Mass loss from the Antarctic Ice Sheet is dominated by ice dynamics, where ocean-driven melt leads to un-buttressing and ice flow acceleration. Long-term ice speed change has been measured in Antarctica over the past four decades; however, there are limited observations of short-term seasonal speed variability on the grounded ice sheet. Here we assess seasonal variations in ice flow speed on 105 glaciers on the west Antarctic Peninsula using Sentinel-1 satellite observations spanning 2014 to 2021. We find an average summer speed-up of 12.4 ± 4.2%, with maximum speed change of up to 22.3 ± 3.2% on glaciers with the most pronounced seasonality. Our results show that over the six-year study period, glaciers on the west Antarctic Peninsula respond to seasonal forcing in the ice–ocean–atmosphere system, indicating sensitivity to changes in terminus position, surface melt plus rainwater flux, and ocean temperature. Seasonal speed variations must be accounted for when measuring the mass balance and sea level contribution of the Antarctic Peninsula, and studies must establish the future evolution of this previously undocumented signal under climate warming scenarios.
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Source data used in this study are available as follows: Copernicus Sentinel-1A/B, available directly from the European Space Agency (https://scihub.copernicus.eu/); Copernicus Marine Service GLORYS12V1 global ocean physics reanalysis data (https://doi.org/10.48670/moi-00021); REMA Antarctic DEM V1 (https://doi.org/10.7910/DVN/SAIK8B); International Bathymetric Chart of the Southern Ocean V1.0 (https://ibcso.org/previous_releases/, https://doi.org/10.1002/grl.50413); and glacier basin inventory (https://doi.org/10.1017/S0954102014000200).
Data produced during this study are available at https://doi.org/10.5281/zenodo.7521416. This includes ice speed time series for all glaciers, calving front positions for eight highlight glaciers, glacier drainage basin scale ice velocity for eight highlight glaciers and RACMO regional climate model data.
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This work was led by the School of Earth and Environment at the University of Leeds. Data processing was undertaken on ARC3, part of the high-performance computing facilities at the University of Leeds, UK. The authors gratefully acknowledge the European Space Agency and the European Commission for the acquisition and availability of Sentinel-1 data and the use of datasets produced through the Copernicus Marine Service. We also acknowledge the Polar Geospatial Center at the University of Minnesota for the availability of the REMA DEM and J. Lea of the University of Liverpool for the public availability of the GEEDiT and MaQiT digitization tools. B.J.W. is supported by the Panorama Natural Environment Research Council (NERC) Doctoral Training Partnership (DTP), under grant NE/S007458/1. A.E.H. and B.J.D. are supported by the NERC DeCAdeS project (NE/T012757/1) and ESA Polar+ Ice Shelves project (ESA-IPL-POE-EF-cb-LE-2019-834). M.R.v.d.B. was supported by the Netherlands Earth System Science Centre (NESSC). J.M.v.W. was supported by the Netherlands Organisation for Scientific Research (NWO) VENI grant VI.Veni.192.083.
The authors declare no competing interests.
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Extended Data Fig. 1 Map of west Antarctic Peninsula glacier drainage basins.
Extended Data Fig. 2 West Antarctic Peninsula ice speed error map and speed interquartile range.
a) Ice velocity error map (m/yr) on the west AP and b) Glacier drainage basins70 shaded by the mean annual ice velocity interquartile range (IQR), which indicates high amplitude (red) and low amplitude (light grey) variability. Inter-annual upper ocean temperature variation is also shown, measured as the annual interquartile range of the depth averaged temperature anomaly in the top 110 m of the water column56. The REMA Antarctica 200 m DEM hill shade71, coastline (black line) and bathymetry from IBSCO v172 is shown on both maps.
Extended Data Fig. 3 Highlight glaciers time series of ice speed.
Time-series of ice speed for 8 highlight glaciers (Fig. 2a-h). Velocity tracking measurements from individual image pair are shown as grey cross and whiskers, where the central cross is the magnitude of the velocity measurement and the central date of the image pair, and the error bar is the ice velocity tracking error defined by Lemos et al. 2018 (see Methods). Red line and shading show the Kalman smoothed speed estimate (red line) with its 95% confidence interval (light red shaded area). Time-series are shown for: a) unnamed North Bone Bay (Fig. 2a, 9), b) Gavin Ice Piedmont (Fig. 2b, 11), c) Leonardo (Fig. 2c, 27), d) Hotine (Fig. 2d, 39), e) Trooz (Fig. 2e, 42), f) Keith (Fig. 2f, 58), g) Cadman (Fig. 2g, 45) and h) Fleming (Fig. 2h, 100) Glaciers. Glaciers ‘a’ to ‘f’ were selected because of their high magnitude seasonal ice speed variability, (autocorrelation values of 0.648, 0.314, 0.586, 0.703, 0.575, 0.575 respectively) while Fleming and Cadman Glaciers were selected because of their recent longer-term ice dynamic change. We showcase a range of mean ice speeds and locations across the west AP.
Extended Data Fig. 4 Highlight glacier summer and winter flow profiles.
Flow-line profile of mean summer (red) and winter (blue) ice speeds, where the 6-year long record of annual speeds (thin line) and the 6-year average (thick line) are both shown. Profiles were extracted when the annual maximum and minimum speeds are measured for each year. Data is shown for 8 highlight glaciers: Flow-line profiles are shown for: a) unnamed North Bone Bay (Fig. 2a, 9), b) Gavin Ice Piedmont (Fig. 2b, 11), c) Leonardo (Fig. 2c, 27), d) Hotine (Fig. 2d, 39), e) Trooz (Fig. 2e, 42), f) Keith (Fig. 2f, 58). Profiles are shown from 0.5 km to 5 km from the terminus.
Extended Data Fig. 5 Antarctic Peninsula scale modelled surface water flux and runoff.
Modelled surface water flux (snowmelt plus rain) (black) and water runoff (orange) for RACMO2.3p2 over the whole model Antarctic Peninsula domain (upper) and the west AP drainage basin73 (lower).
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Wallis, B.J., Hogg, A.E., van Wessem, J.M. et al. Widespread seasonal speed-up of west Antarctic Peninsula glaciers from 2014 to 2021. Nat. Geosci. 16, 231–237 (2023). https://doi.org/10.1038/s41561-023-01131-4