CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation in Community Earth System Model Using Intelligence Algorithms

Abstract

Model error, which results from model parameters, can cause the nonnegligible uncertainty in the North Atlantic Oscillation (NAO) simulation. Conditional nonlinear optimal perturbation related to parameter (CNOP-P) is a powerful approach to investigate the range of uncertainty caused by model parameters under a specific constraint. In this paper, we adopt intelligence algorithms to implement the CNOP-P method and conduct the sensitivity analysis of parameter combinations for NAO events in the Community Earth System Model (CESM). Among 28 model parameters of the atmospheric component, the most sensitive parameter combination for the ${\text{NAO}}^{+}$ consists of parameter for deep convection (cldfrc_dp1), minimum relative humidity for low stable clouds (cldfrc_rhminl), and the total solar irradiance (solar_const). As for the ${\text{NAO}}^{-}$ , the parameter set that can trigger the largest variation of the NAO index (NAOI) is comprised of the constant for evaporation of precip (cldwat_conke), characteristic adjustment time scale (hkconv_cmftau), and the total solar irradiance (solar_const). The most prominent uncertainties of the NAOI ( $\mathrm{\Delta }\text{NAOI}$ ) caused by these two combinations achieve 2.12 for ${\text{NAO}}^{+}$ and −2.72 for ${\text{NAO}}^{-}$ , respectively. In comparison, the maximum level of the NAOI variation resulting from single parameters reaches 1.45 for ${\text{NAO}}^{+}$ and −1.70 for ${\text{NAO}}^{-}$ . It is indicated that the nonlinear impact of multiple parameters would be more intense than the single parameter. These results present factors that are closely related to NAO events and also provide the direction of optimizing model parameters. Moreover, the intelligence algorithms adopted in this work are proved to be adequate to explore the nonlinear interaction of parameters on the model simulation.