Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions

Abstract

With an El Niño prediction model, an advanced approach of conditional nonlinear optimal perturbation (CNOP) is used to reveal the maximum impacts of the errors occurring in initial conditions (ICs) and model parameters (MPs) on the El Niño predictions. The optimally growing initial errors CNOP-I and parameter errors CNOP-P are obtained, as well as their optimally combined mode (denoted by CNOPs). The comparisons among CNOP-I, -P, and CNOPs show that the El Niño predictions are more sensitive to the uncertainties in the MPs than in the ICs. The CNOP-I mainly affects the short-term prediction (less than 3 months), whereas the CNOP-P tends to induce much larger error over a longer prediction time. Both CNOP-I and CNOP-P can induce larger error growth during spring than during other seasons; that is to say, both of them cause the “spring predictability barrier” (SPB) phenomenon. The spring error growth caused by CNOP-I is mainly attributed to the uncertainties of the ocean advection processes, while that caused by the CNOP-P is controlled by thermodynamics. When the errors in ICs and MPs are simultaneously included in predictions, the resultant CNOPs produce much larger error growth and cause much more significant SPB; furthermore, the corresponding mechanism is dominated by the nonlinear advection processes. This certainly indicates that strong nonlinear interactions between the errors in ICs and MPs enhance the SPB, thus deepening our understanding of El Niño predictability. It is obvious that initial and model errors should be simultaneously given great attention to improve the El Niño prediction level.