This data publication presents global high-frequency mass variability that is induced by individual oceanic and atmospheric partial tides. While the atmospheric component is obtained by conducting a tidal analysis of numerical weather data data, the oceanic component has been produced using the hydro-dynamical ocean tide model TiME that was recently upgraded in the framework of the DFG-funded Research Group NEROGRAV and can be used for gravimetric applications. The overall goal of this project is to facilitate the analysis of gravimetric data sets (e.g. GRACE/GRACE-FO) by improving the understanding of sensor data, processing strategies, and background models.

The data set presented herein contributes to this goal as the here described tidally induced mass variations are an important part of the described background models. As tidal variability is usually described as a superposition of so-called partial tides, the presented mass variations can be attributed to individual partial tide frequencies and are thus represented by individual files for each partial tide frequencies. Here, not only the effect of direct gravitation exerted by the ocean and atmospheric mass is included but also gravity variations due to the elastic yielding of the solid Earth in response to water and atmospheric mass redistribution (the load tide) are allowed for. The information describing the partial tides has been transformed to fully normalized Stokes Coefficients describing harmonic in-phase and quadrature component fields as those are especially handy for gravimetric purposes. Additionally, a set of files that allows further expansion of the ensemble of ocean partial tides via linear admittance theory is provided.

A deep understanding of mass distribution and mass transport in System Earth is needed to answer central questions in hydrology, oceanography, glaciology, geophysics and climate research. The necessary information is primarily derived from satellite mission data as observed by GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-on) describing the gravity field of the Earth and its temporal variations.

The research group (RG) „New Refined Observations of Climate Change from Spaceborne Gravity Missions" (NEROGRAV, https://www.lrg.tum.de/iapg/nerograv/), funded by the German Research Foundation (DFG), develops since May 2019 new analysis methods and modeling approaches to improve GRACE and GRACE-FO mission data analysis and focuses on geophysical applications that benefit from significantly reduced error levels in the time series of monthly gravity fields. Phase 1 lasted from May 2019 till April 2022. After successful evaluation in January 2022 the second phase started in January 2023.

The central hypothesis of the research group, slightly updated for phase 2, is: “Only by concurrently improving and better understanding of sensor data, background models, and processing strategies of satellite gravimetry, the resolution, accuracy, and long-term consistency of mass transport series can be significantly increased; the science return in various fields of application improved and the potential of future technological sensor developments fully exploited.“

All groups participating in NEROGRAV have a long-term heritage of expertise in geodetic data acquisition and modeling and will additionally contribute their unique complementary expertise from various neighboring disciplines such as oceanography, hydrology, solid Earth, geophysics and atmospheric and climate sciences. Therefore, it is expected that the second funding phase will not only create significantly improved GRACE/GRACE-FO gravity field models over two decades, but also enable geophysical applications based on this long-term series such as quantifying North Atlantic deep water transports as indicator for variations in the Atlantic Meridional Overturning Circulation (AMOC), assessment of hydrometeorological extreme events or identification of climatic signatures in variations of the terrestrial water storage. Important results and datasets of phase 1 can be found at GFZ´s Data Services.