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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).

On interannual to decadal timescales, surface mass balance (SMB) exerts an important control over variations in the overall mass balance of the Antarctic Ice Sheet (AIS). A proper characterization of this variability is required to determine statistically significant long-term trends in SMB in a robust manner. To do so, we quantify variations in SMB and firn thickness by combining results from multi-mission satellite altimetry and SMB/firn modelling at grid scale. The objective of this study is twofold: First, we characterise the errors in both the altimetry and firn model in a spatially and temporally highly-resolved way. Second, we introduce a new approach that sets the stage for inferring long-term trends in SMB and firn processes at grid scale by taking into account interannual firn thickness variations. We use observed altimetric elevation changes of the AIS from Schröder et al. (2019; doi: 10.5194/tc-13-427-2019) and Nilsson et al. (2022; doi: 10.5194/essd-14-3573-2022) and modelled elevation changes of the AIS from IMAU-FDM v1.2A over the period 05/1992 to 12/2017. We assume that the firn model is able to capture the timing of variations in SMB and firn processes, but not necessarily the amplitude of these variations. The location-dependent amplitude is adjusted to the altimetry observations. We detect highest absolute differences between modelled and observed amplitudes at lower elevations, near the AIS margins. In a relative sense, the largest mismatch in amplitude is found in the dry interior of the East AIS across wind glazed areas of e.g. megadune fields. About 37 % of the variance in altimetry is captured by the adjusted firn thickness variations while about 64 % cannot be explained and is still included in the residuals. These residuals contain both altimetry errors (time-variable penetration and scattering effects of radar signals, intermode and intermission offsets) and firn model errors, like real SMB and firn processes not captured by the model. We identify that the time series of altimetric residuals are highly correlated and conclude that we need to consider an appropriate noise model for determining long-term trends and their uncertainties.

Firn on the Greenland Ice Sheet (GrIS) buffers meltwater, and has a variable thickness, complicating observations of volume change to mass change. In this study, we use a firn model (IMAU-FDM v1.2G) forced by a regional climate model (RACMO2.3p2) to investigate how the GrIS firn layer thickness and pore space have evolved since 1958 in response to variability in the large-scale atmospheric circulation. On interannual timescales, the firn layer thickness and pore space show a spatially heterogeneous response to variability in the North Atlantic Oscillation (NAO). Notably, a stronger NAO following the record warm summer of 2012 led the firn layer in the south and east of the ice sheet to regain thickness and pore space after a period of thinning and reduced pore space. In the southwest, a decrease in melt dominates, whereas in the east the main driver is an increase in snow accumulation. At the same time, the firn in the northwestern ice sheet continued to lose pore space. The NAO also varies on intra-annual timescales, being typically stronger in winter than in summer. This impacts the amplitude of the seasonal cycle in GrIS firn thickness and pore space. In the wet southeastern GrIS, most of the snow accumulates during the winter, when melting and densification are relatively weak, leading to a large seasonal cycle in thickness and pore space. The opposite occurs in other regions, where snowfall peaks in summer or autumn. This dampens the seasonal amplitude of firn thickness and pore space.

In recent years, the existence of firn aquifers in the Antarctic Peninsula (AP) has been confirmed by in-situ observations. Given their importance for understanding the hydrology of the Antarctic ice sheet, a more spatially comprehensive assessment of AP firn aquifers is desirable. The purpose of this study is to map firn aquifers in the AP from space using C-band Synthetic Aperture Radar imagery from ESA’s Sentinel-1 mission. These observations enable the detection of firn aquifers at 1 × 1 km2 resolution. The method presented here is based on quantifying the characteristic, gradual backscatter increase during the (partial) refreezing of the liquid water in the firn layer after the peak melt season. When applied to the available time series, it detects perennial aquifers (existing year-round) for the period 2017 to 2020, as well as seasonal aquifers which do not persist through winter. We acknowledge that the backscatter signature in any given year is indistinguishable for seasonal and perennial aquifers. We detect seasonal firn aquifers in the north and northwest of the AP, as well as on the Wilkins and George VI ice shelves. Only in the north and northwest of the AP, aquifers are detected each year in the observation period, here taken as a proxy for perennial firn aquifers. Both distributions agree with model simulations. Further in situ and modelling studies and longer time series of satellite observations are needed to validate the results of this study.

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

Surface meltwater production impacts the mass balance of the Greenland and Antarctic ice sheets in several ways, both directly (e.g., through runoff in Greenland) and indirectly (e.g., through cryo-hydrologic warming and frontal melt of marine-terminating glaciers in Greenland and hydrofracturing of ice shelves in Antarctica). Despite its importance, the spatial and temporal patterns in melt rates on both ice sheets are still relatively poorly understood. In this contribution we review and contrast surface melt ‘weather ‘(i.e., short term, intra- and interdiurnal variability) and surface melt ‘climate’ (i.e., longer term, interannual variability and future melt) of both ice sheets. We find that in situ observations using suitably equipped (automatic or staffed) weather stations are invaluable for a complete understanding of the melt process, which represents the complex transport of energy by radiation, turbulence, and molecular conduction between the lower atmosphere, the ice/snow surface, and the subsurface ice/snow layers. We provide example time series of ice sheet melt ‘weather’ for the marginal Greenland ice sheet, where warm and humid air masses tend to increase surface melt rate, and for coastal East Antarctica, where the opposite happens. Apart from process understanding, these in situ observations, which especially in Antarctica are scarce in space and time, are also invaluable to validate, evaluate and calibrate satellite- and model-based estimates of ice sheet surface melt rate. We provide examples of modelled melt maps for both ice sheets, and melt projections for a high-warming, fossil-fuelled development scenario. Although important milestones in melt observations (both in-situ and remotely sensed) and melt models (both global and regional) have recently been reached, we identify multiple outstanding research questions pertaining to current and future ice sheet surface melt rates.

The turbulent exchange of heat at the surface, including the sensible heat flux (SHF), is an important component of the surface energy balance (SEB) over glaciers and ice sheets. Yet, the turbulent heat fluxes are parameterized in all SEB models, which makes their contribution to the modelled ice ablation uncertain.In this study, we present several years of continuous, daily, in situ observations of SHF (eddy-covariance) and ice ablation, taken at multiple contrasting sites across the ablation area of the Greenland ice sheet. We then compare these measurements to several SEB models with different settings for the surface roughness lengths.We show that it is possible to accurately model the SHF and the daily ice ablation, provided that the prescribed surface roughness lengths, for both heat and momentum, are accurate. We propose a simple parameterization of these roughness lengths, based on both in-situ measurements and remotely sensed data (UAV, ICESat-2). This updated parameterization can be implemented in SEB- and climate- models for improved simulations of ice sheet ablation and surface mass balance.

This study investigates the sensitivity of modeled surface melt and subsurface heating on the Antarctic ice sheet to a new spectral snow albedo and radiative transfer scheme in the Regional Atmospheric Climate Model (RACMO2), version 2.3p3 (Rp3). We tune Rp3 to observations by performing several sensitivity experiments and assess the impact on temperature and melt by changing one parameter at a time. When fully tuned, Rp3 compares well with in situ and remote sensing observations of surface mass and energy balance, melt, temperature, albedo and snow grain specific surface area. Furthermore, the introduction of subsurface heating in Rp3 significantly improves the snow temperature profile. Near surface snow temperature is especially sensitive to the prescribed fresh snow specific surface area and fresh dry snow metamorphism. These processes, together with the refreezing grain size and subsurface heating, are important for melt around the margins of the Antarctic ice sheet. Moreover, small changes in the albedo and the aforementioned processes can lead to an order of magnitude overestimation of melt, locally leading to runoff and a reduced surface mass balance.

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.

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5. National Science Foundation (NSF)National Science Foundation (NSF); National Center for Atmospheric Research - NSF [1852977]; RUBISCO Scientific Focus Area (SFA) - Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science; Columbia University Presidential Fellowship; U.S. Department of Agriculture NIFA Award [2015-67003-23485]; NASA Interdisciplinary Science Program Award [NNX17AK19G]; U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science programUnited States Department of Energy (DOE) [DE-SC0008317, DESC0016188]; National Science FoundationNational Science Foundation (NSF) [DEB-1153401]; NASA's CARBON program; NASA's TE program; National Aeronautics and Space AdministrationNational Aeronautics & Space Administration (NASA) We would like to thank the reviewers for their insightful comments and helpful suggestions that improved the clarity and presentation of the manuscript. The CESM project is supported primarily by the National Science Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. Computing and data storage resources, including the Cheyenne supercomputer (doi: 10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory (CISL) at NCAR. D. M. L. was supported in part by the RUBISCO Scientific Focus Area (SFA), which is sponsored by the Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research in the U.S. Department of Energy Office of Science. D. K. and P. G. were supported by Columbia University Presidential Fellowship. G. B., D. L. L., W. R. W., and R. Q. T. were supported by the U.S. Department of Agriculture NIFA Award 2015-67003-23485. W. R. W. and G. K. A. were supported by the NASA Interdisciplinary Science Program Award NNX17AK19G. J. B. F. and M. S. carried out the research in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. Government sponsorship acknowledged. All rights reserved. J. B. F. and M. S. were supported in part by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Terrestrial Ecosystem Science program under Awards DE-SC0008317 and DESC0016188; the National Science Foundation Ecosystem Science program (DEB-1153401); and NASA's CARBON and TE programs. All model data are archived and publicly available at the UCAR/NCAR Climate Data Gateway (https://doi.org/10.5065/d6154fwh).

The Antarctic ice sheet mass balance is a major component of the sea level budget and results from the difference of two fluxes of a similar magnitude: ice flow discharging in the ocean and net snow accumulation on the ice sheet surface, i.e. the surface mass balance (SMB). Separately modelling ice dynamics and SMB is the only way to project future trends. In addition, mass balance studies frequently use regional climate models (RCMs) outputs as an alternative to observed fields because SMB observations are particularly scarce on the ice sheet. Here we evaluate new simulations of the polar RCM MAR forced by three reanalyses, ERA-Interim, JRA-55, and MERRA-2, for the period 1979–2015, and we compare MAR results to the last outputs of the RCM RACMO2 forced by ERA-Interim. We show that MAR and RACMO2 perform similarly well in simulating coast-to-plateau SMB gradients, and we find no significant differences in their simulated SMB when integrated over the ice sheet or its major basins. More importantly, we outline and quantify missing or underestimated processes in both RCMs. Along stake transects, we show that both models accumulate too much snow on crests, and not enough snow in valleys, as a result of drifting snow transport fluxes not included in MAR and probably underestimated in RACMO2 by a factor of 3. Our results tend to confirm that drifting snow transport and sublimation fluxes are much larger than previous model-based estimates and need to be better resolved and constrained in climate models. Sublimation of precipitating particles in low-level atmospheric layers is responsible for the significantly lower snowfall rates in MAR than in RACMO2 in katabatic channels at the ice sheet margins. Atmospheric sublimation in MAR represents 363 Gt yr−1 over the grounded ice sheet for the year 2015, which is 16 % of the simulated snowfall loaded at the ground. This estimate is consistent with a recent study based on precipitation radar observations and is more than twice as much as simulated in RACMO2 because of different time residence of precipitating particles in the atmosphere. The remaining spatial differences in snowfall between MAR and RACMO2 are attributed to differences in advection of precipitation with snowfall particles being likely advected too far inland in MAR.

The land ice contribution to global mean sea level rise has not yet been predicted 1 using ice sheet and glacier models for the latest set of socio-economic scenarios, nor using coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects generated a large suite of projections using multiple models 2–8, but primarily used previous-generation scenarios 9 and climate models 10, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections under the new scenarios 11,12 using statistical emulation of the ice sheet and glacier models. We find that limiting global warming to 1.5 degrees Celsius would halve the land ice contribution to twenty-first-century sea level rise, relative to current emissions pledges. The median decreases from 25 to 13 centimetres sea level equivalent (SLE) by 2100, with glaciers responsible for half the sea level contribution. The projected Antarctic contribution does not show a clear response to the emissions scenario, owing to uncertainties in the competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 centimetres SLE under current policies and pledges, with the 95th percentile projection exceeding half a metre even under 1.5 degrees Celsius warming. This would severely limit the possibility of mitigating future coastal flooding. Given this large range (between 13 centimetres SLE using the main projections under 1.5 degrees Celsius warming and 42 centimetres SLE using risk-averse projections under current pledges), adaptation planning for twenty-first-century sea level rise must account for a factor-of-three uncertainty in the land ice contribution until climate policies and the Antarctic response are further constrained.

Projections of the sea level contribution from the Greenland and Antarctic ice sheets (GrIS and AIS) rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous Coupled Model Intercomparison Project phase 5 (CMIP5) effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

The mass of the Greenland ice sheet is declining as mass gain from snowfall is exceeded by mass loss from surface meltwater runoff, marine-terminating glacier calving and submarine melting, and basal melting. Here we use the input/output (IO) method to estimate mass change from 1840 through next week. Mass gains come from three regional climate models (RCMs; HIRHAM/HARMONIE, MAR, and RACMO) and a semi-empirical surface mass balance (SMB) model. Mass losses come from the RCMs, a statistical SMB model, ice discharge at marine terminating glaciers, and ice melted at the base of the ice sheet. From these products we provide an annual estimate of GIS mass balance from 1840 through 1985 and a daily estimate at sector and region scale from 1986 through next week. Compared to other mass balance estimates, this product updates daily, has higher temporal resolution, and is the first IO product to include the basal mass balance which is a source of an additional ~8 % mass loss. Our results demonstrate an accelerating GIS-scale mass loss and general agreement among six other products. Results from this study are available at https://dataverse01.geus.dk/privateurl.xhtml?token=d09976c4-4f89-43ef-8f91-173d269806a4 (Mankoff et al., 2021).

Accurately estimating surface melt volume of the Antarctic Ice Sheet is challenging, and has hitherto relied on climate modelling, or on observations from satellite remote sensing. Each of these methods has its limitations, especially in regions with high surface melt. This study aims to demonstrate the potential of improving surface melt simulations by deploying a deep learning model. A deep-learning-based framework has been developed to correct surface melt from the regional atmospheric climate model version 2.3p2 (RACMO2), using meteorological observations from automatic weather stations (AWSs), and surface albedo from satellite imagery. The framework includes three steps: (1) training a deep multilayer perceptron (MLP) model using AWS observations; (2) correcting moderate resolution imaging spectroradiometer (MODIS) albedo observations, and (3) using these two to correct the RACMO2 surface melt simulations. Using observations from three AWSs at the Larsen B and C Ice Shelves, Antarctica, cross-validation shows a high accuracy (root mean square error = 0.95 mm w.e. per day, mean absolute error = 0.42 mm w.e. per day, and coefficient of determination = 0.95). Moreover, the deep MLP model outperforms conventional machine learning models (e.g., random forest regression, XGBoost) and a shallow MLP model. When applying the trained deep MLP model over the entire Larsen Ice Shelf, the resulting, corrected RACMO2 surface melt shows a better correlation with the AWS observations for two out of three AWSs. However, for one location (AWS 18) the deep MLP model does not show improved agreement with AWS observations, likely due to the heterogeneous drivers of melt within the corresponding coarse resolution model pixels. Our study demonstrates the opportunity to improve surface melt simulations using deep learning combined with satellite albedo observations. On the other hand, more work is required to refine the method, especially for complicated and heterogeneous terrains.

Recent studies indicate that a declining surface mass balance will dominate the Greenland Ice Sheet’s (GrIS) contribution to 21st century sea level rise. It is therefore crucial to understand the liquid water balance of the ice sheet and its response to increasing temperatures and surface melt if we want to accurately predict future sea level rise. The ice sheet firn layer covers ~90% of the GrIS and provides pore space for storage and refreezing of meltwater. Because of this, the firn layer can retain up to ~45% of the surface meltwater and thus act as an efficient buffer to ice sheet mass loss. However, in a warming climate this buffer capacity of the firn layer is expected to decrease, amplifying meltwater runoff and sea-level rise. Dedicated firn models are used to understand how firn layers evolve and affect runoff. Additionally, firn models are used to estimate the changing thickness of the firn layer, which is necessary in altimetry to convert surface height change into ice sheet mass loss.Here, we present the latest version of our firn model IMAU-FDM. With respect to the previous version, changes have been made to the handling of the freshly fallen snow, the densification rate of the firn and the conduction of heat. These changes lead to an improved representation of firn density and temperature. The results have been thoroughly validated using an extensive dataset of density and temperature measurements that we have compiled covering 126 different locations on the GrIS. Meltwater behaviour in the model is validated with upward-looking GPR measurements at Dye-2. Lastly, we present an in-depth look at the evolution firn characteristics at some typical locations in Greenland.Dedicated, stand-alone firn models offer various benefits to using a regional climate model with an embedded firn model. Firstly, the vertical resolution for buried snow and ice layers can be larger, improving accuracy. Secondly, a stand-alone firn model allows for spinning up the model to a more accurate equilibrium state. And thirdly, a stand-alone model is more cost- and time-effective to use. Firn models are increasingly capable of simulating the firn layer, but areas with large amounts of melt still pose the greatest challenge.

Satellite radar altimetry is one of the most important tools for monitoring changes in the mass balance of the world's ice sheets. Different altimetry techniques however, come with their own pitfalls. In radar altimetry, signal penetration into the snowpack introduces ambiguity in the origin of reflected echo, a major issue not present in laser altimetry. Fine tuning the developed processing algorithms for the CryoSat-2 radar altimetry data, using the IceSat2 laser altimetry data as a benchmark, may allow for a more precise surface elevation and snowpack depth estimations. Furthermore, bridging the gap between radar and laser altimetry will result in larger spatial and temporal data coverage when the two data sets are combined. Focusing on Greenland Ice Sheet (GIS), we have developed a processing chain for the estimation of surface elevations and elevation changes from the ESA level-1 product (L1b) Baseline D. We investigated the importance of a retracker type, retracker threshold, Digital Elevation Model (DEM) in the slope correction, and how these affect the estimated surface elevation as compared to the ICESat2 data.Firstly, ESA L1b Baseline-D data was processed at several different thresholds and with various waveform retracker algorithms, including threshold first maxima retracker algorithm (TFMRA) (Helm, 2012; Nilsson, 2015) and the offset center of gravity (OCOG) retracker algorithm (Bamber, 1994; Ricker et al. 2014). We then apply slope correction to adjust for the slope induced error in the radar altimetry data (Hurkmans, 2012), the correction was applied using three different DEMs, ArcticDEM Release 7 (Porter et al., 2018), Greenland Ice Mapping Project (GIMP) DEM (Howat et al., 2017) and ‘Helm’ DEM (Helm, 2014). We checked all of the produced data sets against IceSat-2 data (Smith et al., 2019) corresponding to the same time period, and selected by nearest neighbor calculation for specified maximum distance. We analyze and discuss the differences between IceSat-2 data and CryoSat-2 data and their dependence on several radar altimetry processing parameters and methodologies.

The roughness of a natural surface is an important parameter in atmospheric models, as it determines the intensity of turbulent transfer between the atmosphere and the surface. Unfortunately, this parameter is often poorly known, especially in remote areas where neither high-resolution elevation models nor eddy-covariance measurements are available.In this study, we take advantage of the measurements of the ICESat-2 satellite laser altimeter. We use the geolocated photons product (ATL03) to retrieve a 1-m resolution surface elevation product over the K-transect (West Greenland ice sheet). In combination with a bulk drag partitioning model, the retrieved surface elevation is used to estimate the aerodynamic roughness length (z0m) of the surface.We demonstrate the high precision of the retrieved ICESat-2 elevation using co-located UAV photogrammetry, and then evaluate the modelled aerodynamic roughness against multiple in situ eddy-covariance observations. The results point out the importance to use a bulk drag model over a more empirical formulation.The currently available ATL03 geolocated photons are used to map the aerodynamic roughness along the K-transect (2018-2020). We find a considerable spatiotemporal variability in z0m, ranging between 10−4 m for a smooth snow surface to more than 10−1 m for rough crevassed areas, which confirms the need to incorporate a variable aerodynamic roughness in atmospheric models over ice sheets.

Presently, surface melt over Antarctica is estimated using climate modeling or remote sensing. However, accurately estimating surface melt remains challenging. Both climate modeling and remote sensing have limitations, particularly in the most crucial areas with intense surface melt. The motivation of our study is to investigate the opportunities and challenges in improving the accuracy of surface melt estimation using a deep neural network. The trained deep neural network uses meteorological observations from automatic weather stations (AWS) and surface albedo observations from satellite imagery to improve surface melt simulations from the regional atmospheric climate model version 2.3p2 (RACMO2). Based on observations from three AWS at the Larsen B and C Ice Shelves, cross-validation shows a high accuracy (root mean square error = 0.898 mm.w.e.d−1, mean absolute error = 0.429 mm.w.e.d−1, and coefficient of determination = 0.958). The deep neural network also outperforms conventional machine learning models (e.g., random forest regression, XGBoost) and a shallow neural network. To compute surface melt for the entire Larsen Ice Shelf, the deep neural network is applied to RACMO2 simulations. The resulting, corrected surface melt shows a better correlation with the AWS observations in AWS 14 and 17, but not in AWS 18. Also, the spatial pattern of the surface melt is improved compared to the original RACMO2 simulation. A possible explanation for the mismatch at AWS 18 is its complex geophysical setting. Even though our study shows an opportunity to improve surface melt simulations using a deep neural network, further study is needed to refine the method, especially for complicated, heterogeneous terrain.

Many Greenland glaciers have been attacked by the ocean from below and, in turn, contributed to sea-level rise from the ice sheet., The retreat and acceleration of Greenland glaciers since the mid-1990s have been attributed to the enhanced intrusion of warm Atlantic Waters (AW) into fjords, but this assertion has not been quantitatively tested on a Greenland-wide basis or included in models. Here, we investigate how AW influenced retreat at 226 marine-terminating glaciers using ocean modeling, remote sensing, and in situ observations. We identify 74 glaciers in deep fjords with AW controlling 49% of the mass loss that retreated when warming increased undercutting by 48%. Conversely, 27 glaciers calving on shallow ridges and 24 in cold, shallow waters retreated little, contributing 15% of the loss, while 10 glaciers retreated substantially following the collapse of several ice shelves. The retreat mechanisms remain undiagnosed at 87 glaciers without ocean and bathymetry data, which controlled 19% of the loss. Ice sheet projections that exclude ocean-induced undercutting may underestimate mass loss by at least a factor of 2.