Abstract

Reviewer comments on tc-2021-4, "Supraglacial lake bathymetry automatically derived from ICESat-2 constraining lake depth estimates from multi-source satellite imagery"

Highlights

  • Ice loss from Greenland and Antarctica is the greatest current contributor to rising sea levels, and paleodata and modelling efforts indicate that enhanced mass loss of these ice sheets may become irreversible if certain major tipping points are passed (IPCC 2019, Special Report on the Ocean and Cryosphere in a Changing Climate)

  • Largely driven by increased ocean melting of outlet glaciers (Rignot, 2019), while on the Greenland Ice Sheet mass loss is further promoted by increased surface melt and runoff (Mouginot, 2019)

  • In the summer of 2019, 35 advection of warm, wet mid-latitude air led to a summer mass loss unprecedented in the past 50 years, with widespread surface melt occurring up to the highest regions of the ice sheet (Tedesco and Fettweis, 2020; Sasgen, 2020)

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Summary

Introduction

Ice loss from Greenland and Antarctica is the greatest current contributor to rising sea levels, and paleodata and modelling efforts indicate that enhanced mass loss of these ice sheets may become irreversible if certain major tipping points are passed (IPCC 2019, Special Report on the Ocean and Cryosphere in a Changing Climate). Concurrent with the increase in melt extent and duration, supraglacial lakes - which form when meltwater runoff collects in local topographic lows - are a common feature on large parts of the ice sheets and have become more extensive and have advanced inland toward higher elevations in the past decades (Gledhill and Williamson, 2018; Leeson, 2015; Howat, 2013). Past remote-sensing work has derived lake volumes from high-resolution (~1m) Worldview imagery using a physical optical depth approach as well as an empirical method using in-situ estimates (Moussavi, 2016; Pope, 2016). In addition to bathymetry (supraglacial lake depth) derived from the difference between the air-water and water-ice interface, this algorithm assigns a probability for surface type characteristics to photon returns along-track. We present initial results exploiting this dataset as well as introducing the Watta ICESat-2 surface feature detection algorithm

85 Figure 1
High-resolution imagery near Sermeq Kujalleq
Methods
Imagery Processing
Evaluating lake depths calculated from Watta using ICESat-2 ATL03
Evaluating Data Sources for Imagery-based Depths
Capturing Lake Drainage over the Melt Season
Drainage Mechanisms over Lake
Conclusions
470 Acknowledgements
Full Text
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