A 10-year record of Arctic summer sea ice freeboard from CryoSat-2

Geoffrey Dawson,Jack Landy,Michel Tsamados,Alexander S Komarov,Stephen Howell,Harry Heorton,Thomas Krumpen

doi:10.1016/j.rse.2021.112744

Abstract

Satellite observations of pan-Arctic sea ice thickness have so far been constrained to winter months. For radar altimeters, conventional methods cannot differentiate leads from meltwater ponds that accumulate at the ice surface in summer months, which is a critical step in the ice thickness calculation. Here, we use over 350 optical and synthetic aperture radar (SAR) images from the summer months to train a 1D convolution neural network for separating CryoSat-2 radar altimeter returns from sea ice floes and leads with an accuracy >80%. This enables us to generate the first pan-Arctic measurements of sea ice radar freeboard for May–September between 2011 and 2020. Results indicate that the freeboard distributions in May and September compare closely to those from a conventional ‘winter’ processor in April and October, respectively. The freeboards capture expected patterns of sea ice melt over the Arctic summer, matching well to ice draft observations from the Beaufort Gyre Exploration Program (BGEP) moorings. However, compared to airborne laser scanner freeboards from Operation IceBridge and airborne EM ice thickness surveys from the Alfred Wegener Institute (AWI) IceBird program, CryoSat-2 freeboards are underestimated by 0.02–0.2 m, and ice thickness is underestimated by 0.28–1.0 m, with the largest differences being over thicker multi-year sea ice. To create the first pan-Arctic summer sea ice thickness dataset we must address primary sources of uncertainty in the conversion from radar freeboard to ice thickness.

Highlights

Sea ice extent in the Arctic has declined at an unprecedented rate in recent decades (Stroeve and Notz, 2018), affecting polar amplification of global warming trends (Serreze et al, 2009), changes in precipitation (Webster et al, 2014) and Arctic Ocean freshwater content (Morison et al, 2012)
We used CryoSat-2 data processed from Level-1B to Level-2 for May–September 2011–2020 with the SARvatore and SARINvatore modules provided by the European Space Agency Grid Processing On Demand (GPOD) service (Dinardo et al, 2016) (Data repositories #45 and #46 available from http://wiki.services.eoportal.org/tiki-index.ph p?page=SARvatore+Data+Repository)
In this study we have presented the first estimates of pan-Arctic summer sea ice freeboard from a satellite radar or laser altimeter

Summary

Introduction

Sea ice extent in the Arctic has declined at an unprecedented rate in recent decades (Stroeve and Notz, 2018), affecting polar amplification of global warming trends (Serreze et al, 2009), changes in precipitation (Webster et al, 2014) and Arctic Ocean freshwater content (Morison et al, 2012). A sharp increase in predictability occurs at the onset of the sea ice melting sea son, when the ice-albedo feedback acts to enhance remaining thickness anomalies (Sigmond et al, 2016; Babb et al, 2019). This transition has been termed the “spring predictability barrier” and is a robust feature across most of the GCMs in CMIP5 (Bonan et al, 2019)

Methods

Results

Discussion

Conclusion