Inter-comparison of snow depth over sea ice from multiple methods

Abstract

<jats:p>Abstract. In this study, we compare eight recently developed snow depth products that 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 buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations. Large snow depth discrepancies between the different snow depth data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest spring (March-April) snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest spring snow depths. There is no significant trend in the mean snow depth among all snow products since the 2000s, despite the great differences in regional snow depth. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model (NESOSIM), also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. The snow density in SnowModel-LG is generally higher than climatology, whereas NESOSIM density is generally lower. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology. Inconsistencies in the reconstructed snow parameters among the products, as well as differences between in-situ and airborne observations can in part be 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. </jats:p>

Morphological approaches to understanding Antarctic Sea ice thickness

“…Kwok and Maksym (2014) found that snow depths sampled in situ may be biased due to excessively thin and thick ice not being logistically feasible to sample, while radar-based measurements cannot resolve snow depths below 8 cm, and often have poor returns around deformed ice. Zhou et al (2020) recently showed that different models for snow depth can give high variations for snow density and depth, and in some cases this can be higher than climatology. Similarly, Mallett et al (2020) showed that assuming a constant snow density, and hence a constant radar penetration speed, biases the snow depth estimate.…”

A Textural Approach to Improving Snow Depth Estimates in the Weddell Sea

“…Kwok and Maksym [9] found that snow depths sampled in situ may be biased due to excessively thin and thick ice not being logistically feasible to sample, while radar-based measurements cannot resolve snow depths below 8 cm. Zhou et al [13] recently showed that different models for snow depth can give high variations for snow density and depth, and in some cases this can be higher than climatology. Similarly, Mallett et al [14] showed that assuming a constant snow density, and hence a constant radar penetration speed, biases the snow depth estimate.…”