Improved Global Nonlinear Surface Mass Variation Estimates From Geodetic Displacements and Reconciliation With GRACE Data

Spatiotemporal coverage gaps and the nature of distant signature sources in Global Navigation Satellite System (GNSS) and The Gravity Recovery and Climate Experiment (GRACE) data require global inversions of data combinations for cross validation and complete and continuous monitoring of surface water mass variations. For spectral completeness, accurate data‐based degree‐1 surface mass variation coefficient or equivalent geocenter motion estimates are also desired but very difficult to obtain. To improve the reliability of such estimates, we carry out a new effort to combine different space geodetic technique data in a Kalman filter and time series approach to Terrestrial Reference Frame realization using an advanced formulation. With origin at the center of mass of the Earth system, the new parameterization now includes site displacements in the state vector explicitly for realistic assessment of their covariance matrices. More robust geocenter motion results are achieved when the displacement information is combined further with GRACE gravity data in a unified inversion. Previously, various systematic effects in GNSS data resulted in rather large discrepancies in global surface mass change estimates when compared with those from GRACE gravity data. Here, we show that significantly improved GNSS data after reprocessing and the refined Estimating the Circulation and Climate of the Ocean (ECCO) bottom pressure model reveal a global surface mass variation pattern that has been largely reconciled with that from GRACE data.