Presentation_1_Large-Scale Variability of Physical and Biological Sea-Ice Properties in Polar Oceans.pdf
In this study, we present unique data collected with a Surface and Under-Ice Trawl (SUIT) during five campaigns between 2012 and 2017, covering the spring to summer and autumn transition in the Arctic Ocean, and the seasons of winter and summer in the Southern Ocean. The SUIT was equipped with a sensor array from which we retrieved: sea-ice thickness, the light field at the underside of sea ice, chlorophyll a concentration in the ice (in-ice chl a), and the salinity, temperature, and chl a concentration of the under-ice water. With an average trawl distance of about 2 km, and a global transect length of more than 117 km in both polar regions, the present work represents the first multi-seasonal habitat characterization based on kilometer-scale profiles. The present data highlight regional and seasonal patterns in sea-ice properties in the Polar Ocean. Light transmittance through Arctic sea ice reached almost 100% in summer, when the ice was thinner and melt ponds spread over the ice surface. However, the daily integrated amount of light under sea ice was maximum in spring. Compared to the Arctic, Antarctic sea-ice was thinner, snow depth was thicker, and sea-ice properties were more uniform between seasons. Light transmittance was low in winter with maximum transmittance of 73%. Despite thicker snow depth, the overall under-ice light was considerably higher during Antarctic summer than during Arctic summer. Spatial autocorrelation analysis shows that Arctic sea ice was characterized by larger floes compared to the Antarctic. In both Polar regions, the patch size of the transmittance followed the spatial variability of sea-ice thickness. In-ice chl a in the Arctic Ocean remained below 0.39 mg chl a m−2, whereas it exceeded 7 mg chl a m−2 during Antarctic winter, when water chl a concentrations remained below 1.5 mg chl a m−2, thus highlighting its potential as an important carbon source for overwintering organisms. The data analyzed in this study can improve large-scale physical and ecosystem models, habitat mapping studies and time series analyzed in the context of climate change effects and marine management.