Preprints
https://doi.org/10.5194/tc-2022-94
https://doi.org/10.5194/tc-2022-94
 
11 May 2022
11 May 2022
Status: this preprint is currently under review for the journal TC.

Surface melt on the Shackleton Ice Shelf, East Antarctica (2003–2021)

Dominic Saunderson1, Andrew Mackintosh1, Felicity McCormack1, Richard Selwyn Jones1, and Ghislain Picard2,3 Dominic Saunderson et al.
  • 1School of Earth, Atmosphere and Environment, Monash University, Melbourne, VIC, Australia
  • 2Univ. Grenoble Alpes, CNRS, Institut des Géosciences de l’Environnement (IGE), UMR 5001, Grenoble, France
  • 3Geological Survey of Denmark and Greenland (GEUS), 1350 Copenhagen, Denmark

Abstract. Many ice shelves in Antarctica experience surface melt each summer, with potentially severe consequences for sea level rise. However, large interannual and regional variability in surface melt increases uncertainty in predictions of how ice shelves will react to climate change. Previous studies of surface melt have usually focused on either a process-level understanding of surface melt through energy balance investigations, or used regional melt metrics to quantify interannual variability in satellite observations of surface melt. Here, we use an approach that helps bridge the gap between work at these two scales. Using daily passive microwave observations from the AMSR-E and AMSR-2 sensors, and the machine learning approach of a self-organising map, we identify nine representative spatial distributions (“patterns”) of surface melt on the Shackleton Ice Shelf, East Antarctica, over the previous two decades (2002/03–2020/21). Our results point to a significant role for surface air temperatures in controlling the interannual variability of summer melt, and also reveal the influence of local controls on driving melt. In particular, prolonged melt in the south-east of the shelf and along the grounding line shows the importance of katabatic winds and surface albedo. Our approach highlights the necessity of understanding both local and large-scale controls on surface melt, and demonstrates that self-organising maps can be used to investigate the variability of surface melt on Antarctic ice shelves.

Dominic Saunderson et al.

Status: open (until 06 Jul 2022)

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Dominic Saunderson et al.

Data sets

Processed Melt Data and R Analysis Code Dominic Saunderson https://github.com/polarSaunderson/ShackletonSOM

Dominic Saunderson et al.

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Short summary
We investigate the variability of surface melt on the Shackleton Ice Shelf in East Antarctica over the last two decades (2003–2021). We use daily satellite observations and a machine learning approach called a self-organising map to identify nine common spatial patterns of melt. These patterns allow comparisons of melt within and across melt seasons, and highlight the importance of local controls such as topography, katabatic winds, and albedo on driving surface melt.