A New Sensitivity Analysis Approach Using Conditional Nonlinear Optimal Perturbations and Its Preliminary Application  

作　　者：Qiujie REN Mu MU Guodong SUN Qiang WANG 

机构地区：[1]State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China [2]Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences,Fudan University,Shanghai 200438,China [3]University of Chinese Academy of Sciences,Beijing 100049,China [4]Key Laboratory of Marine Hazards Forecasting,Ministry of Natural Resources,Hohai University,Nanjing 210098,China [5]College of Oceanography,Hohai University,Nanjing 210098,China

出　　处：《Advances in Atmospheric Sciences》2023年第2期285-304,共20页大气科学进展（英文版）

基　　金：supported by the National Nature Science Foundation of China(41975132);the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2020B0301030004).

摘　　要：Simulations and predictions using numerical models show considerable uncertainties,and parameter uncertainty is one of the most important sources.It is impractical to improve the simulation and prediction abilities by reducing the uncertainties of all parameters.Therefore,identifying the sensitive parameters or parameter combinations is crucial.This study proposes a novel approach:conditional nonlinear optimal perturbations sensitivity analysis(CNOPSA)method.The CNOPSA method fully considers the nonlinear synergistic effects of parameters in the whole parameter space and quantitatively estimates the maximum effects of parameter uncertainties,prone to extreme events.Results of the analytical g-function test indicate that the CNOPSA method can effectively identify the sensitivity of variables.Numerical results of the theoretical five-variable grassland ecosystem model show that the maximum influence of the simulated wilted biomass caused by parameter uncertainty can be estimated and computed by employing the CNOPSA method.The identified sensitive parameters can easily change the simulation or prediction of the wilted biomass,which affects the transformation of the grassland state in the grassland ecosystem.The variance-based approach may underestimate the parameter sensitivity because it only considers the influence of limited parameter samples from a statistical view.This study verifies that the CNOPSA method is effective and feasible for exploring the important and sensitive physical parameters or parameter combinations in numerical models.

关 键 词：physical parameters parameter uncertainty sensitivity analysis nonlinear optimization land-surface process 

分 类 号：P45[天文地球—大气科学及气象学]