Abstract ID: 3.12826 | Accepted as Poster | Poster | TBA | TBA

Since the early 21st century, we have conducted annual mass-balance monitoring of the Aldegondabreen glacier (5.2 km², 125–500 m a.s.l.) near Barentsburg. Although our observational record is relatively short, Aldegondabreen is a well-studied glacier with a well-documented geodetic mass balance throughout the 20th century. Recent observations confirm that mass loss has accelerated over the past decade, and in the last five years—exceptionally warm for both Svalbard and the entire Arctic—the glacier’s mass balance has reached unprecedentedly negative values. On average, Aldegondabreen has lost about 2.0 m w.e. per year (2019–2024). Mass balance variations strongly correlate with air temperature on an interannual timescale. However, rising temperatures are not the only significant manifestation of modern climate change. Increasing attention has been given to extreme weather events, such as heatwaves, which have become more frequent and are projected to intensify. To assess their impact on glacier melt, we conduct meteorological and actinometric measurements and apply an energy-balance model in a diagnostic rather than predictive manner. A striking example is the annual mass balance for 2022 (−2.13 m w.e.), one of the most negative on record. We identified four heatwaves (9–19 days) that significantly prolonged the melt season. Additionally, several short-term extreme melt events (1–3 days) were recorded, with melt rates reaching up to 75 mm w.e. day⁻¹. These events correlated strongly (r = 0.87, p < 0.01) with discharge from a proglacial stream and coincided with increased mean daily wind speeds (up to 10.3 m s⁻¹). Given the limitations of the glaciological method at short timescales, hydrological water-level measurements provide valuable validation for high-resolution energy-balance modeling. The impact of extreme weather events on Arctic glacier mass balance remains understudied. Two key challenges for modeling are: (1) low spatial resolution of meteorological datasets, which limits capturing turbulent flux variability, and (2) a lack of high-frequency field data for validation, as ablation stakes cannot resolve short-lived melt events.