A Stochastic Parameter Perturbation Method to Represent Uncertainty in a Microphysics Scheme

Abstract

Current state-of-the art regional numerical weather forecasts are run at horizontal grid spacings of a few kilometers, which permits mediumto large-scale convective systems to be represented explicitly in the model.With the convection parameterization no longer active, much uncertainty in the formulation of subgrid-scale processes moves to other areas such as the cloud microphysical, turbulence, and land surface parameterizations. The goal of this study is to investigate experiments with stochastically perturbed parameters (SPP) within a microphysics parameterization and the model’s horizontal diffusion coefficients. To estimate the ‘‘true’’ uncertainty due to parameter uncertainty, the magnitudes of the perturbations are chosen as realistically as possible and not with a purposeful intent of maximal forecast impact as some prior work has done. Spatial inhomogeneities and temporal persistence are represented using a random perturbation pattern with spatial and temporal correlations. The impact on the distributions of various hydrometeors, precipitation characteristics, and solar and longwave radiation are quantified for a winter case and a summer case. In terms of upscale error growth, the impact is relatively small and consists primarily of triggering atmospheric instabilities in convectively unstable regions. In addition, small in situ changes with potentially large socioeconomic impacts are observed in the precipitation characteristics such as maximum hail size. Albeit the impact of introducing physically based parameter uncertainties within the bounds of aerosol uncertainties is small, their influence on the solar and longwave radiation balances may still have important implications for global model simulations of future climate scenarios.