Impacts of combined microphysical and land-surface uncertainties on convective clouds and precipitation in different weather regimes

To reduce the underdispersion of precipitation in convective-scale ensemble prediction systems, we investigate the relevance of microphysical and land-surface uncertainties for convective-scale predictability. We use three different initial soil moisture fields and study the response of convective precipitation to varying cloud condensation nuclei (CCN) concentrations and different shape parameters of the cloud droplet size distribution (CDSD) by applying a novel combined-perturbation strategy. Using the new ICOsahedral Non-hydrostatic (ICON) model, we construct a 60-member ensemble for cases with summertime convection under weak and strong synoptic-scale forcing over central Europe. We find a systematic positive soil moisture–precipitation feedback for all cases, regardless of the type of synoptic forcing, and a stronger response of precipitation to different CCN concentrations and shape parameters for weak forcing than for strong forcing. While the days with weak forcing show a systematic decrease in precipitation with increasing aerosol loading, days with strong forcing also show nonsystematic responses for some values of the shape parameters. The large magnitudes of precipitation deviations compared to a reference simulation ranging between −23 % and +18 % demonstrate that the uncertainties investigated here and, in particular, their collective effect are highly relevant for quantitative precipitation forecasting of summertime convection in central Europe. A rainwater budget analysis is used to identify the dominating source and sink terms and their response to the uncertainties applied in this study. Results also show a dominating cold-rain process for all cases and a strong but mostly nonsystematic impact on the release of latent heat, which is considered to be the prime mechanism for the upscale growth of small errors affecting the predictability of convective systems. The combined ensemble spread when accounting for all three uncertainties lies in the same range as the ones from an operational convective-scale ensemble prediction system with 20 members determined in previous studies. This indicates that the combination of different perturbations used in our study may be suitable for ensemble forecasting and that this method should be evaluated against other sources of uncertainty

Retrieval of O₂(¹Σ) and O₂(¹Δ) volume emission rates in the mesosphere and lower thermosphere using SCIAMACHY MLT limb scans

We present the retrieved volume emission rates (VERs) from the airglow of both the daytime and twilight O2(1Σ) band and O2(1Δ) band emissions in the mesosphere and lower thermosphere (MLT). The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard the European Space Agency Envisat satellite observes upwelling radiances in limb-viewing geometry during its special MLT mode over the range 50–150 km. In this study we use the limb observations in the visible (595–811 nm) and near-infrared (1200–1360 nm) bands. We have investigated the daily mean latitudinal distributions and the time series of the retrieved VER in the altitude range from 53 to 149 km. The maximal observed VERs of O2(1Δ) during daytime are typically 1 to 2 orders of magnitude larger than those of O2(1Σ). The latter peaks at around 90 km, whereas the O2(1Δ) emissivity decreases with altitude, with the largest values at the lower edge of the observations (about 53 km). The VER values in the upper mesosphere (above 80 km) are found to depend on the position of the sun, with pronounced high values occurring during summer for O2(1Δ). O2(1Σ) emissions show additional high values at polar latitudes during winter and spring. These additional high values are presumably related to the downwelling of atomic oxygen after large sudden stratospheric warmings (SSWs). Accurate measurements of the O2(1Σ) and O2(1Δ) airglow, provided that the mechanism of their production is understood, yield valuable information about both the chemistry and dynamics in the MLT. For example, they can be used to infer the amounts and distribution of ozone, solar heating rates, and temperature in the MLT