Improving ENSO prediction in a hybrid coupled model with an embedded entrainment temperature parameterisation

摘要

Improving El Ni?o/Southern Oscillation (ENSO) forecast remains a great challenge in the climate-predicting community. Previously, an improved solution to sea surface temperature (SST) anomaly simulations in the tropical Pacific was obtained by explicitly embedding into an ocean general circulation model (OGCM) a separate SST anomaly submodel with an empirical parameterisation for the temperature of subsurface water entrained into the ocean mixed layer (T_e). In the present work, the benefit of the approach is explored and demonstrated in terms of ENSO prediction. A hybrid coupled ocean-atmosphere model (HCM) is utilized to perform two retrospective ENSO forecasts, differing in the way SST anomaly fields are taken for their coupling to the atmosphere, one directly from the OGCM (referred to as a standard coupling, HCM_(std)), and another from the embedded SST anomaly submodel with optimized T_e parameterisation (referred to as an embedded coupling, HCM_(embed)). The results indicate that ENSO forecasts can be effectively improved using the embedded approach; the predicted Ni?o-3.4 SST anomaly correlation is higher by 0.1-0.2 at a 12-month lead time in the HCM_(embed) than in the HCM_(std), and the corresponding root-mean-square (RMS) error is lower by 0.1-0.2 °C. Further improvements and applications are discussed.