Abstract. The study of sea ice using airborne remote sensing platforms provides unique capabilities to measure a wide variety of sea ice properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in sea ice properties. In this paper we describe methods for the retrieval of sea ice thickness, freeboard, and snow depth using data from a multisensor suite of instruments on NASA's Operation IceBridge airborne campaign. We assess the consistency of the results through comparison with independent data sets that demonstrate that the IceBridge products are capable of providing a reliable record of snow depth and sea ice thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing IceBridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved sea ice thickness. Lastly, we present results for the 2009 and 2010 IceBridge campaigns, which are currently available in product form via the National Snow and Ice Data Center.

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[1] During the spring of 2009, an ultrawideband microwave radar was deployed as part of Operation IceBridge to provide the first cross-basin surveys of snow thickness over Arctic sea ice. In this paper, we analyze data from three ∼2000 km transects to examine detection issues, the limitations of the current instrument, and the regional variability of the retrieved snow depth. Snow depth is the vertical distance between the air-snow and snow-ice interfaces detected in the radar echograms. Under ideal conditions, the per echogram uncertainty in snow depth retrieval is ∼4-5 cm. The finite range resolution of the radar (∼5 cm) and the relative amplitude of backscatter from the two interfaces limit the direct retrieval of snow depths much below ∼8 cm. Well-defined interfaces are observed over only relatively smooth surfaces within the radar footprint of ∼6.5 m. Sampling is thus restricted to undeformed, level ice. In early April, mean snow depths are 28.5 ± 16.6 cm and 41.0 ± 22.2 cm over first-year and multiyear sea ice (MYI), respectively. Regionally, snow thickness is thinner and quite uniform over the large expanse of seasonal ice in the Beaufort Sea, and gets progressively thicker toward the MYI cover north of Ellesmere Island, Greenland, and the Fram Strait. Snow depth over MYI is comparable to that reported in the climatology by Warren et al. (1999). Ongoing improvements to the radar system and the utility of these snow depth measurements are discussed.

ABSTRACT. Sea ice is generally covered with snow, which can vary in thickness from a few centimeters to >1 m. Snow cover acts as a thermal insulator modulating the heat exchange between the ocean and the atmosphere, and it impacts sea-ice growth rates and overall thickness, a key indicator of climate change in polar regions. Snow depth is required to estimate sea-ice thickness using freeboard measurements made with satellite altimeters. The snow cover also acts as a mechanical load that depresses ice freeboard (snow and ice above sea level). Freeboard depression can result in flooding of the snow/ice interface and the formation of a thick slush layer, particularly in the Antarctic sea-ice cover. The Center for Remote Sensing of Ice Sheets (CReSIS) has developed an ultra-wideband, microwave radar capable of operation on long-endurance aircraft to characterize the thickness of snow over sea ice. The low-power, 100 mW signal is swept from 2 to 8 GHz allowing the air/snow and snow/ ice interfaces to be mapped with 5 cm range resolution in snow; this is an improvement over the original system that worked from 2 to 6.5 GHz. From 2009 to 2012, CReSIS successfully operated the radar on the NASA P-3B and DC-8 aircraft to collect data on snow-covered sea ice in the Arctic and Antarctic for NASA Operation IceBridge. The radar was found capable of snow depth retrievals ranging from 10 cm to >1 m. We also demonstrated that this radar can be used to map near-surface internal layers in polar firn with fine range resolution. Here we describe the instrument design, characteristics and performance of the radar.

Abstract. Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2-6.5 GHz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semiautomated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009-2012. The uncertainty in these radar-derived accumulation rates is on average 14 %. A comparison of the radarderived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and longterm mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.

Abstract. Increased surface melt over the Greenland Ice Sheet (GrIS) is now estimated to account for half or more of the ice sheet's total mass loss. Here, we show that some meltwater is stored, over winter, in buried supraglacial lakes. We use airborne radar from Operation IceBridge between 2009 and 2012 to detect buried supraglacial lakes, and we find that they were distributed extensively around the GrIS margin through that period. Buried supraglacial lakes can persist through multiple winters and are, on average, ∼ 1.9 + 0.2 m below the surface. Most buried supraglacial lakes exist with no surface expression of their occurrence in visible imagery. The few buried supraglacial lakes that do exhibit surface expression have a unique visible signature associated with a darker blue color where subsurface water is located. The volume of retained water in the buried supraglacial lakes is likely insignificant compared to the total mass loss from the GrIS, but the water may have important implications locally for the development of the englacial hydrologic system and ice temperatures. Buried supraglacial lakes represent a small but year-round source of meltwater in the GrIS hydrologic system.

Abstract. Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet (GrIS) through increasing surface melt, emphasizing the need to closely monitor surface mass balance (SMB) in order to improve sea-level rise predictions. Here, we quantify accumulation rates, the largest component of GrIS SMB, at a higher spatial resolution than currently available, using Snow Radar stratigraphy. We use a semi-automated method to derive annual-net accumulation rates from airborne Snow Radar data collected by NASA's Operation IceBridge from 2009 to 2012. An initial comparison of the accumulation rates from the Snow Radar and the outputs of a regional climate model (MAR) shows that, in general, the radar-derived accumulation matches closely with MAR in the interior of the ice sheet but MAR estimates are high over the southeast GrIS. Comparing the radar-derived accumulation with contemporaneous ice cores reveals that the radar captures the annual and long-term mean. The radar-derived accumulation rates resolve large-scale patterns across the GrIS with uncertainties of up to 11 %, attributed mostly to uncertainty in the snow/firn density profile.