Abstract

The Greenland ice sheet is an excellent observatory for global climate change. Meltwater from the 1.8 million km2 large ice sheet influences oceanic temperature and salinity, nutrient fluxes and global sea level (IPCC 2013). Surface reflectivity is a key driver of surface melt rates (Box et al. 2012). Mapping of different ice-sheet surface types provides a clear indicator of where changes in ice-sheet surface reflectivity are most prominent. Here, we present an updated version of a surface classification algorithm that utilises NASA’s Moderate resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite to systematically monitor ice-sheet surface melt (Fausto et al. 2007). Our aim is to determine the areal extent of three surface types over the 2000–2014 period: glacier ice, melting snow (including percolation areas) and dry snow (Cuffey & Paterson 2010). Monthly 1 km2 resolution surface-type grids can be downloaded via the CryoClim internet portal (www.cryoclim.net). In this report, we briefly describe the updated classification algorithm, validation of surface types and inter-annual variability in surface types.

Highlights

  • The Greenland ice sheet is an excellent observatory for global climate change

  • The algorithm uses normalised thresholds (Th) from calibrated radiances (MOD021KM) between the near-infrared band 5 (1230–1250 nm) and the visible band 10 (483– 493 nm). This updated classification improves on Fausto et al (2007) by implementing new surface type thresholds: Thdry snow≤0.86, 0.86

  • A minimum melt extent (GSTmin) is calculated from pixels classified as dry snow at least once per month

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Summary

Classification algorithm

The algorithm uses normalised thresholds (Th) from calibrated radiances (MOD021KM) between the near-infrared band 5 (1230–1250 nm) and the visible band 10 (483– 493 nm). This updated classification improves on Fausto et al (2007) by implementing new surface type thresholds: Thdry snow≤0.86, 0.86

QAS km
Results and discussion
Conclusions
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