TED: a global temperature-driven thermoelastic displacement dataset for GNSS reference stations (2000–2023)

Abstract

Abstract. The nonlinear signals in global GNSS station height time series reflects both non-tidal mass loading (atmospheric, oceanic, and hydrological) and temperature-driven thermoelastic deformation (TED). However, a globally consistent and reproducible TED data product has long been lacking. Here we present a global dataset of vertical TED for ~15,000 GNSS stations spanning 2000–2023, generated using a full-spectrum, layered finite-element model. The model is driven by hourly ERA5 soil-temperature profiles and parameterized with depth-dependent thermophysical properties from the SoilGrids dataset, enabling consistent quantification of TED from semi-diurnal/diurnal variability through seasonal to interannual timescales. Compared with an identical homogeneous-medium benchmark, subsurface stratification typically changes annual amplitudes by ~0.3 mm and shifts the timing of the annual maximum by ~1 month, yielding regionally coherent and smoothly varying spatial patterns. At stations with independent site characterization, the site-constrained solutions agree closely with SoilGrids-based solutions, with annual-amplitude differences of 0.01–0.03 mm and annual-phase differences mostly within 1–3°. Sensitivity tests using ±10% perturbations in thermal expansion, thermal diffusivity, and Young’s modulus indicate that annual-cycle amplitude and phase are robust. Globally, annual TED amplitudes are typically 1–2 mm, exceed 2–3 mm at some stations, and reach peak-to-peak values up to ~5 mm, with the largest signals concentrated in arid inland and continental climate regions. When TED corrections are applied together with non-tidal mass-loading corrections, the residual vertical dispersion decreases at most stations, with vertical scatter reduced by up to ~70 % at selected sites. The dataset is publicly available for direct use in GNSS coordinate time series correction and related geophysical applications: https://doi.org/10.5281/zenodo.18256342 (Lu et al., 2026).

The authors' abstract, as published at the source. Earth system science data, 2026 · DOI ↗