Description: A rise in global air temperatures is expected to increase permafrost thaw and alter ecosystem carbon and water cycles in Arctic regions. The coupling between the soil temperature in the active layer (soil between the ground surface and permafrost) and air temperature is a key component in understanding permafrost stability and ecosystem change. Vegetation can affect soil temperature through a variety of mechanisms such as canopy shading, impacts on soil thermal conductivity via soil organic inputs or soil water uptake, albedo, and winter snow trapping. However, the relative importance of the vegetative effects on soil temperature is uncertain across large spatial scales and across different vegetative communities and ecosystem types. We compiled data on a Pan-Arctic scale pairing air and soil temperature with vegetation and ecosystem data to examine the impacts of vegetation on the decoupling of air and soil temperatures. We analyzed the summer thawing degree days, winter freezing degree days, and n factors (degree days soil/degree days air) from sites across the Arctic. Our results indicate that the decoupling between summer air and soil temperatures is more variable in boreal ecosystems than tundra ecosystems, and boreal ecosystems have lower winter n-factors than tundra ecosystems. Summer n-factors were more variable than winter n-factors, and had high variability within study sites. Vegetative and ecosystem characteristics can be key drivers of spatial and temporal variability in active layer soil temperature, particularly during the summer. Quantifying the impacts of vegetation on active layer temperature is critical to understanding how changes in vegetation under climate change can further affect permafrost stability and soil temperature.

Description: Since 2005 quasi-annual jökulhlaups, or glacial lake outburst floods, are registered at the Zackenberg research station (74°28′N, 20°34′W), NE-Greenland. The jökulhlaups typically happen during the sommer months, but were also observed during winter and spring. The Zackenberg jökulhlaups exhibit rapid-rising outburst type characteristics with durations of 12-24 hours, and maximum discharges of 120 to 380 m³/s. The source is a lake dammed by an outlet glacier of the A.P. Olsen ice cap about 35 km inland of the research station. The recorded maximum ice-dammed lake volumes are varying in between 5.7 to 15.6 x10⁶ m³.The year 2018 showed no drainage event and the lake remained filled until the 2019 melting season, accumulating the largest lake volume so far. The 2019 lake outburst happened with beginning of July rather early, but just drained slowly for more than a week to then switch to a rapid-type outburst emptying the lake in about half a day. The subsequent 2020 and 2021 floods continued to show characteristics of slower and mixed-type drainage events. The overall evolution of the 2019-2021 lake drainage patterns suggest a transition back to rapid-type jökulhlaup events. To our knowledge, the observed mixed-type drainage patterns within a single jökulhlaup event has been never reported before, and is challenging the current understanding of subglacial floods. We present all the available hydrometric data for the Zackenberg river and the ice-dammed lake to discuss potential causes for the observed jökulhlaup perturbations.

Description: Seasonal snow cover in coastal Greenland has important climatic and ecological impacts. Despite the extensive network for monitoring the Greenland Ice Sheet and mountain glaciers, the knowledge about seasonal snow cover in the ice-free parts of the coastal regions is limited. Snow observations from the east and west coasts are available at a few locations and are crucial for understanding dynamics, variability and changes. This study aims to evaluate the ability of a polar regional climate model (RACMO2.3p2) and reanalysis product (CARRA) to simulate observed spatio-temporal patterns of seasonal snow cover in coastal Greenland from 1997 to the present. We compare the model's performance on different latitudes and coasts, and test its ability to capture the large interannual variability detected in the observational data. We will compare several important variables from the model with available observations, focusing on climatic and ecologically relevant metrics such as maximum snow water equivalent, the timing of spring melt, and snow cover build-up. The evaluation of these two products is necessary to see if they can be an addition to the in-situ observations and remote sensing methods. This research is vital in filling the current gap in our understanding of the seasonal snow cover in coastal Greenland and will contribute to a more complete picture of the region's climate.

Description: Greenland´s peripheral glaciers are losing mass and are contributing significantly to sea level rise. As direct observations are scarce our knowledge is mainly based on remote sensing studies, that can hardly identify individual processes. Here, we use the unique combination of remote sensing data and direct observations to quantify the effect of avalanches on the mass balance of Freya Glacier, a small (5.3km²) coastal valley glacier in Northeast Greenland with direct mass balance observations since 2007. We calculate elevation changes and geodetic mass balance of Freya Glacier from repeated high resolution photogrammetric surveys in 2013 and 2021 using a structure from motion workflow. Elevation changes between 18.8. 2013 and 28.7.2021 range from -10 m to +16 m, with an overall mean of +1.5 m. Seasonal mass balance observations show, that the overall positive mass balance is mainly due to the exceptional winter accumulation of 2018 when in addition avalanche deposits affected 1/3 of the glacier area. Areas of high positive elevation changes correspond to avalanche deposits of 2018 and 2016. The traditional stake network is not dense enough to resolve the spatial variability of the observed elevation change pattern. A first comparison shows, that the glaciological glacier-wide mass balance is more negative than the geodetic one. In this study we analyse possible sources for this discrepancy and quantify the uncertainties of both methods.