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  • A time series of surface ice flow speed at a point on Thwaites Glacier, West Antarctica. The point is on grounded ice and is upstream of a sub-shelf cavity on the west flank of the fast-moving core of Thwaites Glacier. There are a total of 589 points. First column = yyyy-mm-dd, second column = speed in kilometres per year. The work was funded by NERC projects NE/P011365/1 and NE/S006605/1.

  • Thwaites Glacier, West Antarctica. A time series of 156 profiles of ice surface elevation along a flowline based on the mean flow direction. The flowline passes through a region of large elevation change that took place between 2014 and 2017. The work was funded by NERC projects NE/P011365/1 and NE/S006605/1.

  • The dataset contains terminus positions and flowlines of the Vincennes Bay Outlet Glaciers, for the years 1963-2022. These are provided as shapefiles, associated with a paper submitted to The Cryosphere entitled Extensive and anomalous grounding line retreat at Vanderford Glacier, Vincennes Bay, Wilkes Land, East Antarctica (Picton et al., 2023). The dataset is divided into three separate folders: (i) flowlines, (ii) sampling boxes, and (iii) terminus positions. The flowlines and sampling boxes, shown in Figure 1B (Picton et al., 2023), were used to facilitate data collection. The terminus positions represent annual terminus positions manually digitised from satellite imagery. Flowlines, sampling boxes and terminus positions are provided for each of the Vincennes Bay outlet glaciers: Vanderford, Adams, Anzac, Bond East, Bond West, and Underwood. Chris Stokes and Stewart Jamieson acknowledge funding from the UK Natural Environment Research Council grant NE/R000824/1.

  • Polarimetric phase-sensitive radar measurements were collected at the Western Antarctic Ice Sheet (WAIS) Divide on the 25th and 26th December 2019. The measurements were conducted at 10 sites along a 6 km-long transect ~5-10 km northeast of the location of the WAIS Divide Deep Ice Core. At each site, a suite of four quadrature (quad-) polarimetric measurements were collected using an autonomous phase-sensitive radio echo sounder (ApRES) in a single-input single-output (SISO) configuration. The study is part of the Thwaites Interdisciplinary Margin Evolution (TIME) project of the International Thwaites Glacier Collaboration (ITGC), and is a collaboration between the United States National Science Foundation (NSF) and the United Kingdom Natural Environment Research Council (NERC). It was funded by UK Natural Environment Research Council (NERC) research grant NE/S006788/1 and USA National Science Foundation (NSF) research grant 1739027.

  • A map of changes in ice surface speed in metres/year for Thwaites Glacier, West Antarctica, between January 2012 and January 2021. Speeds based on feature tracking of satellite synthetic aperture radar data. The work was funded by NERC projects NE/P011365/1 and NE/S006605/1.

  • GPS data recorded from three sites close to the 2023 site of Halley VI Research Station. Data from site LL20 spans 2013 to 2017; Data from site ZZ6A spans 2017 to 2023; Data from site ZMET spans 2022 to 2023. The data are presented as RINEX observation files. The data were collected as part of the Lifetime-of-Halley monitoring programme. This work was funded by NERC grant NE/X014991/1 (RIFT-TIP) and supported by NERC Antarctic Logistics and Infrastructure.

  • This is distributed temperature sensing (DTS) data from a 1,043 m borehole drilled to the base of Sermeq Kujalleq (Store Glacier), Greenland, 28 km inland from the glacier terminus. The DTS system was installed on 5 July 2019, with recordings continuing until cable failure on 13 August 2019. The record resolution is ~0.65 m. This work was primarily funded and conducted as part of the European Research Council RESPONDER project (https://www.erc-responder.eu/) under the European Union''s Horizon 2020 research and innovation program (Grant 683043). Robert Law was supported by Natural Environment Research Council Doctoral Training Partnership studentships (Grant NE/L002507/1).

  • A dataset of ice-margin change (advance/recession) at the south-western sector of the Greenland Ice Sheet, comprising data from 3325 terrestrial, 439 lacustrine and 35 marine ice-margins respectively. The dataset also comprises measures of ice-marginal lake parameters including area and intersect (length of the lake - ice-margin interface). Measurements were made at approximately five year intervals (epochs) from 1987 to 2015. The ice sheet margin and adjacent ice-marginal lakes were delineated by applying the Normalised Difference Snow Index (NDSI) and the Normalised Difference Water Index (NDWI) respectively to Landsat TM, ETM+ and OLI scenes. Ice-margin changes were measured relative to a series of fixed reference points. The dataset was generated to facilitate comparison of changes at the disparate ice-marginal environments of the ice sheet and investigate temporal patterns of ice-margin recession. The dataset was created and processed by researchers in the School of Geography at the University of Leeds and the Institute of Integrative Biology at the University of Liverpool.

  • Glacial outlines of the APIS (Antarctica Peninsula Ice Sheet) for 1988, 2001, 2009. This is now incorporated into the GLIMS (Global Land Ice Measurements from Space) project.

  • We can learn about the flow of ice in Antarctica by evaluating the key parameters that control the flow speed. These parameters include the basal drag coefficient and the ice viscosity. They can be estimated by adjusting their values so that model velocities at the upper surface agree with satellite observations. This dataset was produced using inverse methods to obtain the parameter values. In this approach a cost function that describes the mismatch between model and satellite data is minimised iteratively by making small adjustments to the parameters at each iteration to improve the fit. The result is better information about the flow field in the Antarctic ice sheet. Once the flow field is available it can be used as an initial state from which begin temporally evolving simulations using the model. A number of different examples are included to show how varying different parameters alters the temporally evolving simulations. The contributing datasets used to constrain the model are listed by Arthern et al (2015) and Arthern and Williams (2017). Multidecadal model simulations span up to 100 years of simulation time. This work was funded by NERC standard grant NE/L005212/1.