A Bayesian Examination of Deep Convective Squall-Line Sensitivity to Changes in Cloud Microphysical Parameters

Deep convective cloud content, precipitation distribution and rate, dynamics, and radiative fluxes are known to be sensitive to the details of liquidand ice-phase cloud microphysical processes. Previous studies have explored the multivariate convective response to changes in cloud microphysical parameter values in a framework that isolated the cloud and radiation schemes from the thermodynamic and dynamic environment. This study uses a Bayesian Markov chain Monte Carlo (MCMC) algorithm to generate sets of cloud microphysical parameters consistent with a specific storm environment in a three-dimensional cloud-systemresolving model. These parameter sets, and the corresponding large ensemble of model simulations, contain information about the univariate model sensitivity, as well as parameter–state and parameter–parameter interactions. Examination of the relationships between cloud parameters and in-cloud vertical motion and latent heat release provides information about the influence of microphysical processes on the in-cloud environment. Exploration of the joint dependence of microphysical properties and clear-air relative humidity and temperature allows an assessment of the influence of cloud microphysics on the near-cloud environment. Analysis of the MCMC results indicates the model output is sensitive to a small subset of the parameters. In addition, constraint of cloud microphysics using bulk observations of the hydrologic cycle and TOA radiative fluxes uniquely constrains vertical velocity, latent heat release, and the environmental temperature and relative humidity.