Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects

Abstract

Abstract. This study investigates the hydrometeor development and response to cloud droplet number concentration (CDNC) perturbations in convection-permitting model configurations. We present results from a real-data simulation of deep convection in the Congo basin, an idealised supercell case, and a warm-rain large-eddy simulation (LES). In each case we compare two frequently used double-moment bulk microphysics schemes and investigate the response to CDNC perturbations. We find that the variability among the two schemes, including the response to aerosol, differs widely between these cases. In all cases, differences in the simulated cloud morphology and precipitation are found to be significantly greater between the microphysics schemes than due to CDNC perturbations within each scheme. Further, we show that the response of the hydrometeors to CDNC perturbations differs strongly not only between microphysics schemes, but the inter-scheme variability also differs between cases of convection. Sensitivity tests show that the representation of autoconversion is the dominant factor that drives differences in rain production between the microphysics schemes in the idealised precipitating shallow cumulus case and in a subregion of the Congo basin simulations dominated by liquidphase processes. In this region, rain mass is also shown to be relatively insensitive to the radiative effects of an overlying layer of ice-phase cloud. The conversion of cloud ice to snow is the process responsible for differences in cold cloud bias between the schemes in the Congo. In the idealised supercell case, thermodynamic impacts on the storm system using different microphysics parameterisations can equal those due to aerosol effects. These results highlight the large uncertainty in cloud and precipitation responses to aerosol in convectionpermitting simulations and have important implications not only for process studies of aerosol-convection interaction, but also for global modelling studies of aerosol indirect effects. These results indicate the continuing need for tighter observational constraints of cloud processes and response to aerosol in a range of meteorological regimes.

Bulk and structural convergence at convection‐resolving scales in real‐case simulations of summertime moist convection over land

“…; White et al . ) and convective updraughts (e.g. Lebo and Morrison, ; Jeevanjee, ) to the employed Δ x .…”

“…Bryan et al, 2003;Wyngaard, 2004;Bryan and Morrison, 2012;Ricard et al, 2013), microphysics (e.g. Weverberg et al, 2013;White et al, 2017) and convective updraughts (e.g. Lebo and Morrison, 2015;Jeevanjee, 2017) to the employed Δx.…”

Aerosol–cloud interactions in mixed-phase convective clouds – Part 1: Aerosol perturbations

“…in parameterisations of sub-grid-scale processes, the formulation of the dynamical core, or the spatial resolution (e.g. bulk microphysics: Lebo et al, 2012, Fan et al, 2012, Morrison, 2012, Hill et al, 2015, and White et al, 2017bin microphysics: Lebo and Seinfeld, 2011, Lebo et al, 2012, Fan et al, 2012, and Hill et al, 2015. In particular, the complexity of the employed cloud microphysical scheme can impact predicted aerosol-induced changes (e.g.…”

Possible roles of fall speed parameters of different graupel densities on microphysics and electrification in an idealized thunderstorm

“…Ice‐phase particles are essential for the generation of precipitation in many types of convective system, especially in the midlatitudes. Due to the lack of comprehensive observations and our limited understanding of the microphysical processes of ice‐phase particles, there are many uncertainties not only in the choice of microphysics schemes (Barthlott et al , ; Hazra et al , ) but also in the microphysical parameters used in each scheme (Johnson et al , ; White et al , ). Therefore, parametrizations in microphysical schemes are often tuned so that precipitation intensities and distributions in simulated storms can match observations (Lang et al , ; Qian et al , ).…”