Five years of Arctic sea ice freeboard measurements from the Ice, Cloud and land Elevation Satellite

Abstract

[1] Using data from the first Earth-orbiting laser altimeter, the Geoscience Laser Altimeter System (GLAS), onboard the Ice, Cloud and land Elevation Satellite (ICESat), we analyze sea ice freeboard in the Arctic Ocean. We describe a new method for sea surface height retrieval, which relies on an algorithm that discriminates laser pulse reflections originating over leads or thin ice. The lead detection algorithm is based on surface reflectivity and analysis of parameters associated with the shape of reflected waveforms. Using knowledge of the local sea surface height and sea ice elevation, we estimate sea ice freeboard and present, for the first time, a time series of Arctic freeboard spanning 5 years between March 2003 and 2008. While the autumn (October-November) and winter (February-March) data illustrate the seasonal and interannual variations in freeboard, following the September 2007 record minimum sea ice extent, the autumn 2007 and winter 2008 spatially averaged freeboards are below the seasonal means at À4.5 cm and À6.8 cm, respectively. Over the observation period, mean freeboard has declined at a rate of $À1.8 cm/a during the autumn period and $À1.6 cm/a during the winter period, in the region bounded by the northern limit of ICESat coverage at 86°N. Because of the short 5-year observation period, it is unclear whether these results represent a long-term, downward trend in Arctic freeboard or are part of a natural variability. Furthermore, since the variability of snow thickness is included in the ICESat freeboard signal, a decrease in the freeboard cannot wholly be attributed to a decrease in sea ice thickness.

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

“…However, sea ice freeboard from ICESat will be different from the sea ice freeboard determined in this study because the height of the sea ice freeboard derived by laser altimetry (i.e., from ICESat) includes snow depth on the sea ice. Farrell et al [23] observed a slightly thicker sea ice freeboard between 2003 and 2008 (up to 0.75 m) than the 2011-2014 period in the present study. The major differences were found in the Canadian Archipelago, Northern Greenland and the central Arctic, where the freeboard was observed as being high in Farrell et al [23], while it decreased from 2011-2013 in this study.…”

“…Farrell et al [23] observed a slightly thicker sea ice freeboard between 2003 and 2008 (up to 0.75 m) than the 2011-2014 period in the present study. The major differences were found in the Canadian Archipelago, Northern Greenland and the central Arctic, where the freeboard was observed as being high in Farrell et al [23], while it decreased from 2011-2013 in this study.…”

“…The estimated sea ice thickness was validated with AEM-bird data. Accurate lead detection is crucial in estimating LSSH, which is essential to retrieve the freeboard and thickness [16,23]. The results showed that the proposed lead detection approach successfully estimated the sea ice thickness, compared to the existing methods.…”

“…This is possibly because the two studies used different smoothing approaches to waveform data, lead detection methods and gridding approaches to CryoSat-2 track data. Farrell et al [23] showed two-month averaged sea ice freeboard maps from 2003-2008 using ICESat data. However, sea ice freeboard from ICESat will be different from the sea ice freeboard determined in this study because the height of the sea ice freeboard derived by laser altimetry (i.e., from ICESat) includes snow depth on the sea ice.…”

Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data

“…Data on sea ice properties from satellite altimetry missions have been used to study the behavior of sea ice at regional to global scales (e.g., Laxon et al, 2003;Giles et al, 2008a;Kwok et al, 2009;Farrell et al, 2009;Zwally et al, 2008;Kurtz and Markus, 2012), while data from airborne remote sensing missions have been used to validate satellite data (e.g., Kurtz et al, 2008;Connor et al, 2009;Laxon et al, 2013) and identify new ways to extend the range of sea ice properties that can be studied using satellite remote sensing data (e.g., . The sea ice properties that remote sensing data sets can retrieve depend on the type of instruments used.…”