well.

Corresponding to the pronounced amplitude asymmetry for the central Pacific (CP) and eastern Pacific (EP) types of El Niño, an asymmetry in the strength of the Bjerknes positive feedback is found between these two types of El Niño, which is manifested as a weaker relationship between the zonal wind anomaly and the zonal gradient of sea surface temperature (SST) anomaly in the CP El Niño. The strength asymmetry mainly comes from a weaker sensitivity of the zonal gradient of sea level pressure (SLP) anomaly to that of diabatic heating anomaly during CP El Niño. This weaker sensitivity is caused by (1) a large cancelation induced by the negative SST‐cloud thermodynamic feedback to the positive dynamical feedback for CP El Niño, (2) an off‐equator shift of the maximum SLP anomalies during CP El Niño, and (3) a suppression of the mean low‐level convergence when CP El Niño events occur more often.