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Supraglacial lakes (SGLs) form when meltwater collects in glacial surface depressions. On the Greenland ice sheet (GrIS), SGL position is controlled by bedrock topography, meaning SGLs form in approximately the same locations each melt season. SGLs provide surface water storage and mediate the transfer of surface water to the ice sheet bed. Drainage water from SGLs has been shown to infiltrate to the base of the ice sheet and lubricate the ice sheet-bedrock interface, causing transient increases in basal sliding at the glacier margin. SGL depth is used to calculate the volume of water contained within the basin and thus the likelihood of hydrofracture caused by increased pressure from overlying water. As overlying water pressure is a function of water depth, determining the accuracy of depth estimation techniques is of the utmost importance in obtaining reliable estimates of hydrofracture likelihood and in determining SGL drainage impacts on ice sheet velocity.Here, we present the results of an intercomparison of SGL depth measurements focused on the Watson River region of the GrIS. Previous studies have derived SGL depth by applying the Philpot (1989) radiative transfer equation to satellite-derived optical imagery. These results proved difficult to validate until recent advancements in remote sensing which allowed us to compare the radiative transfer-derived depths to laser altimetry measurements and high-resolution digital elevation modelling. Previous research has separately investigated the use of the Philpot (1989) radiative transfer equation, laser altimetry and digital elevation models, but none have intercompared all three.This research compares estimates of SGL depths in the Watson River region in southwest Greenland that have been derived using three satellite-based approaches; 1) by applying the radiative transfer equation proposed by Philpot (1989) to Sentinel-2 optical satellite imagery, 2) using ICESat-2 laser altimetry and 3) from ArcticDEM digital elevation models.Using the radiative transfer equation, we find the green band overestimates SGL depth and the red band underestimates SGL depth (with caveats) compared to ICESat-2 transects and digital elevation models. In summary, we achieve the first comprehensive intercomparison of these methods and provide insight into the strengths and potential limitations of each method, including levels of agreement between datasets, and associated uncertainties. This work helps to improve confidence in radiative transfer-derived estimates of SGL depth and volume and, consequently, quantitative estimates of meltwater storage on the surface of the GrIS. This research is associated with ESA’s Polar+ 4DGreenland study.

Subglacial lakes may form beneath ice sheets and ice caps, given the availability of water and appropriate bedrock and surface topography to capture the water. On a regional scale, these lakes can modulate the freshwater output to the ocean by acting as reservoirs that may periodically drain and recharge. Several such active subglacial lakes have been documented under the Antarctic ice sheet, while only a few are observed under the Greenland ice sheet. The small size of the hydrologically active subglacial lakes in Greenland compared to those in Antarctica, puts additional demands on our mapping capabilities to resolve in great detail the evolving surface topography over these lakes to document their temporal behavior. Here, we explore the potential of combining CryoSat-2 swath data and high resolution DEMs generated from TanDEM-X scenes and ArcticDEM strips to improve our knowledge of the evolution of four active subglacial lake sites previously documented in the literature. We find that the DEM data complement each other well in terms of time and resolution and thus provide new information about the subglacial lake activity, though the small size of the collapse basins is challenging for CS2, and we are only able to derive useful CS2 data for the two largest of the four investigated lakes. Based on these data sets we can e.g. conclude that the collapse basin at Flade Isblink was actually as deep as 95 m when it formed, which is 30 m deeper than previously documented. We also present evidence of a new active subglacial lake in Southwest Greenland.

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.

The ESA Earth Explorer CryoSat-2 was launched on 8 April 2010 to monitor the precise changes in the thickness of terrestrial ice sheets and marine floating ice. To do that, CryoSat orbits the planet at an altitude of around 720 km with a retrograde orbit inclination of 92∘ and a quasi repeat cycle of 369 d (30 d subcycle). To reach the mission goals, the CryoSat products have to meet the highest quality standards to date, achieved through continual improvements of the operational processing chains. The new CryoSat Ice Baseline-D, in operation since 27 May 2019, represents a major processor upgrade with respect to the previous Ice Baseline-C. Over land ice the new Baseline-D provides better results with respect to the previous baseline when comparing the data to a reference elevation model over the Austfonna ice cap region, improving the ascending and descending crossover statistics from 1.9 to 0.1 m. The improved processing of the star tracker measurements implemented in Baseline-D has led to a reduction in the standard deviation of the point-to-point comparison with the previous star tracker processing method implemented in Baseline-C from 3.8 to 3.7 m. Over sea ice, Baseline-D improves the quality of the retrieved heights inside and at the boundaries of the synthetic aperture radar interferometric (SARIn or SIN) acquisition mask, removing the negative freeboard pattern which is beneficial not only for freeboard retrieval but also for any application that exploits the phase information from SARIn Level 1B (L1B) products. In addition, scatter comparisons with the Beaufort Gyre Exploration Project (BGEP; https://www.whoi.edu/beaufortgyre, last access: October 2019) and Operation IceBridge (OIB; Kurtz et al., 2013) in situ measurements confirm the improvements in the Baseline-D freeboard product quality. Relative to OIB, the Baseline-D freeboard mean bias is reduced by about 8 cm, which roughly corresponds to a 60 % decrease with respect to Baseline-C. The BGEP data indicate a similar tendency with a mean draft bias lowered from 0.85 to −0.14 m. For the two in situ datasets, the root mean square deviation (RMSD) is also well reduced from 14 to 11 cm for OIB and by a factor of 2 for the BGEP. Observations over inland waters show a slight increase in the percentage of good observations in Baseline-D, generally around 5 %–10 % for most lakes. This paper provides an overview of the new Level 1 and Level 2 (L2) CryoSat Ice Baseline-D evolutions and related data quality assessment, based on results obtained from analyzing the 6-month Baseline-D test dataset released to CryoSat expert users prior to the final transfer to operations.

The ESA 4DGreenland project has the objective of performing an integrated assessment of Greenland’s hydrology through maximizing the use of Earth observation (EO) data. However, as meltwater is activated on the surface of the Greenland ice sheet and percolates into the firn, the firn water storage and delayed runoff are unobservable from EO, and we must resort to modeling to quantify this component. The DTU-firn model has previously been used to quantify the change in firn air volume as a leading component of altimetric mass balance estimates and was initially tuned using firn core density observations. Here, we revisit the fundamentals of the model and perform model inversion using as many observational datasets as possible. This observational data ranges from direct measurements of firn compaction derived from “coffee-can” experiments, over traditional observation of densities and temperature from firn cores to regional observation of firn stratigraphy from airborne radar surveys. This large diversity in the data sources ensures the best possible constraints for capturing as many aspects as possible of the firn dynamics. One important model feature of the DTU-firn model is its ability to resolve individual precipitation events as model layers. This feature promotes the capability of retaining water and shows promising results in line with the in-situ observation. The updated DTU-firn model is therefore used within the 4DGreenland project to provide updated estimates of meltwater retention and delayed runoff as a function of the available water at the surface of the firn. Combined with results obtained by the HIRHAM- and MAR-firn models, it also enables better quantification of firn model uncertainties. Having built confidence in the model we can treat the retention and delayed runoff as a function of the available water at the surface of the firn. This then gives the possibility of using the observed surface melt, also acquired within 4DGreenland from satellite microwave data, in the assessments of Greenland’s hydrology and thereby increasing the useability of EO data.

As Earth’s climate warms, surface melting of the Greenland Ice Sheet is projected to intensify, contributing to rising sea levels1–4. Observations5–7 and theory8–10 indicate that meltwater generated at the surface of an ice sheet can drain to its bed via crevasses and moulins, where it flows relatively unhindered to the coast. This understanding of the movement of water within, and beneath, ice sheets, underpins theoretical models which are used to make projections of ice sheet change11. In this study, we show the first evidence of a disruptive drainage pathway in Greenland, whereby a subglacial flood – triggered by a draining subglacial lake – breaks through the ice sheet surface. This unprecedented outburst of water causes fracturing of the ice sheet, and the formation of 25-metre-high ice blocks. These observations reveal a complex, bidirectional coupling between the surface and basal hydrological systems of an ice sheet, which was previously unknown in Greenland. Analysis of over 30 years of satellite imagery confirms that the subglacial lake has drained at least once previously. However, on that occasion the floodwater failed to breach the ice surface. The two contrasting drainage regimes, coupled with the increased rates of ice melting and thinning that have occurred over the past three decades years, suggest that Arctic climate warming may have facilitated a new, disruptive mode of hydrological drainage on the ice sheet. As such, our observations reveal an emerging and poorly understood phenomenon, which is not currently captured in physical ice sheet models.

Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributions. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling, include global mean sea-level (GMSL) anomalies from satellite altimetry; the global mean steric component from Argo drifter data with incorporation of sea surface temperature data; the ocean mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry; the contribution from global glacier mass changes assessed by a global glacier model; the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes, assessed from satellite radar altimetry and from GRACE; and the contribution from land water storage anomalies assessed by the WaterGAP global hydrological model. Over the period Jan 1993–Dec 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend) and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period Jan 2003–Aug 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions (now about 2.22 ± 0.15 mm yr−1, 61 % of the GMSL trend), with the largest increase contributed from Greenland. The SLB of linear trends is closed for P1 and P2, that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB budget, which can be evaluated only for P2, is also closed, that is, the GRACE-based ocean-mass trend agrees with the sum of assessed mass contributions within uncertainties. Combined uncertainties (1-sigma) of the elements involved in the budgets are between 0.26 and 0.40 mm yr−1, about 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.

Satellite altimeters have monitored the surface elevation change of the Greenland ice sheet since 1978 and with an ice-sheet wide coverage since 1991. The satellite altimeters of interest for Greenland mass balance studies operate at different wavelengths; Ku-band radar, Ka-band radar, infrared laser, and visible laser. Some of the applied wavelengths can penetrate the surface in snow-covered regions and map the elevation change of subsurface layers. Especially the longer radar wavelength can penetrate the upper meters of the snow cover, whereas the infrared laser measurements from ICESat observes the snow-air interface of ice sheets. The pure surface elevation change derived from ICESat has been widely used in mass balance studies and may provide a benchmark for altimetric mass balance estimates after being corrected for changes in the firn-air content. The Ku-band radar observation provides the longest time series of ice sheet volume change, but the record is more difficult to convert into mass balance due to climate-induced variations in the surface penetration.Here, we apply machine learning to build an empirical calibration method for converting the observed radar-derived volume change into mass balance. We train the machine learning model during the limited period of coinciding laser and radar satellite altimetry data (2003-2009). The radar and laser datasets are not sufficient to guide the empirical calibration alone. Hence, additional datasets are used to help build a stable predictor needed for radar calibration, such as ice velocity. We focus on the lessons learned from this machine learning approach but also highlight results from the resulting 28-yearlong time series of Greenland ice sheet mass balance. For example, the Greenland Ice Sheet contribution to global sea-level rise has been 12.1±2.3 mm since 1992, with more than 80% of this originating after 2003.

We present the first 1992‐2020 record of Greenland Ice Sheet (GrIS) mass balance derived from multi‐satellite Ku‐band altimetry. We employ an empirical approach as an alternative detailed to radar‐propagation modeling, and instead convert elevation changes observed by radar altimetry into mass changes using spatiotemporal calibration fields. This calibration field is derived from a machine learning approach that optimizes the prediction of a previously published mass balance field as a function of ice sheet variables. Our mass balance record shows a GrIS contribution of 12.1±2.3 mm sea‐level equivalent since 1992, with more than 80% of this contribution occurring after 2003. Our record also suggests that the 2017 hydrological year is the first year in the 21st century which, within uncertainties, the GrIS was in balance. Overall, the 28‐year radar‐derived mass balance record we present highlights the potential of the method to provide operational mass balance estimates derived from multi‐satellite Ku‐band altimetry.

In a changing climate it is important to continuously monitor the Greenland Ice Sheet (GrIS) in relation to global sea level rise (Gardner et al., 2013). The margin of the GrIS is the most sensitive to climate changes and responds quickly.Here, we study how to improve the sensing capabilities of the marginal areas by applying the novel swath processing technique of interferometric SAR radar data, which is only available from the SIRAL altimeter onboard the Cryosat-2 satellite. In contrast to traditional Point-of-closest-approach (POCA) processing of radar altimeter data, the swath processing delivers a band of data far from nadir and beyond the POCA point. Despite the swath processing, in comparison with POCA, delivers millions of extra data points (Foresta et al., 2018) the new estimates come with a lower signal-to-noise ratio and the method can be optimized further. Here, we investigate the added value of, under suitable surface conditions, to lower the coherence limit to derive the optimal number of observation points and still keep an acceptable signal-to-noise ratio. This will allow us to get the most out of each Cryosat-2 waveform. The validation is further aided by the inter-comparison to airborne data collected during the ESA CryoVEx campaigns.

Melting at the surface of the Greenland ice sheet has significantly increased since the early 1990s and this affects the degree to which radar sensors can penetrate beyond the snow surface. Indeed, radars are sensitive to changes in the surface and subsurface properties, up to ~15 m below the snow surface for instruments using the Ku-band (13.5 GHz). When melting occurs, meltwater can percolate in the snowpack or refreeze at the surface and in turn, the degree of radar penetration is sharply reduced. Here we use measurements of near-surface density from firn cores and models and airborne radar and laser data collected during the European Space Agency of ESA’s CRYOsat Validation EXperiment (CRYOVEX) campaigns along a 675 km transect in West Central Greenland between 2006 and 2017 to examine spatial and temporal fluctuations in the near-surface properties and how this affects radar measurements. From airborne data acquired with ASIRAS at Ku-band, we identify internal layers corresponding to melt layers in the snowpack down to 15 m, in good agreement with a firn densification model. We examine the spatial and temporal distribution of these melt layers and we find that the abundance of melt layers is increasing with elevation and depicts a strong inter-annual variability and that these fluctuations are correlated with fluctuations in the degree of the radar penetration depth. For instance, in 2012, the Greenland ice sheet experienced unprecedented melting and this is seen in the radar data by a reduction of 70% of the penetration in the snowpack following this event. The 2012 melt layer is still visible in data recorded 5 years after the melt event at a depth of 5.1 m. As the frequency and extent of extreme melt events is likely to increase in the coming decades, the effects of fluctuations in Ku-band radar penetration are an important consideration for satellite radar altimetry studies. However, we show that despite large fluctuations in volume scattering, there is a good agreement between Ku-band retracked heights and coincident laser measurements of 13.9 ± 19.9 cm using a threshold retracker. Finally, we also investigate the potential of using higher-frequency KAREN Ka-band (34.5 GHz) airborne radar data to limit the impact of temporal variations in the snowpack properties on backscattered power. We show that surface scattering dominates the Ka-band radar echoes and, overall, they penetrate to significantly lower distances into the near-surface firn by comparison to those acquired at Ku-band. This suggests that Ka-band data are less sensitive to extreme melt events and that the impact of such events on Ka-band data are likely to last for a shorter period of time compared to Ku-band data.

The Greenland ice sheet has experienced an average mass loss of 142 ± 49 Gt/yr from 1992 to 2011 (Shepherd et al. 2012), making it a significant contributor to sea-level rise. Part of the ice- sheet mass loss is the result of increased dynamic response of outlet glaciers (Rignot et al. 2011). The ice discharge from outlet glaciers can be quantified by coincident measurements of ice velocity and ice thickness (Thomas et al. 2000; van den Broeke et al. 2016). As part of the Programme for monitoring of the Greenland Ice Sheet (PROMICE; Ahlstrøm et al. 2008), three airborne surveys were carried out in 2007, 2011 and 2015, with the aim of measuring the changes in Greenland ice-sheet thicknesses. The purpose of the airborne surveys was to collect data to assess the dynamic mass loss of the Greenland ice sheet (Andersen et al. 2015). Here, we present these datasets of observations from ice-penetrating radar and airborne laser scanning, which, in combination, make us able to determine the ice thickness precisely. Surface-elevation changes between surveys are also presented, although we do not provide an in-depth scientific interpretation of these.

The shape of the large ice sheets responds rapidly to climate change, making the elevation changes of these ice-covered regions an essential climate variable. Consistent, long time series of these elevation changes are of great scientific value. Here, we present a newly-developed data product of 25 years of elevation changes of the Greenland Ice Sheet, derived from satellite radar altimetry. The data product is made publicly available within the Greenland Ice Sheets project as part of the ESA Climate Change Initiative programme. Analyzing repeated elevation measurements from radar altimetry is widely used for monitoring changes of ice-covered regions. The Greenland Ice Sheet has been mapped by conventional radar altimetry since the launch of ERS-1 in 1991, which was followed by ERS-2, Envisat and currently CryoSat-2. The recently launched Sentinel-3A will provide a continuation of the radar altimetry time series. Since 2010, CryoSat-2 has for the first time measured the changes in the coastal regions of the ice sheet with radar altimetry, with its novel SAR Interferometric (SARIn) mode, which provides improved measurement over regions with steep slopes. Here, we apply a mission-specific combination of cross-over, along-track and plane-fit elevation change algorithms to radar data from the ERS-1, ERS-2, Envisat and CryoSat-2 radar missions, resulting in 25 years of nearly continuous elevation change estimates (1992–2016) of the Greenland Ice Sheet. This analysis has been made possible through the recent reprocessing in the REAPER project, of data from the ERS-1 and ERS-2 radar missions, making them consistent with Envisat data. The 25 years of elevation changes are evaluated as 5-year running means, shifted almost continuously by one year. A clear acceleration in thinning is evident in the 5-year maps of elevation following 2003, while only small elevation changes observed in the maps from the 1990s.

Long-term observations of surface elevation change of the Greenland ice sheet (GrIS) is of utmost importance when assessing the state of the ice sheet. Satellite radar altimetry offers a long time series of data over the GrIS, starting with ERS-1 in 1991. ESA's Cryosat-2 mission, launched in 2010, provides an invaluable radar altimetry dataset for monitoring the current changes of the ice sheets due to its dense spatial and temporal coverage of these areas. Here, we investigate the effects of including different parameters which describe the shape of the return radar waveform (waveform parameters) in the elevation change algorithm, to correct for temporal changes in the ratio between surface- and volume-scatter in Cryosat-2 observations. We present elevation and volume changes for the Greenland ice sheet in the period from 2010 until 2014. The waveform parameters considered here are the backscatter coefficient, and the leading edge width, which are both available in the ESA Cryosat-2 Level-2i data product. Investigations into relocation of radar reflection points are also included. Inter-comparison of the Cryosat-2 derived elevation changes with those derived from Operation IceBridge laser data suggests waveform parameters to be applicable for correcting for changes in volume scattering. The best results in the Synthetic Aperture Radar Interferometric mode area of the GrIS are found when applying only the backscatter correction, whereas the best result in the Low Resolution Mode area is obtained by only applying a leading edge width correction. Using this approach to correct for the scattering properties, a volume loss of −292±38 km 3 yr −1 is found for the GrIS for the time span November 2010 until November 2014. The inclusion of waveform parameter corrections and improved relocation for the GrIS, helps to reconcile the satellite-derived elevation changes with those observed by Operation IceBridge. However, the bias of temporal changes in the scattering horizons of Cryosat-2 is not entirely removed and suggests that future improvements could be made by including climate data and/or additional waveform parameters to make additional corrections in the Cryosat-2 radar altimetry.

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

The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has measured ice-sheet elevation and thickness via repeat airborne surveys circumscribing the ice sheet at an average elevation of 1708 ± 5 m (Sørensen et al. 2018). We refer to this 5415 km survey as the ‘PROMICE perimeter’. Here, we assess ice-sheet mass balance following the input-output approach of Andersen et al. (2015). We estimate ice-sheet output, or the ice discharge across the ice-sheet grounding line, by applying downstream corrections to the ice flux across the PROMICE perimeter. We subtract this ice discharge from ice-sheet input, or the area-integrated, ice sheet surface mass balance, estimated by a regional climate model. While Andersen et al. (2015) assessed ice-sheet mass balance in 2007 and 2011, this updated input-output assessment now estimates the annual sea-level rise contribution from eighteen sub-sectors of the Greenland ice sheet over the 1995–2015 period.

A new methodology for retrieval of glacier and ice sheet elevations and elevation changes from CryoSat-2 data is presented. Surface elevations and elevation changes determined using this approach show significant improvements over ESA's publicly available CryoSat-2 elevation product (L2 Baseline-B). The results are compared to near-coincident airborne laser altimetry from NASA's Operation IceBridge and seasonal height amplitudes from the Ice, Cloud, and Elevation Satellite (ICESat). Applying this methodology to CryoSat-2 data collected in interferometric synthetic aperture mode (SIN) over the high-relief regions of the Greenland Ice Sheet we find an improvement in the root-mean-square error (RMSE) of 27 and 40 % compared to ESA's L2 product in the derived elevation and elevation changes, respectively. In the interior part of the ice sheet, where CryoSat-2 operates in low-resolution mode (LRM), we find an improvement in the RMSE of 68 and 55 % in the derived elevation and elevation changes, respectively. There is also an 86 % improvement in the magnitude of the seasonal amplitudes when compared to amplitudes derived from ICESat data. These results indicate that the new methodology provides improved tracking of the snow/ice surface with lower sensitivity to changes in near-surface dielectric properties. To demonstrate the utility of the new processing methodology we produce elevations, elevation changes, and total volume changes from CryoSat-2 data for the Greenland Ice Sheet during the period January 2011 to January 2015. We find that the Greenland Ice Sheet decreased in volume at a rate of 289 ± 20 km3a−1, with high interannual variability and spatial heterogeneity in rates of loss. This rate is 65 km3a−1 more negative than rates determined from ESA's L2 product, highlighting the importance of CryoSat-2 processing methodologies.

Beam-limited footprints from conventional satellite radar altimeters have diameters of up to tens of kilometers. Topography within the footprint results in a displacement of the reflecting point from Nadir to the point of closest approach relative to the satellite. Several methods exist for correcting for such mispointing errors. Here, two techniques are applied to observations near Jakobshavn Isbrae, acquired with Envisat's Radar Altimeter (RA-2). The a priori knowledge on the surface topography is obtained from a digital elevation model. The methods relocate the measurement location horizontally to agree with the measured range. One method assumes a constant surface slope within the footprint and uses this and the surface aspect to estimate the displacement parameter; the other locates the optimal relocation point using local topography. The results of the two methods are evaluated against airborne laser-scanner data from the airborne topographic mapper. We find that the accuracy of the relocation depends on both the technique and the spatial resolution of the digital elevation model, and that this dependency varies with surface roughness. Thus, the relocation may be associated with significant errors, which will lower the accuracy of cryospheric studies based on radar altimetry data. We find that the most accurate results are obtained when assessing the full local topography. Furthermore, errors in data over the steep margin are minimized the most when using a spatial resolution of $2$ km; the effect of the resolution over regions with a smoother topography is minor.

Through both atmospheric and oceanic circulation, heat is transferred between the equator and the poles. Possible ways in which the Arctic ecological systems can be affected by warmer temperatures include: changes in amount and duration of snow and ice cover; frequency and extent of spring floods; changes in the ratio of precipitation minus evapotranspiration; amounts of water transport from lakes and rivers from snow and permafrost melting; and a decrease in frozen precipitation. A key component in transferring heat is through freshwater exchange in and out of the Arctic. Hence, accurate mapping freshwater fluxes and potentially its changes with time is vital to describe the heat transfer and its possible temporal changes.

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.

The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.

CryoSat-2 data are used to study elevation changes over an area in the interior part of the Greenland Ice Sheet during the extreme melt event in July 2012. The penetration of the radar signal into dry snow depends heavily on the snow stratigraphy, and the rapid formation of refrozen ice layers can bias the surface elevations obtained from radar altimetry. We investigate the change in CryoSat-2 waveforms and elevation estimates over the melt event and interpret the findings by comparing in situ surface and snow pit observations from the North Greenland Eemian Ice Drilling Project camp. The investigation shows a major transition of scattering properties around the area, and an apparent elevation increase of 56 ± 26 cm is observed in reprocessed CryoSat-2 data. We suggest that this jump in elevation can be explained by the formation of a refrozen melt layer that raised the reflective surface, introducing a positive elevation bias.

We show, for the first time over the Greenland ice sheet, that an along track method for deriving rates of elevation change can successfully be applied to Envisat radar altimetry data (2002–2010). The results provide improved resolution and coverage compared to previous results obtained from cross-over methods. Also, we find that temporal changes in the elevation change rate can be derived from Envisat data, and show clear examples of this by generating five-year running means for selected areas of the Greenland ice sheet. For a period between 2003 and 2009, the elevation of the ice sheets was measured by both the laser altimeter on board ICESat and the radar altimeter on board Envisat. We compare rates of elevation change derived from ICESat and Envisat for this time span in which both sensors were operating. We focus on the area above the equilibrium line altitude, in order to specifically derive information on snow parameters. A comparison of the elevation changes observed by the two sensors shows a complex pattern, which can be explained regionally by model output describing the changes in both firn air content and accumulation rates.

Understanding the long-term changes in the ice sheets of Greenland and Antarctica has global climate significance, especially on long term global sea level rise predictions. Having validated satellite data on current and recent past changes of the ice sheets are crucial for validating climate and earth system models, and give good opportunities for space geodesy to play an important role for society. Under the ESA Climate Change Initiative two projects on Greenland and Antarctica ice sheet changes are making past and present space measurements of the ice sheets available for use by scientists, stakeholders and the general public. The data are part of a large set of ECV’s (Essential Climate Variables) made available by the ESA Climate Initiative, as a contribution to the global Climate Observing System. The ECV data produced include detailed elevation change data from radar altimetry ice flow velocities from synthetic aperture radar missions, mass changes from GRACE, as well as data of glacier and ice shelf grounding lines and (for Greenland) glacier calving front locations from radar and optical data.In the poster we highlight current CCI results on changes in Greenland, with special focus on 25 year elevation changes from ERS-1, ERS-2, Envisat and CryoSat, as well as the Greenland-wide velocity mapping, and the GRACE mass change results.

For more than two decades, radar altimetry missions have provided continuous elevation estimates of the Greenland ice sheet (GrIS). Here, we propose a method for using such data to estimate ice-sheet-wide surface elevation changes (SECs). The final data set will be based on observations acquired from the European Space Agency’s Environmental Satellite (ENVISAT), European Remote Sensing (ERS)-1 and -2, CryoSat-2, and, in the longer term, Sentinel-3 satellites. In order to find the best-performing method, an intercomparison exercise has been carried out in which the scientific community was asked to provide their best SEC estimates as well as feedback sheets describing the applied method. Due to the hitherto few radar-based SEC analyses as well as the higher accuracy of laser data, the participants were asked to use either ENVISAT radar or ICESat (Ice, Cloud, and land Elevation Satellite) laser altimetry over the Jakobshavn Isbræ drainage basin. The submissions were validated against airborne laser-scanner data, and intercomparisons were carried out to analyse the potential of the applied methods and to find whether the two altimeters were capable of resolving the same signal. The analyses found great potential of the applied repeat-track and cross-over techniques, and, for the first time over Greenland, that repeat-track analyses from radar altimetry agreed well with laser data. Since topography-related errors can be neglected in cross-over analyses, it is expected that the most accurate, ice-sheet-wide SEC estimates are obtained by combining the cross-over and repeat-track techniques. It is thus possible to exploit the high accuracy of the former and the large spatial data coverage of the latter. Based on CryoSat’s different operation modes, and the increased spatial and temporal data coverage, this shows good potential for a future inclusion of CryoSat-2 and Sentinel-3 data to continuously obtain accurate SEC estimates both in the interior and margin ice sheet.

During the last decade, the GRACE mission has provided valuable data for determining the mass changes of the Greenland and Antarctic ice sheets. Yet, discrepancies still exist in the published mass balance results, and comprehensive analyses on the sources of errors and discrepancies are lacking. Here, we present monthly mass changes together with trends derived from GRACE data at basin scale for both the Greenland and Antarctic ice sheets, and we assess the variability and errors for each of the possible sources of discrepancies, and we do this in an unprecedented systematic way, taking into account mass inference methods, data sets and background models. We find a very good agreement between the monthly mass change results derived from two independent methods, which represents a cross validation. For the monthly solutions, we find that most of the scatter is caused by the use of the two different data sets rather than the two different methods applied. Besides the well-known GIA trend uncertainty, we find that the geocenter motion and the recent de-aliasing corrections significantly impact the trends, with contributions of +13.2 Gt yr−1 and −20 Gt yr−1, respectively, for Antarctica, which is more affected by these than Greenland. We show differences between the use of release RL04 and the new RL05 and confirm a lower noise content in the new release. The overall scatter of the solutions well exceeds the uncertainties propagated from the data errors and the leakage (as done in the past); hence we calculate new sound total errors for the monthly solutions and the trends. We find that the scatter in the monthly solutions caused by applying different estimates of geocenter motion time series (degree-1 corrections) is significant – contributing with up to 40% of the total error. For the whole GRACE period (2003–2011) our trend estimate for Greenland is −234 ± 20 Gt yr−1 and −83 ± 36 Gt yr−1 for Antarctica (−111 ± 15 Gt yr−1 in the western part). We also find a clear (with respect to our errors) increase of mass loss in the last four years.

A new methodology for retrieval of glacier and ice sheet elevations and elevation changes from CryoSat-2 data is presented. Surface elevations and elevation changes determined using this approach show significant improvements over ESA's publically available Cryosat-2 elevation product (L2 Baseline-B). This when compared to near-coincident airborne laser altimetry from NASA's Operation IceBridge and seasonal height amplitudes from the Ice, Cloud, and Elevation Satellite (ICESat). Applying this methodology to CryoSat-2 data collected in Interferometric Synthetic Aperture mode over the high relief regions of the Greenland ice sheet we find an improvement in the root-mean-square-error (RMSE) of 27 % and 40 % compared to ESA's L2 product in the derived elevation and elevation changes, respectively. In the interior part of the ice sheet, where CryoSat-2 operates in Low Resolution Mode, we find an improvement in the RMSE of 68 % and 55 % in the derived elevation and elevation changes, respectively. There is also an 86 % improvement in the magnitude of the seasonal amplitudes when compared to amplitudes derived from ICESat data. These results indicate that the new methodology provides improved tracking of the snow/ice surface with lower sensitivity to changes in near-surface dielectric properties. To demonstrate the utility of the new processing methodology we produce elevations, elevation changes and total volume changes from Cryosat-2 data for Greenland Ice Sheet during the period Jan-2011 to Jan-2015. We find that the Greenland Ice Sheet decreased in volume at rate of 289 ± 16 km3 a−1, with high inter-annual variability and spatial heterogeneity in rates of loss. This rate is 65 km3 a−1 more negative than rates determined from ESA's L2 product, highlighting the importance of Cryosat-2 processing methodologies.

Elevation data from airborne Light Detection and Ranging (LiDAR) campaigns are used in an attempt to evaluate the accuracy of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) in Greenland. The LiDAR elevation data set is characterized by a high spatial resolution of about 1 m and elevation accuracy of 20–30 cm root mean square error (RMSE). The LiDAR data sets used were acquired during ice-monitoring campaigns carried out from 2003 to 2008. The study areas include ice-free regions, local ice caps and the ice sheet margin. A linear error of 15–65 m was derived, which is far greater than the 20-m product specification. This estimation is biased by both the seasonal and the climatic changes in local ice caps because the ASTER GDEM was computed from imagery acquired in the period 2000–2009. High sloping areas along the coastal regions of Greenland and the effect of the number of scenes used to generate the ASTER GDEM as well as relief are associa...

Constraining glacial isostatic adjustment (GIA) i.e. the Earth’s viscoelastic response to past ice changes, is an important task, because GIA is a significant correction in gravity-based ice sheet mass balance estimates. Here, we investigate how temporal variations in the observed and modeled crustal displacements due to the Earth’s response to ongoing ice mass changes can contribute to the process of constraining GIA. We use mass change grids of the Greenland ice sheet (GrIS) derived from NASA’s high resolution Ice, Cloud and land Elevation Satellite (ICESat) data in three overlapping time spans covering the period 2004–2009 to estimate temporal variations in the elastic response due to present day ice mass loss. The modeled crustal displacements (elastic + GIA) are compared with GPS time series from five permanent sites (KELY, KULU, QAQ1, THU2, and SCOR). We find, that the modeled pattern of elastic crustal displacements shows pronounced variation during the observation period, where an increase in elastic displacement is found at the northwest coast of Greenland, while a decrease is found at the southeast coast. This pattern of temporal changes is supported by the GPS observations. We find, that the temporal behavior of the ICESat-based modeled elastic response agrees well with the GPS observations at the sites KELY, QAQ1, and SCOR. This suggests, that our elastic models are able to resolve the temporal changes in the observed uplift, which indicates that the elastic uplift models are reliable at these sites. Therefore, we conclude that these sites are useful for constraining GIA.

Monthly gravity field models from the GRACE satellite mission are widely used to determine ice mass changes of large ice sheets as well as smaller glaciers and ice caps. Here, we investigate in detail the ice mass changes of the Icelandic ice caps as derived from GRACE data. The small size of the Icelandic ice caps, their location close to other rapidly changing ice covered areas and the low viscosity of the mantle below Iceland make this especially challenging. The mass balance of the ice caps is well constrained by field mass balance measurements, making this area ideal for such investigations. We find that the ice mass changes of the Icelandic ice caps derived from GRACE gravity field models are influenced by both the large gravity change signal resulting from ice mass loss in southeast Greenland and the mass redistribution within the Earth mantle due to glacial isostatic adjustment since the Little Ice Age (∼ 1890 AD). To minimize the signal that leaks towards Iceland from Greenland, we employ an independent mass change estimate of the Greenland Ice Sheet derived from satellite laser altimetry. We also estimate the effect of post Little Ice Age glacial isostatic adjustment, from knowledge of the ice history and GPS network constrained crustal deformation data. We find that both the leakage from Greenland and the post Little Ice Age glacial isostatic adjustment are important to take into account, in order to correctly determine Iceland ice mass changes from GRACE, and when applying these an average mass balance of the Icelandic ice caps of -11.4 +/- 2.2 Gt yr-1 for the period 2003-2010 is found. This number corresponds well with available mass balance measurements.

In order to increase the understanding of the changing climate, the European Space Agency has launched the Climate Change Initiative (ESA CCI), a program which joins scientists and space agencies into 13 projects either affecting or affected by the concurrent changes. This work is part of the Ice Sheets CCI and four parameters are to be determined for the Greenland Ice Sheet (GrIS), each resulting in a dataset made available to the public: Surface Elevation Changes (SEC), surface velocities, grounding line locations, and calving front locations. All CCI projects have completed a so-called Round Robin exercise in which the scientific community was asked to provide their best estimate of the sought parameters as well as a feedback sheet describing their work. By inter-comparing and validating the results, obtained from research institutions world-wide, it is possible to develop the most optimal method for determining each parameter. This work describes the SEC Round Robin and the subsequent conclusions leading to the creation of a method for determining GrIS SEC values. The participants used either Envisat radar or ICESat laser altimetry over Jakobshavn Isbræ drainage basin, and the submissions led to inter-comparisons of radar vs. altimetry as well as cross-over vs. repeat-track analyses. Due to the high accuracy of the former and the high spatial resolution of the latter, a method, which combines the two techniques will provide the most accurate SEC estimates. The data supporting the final GrIS analysis stem from the radar altimeters on-board Envisat, ERS-1 and ERS-2. The accuracy of laser data exceeds that of radar altimetry; the Round Robin analysis has, however, proven the latter equally capable of dealing with surface topography thereby making such data applicable in SEC analyses extending all the way from the interior ice sheet to margin regions. This shows good potential for a~future inclusion of ESA CryoSat-2 and Sentinel-3 radar data in the analysis, and thus for obtaining reliable SEC estimates throughout the entire GrIS.

Warming and Melting Mass loss from the ice sheets of Greenland and Antarctica account for a large fraction of global sea-level rise. Part of this loss is because of the effects of warmer air temperatures, and another because of the rising ocean temperatures to which they are being exposed. Joughin et al. (p. 1172 ) review how ocean-ice interactions are impacting ice sheets and discuss the possible ways that exposure of floating ice shelves and grounded ice margins are subject to the influences of warming ocean currents. Estimates of the mass balance of the ice sheets of Greenland and Antarctica have differed greatly—in some cases, not even agreeing about whether there is a net loss or a net gain—making it more difficult to project accurately future sea-level change. Shepherd et al. (p. 1183 ) combined data sets produced by satellite altimetry, interferometry, and gravimetry to construct a more robust ice-sheet mass balance for the period between 1992 and 2011. All major regions of the two ice sheets appear to be losing mass, except for East Antarctica. All told, mass loss from the polar ice sheets is contributing about 0.6 millimeters per year (roughly 20% of the total) to the current rate of global sea-level rise.

During the last decade, the GRACE mission has provided valuable data for determining the mass changes of the Greenland and Antarctic ice sheets. Yet, discrepancies still exist in the published mass balance results, and analyses on the sources of errors and discrepancies are lacking. Here, we present monthly mass changes together with trends derived from GRACE data at basin scale for both the Greenland and Antarctica ice sheets and we assess, for the first time systematically, the variability and errors for each of the possible sources of discrepancies: mass inference methods, data sets and background models. We find a very good agreement between the monthly mass change results derived from two independent methods, which represents a cross validation. For the monthly solutions, we find that most of the variability is caused by the use of different data sets rather than different methods. Besides the well-known GIA trend uncertainty, we find that the degree-1 variability and the recent de-aliasing corrections have significant impact on monthly time series and trends respectively. We also show the remarkable differences between the use of release RL04 and the new RL05, and how the latter results in smaller mass trends for the majority of the basins. The overall variability of the solutions well exceeds the uncertainties propagated from the data errors and the leakage (as done in the past), hence we calculate new sound total errors for the monthly solutions and the trends. For the whole GRACE period our trend estimate for Greenland is −234 ± 20 Gt yr−1 and −83 ± 36 Gt yr−1 for Antarctica (−111 ± 15 Gt yr−1 in the western part). These trends show a clear (with respect to our errors) increase of mass loss in the last four years.

Estimation of ice sheet mass balance from satellite altimetry requires interpolation of point-scale elevation change (dH/dt) data over the area of interest. The largest dH/dt values occur over narrow, fast-flowing outlet glaciers, where data coverage of current satellite altimetry is poorest. In those areas, straightforward interpolation of data is unlikely to reflect the true patterns of dH/dt. Here, four interpolation methods are compared and evaluated over Jakobshavn Isbræ, an outlet glacier for which widespread airborne validation data are available from NASA's Airborne Topographic Mapper (ATM). The four methods are ordinary kriging (OK), kriging with external drift (KED), where the spatial pattern of surface velocity is used as a proxy for that of dH/dt, and their spatiotemporal equivalents (ST-OK and ST-KED). KED assumes a linear relationship between spatial gradients of velocity and dH/dt, which is confirmed for both negative (Pearson's correlation ρ < −0.85) and, to a lesser degree, positive (ρ = 0.73) dH/dt values. When compared to ATM data, KED and ST-KED yield more realistic spatial patterns and higher thinning rates (over 20 m yr−1 as opposed to 7 m yr−1 for OK). Spatiotemporal kriging smooths inter-annual variability and improves interpolation in periods with sparse data coverage and we conclude, therefore, that ST-KED produces the best results. Using this method increases volume loss estimates from Jakobshavn Isbræ by up to 20% compared to those obtained by OK. The proposed interpolation method will improve ice sheet mass balance reconstructions from existing and past satellite altimeter data sets, with generally poor sampling of outlet glaciers.

There are still discrepancies in published ice sheet mass balance results, even between ones based on the same data sets. It can be difficult to conclude from where the discrepancies arise, and it is therefore important to cross calibrate methods, data and models in order to determine the uncertainty associated with these. We present mass change time series at basin scale for both Greenland and Antarctica, derived from GRACE data. We use two independent methods, several different data sets to derive mass changes associated with appropriateerror bars. Then for each basin we show the GIA correction on trends with its uncertainties. The first method applied is based on a mass inversion, while the second one uses integration over a representation given in water equivalent. We find good agreement between the resulting mass changes based on the two independent methods, especially in the behaviour of time series.We compare our GRACE derived regional estimates with independent mass change results based on altimetry data from NASA’s Ice Cloud and land Elevation Satellite.

We study the implications of a recently published mass balance of the Greenland ice sheet (GrIS), derived from repeated surface elevation measurements from NASA’s ice cloud and land elevation satellite (ICESat) for the time period between 2003 and 2008. To characterize the effects of this new, high‐resolution GrIS mass balance, we study the time‐variations of various geophysical quantities in response to the current mass loss. They include vertical uplift and subsidence, geoid height variations, global patterns of sea level change (or fingerprints), and regional sea level variations along the coasts of Greenland. Long‐wavelength uplifts and gravity variations in response to current or past ice thickness variations are obtained solving the sea level equation, which accounts for both the elastic and the viscoelastic components of deformation. To capture the short‐wavelength components of vertical uplift in response to current ice mass loss, which is not resolved by satellite gravity observations, we have specifically developed a high‐resolution regional elastic rebound (ER) model. The elastic component of vertical uplift is combined with estimates of the viscoelastic displacement fields associated with the process of glacial‐isostatic adjustment (GIA), according to a set of published ice chronologies and associated mantle rheological profiles. We compare the sensitivity of global positioning system (GPS) observations along the coasts of Greenland to the ongoing ER and GIA. In notable contrast with past reports, we show that vertical velocities obtained by GPS data from five stations with sufficiently long records and from one tide gauge at the GrIS margins can be reconciled with model predictions based on the ICE‐5G deglaciation model and the ER associated with the new ICESat‐derived mass balance.

A comprehensive understanding of firn processes is of outmost importance, when estimating present and future changes of the Greenland Ice Sheet. Especially, when remote sensing altimetry is used to assess the state of ice sheets and their contribution to global sea level rise, firn compaction models have been shown to be a key component. Now, remote sensing data can also be used to validate the firn models. Radar penetrating the upper part of the firn column in the interior part of Greenland shows a clear layering. The observed layers from the radar data can be used as an in-situ validation of firn models over large length scales. Here, a simple empirical firn model that is forced with the output of high-resolution (5km) regional climate model (HIRHAM5) is explored. The model is capable of describing a layered structure of the firn column and has previously been used to estimate changes of firn compaction to correct ICESat measurements and assessing the present mass loss of the Greenland ice sheet. Validation of the model against the radar data gives good results and confidence in using the model to answer important questions. Questions such as; how large is the firn compaction correction relative to the changes in the elevation of the surface observed with remote sensing altimetry? What model time resolution is necessary to resolved the observed layering? What model refinements are necessary to give better estimates of the surface mass balance of the Greenland ice sheet from remote sensing altimetry?

ICESat has provided surface elevation measurements of the ice sheets since the launch in January 2003, resulting in a unique dataset for monitoring the changes of the cryosphere. Here, we present a novel method for determining the mass balance of the Greenland ice sheet, derived from ICESat altimetry data. Three different methods for deriving elevation changes from the ICESat altimetry dataset are used. This multi-method approach provides a method to assess the complexity of deriving elevation changes from this dataset. The altimetry alone can not provide an estimate of the mass balance of the Greenland ice sheet. Firn dynamics and surface densities are important factors that contribute to the mass change derived from remote-sensing altimetry. The volume change derived from ICESat data is corrected for changes in firn compaction over the observation period, vertical bedrock movement and an intercampaign elevation bias in the ICESat data. Subsequently, the corrected volume change is converted into mass change by the application of a simple surface density model, in which some of the ice dynamics are accounted for. The firn compaction and density models are driven by the HIRHAM5 regional climate model, forced by the ERA-Interim re-analysis product, at the lateral boundaries. We find annual mass loss estimates of the Greenland ice sheet in the range of 191 ± 23 Gt yr−1 to 240 ± 28 Gt yr−1 for the period October 2003 to March 2008. These results are in good agreement with several other studies of the Greenland ice sheet mass balance, based on different remote-sensing techniques.

ICESat has provided surface elevation measurements of the ice sheets since the launch in January 2003, resulting in a unique data set for monitoring the changes of the cryosphere. Here we present a novel method for determining the mass balance of the Greenland ice sheet derived from ICESat altimetry data. Four different methods for deriving the elevation changes from the ICESat altimetry data set are used. This multi method approach gives an understanding of the complexity associated with deriving elevation changes from the ICESat altimetry data set. The altimetry can not stand alone in estimating the mass balance of the Greenland ice sheet. We find firn dynamics and surface densities to be important factors in deriving the mass loss from remote sensing altimetry. The volume change derived from ICESat data is corrected for firn compaction, vertical bedrock movement and an intercampaign elevation bias in the ICESat data. Subsequently, the corrected volume change is converted into mass change by surface density modelling. The firn compaction and density models are driven by a dynamically downscaled simulation of the HIRHAM5 regional climate model using ERA-Interim reanalysis lateral boundary conditions. We find an annual mass loss of the Greenland ice sheet of 210 ± 21 Gt yr−1 in the period from October 2003 to March 2008. This result is in good agreement with other studies of the Greenland ice sheet mass balance, based on different remote sensing techniques.