ALBERT

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: The Arctic is a hotspot of climate change and is currently undergoing rapid changes in particular in its snow and sea-ice cover, and near-surface air temperature. While observations document these changes, the underlying processes and feedbacks contributing to this phenomenon called Arctic amplification are not fully understood, and limit our ability to predict the future evolution of the Arctic climate system.Within this presentation, the 20+ year climate data record of the Clouds and the Earth’s Radiant Energy System (CERES) project will be used to analyze changes of the surface radiation budget across the Arctic, and investigate the role of clouds in these changes. The focus is directed to the identification of significant trends in contrast to internal climate variability. Attention is also directed to regional contrasts and the seasonality of changes. Results are combined with and contrasted to the ERA5 reanalysis, attempting to reconcile differences and interpreting these changes as part of the Arctic surface energy budget.Subsequently, our results are used to provide context for the observations of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC ) expedition. Specifically, we will attempt to answer the following questions: do MOSAiC observations confirm the CERES and ERA5 data sets in terms of cloud radiative effects? Given the availability of 20 years of CERES data before MOSAiC: would we have expected significant differences in the MOSAiC observations of clouds and radiative fluxes, if the expedition had taken place at the start of the CERES data record?

Description: An accurate determination of location and amount of liquid water in clouds is crucial for precipitation formation, cloud lifetime, and cloud radiative effects. Most remote-sensing retrievals, such as Cloudnet use lidar measurements to infer the location of liquid cloud droplets from measurements. However, lidar observations are of very limited use for optically thick or multilayer mixed-phase clouds (MPC) where they usually underestimate the presence of liquid water due to full signal attenuation, leading to large biases in simulated radiative fluxes. At the same time, general circulation models largely overestimate the downwelling shortwave radiation at the bottom of the atmosphere especially in the Southern Ocean regions. We argue that, in order to reduce this shortwave radiation bias in models, we first need better observational-based retrievals for supercooled-liquid detection that can be used for model validation. For this purpose, the machine-learning-based retrieval VOODOO is used to capture the extent of liquid layers over the complete vertical range of the clouds.To conceptualize the latter, a case study from the DACAPO-PESO campaign in Punta Arenas, Chili (53.13° S, 70.88° W) was investigated in detail by performing a radiative closures study. The shortwave cloud radiative effects of multilayer non-precipitating stratiform MPC - with liquid water layers detected by Cloudnet and VOODOO - was determined using a 1-D radiative transfer simulator and validated with downwelling pyranometer observations. The shortwave radiation bias was reduced by a factor of two suggesting that improved liquid-layer detection helps to decrease the shortwave radiation bias in radiative transfer simulations.

Description: To understand the feedbacks driving the amplified changes in the Arctic a quantification of the contribution of the involved processes is necessary. Here a detailed study of low-level Arctic clouds on the surface radiation budget is presented. These clouds frequently occur below the lowest detection range of most state-of-the-art remote-sensing instruments and were observed in summertime during 25% of the time over the marginal sea ice zone. The low altitude of these clouds poses challenges on their observation and characterization by remote-sensing techniques. Ground-based remote sensing and surface radiation flux measurements performed during the Arctic cruise PS106 in 2017 were combined with radiative transfer simulations to study low-level clouds. A multiwavelength lidar PollyXT with near-range observations capabilities down to 50m and a cloud radar with a lowest detection limit at 165m altitude were operated continuously. The liquid-water microphysical properties of clouds missed by the cloud radar were estimated using measurements of a microwave radiometer HATPRO and the lidar-detected cloud base. Thereby the surface radiative effect of these clouds was quantified. A closure between the observed and modelled radiative surface fluxes was achieved with a realistic representation of low-level liquid-containing clouds in the radiative transfer model. When omitting these low-level clouds, the cloud radiative effect at the surface was misestimated by 43Wm−2. The presented study highlights the importance of improving cloud retrievals for low-level liquid-containing clouds as they are frequently encountered in the high Arctic, together with observational capabilities, both in terms of cloud remote sensing and radiative flux observations.

Description: Significant uncertainties in the prediction of future warming in the Arctic arise from our lack of understanding of governing processes, including cloud radiative feedbacks. The present study compares preliminary simulations of 1D radiative fluxes based on the Cloudnet and ShupeTurner cloud retrievals for the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The analysis is conducted in the context of a radiative closure study at the surface and the top-of-the atmosphere for the upward and downward broadband solar and terrestrial radiative fluxes. The consistency of our simulations and satellite-based estimates from the Clouds and the Earth’s Radiant Energy System (CERES) are analysed by considering several atmospheric and surface-type conditions. Particular focus is given to the effect of clouds on the radiation budget. Based on our simulations and CERES estimates, we find that clouds increase the net radiative fluxes at the surface by about 35.5 W/m² for the entire MOSAiC expedition period. Nevertheless, based on in-situ observations, it is argued that significant uncertainties in the solar and terrestrial affect this estimate of cloud radiative effects. Our research will also address the spatiotemporal variability of clouds and how this might impact the comparison between the point-like ground measurements with the CERES satellite footprint.