Abstract. In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. We present VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), a retrieval based on deep convolutional neural networks (CNNs) mapping radar Doppler spectra to the probability of the presence of cloud droplets (CD). The training of the CNN was realized using the Cloudnet processing suite as supervisor. Once trained, VOODOO yields the probability for CD directly at Cloudnet grid resolution. Long-term predictions of 18 months in total from two mid-latitudinal locations, i.e., Punta Arenas, Chile (53.1∘ S, 70.9∘ W), in the Southern Hemisphere and Leipzig, Germany (51.3∘ N, 12.4∘ E), in the Northern Hemisphere, are evaluated. Temporal and spatial agreement in cloud-droplet-bearing pixels is found for the Cloudnet classification to the VOODOO prediction. Two suitable case studies were selected, where stratiform, multi-layer, and deep mixed-phase clouds were observed. Performance analysis of VOODOO via classification-evaluating metrics reveals precision > 0.7, recall ≈ 0.7, and accuracy ≈ 0.8. Additionally, independent measurements of liquid water path (LWP) retrieved by a collocated microwave radiometer (MWR) are correlated to the adiabatic LWP, which is estimated using the temporal and spatial locations of cloud droplets from VOODOO and Cloudnet in connection with a cloud parcel model. This comparison resulted in stronger correlation for VOODOO (≈ 0.45) compared to Cloudnet (≈ 0.22) and indicates the availability of VOODOO to identify CD beyond lidar attenuation. Furthermore, the long-term statistics for 18 months of observations are presented, analyzing the performance as a function of MWR–LWP and confirming VOODOO's ability to identify cloud droplets reliably for clouds with LWP > 100 g m−2. The influence of turbulence on the predictive performance of VOODOO was also analyzed and found to be minor. A synergy of the novel approach VOODOO and Cloudnet would complement each other perfectly and is planned to be incorporated into the Cloudnet algorithm chain in the near future.

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Abstract. The paper presents the measurement strategy and dataset collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and Lidar Planner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The preliminary results show an undocumented variation of the lateral coherence with the distance from the coast. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines.

Abstract. The paper presents the measurement strategy and data set collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and LidarPlanner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines. The preliminary results show limited variations of the lateral coherence with the scanning distance. A slight increase in the identified Davenport decay coefficient with the height is partly due to the limited pointing accuracy of the instruments. These results underline the importance of achieving pointing errors under 0.1∘ to study properly the lateral coherence of turbulence at scanning distances of several kilometres.

Abstract. The Arctic has warmed much more than the global mean during past decades. The lapse-rate feedback (LRF) has been identified as large contributor to the Arctic amplification (AA) of climate change. This particular feedback arises from the vertically non-uniform warming of the troposphere, which in the Arctic emerges as strong near-surface, and muted free-tropospheric warming. Stable stratification and meridional energy transport are two characteristic processes that are evoked as causes for this vertical warming structure. Our aim is to constrain these governing processes by making use of detailed observations in combination with the large climate model ensemble of the 6th Coupled Model Intercomparison Project (CMIP6). We build on the result that CMIP6 models show a large scatter in Arctic LRF and AA, which are positively correlated for the historical period 1951–2014. Thereby, we present process-oriented constraints by linking characteristics of the current climate to historical climate simulations. In particular, we compare a large consortium of present-day observations to co-located model data from subsets with weak and strong simulated AA and Arctic LRF in the past. Our results firstly suggest that local Arctic processes mediating the lower thermodynamic structure of the atmosphere are more realistically depicted in climate models with weak Arctic LRF and AA (CMIP6/w) in the past. In particular, CMIP6/w models show stronger inversions at the end of the simulation period (2014) for boreal fall and winter, which is more consistent with the observations. This result is based on radiosonde observations from the year-long MOSAiC expedition in the central Arctic, together with long-term radio soundings at the Utqiaǵvik site in Alaska, USA, and dropsonde measurements from aircraft campaigns in the Fram Strait. Secondly, remote influences that can further mediate the warming structure in the free troposphere are more realistically represented by models with strong simulated Arctic LRF and AA (CMIP6/s) in the past. In particular, CMIP6/s models systemically simulate a stronger Arctic energy transport convergence in the present climate for boreal fall and winter, which is more consistent with reanalysis results. Locally, we find links between changes in transport pathways and vertical warming structures that favor a positive LRF in the CMIP6/s simulations. This hints to the mediating influence of advection on the Arctic LRF. We emphasise that one major attempt of this work is to give insights in different perspectives on the Arctic LRF. We present a variety of contributions from a large collaborative research consortium to ultimately find synergy among them in support of advancing our understanding of the Arctic LRF.