the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jan 2026
GRACE and GRACE-FO Mascons for Ocean Dynamic Applications
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).
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-718', Anonymous Referee #1, 26 Feb 2026
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RC2: 'Comments on essd-2025-718', Anonymous Referee #2, 13 Mar 2026
General comments:
This paper provides a detailed description of a GRACE/GRACE-FO – derived mascon dataset designed for studies of ocean dynamics.
The manuscript presents the data processing steps in a clear and systematic way, including comparisons with other existing GRACE/GRACE-FO solutions and explanations of the methodological choices made by the authors. These descriptions help the reader understand how the various processing decisions contribute to the final product and to reducing uncertainties in the dataset.
Overall, the product is well thought out and grounded in prior studies and developments within the GRACE/GRACE-FO community. The authors carefully consider background models, previously identified issues, and lessons learned from earlier solutions produced by different GRACE data centers. The result is a dataset intended for the community studying ocean dynamics – such as ocean mass variability, redistribution of mass, and ocean circulation – that can be used directly without requiring additional corrections or preprocessing steps.
The uniqueness and usefulness of this dataset are primarily highlighted by two aspects of the data processing. First, the authors implement a method that successfully removes gravity signals associated with major earthquakes, an issue that has affected GRACE/-FO observations and has been the subject of ongoing efforts in the community for many years. Second, the dataset shows substantial improvements in ocean bottom pressure estimates in the Arctic and sub-Arctic oceans, regions that have historically been affected by leakage of land signals from glacial mass changes into the ocean domain. This leakage has often produced unrealistic trends or spurious spikes in previous GRACE-based estimates, and the mitigation of this problem represents a meaningful advance for Arctic studies, particularly in the region around Greenland, and in the region surrounding Franz Joseph Land.
I downloaded the dataset and explored it by generating a few plots. It appears to be comprehensive and has a structure that closely aligns with previous mascon solutions. This consistency makes it straightforward for existing GRACE users to work with the data. I hope that this product will continue to be updated throughout the lifetime of the GRACE-FO mission and potentially extended for the future gravity missions.
Overall, the dataset is of high quality and will likely be widely used within the oceanographic community. It may also prove valuable for the modeling community, particularly for groups that already assimilate – or may soon begin assimilating – GRACE/GRACE-FO observations. The manuscript itself is well written, generally concise, and well supported by explanations and references. I am happy to recommend this for publication after considering the minor comments below:
1. Comparison with ECCOv4.4
The authors compare the DO dataset with ECCOv4.4. However, if I understand correctly, ECCOv4.4 already assimilates a version of GRACE/GRACE-FO data, but the ECCOv4.4 website does not indicate which. While the authors make efforts to ensure a consistent comparison (for example by removing atmospheric signals from ECCOv4.4), it would be helpful to briefly discuss the implications of ECCOv4.4 already assimilating GRACE ocean bottom pressure – likely a JPL mascon version.
I recommend that the authors add a brief explanation (max 2-3 sentences) describing, to the best of their knowledge, which GRACE product is assimilated in ECCOv4.4 and how this may affect the comparison presented in the manuscript. Clarifying this point would help readers better interpret the similarities or differences between the datasets. It would also likely make a stronger case for the use of this new product to be assimilated in modeling efforts like future versions of ECCO, which would likely yield an improved representation of the real world compared to what ECCOv4.4 and others currently do.
2. Equivalent thickness definition
The manuscript would benefit from a brief mention of the density used for the conversion from pressure to water equivalent thickness. I recognize that the dataset itself already includes this information (i.e., 1025 kg/m3) within the list of data attributes, but since this product is strongly targeting an oceanography audience, I think it would be relevant to mention this upfront in the manuscript.
3. Uncertainty estimates
The manuscript emphasizes that several sources of noise – such as earthquake signals and Arctic leakage effects – have been mitigated in this product. I appreciate the estimate of average uncertainty of ~0.8 cm equivalent water thickness for the DO product. However, it would be helpful if the authors could clarify whether this estimate refers to the global oceans or specifically to equatorial regions. In addition, could the authors provide an estimate of the uncertainty specific to the Arctic and subarctic regions? The improvement of the ocean bottom pressure product appears to be particularly significant in these areas, in addition to the lower-latitude regions with the earth-quake-related signals removed. Overall, this measure of uncertainty is broadly consistent with improvements reported in previous GRACE/-FO solutions. For example, JPL mascons RL05 report a global ocean average uncertainty of ~1cm after applying a Coastline Resolution Improvement filter by Wiese et al. (2016); CSR RL05 mascons consider a global average of 2cm by Save et al. (2016); and Chambers and Bonin (2012), find standard error of Rel05 spherical harmonics -based solution to be ~1cm in low-mid latitudes, and between 1.5-2cm in the polar and subpolar regions. Given the limited availability of multi-year in situ bottom pressure records, it is understandable that the uncertainty estimate relies on comparisons with ocean models. However, as noted above, the ECCOv4.4 solution is not fully independent of GRACE data. I suggest that the authors include a brief cautionary note in the manuscript indicating that, because ECCOv4.4 assimilates GRACE, the estimated uncertainty of ~0.8 cm may not be entirely independent of the product being evaluated.
Citation: https://doi.org/10.5194/essd-2025-718-RC2
Data sets
CSR GRACE & GRACE-FO Dynamic Ocean Mascons RL06.2DO Nadège Pie et al. https://doi.org/10.18738/T8/3VUPEW
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- 1
The paper describes a new ocean bottom pressure product based on GRACE/FO data, which includes corrections for four major earthquakes and for gravitation, rotation and deformation (GRD) effects related to land water and ice changes. These so-called dynamic ocean mascons (DOM) include additional changes in data processing that lead to better representation of ocean bottom pressure signals around Franz Josef Land. The new dataset is timely and fills a major need when using GRACE/FO data products for studies of ocean dynamics. The methodology is well developed and clearly described in section 2. The statistical evaluation of the DOM product is basically fine but a couple of improvements to the dataset would seem essential, as elaborated below.
I find the lack of treatment of GRD effects from atmospheric mass changes over land a major shortcoming of this dataset. It is unclear why these effects are not included. They are clearly important around Eurasia and Antarctica at the annual time scale, judging from previous works including the cited paper by Tamisiea et al. (2010). Just because they are not provided in other GRD calculations, as stated in lines 363-364, is not a good justification for leaving those potentially important effects out of this dataset. Quite the contrary! An implementation of such a correction could use surface atmospheric fields from the AOD model or some other atmospheric reanalysis product of choice. I strongly recommend that the authors update their dataset with this extra correction.
Another important issue is the characterization of uncertainty for the newly applied corrections or in some sense the remaining GRD and earthquake signals after correction. In this regard, a number of other similar products, dealing with these corrections, have been recently made available (e.g., Landerer and Wiese, https://doi.org/10.5067/HMOGD-4JM01; Dahle et al. 2025, https://doi.org/10.5194/essd-17-611-2025). Discussion of the new dataset in the context of other similar products would be very useful. Moreover, comparisons with these other corrections, particularly for the case of GRD, could provide a measure of uncertainty. The only quantitative discussion of uncertainty refers to the DOM fields and is based on crude comparisons with ECCO (lines 437-441). Any measure of uncertainty for the corrections provided would be useful when applying the DOM fields to ocean circulation and data assimilation studies.
A final concern is the DOM evaluations performed using comparisons with ECCOv4r4. First, such comparisons need to acknowledge that the ECCOv4r4 solution is itself constrained by GRACE data (in that case JPL mascons). Thus, interpreting similarities between GRACE and ECCOv4r4 as a sign of the quality of DOM derived in the paper (e.g., lines 425-429) is at best ambiguous. Second, perhaps more important is to show quantitatively whether DOM estimates are more or less consistent with the ECCO fields than the regular (uncorrected) mascons. Similar attempts are reported by Ponte et al. (2024, https://doi.org/10.1029/2024EA003661) for the seasonal cycle. See also comment on Figure 11 below.
MINOR ISSUES (listed with line numbers)
8/ Capitalize “arctic” here and elsewhere when referring to region, ocean, etc.
50/ Sounds too “assertive”. You have tried to remove non-oceanographic signals from earthquakes, but not all earthquakes. Moreover, there are possibly other non-oceanographic signals (e.g., Mandea et al. 2015; https://doi.org/10.1002/2015JB012048) that are ignored in this work. While outside the scope of this paper, these signals could be mentioned to provide broader context.
52/ Define mascon at first mention (line 36?).
80/ Define ECCOv4r4.
118-119/ This statement also seems too assertive (see comment on line 50).
139/ Define ellipsoid correction.
135/ What “idea across”? Too colloquial? It would be more informative to say that the approximation is good to within some percentage number or something equivalent.
346-347/ Could be written more clearly.
355/ Imprecise phrasing: pressure variations do not arise from geostrophic balance, instead they lead to geostrophic balance as currents driven by those pressure variations feel the Coriolis force in a rotating planet.
356/ “Because internal pressure variations should average out to zero,…”
Figure 7/ It is not easy to see the subtle differences in these panels. Perhaps showing a difference of trends and amplitudes can provide a better quantitative assessment of the importance of a spatially varying GRD vs spatially constant correction. The differences in annual amplitude can also miss possible differences coming from changes in phase. If differences in phase are important, showing the root-mean-square difference of the annual cycles might be a better alternative.
397/ Effects of GRD and comparisons with ECCO are also discussed by Ponte et al. (2024; https://doi.org/10.1029/2024EA003661) in the context of the seasonal cycle. A discussion of how the current results relate to their findings would be useful to add in section 3.
420-421/Remaining earthquake signals can be seen in side lobes away from the epicenter in both trend and rms plots (figure 6b,d). Is a linear trend the best model to remove? Some more insight into this would be useful for the user, including some discussion of why the current models seem to miss significant parts of the earthquake signal.
428/ Define f/H.
Figure 11/ Do the DOM behave differently compared to the regular mascons? The focus should be on what improvements are brought by the DOM and this figure and its discussion do not address the key reason for the analysis.
459-463/ The Argentine basin issue has been noted and discussed before in several papers. Moreover, the DOM dataset does not bring anything new to this issue, as far as I can grasp from this brief discussion. I suggest deleting this text.
512/ “…and the effect of global atmospheric pressure”
516/ The text here reads “mascons are designed to be comparable to ocean models” but two lines below we have “mascons should not be compared to…models of ocean bottom pressure”. Please rewrite more clearly.
Appendix/ Substantial text in the appendix is basically a repetition word for word of the main text. This is a bad practice and really unnecessary. All repeated text should be deleted.
References/ Please double check your list for completeness. I could not find Pie et al. (2025), Ponte et al. (2018), Save (2019), Sun et al. (2016).