Meteorological measurements from 8 dropsondes released during POLAR 4 flight on 1993-03-04 along a track orthogonal to the pack ice edge north west of Svalbard

Different parameterizations of subgrid-scale fluxes are utilized in a nonhydrostatic and anelastic mesoscale model to study their influence on simulated Arctic cold air outbreaks. A local closure, a profile closure and two nonlocal closure schemes are applied, including an improved scheme, which is based on other nonlocal closures. It accounts for continuous subgrid-scale fluxes at the top of the surface layer and a continuous Prandtl number with respect to stratification. In the limit of neutral stratification the improved scheme gives eddy diffusivities similar to other parameterizations, whereas for strong unstable stratifications they become much larger and thus turbulent transports are more efficient. It is shown by comparison of model results with observations that the application of simple nonlocal closure schemes results in a more realistic simulation of a convective boundary layer than that of a local or a profile closure scheme. Improvements are due to the nonlocal formulation of the eddy diffusivities and to the inclusion of heat transport, which is independent of local gradients (countergradient transport).

Land cover classification of Samoylov Island and Landsat subpixel water cover of Lena River Delta, Siberia, with links to ESRI grid files

Ignoring small-scale heterogeneities in Arctic land cover may bias estimates of water, heat and carbon fluxes in large-scale climate and ecosystem models. We investigated subpixel-scale heterogeneity in CHRIS/PROBA and Landsat-7 ETM+ satellite imagery over ice-wedge polygonal tundra in the Lena Delta of Siberia, and the associated implications for evapotranspiration (ET) estimation. Field measurements were combined with aerial and satellite data to link fine-scale (0.3 m resolution) with coarse-scale (upto 30 m resolution) land cover data. A large portion of the total wet tundra (80%) and water body area (30%) appeared in the form of patches less than 0.1 ha in size, which could not be resolved with satellite data. Wet tundra and small water bodies represented about half of the total ET in summer. Their contribution was reduced to 20% in fall, during which ET rates from dry tundra were highest instead. Inclusion of subpixel-scale water bodies increased the total water surface area of the Lena Delta from 13% to 20%. The actual land/water proportions within each composite satellite pixel was best captured with Landsat data using a statistical downscaling approach, which is recommended for reliable large-scale modelling of water, heat and carbon exchange from permafrost landscapes.

Sea ice thickness distribution (ITD) Model for the Arctic, links to NetCDF files

Sea ice models contain many different parameterizations of which one of the most commonly used is a subgrid-scale ice thickness distribution (ITD). The effect of this model component and the associated ice strength formulation on the reproduction of observed Arctic sea ice is assessed. To this end the model's performance in reproducing satellite observations of sea ice concentration, thickness and drift is evaluated. For an unbiased comparison, different model configurations with and without an ITD are tuned with an automated parameter optimization. The original combination of ITD and ice strength parameterization does not lead to better results than a simple single category model. Yet changing to a simpler ice strength formulation, which depends linearly on the mean ice thickness across all thickness categories, allows to clearly improve the model-data misfit when using an ITD. In the original formulation, the ice strength depends strongly on the number of thickness categories, so that introducing more categories can lead to thicker albeit weaker ice on average.

Homogeneous Spatial Units (HSU) - a Pan-European geographical basis for environmental and socio-economic modelling

The spatial data set delineates areas with similar environmental properties regarding soil, terrain morphology, climate and affiliation to the same administrative unit (NUTS3 or comparable units in size) at a minimum pixel size of 1km2. The scope of developing this data set is to provide a link between spatial environmental information (e.g. soil properties) and statistical data (e.g. crop distribution) available at administrative level. Impact assessment of agricultural management on emissions of pollutants or radiative active gases, or analysis regarding the influence of agricultural management on the supply of ecosystem services, require the proper spatial coincidence of the driving factors. The HSU data set provides e.g. the link between the agro-economic model CAPRI and biophysical assessment of environmental impacts (updating previously spatial units, Leip et al. 2008), for the analysis of policy scenarios. Recently, a statistical model to disaggregate crop information available from regional statistics to the HSU has been developed (Lamboni et al. 2016). The HSU data set consists of the spatial layers provided in vector and raster format as well as attribute tables with information on the properties of the HSU. All input data for the delineation the HSU is publicly available. For some parameters the attribute tables provide the link between the HSU data set and e.g. the soil map(s) rather than the data itself. The HSU data set is closely linked the USCIE data set.

Drivers of multi-scale plant community structure in agricultural field margins of South Korea

This data set describes the distribution of a total of 90 plant species growing on field margins of an agricultural landscape in the Haean-myun catchment in South Korea. We conducted our survey between July and August 2011 in 100 sampling plots, covering the whole catchment. In each plot we measured three environmental variables: slope, width of the field margin, and management type (i.e. "managed" for field margins that had signs of management activities from the ongoing season such as cutting or spraying herbicides and "unmanaged" for field margins that had been left untouched in the season). For the botanical survey each plot was sampled using three subplots of one square meter per subplot; subplots were 4 m apart from each other. In each subplot, we estimated three different vegetation characteristics: vegetation cover (i.e. the percentage of ground covered by vegetation), species richness (i.e. the number of observed species) and species abundance (i.e. the number of observed individuals / species). We calculated the percentage of the non-farmed habitats by creating buffer zones of 100, 200, 300, 400 and 500 m radii around each plot using data provided by (Seo et al. 2014). Non-farmed habitats included field margins, fallows, forest, riparian areas, pasture and grassland.