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 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 ( Δ NAOI ) 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.
Highlights
North Atlantic Oscillation (NAO) has the most prominent seesaw-type oscillation in atmospheric circulation between subtropical high and subpolar low [1]
Several scholars utilized Community Earth System Model (CESM) to simulate the evolution of NAO with related factors, such as temperature [30]. e CESM assembles atmosphere, ocean, land, and sea-ice component models coupled through a coupler. e corresponding atmospheric component is derived from the Community Atmosphere Model (CAM), and the land component is the Community Land Model (CLM)
ΔNAOI describes the NAO index (NAOI) difference compared with the reference state. e reference state can be obtained by running CESM with default parameter values. e greatest variation of positive ΔNAOI and negative ΔNAOI triggered by single parameters is illustrated in Figures 3 and 4. e range of ΔNAOI+ is from 0.22 to 1.45, and p10 (Minimum relative humidity for low stable clouds) achieves the largest difference, while p1 (CH4 volume mixing ratio) has the least influence on the NAO+
Summary
North Atlantic Oscillation (NAO) has the most prominent seesaw-type oscillation in atmospheric circulation between subtropical high and subpolar low [1]. NAO presents the characteristic of abnormal and frequently changed, especially for winter NAO [2]. Recent research indicates that the NAO+ (NAO− ) leads to increased (reduced) temperature [3], and extremely cold weather along with snowstorms in Europe is closely associated with NAO− in a large-scale NAO circulation [4, 5]. E low-frequency change and seasonal features of NAO have a significant impact on the global climate [6, 7], estimating the uncertainty of NAO simulation and prediction has practical implications [8]. Several models have been developed to describe the nonlinear scale interaction and the life cycle of NAO [13, 14], such as the CERFACS forecast system [15], CMCCINGV [16], and Community Earth System Model (CESM) [17]. The climate simulation using dynamic models would be influenced by both initial condition errors and model errors [18], which have become two major
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