Seasonal thickness changes of Arctic sea ice north of Svalbard and implications for satellite remote sensing, ecosystem, and environmental management

Sea-ice thickness is a crucial parameter to consider when assessing the status of Arctic sea ice, whether for environmental management, monitoring projects, or regional or pan-arctic assessments. Modern satellite remote sensing techniques allow us to monitor ice extent and to estimate sea-ice thickness changes; but accurate quantifications of sea-ice thickness distribution rely on in situ and airborne surveys. From January to June 2015, an international expedition (N-ICE2015) took place in the Arctic Ocean north of Svalbard, with the Norwegian research vessel RV Lance frozen into drifting sea ice. In total, four drifts, with four different floes were made during that time. Sea-ice and snow thickness measurements were conducted on all main ice types present in the region, first year ice, multiyear ice, and young ice. Measurement methods included ground and helicopter based electromagnetic surveys, drillings, hot-wire installations, snow-sonde transects, snow stakes, and ice mass balance and snow buoys. Ice thickness distributions revealed modal thicknesses in spring between 1.6 and 1.7 m, which is lower than reported for the region from comparable studies in 2009 (2.4 m) and 2011 (1.8 m). Knowledge about the ice thickness distribution in a region is crucial to the understanding of climate processes, and also relevant to other disciplines. Sea-ice thickness data collected during N-ICE2015 can also give us insights into how ice and snow thicknesses affect ecosystem processes. In this presentation, we will explore the influence of snow cover and ocean properties on ice thickness, and the role of sea-ice thickness in air-ice-ocean interactions. We will also demonstrate how information about ice thickness aids classification of different sea ice types from SAR satellite remote sensing, which has real-world applications for shipping and ice forecasting, and how sea ice thickness data contributes to climate assessments.