In this study, PNNL researchers and their collaborators at the University of Wisconsin-Milwaukee, the National Center for Atmospheric Research, and the Institute of Atmospheric Physics in Beijing used a quasi-Monte Carlo sampling approach to explore 35 parameters in a newly implemented cloud parameterization called Cloud Layers Unified By Binormals, or CLUBB. In a generalized linear model, they studied how simulated cloud fields respond to different tunable parameters. They configured one instance of stratocumulus and two of shallow cumulus clouds at coarse and fine vertical resolutions using the single-column version of the Community Atmosphere Model (CAM5) to reduce computational cost.

This study improves understanding of the newly implemented CLUBB showing that among many tunable parameters, only a handful are influential. The researchers identified different influential parameters for different types of clouds. For example, in stratocumulus, it is those parameters that appear in water and heat flux equations. In shallow cumulus, the parameters related to the skewness of vertical velocity are influential. For stratocumulus clouds, the influential parameters are also sensitive to the version resolution is used.

The findings help reduce the number of tunable parameters in future studies of sensitivity and calibration in global simulations. The results show that a small subset of tunable parameters can explain most of the variance in simulated cloud fields in the single-column version of CAM5.