Carbon uptake rate calculated for melt pond water samples during POLARSTERN cruise ARK-XXVII/3 (IceArc) in 2012

Net Primary Production was measured using the 14**C uptake method with minor modifications. Melt pond samples were spiked with 0.1µCi ml**-1 of 14**C labelled sodium bicarbonate (Moravek Biochemicals, Brea, USA) and distributed in 10 clear bottles (20 ml each). Subsequently they were incubated for 12 h at -1.3°C under different scalar irradiances (0-420 µmol photons m**-2 s**-1) measured with a spherical sensor (Spherical Micro Quantum Sensor US-SQS/L, Heinz Walz, Effeltrich, Germany). At the end of the incubation, samples were filtered onto 0.2 µm nitrocellulose filters and the particulate radioactive carbon uptake was determined by liquid scintillation counting using Filter count scintillation cocktail (Perkin Elmer, Waltham, USA). The carbon uptake values in the dark were subtracted from the carbon uptake values measured in the light incubations. Dissolved inorganic carbon (DIC) was measured for each sample using the flow injection system (Hall and Aller, 1992). The DIC concentration was taken into account to calculate the amount of labeled bicarbonate incorporated into the cell. Carbon fixation rates were normalized volumetrically and by chlorophyll a. Photosynthesis-irradiance curves (PI curves) were fitted using MATLAB® according to the equation proposed by Platt et al. (1980) including a photoinhibition parameter (beta) and providing the main photosynthetic parameters: maximum Chla normalized carbon fixation rate if there were no photoinhibition (Pb) and the initial slope of the saturation curve (alpha). The derived parameters: light intensity at which photosynthesis is maximal (Im), the carbon fixation rate at that maximal irradiance (Pbm) and the adaptation parameter or photoacclimation index (Ik) were calculated according to Platt et al. (1982).

Persistent organic pollutants, d13C and d15N in zooplankton, fish and bird species in Kongsfjorden according to season

Seasonality in biomagnification of persistent organic pollutants (POPs; polychlorinated biphenyls, chlorinated pesticides, and brominated flame retardants) in Arctic marine pelagic food webs was investigated in Kongsfjorden, Svalbard, Norway. Trophic magnification factors (TMFs; average factor change in concentration between two trophic levels) were used to measure food web biomagnification in biota in May, July, and October 2007. Pelagic zooplankton (seven species), fish (five species), and seabirds (two species) were included in the study. For most POP compounds, highest TMFs were found in July and lowest were in May. Seasonally changing TMFs were a result of seasonally changing POP concentrations and the d15N-derived trophic positions of the species included in the food web. These seasonal differences in TMFs were independent of inclusion/exclusion of organisms based on physiology (i.e., warm- versus cold-blooded organisms) in the food web. The higher TMFs in July, when the food web consisted of a higher degree of boreal species, suggest that future warming of the Arctic and increased invasion by boreal species can result in increased food web magnification. Knowledge of the seasonal variation in POP biomagnification is a prerequisite for understanding changes in POP biomagnification caused by climate change.

Surface temperature measurements near Ny-Ålesund, Svalbard, Norway in 2008 with links to data files

The ground surface temperature is one of the key parameters that determine the thermal regime of permafrost soils in arctic regions. Due to remoteness of most permafrost areas, monitoring of the land surface temperature (LST) through remote sensing is desirable. However, suitable satellite platforms such as MODIS provide spatial resolutions, that cannot resolve the considerable small-scale heterogeneity of the surface conditions characteristic for many permafrost areas. This study investigates the spatial variability of summer surface temperatures of high-arctic tundra on Svalbard, Norway. A thermal imaging system mounted on a mast facilitates continuous monitoring of approximately 100 x 100 m of tundra with a wide variability of different surface covers and soil moisture conditions over the entire summer season from the snow melt until fall. The net radiation is found to be a control parameter for the differences in surface temperature between wet and dry areas. Under clear-sky conditions in July, the differences in surface temperature between wet and dry areas reach up to 10K. The spatial differences reduce strongly in weekly averages of the surface temperature, which are relevant for the soil temperature evolution of deeper layers. Nevertheless, a considerable variability remains, with maximum differences between wet and dry areas of 3 to 4K. Furthermore, the pattern of snow patches and snow-free areas during snow melt in July causes even greater differences of more than 10K in the weekly averages. Towards the end of the summer season, the differences in surface temperature gradually diminish. Due to the pronounced spatial variability in July, the accumulated degree-day totals of the snow-free period can differ by more than 60% throughout the study area. The terrestrial observations from the thermal imaging system are compared to measurements of the land surface temperature from the MODIS sensor. During periods with frequent clear-sky conditions and thus a high density of satellite data, weekly averages calculated from the thermal imaging system and from MODIS LST agree within less than 2K. Larger deviations occur when prolonged cloudy periods prevent satellite measurements. Futhermore, the employed MODIS L2 LST data set contains a number of strongly biased measurements, which suggest an admixing of cloud top temperatures. We conclude that a reliable gap filling procedure to moderate the impact of prolonged cloudy periods would be of high value for a future LST-based permafrost monitoring scheme. The occurrence of sustained subpixel variability of the summer surface temperature is a complicating factor, whose impact needs to be assessed further in conjunction with other spatially variable parameters such as the snow cover and soil properties.