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Knowledge of contemporaneous snow depth on Arctic sea ice is important both to constrain the regional climatology and to improve the accuracy of satellite altimeter estimates of sea ice thickness. We assess new data available from the NASA Operation IceBridge snow radar instrument and derive snow depth estimates across the western Arctic ice pack using a novel methodology based on wavelet techniques that define the primary reflecting surfaces within the snow pack. We assign uncertainty to the snow depth estimates based upon both the radar system parameters and sea ice topographic variability. The accuracy of the airborne snow depth estimates are examined via comparison with coincident measurements gathered in situ across a range of ice types in the Beaufort Sea. We discuss the effect of surface morphology on the derivation, and consequently the accuracy, of airborne snow depth estimates. We find that snow depths derived from the airborne snow radar using the wavelet-based technique are accurate to 1 cm over level ice. Over rougher surfaces including multiyear and ridged ice, the radar system is impacted by ice surface morphology. Across basin scales, we find the snow-radar-derived snow depth on first-year ice is at least 60% of the value reported in the snow climatology for the Beaufort Sea, Canada Basin, and parts of the central Arctic, since these regions were previously dominated by multiyear ice during the measurement period of the climatology. Snow on multiyear ice is more consistent with the climatology.

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Pressure ridges impact the mass, energy and momentum budgets of the sea-ice cover and present an obstacle to transportation through ice-infested waters. Quantifying ridge characteristics is important for understanding total sea-ice mass and for improving the representation of sea-ice dynamics in high-resolution models. Multi-sensor measurements collected during annual Operation IceBridge (OIB) airborne surveys of the Arctic provide new opportunities to assess the sea ice at the end of winter. We present a new methodology to derive ridge sail height from high-resolution OIB Digital Mapping System (DMS) visible imagery. We assess the efficacy of the methodology by mapping the full sail height distribution along 12 pressure ridges in the western and central Arctic. Comparisons against coincident Airborne Topographic Mapper (ATM) elevation anomalies are used to demonstrate the methodology and evaluate DMS-derived sail heights. Sail heights and elevation anomalies were correlated at 0.81 or above. On average mean and maximum sail height agreed with ATM elevation to within 0.11 and 0.49 m, respectively. Of the ridges mapped, mean sail height ranged from 0.99 to 2.16 m, while maximum sail height ranged from 2.1 to 4.8 m. DMS also delivered higher sampling along ridge crests than coincident ATM data.

ABSTRACT. With the conclusion of the science phase of the Ice, Cloud and land Elevation Satellite (ICESat) mission in late 2009, and the planned launch of ICESat-2 in late 2015, NASA has recently established the IceBridge program to provide continuity between missions. A major goal of IceBridge is to obtain a sea-ice thickness time series via airborne surveys over the Arctic and Southern Oceans. Typically two laser altimeters, the Airborne Topographic Mapper (ATM) and the Land, Vegetation and Ice Sensor (LVIS), are utilized during IceBridge flights. Using laser altimetry simulations of conventional analogue systems such as ICESat, LVIS and ATM, with the multi-beam system proposed for ICESat-2, we investigate differences in measurements gathered at varying spatial resolutions and the impact on seaice freeboard. We assess the ability of each system to reproduce the elevation distributions of two seaice models and discuss potential biases in lead detection and sea-surface elevation, arising from variable footprint size and spacing. The conventio nal systems accurately reproduce mean freeboard over 25 km length scales, while ICESat-2 offers considerable improvements over its predecessor ICESat. In particular, its dense along-track sampling of the surface will allow flexibility in the algorithmic approaches taken to optimize the signal-to-noise ratio for accurate and precise freeboard retrieval.