16 Feb 2022
16 Feb 2022
Status: a revised version of this preprint is currently under review for the journal TC.

Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection

Alek Aaron Petty1,2, Nicole Keeney1,2, Alex Cabaj3, Paul Kushner3, and Marco Bagnardi1,4 Alek Aaron Petty et al.
  • 1Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 2Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 3University of Toronto, Toronto, Canada
  • 4ADNET Systems Inc., Bethesda, MD, USA

Abstract. Reliable basin-scale estimates of sea ice thickness are urgently needed to improve our understanding of recent changes and future projections of polar climate. Data collected by NASA’s ICESat-2 mission have provided new, high-resolution, estimates of sea ice freeboard across both hemispheres since data collection started in October 2018. These data have been used in recent work to produce estimates of winter Arctic sea ice thickness using snow loading estimates from the NASA Eulerian Snow On Sea Ice Model (NESOSIM). Here we provide an impact assessment of upgrades to both the ICESat-2 freeboard data (ATL10) and NESOSIM snow loading on estimates of winter Arctic sea ice thickness. Misclassified leads were removed from the freeboard algorithm in the third release (rel003) of ICESat-2 freeboard data, which increased freeboards in January and April 2019, and increased the fraction of low freeboards in November 2018, compared to rel002. These changes improved comparisons of sea ice thickness (lower mean biases and standard deviations, higher correlations) with monthly gridded thickness estimates produced from ESA’s CryoSat-2 (using the same input snow and ice density assumptions). Later releases (rel004 and rel005) of ICESat-2 ATL10 freeboards result in less significant changes in the freeboard distributions and thus thickness. The latest version of NESOSIM (version 1.1), forced by CloudSat-scaled ERA5 snowfall, has been re-calibrated using snow depth estimates obtained by NASA’s Operation IceBridge airborne mission. The upgrade from NESOSIM v1.0 to v1.1 results in only small changes in snow depth which have a less significant impact on thickness compared to the rel002 to rel003 freeboard changes. Finally, we present our updated monthly gridded winter Arctic sea ice thickness dataset and highlight key changes over the past three winter seasons of data collection (November 2018–April 2021). Strong differences in total winter Arctic thickness across the three winters are observed, linked to clear differences in the multiyear ice thickness at the start of each winter. Interannual changes in snow depth provide significant impacts on our thickness results on regional and seasonal scales. Our analysis of recent winter Arctic sea ice thickness variability is provided online in a novel Jupyter Book format to increase transparency and user engagement with our derived gridded monthly thickness dataset.

Alek Aaron Petty 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-2022-39', Anonymous Referee #1, 06 Apr 2022
  • AC1: 'Comment on tc-2022-39', Alek Petty, 08 Apr 2022
  • RC2: 'Comment on tc-2022-39', Anonymous Referee #2, 14 Apr 2022

Alek Aaron Petty et al.

Alek Aaron Petty et al.


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Short summary
Here we show recent upgrades to freeboard measurements from NASA's ICESat-2 laser altimeter, snow depth and density from a snow reconstruction and their collective impact on estimates of total winter Arctic sea ice thickness. Our new results show better agreement with estimates obtained by ESA's CryoSat-2. We present three winters of thickness estimates across the Arctic and explore the various factors influencing the observed differences, including thinning of the older, multi-year ice.