An Improved ENSO Ensemble Forecasting Strategy Based on Multiple Coupled Model Initialization Parameters

Abstract

Accurate prediction of El Niño–Southern Oscillation (ENSO) at relatively long timescales is propitious to forecasting other climate variables and meteorological disasters. Here, we use a coupled general circulation model, ICMv2, to investigate the influence of an initialization parameter, sea surface temperature (SST)-nudging strength, on ENSO prediction skill in the model's 1981–2010 hindcast experiments and suggest a multiple initialization parameter ensemble (MIPE) forecast as a new ensemble forecasting strategy to improve ENSO prediction skill. Different SST-nudging strengths produce different ENSO prediction skill via the generated various initial values. Selecting initial values closest to the observed SST (represented by reanalysis data) and increasing the ensemble size is inefficient in improving the skill of single initialization parameter ensemble (SIPE) forecasts. With ensemble members from different SST-nudging strength groups, the MIPE forecasts are significantly more skillful than the SIPE forecasts at 1- to 10-month lead time. More than 96% of 20,000 MIPE experiments generated by a Monte Carlo approach have larger anomaly correlation coefficient than SIPE at 1- to 9-month lead time. Our findings suggest that MIPE forecasting is another efficient strategy that can improve ENSO prediction skill besides multimodel and multimember ensembles using different initial values with random disturbances.