Seasonal glacier and snow loading in Svalbard recovered from geodetic observations

TL;DR

This study uses advanced filtering and modeling techniques on GNSS and VLBI data to accurately measure seasonal glacier and snow loading effects in Svalbard, demonstrating the importance of filtering for millimeter-level geophysical signals.

Contribution

It introduces an enhanced Common Mode filtering method combined with regional mass loading models to improve the accuracy of seasonal vertical displacement measurements.

Findings

Significant reduction in solution discrepancies using CM filtering.

Detection of the entire vertical loading signal at 95% confidence.

Refinement of land water storage models reduces annual amplitude from 2.1 mm to 1.1 mm.

Abstract

We processed time series from seven Global Navigation Satellite System (GNSS) stations and one Very Long Baseline Interferometry (VLBI) station in Svalbard. The goal was to capture the seasonal vertical displacements caused by elastic response of variable mass load due to ice and snow accumulation. We found that estimates of the annual signal in different GNSS solutions disagree by more than 3 mm which makes geophysical interpretation of raw GNSS time series problematic. To overcome this problem, we have used an enhanced Common Mode (CM) filtering technique. The time series are differentiated by the time series from remote station BJOS with known mass loading signals removed a priori. Using this technique, we have achieved a substantial reduction of the differences between the GNSS solutions. We have computed mass loading time series from a regional Climatic Mass Balance (CMB) and snow model that provides the amount of water equivalent at a 1 km resolution with a time step of 7 days. We found that the entire vertical loading signal is present in data of two totally independent techniques at a statistically significant level of 95%. This allowed us to conclude that the remaining errors in vertical signal derived from the CMB model are less than 0.2 mm at that significance level. Refining the land water storage loading model with a CMB model resulted in a reduction of the annual amplitude from 2.1 mm to 1.1 mm in the CM filtered time series, while it had only a marginal impact on raw time series. This provides a strong evidence that CM filtering is essential for revealing local periodic signals when a millimetre level of accuracy is required.