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Clouds play a potentially important role in Arctic climate change but are poorly represented in current atmospheric models across scales. To improve the representation of Arctic clouds in models, it is necessary to compare models to observations to consequently reduce this uncertainty. This study compares aircraft observations from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign around Svalbard, Norway, in May–June 2017 and simulations using the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather prediction (NWP) setup at 1.2 km horizontal resolution. By comparing measurements of solar and terrestrial irradiances during ACLOUD flights to the respective properties in ICON, we showed that the model systematically overestimates the transmissivity of the mostly liquid clouds during the campaign. This model bias is traced back to the way cloud condensation nuclei (CCN) get activated into cloud droplets in the two-moment bulk microphysical scheme used in this study. This process is parameterized as a function of grid-scale vertical velocity in the microphysical scheme used, but in-cloud turbulence cannot be sufficiently resolved at 1.2 km horizontal resolution in Arctic clouds. By parameterizing subgrid-scale vertical motion as a function of turbulent kinetic energy, we are able to achieve a more realistic CCN activation into cloud droplets. Additionally, we showed that by scaling the presently used CCN activation profile, the hydrometeor number concentration could be modified to be in better agreement with ACLOUD observations in our revised CCN activation parameterization. This consequently results in an improved representation of cloud optical properties in our ICON simulations.

The atmospheric energy budget is analysed in numerical simulations of tropical cloud systems to better understand the physical processes behind aerosol effects on the atmospheric energy budget. The simulations include both shallow convective clouds and deep convective tropical clouds over the Atlantic Ocean. Two different sets of simulations, at different dates (10–12 and 16–18 August 2016), are simulated with different dominant cloud modes (shallow or deep). For each case, the cloud droplet number concentration (CDNC) is varied as a proxy for changes in aerosol concentrations without considering the temporal evolution of the aerosol concentration (for example due to wet scavenging, which may be more important under deep convective conditions). It is shown that the total column atmospheric radiative cooling is substantially reduced with CDNC in the deep-cloud-dominated case (by ∼10.0 W m−2), while a much smaller reduction (∼1.6 W m−2) is shown in the shallow-cloud-dominated case. This trend is caused by an increase in the ice and water vapour content at the upper troposphere that leads to a reduced outgoing longwave radiation, an effect which is stronger under deep-cloud-dominated conditions. A decrease in sensible heat flux (driven by an increase in the near-surface air temperature) reduces the warming by ∼1.4 W m−2 in both cases. It is also shown that the cloud fraction response behaves in opposite ways to an increase in CDNC, showing an increase in the deep-cloud-dominated case and a decrease in the shallow-cloud-dominated case. This demonstrates that under different environmental conditions the response to aerosol perturbation could be different.

Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. In this work, a classification system (Identification and Classification of Cirrus or IC-CIR) is introduced to identify cirrus clouds by the cloud formation mechanism. Using reanalysis and satellite data, cirrus clouds are separated into four main types: orographic, frontal, convective and synoptic. Through a comparison to convection-permitting model simulations and back-trajectory-based analysis, it is shown that these observation-based regimes can provide extra information on the cloud-scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes. Despite having different cloud formation mechanisms, the radiative properties of the regimes are not distinct, indicating that retrieved cloud properties alone are insufficient to completely describe them. This classification is designed to be easily implemented in GCMs, helping improve future model–observation comparisons and leading to improved parametrisations of cirrus cloud processes.

The spatio-temporal variability of integrated water vapour (IWV) on small scales of less than 10 km and hours is assessed with data from the 2 months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sun photometer, radiosondes, Raman lidar, infrared and near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of random differences (standard deviation ≤1 kg m−2) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering. For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Nonhydrostatic modelling framework (ICON), which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3–4 km or time of 10–15 min induce IWV variabilities on the order of 0.4 kg m−2. This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds. Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m−2) even at very short time scales of a few minutes. These cannot be captured by the temporally lower-resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.