| 24 Feb 2026
Improving ocean bottom pressure fields using space gravity data in state estimation
Abstract. Ocean bottom pressure (pb) is critical for monitoring and understanding ocean variability, yet global observations from GRACE and GRACE Follow-On suffer from limited spatiotemporal coverage. State estimation methods allow for the dynamical interpolation of sparse data by optimally combining observations with models. Here we examine the effects of assimilating GRACE data (local pb anomalies and global mean), along with other datasets, on state estimates produced by the project for Estimating the Circulation and Climate of the Ocean (ECCO). The ECCO optimization leads to large adjustments in pb fields at monthly and longer timescales. A substantial part of those adjustments is directly induced by GRACE constraints, with largest impacts occurring at high latitudes. Additionally, the mean ocean mass constraint is essential for mitigating large imbalances in freshwater fluxes derived from atmospheric reanalyses (used as prior forcing) and for producing a realistic barystatic sea level curve. Interpretation of remaining ECCO and GRACE differences highlights issues with non-oceanographic data signals. Our findings indicate that GRACE data contain information complementary to that available in other datasets, quantifying their value for determining pb and associated circulation fields.
The authors aim to examine the effect of assimilating GRACE data, along with other datasets, on state estimates produced by the project for Estimating the Circulation and Climate of the Ocean (ECCO). By way of comparing the results against a reference run that does not include any data constraints the authors claim that the ECCO optimization leads to large adjustments in bottom pressure (pb) fields at monthly and longer timescales largely upon assimilation of GRACE data. Another conclusion drawn is that a mean ocean mass constraint is essential for mitigating large imbalances in freshwater fluxes derived from atmospheric reanalyses (used as prior forcing) and for producing a realistic barystatic sea level curve. Inspecting the residuals, the authors also point to problems with the GRAC data that appear to be inconsistent with other information about the ocean circulation and its variability.
The subject of the paper is important, and ultimately the manuscript should be published. However, some mayor shortcomings must be remedied first. First and foremost, the experimental setup is flawed in that only one assimilation experiment constraint simultaneously by many data sets is being used to pinpoint the influence of a specific data set – the GRACE data - by comparing the results against a reference run in which no data were assimilated at all. Obviously, this cannot work and conclusions drawn are not backed up by the results shown. This holds even more so as the GRACE data kick in at the same time when Argo data become available and so the solution will be impacted in all its aspect by both data sets and all other data as well. Ideally, exactly the same set-up should be run twice with and without GARCE data involved. At a minimum, what the authors need to do here is compare results against a previous optimization run (Release 4 described in detail by Fukumori et al. (2019) which included almost all data as constraints, but not the GRACE data. Moreover, many important details are missing in the paper regarding the approach but also the assimilation experiment itself that need to be added. More detailed comments are provided below. Once all those have been addressed satisfactorily I belief that the paper can become a significant contribution.
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