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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]

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]

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]

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]

Abstract: Within the last decade, the Greenland ice sheet (GrIS) and its surroundings have experienced record high surface temperatures (), ice sheet melt extent () and record-low summer sea-ice extent (). 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; ), surface-ice velocities from Inteferometric Synthetic Aperture Radar (InSAR; ) together with output of the regional atmospheric climate modelling (RACMO2/GR; ), and surface-elevation changes from the Ice, cloud and land elevation satellite (ICESat; ). 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). [Copyright &y& Elsevier]

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 ~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. [ABSTRACT FROM AUTHOR]

Satellite gravimetry data acquired by the Gravity Recovery and Climate Experiment (GRACE) allows to derive the temporal evolution in ice mass for both the Antarctic Ice Sheet (AIS) and the Greenland Ice Sheet (GIS). Various algorithms have been used in a wide range of studies to generate Gravimetric Mass Balance (GMB) products. Results from different studies may be affected by substantial differences in the processing, including the applied algorithm, the utilised background models and the time period under consideration. This study gives a detailed description of an assessment of the performance of GMB algorithms using actual GRACE monthly solutions for a prescribed period as well as synthetic data sets. The inter-comparison exercise was conducted in the scope of the European Space Agency's Climate Change Initiative (CCI) project for the AIS and GIS, and was, for the first time, open to everyone. GMB products generated by different groups could be evaluated and directly compared against each other. For the period from 2003-02 to 2013-12, estimated linear trends in ice mass vary between −99 Gt/yr and −108 Gt/yr for the AIS and between −252 Gt/yr and −274 Gt/yr for the GIS, respectively. The spread between the solutions is larger if smaller drainage basins or gridded GMB products are considered. Finally, findings from the exercise formed the basis to select the algorithms used for the GMB product generation within the AIS and GIS CCI project. [ABSTRACT FROM AUTHOR]

Glaciers distinct from the Greenland and Antarctic Ice Sheets are losing large amounts of water to the world's oceans. However, estimates of their contribution to sea level rise disagree. We provide a consensus estimate by standardizing existing, and creating new, mass-budget estimates from satellite gravimetry and altimetry and from local glaciological records. In many regions, local measurements are more negative than satellite-based estimates. All regions lost mass during 2003-2009, with the largest losses from Arctic Canada, Alaska, coastal Greenland, the southern Andes, and high-mountain Asia, but there was little loss from glaciers in Antarctica. Over this period, the global mass budget was -259 ± 28 gigatons per year, equivalent to the combined loss from both ice sheets and accounting for 29 ± 13% of the observed sea level rise.