A sensitivity study of radiative fluxes at the top of atmosphere to cloud-microphysics and aerosol parameters in the community atmosphere model CAM5

摘要

In this study, we investigated the sensitivity of netradiative fluxes (FNET) at the top of atmosphere (TOA) to 16 selecteduncertain parameters mainly related to the cloud microphysics and aerosolschemes in the Community Atmosphere Model version 5 (CAM5). We adopted aquasi-Monte Carlo (QMC) sampling approach to effectively explore the high-dimensional parameter space. The output response variables (e.g., FNET) aresimulated using CAM5 for each parameter set, and then evaluated using thegeneralized linear model analysis. In response to the perturbations of these16 parameters, the CAM5-simulated global annual mean FNET ranges from −9.8to 3.5 W m compared to 1.9 W m with the default parametervalues. Variance-based sensitivity analysis is conducted to show therelative contributions of individual parameter perturbations to the globalFNET variance. The results indicate that the changes in the global mean FNETare dominated by changes in net cloud forcing (CF) within the parameterranges being investigated. The threshold size parameter related toauto-conversion of cloud ice to snow is identified as one of the mostinfluential parameters for FNET in CAM5 simulations. The strongheterogeneous geographic distribution of FNET variance shows that parametershave a clear localized effect over regions where they are acting. However,some parameters also have non-local impacts on FNET variance. Althoughexternal factors, such as perturbations of anthropogenic and naturalemissions, largely affect FNET variance at the regional scale, their impactis weaker than that of model internal parameters in terms of simulatingglobal mean FNET. The interactions among the 16 selected parameterscontribute a relatively small portion to the total FNET variance over mostregions of the globe. This study helps us better understand the parameteruncertainties in the CAM5 model, and thus provides information for furthercalibrating uncertain model parameters with the largest sensitivity.