Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is
poor but is critical to obtaining sea ice thickness from satellite altimetry
measurements of the freeboard. We examine the usefulness of various snow
products to provide snow depth information over Antarctic fast ice in McMurdo
Sound with a focus on a novel approach using a high-resolution numerical snow
accumulation model (SnowModel). We compare this model to results from
ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow
depths and in situ measurements at the end of the sea ice growth season in
2011. The fast ice was segmented into three areas by fastening date and the
onset of snow accumulation was calibrated to these dates. SnowModel captures
the spatial snow distribution gradient in McMurdo Sound and falls within
2 cm snow water equivalent (s.w.e) of in situ measurements across the entire
study area. However, it exhibits deviations of 5 cm s.w.e. from these
measurements in the east where the effect of local topographic features has
caused an overestimate of snow depth in the model. AMSR-E provides s.w.e.
values half that of SnowModel for the majority of the sea ice growth season.
The coarser-resolution ERA-Interim produces a very high mean s.w.e. value
20 cm higher than the in situ measurements. These various snow datasets and
in situ information are used to infer sea ice thickness in combination with
CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal
trend of sea ice freeboard growth but thickness results are highly dependent
on what interface the retracked CS-2 height is assumed to represent. Because
of this ambiguity we vary the proportion of ice and snow that represents the
freeboard – a mathematical alteration of the radar penetration into the snow
cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice
thickness uncertainty within these bounds, as means of the entire growth season, are 1.08,
4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an
interpolated in situ snow dataset we find the best agreement between
CS-2-derived and in situ thickness when this interface is assumed to be
0.07 m below the snow surface.