We report an increase in winter (DJF) cyclone densities in the areas around Svalbard and in northwestern Barents Sea and a decrease in cyclone densities in southeastern Barents Sea during 1979–2016. Despite high interannual variability, the trends are significant at the 90% confidence level. The changes appear as a result of a shift into a more meridional winter storm track in the high‐latitude North Atlantic, associated with a positive trend in the Scandinavian Pattern. A significant decrease in the Brunt–Väisälä frequency east of Svalbard and a significant increase in the Eady Growth Rate north of Svalbard indicate increased baroclinicity, favouring enhanced cyclone activity in these regions. For the first time, we apply composite analysis to explicitly address regional consequences of these wintertime changes in the high‐latitude North Atlantic. We find a tendency toward a warmer and more moist atmospheric state in the Barents Sea and over Svalbard with increased cyclone activity around Svalbard.
<p>Potentially high-impact warm and wet winter conditions have become increasingly common in recent decades in the arctic archipelago of Svalbard. In this study, we document present 2m temperature, precipitation and rain-on-snow (ROS) climate conditions in Svalbard and relate them to different atmospheric circulation (AC) types. For this purpose, we utilise a set of observations together with output from the high resolution numerical weather prediction model AROME-Arctic. We find that 2m median temperatures vary the most across AC types in winter and spring, and the least in summer. Southerly and southwesterly flow is associated with 10th percentile 2m temperatures above freezing in all seasons. In terms of precipitation, we find the highest amounts and intensities with onshore flow over open water. Sea ice appears to play a strong role in the local variability in both 2m temperature and precipitation. ROS is a frequent phenomenon in the study period, in particular below 250 m ASL. In winter, ROS only occurs with AC types from the southerly sector or during the passage of a low pressure centre or trough. Most of these events occur during southwesterly flow, with a low pressure center west of Svalbard.</p><p>&#160;</p>
The Svalbard Archipelago has undergone rapid warming in the recent decades leading to warmer and wetter winter conditions. This study relates the present (2013-2018) 2 m temperature, precipitation, and rain-on-snow (ROS) climate in Svalbard to different atmospheric circulation (AC) types utilizing the high-resolution numerical weather prediction model Application of Research to Operations at Mesoscale (AROME)-Arctic. We find that the 2 m median temperatures vary most across AC types in winter and spring and in summer they vary the least. In all seasons the 10 th percentile 2 m temperatures are above 0°C with southwesterly AC types over Svalbard. In comparison, the relationship between AC type and precipitation varies more spatially, with most accumulated precipitation and highest median precipitation intensities with onshore flow over open water. Our results suggest that sea ice explains a large part of the local variability in both 2 m temperature and precipitation. In the studied period ROS is a frequent phenomenon up to 150 m above sea level (ASL) on land, with most events in the southwestern parts of the archipelago (57 cases during five winter seasons). ROS events in winter occur predominantly with AC types from the southerly sector or during a low-pressure center/trough passage. The southwesterly cyclonic AC type, with a low-pressure center west of Svalbard, is the most frequent AC type for ROS events. In addition to being the most frequent, the southwesterly AC has the largest spatial coverage of ROS. Plain Language SummaryThe Svalbard Archipelago has undergone rapid warming in the recent decades leading to warmer and wetter winter conditions. This study relates the present (2013-2018) 2 m temperature, precipitation, and rain-on-snow (ROS) climate to different atmospheric circulation (AC) types utilizing the high-resolution numerical weather prediction model Application of Research to Operations at Mesoscale (AROME)-Arctic. We found that winter and spring 2 m median temperatures vary most across AC types and in summer they vary the least. Our results suggest that sea ice explains a large part of the local variability in both 2 m temperature and precipitation. In the studied period ROS is a frequent phenomenon up to 150 m ASL on land, with most events in the southwestern parts of the archipelago (57 cases during five winter seasons). The majority of these events occur in southwesterly cyclonic AC type, with a low-pressure center west of Svalbard. In addition to being the most frequent, the southwesterly AC has the largest spatial coverage of ROS.
Abstract. Atmospheric circulation exerts an important control on a region's snow avalanche activity by broadly determining the mountain weather patterns that influence snowpack development and avalanche release. In central Spitsbergen, the largest island in the High Arctic Svalbard archipelago, avalanches are a common natural hazard throughout the winter months. Previous work has identified a unique snow climate reflecting the region's climatically dynamic environmental setting but has not specifically addressed the synoptic-scale control of atmospheric circulation on avalanche activity here. In this work, we investigate atmospheric circulation's control on snow avalanching in the Nordenskiöld Land region of central Spitsbergen by first constructing a four-season (2016/2017–2019/2020) regional avalanche activity record using observations available on a database used by the Norwegian Water Resources and Energy Directorate (NVE). We then analyze the synoptic atmospheric conditions on days with differing avalanche activity situations. Our results show atmospheric circulation conducive to elevated precipitation, wind speeds, and air temperatures near Svalbard are associated with increased avalanche activity in Nordenskiöld Land, but different synoptic signals exist for days characterized by dry, mixed, and wet avalanche activity. Differing upwind conditions help further explain differences in the frequency and nature of avalanche activity resulting from these various atmospheric circulation patterns. We further employ a daily atmospheric circulation calendar to help contextualize our results in the growing body of literature related to climate change in this location. This work helps expand our understanding of snow avalanches in Svalbard to a broader spatial scale and provides a basis for future work investigating the impacts of climate change on avalanche activity in Svalbard and other locations where avalanche regimes are impacted by changing climatic and synoptic conditions.
<p>We present results from a set of field campaigns conducted in an arctic valley and fjord environment in central Spitsbergen, Svalbard. These field campaigns, which are conducted as part of a graduate class at the University Centre in Svalbard (UNIS), address a range of phenomena typical for the arctic atmospheric boundary layer using both observational and numerical means. These phenomena include low-level jets, cold pools, drainage flows, and air-sea interactions, several of which typically are challenging to accurately model. On the observational side, we utilise a range of sensors and instrumentation platforms, such as portable weather stations, a tethersonde (anchored weather balloon), small temperature sensors (TinyTags), sonic anemometers, automatic weather stations, and drones. As of this year, the sensor suite will also constitute a wind lidar and a microwave temperature profiler. The resulting datasets represent a unique model-independent data set from a region where observations are otherwise sparse. On the numerical side, we utilise data from the high-resolution (2.5 km horizontal grid spacing) AROME-Arctic weather prediction model. AROME Arctic is run operationally by the Norwegian Meteorological Institute (MET Norway) for a domain covering Northern Fennoscandia, larger parts of the Barents Sea, and Svalbard. We use the model data both to plan our fieldwork and for interpreting our observations. In turn, we use the observations for improving our understanding of the mentioned phenomena and also for validating the model.</p>
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