We invite observational and model-based presentations on past reconstructions and future projections of the SMB and SEB over glaciers, ice sheets and ice shelves. We promote research that identifies drivers and further explores changes in rain/snow accumulation and redistribution, meltwater production, retention and refreezing in firn, and subsequent surface runoff, sublimation and blowing snow erosion. We welcome studies using global/regional climate models, SMB-SEB and positive degree day (PDD) models, or machine learning techniques that enhance our understanding of glacial surface processes from local to regional scales. Works combining SMB models with in-situ or remote sensing observations to quantify glacial mass change are also encouraged.
Orals: Wed, 6 May, 16:15–17:50 | Room 1.14
High-sensitivity borehole pressure sensors provide a novel means of monitoring the surface mass balance (SMB) of Earth’s great ice sheets. When properly coupled to the ice, these instruments are expected to detect millimeter-scale changes in ice thickness corresponding to variations in snow accumulation. The Boussinesq solution guarantees that a pressure sensor buried at a depth of 1000 m will represent a spatially weighted average of surface mass load variations with an approximate radius of 1000 m at the surface. The sensing system itself consists of a pressure sensor connected to a pressure coupling element such as a sack filled with antifreeze. Once freeze-in related transients have passed, the internal fluid pressure in the sack reflects the full overburden stress exerted by the overlying ice column. Long term drift of the sensors requires special engineering attention and may be corrected for using some combination of pre-deployment pressurization, in situ calibration, and/or the use of multiple in situ sensors. We discuss deployment considerations using the IceDiver thermal melt probe for a potential deployment at Greenland Summit Station. The borehole SMB observatory described here supports altimetry-based inference of glacier mass change, provides a critical dataset for validating firn densification models, and establishes a fundamentally new approach to measuring glacier mass balance.
How to cite: Lipovsky, B., Brand, B., Burnett, J., Michel-Hart, N., Smith, B., and Winebrenner, D.: A New View from Within: Borehole Pressure Sensors and the Measurement of Glacier Mass Balance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11424, https://doi.org/10.5194/egusphere-egu26-11424, 2026.
As one of the main contributors of global sea level rise, the Greenland Ice Sheet (GrIS) has been experiencing significant thinning and losing ice mass since the Earth observation era using satellites. ICESat-2, equipped with the photon-counting altimetric technology, provides an unprecedented elevation accuracy of 2-4 cm and extremely high spatio-temporal coverages for reducing uncertainties in mass balance estimation of the ice sheet. We have developed a multi-temporal elevation change estimation model (MECEM) that is especially tailored for processing the ICESat-2 data and estimating mass changes. Here, we present our assessment of surface elevation change across the GrIS and its peripheral glaciers, using ICESat-2 observations acquired between March 2019 and March 2024. A validation against GNSS-derived elevation change rates near the Summit Station during 2022–2024 demonstrates a close agreement. In addition, a comparison with independent elevation change products indicates a strong consistency, particularly over interior high-elevation regions. Our results reveal increased thinning in the southeast and southwest coastal regions and major outlet glacier systems, such as Jakobshavn Isbræ and Helheim Glacier. The ablation zone (< 1500 m) experiences a rapid thinning at an average rate of −0.48 ± 0.04 m yr⁻¹, while the interior region (≥ 1500 m) remains relatively stable with a slight thickening. The mean surface elevation change rate over the entire GrIS is −0.10 ± 0.02 m yr⁻¹. We further convert the surface elevation changes to ice mass changes by using corrections of firn air content and solid earth. A comparison with other studies and a discussion are also provided.
How to cite: Li, R., Liu, M., Chen, Q., He, Y., Wang, X., and Qiao, G.: Surface Elevation Changes and Mass Balance of the Greenland Ice Sheet from 2019 to 2024 from ICESat-2 Observations using a new MECEM model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8541, https://doi.org/10.5194/egusphere-egu26-8541, 2026.
Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) satellite missions, altogether referred to as GRACE hereafter, are a powerful tool to provide Greenland monthly mass anomaly estimates over a time span of more than two decades. Here we present a comparison between GRACE seasonal mass anomalies and surface mass balance (SMB) estimates from the Regional Atmospheric Climate Model RACMO2.3p2. The latter provides estimates of SMB and its individual components over Greenland with a daily temporal sampling and high spatial resolution, i.e. 5.5 km in the tundra area and 1 km over glaciated areas, using statistical downscaling. Our goals are to provide frameworks to independently evaluate the model and to estimate buffered water storage (BWS), i.e. temporal storage of meltwater in the deeper ice sheet system subject to discharge into the ocean during the melt season or soon thereafter.
The comparison spans the time interval between February 2003 and July 2022. Long-term (slow) trends are removed to exclude signals related to ice discharge variability and glacial isostatic adjustment (assuming the seasonal variability of these signals is negligible). The GRACE- and RACMO-based estimates are inverted into mean mass anomalies per calendar month to extract their typical seasonal patterns. The study area is limited to the coastal zone of Greenland, including the ice sheet ablation zone where signals related to BWS and under- or over-estimation of runoff are concentrated. The coastal zone is further divided into 12 areas for a detailed analysis. We compare the time series of seasonal patterns in terms of RMS differences, Pearson correlation coefficient, and Nash-Sutcliffe Efficiency (NSE). Over the entire coastal zone, the RMS difference is 13.7 Gt or 1.3 cm in terms of mean equivalent water height (about 14% of the total signal); the correlation coefficient and NSE are 0.992 and 0.980, respectively, indicating a sufficient match between the overall seasonal patterns of the two datasets. A few individual coastal zones also show a good agreement between GRACE- and RACMO-based estimates, with the RMS differences below 2 cm. In two northwestern coastal areas, however, GRACE- and RACMO-based mass anomalies show large discrepancies (e.g. NSE lower than 0.7), potentially due to an overestimation of modelled runoff. In two southwestern coastal areas, a mismatch is observed as well, with maximum differences occurring in July, in concert with the timing of BWS documented in earlier studies.
While previous studies already attempted to quantify BWS in Greenland, they did not account for the scaling of modelled runoff, introducing a biased estimation of BWS. In light of our study, a more comprehensive approach can be adopted for BWS quantification. Such an approach can also benefit from using other regional climate models for BWS estimation.
How to cite: Li, W., Ditmar, P., van den Broeke, M., Noël, B., and Wouters, B.: Comparison of GRACE/GFO- and RACMO-based seasonal mass anomalies over the coastal zone of Greenland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10199, https://doi.org/10.5194/egusphere-egu26-10199, 2026.
The Greenland ice sheet (GrIS) constitutes a major contributor to sea level rise, with significant mass losses dominated by surface melt and runoff over recent decades. The enhanced surface melt is concomitant with an observed decrease in albedo and a retreat of the snow line to higher elevations, amplified by melt-albedo feedbacks. A realistic representation of snow and ice albedo over the GrIS is therefore essential in climate models to correctly represent the surface energy balance, the meltwater production and the ice sheet surface mass balance.
In ORCHIDEE, the land surface component of the IPSL-CM Global Climate Model, snow albedo is currently represented with a decay parameterization based on snow age. Such simplified parametrizations are common in land surface schemes of CMIP earth system models. Our objective is to improve the representation of snow albedo over the GrIS using observations from PROMICE stations and MODIS retrievals. Our approach relies on the use of the History Matching calibration tool, based on Gaussian processes emulators, to identify the snow albedo parameters best adapted to Greenland conditions.
We first perform an offline calibration over Greenland to assess the ability of the ORCHIDEE model to reproduce the snow albedo decay and the bare ice extent in summer. We show that a single year calibration allows for a good representation of snow albedo decay over the 2000–2019 period. We aim to revisit this approach by using the atmospheric component of IPSL-CM, LMDZ, coupled with ORCHIDEE, in a regional configuration. This will allow us to assess whether atmosphere-surface feedback needs to be taken into account during the calibration procedure. Finally, we assess if the improved representation of albedo decay enables the IPSL model to produce a more realistic decrease of the GrIS surface mass balance over recent decades.
How to cite: Conesa, P., Agosta, C., Charbit, S., and Beylat, S.: Control of snow albedo decay on Greenland surface melt using the land surface model ORCHIDEE, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12329, https://doi.org/10.5194/egusphere-egu26-12329, 2026.
Accurate simulation of the Greenland Ice Sheet (GrIS) surface mass balance (SMB) is essential for quantifying its contribution to sea-level rise. The regional atmospheric climate model MAR is widely used to study GrIS SMB changes and force ice-sheet models, highlighting the importance of assessing its performance and sensitivity to horizontal resolution. Here, we evaluate the latest MARv3.14 version at 5 km resolution and assess the impact of coarser resolutions (10–30 km) on simulated GrIS SMB and its components.
MAR outputs are evaluated against a range of independent observations, including in situ SMB measurements, automatic weather station records of near-surface meteorological variables, and satellite-derived melt extent and albedo products. At 5 km resolution, MAR reproduces observed SMB with a root-mean-square error of 0.53 m w.e. yr⁻¹. For near-surface meteorological variables and surface energy budget fluxes, model errors are smaller than the corresponding observed variability. The assimilation of bare-ice albedo, implemented in the latest MAR version, improves model performance in the ablation zone. Remaining biases in the accumulation zone suggest that improvements to the snow albedo scheme are further required. Simulated melt timing is consistent with satellite-based melt extent products.
Comparing simulations at different spatial resolutions, we find that SMB discrepancies mainly occur at the ice-sheet margins, characterized by strong topographic gradients. While integrated SMB differences generally remain within interannual variability, precipitation and runoff are highly sensitive to the model spatial resolution.
Corrections based on a vertical SMB gradient (as in Franco et al., 2012) or a sub-pixel methodology allowing the surface scheme (SISVAT) to be run at a higher resolution than the atmospheric model could deal in part with runoff discrepancies vs spatial resolution but precipitation anomalies remain a challenge. We conclude by discussing ongoing model developments, in particular the implementation of a sub-pixel methodology.
How to cite: Timmermans, G., Brice, N., Kittel, C., Thomas, D., Nicolas, G., Clara, L., and Xavier, F.: Evaluation of MARv3.14 over Greenland and the Impact of Model Resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11626, https://doi.org/10.5194/egusphere-egu26-11626, 2026.
The temporal evolution of near-surface ice slabs (> 1m thick) on the Greenland Ice Sheet (GrIS) has significant implications for understanding both past variability and future changes in ice sheet mass balance. These ice slabs, formed through the refreezing of meltwater in the firn layer, modulate surface runoff dynamics and affect the ice sheet’s meltwater retention capacity, which is critical for understanding future surface mass balance (SMB) sensitivity to climate change. With ongoing climate warming, the mechanisms driving the formation and expansion of these ice slabs are likely to intensify. There is a need for coupled SMB and sub-surface firn models that can effectively simulate near-surface firn and ice slab evolution, allowing us to predict their long-term impact on Greenland's contribution to global sea-level rise.
In this study we present a 1-D physically based model with high vertical resolution (1 cm) to simulate surface melt, percolation, refreezing, ice slab evolution, runoff, and surface mass balance (SMB) across the GrIS. The high vertical grid resolution facilitates continuous simulation of the evolution of ice layers with thicknesses ranging from 1 cm to several metres. We applied a novel, laboratory defined, temperature-dependent ice layer permeability criterion whereby all ice layers are permeable above –0.15 °C but become impermeable beyond 1 m thickness. The model incorporates a novel parameterization of vertical snow and firn compaction, replacing frequently used theoretical relationships derived for dry snow compaction with a laboratory-derived, viscosity-based relationship for refrozen snow / firn more commonly found within percolation zones.
We applied the model to the GrIS from 1999 to the end of the 21st century at a spatial resolution of 0.11° and a temporal resolution of 15 minutes. The simulations reproduce the observed distribution of past ice slab occurrences across the GrIS and generate near-surface firn density profiles and proglacial discharge hydrographs that closely match available field measurements. The model also captures the observed interannual variability in the runoff limit, demonstrating strong consistency with satellite-derived estimates. We forced the model with future climate projections to investigate the projected evolution of near-surface ice slabs and their implications for future vertical firn densification, meltwater runoff and surface mass balance.
How to cite: Laha, S. and Mair, D. W. F.: Temporal Evolution of Near-Surface Ice Slabs on the Greenland Ice Sheet: From Past Variability to Future Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3362, https://doi.org/10.5194/egusphere-egu26-3362, 2026.
Accurate projections of ice sheet surface mass balance (SMB) are critical for sea-level rise estimates. Polar regional climate models (RCMs) coupled with firn models are the primary tools for simulating melt and projecting future SMB. However, the projections from different RCMs significantly deviate from each other. Additionally, the computational costs of polar RCMs and their firn models limit the creation of large ensembles needed to statistically assess the likely range in future melt and runoff.
Machine learning emulators offer a promising solution by enabling rapid predictions at a fraction of the computational cost. We therefore present machine learning emulators that predict daily surface melt and runoff from atmospheric variables from their associated polar RCM over Greenland. The emulators use a novel physics-informed, modular architecture that combines short-term weather patterns with long-term climate memory, capturing both immediate atmospheric forcing and accumulated firn characteristics.
Our work demonstrates that machine learning can successfully emulate firn model behavior from climate forcing alone. This represents a crucial first step toward computationally efficient emulation of polar RCMs, facilitating generation of large ensembles, sensitivity analysis, and potential integration as surrogate models within Earth system models.
How to cite: Schlager, E., Langen, P. L., Mottram, R. H., Scher, S., and Ahlstrøm, A. P.: Emulating Greenland Ice Sheet melt and runoff from polar RCMs with machine learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7895, https://doi.org/10.5194/egusphere-egu26-7895, 2026.
Regional climate models (RCM) with good snow schemes can provide high-resolution surface mass balance (SMB) estimates over Antarctica, but come with high computational costs limiting the number of realizations across Earth System Models (ESM) and shared socioeconomic pathways (SSP). SMB’s great local variability as observable in regions characterized by complex topography such as the Antarctic Peninsula, requires high resolution. Recent machine learning approaches have shown promising downscaling results as cost efficient alternative to RCMs. However, their limited transferability across forcings remains a drawback for practical application.
Here, we investigate transferability of a Convolutional Neural Network (CNN) based emulator predicting SMB over the Antarctic Peninsula utilizing multiple SSPs. Two training scenarios are compared: a) a perfect model setup where training and prediction are performed under the same SSP, and b) an imperfect model setup in which the emulator is trained on a higher emission scenario and applied to a lower emission scenario. The latter represents an interpolation along SSPs, therefore comparable performance to the perfect model setup is expected.
Preliminary analyses suggest consistency in large-scale statistics over the full domain with benchmarks set in earlier studies. However, pronounced local variability in model performance is observable, particularly in regions of high melt or precipitation. Ongoing work aims to quantify these differences, detect potential causes, and their implications for predictions in the imperfect model setup.
Positive results could enable the generation of additional SSP-runs from existing RCM simulations, therefore substantially reducing total computational cost relative to the number of predictions.
How to cite: Zirkel, E., Mottram, R., Verro, K., and Lhermitte, S.: Emulating Surface Mass Balance in the Antarctic Peninsula Under Changing Forcings: A Transferability Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18429, https://doi.org/10.5194/egusphere-egu26-18429, 2026.
Direct observations of the surface mass balance (SMB) over the Greenland Ice Sheet are currently limited to basin-scale estimates and sparse in situ measurements, making a comprehensive observational assessment challenging. Therefore, RCMs remain the best option for producing spatially and temporally continuous SMB estimates, but models show substantial regional differences, even under present-day conditions. While satellite observations provide extensive information about surface processes, a fully satellite-based SMB product remains to be seen.
In this study, we present SMBnet, a deep learning model that estimates SMB over central West Greenland by integrating satellite observations, reanalysis data, and simple physical constraints. SMBnet combines satellite-derived ice surface temperature and albedo with ERA5 reanalysis data, ice velocity, and GRACE/-FO mass anomalies to produce spatially and temporally continuous SMB estimates. The model employs a U-Net architecture and is trained using multiple loss terms that enforce consistency with observations and incorporate physical knowledge of accumulation and ablation processes. Although applied here to central West Greenland, the approach shows clear potential for extending it to the entire ice sheet, offering a computationally efficient, observation-driven complement to traditional RCMs for estimating present-day SMB.
How to cite: Puggaard, A., Solgaard, A., S. Sørensen, L., Mottram, R., and B. Simonsen, S.: A physics-guided Deep Learning Model for SMB of Central West Greenland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21387, https://doi.org/10.5194/egusphere-egu26-21387, 2026.
Enhanced climate warming across Arctic glaciers, ice caps and the Greenland Ice Sheet has intensified melt and refreezing processes, producing more widespread regions of melt-affected, near-surface (< 12 m depth) firn within accumulation areas. Accurately simulating this near-surface region is important, as it governs the evolving capacity of ice sheets to retain meltwater through refreezing and thus strongly influences surface mass balance. Although over short timescales (days to months) refreezing of meltwater dominates changes in the density of melt-affected firn, over longer timescales (decades to centuries), densification through vertical compaction of refrozen firn remains significant. Whilst there is a substantial body of both field and theoretical work addressing the issue of dry firn densification by compaction of snow into firn and ice, i.e. in the absence of melting and refreezing, there is a need for better understanding of densification by compaction in melt-affected snow / firn increasingly found across accumulation areas of ice sheets and glaciers.
Firn densification can be defined via a constitutive equation, calculating the vertical compression rate due to stress from the overburden pressure dependent on firn viscosity. Laboratory studies have demonstrated the potential of controlled experiments to constrain the viscosity of dry snow of varying densities. However, similar experimental constraints do not yet exist for refrozen, melt-affected firn. In this study we extend such laboratory approaches to refrozen snow/firn to quantify their effective viscosities. We undertook confined uniaxial compression tests on snow/firn samples with known grain sizes and densities (c. 400 – 600 kg m-3), simulating expected ranges of overburden pressures to c. 12 m depth to establish the stress-strain rate relationships that define near-surface firn viscosities. Our experiments indicate an exponential increase in viscosity with density. The viscosity values calculated here for melt-affected snow/firn are broadly consistent with the lower envelope of viscosities reported in previous studies on cold dry firn, though they exceed those made in the field for temperate firn. The relationship of viscosity to snow/firn density allows our findings to be incorporated into large-scale firn densification and surface mass balance models, where resolving detailed microstructural processes and form is often impractical. By providing empirically constrained viscosity estimates tied directly to density, this work bridges a gap between field observations of melt-affected firn and the parameterizations required by regional and continental-scale models for more robust future projections of near-surface refreezing capacity of ice sheets and glaciers.
How to cite: Mair, D., Brown, G., and Laha, S.: Viscosity of melt-affected snow/firn derived from laboratory-based uniaxial compression experiments., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4980, https://doi.org/10.5194/egusphere-egu26-4980, 2026.
Ice shelves currently stabilize the Antarctic ice sheet but are vulnerable to hydrofracturing in the future. A faster increase in surface melt compared to accumulation will cause a reduction in available firn pore space and will thus eventually lead to meltwater ponding at the surface. However, current climate models disagree on the total volume of meltwater production and accumulation, and thus on the evolution of firn density over ice shelves. A lack of direct melt and refreezing observations on ice shelves, especially in areas of high melt, prevents a detailed benchmarking of climate and firn models.
Since 2022, a dedicated experiment takes place at two contrasting locations with existing long-term automatic weather observations on the Larsen C ice shelf, which is at risk of collapsing in the future. Here we present results from 3 years of data from an eddy covariance system, which measured the sensible and latent heat fluxes, a GNSS antenna that measured snow height change by interferometry (GNSSir), and a snow cosmic ray counter that measured the change in snow mass and thus bulk snow density.
We discuss the main challenges and recommendations in acquiring such data, the derived surface energy balance (SEB) fluxes, and the variation of bulk snow density due to accumulation and meltwater refreezing. We demonstrate that the turbulent fluxes can be correctly simulated with a bulk turbulence model, that the variation of surface roughness in time can also be extracted from GNSSir, and that the hourly melt fluxes and refreezing can be simulated within 10% accuracy using a skin energy balance model forced by standard single level observations.
How to cite: Van Tiggelen, M., Smeets, P., Reijmer, C., and van den Broeke, M.: Observations of turbulent heat exchange, surface melt and refreezing on the Larsen C ice shelf, Antarctica (2022-2025), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6842, https://doi.org/10.5194/egusphere-egu26-6842, 2026.
The Getz Ice Shelf is a region of high surface melt potential, yet its annual melt days are typically fewer than 10. In contrast, the upstream region of the Amery Ice Shelf, which has similar latitude and altitude, is more prone to surface melting, with some areas recording over 50 melt days throughout the year. Ice surface temperature (IST) is a critical indicator of its surface energy budget and mass balance. To investigate the differences in IST between the Getz and Amery ice shelves, this study employed high-resolution thermal infrared data from the FY-3A MERSI-Ⅰ and FY-3D MERSI-Ⅱ satellites to retrieve summer IST over both ice shelves for 2008–2014 and 2019–2024, and conducted a comparative analysis of their spatiotemporal variation patterns. Time series analysis of the Getz Ice Shelf revealed no statistically significant IST trend in either period. However, an anomalous warming event exceeding 5 K was observed in March 2013, abruptly reversing the typical seasonal cooling and resulting in a higher monthly average IST for March than for February. Meteorological analysis linked this anomaly to an anticyclone in the Amundsen Sea, which drove strong poleward transport of moisture and heat. The consequent increase in mid- and low-level cloud cover enhanced downward longwave radiation, leading to rapid surface warming. Also for the Amery Ice Shelf, time series analysis revealed no statistically significant IST trend in either period. Comparative analysis of summer IST reveals similar average values for the Getz and Amery ice shelves, with Getz even warmer by nearly 2 K in some years. However, surface melting on Getz remains lower. Analysis incorporating precipitation data indicates that the Getz Ice Shelf receives significantly higher precipitation than the Amery Ice Shelf. The greater snowfall replenishes surface fresh snow, increases albedo, and thereby suppresses melting. This suggests that wet ice shelves surface with high accumulation have a higher temperature threshold for surface melting. Notably, surface precipitation over the Getz Ice Shelf during the summer warming period (October–December) showed a significant declining trend from 2012 to 2023. Given the absence of pronounced changes in IST, this reduction in precipitation may elevate the future risk of surface melting on the Getz Ice Shelf.
How to cite: Liu, T., Zhang, X., Pang, X., and Yang, Y.: A Study on Summer Surface Temperature of Antarctic Ice Shelves Based on FY-3 Satellite: A Case Study of the Getz and Amery Ice Shelves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11304, https://doi.org/10.5194/egusphere-egu26-11304, 2026.
Firn densification turns snow into glacial ice. At the surface of ice sheets, ice caps, and glaciers, firn modulates the interaction between atmosphere and ice, and is in turn affected by temperature, precipitation, wind, deposition, and ice dynamics. As climatic factors vary, the rate of firn densification changes even as the mass stays constant. These densification rates are used to correct satellite altimetry measurements of mass balance across the Greenland and Antarctic Ice Sheets. The IMAU Firn Densification Model (IMAU-FDM) is a 1D, semi-empirical model that simulates the evolution of snow grain size, firn density, firn air content, temperature, and liquid water content commonly used for continent-wide altimetry corrections. We will present the results of the FDM from an extended timeseries (1939-2023) and updated forcing (ERA5-forced RACMO2p3.2), along with a toolkit for post-processing and using the output. We show the impact of longer-term forcing changes the overall firn air content and densification rates, and the effects of a longer, earlier spinup period.
How to cite: Case, E., Kuipers-Munneke, P., Brils, M., van de Berg, W.-J., Tijm-Reijmer, C., and van den Broeke, M.: Changes in Greenland firn densification from extended IMAU-FDM runs (1939-2023), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13516, https://doi.org/10.5194/egusphere-egu26-13516, 2026.
The firn layer regulates how an ice sheet responds to climate change, by modifying how changes in surface temperature, snow accumulation and ablation affects the ice sheet mass balance. The firn layer can be simulated with a firn densification model. However, a variety of climate forcing time steps have been used to force such firn models in literature, ranging from 3-hourly to daily, monthly, or even annually. Reasons for selecting certain climate forcing time steps are most often practical: the data are not available at smaller time steps, the amount of forcing data becomes too large, or computational resources are insufficient. Evaluation of the impact of the selected forcing time step is often absent or based on different lines of reasoning.
In this study, we force the firn model IMAU-FDM with different surface climate forcing time steps for the Antarctic Peninsula and southern Greenland Ice Sheet. We show that the modelled firn layer contains more pore space for larger forcing time steps. Locations with limited firn pore space due to seasonal melt are most sensitive.
The largest differences in modelled firn pore space arise when there is no diurnal cycle in the climate forcing input data. This allows for coexisting snowmelt and sub-zero surface temperatures, leading to immediate shallow refreezing of meltwater. We also found that parameterizations for e.g. fresh snow density can become unsuitable when applied outside the physical conditions or climate forcing time step on which they are based. We argue that (1) firn models require input with at least a sub-daily forcing time step, (2) use of parameterizations should be critically assessed and only used consistently with the way they were originally developed, and (3) the forcing time step should be considered when interpreting firn model output.
How to cite: van den Aker, T., Kuipers Munneke, P., van de Berg, W. J., Immerzeel, W., and van den Broeke, M.: Climate forcing time step requirements for firn modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7541, https://doi.org/10.5194/egusphere-egu26-7541, 2026.
How to cite: van der Aa, K., Reijmer, C., Kuipers Munneke, P., van de Berg, W. J., and van Dalum, C.: Updated Climatology and Surface Mass Balance for the period 1979-2024 of the Antarctic Peninsula using RACMO2.4p1, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12210, https://doi.org/10.5194/egusphere-egu26-12210, 2026.
In the last decades, the Greenland ice sheet (GrIS) and its peripheral ice caps (GICs) have been rapidly losing mass through increased solid ice discharge and declining surface mass balance (SMB), i.e., the difference between precipitation accumulation and ablation from sublimation and surface runoff. Capturing SMB and its components at the local scale, notably across rapidly melting glaciers and ice caps, is therefore essential to accurately quantify Greenland’s contribution to global sea-level rise.
Here we compare long-term SMB reconstructions from two ERA5-forced regional climate models at ~5 km (MARv3.14 and RACMO2.3p2), further statistically downscaled to 500 m spatial resolution (1940-2024). These products are evaluated using in-situ measurements covering accumulation and ablation zones, as well as remotely sensed mass change records, showing good agreement. While the two models align Greenland-wide in terms of integrated SMB, individual components differ suggesting error compensation. MAR generally experiences both higher inland precipitation accumulation and larger surface runoff in marginal ablation zones than RACMO2, yielding approximately equivalent integrated SMB estimates. We examine these differences at the GrIS and GICs scale and further explore our products’ sensitivity to spatial resolution using corresponding downscaled SMB at 1 km.
How to cite: Noël, B., Fettweis, X., van de Berg, W. J., and van den Broeke, M.: Differing high-resolution Greenland ice sheet and peripheral ice caps surface mass balance since 1940, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6601, https://doi.org/10.5194/egusphere-egu26-6601, 2026.
Freshwater fluxes from the Antarctic and Greenland ice sheets play a critical role in sea-level rise, ocean circulation, and the global climate system. These fluxes arise from both dynamical processes—such as ice discharge, iceberg calving, and basal melting of ice shelves—and from surface mass balance processes. In Greenland, surface meltwater runoff is already a major contributor to freshwater input, and it is projected to become increasingly important in Antarctica as climate warming progresses.
While regional climate models (RCMs) are key to studying climate at regional and local scales, relatively few are equipped with advanced snow and firn models capable of producing accurate surface mass balance results. Here, we present a comprehensive, state-of-the-art collection of regional climate model simulations (RACMO2, MAR, and HIRHAM5) for both Greenland and Antarctica, forced by historical and SSP-scenario CMIP6 Earth System Models and extending to the year 2100. We briefly assess the modelled surface mass balance, accumulation, melt, and runoff, and highlight aspects of atmosphere–snow/ice interactions that remain an active area of model development. This dataset can be used to prescribe freshwater fluxes from surface mass balance to oceanic or climate modelling experiments, or as a comparison against in situ observational datasets.
How to cite: Verro, K., Hofsteenge, M., Amory, C., van de Berg, W. J., Boberg, F., van den Broeke, M., Carney, M., Case, E., van Dalum, C., Fettweis, X., Hansen, N., Mottram, R., Olesen, M., and van Tiggelen, M.: Historical and Future Surface Mass Balance Contributions to Antarctic and Greenland Ice Sheet Freshwater Fluxes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3836, https://doi.org/10.5194/egusphere-egu26-3836, 2026.
Surface mass balance (SMB) of polar ice sheets is a key component in quantifying their contribution to global sea-level rise. Accurate, high-resolution projections of SMB are therefore required to force ISMIP7 ice sheet models. In this study, we present preliminary results from SMBMIP, a comparison of several high-resolution SMB products over the Greenland Ice Sheet within the ISMIP7 framework. These products include, among others, SMB projections from the regional climate model MAR, statistical downscaling of MAR and the Earth System Model CESM2, and machine-learning outputs. For a meaningful comparison, participating projections are based on a same CESM2 historical climate and emission scenario. The aim is to explore uncertainties, performance and efficiency of various modelling approaches.
How to cite: Lambin, C., Dunmire, D., Senthil, N., Schlegel, N., Nowicki, S., and Noël, B.: ISMIP7 SMBMIP: a preliminary comparison of Greenland ice sheet surface mass balance products, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10038, https://doi.org/10.5194/egusphere-egu26-10038, 2026.
When you want to know the future of the Greenland ice sheet, you are confronted with the question: What will be Greenland's surface mass balance (SMB) in the future?
The Copenhagen Ice-Snow Surface Energy and Mass Balance Model (CISSEMBEL) is a unified surface mass balance model developed at the Danish Meteorological Institute (DMI) that serves dual purposes in climate modeling. First, it operates as a standalone model to compute surface mass balance (SMB) from a wide range of atmospheric forcing data, including reanalysis products, meteorological forecasts, and automatic weather station (AWS) observations. Second, it acts as an innovative coupling framework that integrates SMB modeling into an atmospheric model. Since CISSEMBEL corrects dynamically elevation differences between the coarsely resolved orography in global atmosphere models and the high-resolution target during runtime, CISSEMBEL delivers boundary conditions essential for accurate atmosphere-ice sheet interactions. Ultimately, it enables a seamless integration of Ice Sheet Models (ISMs) into Earth System Models (ESMs).
In this presentation, we show CISSEMBEL results following the protocol of the Greenland Ice Sheet (GrIS) SMB model intercomparison project (GrSMBMIP). We present results on the grid of the used EraInterim (EraI) forcing as well as higher-resolution simulations on an Ice Sheet Model Intercomparison (ISMIP) grid. Our model results show that initialization affects the final results. Also, the different downscaling approaches (directly to a higher-resolution target or via height classes) available in CISSEMBEL deliver similar results. Since various parameterizations are available, the user can analyze their impact on results and explore the consequences on the SMB that determines Greenland’s future.
How to cite: Rodehacke, C., Verro, K., Hansen, N., Krebs-Kanzow, U., and Mottram, R.: Surface Mass Balance Model CISSEMBEL linking ice sheets and ESMs: Results of standalone GrSMBMIP simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17009, https://doi.org/10.5194/egusphere-egu26-17009, 2026.
