PNNL

Abstract

In this study, we investigated the sensitivity of net radiative fluxes (FNET) at the top of atmosphere (TOA) to 16 selected uncertain parameters mainly related to the cloud microphysics and aerosol schemes in the Community Atmosphere Model version 5 (CAM5). We adopted a quasi-Monte Carlo (QMC) sampling approach to effectively explore the high dimensional parameter space. The output response variables (e.g., FNET) were simulated using CAM5 for each parameter set, and then evaluated using generalized linear model analysis. In response to the perturbations of these 16 parameters, the CAM5-simulated global annual mean FNET ranges from -9.8 to 3.5 W m-2 compared to the CAM5-simulated FNET of 1.9 W m-2 with the default parameter values. Variance-based sensitivity analysis was conducted to show the relative contributions of individual parameter perturbation to the global FNET variance. The results indicate that the changes in the global mean FNET are dominated by those of cloud forcing (CF) within the parameter ranges being investigated. The size threshold parameter related to auto-conversion of cloud ice to snow is confirmed as one of the most influential parameters for FNET in the CAM5 simulation. The strong heterogeneous geographic distribution of FNET variation shows parameters have a clear localized effect over regions where they are acting. However, some parameters also have non-local impacts on FNET variance. Although external factors, such as perturbations of anthropogenic and natural emissions, largely affect FNET variations at the regional scale, their impact is weaker than that of model internal parameters in terms of simulating global mean FNET in this study. The interactions among the 16 selected parameters contribute a relatively small portion of the total FNET variations over most regions of the globe. This study helps us better understand the CAM5 model behavior associated with parameter uncertainties, which will aid the next step of reducing model uncertainty via calibration of uncertain model parameters with the largest sensitivity.