Preprints
https://doi.org/10.5194/tc-2021-135
https://doi.org/10.5194/tc-2021-135

  25 May 2021

25 May 2021

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

Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay sea ice thickness

Isolde A. Glissenaar1, Jack C. Landy1,2, Alek A. Petty3,4, Nathan T. Kurtz3, and Julienne C. Stroeve5,6,7 Isolde A. Glissenaar et al.
  • 1Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol, UK
  • 2The Earth Observation Laboratory, Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
  • 3Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 4Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 5Centre for Earth Observation Science, University of Manitoba, Winnipeg, MB, Canada
  • 6Department of Earth Science, University College London, London, United Kingdom
  • 7National Snow and Ice Data Center, University of Colorado Boulder, Boulder, CO, USA

Abstract. In the Arctic, multi-year sea ice is being rapidly replaced by seasonal sea ice. Baffin Bay, situated between Greenland and Canada, is part of the seasonal ice zone. In this study, we present a long-term multi-mission assessment (2003–2020) of spring sea ice thickness in Baffin Bay from satellite altimetry and sea ice charts. Sea ice thickness within Baffin Bay is calculated from Envisat, ICESat, CryoSat-2 and ICESat-2 freeboard estimates, alongside a proxy from the ice chart stage of development that closely matches the altimetry data. We study the sensitivity of sea ice thickness results estimated from an array of different snow depth and snow density products and methods for redistributing low resolution snow data onto along-track altimetry freeboards. The snow depth products that are applied include a reference estimated from the Warren climatology, a passive microwave snow depth product, and the dynamic snow scheme SnowModel-LG. We find that applying snow depth redistribution to represent small-scale snow variability has a considerable impact on ice thickness calculations from laser freeboards but was unnecessary for radar freeboards. Decisions on which snow loading product to use and whether to apply snow redistribution can lead to different conclusions on trends and physical mechanisms. For instance, we find an uncertainty envelope around the March mean sea ice thickness of 13 % for different snow depth/density products and redistribution methods. Consequently, trends in March sea ice thickness from 2003–2020 range from −23 cm/dec to 17 cm/dec, depending on which snow depth/density product and redistribution method is applied. Over a longer timescale, since 1996, the proxy ice chart thickness product demonstrates statistically significant thinning within Baffin Bay of 7 cm/dec. Our study provides further evidence for long-term asymmetrical trends in Baffin Bay sea ice thickness (with −17.6 cm/dec thinning in the west and 10.8 cm/dec thickening in the east of the bay) since 2003. This asymmetrical thinning is consistent for all combinations of snow product and processing method, but it is unclear what may have driven these changes.

Isolde A. Glissenaar et al.

Status: open (until 20 Jul 2021)

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Isolde A. Glissenaar et al.

Data sets

Sea ice thickness and snow depth in Baffin Bay (March 2003-2020) Isolde Glissenaar https://data.bris.ac.uk/data/dataset/2peywz756l8182cpwlanm4ratz

Isolde A. Glissenaar et al.

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Short summary
Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.