Abstract
AbstractESA's CryoSat‐2 has transformed the way we monitor Arctic sea ice, providing routine measurements of the ice thickness with near basin‐wide coverage. Past studies have shown that uncertainties in the sea ice thickness retrievals can be introduced at several steps of the processing chain, for instance, in the estimation of snow depth, and snow and sea ice densities. Here, we apply a new physical model to CryoSat‐2, which further reveals sea ice surface roughness as a key overlooked feature of the conventional retrieval process. High‐resolution airborne observations demonstrate that snow and sea ice surface topography can be better characterized by a lognormal distribution, which varies based on the ice age and surface roughness within a CryoSat‐2 footprint, than a Gaussian distribution. Based on these observations, we perform a set of simulations for the CryoSat‐2 echo waveform over “virtual” sea ice surfaces with a range of roughness and radar backscattering configurations. By accounting for the variable roughness, our new lognormal retracker produces sea ice freeboards that compare well with those derived from NASA's Operation IceBridge airborne data and extends the capability of CryoSat‐2 to profile the thinnest/smoothest sea ice and thickest/roughest ice. Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first‐year ice and 30% over multiyear ice, representing one of the principal sources of pan‐Arctic sea ice thickness uncertainty.
Highlights
Basin‐scale estimates of sea ice thickness have been produced since the early 1990s by satellite radar altimeters including the European Space Agency's ERS‐1 and ERS‐2, Envisat, and, more recently, CryoSat‐2 (Laxon et al, 2003; Laxon et al, 2013; Paul et al, 2018)
Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first‐year ice and 30% over multiyear ice, representing one of the principal sources of pan‐Arctic sea ice thickness uncertainty
It has been conventionally estimated that CryoSat‐2 has a minimum ice thickness retrieval limit of ~0.5 m, owing to the challenge measuring radar freeboards thinner than ~5 cm through radar altimetry (Ricker et al, 2017)
Summary
Basin‐scale estimates of sea ice thickness have been produced since the early 1990s by satellite radar altimeters including the European Space Agency's ERS‐1 and ERS‐2, Envisat, and, more recently, CryoSat‐2 (Laxon et al, 2003; Laxon et al, 2013; Paul et al, 2018). The empirical retracking algorithms obtain the scattering surface elevation directly from the detected waveform, rather than an ideal echo fit to the waveform, and are several orders of magnitude faster than physical retrackers (Kurtz et al, 2014) While this makes them attractive for operational sea ice thickness products, they do not account for physically realistic variations in the tracking point threshold owing to variable sea ice properties. We characterize the statistical properties of sea ice surface roughness, at both the air‐snow and snow‐ice interfaces, using airborne observations acquired as part of NASA's Operation IceBridge (OIB) campaign Building on these results, we apply a set of simulations from the model of Landy et al (2019) to develop a new algorithm (hereafter the Lognormal Altimeter Retracker Model [LARM]) for retracking. We contrast this uncertainty with other systematic uncertainties of the sea ice thickness processing chain, introduced by snow depth or density, or ice density, for instance
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