摘要：The growing demand for skillful near-term climate prediction encourages an improved prediction of low-frequency sea surface temperature (SST) variabilities such as the El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). This study assesses their seasonal-to-decadal prediction skills using large ensembles of the Coupled Model Intercomparison Project phases 5 and 6 retrospective decadal predictions. A multi-model ensemble reforecast successfully predicts ENSO over a year in advance. While its seasonal prediction skill in the following spring and summer is achieved by multi-model ensemble averaging of relatively smaller ensemble members, the multi-year prediction of winter ENSO needs a larger ensemble size. The PDO is significantly predicted at a lead time of five-to-nine years but such a long-lead prediction is sourced from external radiative forcing instead of initialization, as evidenced from uninitialized historical simulations. The effect of model initialization lasts only two years. These results confirm that both the model initialization and the proper estimate of near-term radiative forcing are required to improve the seasonal-to-decadal prediction in the Pacific Basin.
