Arctic cloud properties derived from ground-based sensor synergy at Ny-Ålesund

Contemporary climate models show that clouds are one of the key components in the climate of the Arctic region experiencing rapid surface warming. Modeling of the cloud impact on the Arctic amplification is still uncertain not only because cloud life cycle is defined by large number of processes, but also because the clouds are closely related to other components of the Arctic climate, such as atmospheric water vapor, ocean, sea ice, and long-range air transport. In order to better understand the role of clouds in the Arctic, in June 2016 the French-German Arctic research station situated in Ny-Ålesund, Norway was complemented with a W-band cloud radar within the Transregional Collaborative Research Center (TRR 172) "Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC)³". This observation site became one of a few Arctic sites capable of state-of-the-art cloud profiling with high temporal and spatial resolution. This thesis summarizes the cloud macro and microphysical properties of clouds based on the first two and a half years of cloud measurements at Ny-Ålesund. The total occurrence of clouds was found to be ~81%. The most predominant type of clouds is multi-layer clouds with the frequency of occurrence of 44.8%. Single-layer clouds occur 36% of the time. The most common type of single-layer clouds is mixed-phase with a frequency of occurrence of 20.6%. The total occurrences of single-layer ice and liquid clouds are 9% and 6.4%, respectively. A comparison of cloud occurrence at Ny-Ålesund with a numerical weather prediction model revealed an overestimation in the occurrence of single layer ice clouds and underestimation of the occurrence of mixed-phase clouds. The cloud properties were further related to occurrence of anomalous atmospheric conditions often caused by transport of relatively warm and moist air from the North Atlantic and circulation of dry and cold air in the Arctic region. Dry anomalies are related to about 30% less cloud occurrence with respect to normal conditions. In contrast, during moist conditions the cloud occurrence typically reaches 90-99%. Excess and shortage in water vapor typically increases and decreases the amount of condensed water in cloud, respectively. The changes in cloud properties during moist and dry anomalies in turn affect the surface cloud radiative effect (CRE). In winter, spring, and autumn the net surface CRE is dominated by the longwave (LW) CRE and, therefore, during these seasons dry and moist conditions are related to lower and higher cloud related surface warming in Ny-Ålesund, respectively. In summer, shortwave CRE becomes dominant and moist conditions cause stronger surface cooling relative to normal cases, while dry conditions tend to reduce the cloud related surface cooling.Moist anomalies show significant positive trends varying for different seasons from 2.8 to 6.4%/decade. In contrast, the occurrence of dry anomalies has been declining at rates from -12.9 to -4%/decade. A novel technique for the estimation of LW CRE developed within this study shows that the long-term trends in the thermodynamic conditions at Ny-Ålesund are related to significant positive trends in longwave CRE of 3.4 and 2.2 W/(m² decade) in winter and autumn, respectively. In summer, a negative trend of -1.8 W/(m² decade) was found, while no significant trends were found for the spring season. The database with cloud profiles obtained within this work can be used for an evaluation of numerical weather prediction models, while radiative cloud properties estimated from reanalysis models can be evaluated with long-term LW CRE retrieved with the developed method

A Second Mortuary Hiatus on Lake Baikal in Siberia and the Arrival of Small-Scale Pastoralism

Peer reviewedPublisher PD

Domestication as Enskilment : Harnessing Reindeer in Arctic Siberia

Acknowledgements Funding for this project was provided by the Wenner-Gren Foundation (SFR1725) to R. Losey, the JPI HUMANOR project (ESRC ES/M011054/1) to D. Anderson, ERC GRETPOL to D. Arzyutov, and the Russian Foundation for Basic Research to N.Fedorova (18-09-40011). The authors wish to express their gratitude to the Nenets families, the Okotettos and Yaungads, who hosted us during our stay in Iamal, which is greatly appreciated. Special thanks are also offered to the staff of Iamal-Nenets Autonomous District, and the staff of the Iamal-Nenets Regional Museum and Exhibition Complex of I.S. Shemanovskii, Peter the Great Museum of Anthropology and Ethnography, and British Museum for providing access to their collections.Peer reviewedPostprin

A systematic assessment of water vapor products in the Arctic: from instantaneous measurements to monthly means

Water vapor is an important component in the water and energy cycle of the Arctic. Especially in light of Arctic amplification, changes in water vapor are of high interest but are difficult to observe due to the data sparsity of the region. The ACLOUD/PASCAL campaigns performed in May/June 2017 in the Arctic North Atlantic sector offers the opportunity to investigate the quality of various satellite and reanalysis products. Compared to reference measurements at R/V Polarstern frozen into the ice (around 82∘ N, 10∘ E) and at Ny-Ålesund, the integrated water vapor (IWV) from Infrared Atmospheric Sounding Interferometer (IASI) L2PPFv6 shows the best performance among all satellite products. Using all radiosonde stations within the region indicates some differences that might relate to different radiosonde types used. Atmospheric river events can cause rapid IWV changes by more than a factor of 2 in the Arctic. Despite the relatively dense sampling by polar-orbiting satellites, daily means can deviate by up to 50 % due to strong spatio-temporal IWV variability. For monthly mean values, this weather-induced variability cancels out, but systematic differences dominate, which particularly appear over different surface types, e.g., ocean and sea ice. In the data-sparse central Arctic north of 84∘ N, strong differences of 30 % in IWV monthly means between satellite products occur in the month of June, which likely result from the difficulties in considering the complex and changing surface characteristics of the melting ice within the retrieval algorithms. There is hope that the detailed surface characterization performed as part of the recently finished Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) will foster the improvement of future retrieval algorithms

Cloud microphysical properties retrieved from ground-based remote sensing at Ny-Ålesund (10 June 2016 - 8 October 2018)

The data contain time-height series of cloud liquid water content, ice water content, droplet and ice effective radii retrieved from ground-based remote sensing observations at the AWIPEV atmospheric observatory for the time period 10 June 2016 to 8 October 2018. The application of the cloud microphysical retrieval algorithms is based on the Cloudnet Categorization product (Illingworth et al., 2007, doi:10.1175/BAMS-88-6-883), which can be downloaded via http://devcloudnet.fmi.fi. Time and height resolution are same as in the Cloudnet Categorization product. The radar reflectivity values given in the Cloudnet Categorization product have been bias corrected. 4.66 dB (3 dB) have been added to the original data for the time period 10 June 2016 - 26 July 2017 (29 July 2017 - 8 October 2018). In case of single-layer liquid clouds containing cloud droplets only, liquid water content and liquid effective radius have been retrieved following Frisch et al. (1998, doi:10.1029/98JD01827) and Frisch (2002, doi:10.1175/1520-0426(2002)0192.0.CO;2), respectively. In case of clouds containing liquid droplets only, a scaled adiabatic liquid water content profile has also been retrieved so that its integral matches the liquid water path from the microwave radiometer. For all height bins, where the Cloudnet Categorization product indicates the presence of ice, ice water content and ice effective radius have been retrieved as described by Hogan et al. (2006, doi:10.1175/JAM2340.1) and Delanoë et al. (2007, doi:10.1175/JAM2543.1)

Integrated water vapor of HATPRO microwave radiometer at AWIPEV, Ny-Ålesund (2017)

The microwave radiometer HATPRO has seven K band (22.235-31.4 GHz) channels that are sensitive to the path integrated amount of cloud liquid water (clwvi) and the path integrated amount of water vapor (precipitable water, prw). These files contain prw as a function of measurement time, elevation and azimuth angle observing direction. prw is based on multi-variate regression retrievals (see also Crewell and Löhnert, 2003; doi:10.1029/2002RS002654). Provided uncertainties describe the expected standard error of prw. Note, that prw values are given for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. Additionally included are temperature, pressure and humidity at the instrument location as well quality flags characterizing the instrument and retrieval performance

Liquid water path of HATPRO microwave radiometer at AWIPEV, Ny-Ålesund (2018)

The microwave radiometer HATPRO has seven K band (22.235-31.4 GHz) channels that are sensitive to the path integrated amount of cloud liquid water (clwvi) and the path integrated amount of water vapor (precipitable water, prw). These files contain clwvi as a function of measurement time, elevation and azimuth angle observing direction. clwvi is based on multi-variate regression retrievals (see also Crewell and Löhnert, 2003; doi:10.1029/2002RS002654). Provided uncertainties describe the expected standard error of clwvi. Note, that clwvi values are given for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. Additionally included are temperature, pressure and humidity at the instrument location as well quality flags characterizing the instrument and retrieval performance

Integrated water vapor of HATPRO microwave radiometer at AWIPEV, Ny-Ålesund (2016)

The microwave radiometer HATPRO has seven K band (22.235-31.4 GHz) channels that are sensitive to the path integrated amount of cloud liquid water (clwvi) and the path integrated amount of water vapor (precipitable water, prw). These files contain prw as a function of measurement time, elevation and azimuth angle observing direction. prw is based on multi-variate regression retrievals (see also Crewell and Löhnert, 2003; doi:10.1029/2002RS002654). Provided uncertainties describe the expected standard error of prw. Note, that prw values are given for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. Additionally included are temperature, pressure and humidity at the instrument location as well quality flags characterizing the instrument and retrieval performance

Liquid water path of HATPRO microwave radiometer at AWIPEV, Ny-Ålesund (2017)

The microwave radiometer HATPRO has seven K band (22.235-31.4 GHz) channels that are sensitive to the path integrated amount of cloud liquid water (clwvi) and the path integrated amount of water vapor (precipitable water, prw). These files contain clwvi as a function of measurement time, elevation and azimuth angle observing direction. clwvi is based on multi-variate regression retrievals (see also Crewell and Löhnert, 2003; doi:10.1029/2002RS002654). Provided uncertainties describe the expected standard error of clwvi. Note, that clwvi values are given for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. Additionally included are temperature, pressure and humidity at the instrument location as well quality flags characterizing the instrument and retrieval performance

HATPRO microwave radiometer measurements at AWIPEV, Ny-Ålesund (2016-2018)

The microwave radiometer HATPRO has seven K band (22.235-31.4 GHz) channels that are sensitive to the path integrated amount of cloud liquid water (clwvi) and the path integrated amount of water vapor (precipitable water, prw). It also has seven V band (51.8-58.8 GHz) channels that are sensitive to atmospheric temperature (ta). The data from 2016-2018 are given in this collection