Abstract
Abstract. In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.
Highlights
Snow on sea ice plays an important role in the Arctic climate system
Eight snow depth data sets are included in this intercomparison study (Table 1). They mainly fall into two categories: (1) snow reconstruction using atmospheric reanalysis data as input to a snow accumulation model together with snow redistribution by sea ice drift; (2) snow depth retrieved from satellite data, including passive-microwavebased snow retrieval, blended satellite-derived radar sea ice freeboards at two different frequencies, and active–passive satellite data synergy
The algorithm is adapted from Markus and Cavalieri (1998), which was derived from a series of passive microwave sensors, such as the Scanning Multichannel Microwave Radiometer (SMMR), and continuing on through the Special Sensor Microwave/Imager (SSM/I) and SSM/I Sounder (SSMIS)
Summary
Snow on sea ice plays an important role in the Arctic climate system. Snow provides freshwater for melt pond development and, when the melt ponds drain, freshwater to the Published by Copernicus Publications on behalf of the European Geosciences Union. With the launch of ICESat-2, additional possibilities exist to combine radar and lidar altimeters to directly retrieve snow depth (Kwok and Markus, 2018) Such approaches are paving the way for proposed future satellite missions (i.e., ESA’s CRISTAL, Kern et al, 2019). In recognition of the numerous new snow data products available, it is timely to provide an inter-comparison of these products so that recommendations can be made to the science community as to which data product best suits their needs Towards this end, we provide a comprehensive intercomparison between eight new snow depth products and evaluate them against various in situ observations and different NASA Operation IceBridge (OIB) snow depth products.
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