Modeling gravimetric signatures of third-degree ocean tides and their detection in superconducting gravimeter records

We employ the barotropic, data-unconstrained ocean tide model TIME to derive an atlas for degree-3 tidal constituents including monthly to terdiurnal tidal species. The model is optimized with respect to the tide gauge data set TICON-td that is extended to include the respective tidal constituents of diurnal and higher frequencies. The tide gauge validation shows a rootmean-square (RMS) deviation of 0.9-1.3 mm for the individual species. We further model the load tide-induced gravimetric signals by two means (1) a global load Love number approach and (2) evaluating Greens-integrals at 16 selected locations of superconducting gravimeters. The RMS deviation between the amplitudes derived using both methods is below 0.5 nGal (1 nGal = 0.011 nm/S-2) when excluding near-coastal gravimeters. Utilizing ETERNA-x, a recently upgraded and reworked tidal analysis software, we additionally derive degree-3 gravimetric tidal constituents for these stations, based on a hypothesis-free wave grouping approach. We demonstrate that this analysis is feasible, yielding amplitude predictions of only a few 10 nGal, and that it agrees with the modeled constituents on a level of 63-80% of the mean signal amplitude. Larger deviations are only found for lowest amplitude signals, near-coastal stations, or shorter and noisier data sets.

Automated summary

This study employs the TIME model, a barotropic and data-unconstrained ocean tide model, to derive a atlas for degree-3 tidal constituents at monthly and weekly scales. The model is optimized using the TICON-td data set and validated against tide gauge data, with a root-mean-square deviation of 0.9-1.3 mm. Furthermore, the study models the load-induced gravimetric signals using two methods, and the deviation between the amplitudes derived from these methods is below 0.5 nGal. The study also utilizes the ETERNA-x software to derive gravimetric tidal constituents for selected stations, showing good agreement with modeled constituents.