Abstract The usefulness of Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GRACE/FO) data products for ocean studies remains limited in a few areas, where large gravity signals from the 2011 Japan earthquake and three major earthquakes in the Andaman‐Sumatra area (in 2004, 2007, and 2012) obscure vastly smaller signals of interest related to ocean dynamics. Furthermore, these earthquake effects can not be simply removed from the GRACE/FO products via a model during post‐processing, due to the loosening of constraints needed to allow the signal into current GRACE/FO products. Here, we create a multi‐earthquake empirical model from GRACE/GRACE‐Follow On data, estimating the equivalent water thickness changes as seen in the Center for Space Research (CSR) mascons due to the four major earthquakes mentioned. An iterative principal component analysis was used to compute piecewise linear approximations of the signals in these regions, which describe the co‐seismic bias changes during the known month of each earthquake as well as post‐seismic trends in local mass after/between quakes. This model is then applied as part of the estimation background field during a new iteration of CSR mascon processing designed for oceanic use, using tightened regularization to reduce noise in the near‐earthquake regions. By removing the CSR‐derived earthquake model and reducing month‐to‐month variability via the new regularization, we have reduced the GRACE/FO RMS in ocean mascons near the quake epicenters from more than 50 cm to under 5 cm, comparable to ocean mascon signals elsewhere. The resulting mascon series is more suitable for oceanic studies near Japan and Andaman Bay.

A model of ocean tides is generally used to remove the dynamic tidal signal in satellite gravimetric observations, such as GRACE(-FO). Model imperfections can cause stripes and long period aliasing errors in monthly gravity solutions. Recent developments in ocean tide modeling have substantially improved accuracy at high latitudes and in shallow water regions. In this study, we compared the 8 major tidal constituents from three recent ocean tide models, namely EOT20, FES22 and GOT5.6, as well as GOT4.8, using 13-year long GRACE ranging observations. The improvements of the three recent models are significant, showing more than 2 postfit variance reduction. Among them, GOT5.6 generally performs the best in terms of postfit variance reduction (11% vs. GOT4.8) and residual ocean tide fitting, particularly near West Antarctica. The residual ocean mass RMS, computed from monthly gravity solutions using GOT5.6, is on average 1 mm (GOT4.8), 0.3 mm (EOT20) and 0.1 mm (FES22) smaller. These results make it our preferred model for the upcoming GRACE(-FO) Release 07 (RL07) re-processing. Regarding implementation, utilizing the minor tides in GOT5.6 and long period tides in FES22 as well as considering geographically varying seawater density can further enhance model performance (denoted GOT5.6p3), which on average reduces the residual ocean mass RMS by an additional 7%. Notably, using lateral varying seawater density largely reduces the anomalous M2 residuals in the North Atlantic Ocean. We anticipate that GOT5.6p3 will substantially reduce stripes and aliasing errors in forthcoming RL07 monthly solutions. The improvements will also benefit GOCE reprocessing and GRACE(-FO) sub-monthly solutions.

Abstract. A new series of mascons are made from GRACE and GRACE-FO data, specifically designed for use by oceanographers interested in studying variations in ocean mass transport and circulation. This series has pre-removed those changes in ocean mass distribution caused by barystatic gravity, rotation, and deformation (GRD) signals, as well as the non-oceanographic signals caused by four major oceanic earthquakes, neither of which impact circulation. Subtle changes in the processing and regularization schemes also help reduce the visibility of instrument/orbital errors in the ocean signal, particularly in the arctic and near the sites of the removed earthquakes. The primary benefit of this data set is increased ease of use for researchers interested in ocean dynamics, as the product is designed to be used "off the shelf" with no additional corrections required, even by those less familiar with GRACE data usage. The complete dataset is available at https://doi.org/10.18738/T8/3VUPEW (Pie et al., 2025).

Previous work has demonstrated a significant correlation between the pattern of sea level change computed from a satellite-altimeter-based inference of Greenland ice mass flux from 1993-2019 and satellite sea surface height (SSH) observations adjacent to the island. However, a key question is unanswered in this detection; namely, what constraints on ice mass flux do the SSH observations provide? To address this issue, we perform a series of inversions of the available SSH data offshore Greenland. Our results indicate that such inversions are highly non-unique. However, we also demonstrate that robust inferences can be obtained by incorporating reasonable a priori constraints, in our case limiting the ice model to a small set of discs associated with the major drainage basins of the ice sheet that are proximal to the SSH observations. Our inversions in this case yield estimates of average ice mass loss in the range 0.62-0.70 mm yr-1 in units of equivalent global mean sea level change over the period 1993-2019, when the observations are corrected for the signal of dynamic sea level change. This inference agrees with independent ice altimeter-based estimates of Greenland ice sheet mass flux rates, showing broadly consistent relative ice mass loss rates across southern Greenland basins. Our analysis is the first to directly invert SSH observations for ice mass changes and we conclude that the consideration of such data, particularly in combination with other data sets (e.g. GRACE gravity, ice altimeter measurements, GNSS observations) has the potential to improve constraints on ice sheet mass changes in a warming world.

The fifteen and half years long GRACE temporal gravity data has greatly contributed to interdisciplinary studies especially in climate and solid Earth sciences. To further improve the noise level and error characteristic of the GRACE temporal gravity solutions, as well as provide an archival record for supporting a multidecade mass change measurement based on measurement continuity between the GRACE and GRACE Follow-On mission, the GRACE RL07 re-processing has been carried out at CSR. We will use an initial version of the GRACE Level1B  data (i.e. version 04P), updated background models, new GPS observation strategy and improved observation noise model for the RL07 re-processing. In this poster, we will present preliminary results from the GRACE RL07 re-processing and compare them with the GRACE RL06 solutions using different metrics.

We have developed a specialized set of CSR mascons for ocean dynamics studies. The standard GRACE/GRACE-FO mascons over the ocean represent ocean bottom pressure. Thus, they have contributions from the average atmospheric pressure change over the ocean, the barystatic (global mean) mass change and the geographically variable redistribution of mass caused by gravitation, rotation, and deformation (GRD) changes, none of which drive ocean dynamics. In addition, there are large signals associated with the Andaman-Sumatra and Tohoku earthquakes. In this presentation, we describe the differences in the signal content and regularization of new ocean mascons from the standard release. We have calculated the barystatic-GRD fingerprints consistent with the mascons' estimate of continental mass changes, as well as explored the atmospheric and dynamic ocean effects on the fingerprints. In addition, we have estimated the earthquake signals using localized empirical orthogonal functions. All these contributions are removed, allowing the new product to be directly utilized in ocean dynamic studies.

Rapid melting of ice sheets and glaciers drives a unique geometry, or fingerprint, of sea level change. However, the detection of individual fingerprints has been challenging because of sparse observations at high latitudes and the difficulty of disentangling ocean dynamic variability from the signal. We predict the fingerprint of Greenland Ice Sheet (GrIS) melt using recent ice mass loss estimates from radar altimetry data and model reconstructions of nearby glaciers and compare this prediction to an independent, altimetry-derived sea surface height trend corrected for ocean dynamic variability in the region adjacent to the ice sheet. A statistically significant correlation between the two fields (P < 0.001) provides an unambiguous observational detection of the near-field sea level fingerprint of recent GrIS melting in our warming world.

We carry out a comprehensive error assessment of Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GFO) Release‐6 (RL06) solutions from the Center for Space Research (CSR) at the University of Texas at Austin, NASA Jet Propulsion Laboratory (JPL), and Geoforschungszentrum (GFZ). The study covers the period April 2002 to August 2020 and uses two different methods, one based upon open ocean residuals (OOR) and the other a Three‐Cornered Hat (TCH) calculation. General results from the two methods are similar. With 300 km Gaussian smoothing OOR RMS errors for CSR, JPL, and GFZ solutions are ∼2.01, 3.19, and 3.67 cm, respectively. With additional decorrelation filtering OOR RMS values are reduced to ∼1.24, 1.53, and 1.69 cm, respectively. TCH analysis also shows that CSR has the lowest noise levels with similar RMS values, and additional decorrelation filtering reduces error levels. TCH may underestimate errors if there are common errors among geophysical background models. Errors in GFO's first two years (25 solutions for 2018.06 to 2020.08) are comparable to those of GRACE when zonal degree 2 and 3 coefficients are replaced by Satellite Laser Ranging estimates. The OOR method reveals mismodeled intra‐seasonal dynamic ocean signals associated with the Argentine Gyre during de‐aliasing, while the TCH method shows differences between ocean tide models near Australia and Antarctica. Both OOR and TCH RMS analysis offer a means to assess the noise level of GRACE/GFO estimated mass change. The actual uncertainty of GRACE/GFO estimate averaged (or totaled) over a given region is also affected by other error sources.

Since June, 2018, the Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) is extending the 15‐year monthly mass change record of the GRACE mission, which ended in June 2017. The GRACE‐FO instrument and flight system performance has improved over GRACE. Better attitude solutions and enhanced pointing performance result in reduced fuel consumption and gravity range rate post‐fit residuals. One accelerometer requires additional calibrations due to unexpected measurement noise. The GRACE‐FO gravity and mass change fields from June 2018 through December 2019 continue the GRACE record at an equivalent precision and spatiotemporal sampling. During this period, GRACE‐FO observed large interannual terrestrial water variations associated with excess rainfall (Central US, Middle East), drought (Europe, Australia), and ice melt (Greenland). These observations are consistent with independent mass change estimates, providing high confidence that no intermission biases exist from GRACE to GRACE‐FO, despite the 11‐month gap. GRACE‐FO has also successfully demonstrated satellite‐to‐satellite laser ranging interferometry.

Changes in sea level lead to some of the most severe impacts of anthropogenic climate change. Consequently, they are a subject of great interest in both scientific research and public policy. This paper defines concepts and terminology associated with sea level and sea-level changes in order to facilitate progress in sea-level science, in which communication is sometimes hindered by inconsistent and unclear language. We identify key terms and clarify their physical and mathematical meanings, make links between concepts and across disciplines, draw distinctions where there is ambiguity, and propose new terminology where it is lacking or where existing terminology is confusing. We include formulae and diagrams to support the definitions.

Time-resolved satellite gravimetry has revolutionized understanding of mass transport in the Earth system. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has enabled monitoring of the terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure variations, as well as understanding responses to changes in the global climate system. Initially a pioneering experiment of geodesy, the time-variable observations have matured into reliable mass transport products, allowing assessment and forecast of a number of important climate trends, and improvements in service applications such as the United States Drought Monitor. With the successful launch of the GRACE Follow-On mission, a multi-decadal record of mass variability in the Earth system is within reach.

We analyze global mean ocean mass (GMOM) change over the 12‐year period (January 2005 to December 2016) using three different Gravity Recovery and Climate Experiment (GRACE) RL05 monthly time‐variable gravity solutions and compare GRACE results with independent observations from satellite altimeter and Argo floats (i.e., Altimeter‐Argo). The new results from both GRACE and Altimeter‐Argo show substantially larger GMOM rates than previous estimates, attributed to increased ice losses over land in recent years. Altimeter measurements show an average sea level rise rate of 3.87 ± 0.16 mm/year over the 12‐year period, with 1.12 ± 0.08 mm/year driven by steric effect based on Argo observations. GRACE‐observed ocean mass change agrees well with Altimeter‐Argo estimates at seasonal time scales, but notable discrepancies exist at long‐term time scales. GRACE‐estimated GMOM rates, ranging from 2.17 ± 0.12 to 2.39 ± 0.12 mm/year, are notably smaller than the Altimeter‐Argo estimate of 2.75 ± 0.18 mm/year. However, when GRACE degree‐2 zonal terms are retained (instead of being replaced by satellite laser ranging measurements), the forward modeled GRACE GMOM rate is 2.70 ± 0.16 mm/year, consistent with the Altimeter‐Argo estimate. Accurate quantification of GMOM change using GRACE is challenging and relies on accurate correction for contributions from geocenter motion and Earth oblateness change using independent observations and the Glacial Isostatic Adjustment effect using model predictions. Our analysis indicates that long‐term geocenter motion alone may contribute ~0.3 mm/year to GRACE GMOM rates, and different data processing methods have notable effect on GRACE GMOM estimates.

Estimates of regional and global average sea level change remain a focus of climate change research. One complication in obtaining coherent estimates is that geodetic datasets measure different aspects of the sea level field. Satellite altimetry constrains changes in the sea surface height (SSH; or absolute sea level), whereas tide gauge data provide a measure of changes in SSH relative to the crust (i.e., relative sea level). The latter is a direct measure of changes in ocean volume (and the combined impacts of ice sheet melt and steric effects), but the former is not since it does not account for crustal deformation. Nevertheless, the literature commonly conflates the two estimates by directly comparing them. We demonstrate that using satellite altimetry records to estimate global ocean volume changes can lead to biases that can exceed 15%. The level of bias will depend on the relative contributions to sea level changes from the Antarctic and Greenland Ice Sheets. The bias is also more sensitive to the detailed geometry of mass flux from the Antarctic Ice Sheet than the Greenland Ice Sheet due to rotational effects on sea level. Finally, in a regional sense, altimetry estimates should not be compared to relative sea level changes because radial crustal motions driven by polar ice mass flux are nonnegligible globally.