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  • 1
    Publication Date: 2023-11-13
    Description: Future projections of global mean sea level change are uncertain, partly because of our limited understanding of the dynamics of Greenland’s outlet glaciers. Here we study Nioghalvfjerdsbræ, an outlet glacier of the Northeast Greenland Ice Stream that holds 1.1 m sea-level equivalent of ice. We use GPS observations and numerical modelling to investigate the role of tides as well as the elastic contribution to glacier flow. We find that ocean tides alter the basal lubrication of the glacier up to 10 km inland of the grounding line, and that their influence is best described by a viscoelastic rather than a viscous model. Further inland, sliding is the dominant mechanism of fast glacier motion, and the ice flow induces persistent elastic strain. We conclude that elastic deformation plays a role in glacier flow, particularly in areas of steep topographic changes and fast ice velocities.
    Description: Ice flow dynamics in Greenland’s outlet glaciers are influenced by elastic deformation, both in the area of tidal influence up to 14 km inland from the grounding line and further upstream, suggest analyses of GPS observations and numerical simulations.
    Description: Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) https://doi.org/10.13039/501100002347
    Description: European Union’s Horizon 2020 Research and Innovation Programme
    Description: https://doi.org/10.1594/PANGAEA.928940
    Description: https://nsidc.org/data/IDBMG4
    Description: https://gitlab.awi.de/jchristm/viscoelastic-79ng-greenland
    Description: https://doi.org/10.5281/zenodo.5507115
    Description: https://doi.org/10.5281/zenodo.5506953
    Keywords: ddc:551.31 ; Climate change ; Cryospheric science ; Hydrology
    Language: English
    Type: doc-type:article
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  • 2
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    PANGAEA
    In:  Supplement to: Schumacher, Maike; King, Matt; Rougier, Jonathan C; Sha, Zhe; Khan, Shfaqat Abbas; Bamber, Jonathan L (2018): A new global GPS data set for testing and improving modelled GIA uplift rates. Geophysical Journal International, 214(3), 2164-2176, https://doi.org/10.1093/gji/ggy235
    Publication Date: 2023-01-13
    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.
    Keywords: LATITUDE; LONGITUDE; Station label; Velocity, vertical; Velocity, vertical, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 12216 data points
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  • 3
    Publication Date: 2023-01-13
    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.
    Keywords: MULT; Multiple investigations; Upernavik_Isstrom; West Greenland
    Type: Dataset
    Format: application/zip, 89.2 kBytes
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  • 4
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    PANGAEA
    In:  Supplement to: Vijay, Saurabh; Khan, Shfaqat Abbas; Kusk, Anders; Solgaard, Anne M; Moon, Twila; Bjørk, Anders Anker (2019): Resolving Seasonal Ice Velocity of 45 Greenlandic Glaciers With Very High Temporal Details. Geophysical Research Letters, https://doi.org/10.1029/2018GL081503
    Publication Date: 2023-02-22
    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.
    Keywords: Event label; File format; File name; File size; NorthEast_Greenland; NorthWest_Greenland; SouthEast_Greenland; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 52 data points
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  • 5
    Publication Date: 2023-05-12
    Keywords: AASI; Area/locality; Calculated from GPS; Disko Island, West Greenland; Event label; ILUL; KAGA; Latitude of event; Longitude of event; Number; QEQE; Standard deviation; Uplift rate; Uplift rate, standard deviation; West Greenland
    Type: Dataset
    Format: text/tab-separated-values, 35 data points
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  • 6
    Publication Date: 2023-05-12
    Keywords: AASI; Amplitude; Angle; Area/locality; Calculated from GPS; Disko Island, West Greenland; Event label; ILUL; KAGA; Latitude of event; Longitude of event; QEQE; Standard deviation; Uplift rate; Uplift rate, standard deviation; West Greenland
    Type: Dataset
    Format: text/tab-separated-values, 28 data points
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  • 7
    Publication Date: 2023-05-12
    Keywords: AASI; Area/locality; Calculated from GPS; Disko Island, West Greenland; Event label; ILUL; KAGA; Latitude of event; Longitude of event; QEQE; Uplift rate; Uplift rate, standard deviation; West Greenland
    Type: Dataset
    Format: text/tab-separated-values, 36 data points
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  • 8
    Publication Date: 2023-05-12
    Keywords: AASI; Area/locality; Calculated from GPS; Disko Island, West Greenland; Event label; ILUL; KAGA; Latitude of event; Longitude of event; QEQE; Uplift rate; Uplift rate, standard deviation; West Greenland
    Type: Dataset
    Format: text/tab-separated-values, 55 data points
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  • 9
    Publication Date: 2023-11-09
    Keywords: 79°N Glacier; ALTITUDE; AWI Arctic Land Expedition; DATE/TIME; GL-Land_2017_iGRIFF; GNSS; GNSS Receiver; GPS; GPS-GL-45; Greenland; iGRIFF 79°N Glacier Expedition; LATITUDE; LONGITUDE; Nioghalvfjerdsbraeen Glacier; Northeast Greenland; Waypoint GravNav 8.8 processing software
    Type: Dataset
    Format: text/tab-separated-values, 0 data points
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  • 10
    Publication Date: 2023-11-24
    Description: Processed GNSS data of four stations at 79°N Glacier (Nioghalvfjerdsbraeen Glacier) in northeast Greenland from 2017 (see link in "Further details"). The GNSS data were processed using the GIPSY-OASIS software Package with high-precision kinematic data processing methods (Nettles et al., 2008) with ambiguity resolution using Jet Propulsion Laboratory (JPL)'s orbit and clock products, constraint on kinematic position solution. We use the GIPSY-OASIS version 6.4 developed at JPL, and released in January 2020 (Bertiger et al., 2020). We use JPL final orbit products which include satellite orbits, satellite clock parameters and Earth orientation parameters. The orbit products take the satellite antenna phase center offsets into account. The atmospheric delay parameters are modelled using the Vienna Mapping Function 1 (VMF1) with VMF1grid nominals (Boehm et al., 2006). Corrections are applied to remove the solid Earth tide and ocean tidal loading. The amplitudes and phases of the main ocean tidal loading terms are calculated using the Automatic Loading Provider (http://holt.oso.chalmers.se/loading/) applied to the FES2014b (Lyard et al., 2006) ocean tide model including correction for centre of mass motion of the Earth due to the ocean tides. The site coordinates are computed in the IGS14 frame (Altamimi et al., 2016). We convert the Cartesian coordinates at 5 min intervals to local up, north and east for each GNSS site monitored at the surface of the 79°N Glacier. In addition, we use Waypoint GravNav 8.8 processing software. We applied kinematic PPP processing using precise satellite orbits and clocks. The site coordinates are computed in the IGS14 frame and converted to WGS84 during data export at 15 seconds interval. To avoid jumps between daily solutions of the Waypoint PPP product, as the data is recorded in daily files, we merged three successive files prior to processing to enable full day overlaps. In a second step, the 3-day solutions are combined using relative point to point distances. To avoid edge effects, we combined the files in the middle of each 1-day overlap and removed outliers. The data were re-sampled to 5 min interval to match the GIPSY-OASIS product.
    Keywords: 79°N Glacier; GNSS; GPS; Greenland - Ice Sheet/Ocean Interaction: From process understanding to an analysis of the regional system; GROCE; Nioghalvfjerdsbraeen Glacier; Northeast Greenland
    Type: Dataset
    Format: application/zip, 7 datasets
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