Aerosol–cloud interactions in mixed-phase convective clouds – Part 2: Meteorological ensemble

Abstract

Abstract. The relative contribution of variations in meteorological and aerosol initial and boundary conditions to the variability in modelled cloud properties is investigated with a high-resolution ensemble (30 members). In the investigated case, moderately deep convection develops along sea-breeze convergence zones over the southwestern peninsula of the UK. A detailed analysis of the mechanism of aerosol–cloud interactions in this case has been presented in the first part of this study (Miltenberger et al., 2018). The meteorological ensemble (10 members) varies by about a factor of 2 in boundary-layer moisture convergence, surface precipitation, and cloud fraction, while aerosol number concentrations are varied by a factor of 100 between the three considered aerosol scenarios. If ensemble members are paired according to the meteorological initial and boundary conditions, aerosol-induced changes are consistent across the ensemble. Aerosol-induced changes in CDNC (cloud droplet number concentration), cloud fraction, cell number and size, outgoing shortwave radiation (OSR), instantaneous and mean precipitation rates, and precipitation efficiency (PE) are statistically significant at the 5 % level, but changes in cloud top height or condensate gain are not. In contrast, if ensemble members are not paired according to meteorological conditions, aerosol-induced changes are statistically significant only for CDNC, cell number and size, outgoing shortwave radiation, and precipitation efficiency. The significance of aerosol-induced changes depends on the aerosol scenarios compared, i.e. an increase or decrease relative to the standard scenario. A simple statistical analysis of the results suggests that a large number of realisations (typically >100) of meteorological conditions within the uncertainty of a single day are required for retrieving robust aerosol signals in most cloud properties. Only for CDNC and shortwave radiation small samples are sufficient. While the results are strictly only valid for the investigated case, the presented evidence combined with previous studies highlights the necessity for careful consideration of intrinsic predictability, meteorological conditions, and co-variability between aerosol and meteorological conditions in observational or modelling studies on aerosol indirect effects.

Cloud Ice Processes Enhance Spatial Scales of Organization in Arctic Stratocumulus

“…Miltenberger et al (2018a) found substantially larger cloud cells in simulations of summertime deep convective clouds with higher aerosol concentrations compared to a low aerosol environment. In addition, these aerosol-induced changes were statistically significant compared to the uncertainty introduced by a meteorological ensemble (Miltenberger et al, 2018b). However, their aerosol concentrations are much larger than observed for the Arctic environment and in deep convective clouds latent heating from condensation and turbulence are stronger than in shallow stratocumulus.…”

Relative impact of aerosol, soil moisture and orographyperturbations on deep convection

“…An important finding is the fact that a heterogeneous CCN distribution with a mean concentration corresponding to that of the reference run (continental assumption), can yield to precipitation deviations ranging in the same order of magnitude than changing the total CCN concentration. The fact that soil moisture and aerosol perturbations contribute in a similar magnitude to the precipitation totals suggests that aerosols are indeed important for quantitative precipitation forecast (Miltenberger et al, 2018). The smallest deviations from the reference runs occurred when introducing orography uncertainties.…”

Sensitivity of Hail Precipitation to Ensembles of Uncertainties of Representative Initial Environmental Conditions From ECMWF

“…Due to a low vertical resolution in the ECMWF ensemble, low‐level variations (e.g., low‐level wind shear) among the ensemble members may not be captured representatively. Considerable uncertainty remains in our understanding and modeling of deep convective clouds and their nonlinear interactions with different processes, because model grid scales are typically several orders of magnitude larger than the process scales (Miltenberger et al, ). Real atmospheric conditions are much more complicated than the idealized conditions proposed here, particularly in environments favorable for deep convection.…”

“…Although the importance of intrinsic predictability limits has been acknowledged in weather forecasting, its implications for the evaluation of cloud microphysics parameterizations has been less well addressed (Miltenberger et al, 2018). The uncertainty was also determined for the six hydrometeor species employed in the microphysics scheme, so that we could determine for which species the results were most uncertain under the different perturbations.…”

“…Considerable efforts have been made to explore the predictability of severe storms (e.g., Flora et al, 2018;Weyn & Durran, 2017). Ensemble forecasting is now an important component of operational forecasting and is increasingly used at convection-permitting or even higher spatial resolutions (Miltenberger et al, 2018). Past studies have demonstrated the ability of storm-scale ensemble analyses to simulate severe storms (Flora et al, 2018;Potvin et al, 2017;Zhang et al, 2016).…”