Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

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.

Winter Snow Depth on Arctic Sea Ice From Satellite Radiometer Measurements (2003–2020): Regional Patterns and Trends

“…SnowModel-LG shows a similar spatial distribution of h s trends but with stronger negative trends over FYI and mixed areas and with less organized positive trends over MYI area (Figure 3c). However, over a longer period from 1991 to 2015, reanalysis-based h s data set show significant positive h s trends on MYI over the north of Canadian Archipelago (Zhou et al, 2021), in agreement with this study. It is interesting to note that this positive h s trend is hardly found in the improved GR-method (Figure S5a).…”

“…In general, h s over the MYI area is found to be deeper than over the FYI area because the snow accumulation period is longer on MYI. This is also seen in other model-based and satellite-based h s products (Rostosky et al, 2018;Zhou et al, 2021). The overall mean h s distribution is generally consistent with mW99 (Figure 2d).…”

Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness

“…Uncertainty in snow depth is one of the largest error sources in sea ice thickness determination from satellite altimetry, contributing up to 50 % of the total radar altimetry thickness uncertainty (Giles et al, 2007) and potentially 70 % of laser altimetry thickness uncertainty (Zygmuntowska et al, 2014). Multiple snow depth products are available to retrieve sea ice thickness from satellite altimetry (Zhou et al, 2021). Past studies have generally used the estimation of snow depth across the Arctic from the Warren climatology (Warren et al, 1999).…”

Changes in Under‐Ice Primary Production in the Chukchi Sea From 1988 to 2018

“…Snow and melt pond thickness were computed using the mean of two Arctic Ocean snow depth models, SnowModel‐LG (Liston et al., 2020; Stroeve et al., 2020, 25 km resolution, ERA5 forcing) and CPOM (Zhou et al., 2021, 12.5 km resolution, ERA‐Interim forcing). Sea ice thickness was derived from ice age (Tschudi et al., 2019, 62.5 km resolution, ERA‐Interim forcing) by fitting a compilation of average February–March (2004–2008) ice thickness by age (Tschudi et al., 2016).…”