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

• Published in: Advances in Meteorology
• Main Authors: Bin Mu, Jing Li, Shijin Yuan, Xiaodan Luo, Guokun Dai
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: Meteorology. Climatology; QC851-999; North Atlantic; North Atlantic oscillation
• Online Access: https://doi.org/10.1155/2020/6070789; https://doaj.org/article/e3ac82da3ca44a5c94691ba5bbb75bc7

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