Surface-mass-balance (SMB) and firn-densification (FD) models are widely used in altimetry studies as a tool to separate atmospheric-driven from ice-dynamics-driven ice-sheet mass changes and to partition observed volume changes into ice-mass changes and firn-air-content changes. Until now, SMB models have been principally validated based on comparison with ice core and weather station data or comparison with widely separated flight radar-survey flight lines. Firn-densification models have been primarily validated based on their ability to match net densification over decades, as recorded in firn cores, and the short-term time-dependent component of densification has rarely been evaluated at all. The advent of systematic ice-sheet-wide repeated ice-surface-height measurements from ICESat-2 (the Ice Cloud, and land Elevation Satellite, 2) allows us to measure the net surface-height change of the Greenland ice sheet at quarterly resolution and compare the measured surface-height differences directly with those predicted by three FD–SMB models: MARv3.5.11 and GSFCv1.1 and GSFCv1.2. By segregating the data by season and elevation, and based on the timing and magnitude of modelled processes in areas where we expect minimal ice-dynamics-driven height changes, we investigate the models' accuracy in predicting atmospherically driven height changes. We find that while all three models do well in predicting the large seasonal changes in the low-elevation parts of the ice sheet where melt rates are highest, two of the models (MARv3.5.11 and GSFCv1.1) systematically overpredict, by around a factor of 2, the magnitude of height changes in the high-elevation parts of the ice sheet, particularly those associated with melt events. This overprediction seems to be associated with the melt sensitivity of the models in the high-elevation part of the ice sheet. The third model, GSFCv1.2, which has an updated high-elevation melt parameterization, avoids this overprediction.

Commonly used mass-concentration (mascon) solutions estimated from Level-1B Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On data, provided by processing centers such as the Jet Propulsion Laboratory (JPL) or the Goddard Space Flight Center (GSFC), do not give users control over the placement of mascons or inversion assumptions, such as regularization. While a few studies have focused on regional or global mascon optimization from spherical harmonics data, a global optimization based on the geometry of geophysical signal as a standardized product with user-defined points has not been addressed. Finding the optimal configuration with enough coverage to account for far-field leakage is not a trivial task and is often approached in an ad-hoc manner, if at all. Here, we present an automated approach to defining non-uniform, global mascon solutions that focus on a region of interest specified by the user, while maintaining few global degrees of freedom to minimize noise and leakage. We showcase our approach in High Mountain Asia (HMA) and Alaska, and compare the results with global uniform mascon solutions from range-rate data. We show that the custom mascon solutions can lead to improved regional trends due to a more careful sampling of geophysically distinct regions. In addition, the custom mascon solutions exhibit different seasonal variation compared to the regularized solutions. Our open-source pipeline will allow the community to quickly and efficiently develop optimized global mascon solutions for an arbitrary point or polygon anywhere on the surface of the Earth.

A series of traverses has been conducted for validation of the National Aeronautics and Space Administration Ice, Cloud, and land Elevation Satellite 2 (ICESat‐2) on the flat interior of the Antarctic ice sheet. Global Navigation Satellite System data collected on three separate 88S Traverses intersect 20% of the ICESat‐2 reference ground tracks and have precisions of better than ±7 cm and biases of less than ~4 cm. Data from these traverses were used to assess heights from ICESat‐2, CryoSat‐2, and Airborne Topographic Mapper (ATM). ICESat‐2 heights have better than ±3.3 cm bias and better than ±7.2 cm precision. ATM heights have better than 9.3 cm bias and better than ±9.6 cm precision. CryoSat‐2 heights have –38.9 cm of bias and ±47.3 cm precision. These best case results are from the flat ice‐sheet interior but provide a characterization of the quality of satellite and airborne altimetry.

Greenland Ice Sheet surface elevation is changing as mass loss accelerates. In understanding elevation change, the magnitudes of physical processes involved are important for interpretation of altimetry and assessing changes in these processes. The four key processes are surface mass balance (SMB), firn densification, ice dynamics, and isostatic adjustment. We quantified these processes at Summit, Greenland, where monthly Global Navigation Satellite System (GNSS) snowmobile traverses measured elevation change from 2008 to 2018. We find an elevation increase of 0.019 m a^-1. The sum of the effects of the four processes reproduces the measured elevation time series, in linear trend and in intra-annual variability. The short-term variability in elevation is primarily explained by the variability in SMB. Since SMB has not changed significantly over the last century, and the other processes change over longer time scales, the elevation change likely has been ongoing for at least the last 100 years.

We examine data continuity between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (FO) missions over Greenland and Antarctica using independent data from the mass budget method, which calculates the difference between ice sheet surface mass balance and ice discharge at the periphery. For both ice sheets, we find consistent GRACE/GRACE‐FO time series across the data gap, at the continental and regional scales, and the data gap is confidently filled with mass budget method data. In Greenland, the GRACE‐FO data reveal an exceptional summer loss of 600 Gt in 2019 following two cold summers. In Antarctica, ongoing high mass losses in the Amundsen Sea Embayment of West Antarctica, the Antarctic Peninsula, and Wilkes Land in East Antarctica cumulate to 2130, 560, and 370 Gt, respectively, since 2002. A cumulative mass gain of 980 Gt in Queen Maud Land since 2009, however, led to a pause in the acceleration in mass loss from Antarctica after 2016.

The Greenland Ice Sheet has been a major contributor to global sea-level rise in recent decades and it is expected to continue to be so. Although increases in glacier flow and surface melting have been driven by oceanic and atmospheric warming, the magnitude and trajectory of the ice sheet's mass imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet's volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. The ice sheet was close to a state of balance in the 1990s, but annual losses have risen since then, peaking at 345 ± 66 billion tonnes per year in 2011. In all, Greenland lost 3,902 ± 342 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.8 ± 0.9 millimetres. Using three regional climate models, we show that the reduced surface mass balance has driven 1,964 ± 565 billion tonnes (50.3 per cent) of the ice loss owing to increased meltwater runoff. The remaining 1,938 ± 541 billion tonnes (49.7 per cent) of ice loss was due to increased glacier dynamical imbalance, which rose from 46 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. The total rate of ice loss slowed to 222 ± 30 billion tonnes per year between 2013 and 2017, on average, as atmospheric circulation favoured cooler conditions and ocean temperatures fell at the terminus of Jakobshavn Isbrae. Cumulative ice losses from Greenland as a whole have been close to the rates predicted by the Intergovernmental Panel on Climate Change for their high-end climate warming scenario, which forecast an additional 70 to 130 millimetres of global sea-level rise by 2100 compared with their central estimate.

Measurements of time‐variable gravity from the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow‐on (GRACE‐FO) missions are an invaluable tool for monitoring changes in the mass of the Earth's glaciated regions. We improve upon estimates of glacier and ice sheet mass balance from time‐variable gravity by including instantaneous spatiotemporal variations in sea level. Here, a least squares mascon technique is combined with solutions to the sea level equation to iteratively correct the GRACE/GRACE‐FO data for the induced sea level response on a monthly basis. We find that variations in regional sea level affect ice sheet mass balance estimates in Greenland by approximately 4% and in Antarctic by approximately 5%. Since 2002, the Greenland ice sheet has been losing mass at an average rate of 263 ± 23 Gt/yr, and the Antarctic ice sheet has been losing mass at average rates between 90 ± 52 and 122 ± 53 Gt/yr depending on the rate of glacial isostatic adjustment. The mass losses from both ice sheets represent an increase of 15.6 ± 2.0 mm to global mean sea levels since 2002.

Geocenter variations relate the motion of the Earth’s center of mass with respect to its center of figure, and represent global-scale redistributions of the Earth’s mass. We investigate different techniques for estimating of geocenter motion from combinations of time-variable gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions, and bottom pressure outputs from ocean models. Here, we provide self-consistent estimates of geocenter variability incorporating the effects of self-attraction and loading, and investigate the effect of uncertainties in atmospheric and oceanic variation. The effects of self-attraction and loading from changes in land water storage and ice mass change affect both the seasonality and long-term trend in geocenter position. Omitting the redistribution of sea level affects the average annual amplitudes of the x, y, and z components by 0.2, 0.1, and 0.3 mm, respectively, and affects geocenter trend estimates by 0.02, 0.04 and 0.05 mm/yr for the the x, y, and z components, respectively. Geocenter estimates from the GRACE Follow-On mission are consistent with estimates from the original GRACE mission.

We calculate rates of ice thickness change and bottom melt for ice shelves in West Antarctica and the Antarctic Peninsula from a combination of elevation measurements from NASA–CECS Antarctic ice mapping campaigns and NASA Operation IceBridge corrected for oceanic processes from measurements and models, surface velocity measurements from synthetic aperture radar, and high-resolution outputs from regional climate models. The ice thickness change rates are calculated in a Lagrangian reference frame to reduce the effects from advection of sharp vertical features, such as cracks and crevasses, that can saturate Eulerian-derived estimates. We use our method over different ice shelves in Antarctica, which vary in terms of size, repeat coverage from airborne altimetry, and dominant processes governing their recent changes. We find that the Larsen-C Ice Shelf is close to steady state over our observation period with spatial variations in ice thickness largely due to the flux divergence of the shelf. Firn and surface processes are responsible for some short-term variability in ice thickness of the Larsen-C Ice Shelf over the time period. The Wilkins Ice Shelf is sensitive to short-timescale coastal and upper-ocean processes, and basal melt is the dominant contributor to the ice thickness change over the period. At the Pine Island Ice Shelf in the critical region near the grounding zone, we find that ice shelf thickness change rates exceed 40 m yr−1, with the change dominated by strong submarine melting. Regions near the grounding zones of the Dotson and Crosson ice shelves are decreasing in thickness at rates greater than 40 m yr−1, also due to intense basal melt. NASA–CECS Antarctic ice mapping and NASA Operation IceBridge campaigns provide validation datasets for floating ice shelves at moderately high resolution when coregistered using Lagrangian methods.