The ice mass balance of the Antarctic and Greenland Ice Sheets and the World's Glaciers and Ice Caps (GIC) has a major impact on sea level rise. 

Here we present an update on monthly estimates of glaciers and ice sheet mass balance from the GRACE/GRACE-FO mission up to 2024 using the Microwave Instrument (MWI). We report a slow-down in mass loss in Greenland after 2012 and a mass gain in Antarctica in 2021-2023. In recent years, we note a pause in the mass loss of Antarctica, a mass loss in Greenland that is no longer accelerating, and a steady loss from the GIC. The ice sheet mass loss has slowed down to average 255 Gt/yr for Greenland and 108 Gt/yr for Antarctica during 2002-2024. Combining the GRACE/GRACE-FO data with a longer-time record for 1979-2024, however, we find that the ice sheet mass loss has been accelerating for both Greenland and Antarctica, i.e., the recent slowdown is a fluctuation on top of a long-term decrease.

How to cite: Velicogna, I.: Improving ice sheet and glaciers mass balance estimates using Microwave Instrument (MWI) GRACE-FO and GRACE data , GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-83, https://doi.org/10.5194/gstm2024-83, 2024.

The Greenland Ice Sheet (GrIS) plays an important role in the climate system. Since the twenty-first century, GrIS has been melting rapidly due to oceanic and atmospheric warming, leading to a global mean sea level rise of more than 1 millimeter per year. Since 2002, the total amount of GrIS mass changes can be accurately quantified from gravity anomaly observed by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite missions. GRACE(-FO) missions reveal that the GrIS has lost an average of over 260 billion tonnes of ice every year, but they cannot quantify the detailed spatial distribution of these mass changes due to a limiting spatial resolution of about 300 kilometers. In this study, we develop a deep learning method to downscale the GrIS mass anomaly products to 5-km spatial resolution by benefiting from other data sources, including satellite altimetry measurements and surface mass balance modelling. To consider Greenland's complex coastlines and reduce land-ocean leakages, we convert the gridded products into graphs, which allows us to have more flexible shapes of target areas. We test different variants of graph neural networks (GNNs), including graph convolutional networks (GCNs) and graph attention networks (GATs), to estimate the connections among neighboring locations. The GrIS mass anomalies are decomposed into long-term trends and monthly variations, which are later estimated separately by considering different high-resolution products. For the long-term trends, we use 5-year elevation changes measured by multiple altimetry missions as input to provide high spatial resolution information. The surface mass balance estimations from regional climate models and monthly elevation changes inferred from satellite altimetry are considered on the monthly scale. At the same time, the large-scale averages of downscaled mass anomalies are forced to follow GRACE(-FO) products. Ultimately, the downscaled GrIS mass anomalies agree well with GRACE(-FO) products, with large-scale RMSE of around one centimeter per year, and improved more than 60% compared to the altimetry-only estimations. The high-resolution spatial information of 5 kilometers measured by altimetry missions is successfully retained with an average pixel-wise correlation over 0.96. The downscaled GrIS mass anomaly product is valuable for understanding the spatial distribution of GrIS mass changes and is especially beneficial for analyzing individual glacier systems, which is not possible with current available GrIS mass anomaly products.

How to cite: Gou, J., Willen, M., Wilms, J., Flechtner, F., Horwath, M., and Soja, B.: High-resolution Greenland ice sheet mass anomalies from data fusion using graph neural networks, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-38, https://doi.org/10.5194/gstm2024-38, 2024.

The Gravity Recovery and Climate Experiment and its follow-on mission (GRACE and GRACE-FO) have provided us with the most direct and near-continuous observation of the mass variation of the Earth’s surface since 2002. Combined with a glacial isostatic adjustment model, we can resolve the temporal gravity field signal related to the Antarctic Ice Sheet (AIS) to estimate the corresponding mass changes. However, the native spatial resolution of the GRACE/GRACE-FO is limited to ~300 x 300 km, hindering our ability to resolve the origin of smaller-scale mass changes. Satellite altimetry can resolve mass changes of the AIS with much finer spatial resolution. However, conversion from altimeter-measured surface height to mass anomalies required additional corrections for firn air content changes and ice density profile, introducing extra uncertainty to the altimeter mass balance estimate. The finer spatial resolution also means that satellite altimeters take longer to achieve fully coverage of observations of the Earth. Here, we combine the strengths of these two techniques to obtain a GRACE-FO solution that can better resolve the origin of mass change while still having a monthly time step. This method combined the ICESat-2 dataset with the GRACE-FO inversion process at the normal equation level. Instead of using regular gridded interpolated data, we employ the along-track ATL11 data set. This approach allows versatile mass balance estimates that easily cope with different temporal steps and spatial patterns.

In this talk, we will present results of inversions with simulated ICESat-2 and GRACE-FO data. The data were simulated using a “truth” temporal gravity field, the actual orbital data of the ICESat-2 and GRACE-FO and noise to represent uncertainty in firn and GIA corrections. Results show that our method can better resolve the simulated “truth” temporal gravity field. However, how much the combined inversion can improve the accuracy of the solution depends on the level of uncertainty related to the ICESat-2 data. Other than the original uncertainty of the ICESat-2 dataset, firn air content is another major source of uncertainty in coastal regions. Moreover, the coverage of the ICESat-2 dataset over a single mascon is important when calculating the mean height anomaly of a mascon as sampled by ICESat-2. When the height changes within a mascon have high spatial variability, significant mass changes occurring in a small area can alter even the sign of the mean height anomalies estimates if they were not sampled by the ICESat-2 groundtracks. Therefore, a scaling factor related to the intra-mascon variability of each mascon is required to account for this in the inversion process.

How to cite: Leong, K., Tregoning, P., and McGirr, R.: Combined inversion of GRACE-FO and ICESat-2 data for Antarctic Ice Sheet mass balance estimation, GRACE/GRACE-FO Science Team Meeting, Potsdam, Germany, 8–10 Oct 2024, GSTM2024-60, https://doi.org/10.5194/gstm2024-60, 2024.