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 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 , 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 () caused by these two combinations achieve 2.12 for and −2.72 for , respectively. In comparison, the maximum level of the NAOI variation resulting from single parameters reaches 1.45 for and −1.70 for . 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.

中文翻译：

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基于CNOP-P的智能算法对社区地球系统模型中北大西洋涛动的参数敏感性分析

由模型参数引起的模型误差可导致北大西洋涛动（NAO）模拟中的不确定性不可忽略。与参数有关的条件非线性最优摄动（CNOP-P）是研究在特定约束下由模型参数引起的不确定性范围的有效方法。在本文中，我们采用智能算法来实现CNOP-P方法，并在社区地球系统模型（CESM）中对NAO事件的参数组合进行敏感性分析。在大气成分的28个模型参数中，最敏感的参数组合包括深对流参数（cldfrc_dp1），低稳定云的最小相对湿度（cldfrc_rhminl），以及总太阳辐照度（solar_const）。至于，参数集可触发NAO索引的最大变化（直井）包括用于雨（蒸发恒定的cldwat_conke），特性调整时标（hkconv_cmftau），和总的太阳辐照度（solar_const）。所述直井的最突出的不确定性（）引起的这两个的组合实现2.12对于和-2.72 ，分别。相比之下，单个参数导致的NAOI变化的最大水平为时为1.45，对于时为-1.70 。结果表明，多个参数的非线性影响将比单个参数更强烈。这些结果提出了与NAO事件密切相关的因素，也为优化模型参数提供了方向。此外，证明了本文采用的智能算法足以探索模型仿真中参数的非线性相互作用。