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Because Antarctic surface melt is mostly driven by local processes, its simulation necessitates high‐resolution regional climate models (RCMs). However, the current horizontal resolution of RCMs (≈25–30 km) is inadequate for capturing small‐scale melt processes. To address this limitation, we present SUPREME (SUPer‐REsolution‐based Melt Estimation over Antarctica), a deep learning method to downscale surface melt to 5.5 km resolution using a physically‐informed super‐resolution model. The physical information integrated into the model originates from observations tied to surface melt, specifically remote sensing‐derived albedo and elevation. These remote sensing data, in addition to a Regional Atmospheric Climate Model (RACMO) run at 27 km resolution, account for the diverse drivers of surface melt across Antarctica, facilitating effective generalization beyond the training region of the Antarctic Peninsula. A comparison of SUPREME with a dynamically downscaled RACMO run at 5.5 km over the Antarctic Peninsula shows high accuracy, with average yearly RMSE and bias of 5.5 mm w.e. yr−1 and 4.5 mm w.e. yr−1, respectively. Validation at five automatic weather stations reveals SUPREME's marked improvement with substantially lower average RMSE (81 mm w.e.) compared to RACMO 27 km (129 mm w.e.). Beyond the training region, SUPREME aligns more closely with remote sensing products associated with surface melt than super‐resolution models lacking physical constraints. While further validation of SUPREME is needed, our study highlights the potential of super‐resolution techniques with physical constraints for high‐resolution surface melt monitoring in Antarctica, providing insights into the impacts of localized melting on processes affecting ice shelf integrity such as hydrofracturing. Plain Language Summary: To improve surface melt monitoring in Antarctica, high‐resolution climate models are essential. Existing models, like the Regional Atmospheric Climate Model (RACMO), do not have a fine enough spatial resolution to capture small‐scale melt processes. To overcome this, we introduce SUPREME (SUPer‐REsolution‐based Melt Estimation over Antarctica), a method that refines surface melt data to a higher resolution of 5.5 km using an advanced super‐resolution model. We enhance this model with physical information derived from observations directly related to surface melt, specifically using albedo and elevation data from remote sensing. By incorporating these observations, along with RACMO data at 27 km resolution, we account for the various triggers of surface melt across Antarctica. SUPREME accurately predicts high‐resolution surface melt beyond the training region of the Antarctic Peninsula, showing promising results compared to existing melt observations. Further validation is needed, but this approach, combining super‐resolution techniques and remote sensing data, holds potential for accurate surface melt monitoring in Antarctica. This may advance our understanding of the impacts of localized features on processes affecting ice shelf integrity such as meltwater‐induced hydrofracturing. Key Points: Our method downscales Antarctic surface melt from a regional climate model, employing a physically‐informed super‐resolution architectureThe super‐resolution model relies on physical information derived from remote sensing data, specifically surface albedo and elevationIncorporating physical information boosts model generalization, enabling accurate high‐resolution surface melt beyond the training region [ABSTRACT FROM AUTHOR]

Within the last decade, the Greenland ice sheet (GrIS) and its surroundings have experienced record high surface temperatures (Mote, 2007; Box et al., 2010), ice sheet melt extent (Fettweis et al., 2011) and record-low summer sea-ice extent (Nghiem et al., 2007). Using three independent data sets, we derive, for the first time, consistent ice-mass trends and temporal variations within seven major drainage basins from gravity fields from the Gravity Recovery and Climate Experiment (GRACE; Tapley et al., 2004), surface-ice velocities from Inteferometric Synthetic Aperture Radar (InSAR; Rignot and Kanagaratnam, 2006) together with output of the regional atmospheric climate modelling (RACMO2/GR; Ettema et al., 2009), and surface-elevation changes from the Ice, cloud and land elevation satellite (ICESat; Sørensen et al., 2011). We show that changing ice discharge (D), surface melting and subsequent run-off (M/R) and precipitation (P) all contribute, in a complex and regionally variable interplay, to the increasingly negative mass balance of the GrIS observed within the last decade. Interannual variability in P along the northwest and west coasts of the GrIS largely explains the apparent regional mass loss increase during 2002-2010, and obscures increasing M/R and D since the 1990s. In winter 2002/2003 and 2008/2009, accumulation anomalies in the east and southeast temporarily outweighed the losses by M/R and D that prevailed during 2003-2008, and after summer 2010. Overall, for all basins of the GrIS, the decadal variability of anomalies in P, M/R and D between 1958 and 2010 (w.r.t. 1961-1990) was significantly exceeded by the regional trends observed during the GRACE period (2002-2011). © 2012 Elsevier B.V.

Mass budget calculations, validated with satellite gravity observations [from the Gravity Recovery and Climate Experiment (GRACE) satellites], enable us to quantify the individual components of recent Greenland mass loss. The total 2000-2008 mass loss of approximately 1500 gigatons, equivalent to 0.46 millimeters per year of global sea level rise, is equally split between surface processes (runoff and precipitation) and ice dynamics. Without the moderating effects of increased snowfall and refreezing, post-1996 Greenland ice sheet mass losses would have been 100% higher. Since 2006, high summer melt rates have increased Greenland ice sheet mass loss to 273 gigatons per year (0.75 millimeters per year of equivalent sea level rise). The seasonal cycle in surface mass balance fully accounts for detrended GRACE mass variations, confirming insignificant subannual variation in ice sheet discharge.

Geoscientific models are facing increasing challenges to exploit growing datasets coming from remote sensing. Universal differential equations (UDEs), aided by differentiable programming, provide a new scientific modelling paradigm enabling both complex functional inversions to potentially discover new physical laws and data assimilation from heterogeneous and sparse observations. We demonstrate an application of UDEs as a proof of concept to learn the creep component of ice flow, i.e. a nonlinear diffusivity differential equation, of a glacier evolution model. By combining a mechanistic model based on a two-dimensional shallow-ice approximation partial differential equation with an embedded neural network, i.e. a UDE, we can learn parts of an equation as nonlinear functions that then can be translated into mathematical expressions. We implemented this modelling framework as ODINN.jl, a package in the Julia programming language, providing high performance, source-to-source automatic differentiation (AD) and seamless integration with tools and global datasets from the Open Global Glacier Model in Python. We demonstrate this concept for 17 different glaciers around the world, for which we successfully recover a prescribed artificial law describing ice creep variability by solving ∼ 500 000 ordinary differential equations in parallel. Furthermore, we investigate which are the best tools in the scientific machine learning ecosystem in Julia to differentiate and optimize large nonlinear diffusivity UDEs. This study represents a proof of concept for a new modelling framework aiming at discovering empirical laws for large-scale glacier processes, such as the variability in ice creep and basal sliding for ice flow, and new hybrid surface mass balance models. [ABSTRACT FROM AUTHOR]

In recent years, the existence of perennial firn aquifers in the Antarctic Peninsula (AP) has been confirmed by in situ observations. Previous studies have suggested that these subsurface aquifers, together with meltwater ponds at the surface, provide a reservoir of liquid water to feed propagating fractures, promoting hydrofracture-driven ice-shelf disintegration. This study maps perennial firn aquifers in the AP from space using C-band Synthetic Aperture Radar imagery from ESA's Sentinel-1 (S1) mission. With these observations, we detect firn aquifers at 1 km × 1 km resolution, for the period 2017 to 2020. Existing methods, that use S1 data and rely on a backscatter intensity difference threshold approach, are prone to misclassify late-melt events as aquifers, when applied to the AP. Therefore, we have developed and evaluated a new approach that is better suited to the Antarctic environment. The new method exploits the characteristic, gradual backscatter increase during the (partial) refreezing of the liquid water in the firn layer after the peak of the melt season. Most firn aquifers are detected in the north and northwest of the AP, as well as on the Wilkins and George VI ice shelves. Aquifer locations detected with the present methodology agree with in situ observations and with model simulations of firn water content. [ABSTRACT FROM AUTHOR]

The intrusion of Circumpolar Deep Water in the Amundsen and Bellingshausen Sea embayments of Antarctica causes ice shelves in the region to melt from below, potentially putting their stability at risk. Earlier studies have shown how digital elevation models can be used to obtain ice shelf basal melt rates at a high spatial resolution. However, there has been limited availability of high-resolution elevation data, a gap the Reference Elevation Model of Antarctica (REMA) has filled. In this study we use a novel combination of REMA and CryoSat-2 elevation data to obtain high-resolution basal melt rates of the Dotson Ice Shelf in a Lagrangian framework, at a 50 m spatial posting on a 3-yearly temporal resolution. We present a novel method: Basal melt rates Using REMA and Google Earth Engine (BURGEE). The high resolution of BURGEE is supported through a sensitivity study of the Lagrangian displacement. The high-resolution basal melt rates show a good agreement with an earlier basal melt product based on CryoSat-2. Both products show a wide melt channel extending from the grounding line to the ice front, but our high-resolution product indicates that the pathway and spatial variability of this channel is influenced by a pinning point on the ice shelf. This result emphasizes the importance of high-resolution basal melt rates to expand our understanding of channel formation and melt patterns. BURGEE can be expanded to a pan-Antarctic study of high-resolution basal melt rates. This will provide a better picture of the (in)stability of Antarctic ice shelves. [ABSTRACT FROM AUTHOR]

With the development of highly sensitive bioanalytical techniques, the volume of samples necessary for accurate analysis has reduced. Microsampling, the process of obtaining small amounts of blood, has thus gained popularity as it offers minimal‐invasiveness, reduced logistical costs and biohazard risks while simultaneously showing increased sample stability and a potential for the decentralization of the approach and at‐home self‐sampling. Although the benefits of microsampling have been recognised, its adoption in clinical practice has been slow. Several microsampling technologies and devices are currently available and employed in research studies for various biomedical applications. This review provides an overview of the state‐of‐the‐art in microsampling technology with a focus on the latest developments and advancements in the field of microsampling. Research published in the year 2022, including studies (i) developing strategies for the quantitation of analytes in microsamples and (ii) bridging and comparing the interchangeability between matrices and choice of technology for a given application, is reviewed to assess the advantages, challenges and limitations of the current state of microsampling. Successful implementation of microsampling in routine clinical care requires continued efforts for standardization and harmonization. Microsampling has been shown to facilitate data‐rich studies and a patient‐centric approach to healthcare and is foreseen to play a central role in the future digital revolution of healthcare through continuous monitoring to improve the quality of life. [ABSTRACT FROM AUTHOR]

The intrusion of warm circumpolar deep water causes ice shelves in the Amundsen and Bellinghausen Sea Embayments of Antarctica to melt from below, thereby potentially putting their stability at risk. Earlier studies have shown how digital elevation models can be used to obtain high-resolution ice shelf basal melt rates. However, there has been limited availability of high-resolution elevation data, a gap the Reference Elevation Model of Antarctica (REMA) has filled. In this study we use a novel combination of REMA and CryoSat-2 elevation data to obtain high-resolution basal melt rates of the Dotson Ice Shelf in a Lagrangian framework, at a 50m spatial resolution on a 3-yearly temporal resolution. We present a novel Basal melt rates Using REMA and Google Earth Engine (BURGEE) method, which high resolution is supported through a sensitivity study assuming that the quality of the Lagrangian displacement is the main error source. The high-resolution basal melt rates show a good agreement with an earlier 500-m resolution basal melt product based on CryoSat-2, with a wide melt channel extending from the grounding line to the ice shelf front. Our high-resolution product indicates that the pathway and spatial variability of the main channel is influenced by a pinning point on the ice shelf. Additionally, it reveals a narrower coastal channel running along the western margin, which was not identified in the lower-resolution melt product, but does show up in a recent ocean modeling study. This emphasizes the importance of high-resolution basal melt rates to expand our understanding of channel formation and melt patterns. BURGEE can be expanded to a Pan-Antarctic study of high-resolution basal melt rates. This will provide a better picture of the (in)stability of Antarctic ice shelves. [ABSTRACT FROM AUTHOR]

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 x 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. [ABSTRACT FROM AUTHOR]

In recent years a vast amount of glacier surface velocity data from satellite imagery has emerged based on correlation between repeat images. Thereby, much emphasis has been put on the fast processing of large data volumes and products with complete spatial coverage. The metadata of such measurements are often highly simplified when the measurement precision is lumped into a single number for the whole dataset, although the error budget of image matching is in reality neither isotropic nor constant over the whole velocity field. The spread of the correlation peak of individual image offset measurements is dependent on the image structure and the non-uniform flow of the ice and is used here to extract a proxy for measurement uncertainty. A quantification of estimation error or dispersion for each individual velocity measurement can be important for the inversion of, for instance, rheology, ice thickness and/or bedrock friction. Errors in the velocity data can propagate into derived results in a complex and exaggerating way, making the outcomes very sensitive to velocity noise and outliers. Here, we present a computationally fast method to estimate the matching precision of individual displacement measurements from repeat imaging data, focusing on satellite data. The approach is based upon Gaussian fitting directly on the correlation peak and is formulated as a linear least-squares estimation, making its implementation into current pipelines straightforward. The methodology is demonstrated for Sermeq Kujalleq (Jakobshavn Isbræ), Greenland, a glacier with regions of strong shear flow and with clearly oriented crevasses, and Malaspina Glacier, Alaska. Directionality within an image seems to be the dominant factor influencing the correlation dispersion. In our cases these are crevasses and moraine bands, while a relation to differential flow, such as shear, is less pronounced on the correlation spread. [ABSTRACT FROM AUTHOR]

In Antarctica, Global Positioning System (GPS) vertical time series exhibit non‐linear signals over a wide range of temporal scales. To explain these non‐linearities, a number of hypotheses have been proposed, among them the short‐term rapid solid Earth response to contemporaneous ice mass change. Here we use GPS vertical time series to reveal the solid Earth response to variations in surface mass balance (SMB) in the Southern Antarctic Peninsula (SAP). At four locations in the SAP we show that interannual variations of SMB anomalies cause measurable elastic deformation. We use regional climate model SMB products to calculate the induced displacement assuming a perfectly elastic Earth. Our results show a reduction of the misfit when fitting a linear trend to GPS time series corrected for the elastic response to SMB variations. Our results imply that, for a better understanding of the glacial isostatic adjustment signal in Antarctica, SMB variability must be considered. Plain Language Summary: The Global Positioning System (GPS) allows us to measure the changing shape of the Earth's surface with high accuracy. These changes reflect multiple processes operating both within the Earth and at the surface. Using continuously recording GPS instruments in the Antarctic Peninsula, we study the response of the solid Earth to seasonal and annual variations in snow and ice accumulation over the past decade. We show that such loading triggers a measurable response that varies between years, complicating efforts to calculate the long‐term Earth response to past ice sheet change. We discuss the importance of our findings for understanding feedbacks between the solid Earth and the Antarctic ice sheet. Key Points: Global Positioning System time series of vertical deformation in the Southern Antarctic Peninsula show transient signalsModeled elastic deformation due to surface mass balance (SMB) variation can largely explain these signalsConsidering SMB elastic deformation improves estimates of linear vertical velocities [ABSTRACT FROM AUTHOR]

Icelandic glaciers have been losing mass since the Little Ice Age in the mid‐to‐late 1800s, with higher mass loss rates in the early 21st century, followed by a slowdown since 2011. As of yet, it remains unclear whether this mass loss slowdown will persist in the future. By reconstructing the contemporary (1958–2019) surface mass balance of Icelandic glaciers, we show that the post‐2011 mass loss slowdown coincides with the development of the Blue Blob, an area of regional cooling in the North Atlantic Ocean to the south of Greenland. This regional cooling signal mitigates atmospheric warming in Iceland since 2011, in turn decreasing glacier mass loss through reduced meltwater runoff. In a future high‐end warming scenario, North Atlantic cooling is projected to mitigate mass loss of Icelandic glaciers until the mid‐2050s. High mass loss rates resume thereafter as the regional cooling signal weakens. Plain Language Summary: Icelandic glaciers are currently losing mass, but with a recent slowdown in the last decade. We show that this slowdown coincides with the development of an area of regional cooling in the North Atlantic Ocean to the south of Greenland, called Blue Blob. Cooling in the Blue Blob has been mitigating atmospheric warming in Iceland since 2011, reducing glacier melt. In a future warmer climate, North Atlantic cooling is projected to persist until the mid‐2050s, further slowing down mass loss of Icelandic glaciers. High mass loss resumes thereafter as the regional cooling in the Blue Blob weakens. Key Points: We project daily surface mass balance of Icelandic glaciers at 500 m spatial resolution under a high‐end warming scenario until 2100We correlate the post‐2011 glacier mass loss slowdown with a local cooling in the North Atlantic OceanWe predict that local North Atlantic cooling will continue mitigating Icelandic glaciers mass loss until the mid‐2050s [ABSTRACT FROM AUTHOR]

Meltwater from Himalayan glaciers sustains the flow of rivers such as the Ganges and Brahmaputra on which over half a billion people depend for day-to-day needs. Upstream areas are likely to be affected substantially by climate change, and changes in the magnitude and timing of meltwater supply are expected to occur in coming decades. About 10 % of the Himalayan glacier population terminates into proglacial lakes, and such lake-terminating glaciers are known to exhibit higher-than-average total mass losses. However, relatively little is known about the mechanisms driving exacerbated ice loss from lake-terminating glaciers in the Himalaya. Here we examine a composite (2017–2019) glacier surface velocity dataset, derived from Sentinel 2 imagery, covering central and eastern Himalayan glaciers larger than 3 km 2. We find that centre flow line velocities of lake-terminating glaciers (N = 70; umedian : 18.83 m yr -1 ; IQR – interquartile range – uncertainty estimate: 18.55–19.06 m yr -1) are on average more than double those of land-terminating glaciers (N = 249; umedian : 8.24 m yr -1 ; IQR uncertainty estimate: 8.17–8.35 m yr -1) and show substantially more heterogeneity than land-terminating glaciers around glacier termini. We attribute this large heterogeneity to the varying influence of lakes on glacier dynamics, resulting in differential rates of dynamic thinning, which causes about half of the lake-terminating glacier population to accelerate towards the glacier termini. Numerical ice-flow model experiments show that changes in the force balance at the glacier termini are likely to play a key role in accelerating the glacier flow at the front, with variations in basal friction only being of modest importance. The expansion of current glacial lakes and the formation of new meltwater bodies will influence the dynamics of an increasing number of Himalayan glaciers in the future, and these factors should be carefully considered in regional projections. [ABSTRACT FROM AUTHOR]

We introduce an algorithm (Watta) which automatically calculates supraglacial lake bathymetry and detects potential ice layers along tracks of the ICESat-2 (Ice, Cloud, and Land Elevation Satellite) laser altimeter. Watta uses photon heights estimated by the ICESat-2 ATL03 product and extracts supraglacial lake surface, bottom, and depth corrected for refraction and (sub-)surface ice cover in addition to producing surface heights at the native resolution of the ATL03 photon cloud. These measurements are used to constrain empirical estimates of lake depth from satellite imagery, which were thus far dependent on sparse sets of in situ measurements for calibration. Imagery sources include Landsat 8 Operational Land Imager (OLI), Sentinel-2, and high-resolution Planet Labs PlanetScope and SkySat data, used here for the first time to calculate supraglacial lake depths. The Watta algorithm was developed and tested using a set of 46 lakes near Sermeq Kujalleq (Jakobshavn) glacier in western Greenland, and we use multiple imagery sources (available for 45 of these lakes) to assess the use of the red vs. green band to extrapolate depths along a profile to full lake volumes. We use Watta-derived estimates in conjunction with high-resolution imagery from both satellite-based sources (tasked over the season) and nearly simultaneous Operation IceBridge CAMBOT (Continuous Airborne Mapping By Optical Translator) imagery (on a single airborne flight) for a focused study of the drainage of a single lake over the 2019 melt season. Our results suggest that the use of multiple imagery sources (both publicly available and commercial), in combination with altimetry-based depths, can move towards capturing the evolution of supraglacial hydrology at improved spatial and temporal scales. [ABSTRACT FROM AUTHOR]

The aerodynamic roughness of heat, moisture, and momentum of a natural surface are important parameters in atmospheric models, as they co-determine 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 adapt a bulk drag partitioning model to estimate the aerodynamic roughness length (z0m) such that it can be applied to 1D (i.e. unidirectional) elevation profiles, typically measured by laser altimeters. We apply the model to a rough ice surface on the K-transect (west Greenland Ice Sheet) using UAV photogrammetry, and we evaluate the modelled roughness against in situ eddy-covariance observations. We then present a method to estimate the topography at 1 m horizontal resolution using the ICESat-2 satellite laser altimeter, and we demonstrate the high precision of the satellite elevation profiles against UAV photogrammetry. The currently available satellite profiles are used to map the aerodynamic roughness during different time periods along the K-transect, that is compared to an extensive dataset of in situ observations. We find a considerable spatio-temporal variability in z0m , ranging between 10-4 m for a smooth snow surface and 10-1 m for rough crevassed areas, which confirms the need to incorporate a variable aerodynamic roughness in atmospheric models over ice sheets. [ABSTRACT FROM AUTHOR]

Surface melting occurs during summer on the Antarctic and Greenland ice sheets, but the volume of stored surface meltwater has been difficult to quantify due to a lack of accurate depth estimates. NASA's ICESat‐2 laser altimeter brings a new capability: photons penetrate water and are reflected from both the water and the underlying ice; the difference provides a depth estimate. ICESat‐2 sampled Amery Ice Shelf on January 2, 2019 and showed double returns from surface depressions, indicating meltwater. For four melt features, we compared depth estimates from eight algorithms: six based on ICESat‐2 and two from coincident Landsat‐8 and Sentinel‐2 imagery. All algorithms successfully identified surface water at the same locations. Algorithms based on ICESat‐2 produced the most accurate depths; the image‐based algorithms underestimated depths (by 30%–70%). This implies that ICESat‐2 depths can be used to tune image‐based algorithms, moving us closer to quantifying stored meltwater volumes across Antarctica and Greenland. Plain Language Summary: Summer surface melting on Antarctica's ice shelves is a small component of overall ice sheet mass loss but can be important for individual ice shelves and may increase as the climate warms. However, the volume of meltwater has been difficult to monitor because depth estimates are challenging. NASA's ICESat‐2 laser altimetry mission brings a new capability to this problem. ICESat‐2 532 nm photons (green light) are able to pass through water and reflect from both the water surface and the underlying ice surface; the difference in elevation provides meltwater depth estimates. In this pilot study, we compared depths from eight algorithms (six ICESat‐2 and two image based) over four Amery Ice Shelf meltwater lakes for an ICESat‐2 pass in early January 2019. The ICESat‐2 algorithms all produced more reliable depth estimates, and the image‐based algorithms underestimated the depths. This implies that ICESat‐2 water depths can be used to tune image‐based depth retrieval algorithms, enabling improved performance and allowing us to estimate more accurately how much surface melt is stored in melt ponds on the ice sheets each summer. Key Points: ICESat‐2 photons penetrate surface melt lakes and reflect from both the water surface and the underlying ice, providing depth estimatesWe compared depths from eight algorithms (six ICESat‐2 and two image‐based) for four lakes present on Amery Ice Shelf in January 2019Depths from ICESat‐2 were more accurate than from imagery (30%–70% too low); merging these data will improve estimates ice‐sheet wide [ABSTRACT FROM AUTHOR]

We present a reconstruction of historical (1950–2014) surface mass balance (SMB) of the Greenland ice sheet (GrIS) using a high-resolution regional climate model (RACMO2; ∼11 km) to dynamically downscale the climate of the Community Earth System Model version 2 (CESM2; ∼111 km). After further statistical downscaling to 1 km spatial resolution, evaluation using in situ SMB measurements and remotely sensed GrIS mass change shows good agreement. Comparison with an ensemble of previously conducted RACMO2 simulations forced by climate reanalysis demonstrates that the current product realistically represents the long-term average and variability of individual SMB components and captures the recent increase in meltwater runoff that accelerated GrIS mass loss. This means that, for the first time, climate forcing from an Earth system model (CESM2), which assimilates no observations, can be used without additional corrections to reconstruct the historical GrIS SMB and its recent decline that initiated mass loss in the 1990s. This paves the way for attribution studies of future GrIS mass loss projections and contribution to sea level rise. [ABSTRACT FROM AUTHOR]

Surface mass balance (SMB) provides mass input to the surface of the Antarctic and Greenland Ice Sheets and therefore comprises an important control on ice sheet mass balance and resulting contribution to global sea level change. As ice sheet SMB varies highly across multiple scales of space (meters to hundreds of kilometers) and time (hourly to decadal), it is notoriously challenging to observe and represent in models. In addition, SMB consists of multiple components, all of which depend on complex interactions between the atmosphere and the snow/ice surface, large‐scale atmospheric circulation and ocean conditions, and ice sheet topography. In this review, we present the state‐of‐the‐art knowledge and recent advances in ice sheet SMB observations and models, highlight current shortcomings, and propose future directions. Novel observational methods allow mapping SMB across larger areas, longer time periods, and/or at very high (subdaily) temporal frequency. As a recent observational breakthrough, cosmic ray counters provide direct estimates of SMB, circumventing the need for accurate snow density observations upon which many other techniques rely. Regional atmospheric climate models have drastically improved their simulation of ice sheet SMB in the last decade, thanks to the inclusion or improved representation of essential processes (e.g., clouds, blowing snow, and snow albedo), and by enhancing horizontal resolution (5–30 km). Future modeling efforts are required in improving Earth system models to match regional atmospheric climate model performance in simulating ice sheet SMB, and in reinforcing the efforts in developing statistical and dynamic downscaling to represent smaller‐scale SMB processes. Plain Language Summary: Ice sheets, the largest class of glaciers, contain the majority of ice on Earth. The amount of ice contained in ice sheets changes constantly with the addition of new snow and ice, and melting taking place at the surface, base, and terminus of ice sheets. The balance between these inputs and outputs is known as the "mass balance." Processes affecting the addition and removal of snow on top of the ice sheet are termed the "surface mass balance" and include rainfall, moisture evaporation, snow‐transporting winds, and melting due to temperature changes. Scientists can now monitor these processes with tools on‐site, such as automated weather stations, Global Positioning Systems, and sensors that record high‐energy radiation (cosmic rays) originating outside the Earth's atmosphere. Several methods are also available where Earth‐orbiting satellites measure how ice is changing. Data collected in these ways have revealed how the surface mass balance varies over time and space. A better understanding of these processes is critical to predicting future behavior of ice sheets and their effect on sea level. Improvements to regional‐scale models in the past decade have allowed good simulations of surface mass balance, and the next step is to build models that work at a global scale. Key Points: Emerging (remote) observational techniques provide enhanced insights in spatial and temporal variability of ice sheet surface mass balance (SMB)Regional climate models can be used to assess ice sheet SMB, although deficiencies remain in representing subgrid processesIn the near future, Earth System Models can be used to assess internal variability, forced change, and positive feedbacks on ice sheet SMB [ABSTRACT FROM AUTHOR]