Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model

Sea ice is an important component of the global climate system. The presence of a snowpack covering sea ice can strongly modify the thermodynamic behavior of the sea ice, due to the low thermal conductivity and high albedo of snow. The snowpack can be stratified and change properties (density, water content, grain size and shape) throughout the seasons. Melting snow provides freshwater which can form melt ponds or cause flushing of salt out of the underlying sea ice, while flooding of the snow layer by saline ocean water can strongly impact both the ice mass balance and the freezing point of the snow. To capture the complex dynamics from the snowpack, we introduce modifications to the physics-based, multi-layer SNOWPACK model to simulate the snow-sea-ice system. Adaptations to the model thermodynamics and a description of water and salt transport through the snow-sea-ice system by coupling the transport equation to the Richards equation were added. These modifications allow the snow microstructure descriptions developed in the SNOWPACK model to be applied to sea ice conditions as well. Here, we drive the model with data from snow and ice mass-balance buoys installed in the Weddell Sea in Antarctica. The model is able to simulate the temporal evolution of snow density, grain size and shape, and snow wetness. The model simulations show abundant depth hoar layers and melt layers, as well as superimposed ice formation due to flooding and percolation. Gravity drainage of dense brine is underestimated as convective processes are so far neglected. Furthermore, with increasing model complexity, detailed forcing data for the simulations are required, which are difficult to acquire due to limited observations in polar regions

Validation of SMOS sea ice thickness retrieval in the northern Baltic Sea

The Soil Moisture and Ocean Salinity (SMOS) mission observes brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) with a daily coverage of the polar regions. L-band radiometry has been shown to provide information on the thickness of thin sea ice. Here, we apply a new emission model that has previously been used to investigate the impact of snow on thick Arctic sea ice. The model has not yet been used to retrieve ice thickness. In contrast to previous SMOS ice thickness retrievals, the new model allows us to include a snow layer in the brightness temperature simulations. Using ice thickness estimations from satellite thermal imagery, we simulate brightness temperatures during the ice growth season 2011 in the northern Baltic Sea. In both the simulations and the SMOS observations, brightness temperatures increase by more than 20 K, most likely due to an increase of ice thickness. Only if we include the snow in the model, the absolute values of the simulations and the observations agree well (mean deviations below 3.5 K). In a second comparison, we use high-resolution measurements of total ice thickness (sum of ice and snow thickness) from an electromagnetic (EM) sounding system to simulate brightness temperatures for 12 circular areas. While the SMOS observations and the simulations that use the EM modal ice thickness are highly correlated (r2=0.95), the simulated brightness temperatures are on average 12 K higher than observed by SMOS. This would correspond to an 8-cm overestimation of the modal ice thickness by the SMOS retrieval. In contrast, if the simulations take into account the shape of the EM ice thickness distributions (r2=0.87), the mean deviation between simulated and observed brightness temperatures is below 0.1 K

Snow Cover Impacts on Antarctic Sea ice

The snow cover on Antarctic sea ice impacts the energy, mass, and momentum balance of the sea ice cover, which in turn strongly influences fluxes between ocean, sea ice, and atmosphere. Despite the fact that snow depth is qualified as an Essential Climate Variable, the knowledge of the snow cover distribution and properties is still mostly vague and no large-scale (e.g. Antarctic wide) snow depth data product is available. Snow on Antarctic sea ice is characterized through a high spatial and temporal variability, and shows a highly heterogeneous internal stratification. This poses a challenge to air or space borne snow depth retrieval algorithms. Similarly, sea ice models are not yet able to resolve snow processes with enough accuracy. Here we present measurements of snow depth and physical snow properties along drift trajectories of autonomous Snow Buoys, which were deployed during several Polarstern cruises in the Weddell Sea since 2014. Resulting time series of snow depth show an event driven snow accumulation even during austral summer, whereas melting and a significant decrease of snow depth is only observed along the marginal sea ice zone. Additional analysis with the 1D multi-layer thermodynamic snow model SNOWPACK provides insights into internal processes such as snow to ice development cover along the trajectories. For these studies, SNOWPACK, which was previously used for land-based snow, has been extended with a sea ice module and is forced with re-analysis data. Comparisons between model and in-situ measurements show the capability of the model to reproduce the prevalent snow stratigraphy

Observations of snow cover processes on Antarctic sea ice from in-situ and model studies

The snow cover on Antarctic sea ice impacts the energy, mass, and momentum balance of the sea ice cover, which in turn strongly influences fluxes between ocean, sea ice, and atmosphere. Despite the fact that snow depth is qualified as an Essential Climate Variable, the knowledge of the snow cover distribution and properties is still mostly vague and no large-scale (e.g. Antarctic wide) snow depth data product is available. Snow on Antarctic sea ice is characterized through a high spatial and temporal variability, and shows a highly heterogeneous internal stratification. This poses a challenge to air or space borne snow depth retrieval algorithms. Similarly, sea ice models are not yet able to resolve snow processes with enough accuracy. Here we present measurements of snow depth and physical snow properties along drift trajectories of autonomous Snow Buoys, which were deployed during several Polarstern cruises in the Weddell Sea since 2014. Resulting time series of snow depth show an event driven snow accumulation even during austral summer, whereas melting and a significant decrease of snow depth is only observed along the marginal sea ice zone. Additional analysis with the 1D multi-layer thermodynamic snow model SNOWPACK provides insights into internal processes such as snow to ice development cover along the trajectories. For these studies, SNOWPACK, which was previously used for land-based snow, has been extended with a sea ice module and is forced with re-analysis data. Comparisons between model and in-situ measurements show the capability of the model to reproduce the prevalent snow stratigraphy

From snow accumulation to snow depth distributions by quantifying meteoric ice fractions in the Weddell Sea

Year-round snow cover is a characteristic of the entire Antarctic sea ice cover, which has significant implications for the energy and mass budgets of sea ice, e.g., by preventing surface melt in summer and enhancing sea ice growth through extensive snow ice formation. However, substantial observational gaps in the seasonal cycle of Antarctic sea ice and its snow cover limit the understanding of important processes in the ice-covered Southern Ocean. They also introduce large uncertainties in satellite remote sensing applications and climate studies. Here we present results from 10 years of autonomous snow observations from Snow Buoys in the Weddell Sea. To distinguish between actual snow depth and potential snow ice thickness within the accumulated snowpack, a one-dimensional thermodynamic sea ice model is applied along the drift trajectories of the buoys. The results show that potential snow ice formation, with an average maximum thickness of 35cm, was detected along 41% of the total track length of the analyzed Snow Buoy tracks, which corresponds to about one-quarter of the snow accumulation. In addition, we simulate the evolution of internal snow properties along the drift trajectories with the more complex SNOWPACK model, which results in superimposed ice thicknesses between 0 and 14cm on top of the snow ice layer. These estimates will provide an important reference dataset for both snow depth and meteoric ice rates in the Southern Ocean

Zum Zusammenhang von Mediennutzung, Verhaltensauffälligkeiten und ADHS

Maaß EE, Hahlweg K, Heinrichs N, Kuschel A, Döpfner M. Bildschirmmedien im Kindergartenalter. Zeitschrift für Gesundheitspsychologie. 2010;18(2):55-68.Zusammenfassung. Die vorliegende Querschnittsuntersuchung (N = 708 Familien) versuchte unter Berücksichtigung wichtiger Kovariaten wie z.B. des elterlichen Erziehungsverhaltens oder sozioökonomischer Variablen Zusammenhänge zwischen der Nutzung elektronischer Medien, Verhaltensauffälligkeiten und ADHS zu identifizieren. Nach der SÖS (sozioökonomischer Status) – Mainstreaming Hypothese würden die stärksten Zusammenhänge für Kinder mit hohem SÖS erwartet, währenddessen nach der SÖS-Resonanz Hypothese diese vor allem für Kinder mit niedrigem SÖS erwartet werden würden. Ergebnisse: Es zeigten sich signifikante Zusammenhänge zwischen der täglichen Fernsehnutzung und vermehrten Schlafproblemen, aggressivem Verhalten sowie Aufmerksamkeitsproblemen. Eine tägliche Computernutzung wies hingegen einen Zusammenhang mit geringeren Aufmerksamkeitsproblemen auf. In Abhängigkeit des SÖS der Mütter folgten die Zusammenhänge (v.a.) hinsichtlich der Fernsehnutzung und internalisierendem Verhalten in gewissem Maße den Vorhersagen einer Resonanz-Hypothese, währenddessen in Abhängigkeit des SÖS der Väter diese eher einer Mainstreaming-Hypothese entsprachen. Zukünftige Forschungsarbeiten können profitieren, wenn diese Moderationen im Untersuchungsdesign stärker berücksichtigt würden