Fine-tuning Atmospheric Parameters for Improving ENSO Simulation in the Zebiak-Cane Model

Zebiak-Cane (ZC) model, renowned as a coupled ocean-atmosphere model specifically designed to simulate and predict the El Niño-Southern Oscillation (ENSO), is an indispensable tool for ENSO studies. However, the original ZC model exhibits certain biases in reproducing the ENSO-related sea surface temperature anomalies (SSTA) and heating anomalies, limiting its broader applicability. To improve the accuracy of ENSO simulation, we proposed a modified ZC model named MZC_XJH model through incorporating a new parameter <i>δ</i>₀ to refine the heating parameterization scheme. The performance in simulating the nonlinear SST-precipitation relationship with different values of <i>δ</i>₀ in the MZC_XJH model was firstly elaborated. Then we investigated the impacts of three key atmospheric parameters on ENSO simulation by conducting experiments with the MZC_XJH model. Through assessing the performance in simulating five fundamental ENSO metrics (amplitude, periodicity, seasonality, diversity and skewness), we uncovered that the sensitivities of simulated ENSO behaviors to different parameters are distinct. Moreover, we explained why a particular parameter greatly affects some simulated ENSO behaviors while others exert minor influence. We also revealed that the nonlinear effect due to the covariation of multi-parameters on ENSO simulation warrants careful consideration when tuning multi-parameters synchronously. Lastly, we presented an updated version of MZC_XJH model, in which some biases have been mitigated but some remain obvious. Although there are no universally optimal parameters that would ensure flawless performance in simulating every aspect of ENSO, this study provides a valuable reference for tuning atmospheric parameters in the MZC_XJH model, rendering the MZC_XJH model applicable to some research objectives.