Preprints
https://doi.org/10.5194/tc-2021-300
https://doi.org/10.5194/tc-2021-300

  21 Sep 2021

21 Sep 2021

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

Brief communication: Estimating the ice thickness of the Müller Ice Cap using an inversion of the shallow ice approximation

Ann-Sofie Priergaard Zinck1,2 and Aslak Grinsted2 Ann-Sofie Priergaard Zinck and Aslak Grinsted
  • 1Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, Netherlands
  • 2Physics of Ice, Climate and Earth, University of Copenhagen, Copenhagen, Denmark

Abstract. The Müller Ice Cap will soon set the scene for a new drilling project. Therefore, ice thickness estimates are necessary for planning since thickness measurements of the ice cap are sparse. Here, two models are presented and compared, i) a simple inversion of the shallow ice approximation (SIA inversion) by the use of a single radar line in combination with the glacier outline, surface slope, and elevation, and ii) an iterative inverse method using the Parallel Ice Sheet Model (PISM). The two methods mostly agree about a good drill site candidate. However, the new semi-empirical SIA inversion is insensitive to mass balance, computationally fast, and provides better fits.

Ann-Sofie Priergaard Zinck and Aslak Grinsted

Status: open (until 16 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-300', Anonymous Referee #1, 25 Oct 2021 reply

Ann-Sofie Priergaard Zinck and Aslak Grinsted

Ann-Sofie Priergaard Zinck and Aslak Grinsted

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Short summary
The Müller Ice Cap will soon set the scene for a new drilling project. To obtain an ice core with stratified layers and good time resolution, thickness estimates are necessary for the planning. Here we present a new and fast method of estimating ice thicknesses from sparse data, and compares it to an existing ice flow model. We find that the new semi-empirical method is insensitive to mass balance, computationally fast, and provides better fits than the other method.