Consistent Initial Error Modes Causing the Largest Prediction Errors and the Strongest Predictability Barrier for Two Types of El Niño Events in CMIP6

Abstract

Based on the coupled conditional nonlinear optimal perturbation (C-CNOP) method, this study explores the season-dependent predictability barrier (PB) affecting the forecasts of two types of El Niño (central Pacific, CP; eastern Pacific, EP) events by using CMIP6 models. It is found that CP (EP) El Niño forecasts often occurs summer (spring) PB, and only powerful season-dependent PB can lead to large prediction errors. Further investigating the initial causes of the largest prediction errors and strongest PB, we find that the spatial pattern of initial errors consistently exhibits the sea temperature anomaly dipole of east positive–west negative in the equatorial Pacific, and errors over upper layers of North (South) Pacific are similar to the negative Victoria mode (South Pacific Meridional Mode). Physically, the mode evolution of initial errors in the equatorial Pacific, North and South Pacific are all positive feedback processes, which together ultimately lead to large cold biases over the central-eastern (CP) or eastern (EP) equatorial Pacific in December. Analysis shows that the initial error mode of North Pacific mainly affects the cold bias of the central Pacific, whereas the mode of South Pacific mostly controls the bias in the eastern Pacific. These initial error modes found in this study can have more serious impacts on forecasts of two types of El Niño events than that in previous studies. The results of this study offer valuable scientific guidance for the adaptive observation of ENSO, which will likely be able to maximize the prediction skills for two types of El Niño events.