Preprints
https://doi.org/10.5194/tc-2021-177
https://doi.org/10.5194/tc-2021-177

  13 Jul 2021

13 Jul 2021

Review status: this preprint is currently under review for the journal TC.

Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

Peter A. Tuckett1, Jeremy C. Ely1, Andrew J. Sole1, James M. Lea2, Stephen J. Livingstone1, Julie M. Jones1, and J. Melchior van Wessem3 Peter A. Tuckett et al.
  • 1Department of Geography, University of Sheffield, Sheffield, S3 7ND, UK
  • 2Department of Geography, University of Liverpool, Liverpool, UK
  • 3Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands

Abstract. Surface meltwater is widespread around the margin of the Antarctic Ice Sheet and has the potential to influence ice-shelf stability, ice-dynamic processes and ice-albedo feedbacks. Whilst the general spatial distribution of surface meltwater across the Antarctic continent is now relatively well known, our understanding of the seasonal and multi-year evolution of surface meltwater is limited. Attempts to generate robust time series of melt cover have largely been constrained by computational expense or limited ice surface visibility associated with mapping from optical satellite imagery. Here, we implement an existing meltwater detection method alongside a novel method for calculating visibility metrics within Google Earth Engine. This enables us to quantify uncertainty induced by cloud cover and variable image data coverage, allowing us to automatically generate time series of surface melt area over large spatial and temporal scales. We demonstrate our method on the Amery Ice Shelf region of East Antarctica, analysing 4,164 Landsat 7 and 8 optical images between 2005 and 2020. Results show high interannual variability in surface meltwater cover, with mapped cumulative lake area totals ranging from 384 km2 to 3898 km2 per melt season. However, by incorporating image visibility assessments into our results, we estimate that cumulative total lake areas are on average 42 % higher than minimum mapped values, highlighting the importance of accounting for variations in image visibility when mapping lake areas. In a typical melt season, total lake area remains low throughout November and early December, before increasing, on average, by an order of magnitude during the second half of December. Peak lake area most commonly occurs during January, before decreasing during February as lakes freeze over. We show that modelled melt predictions from a regional climate model provides a good indication of lake cover in the Amery region, and that annual lake coverage is strongly associated with phases of the Southern Annular Mode (SAM); surface melt is typically highest in years with a negative austral summer SAM index. Furthermore, we suggest that melt-albedo feedbacks modulate the spatial distribution of meltwater in the region, with the exposure of blue ice from persistent katabatic wind scouring influencing the susceptibility of melt ponding. Results demonstrate how our method could be scaled up to generate a multi-year time series record of surface water extent from optical imagery at a continent-wide scale.

Peter A. Tuckett et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-177', Sammie Buzzard, 17 Aug 2021
    • AC1: 'Reply on RC1', Peter Tuckett, 11 Oct 2021
  • RC2: 'Comment on tc-2021-177', Anonymous Referee #2, 23 Aug 2021
    • AC2: 'Reply on RC2', Peter Tuckett, 11 Oct 2021

Peter A. Tuckett et al.

Peter A. Tuckett et al.

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Short summary
Lakes form on the surface of the Antarctic Ice Sheet during the summer. These lakes can generate further melt, break up floating ice shelves and alter ice dynamics. Here, we describe a new automated method for mapping surface lakes, and apply our technique to the Amery Ice Shelf between 2005 and 2020. Lake area is highly variable between years, driven by large-scale climate patterns. This technique will help us understand the role of Antarctic surface lakes in our warming world.