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The Svalbard archipelago in the Arctic North Atlantic is experiencing rapid changes in the surface climate and sea ice distribution, with impacts for the coupled climate system and the local society. This study utilizes observational data of surface air temperature (SAT) from 1980–2016 across the whole Svalbard archipelago, and sea ice extent (SIE) from operational sea ice charts to conduct a systematic assessment of climatologies, long‐term changes and regional differences. The proximity to the warm water mass of the West Spitsbergen Current drives a markedly warmer climate in the western coastal regions compared to northern and eastern Svalbard. This imprints on the SIE climatology in southern and western Svalbard, where the annual maxima of 50–60% area ice coverage are substantially less than 80–90% in the northern and eastern fjords. Owing to winter‐amplified warming, the local climate is shifting towards more maritime conditions, and SIE reductions of between 5 and 20% per decade in particular regions are found, such that a number of fjords in the west have been virtually ice‐free in recent winters. The strongest decline comes along with SAT forcing and occurs over the most recent 1–2 decades in all regions; while in the 1980s and 1990s, enhanced northerly winds and sea ice drift can explain 30–50% of SIE variability around northern Svalbard, where they had correspondingly lead to a SIE increase. With an ongoing warming it is suggested that both the meteorological and cryospheric conditions in eastern Svalbard will become increasingly similar to what is already observed in the western fjords, namely suppressed typical Arctic climate conditions. [ABSTRACT FROM AUTHOR]

Knowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% ‘thin’, 74.4% ‘level’, and 14.4% ‘deformed’. The area fractions of the simplified classes from ground- and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites. [ABSTRACT FROM AUTHOR]

Snow thickness, duration of snow coverage and amount of ice coveringthe soil are crucial for the development of biota in the Arctic tundra environment. The snow thickness and optical properties control theamount of Photosynthetically Active Radiation (PAR) that is available for vegetation. A late snow cover may prevent birds from nesting onthe ground. Furthermore, ice at the snow/soil interface can be an obstacle for grazing of Svalbard reindeer and affect the microfauna population. Snow and ice thickness, and the physical and optical properties of snow covering Arctic tundra were measured on the Broggerhalvoya peninsula on western Svalbard in spring of 1997. Additionally, thicknesses of ground-covering ice were measured in spring of 1998. The initial maximum thickness of snow in the observed areas varied from 0.4 to 0.9 m. The snow around Ny-Alesund began to disappear by the beginning of June, with the entire snow pack disappearing within 2-3 weeks. At the bottom of the snow pack, there was a soil-covering ice layer between 0.05 and 0.1 m thick. We obtained radiation and reflectanceparameters (spectral albedo, attenuation of PAR and global radiation) as well as physical properties of snow (e.g. temperature and density) over six weeks from early May to late June. Electrolytic conductivity of melted snow samples from snow pits shows clearly different conductivity for different stratigraphic sections within the snow pack in early June. Later on, these contrasts disappeared as internal ice layers melted and the snow pack underwent percolation. The albedo maximum before melt onset exceeded 0.9, whereas in the later phase of melting snow surfaces exhibited significantly lower albedo due to metamorphosis, thinning, and blackening by soil-particle contamination. However, even an apparently clean' snow surface had about 30% lower albedo in mid-June than in mid-May. Observations from under-snow PAR measurements are verified using a physically based radiative transfer model. [ABSTRACT FROM AUTHOR]

Since 1981, hourly values of albedo have been measured routinely at Norwegian Polar Institute's research station in Ny-A˚lesund, Svalbard. We have undertaken statistical analysis of the time series 1981–1997 to investigate potential long-term variability and trends in the albedo data set. The following questions have been raised and answered by regression analysis: (i) Has the time of beginning of snow melt changed? (ii) Have melt rates changed? (iii) Has the time of snow arrival in fall changed? (iv) Has the period without snow cover changed? The period without snow on the ground is studied because of its importance for tundra characteristics as a habitat for biota, e.g. length of the growth season. Our data show that albedo varies seasonally, with large variations in spring and autumn and much smaller variations in winter and summer. The variability is reasonably constant within each season. Density estimates of the albedo data suggest that the dates with highest likelihood for (i) start of snow melt and (ii) start of snow formation are 5th of June and 17th of September, respectively. Highest probability for the length of snow-free season is 94 days. None of the tests indicated any significant trends (or indications of climate change) in the 17-year record of albedo, that means that the four questions above were all answered by “no.” Correlation with the North Atlantic Oscillation (NAO) index is also investigated. No correlation between the NAO index and albedo nor temperature or precipitation was found. Even so, because of the short duration that our data set spans, we cannot rule out that such a correlation exists on decadal time scales. [Copyright &y& Elsevier]