ALBERT

Description: The synergy between airborne lidar, radar, passive microwave, and passive imaging spectrometer measurements was used to characterize the vertical and small-scale (down to 10 m) horizontal distribution of the cloud thermodynamic phase. Two case studies of low-level Arctic clouds in a cold air outbreak and a warm air advection observed during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) were investigated. Both clouds exhibited the typical vertical mixed-phase structure with mostly liquid water droplets at cloud top and ice crystals in lower layers. The cloud top horizontal small-scale variability observed during the cold air outbreak is dominated by the liquid water close to the cloud top and shows no indication of ice in lower cloud layers. Contrastingly, the cloud top variability of the case observed during a warm air advection showed some ice in areas of low reflectivity or cloud holes. Radiative transfer simulations considering homogeneous mixtures of liquid water droplets and ice crystals were able to reproduce the horizontal variability of this warm air advection. To account for more realistic vertical distributions of the thermodynamic phase, large eddy simulations (LES) were performed to reconstruct the observed cloud properties and were used as input for radiative transfer simulations. The simulations of the cloud observed during the cold air outbreak, with mostly liquid water at cloud top, realistically reproduced the observations. For the warm air advection case, the simulated cloud field underestimated the ice water content (IWC). Nevertheless, it revealed the presence of ice particles close to the cloud top and confirmed the observed horizontal variability of the cloud field. It is concluded that the cloud top small-scale horizontal variability reacts to changes in the vertical distribution of the cloud thermodynamic phase. Passive satellite-borne imaging spectrometer observations with pixel sizes larger than 100 m miss the small-scale cloud top structures, which limits their capabilities to provide indications about the cloud vertical structure.

Description: Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC)3 project was established in 2016 (www.ac3-tr.de/). It comprises modeling and data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, shipborne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data. For example, a distinct atmospheric moistening, an increase of regional storm activities, an amplified winter warming in the Svalbard and North Pole regions, and a decrease of sea ice thickness in the Fram Strait and of snow depth on sea ice have been identified. A positive trend of tropospheric bromine monoxide (BrO) column densities during polar spring was verified. Local marine/biogenic sources for cloud condensation nuclei and ice nucleating particles were found. Atmospheric–ocean and radiative transfer models were advanced by applying new parameterizations of surface albedo, cloud droplet activation, convective plumes and related processes over leads, and turbulent transfer coefficients for stable surface layers. Four modes of the surface radiative energy budget were explored and reproduced by simulations. To advance the future synthesis of the results, cross-cutting activities are being developed aiming to answer key questions in four focus areas: lapse rate feedback, surface processes, Arctic mixed-phase clouds, and airmass transport and transformation.

Description: Clouds play a role in the changing Arctic climate, and are currently the cause for large uncertainties in climate projections. Therefore, we used the ICON model and a large set of observations to study the representation of clouds in the model for an Arctic location (Ny-Ålesund, Svalbard). Using several months of high-resolution ICON simulations, we evaluated the representation of the liquid water path, integrated water vapour, as well as vertical profiles of humidity and temperature. We found a good agreement in the large-scale dynamics and variables between the model and observations we used from the super-site AWIPEV which is located in Ny-Ålesund. As next step we are working on understanding the deficiencies which we found related to the phase-partitioning in the clouds. The phase-partitioning showed too much ice production in the model. To achieve a better understanding of the deficiencies we created a tool to output the process tendencies of the 2-moment microphysics scheme, implemented in ICON. With this we can evaluate the role each microphysical process plays for the development and evolution of the clouds. The previously run simulations are used to identify the influence of specific processes under certain environmental conditions on the increased ice production. We will show first results of this analysis and suggestions for processes that have to be improved to gain a more accurate phase-partitioning.

Description: The representation of Arctic mixed-phase clouds - their formation, evolution as well as persistence – is still rather uncertain in coarse-scale regional models. In order to first evaluate and potentially improve the respective microphysical parameterizations, a more detailed process understanding becomes necessary. We performed several simulations at hectometer scale – and by that approaching cloud-resolving scales – around different observational supersites, which are equipped with remote sensing instrumentations for cloud observations. We used the ICON model in a large-eddy configuration on a limited area domain, allowing heterogeneous surface as well as lateral boundary conditions. And we applied a two-moment microphysics parameterization, but no cloud fraction parameterization. By simulating several days, the representation of mixed-phase clouds could be compared to the supersite observations and investigated in a statistical way. While the model shows a great potential in capturing the general structure and timing of mixed-phase clouds – especially at Ny-Ålesund (Svalbard) – we will also discuss existing sensitivities and uncertainties e.g. with respect to ice-formation processes, which led to too many simulated ice clouds. Beyond the detailed microphysical aspects, another uncertainty in the Arctic comes from the uncertain surface properties, especially over sea-ice. To tackle this question and to estimate the sensitivity, we simulated and compared our model simulations at different places across the Arctic. As a second aspect, we will show the influence of different surface and large-scale conditions on the representation of Arctic mixed-phase clouds and point out potential issues to be considered and improved especially in coarse-scale simulations.