Recent progress has been made in quantifying snowmelt in the Himalaya. Although the conditions are favorable for refreezing, little is known about the spatial variability of meltwater refreezing, hindering a complete understanding of seasonal snowmelt dynamics. This study aims to improve our understanding about how refreezing varies in space and time. We simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment, Nepalese Himalaya, covering a 5-year period. Meteorological forcing data were derived from a unique elaborate network of meteorological stations and high-resolution meteorological simulations. The results show that the annual catchment average refreezing amounts to 122 mm w.e. (21% of the melt), and varies strongly in space depending on elevation and aspect. In addition, there is a seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. Substantial intra-annual variability resulted from fluctuations in snowfall. Daily refreezing simulations decreased by 84% (annual catchment average of 19 mm w.e.) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture melt–refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to changes in air temperature as a 2°C increase leads to a refreezing decrease of 35%.

Mass loss from the West Antarctic Ice Sheet is dominated by glaciers draining into the Amundsen Sea Embayment (ASE), yet the impact of anomalous precipitation on the mass balance of the ASE is poorly known. Here we present a 25-year (1996–2021) record of ASE input-output mass balance and evaluate how two periods of anomalous precipitation affected its sea level contribution. Since 1996, the ASE has lost 3331 ± 424 Gt ice, contributing 9.2 ± 1.2 mm to global sea level. Overall, surface mass balance anomalies contributed little (7.7%) to total mass loss; however, two anomalous precipitation events had larger, albeit short-lived, impacts on rates of mass change. During 2009–2013, persistently low snowfall led to an additional 51 ± 4 Gt yr−1 mass loss in those years (contributing positively to the total loss of 195 ± 4 Gt yr−1). Contrastingly, extreme precipitation in the winters of 2019 and 2020 decreased mass loss by 60 ± 16 Gt yr−1 during those years (contributing negatively to the total loss of 107 ± 15 Gt yr−1). These results emphasise the important impact of extreme snowfall variability on the short-term sea level contribution from West Antarctica.

Surface melt is an important driver of ice shelf disintegration and its consequent mass loss over the Antarctic Ice Sheet. Monitoring surface melt using satellite remote sensing can enhance our understanding of ice shelf stability. However, the sensors do not measure the actual physical process of surface 5 melt, but rather observe the presence of liquid water. Moreover, the sensor observations are influenced by the sensor characteristics and surface properties. Therefore, large inconsistencies can exist in the derived melt estimates from different sensors. In this study, we apply state-of-the-art melt detection algorithms to four 10 frequently used remote sensing sensors: two active microwave sensors, ASCAT (Advanced Scatterometer) and Sentinel-1, a passive microwave sensor SSMIS (Special Sensor Microwave Imager/Sounder), and an optical sensor MODIS (Moderate Resolution Imaging Spectroradiometer). We intercompare the 15 melt detection results over the entire Antarctic Ice Sheet and four selected study regions for the melt seasons 2015-2020. Our results show large spatiotemporal differences in detected melt between the sensors, with particular disagreement in blue ice areas, in aquifer regions, and during wintertime surface melt. 20We discuss that discrepancies between sensors are mainly due to (1) cloud obstruction and polar darkness, (2) frequencydependent penetration of satellite signals, (3) temporal resolution, and (4) spatial resolution, as well as (5) the applied melt detection methods. Nevertheless, we argue that different sensors can 25 complement each other, enabling improved detection of surface melt over the Antarctic Ice Sheet.

Abstract. Firn simulations are essential for understanding Antarctic ice sheet mass change as they enable us to convert satellite altimetry observed volume changes to mass changes, and to quantify the meltwater buffering capacity of firn. Here, we present and evaluate a simulation of the contemporary Antarctic firn layer using the updated semi-empirical firn model IMAU-FDM for the period 1979–2020. In IMAU-FDM, we have improved the fresh snow density and firn compaction parameterizations, and used improved 5 atmospheric forcing. In addition, the model has been tuned and evaluated against 148 in situ observations across the ice sheet. The updated model captures the observed strong spatial variation in firn thickness and density. The temporal variation can be split into a rather stable seasonal cycle driven by snowfall, compaction and melt seasonal cycles, and more irregular decadal variations driven by snowfall anomalies. Comparison of simulated surface elevation change with altimetry shows that the decadal trends agree reasonably well, and that the performance of the updated model has improved, notably in Dronning Maud Land and Wilkins Land.

Abstract. Firn simulations are essential for understanding Antarctic ice sheet mass change, as they enable us to convert satellite altimetry observed volume changes to mass changes and column thickness to ice thickness and to quantify the meltwater buffering capacity of firn. Here, we present and evaluate a simulation of the contemporary Antarctic firn layer using the updated semi-empirical IMAU Firn Densification Model (IMAU-FDM) for the period 1979–2020. We have improved previous fresh-snow density and firn compaction parameterizations and used updated atmospheric forcing. In addition, the model has been calibrated and evaluated using 112 firn core density observations across the ice sheet. We found that 62 % of the seasonal and 67 % of the decadal surface height variability are due to variations in firn air content rather than firn mass. Comparison of simulated surface elevation change with a previously published multi-mission altimetry product for the period 2003–2015 shows that performance of the updated model has improved, notably in Dronning Maud Land and Wilkes Land. However, a substantial trend difference (>10 cm yr−1) remains in the Antarctic Peninsula and Ellsworth Land, mainly caused by uncertainties in the spin-up forcing. By estimating previous climatic conditions from ice core data, these trend differences can be reduced by 38 %.