Surface melt and subsequent firn air depletion can ultimately lead to disintegration of Antarctic ice shelves1, 2 causing grounded glaciers to accelerate3 and sea level to rise. In the Antarctic Peninsula, foehn winds enhance melting near the grounding line4, which in the recent past has led to the disintegration of the most northerly ice shelves5, 6. Here, we provide observational and model evidence that this process also occurs over an East Antarctic ice shelf, where meltwater-induced firn air depletion is found in the grounding zone. Unlike the Antarctic Peninsula, where foehn events originate from episodic interaction of the circumpolar westerlies with the topography, in coastal East Antarctica high temperatures are caused by persistent katabatic winds originating from the ice sheet’s interior. Katabatic winds warm and mix the air as it flows downward and cause widespread snow erosion, explaining >3 K higher near-surface temperatures in summer and surface melt doubling in the grounding zone compared with its surroundings. Additionally, these winds expose blue ice and firn with lower surface albedo, further enhancing melt. The in situ observation of supraglacial flow and englacial storage of meltwater suggests that ice-shelf grounding zones in East Antarctica, like their Antarctic Peninsula counterparts, are vulnerable to hydrofracturing@en

Runoff has recently become the main source of mass loss from the Greenland Ice Sheet and is an important contributor to global sea level rise. Linking runoff to surface meltwater production is complex, as meltwater can be retained within the firn by refreezing or perennial liquid water storage. To constrain these uncertainties, the outputs of two offline snow/firn models of different complexity (IMAU-FDM and SNOWPACK) are compared to assess the sensitivity of meltwater retention to the model formulation (e.g., densification, irreducible water content, vertical resolution). Results indicate that model differences are largest in areas where firn aquifers form, i.e., particularly along the south-eastern margin of the ice sheet. The IMAU-FDM simulates higher densification rates for such climatic conditions and prescribes a lower irreducible water content than SNOWPACK. As a result, the model predicts substantially lower amounts of refreezing and liquid water storage. SNOWPACK performs better for this area, confirmed both by density profiles from firn cores and radar-inferred observations. Refreezing integrated over the entire ice sheet and averaged for the period 1960–2014 amounts to 216 Gt a−1 (IMAU-FDM) and 242 Gt a−1 (SNOWPACK), which is 41 and 46% of the total liquid water input (snowmelt and rainfall). The mean areal extents of perennial firn aquifers for 2010–2014 simulated by the models are 55,700 km2 (IMAU-FDM) and 90,200 km2 (SNOWPACK). Discrepancies between modeled firn profiles and observations emphasize the importance of processes currently not accounted for in most snow/firn models, such as vertical heterogeneous percolation, ponding of water on impermeable layers, lateral (sub-)surface water flow, and the issue of ill-constrained refreezing conditions at the base of firn aquifers.@en

The Greenland ice sheet (GrIS) is highly sensitive to climate forcing, as shown by current observations. Here, we use one of the few coupled ice sheet and ocean-atmosphere general circulation models to examine the coupling between the GrIS surface mass balance (SMB), elevation and dynamical flow. Surface melt is calculated with an energy balance scheme, avoiding the use of empirical melt-temperature relationships (e.g., positive degree days). Despite the course horizontal resolution of the atmospheric model (ECHAM5T31, 3.75 degrees), the model shows reasonable skill in the simulation of the GrIS surface climate and surface melt when compared with a regional model (RACMO2). Our results reveal a growing present-day GrIS in the absence of anthropogenic forcing, in response to reduced insolation forcing since the mid-Holocene. Biases in the simulation of the present-day GrIS are partially attributed to atmospheric sources. We assess the sensitivity of the GrIS to future anthropogenic greenhouse gas forcing through three Representative Concentration Pathways and their extensions until A.D. 2300, as well as to climate variability through a small ensemble of historical and RCP/ECP8.5 simulations. The elevation-SMB feedback enhances future GrIS decay with 8-11% (by 2100) and 24-31% (by 2300), depending on the scenario. The small ensemble shows a 2.5 times spread in present-day GrIS decay rates. @en

In this study, we use satellite gravimetry data from the Gravity Recovery and Climate Experiment (GRACE) to estimate regional mass change of the Greenland ice sheet (GrIS) and neighboring glaciated regions using a least squares inversion approach. We also consider results from the input–output method (IOM). The IOM quantifies the difference between the mass input and output of the GrIS by studying the surface mass balance (SMB) and the ice discharge (D). We use the Regional Atmospheric Climate Model version 2.3 (RACMO2.3) to model the SMB and derive the ice discharge from 12 years of high-precision ice velocity and thickness surveys. We use a simulation model to quantify and correct for GRACE approximation errors in mass change between different subregions of the GrIS, and investigate the reliability of pre-1990s ice discharge estimates, which are based on the modeled runoff. We find that the difference between the IOM and our improved GRACE mass change estimates is reduced in terms of the long-term mass change when using a reference discharge derived from runoff estimates in several subareas. In most regions our GRACE and IOM solutions are consistent with other studies, but differences remain in the northwestern GrIS. We validate the GRACE mass balance in that region by considering several different GIA models and mass change estimates derived from data obtained by the Ice, Cloud and land Elevation Satellite (ICESat). We conclude that the approximated mass balance between GRACE and IOM is consistent in most GrIS regions. The difference in the northwest is likely due to underestimated uncertainties in the IOM solutions.@en