Johan Nilsson

We apply Gaussian Process Regression (GPR) to ICESat-2 along-track height change data to generate spatially continuous glacier height change fields across two glaciated regions with complex topography and dynamic behaviour: Larsen-B (Antarctic Peninsula) and central Southern Svalbard. For Larsen-B, GPR-derived average height change rates from 2021–2024 are −0.61 ± 0.02 m a ⁻¹ , corresponding to a volume loss of 4.55 ± 0.17 km ³ a ⁻¹ , similar to independent estimates from TanDEM-X. In Svalbard, we observe widespread average thinning (−1.62 ± 0.03 m a ⁻¹ ) and detect clear signals of surging glaciers. GPR's multi-dimensional, uncertainty-aware framework enables accurate interpolation across data gaps and supports the detection of localized dynamic events, such as surges. Sensitivity tests show that interpolation errors increase with gap size, slope, and extrapolation distance. Our findings demonstrate that GPR is well-suited for enhancing the spatial and temporal resolution of altimetry-based glacier monitoring, enabling improved estimates of height and volume change while reconstructing spatially localized phenomena such as surge activity and other transient glacier dynamics.

We present two decades of monthly Antarctic mass change at a spatial resolution of (100 km)2, representing an order of magnitude improvement over the current state-of-the-art for gravity-derived estimates. This advancement is achieved by leveraging the accuracy of satellite gravimetry over larger spatial scales and the spatial resolution of altimetry at finer spatial scales. We do this through a formal data combination of satellite gravimetry and altimetry observations at the level of the gravimetry normal equations, estimating 1° circular disk mascon elements. The data combination yields independent mascon estimates, yielding effective spatial resolution of 1°. Gravimetry observations dominate the solution at long wavelengths, while altimetry observations are the dominant contributor at the spatial scale of an individual mascon. This yields a mass change solution that is closely aligned with gravimetry-only solutions from GRACE and GRACE-FO over larger spatial scales, yet spatially resolves mass change variability at finer scales. Additionally, we provide a set of gain factors to further downscale the mass within each mascon, providing monthly mass change estimates at a 1.92 km spatial scale. Uncertainties from measurement systems and model corrections (glacial isostatic adjustment, firn air content) are propagated through the data combination and presented. The methodology presented is extensible to other regions around the globe.

The integration of data from multiple satellite altimetry missions, each offering unique observational characteristics, has enabled us to discern both short-term variability and long-term climate trends affecting Greenland's peripheral glaciers and the Greenland Ice Sheet (GrIS). Our methodology, informed by lessons learned from analogous efforts in Antarctica and by improved incorporation of external velocity and elevation data to reduce bias in elevation-change estimates, ensures the consistency and reliability of the derived dataset. An additional enhancement of this product is the inclusion of a digital elevation model, which facilitates the analysis of changes in absolute elevation. The dataset covers the years 1992–2023 and is publicly available as part of NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project (https://doi.org/10.5067/ICFVI7DKHZJV, Nilsson et al., 2026). Our analysis reveals significant patterns of mass loss across the GrIS. We find that the ice sheet and peripheral glaciers experienced average mass losses of −160 ± 17 and −23 ± 5 Gt a ⁻¹ , respectively, over the 1992–2023 period, with notable temporal variations. Specifically, the early years of the record exhibit a positive mass balance, likely driven by anomalously positive surface mass balance. However, this trend reverses in later years, with a pronounced increase in mass-loss rates that highlights the accelerating impact of climate change on ice-sheet dynamics and surface mass balance. Moreover, our analysis underscores the importance of considering peripheral glaciers alongside the continental ice sheet when assessing overall mass trends. By incorporating data from peripheral glaciers, we provide a more comprehensive understanding of Greenland's total contributions to global sea-level rise. Our findings reveal not only the magnitude of mass loss but also its evolution through time, emphasizing the need for continued monitoring and research to better understand the impacts of climate change on Earth's cryosphere.

We apply Gaussian Process Regression (GPR) to ICESat-2 along-track height change data to generate spatially continuous glacier height change fields across two glaciated regions with complex topography and dynamic behaviour: Larsen-B (Antarctic Peninsula) and central Southern Svalbard. For Larsen-B, GPR-derived height change rates from 2021–2024 average −0.61 ± 0.02 m a–1, corresponding to a volume loss of 4.55 ± 0.17 km³ a–1, similar to independent estimates from TanDEM-X. In Svalbard, we observe widespread thinning (−1.57 ± 0.03 m a–1) and detect clear signals of surging glaciers. GPR's multi-dimensional, uncertainty-aware framework enables accurate interpolation across data gaps and supports the detection of localized dynamic events, such as surges. Sensitivity tests show that interpolation errors increase with gap size, slope, and extrapolation distance. Our findings demonstrate that GPR is well-suited for enhancing the spatial and temporal resolution of altimetry-based glacier monitoring, enabling improved estimates of height and volume change while reconstructing spatially localized phenomena such as surge activity and other transient glacier dynamics.

Satellite radar altimetry has provided continuous observations of Antarctic Ice Sheet (AIS) surface elevation change since 1992. However, uncertainties in radar-derived elevation estimates remain substantial, primarily due to the influence of local surface topography and time-variable signal penetration into snow and firn. The launch of the ICESat-2 laser altimetry mission in late 2018 established a new benchmark for high-accuracy surface elevation measurements, enabling inter-comparison with radar altimetry results and improved assessment of associated uncertainties. In this study, we use the ICESat-2 measurements to evaluate radar altimetry-derived elevation change estimates from CryoSat-2 over the 6 905 000 km2 large and relatively flat interior of the AIS, where topography-related errors are small. We apply a suite of radar-specific correction methods to the CryoSat-2 measurements, including multiple retracking algorithms and empirical corrections for the time-variable surface and volume scattering of the radar signal. We analyse a 5.5-year overlap period between ICESat-2 and CryoSat-2 (April 2019-October 2024) to assess how the different correction methods influence the CryoSat-2 surface elevation change estimates and their uncertainties. ICESat-2 observations indicate a thickening of 97 ± 4 km3 yr-1, coinciding with several events of excess snowfall during 2019-2024. All CryoSat-2 solutions yield systematically lower thickening trends, with the smallest bias (0.6 ± 1.0 cm yr−1 or 42 km3 yr-1) obtained using the AWI-ICENet1 convolutional neural network retracker. The remaining trend differences correlate with the ICESat-2 trend signal itself. We discuss possible causes of these systematic differences, one of which is the hypothesis that temporal variations in radar signal penetration associated to temporal variations in snow properties continue to induce systematic errors in inferred surface elevation changes. If the mean trend difference here were representative of the entire grounded AIS (12 352 700 km2), it would correspond to an underestimation of AIS volume and mass trends by approximately 74 km3 yr−1 and 28 Gt yr−1, respectively. These results underscore the challenges of using radar altimetry to resolve subtle, long-term trends related to surface mass balance changes, while also demonstrating the potential of combined laser-radar altimetry analysis to reduce uncertainties in AIS volume and mass balance estimates.

TanDEM-X radar penetration into polar ice introduces significant uncertainties in digital elevation models (DEMs), consequently affecting the accuracy of glacial mass balance estimations. This limitation arises from the variable penetration depth of radar signals in snow and ice, which can cause the DEM surface to deviate from the true physical surface. X-band radars, operating in the 8–12 GHz range, interact with ice through absorption, reflection, and transmission. These interactions heavily depend on variables such as ice temperature, water content, snow density, and salinity, which directly influence microwave penetration depth and signal behaviour. Advancements in remote sensing have leveraged X-band synthetic aperture radar (SAR) to characterize snow and ice surfaces, facilitating applications such as sea ice classification and snow layer analysis. However, X-band SAR signals often exhibit biases in elevation measurements due to their partial penetration into snow and ice. This bias complicates efforts to integrate SAR-derived digital elevation models (DEMs) with laser altimetry data like ICESat-2, where penetration is negligible. Therefore, correcting X-band biases is critical for achieving high-accuracy surface elevation models and reliable glaciological assessments. Here we address these challenges by integrating neural network techniques with TanDEM-X (TDX) DEMs, Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data, and environmental parameters from European Centre for Medium-Range Weather Forecasts (ECMWF). We leverage about 300,000 ICESat-2 pointwise measurements acquired within 30 days of TDX measurements spanning 2021–2024 in Antarctica. Additionally, we consider a diverse dataset of snow and atmospheric variables including temperature, snowfall, snow depth, and wind speed to model and predict X-band penetration biases across Antarctica. This approach advances existing methods by automating bias correction and enhancing the integration of SAR and altimetry datasets achieving mean bias correction of the order 1 cm with a Root Mean Square Error (RMSE) of about 1 m and maximum errors of the order of 10 m. Findings from this work provide actionable insights for improving elevation model accuracy in different ways. First, we offer the retrieved pointwise TanDEM-X, ICESat-2 and ECMWF dataset open access for future studies through a dedicated Zenodo page. Second, we provide both our trained network weights and our algorithms in the form of Jupyter Notebooks on Github for improved reproducibility. Most importantly, we discuss the broader efforts in the NASA Surface Topography Elevation (STV) decadal survey incubation study and polar ice monitoring by addressing penetration biases which is one of the most critical uncertainties in radar remote sensing of snow and ice.

The integration of data from multiple satellite altimetry missions, each offering unique observational characteristics, has enabled us to discern both short-term variability and long-term climate trends affecting Greenland’s peripheral glaciers and the Greenland Ice Sheet (GrIS). Our methodology, adapted and informed by lessons learned from our similar efforts in Antarctica, ensures the consistency and reliability of the derived elevation change dataset. The data covers the years 1992–2023 and is made publicly available as part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Inter-Mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project. Our analysis of the dataset reveals significant patterns of mass loss across the GrIS. We find that the ice sheet and peripheral glaciers have experienced an average mass loss of -173 ± 19 Gt a-1 and -23 ± 5 Gt a-1 respectively over the 1992–2022 time period (given temporal availability of selected firn models), with notable variations over time. Specifically, the early years of the record exhibit a positive mass balance, likely due to anomalously positive surface mass balance. However, this trend shifts in subsequent years, with a pronounced increase in mass loss rates, highlighting the accelerating impact of a changing climate on ice sheet dynamics and surface mass balance. Moreover, our analysis underscores the importance of considering peripheral glaciers in addition to the continental ice sheet when assessing overall mass trends. By incorporating data from peripheral glaciers, we provide a more comprehensive understanding of Greenland’s total contributions to global sea level rise. Our findings reveal not only the magnitude of mass loss but also its evolution over time, emphasizing the need for continued monitoring and research to better understand the impacts of climate change on Earth's cryosphere.