Evaluating uncertainty in convective cloud microphysics using statistical emulation

The microphysical properties of convective clouds determine their radiative effects on climate, the amount and intensity of precipitation as well as dynamical features. Realistic simulation of these cloud properties presents a major challenge. In particular, because models are complex and slow to run, we have little understanding of how the considerable uncertainties in parameterized processes feed through to uncertainty in the cloud responses. Here we use statistical emulation to enable a Monte Carlo sampling of a convective cloud model to quantify the sensitivity of 12 cloud properties to aerosol concentrations and nine model parameters representing the main microphysical processes. We examine the response of liquid and ice-phase hydrometeor concentrations, precipitation, and cloud dynamics for a deep convective cloud in a continental environment. Across all cloud responses, the concentration of the Aitken and accumulation aerosol modes and the collection efficiency of droplets by graupel particles have the most influence on the uncertainty. However, except at very high aerosol concentrations, uncertainties in precipitation intensity and amount are affected more by interactions between drops and graupel than by large variations in aerosol. The uncertainties in ice crystal mass and number are controlled primarily by the shape of the crystals, ice nucleation rates, and aerosol concentrations. Overall, although aerosol particle concentrations are an important factor in deep convective clouds, uncertainties in several processes significantly affect the reliability of complex microphysical models. The results suggest that our understanding of aerosol-cloud interaction could be greatly advanced by extending the emulator approach to models of cloud systems.