Improving ocean bottom pressure fields using space gravity data in state estimation

Abstract. Ocean bottom pressure (p_b) 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 p_b 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 p_b 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 p_b and associated circulation fields.