Cao, X.; Su, S.; Leng, H., and Liu, B., 2020. Global sensitivity analysis of parameters in the ENSO model based on Sobol' method. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 340–345. Coconut Creek (Florida), ISSN 0749-0208.

In this work, a global sensitivity analysis (GSA) is performed on physical parameters and initial values of a nonlinear El Niño Southern Oscillation (ENSO) model, to determine the influential parameters at different prediction times. The anomaly of sea surface temperature (SST) at each prediction time is viewed as the response quantity of interest in the sensitivity analysis by Sobol' method. And the Latin Hypercube Sampling technique is applied to all input parameters. Then, the first-order sensitivity indexes (FSI) and total sensitivity indexes (TSI) of physical parameters and initial values are calculated, respectively. The results of numerical simulations show that the proposed method is very effective and feasible for sensitivity analysis of nonlinear ENSO model. It is concluded that the sensitive parameters and relative ranking vary considerably at different prediction times. In the beginning period, initial values of the model have a great influence on the SST anomaly forecast, while for the long-term prediction, physical coefficients become main sensitive parameters, and the initial values have little effect on the output variable. In addition, the interaction between input parameters in the model increases with forecast time, which is indicated by that the difference between the TSI and FSI of each parameter becomes larger.

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