ALBERT

Description: The continuously operating Global Positioning System (GPS) sites mounted on bedrock around the coast of Greenland provide important geodetic datasets to quantify the solid Earth's response to historical and present-day ice mass variations. The presence of colored noise and changeable seasonal signals makes it difficult to detect transient changes in GPS time series. Here we apply the Multichannel Singular Spectral Analysis to the combination of GPS data and Gravity Recovery and Climate Experiment (GRACE) data so that we can identify and fully utilize the spatial correlations from these two independent datasets. Using the GPS and GRACE data near Upernavik Isstrøm in West Greenland as an example, we demonstrate that this method successfully detects two transient signals in ice mass variations during 2008 and 2014. Our forward modeling of loading displacements due to changes in surface mass balance (SMB) and ice dynamics suggests that the transient change starting in mid-2008 was due to the combined contributions from dynamically-induced mass loss and SMB. The transient change starting in mid-2011 was mainly due to ablation Specifically, the ice melted more in 2012 and less in 2013 with little contribution from anomalies in accumulation. This dataset includes: (1) Vertical displacements inferred from GPS and GRACE with atmospheric loading, non-tidal ocean loading, and terrestrial water storage loading removed. Gaps are filled and linear trends are also removed. This data are used for multi-channel singular spectral analysis (M-SSA) in the paper. (2) Transient and seasonal signals extracted from GPS and GRACE data by M-SSA. (3) Transient and seasonal signals extracted from surface mass balance data by M-SSA. (4) Transient signals inferred from glacial dynamics.

Description: Seasonal glacier ice velocities are important for precisely estimating annual ice discharge and understanding controlling mechanisms, but these measurements for a large number of Greenlandic glaciers are limited by low temporal resolution. We present seasonal changes in ice velocities, radar backscatter to mark the onset and extent of melt season and ice front positions of 45 Greenlandic glaciers using Sentinel-1 SAR data for the period 2015-2017. Seasonal velocity fluctuations of roughly half of the glaciers appear to be primarily controlled by surface-melt induced changes in the subglacial hydrology. This includes glaciers that speedup with the onset of surface melt and glaciers with comparable late winter and early melt season velocities showing significant slowdown during most of the melt season and speedup winter. Nearly 25% glaciers show strong correspondence between ice speed and terminus changes. Our results pinpoint the seasonal variations highlighting the variable influence of meltwater on year-around ice velocities.

Description: We analyze 2006-2009 data from four continuous Global Positioning System (GPS) receivers located between 5 and 150 km from the glacier Jakobshavn Isbrae, West Greenland. The GPS stations were established on bedrock to determine the vertical crustal motion due to the unloading of ice from Jakobshavn Isbrae. All stations experienced uplift, but the uplift rate at Kangia North, only 5 km from the glacier front, was about 10 mm/yr larger than the rate at Ilulissat, located only ~45 km further away. This suggests that most of the uplift is due to the unloading of the Earth's surface as Jakobshavn thins and loses mass. Our estimate of Jakobshavn's contribution to uplift rates at Kangia North and Ilulissat are 14.6 ± 1.7 mm/yr and 4.9 ± 1.1 mm/yr, respectively. The observed rates are consistent with a glacier thinning model based on repeat altimeter surveys from NASA's Airborne Topographic Mapper (ATM), which shows that Jakobshavn lost mass at an average rate of 22 ± 2 km**3/yr between 2006 and 2009. At Kangia North and Ilulissat, the predicted uplift rates computed using thinning estimates from the ATM laser altimetry are 12.1 ± 0.9 mm/yr and 3.2 ± 0.3 mm/yr, respectively. The observed rates are slightly larger than the predicted rates. The fact that the GPS uplift rates are much larger closer to Jakobshavn than further away, and are consistent with rates inferred using the ATM-based glacier thinning model, shows that GPS measurements of crustal motion are a potentially useful method for assessing ice-mass change models.

Description: We have produced a global dataset of ~4000 GPS vertical velocities that can be used as observational estimates of glacial isostatic adjustment (GIA) uplift rates. GIA is the response of the solid Earth to past ice loading, primarily, since the Last Glacial Maximum, about 20 K yrs BP. Modelling GIA is challenging because of large uncertainties in ice loading history and also the viscosity of the upper and lower mantle. GPS data contain the signature of GIA in their uplift rates but these also contain other sources of vertical land motion (VLM) such as tectonics, human and natural influences on water storage that can mask the underlying GIA signal. A novel fully-automatic strategy was developed to post-process the GPS time series and to correct for non-GIA artefacts. Before estimating vertical velocities and uncertainties, we detected outliers and jumps and corrected for atmospheric mass loading displacements. We corrected the resulting velocities for the elastic response of the solid Earth to global changes in ice sheets, glaciers, and ocean loading, as well as for changes in the Earth's rotational pole relative to the 20th century average. We then applied a spatial median filter to remove sites where local effects were dominant to leave approximately 4000 GPS sites. The resulting novel global GPS dataset shows a clean GIA signal at all post-processed stations and is suitable to investigate the behaviour of global GIA forward models. The results are transformed from a frame with its origin in the centre of mass of the total Earth's system (CM) into a frame with its origin in the centre of mass of the solid Earth (CE) before comparison with 13 global GIA forward model solutions, with best fits with Pur-6-VM5 and ICE-6G predictions. The largest discrepancies for all models were identified for Antarctica and Greenland, which may be due to either uncertain mantle rheology, ice loading history/magnitude and/or GPS errors.