Using Emulators to Understand the Sensitivity of Deep Convective Clouds and Hail to Environmental Conditions

This study aims to identify model parameters describing atmospheric conditions such as wind shear and cloud condensation nuclei (CCN) concentration, which lead to large uncertainties in the prediction of deep convective clouds. In an idealized setup of a cloud-resolving model including a two-moment microphysics scheme we use the approach of statistical emulation to allow for a Monte Carlo sampling of the parameter space, which enables a comprehensive sensitivity analysis. We analyze the impact of six uncertain input parameters on cloud properties (vertically integrated content of six hydrometeor classes), precipitation, and the size distribution of hail. Furthermore, we investigate whether the sensitivities are robust for different trigger mechanisms of convection. We find that the uncertainties of most cloud and precipitation outputs are dominated by the uncertainty in the temperature profile and the CCN concentration while the contributions of other input parameters to the uncertainties may vary. The temperature profile is also an important factor in determining the size distribution of surface hail. We also notice that the sensitivities of cloud water and hail to the CCN concentration depend on environmental conditions. Our results show that depending on the choice of the trigger mechanism, the contribution of the input parameters to the uncertainty varies, which means that studies with different trigger mechanisms might not be comparable. Overall, the emulator approach appears to be a powerful tool for the analysis of complex weather prediction models in an idealized setup.