Evaluating uncertainty in convective cloud microphysics using statistical emulation

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.