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Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) from the spaceborne interferometric radar mission TanDEM-X hold a large potential for glacier change assessments. However, a bias is potentially introduced through the penetration of the X-band signal into snow and firn. To improve our understanding of radar penetration on glaciers, we compare DEMs derived from the almost synchronous acquisition of TanDEM-X and Pléiades optical stereo-images of Grosser Aletschgletscher in March 2021. We found that the elevation bias - averaged per elevation bin - can reach up to 4-8m in the accumulation area, depending on post co-registration corrections. Concurrent in situ measurements (ground-penetrating radar, snow cores, snow pits) reveal that the signal is not obstructed by the last summer horizon but reaches into perennial firn. Because of volume scattering, the TanDEM-X surface is determined by the scattering phase centre and does not coincide with a specific firn layer. We show that the bias corresponds to more than half of the decadal ice loss rate. To minimize the radar penetration bias, we recommend to select DEMs from the same time of the year and over long observation periods. A correction of the radar penetration bias is recommended, especially when combining optical and TanDEM-X DEMs. [ABSTRACT FROM AUTHOR]

Modeling the short‐term (<50 years) evolution of glaciers is difficult because of issues related to model initialization and data assimilation. However, this timescale is critical, particularly for water resources, natural hazards, and ecology. Using a unique record of satellite remote‐sensing data, combined with a novel optimisation and surface‐forcing‐calculation method within the framework of the deep‐learning‐based Instructed Glacier Model, we are able to ameliorate initialization issues. We thus model the committed evolution of all glaciers in the European Alps up to 2050 using present‐day climate conditions, assuming no future climate change. We find that the resulting committed ice loss exceeds a third of the present‐day ice volume by 2050, with multi‐kilometer frontal retreats for even the largest glaciers. Our results show the importance of modeling ice dynamics to accurately retrieve the ice‐thickness distribution and to predict future mass changes. Thanks to high‐performance GPU processing, we also demonstrate our method's global potential. Plain Language Summary: Modeling glaciers is highly challenging over the next few decades because setting up models correctly is a big issue. This is unfortunate, because we really want to know what is going to happen on that timescale, as it will directly affect our lives, homes and jobs. We present a new modeling approach, taking advantage of new data and machine‐learning methods, that allows us to set our model up much more effectively. We thus work out how much ice will be lost in the European Alps between now and 2050, even if the climate does not change further, and find that a third of the ice will be lost, come what may. Even the largest glacier fronts will retreat by several kilometers. We show that modeling glaciers properly, with a well‐set‐up model, is really important to make accurate predictions. Key Points: We present a novel, fast, accurate deep‐learning method for glacier model initialization and forward simulation at a regional scaleWe calculate glacier climatic surface elevation change without needing calibration across the whole of the European Alps for the first timeWe find committed ice loss in the European Alps to be 34% by 2050, rising to 46% with linear extrapolation of 2000–2020 mass‐balance trends [ABSTRACT FROM AUTHOR]

Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea-level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing strategies. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods, but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty in order to enhance inter-comparison and empower physical process insights across glacier elevation-change studies. [ABSTRACT FROM AUTHOR]

Glaciers in the Andes are among the fastest shrinking and largest contributors to sea level rise. They also represent crucial water resources in the tropical and semi-arid portions of this large mountain range. Yet, the magnitude of recent ice loss for the entire mountain range is poorly constrained. Here, we present glacier mass changes for the Andes (from 10°N to 56°S) between 2000 and 2018 using time series of digital elevation models derived from ASTER stereo images. The total glacier mass change over this period is -20.0 ± 4.0 Gt yr-1 (-0.63 ± 0.15 m w.e. yr-1), with largest mass losses in the Patagonian Andes (-0.68 ± 0.17 m w.e. yr-1) and the Tropical Andes (-0.41 ± 0.16 m w.e. yr-1), and relatively moderate losses (-0.25 ± 0.14 m w.e. yr-1) between 26° and 37°S in the Dry Andes. Sub-period analysis (2000-2009 vs. 2009-2018) reveals steady mass loss in the Tropics and south of 45°S. Conversely, a shift from slightly positive to strongly negative mass balance is measured between 26° and 45°S. In this latter region, the rapid glacier loss in recent years coincides with extremely dry conditions since 2010 and partially helped to mitigate the negative impacts of this sustained drought. This first fully resolved, multi-decadal dataset of recent glacier mass changes at an Andes scale, improves our understanding of local and regional glacier variations in response to climate change and constitutes a relevant validation basis for climatic, hydrological and glaciological models. [ABSTRACT FROM AUTHOR]