Space geodetic data and validation method for global and regional glacial isostatic adjustment model optimization

Space geodetic data contain a glacial isostatic adjustment (GIA) signal, which is most prominent in formerly glaciated areas with present-day crustal uplift rates exceeding 10 mm/year in NE Canada and Central Sweden. Employing GNSS, VLBI, DORIS, and SLR data ingested into the latest international te...

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Bibliographic Details
Main Authors: Bagge, M., Boergens, E., Balidakis, K., Albrecht, T., Klemann, V., Dobslaw, H., Steinberger, B., Haeger, C.
Format: Conference Object
Language:English
Published: 2023
Subjects:
Online Access:https://gfzpublic.gfz-potsdam.de/pubman/item/item_5019107
Description
Summary:Space geodetic data contain a glacial isostatic adjustment (GIA) signal, which is most prominent in formerly glaciated areas with present-day crustal uplift rates exceeding 10 mm/year in NE Canada and Central Sweden. Employing GNSS, VLBI, DORIS, and SLR data ingested into the latest international terrestrial reference frame (ITRF2020) we create a global dataset of GIA present-day uplift rates. We employ a multi-analysis-centre ensemble of GNSS station and geocentre motion coordinate solutions. Tectonic and weather signatures were reduced in estimating GNSS-derived velocities, and the trend signal is extracted from these GNSS time series with the STL method (seasonal-trend decomposition based on Loess). In addition, we develop a validation method for GIA model – data comparisons. As the geodetic stations are unevenly distributed, we employ a weighting scheme that involves network density and cross-correlation of the stations’ displacement time series. As measures of agreement for global and regional cases, we employ the weighted root mean square error (RMSE) and the weighted mean absolute error (MAE). We apply the validation method to a large suite of GIA model simulations capturing uncoupled and coupled Solid Earth – Ice Sheet models, as well as laterally homogeneous and heterogeneous viscosity structures of the Earth’s mantle, which are derived from a broad spectrum of geophysical data. The results suggest constraints on global and regional GIA model parameterisations in view of the considered observational data.