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International audience; Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9 mm to global mean sea level, with the rate of mass loss rising from 105 Gt yr−1 between 1992 and 1996 to 372 Gt yr−1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9 Gt yr−1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86 Gt yr−1 in 2017 to 444 Gt yr−1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9 Gt yr−1) and, to a lesser extent, from the Antarctic Peninsula (13±5 Gt yr−1). East Antarctica remains close to a state of balance, with a small gain of 3±15 Gt yr−1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021).

The Greenland ice sheet mass loss is one of the main sources of contemporary sea-level rise. The mass loss is primarily caused by surface melt and the resulting runoff. During the melt season, the ice sheet’s surface receives energy from sunlight absorption and sensible heating, which subsequently heats the subsurface snow and ice. The energy from the previous melt season can also enhance melting in the following summer as less heating is required to bring the snow and ice to the melting point. Subsurface temperatures are therefore both a result and a driver of the timing and magnitude of surface melt on the ice sheet. We present a dataset of more than 3900 measurements of ice, snow and firn temperature at 10 m depth across the Greenland ice sheet spanning the years from 1912 to 2022. We construct an artificial neural network (ANN) model that takes as input the ERA5 reanalysis monthly near-surface air temperature and snowfall for the 1954-2022 period and train it on our compilation of observed 10-meter temperature. We use our dataset and the ANN to evaluate three broadly used regional climate models (RACMO, MAR and HIRHAM). Our ANN model provides an unprecedented and observation-based description of the recent warming of the ice sheet’s near-surface and our evaluation of the three climate models highlights future development for the models. Overall, these findings improve our understanding of the ice sheet’s response to recent atmospheric warming and will help reduce uncertainties of ice sheet surface mass balance estimates.

The Horizon 2020 project PolarRES is coordinating a large international consortium of regional climate modelling groups in building a new ensemble of regional climate projections out to 2100. The ensemble is built at very high resolution (~12km) and using common domains, and set-ups to give directly comparable model outputs. At the same time, all regional climate models have been upgraded to a next-generation set-up, producing an ensemble of unprecedented sophistication.We use a storyline approach, focused on Arctic amplification and cyclones in the northern hemisphere and Southern Annular Mode variability in Antarctica, to select global climate models for forcing on the boundaries. Each regional climate modelling group will downscale ERA5 and multiple global climate models. The data produced from these simulations will be used to improve process understanding under present and future conditions as well as to identify impacts of climate change in the polar regions.Here, we present the experimental protocol developed in PolarRES and give details of the different regional climate models used, their setup, processes and domains as well as an overview of the outputs and planned applications. We show preliminary analysis of hindcast outputs to assess the performance of the ensemble. We invite other regional climate modelling groups outside the PolarRES consortium to consider using the same CORDEX -compatible model set-up and we are happy to receive suggestions of further spin-off studies or requests for collaboration.

We present results from a new high resolution regional climate model, configured for both the Arctic and the Antarctic, assessed with a range of in-situ and remote sensing datasets. Under the Horizon 2020 PolarRES project, a set of simulations are performed at a spatial resolution of ~12 km over the Arctic and Antarctic regions using the latest version (cy43) of the HCLIM-ALADIN regional climate model. The model includes a thermodynamic sea ice scheme and has been updated with the latest ice sheet masks and improved topography and other physiographic fields. The model will be used to provide climate projections over the 100-year period 2001-2100 for two emission scenarios, and driven on the boundaries by General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project (CMIP6). We also present and evaluate hindcast simulations for the period of 2001 to 2020 over both domains, forced by ERA5 on the boundaries. Model precipitation, temperature, sea ice, and other variables are evaluated with observations from automatic weather stations and satellite data in the polar regions, and additionally compared against the new high resolution (2.5km) Copernicus Arctic Regional ReAnalysis (CARRA) dataset. We also examine the effect of spectral nudging on simulation output. Preliminary results show that HCLIM improves on ERA5, capturing the precipitation, temperature, sea ice cover and ice sheet surface mass balance in both polar regions.In addition, we show that the wealth of earth observation data now available via the ESA climate change initiative and the EUMETSAT climate data programmes are extremely useful tools to the regional climate modelling community. We use example scripts for model evaluation using EO data via an open repository and present user cases that can be replicated by other modelling groups.

We use the land-ice surface height data product (ATL06 release 5) from NASA’s latest satellite laser altimetry, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to compute surface elevation changes (SEC) from October 2018 to September 2021 over both Antarctica and Greenland. To convert the SEC to mass change we need to remove the non-ice related SEC processes. To remove the signal from the firn compaction, we use an offline surface energy and firn model. The model is driven by outputs from the atmospheric regional climate model HIRHAM5, forced with reanalysis dataset ERA5, and it simulates the physics of the firn pack. The vertical bedrock movement also creates non-ice related signals, the glacial isostatic adjustment has been computed using the ICE-7G model and SELEN4, and the elastic rebound has been computed using a modified version of the REAR code. When the SEC are corrected for signals that are not associated with a change in snow or ice mass, we convert to mass change by multiplying the height change with an appropriate density. The corrected SEC can result from a change in either melt, snow accumulation, or dynamical behavior, this means that the appropriate density depends on which physical processes are driving the observed SEC. In this study, we have made a new density parametrization to convert the volume change into mass change. The density parametrization determines if one should multiply with snow densities (250-350 kg/m³) or ice density (917 kg/m³) based on a number of criteria; the sign of SEC, ice flow velocity, and the altitude of the area. With our new density parametrization, we get that the Greenland Ice Sheet has lost 237.5±10.3 Gt/year and the grounded Antarctic Ice Sheet has lost -137.6±27.2 Gt/year in the period. These results are in agreement with other mass balance estimates derived with different methods.

Arctic amplification is causing global warming to have a more intense impact on arctic regions, with consequences on the surface mass balance and glacier coverage of Greenland. The glaciers of Greenland are also shrinking, contributing to sea level rise as well. Projecting the future evolution of these changes is crucial for understanding the likely impacts of climate change on sea level rise.In this study, we compared three state-of-the-art Regional Climate Models (RCMs) (MAR, RACMO, and HIRHAM) using a common grid and forcing data from Earth System Models to assess their ability to project future changes in Greenland's surface mass balance up to 2100. We also considered the impact of different Earth System Models and Shared Socioeconomic Pathways.The results of this comparison showed significant differences in the projections produced by these different models, with a factor-2 difference in mass loss between MAR and RACMO on cumulative Surface Mass Balance anomalies. These differences are important as RCMs are often used as inputs for ice sheet models, which are used to make predictions about sea level rise. Furthermore, we aim to investigate the causes of these differences, as understanding them will be key to improving the accuracy of sea level rise projections.The uncertainty of the RCMs projections are translated into uncertainties in Sea-Level-Rise projections. The results presented here open the door for deeper investigations in the climate modeling community and the physical reasons linked to these divergences. Our study highlighted the importance of continued research and development of RCMs to better understand the physics implemented in these models and ultimately improve the accuracy of future sea level rise projections.

The ever increasing number of scientific datasets made available by authoritative data providers (NASA, Copernicus, etc.) and provided by the scientific community opens new possibilities for advancing the state of the art in many areas of the natural sciences. As a result, researchers, innovators, companies and citizens need to acquire computational and data analysis skills to optimally exploit these datasets. Several educational programs dispense basic courses to students, and initiatives such as “The Carpentries” (https://carpentries.org/) complement this offering but also reach out to established researchers to fill the skill gap thereby empowering them to perform their own data analysis. However, most researchers find it challenging to go beyond these training sessions and face difficulties when trying to apply their newly acquired knowledge to their own research projects. To this regard, hackathons have proven to be an efficient way to support researchers in becoming competent practitioners but organising good hackathons is difficult and time consuming. In addition, the need for large amounts of computational and storage resources during the training and hackathons requires a flexible solution. Here, we propose an approach where researchers work on realistic, large and complex data analysis problems similar to or directly part of their research work. Researchers access an infrastructure deployed on the European Ocean Science Cloud (EOSC) that supports intensive data analysis (large compute and storage resources). EOSC is a European Commission initiative for providing a federated and open multi-disciplinary environment where data, tools and services can be shared, published, found and re-used. We used jupyter book for delivering a collection of FAIR training materials for data analysis relying on Pangeo EOSC deployments as its primary computing platform. The training material (https://pangeo-data.github.io/foss4g-2022/intro.html, https://pangeo-data.github.io/clivar-2022/intro.html, https://pangeo-data.github.io/escience-2022/intro.html) is customised (different datasets with similar analysis) for different target communities and participants are taught the usage of Xarray, Dask and more generally how to efficiently access and analyse large online datasets. The training can be completed by group work where attendees can work on larger scale scientific datasets: the classroom is split into several groups. Each group works on different scientific questions and may use different datasets. Using the Pangeo (http://pangeo.io) ecosystem is not always new for all attendees but applying Xarray (http://xarray.pydata.org) and Dask (https://www.dask.org/) on actual scientific “mini-projects” is often a showstopper for many researchers. With this approach, attendees have the opportunity to ask questions, collaborate with other researchers as well as Research Software Engineers, and apply Open Science practices without the burden of trying and failing alone. We find the involvement of scientific computing research engineers directly in the training is crucial for success of the hackathon approach. Feedback from attendees shows that it provides a solid foundation for big data geoscience and helps attendees to quickly become competent practitioners. It also gives infrastructure providers and EOSC useful feedback on the current and future needs of researchers for making their research FAIR and open. In this presentation, we will provide examples of achievements from attendees and present the feedback EOSC providers have received.

Our ability to improve prognostic modeling of the Greenland Ice Sheet relies on understanding the long-term relationships between climate and mass flux (via iceberg calving) from marine-terminating tidewater glaciers (TWGs). Observations of recent TWG behavior are widely available, but long-term records of TWG advance are currently lacking. We present glacial geomorphological, sedimentological, archaeological, and modeling data to reconstruct the ~20 km advance of Kangiata Nunaata Sermia (KNS; the largest tidewater glacier in southwest Greenland) during the first half of the past millennium. The data show that KNS advanced ~15 km during the 12th and 13th centuries CE at a rate of ~115 m a−1, contemporaneous with regional climate cooling toward the Little Ice Age and comparable to rates of TWG retreat witnessed over the past ~200 years. Presence of Norse farmsteads proximal to KNS demonstrates their resilience to climate change, manifest as a rapidly advancing TWG in a cooling climate. The results place limits on the magnitude of ice-margin advance and demonstrate TWG sensitivity to climate cooling as well as warming. These data combined with our grounding-line stability analysis provide a long-term record that validates approaches to numerical modeling aiming to link calving to climate.

Ice losses from the Greenland and Antarctic Ice Sheets have accelerated since the 1990s, accounting for a significant increase in global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume and in Earth’s gravity field. Between 1992 and 2020, the ice sheets contributed 21.0 ± 1.9 mm to global mean sea-level, with the rate of mass loss rising from 105 Gt yr-1 between 1992 and 1996 to 372 Gt yr-1 between 2016 and 2020. In Greenland, the rate of mass loss is 169 ± 9 Gt yr-1 between 1992 and 2020 but there are large inter-annual variations in mass balance with mass loss ranging from 86 Gt yr-1 in 2017 to 444 Gt yr-1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (-82 ± 9 Gt yr-1) and to a lesser extent from the Antarctic Peninsula (-13 ± 5 Gt yr-1). East Antarctica remains close to a state of balance (3 ± 15 Gt yr-1), but is the most uncertain component of Antarctica’s mass balance.

Surface mass balance (SMB) is computed from regional climate models (RCM) using reanalysis data. Estimates of the SMB vary between RCMs due to differences such as the model set-up, physical parameterizations, and topography as well as ice mask. The ice mask in a model defines the surface covered by glacier ice. The differences in ice masks appear small, however it is here shown that it leads to important differences in SMB when integrated over the continent. To circumvent this area-dependent bias, intercomparison studies of modelled SMB use a common ice mask (Mottram et al., 2021). The SMB in areas outside the common ice mask is discarded. By comparing the native ice masks with the common ice mask used in Mottram et al. 2021 we find differences in integrated SMB of between 20.1 and 102.4 Gt per year over the grounded ice sheet when compared to the ensemble mean from Mottram et al. 2021. These differences are nearly equivalent to the entire Antarctic ice sheet mass imbalance identified in the IMBIE study. SMB is particularly essential when estimating the total mass balance of an ice sheet via the input-output method, by subtracting ice discharge from the SMB to derive the mass change. We use the RCM HIRHAM5 to simulate the Antarctic climate and force a newly develop offline subsurface firn model, to simulate the Antarctic SMB from 1980 to 2017. We use discharge estimates from two previously published studies to calculate the regional scale mass budget. To validate the results from the input-output method, we compared the results to the gravimetry-derived mass balance from the GRACE/GRACE-FO mass loss time series, computed for the period 2002–2020. We find good agreement between the two input-output results and GRACE in West Antarctica, however, there are large disagreements between the two input-output methods in East Antarctica and over the Antarctic Peninsula. Over the entire grounded ice sheet, GRACE detects a mass loss of 900 Gt for the period 2002-2017, whereas the two input-output results show a mass gain of 500 Gt and a mass loss of 4000 Gt, depending on which discharge dataset is used. These results are integrated over the native HIRHAM5 ice mask. If we instead integrate over the common ice mask from Mottram et al. 2021, the results change from a mass gain of 500 Gt to a mass loss of 500 Gt, and a mass loss of 4000 Gt to a mass loss of 5000 Gt, over the grounded ice sheet for the period 2002-2017. While the differences in ice discharge remain the largest sources of uncertainty in the Antarctic ice sheet mass budget, our analysis shows that even a small area bias in ice mask can have a huge impact in high precipitation areas and therefore SMB estimates. We conclude there is a pressing need for a common ice mask protocol, to create an accurate harmonized updated ice mask.

Surface Mass Balance (SMB) is the key driver of ice sheet mass budget. It delivers the snow that nourishes ice sheets and the surface melt that balances snowfall and, along with ocean interactions, drives ice flow. We here present alternative future projections for both the Greenland and Antarctic ice sheets, driven by two different earth system models (ESMs), EC-Earth and CESM2, for two different emissions pathways (SSP585, 245) and in the case of EC-Earth for two different CMIP versions (EC-EARTH2 inCMIP5 and EC-EARTH3 in CMIP6).We use the regional climate model (RCM) HIRHAM5 to downscale the global models to 5.5km resolution over the Greenland ice sheet and 12km resolution over Antarctica. HIRHAM5 output is then used to drive a surface mass budget model for both ice sheets.The matrix of models and scenarios gives us the opportunity to examine how different factors, including atmospheric circulation indices, model resolution, ocean dynamics, sea ice and SMB components affect mass budget and sea level rise estimates over the course of the 21st century. About half the difference between CMIP5 and CMIP6 SMB estimates is related to differences in the scenarios compared to the SSPs and about half is related to differences in the driving models. In addition, we compare with other published downscaled SMB estimates from different RCMs (MAR and RACMO) to assess the envelope of likely ice sheet evolution out to 2100. Both CESM2 and EC-EARTH3 have high equilibrium climate sensitivity, and our study correspondingly shows high ice sheet mass loss particularly from Greenland by the end of the century, in line with other published estimates under high emissions scenarios. Melt is increasingly important in both ice sheets, but especially Greenland over the course of the 21st century and scales by temperature and therefore emissions pathway. All model projections show an increase in precipitation, but internal variability in circulation in the Southern Ocean still dominates the patterns in Antarctica and masks to some extent climate change signal in SMB.Future work will extend the ensemble of SMB estimate with a direct statistically based method, that allows fast downscaling of ESM output directly to SMB using the Copenhagen Ice Sheet Surface Energy and Mass Balance modEL (CISSEMBEL) and we also present some early preliminary results comparing different downscaling techniques.

Regional climate models compute ice sheet surface mass balance (SMB) over a mask that defines the area covered by glacier ice, but ice masks have not been harmonised between models. Intercomparison studies of modelled SMB therefore use a common ice mask. The SMB in areas outside the common ice mask, which are typically coastal and high-precipitation regions, is discarded. Ice mask differences change integrated SMB by between 40.5 and 140.6 Gt yr−1 (1.8 % to 6.0 % of ensemble mean SMB), equivalent to the entire Antarctic mass imbalance. We conclude there is a pressing need for a common ice mask protocol.

The Greenland Ice Sheet (GIS) is subject to amplified impacts of climate change and its monitoring is essential for understanding and improving scenarios of future climate conditions. Surface temperature over the GIS is an important variable as it regulates processes related to the exchange of energy and water between the surface and the atmosphere. As few key local observation sites exist, an important alternative to obtain surface temperature observations over the GIS is space-borne sensors that carry thermal infrared instruments. These offer several passes per day with a wide view and are the basis of deriving Ice Surface Temperature (IST) products. The aim of this study was to compare several satellite IST products for the GIS and develop and validate the first multi-sensor, gap-free (Level 4, L4) product for 2012. High resolution Level 2 (L2) IST products from the European Space Agency (ESA) Land Surface Temperature Climate Change Initiative (LST_cci) project and the Arctic & Antarctic Ice Surface Temperatures from Thermal Infrared Satellite Sensors (AASTI) dataset, were assessed using observations from the PROMICE stations and IceBridge flight campaigns. The AASTI data showed overall better performance compared to LST_cci data, that in return had superior spatial coverage and availability. Both datasets were further utilised to construct a daily, gap-free, L4 IST product using the optimal interpolation (OI) method. The resulting L4 IST product performed satisfactorily in terms of quality when compared with surface temperature observations from the PROMICE stations and IceBridge flight campaigns. By combining the advantages of the upstream satellite datasets, the gap-free L4 IST product allowed for the analysis of IST over the GIS during the year 2012, when a significant melt event occurred. Mean summer (June–August) IST over the GIS was −5.5 °C ± 4.5 °C, with an annual mean of −22.1 °C ± 5.4 °C. Mean IST during the melt season (May–August) ranged from −15 °C to −1 °C, while almost the entire GIS experienced at least between 1 and 5 melt days when temperatures were −1 °C or higher. Finally, this study assessed the potential for using the satellite L4 IST product to improve model simulations of the GIS surface mass budget (SMB). The new L4 IST product was first used to evaluate, and then it was assimilated into an SMB model of snow and firn processes for the year 2012, to assess the impact of including a high resolution IST product on the SMB model. Compared with independent observations from the PROMICE stations and IceBridge flight campaigns, inclusion of the L4 IST dataset improved the model performance during the key onset of the melt season, where model biases are typically large. Reassuringly, internal model parameterisations performed well compared to the L4 IST dataset outside of this season, providing more confidence in modelled GIS surface mass budget (SMB) estimates.

The Greenland Ice Sheet (GIS) is subject to amplified impacts of climate change and its monitoring is essential for understanding and improving scenarios of future climate conditions. Surface temperature over the GIS is an important variable, regulating processes related to the exchange of energy and water between the surface and the atmosphere. Few local observation sites exist; thus spaceborne platforms carrying thermal infrared instruments offer an alternative for surface temperature observations and are the basis for deriving ice surface temperature (IST) products. In this study several satellite IST products for the GIS were compared, and the first multi-sensor, gap-free (Level 4, L4) product was developed and validated for 2012. High-resolution Level 2 (L2) products from the European Space Agency (ESA) Land Surface Temperature Climate Change Initiative (LST_cci) project and the Arctic and Antarctic Ice Surface Temperatures from Thermal Infrared Satellite Sensors (AASTI) dataset were assessed using observations from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) stations and IceBridge flight campaigns. AASTI showed overall better performance compared to LST_cci data, which had superior spatial coverage and availability. Both datasets were utilised to construct a daily, gap-free L4 IST product using the optimal interpolation (OI) method. The resulting product performed satisfactorily when compared to surface temperature observations from PROMICE and IceBridge. Combining the advantages of satellite datasets, the L4 product allowed for the analysis of IST over the GIS during 2012, when a significant melt event occurred. Mean summer (June–August) IST was −5.5 ± 4.5 ∘C, with an annual mean of −22.1 ± 5.4 ∘C. Mean IST during the melt season (May–August) ranged from −15 to −1 ∘C, while almost the entire GIS experienced at least between 1 and 5 melt days when temperatures were −1 ∘C or higher. Finally, this study assessed the potential for using the satellite L4 IST product to improve model simulations of the GIS surface mass balance (SMB). The L4 IST product was assimilated into an SMB model of snow and firn processes during 2012, when extreme melting occurred, to assess the impact of including a high-resolution IST product on the SMB model. Compared with independent observations from PROMICE and IceBridge, inclusion of the L4 IST dataset improved the SMB model simulated IST during the key onset of the melt season, where model biases are typically large and can impact the amount of simulated melt.

Observed ice-sheet losses track the upper range of the IPCC Fifth Assessment Report sea-level predictions, recently driven by ice dynamics in Antarctica and surface melting in Greenland. Ice-sheet models must account for short-term variability in the atmosphere, oceans and climate to accurately predict sea-level rise.

In recent decades, meltwater runoff has accelerated to become the dominant mechanism for mass loss in the Greenland ice sheet1,2,3. In Greenland’s high- elevation interior, porous snow and firn accumulate; these can absorb surface meltwater and inhibit runoff4, but this buffering effect is limited if enough water refreezes near the surface to restrict percolation5,6. However, the influence of refreezing on runoff from Greenland remains largely unquantified. Here we use firn cores, radar observations and regional climate models to show that recent increases in meltwater have resulted in the formation of metres-thick, low-permeability ‘ice slabs’ that have expanded the Greenland ice sheet’s total runoff area by 26 ± 3 per cent since 2001. Although runoff from the top of ice slabs has added less than one millimetre to global sea-level rise so far, this contribution will grow substantially as ice slabs expand inland in a warming climate. Runoff over ice slabs is set to contribute 7 to 33 millimetres and 17 to 74 millimetres to global sea-level rise by 2100 under moderate- and high-emissions scenarios, respectively—approximately double the estimated runoff from Greenland’s high-elevation interior, as predicted by surface mass balance models without ice slabs. Ice slabs will have an important role in enhancing surface meltwater feedback processes, fundamentally altering the ice sheet’s present and future hydrology.

The future rates of ice sheet melt in Greenland and Antarctica are an important factor when making estimates of the likely rate of sea level rise. Global climate models that took part in the fifth Coupled Model Intercomparison Project (CMIP5) have generally been unable to replicate observed rates of ice sheet melt. With the advent of the sixth Coupled Model Intercomparison Project (CMIP6), with a general increase in the equilibrium climate sensitivity, we here compare two versions of the global climate model EC-Earth using the regional climate model HIRHAM5 downscaling EC-Earth for Greenland and Antarctica. One version (v2) of EC-Earth is taken from CMIP5 for the high-emissions Representative Concentration Pathways (RCP8.5) scenario and the other (v3) from CMIP6 for the comparable high-emissions Shared Socioeconomic Pathways (SSP5-8.5) scenario). For Greenland, we downscale the two versions of EC-Earth for the historical period 1991–2010 and for the scenario period 2081–2100. For Antarctica, the periods are 1971–2000 and 2071–2100, respectively. For the Greenland Ice Sheet, we find that the mean change in temperature is 5.9 °C when downscaling EC-Earth v2 and 6.8 °C when downscaling EC-Earth v3. Corresponding values for Antarctica are 4.1 °C for v2 and 4.8 °C for v3. The mean change in surface mass balance at the end of the century under these high emissions scenarios is found to be −210 Gt yr−1 (v2) and −1150 Gt yr−1 (v3) for Greenland and 420 Gt yr−1 (v2) and 80 Gt yr−1 (v3) for Antarctica. These distinct differences in temperature change and particularly surface mass balance change are a result of the higher equilibrium climate sensitivity in EC-Earth v3 (4.3 K) compared with 3.3 K in EC-Earth v2 and the differences in greenhouse gas concentrations between the RCP8.5 and the SSP5-8.5 scenarios.

The development of the HARMONIE model system has led to huge advances in numerical weather prediction, including over Greenland where a numerical weather prediction (NWP) model is used to forecast daily surface mass budget over the Greenland ice sheet as presented on polarportal.dk. The new high resolution Copernicus Arctic Reanalysis further developed the possibilities in HARMONIE with full 3DVar data assimilation and extended use of quality-controlled local observations. Here, we discuss the development and current status of the climate version of the HARMONIE Climate model (HCLIM). The HCLIM system has opened up the possibility for flexible use of the model at a range of spatial scales using different physical schemes including HARMONIE-AROME, ALADIN and ALARO for different spatial and temporal resolutions and assimilating observations, including satellite data on sea ice concentration from ESA CCI+, to improve hindcasts. However, the range of possibilities means that documenting the effects of different physics and parameterisation schemes is important before widespread application. Here, we focus on HCLIM performance over the Greenland ice sheet, using observations to verify the different plausible set-ups and investigate biases in climate model outputs that affect the surface mass budget (SMB) of the Greenland ice sheet. The recently funded Horizon 2020 project PolarRES will use the HCLIM model for very high resolution regional downscaling, together with other regional climate models in both Arctic and Antarctic regions, and our analysis thus helps to optimise the use of HCLIM in the polar regions for different modelling purposes.

The regional climate model HIRHAM5 developed for Greenland ice sheet applications has now been updated to also simulate Antarctic conditions. The outputs of the Antarctic runs have been used to force an offline subsurface model, to give estimates of the Antarctic surface mass balance (SMB) from 1980 to 2017. Here, we compare two different versions of this offline subsurface model and evaluate how they simulate the physical properties of the uppermost part of the Antarctic firn pack. We find that the total calculated SMB of Antarctica is sensitive to the subsurface model variant. One model version uses an Eulerian framework, meaning that mass is advected through layers of fixed mass. When snowfall occurs at the surface, it is added to the first layer and an equal mass from that layer is shifted to the underlying layer. The same goes for each layer in the model column, and vice versa for mass loss. The other model version uses a Lagrangian framework for the layer development. Layers evolve through splitting and merging dynamically based on a number of weighted criteria.The model simulations are validated against in situ observations of firn temperature and subsurface density. We find a mean temperature bias of 0.42-0.52 ℃ and a mean bias in modelled density of -24.0±18.4 kg m⁻³ and -8.2±15.3 kg m⁻³ for layers less than 550 kg m⁻³ for the Eulerian and Lagrangian framework, respectively. For layers with a density above 550 kg m⁻³ the bais is -31.7±23.4 kg m⁻³ and -35.0±23.7 kg m⁻³ for the Eulerian and Lagrangian framework, respectively. The modelling framework also affects the resulting SMB. The Lagrangian framework, estimates a total SMB of 2473.5±114.4 Gt yr⁻¹ while the Eulerian framework results in slightly higher modelled SMB of 2564.8±113.7 Gt yr⁻¹. The majority for this difference in modelled SMB is pinpointed to the ice shelves (the SMB over grounded ice only differs 30 Gt yr⁻¹) and demonstrates the importance of firn modelling in areas with substantial melt. Both the Eulerian and the Lagrangian SMB estimates are within each other's uncertainties and within range of other SMB studies. However, the Lagrangian version has the best statistics for modelling the densities. Given the importance of precipitation to Antarctic SMB, climate models must accurately simulate regional circulation patterns that modulate precipitation rates. We therefore investigate the relationship between SMB in individual drainage basins and the southern annular mode (SAM), using Monte Carlo simulations. This shows a robust relationship between SAM and SMB in half of the basins (13 out of 27). In general, when SAM is positive there is a lower SMB over the Plateau and a higher SMB on the westerly side of the Antarctic Peninsula, and vice versa when the SAM is negative.

In a warming climate concise knowledge of the mass balance of the Greenland ice sheet is of utter importance. Speculations that current warming will increase the snow accumulation and mitigate mass balance losses are unconstrained as accumulation data across large regions of the northern ice sheet are scarce. We reconstructed the accumulation from six north Greenland shallow firn cores (~10 m) and eight snow cores (~2 m) to constrain recent accumulation patterns in northern Greenland and calculated recent warming in the same area using borehole temperature measurements. We find an increase in temperatures in the north Greenland interior of 0.9 to 2.5 °C (method and site dependent) per decade over the past two decades in line with an Arctic amplified anthropogenic warming. We compare annual reconstructed accumulation from the firn cores (1966–2015) to radar estimates and to annual re-analysis data (1980–2016) of precipitation subtracted evaporation from the regional climate model HIRHAM5, operated by the Danish Meteorological Institute. The spatial variability resembles that observed in earlier estimates with a clear increase west of the topographic divide and a low accumulation area across the north-eastern ice sheet. Our accumulation results are comparable to earlier firn core estimates, despite being larger in the east. We only find a positive significant trend in the accumulation for the period 2000–2010 to the northwest. In the vicinity of the EGRIP deep ice core drilling site, we find variable accumulation patterns for two 15 km apart firn cores likely owing to local topographic effects as a result of the North East Greenland Ice Stream dynamics.

The future rates of ice sheet melt in Greenland and Antarctica are an important factor when making estimates of the likely rate of sea level rise. Global climate models that took part in the fifth Coupled Model Intercomparison Project (CMIP5) have generally been unable to replicate observed rates of ice sheet melt. With the advent of the sixth Coupled Model Intercomparison Project (CMIP6), with a general increase in the equilibrium climate sensitivity, we here compare two versions of the global climate model EC-Earth using the regional climate model HIRHAM5 downscaling EC-Earth for Greenland and Antarctica. One version (v2) of EC-Earth is taken from CMIP5 for the high-emissions Representative Concentration Pathways (RCP8.5) scenario and the other (v3) from CMIP6 for the comparable high-emissions Shared Socioeconomic Pathways (SSP5-8.5) scenario). For Greenland, we downscale the two versions of EC-Earth for the historical period 1991–2010 and for the scenario period 2081–2100. For Antarctica, the periods are 1971–2000 and 2071–2100, respectively. For the Greenland Ice Sheet, we find that the mean change in temperature is 5.9 °C when downscaling EC-Earth v2 and 6.8 °C when downscaling EC-Earth v3. Corresponding values for Antarctica are 4.1 °C for v2 and 4.9 °C for v3. The mean change in surface mass balance at the end of the century under these high emissions scenarios is found to be −210 Gt yr−1 (v2) and −1150 Gt yr−1 (v3) for Greenland and +150 Gt yr−1 (v2) and −710 Gt yr−1 (v3) for Antarctica. These distinct differences in temperature change and particularly surface mass balance change are a result of the higher equilibrium climate sensitivity in EC-Earth v3 (4.3 K) compared with 3.3 K in EC-Earth v2 and the differences in greenhouse gas concentrations between the RCP8.5 and the SSP5-8.5 scenarios.

The North Atlantic Oscillation (NAO) is an important control on both northern European weather and Greenland ice sheet surface mass budget via the path of storm tracks that deliver precipitation, particularly in the winter, and by the strength and persistence of the Greenland blocking high that promotes melt in summer. Within CMIP5 models, atmospheric blocking was generally poorly represented regardless of location, we here examine an ensemble of 10 CMIP6 fully coupled earth system models (ESMs) that were available by Summer 2019 in order to examine if model improvements better represent the NAO in CMIP6.We examine temperature over Greenland and the north Atlantic region as well as NAO position, persistence and strength in winter and summer for each model in the historical scenario. No single model performs well on all characteristics but the UKESM and EC-EARTH3 perform the best when compared to the ERA5 climate reanalysis.We also show how the NAO is expected to change in 8 of these models under different future climate scenarios. The location of the Icelandic low in particular migrates northwards by varying amounts, likely related to Arctic sea ice changes within the models and with a consequent impact on precipitation.Downscaling experiments carried out using the HIRHAM5 regional climate model over the Greenland ice sheet show the importance of accurately characterising the NAO in order to correctly estimate both winter accumulation and summer melt and the combination that gives the ice sheet mass budget. Our study emphasises the importance of assessing a range of different climate and weather variables when selecting models to downscale for obtaining ice sheet mass balance. We also note that while some progress has been made in better representing atmospheric blocking in ESMs, largely down to higher resolution in atmospheric models, there is still a substantial improvement required before ESMs can be said to accurately characterise the climate of the North Atlantic region with consequent impacts on ice sheet surface mass budget projections.

Mass loss from glacier surface melt is buffered by percolation and refreezing in the underlying snowpack, processes of particular importance in the percolation zone of the Greenland ice sheet and increasingly in Antarctica under a warming climate. Retention and refreezing is dependent on a number of micro-scale factors such as snow grain size, density and temperature that are heavily parameterized in models. Melt and snowfall in preceding seasons are also important in determining retention rates in the current season due to initialization of the snowpack .In the retention model intercomparison project (RetMIP) we use a common atmospheric forcing from the HIRHAM5 regional climate model to drive participating models, to study the effect of different internal parameterisations. We compare 9 different 1D models and four 2D models with each other and with observations from 4 key field sites. We show that initialisation of snowpack models is important but evolution of retention through time is strongly determined by melt rates.Models that explicitly account for deep meltwater percolation tend to overestimate percolation depth and consequently firn temperature at the percolation and ice slab sites although they simulate accurately the recharge of the firn aquifer. Models using Darcy’s law and bucket scheme compare favourably to observations at the percolation site but only the Darcy models accurately simulate firn temperature and thus meltwater percolation at the ice slab site. We find that Eulerian models that transfer firn through fixed layers, diffuse over time the gradients in firn temperature and density. No model outperforms all others at our four test sites indicating that all models have potential for development.A first look at Antarctic firn processes emphasises the importance of long spin-up times to initialise the snowpack and as the ice sheet surface evolves in the future parameterising the specific Antarctic retention process is likely to become more important.

We use remotely sensed ice velocities in combination with observations of surface elevation and glacier area change to investigate the dynamics of Hagen Bræ, North Greenland in high detail over the last 35 years. From our data, we can establish for the first time that Hagen Bræ is a surge‐type glacier with characteristics of both Alaskan‐ and Svalbard‐type surging glaciers. We argue that the observed surge was preconditioned by the glacier geometry and triggered by englacially stored meltwater. At present, the glacier is in a transitional state between active and quiescence phases and is not building up to its pre‐surge geometry. We suggest that the glacier is adjusting to the loss of its floating section, general thinning, and changes in fjord conditions that occurred over the study period which are unrelated to the surge behavior. The high temporal resolution of the ice velocity data gives insight to the sub‐annual glacier flow., Key Points Thirty‐five years of remotely sensed data document the recent dynamics and surge cycle of Hagen Bræ in high detailWe argue that the surge was preconditioned by geometry but triggered by englacially stored meltwaterThe onset of the observed surge was abrupt while its termination was gradual

We provide an updated analysis of instrumental Greenland monthly temperature data to 2019, focusing mainly on coastal stations but also analysing ice‐sheet records from Swiss Camp and Summit. Significant summer (winter) coastal warming of ~1.7 (4.4)°C occurred from 1991–2019, but since 2001 overall temperature trends are generally flat and insignificant due to a cooling pattern over the last 6–7 years. Inland and coastal stations show broadly similar temperature trends for summer. Greenland temperature changes are more strongly correlated with Greenland Blocking than with North Atlantic Oscillation changes. In quantifying the association between Greenland coastal temperatures and Greenland Ice Sheet (GrIS) mass‐balance changes, we show a stronger link of temperatures with total mass balance rather than surface mass balance. Based on Greenland coastal temperatures and modelled mass balance for the 1972–2018 period, each 1°C of summer warming corresponds to ~(91) 116 Gt·yr−1 of GrIS (surface) mass loss and a 26 Gt·yr−1 increase in solid ice discharge. Given an estimated 4.0–6.6°C of further Greenland summer warming according to the regional model MAR projections run under CMIP6 future climate projections (SSP5‐8.5 scenario), and assuming that ice‐dynamical losses and ice sheet topography stay similar to the recent past, linear extrapolation gives a corresponding GrIS global sea‐level rise (SLR) contribution of ~10.0–12.6 cm by 2100, compared with the 8–27 cm (mean 15 cm) “likely” model projection range reported by IPCC in 2019 (SPM.B1.2). However, our estimate represents a lower limit for future GrIS change since fixed dynamical mass losses and amplified melt arising from both melt‐albedo and melt‐elevation positive feedbacks are not taken into account here. publishedVersion

The ability of state-of-the-art regional climate models (RCMs) to simulate the trends of intense cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble in winter and summer are compared with the results from four reanalyses (ERA-Interim, NCEP-CFSR, NASA-MERRA2 and JMA-JRA55) in winter and summer for 1981-2010 period. © Published under licence by IOP Publishing Ltd.

Simulations show that the Greenland Ice Sheet could disappear within a millennium if greenhouse gas emissions continue unabated., The Greenland Ice Sheet holds 7.2 m of sea level equivalent and in recent decades, rising temperatures have led to accelerated mass loss. Current ice margin recession is led by the retreat of outlet glaciers, large rivers of ice ending in narrow fjords that drain the interior. We pair an outlet glacier–resolving ice sheet model with a comprehensive uncertainty quantification to estimate Greenland’s contribution to sea level over the next millennium. We find that Greenland could contribute 5 to 33 cm to sea level by 2100, with discharge from outlet glaciers contributing 8 to 45% of total mass loss. Our analysis shows that uncertainties in projecting mass loss are dominated by uncertainties in climate scenarios and surface processes, whereas uncertainties in calving and frontal melt play a minor role. We project that Greenland will very likely become ice free within a millennium without substantial reductions in greenhouse gas emissions.

Changes in the characteristics of cyclone activity (frequency, depth and size) in the Arctic are analyzed based on simulations with state-of-the-art regional climate models (RCMs) from the Arctic-CORDEX initiative and global climate models (GCMs) from CMIP5 under the Representative Concentration Pathway (RCP) 8.5 scenario. Most of RCMs show an increase of cyclone frequency in winter (DJF) and a decrease in summer (JJA) to the end of the 21st century. However, in one half of the RCMs, cyclones become weaker and substantially smaller in winter and deeper and larger in summer. RCMs as well as GCMs show an increase of cyclone frequency over the Baffin Bay, Barents Sea, north of Greenland, Canadian Archipelago, and a decrease over the Nordic Seas, Kara and Beaufort Seas and over the sub-arctic continental regions in winter. In summer, the models simulate an increase of cyclone frequency over the Central Arctic and Greenland Sea and a decrease over the Norwegian and Kara Seas by the end of the 21st century. The decrease is also found over the high-latitude continental areas, in particular, over east Siberia and Alaska. The sensitivity of the RCMs' projections to the boundary conditions and model physics is estimated. In general, different lateral boundary conditions from the GCMs have larger effects on the simulated RCM projections than the differences in RCMs' setup and/or physics.

During two exceptionally large July 2012 multiday Greenland ice sheet melt episodes, nonradiative energy fluxes (sensible, latent, rain, and subsurface collectively) dominated the ablation area surface energy budget of the southern and western ice sheet. On average the nonradiative energy fluxes contributed up to 76% of daily melt energy at nine automatic weather station sites in Greenland. Comprising 6% of the ablation period, these powerful melt episodes resulted in 12–15% of the south and west Greenland automatic weather station annual ablation totals. Analysis of high resolution (~5 km) HIRHAM5 regional climate model output indicates widespread dominance of nonradiative energy fluxes across the western ablation area during these episodes. Yet HIRHAM5 still underestimates melt by up to 56% during these episodes due to a systematic underestimation of turbulent energy fluxes typical of regional climate models. This has implications for underestimating future melt, when exceptional melt episodes are expected to occur more frequently.

We review the findings of approximately 60 years of in situ and remote sensing studies of glacier crevasses, as well as the three broad classes of numerical models now employed to simulate crevasse fracture. The relatively new insight that mixed-mode fracture in local stress equilibrium, rather than downstream advection alone, can introduce nontrivial curvature to crevasse geometry may merit the reinterpretation of some key historical observation studies. In the past three decades, there have been tremendous advances in the spatial resolution of satellite imagery, as well as fully automated algorithms capable of tracking crevasse displacements between repeat images. Despite considerable advances in developing fully transient three-dimensional ice flow models over the past two decades, both the zero stress and linear elastic fracture mechanics crevasse models have remained fundamentally unchanged over this time. In the past decade, however, multidimensional and transient formulations of the continuum damage mechanics approach to simulating ice fracture have emerged. The combination of employing damage mechanics to represent slow upstream deterioration of ice strength and fracture mechanics to represent rapid failure at downstream termini holds promise for implementation in large-scale ice sheet models. Finally, given the broad interest in the sea level rise implications of recent and future cryospheric change, we provide a synthesis of 10 mechanisms by which crevasses can influence glacier mass balance.

The Greenland ice sheet is a major contributor to sea level rise, adding an estimated 0.47 +/− 0.23 mm/yr to global mean sea level between 1991 and 2015 (van den Broeke et al., 2016). Making sea level rise projections for the future and understanding the processes controlling current observed rates of sea level rise are crucially dependent on understanding the present-day state of the ice sheet. Here, we provide an overview of the current state of the mass budget of Greenland based on satellite gravimetry and remote sensing observations of surface elevation change, ice sheet velocity and calving front positions. We also combine these essential climate variables with a regional climate model (RCM) output from an ice sheet model (ISM) to gain insight into poorly understood ice sheet dynamical and surface mass processes. On average from 1992 to 2017 the ice sheet in some locations has lost up −2.65 m/yr in elevation based on ESA Radar altimetry analysis. Calving fronts have retreated all around Greenland since the 1990s and in only two out of 28 study locations have they remained stable. The locations of grounding lines at 5 key glaciers with floating ice tongues have remained stable over the observation period. However a detailed case study at Petermann glacier with an ice fracture model shows the sensitivity of these floating ice shelves to future climate change. GRACE gravimetric mass balance (GMB) data allows us to tie together disparate lines of evidence showing that Greenland has lost about 265 +/− 25 Gt/yr of ice over the period 2002 to 2015. RCM and ISM simulations show that surface mass processes dominate the overall Greenland ice sheet mass budget except for areas of fast ice sheet flow but marked differences between models and between models and observations indicate that not all processes are captured accurately, indicating areas of greater uncertainty and directions of future research for future sea level rise projections.

Using regional climate-model runs with a horizontal resolution of 5.5 km for two future scenarios and two time slices (representative concentration pathway [RCP] 4.5 and 8.5; 2031–2050 and 2081–2100) relative to a historical period (1991–2010), we study the climate change for the Qeqqata municipality in general and for Kangerlussuaq in particular. The climate-model runs are validated against observations of temperature and surface mass balance and a reanalysis simulation with the same model setup as the scenario runs, providing high confidence in the results. Clear increases in temperature and precipitation for the end of the 21st century are shown, both on and off the ice sheet, with an off–ice sheet mean annual temperature increase of 2.5–3°C for the RCP4.5 scenario and 4.8–6.0°C for the RCP8.5 scenario, and for precipitation an increase of 20–30% for the RCP4.5 scenario and 30–80% for the RCP8.5 scenario. Climate analogs for Kangerlussuaq for temperature and precipitation are provided, indicating that end-of-the-century Kangerlussuaq mean annual temperature is comparable with temperatures for the south of Greenland today. The extent of glacial retreat is also estimated for the Qeqqata municipality, suggesting that most of the ice caps south of the Kangerlussuaq fjord will be gone before the end of this century. Furthermore, the high-resolution runs are compared with an ensemble of six models run at a 50 km resolution, showing the need for high-resolution model simulations over Greenland.

The ability of state-of-the-art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA-Interim, National Centers for Environmental Prediction-Climate Forecast System Reanalysis, National Aeronautics and Space Administration-Modern-Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency-Japanese 55-year reanalysis) in winter and summer for 1981-2010 period. In addition, we compare cyclone statistics between ERA-Interim and the Arctic System Reanalysis reanalyses for 2000-2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large-scale spectral nudging show a better agreement with ERA-Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables. ©2018. American Geophysical Union.

Freshwater runoff to fjords with marine-terminating glaciers along the Greenland Ice Sheet margin has an impact on fjord circulation and potentially ice sheet mass balance through increasing heat transport to the glacier front. Here, the authors use the high-resolution (5.5 km) HIRHAM5 regional climate model, allowing high detail in topography and surface types, to estimate freshwater input to Godthåbsfjord in southwest Greenland. Model output is compared to hydrometeorological observations and, while simulated daily variability in temperature and downwelling radiation shows high correlation with observations (typically >0.9), there are biases that impact the results. In particular, overestimated albedo leads to underestimation of melt and runoff at low elevations. In the model simulation (1991–2012), the ice sheet experiences increasing energy input from the surface turbulent heat flux (up to elevations of 2000 m) and shortwave radiation (at all elevations). Southerly wind anomalies and declining cloudiness due to an increase in atmospheric pressure over north Greenland contribute to increased summer melt. This results in declining surface mass balance (SMB), increasing surface runoff, and upward shift of the equilibrium line altitude. SMB is reconstructed back to 1890 though regression between simulated SMB and observed temperature and precipitation, with added uncertainty in the period 1890–1952 because of possible inhomogeneity in the precipitation record. SMB as low as in recent years appears to have occurred before, most notably around 1930, 1950, and 1960. While previous low SMBs were mainly caused by low accumulation, those around 1930 and in the 2000s are mainly due to warming.

Terrestrial freshwater runoff strongly influences physical and biogeochemical processes at the fjord scale and can have global impacts when considered at the Greenland scale. We investigate the performance of the HIRHAM5 regional climate model over the catchments delivering freshwater to Tyrolerfjord and Young Sound by comparing to the unique Greenland Ecological Monitoring database of in situ observations from this region. Based on these findings, we estimate and discuss the fraction of runoff originating from glacierized and non-glacierized land delivered at the daily scale between 1996 and 2008. We find that glaciers contributed on average 50–80% of annual terrestrial runoff when considering different sections of Tyrolerfjord–Young Sound, but snowpack depletion on land and consequently runoff happens about one month earlier in the model than observed in the field. The temporal shift in the model is a likely explanation why summer surface salinity in the inner fjord did not correlate to modelled runoff. Electronic supplementary material The online version of this article (doi:10.1007/s13280-016-0876-4) contains supplementary material, which is available to authorized users.

To improve Greenland Ice Sheet surface mass balance (SMB) simulation, the subsurface scheme of the HIRHAM5 regional climate model was extended to include snow densification, varying hydraulic conductivity, irreducible water saturation and other effects on snow liquid water percolation and retention. Sensitivity experiments to investigate the effects of the additions and the impact of different parameterization choices are presented. Compared with 68 accumulation area ice cores, the simulated mean annual net accumulation bias is -5% (correlation coefficient of 0.90). Modeled SMB in the ablation area compares favorably with 1041 PROMICE observations with regression slope of 0.95-0.97 (depending on model configuration), correlation coefficient of 0.75-0.86 and mean bias -3%. Weighting ablation area SMB biases at low- and high-elevation with the amount of runoff from these areas, we estimate ice sheet-wide mass loss biases in the ablation area at -5% and -7% using observed (MODIS-derived) and internally calculated albedo, respectively. Comparison with observed melt day counts shows that patterns of spatial (correlation ~0.9) and temporal (correlation coefficient of ~0.9) variability are realistically represented in the simulations. However, the model tends to underestimate the magnitude of inter-annual variability (regression slope ~0.7) and overestimate that of spatial variability (slope ~1.2). In terms of subsurface temperature structure and occurrence of perennial firn aquifers and perched ice layers, the most important model choices are the albedo implementation and irreducible water saturation parameterization. At one percolation area location, for instance, the internally calculated albedo yields too high subsurface temperatures below 5 m, but when using an implementation of irreducible saturation allowing higher values, an ice layer forms in 2011, reducing the deep warm bias in subsequent years. On the other hand, prior to the formation of the ice layer, observed albedos combined with lower irreducible saturation give the smallest bias. Perennial firn aquifers and perched ice layers occur in varying thickness and area for different model parameter choices. While the occurrence of these features has an influence on the local-scale subsurface temperature, snow, ice and water fields, the Greenland-wide runoff and SMB are - in the model’s current climate - dominated by the albedo implementation.