A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses. Issue 10 (1st October 2020)

• Record Type: Journal Article
• Authors: Stroeve, Julienne; Liston, Glen E.; Buzzard, Samantha; Zhou, Lu; Mallett, Robbie; Barrett, Andrew; Tschudi, Mark; Tsamados, Michel; Itkin, Polona; Stewart, J. Scott
• Is Part Of: Journal of geophysical research. Volume 125:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-01
• Subjects: snow on sea ice -- Arctic -- climate change; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2019JC015900
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response to this data gap, a new Lagrangian snow evolution model (SnowModel‐LG) was developed to simulate snow depth, density, and grain size on a pan‐Arctic scale, daily from August 1980 through July 2018. In this study, we evaluate the results from this effort against various data sets, including those from Operation IceBridge, ice mass balance buoys, snow buoys, MagnaProbes, and rulers. We further compare modeled snow depths forced by two reanalysis products (Modern Era Retrospective‐Analysis for Research and Applications, Version 2 and European Centre for Medium‐Range Weather Forecasts Reanalysis, 5th Generation) with those from two historical climatologies, as well as estimates over first‐year and multiyear ice from satellite passive microwave observations. Our results highlight the ability of our SnowModel‐LG implementation to capture observed spatial and seasonal variability in Arctic snow depth and density, as well as the sensitivity to the choice of reanalysis system used to simulate snow depths. Since 1980, snow depth is found to decrease throughout most regions of the Arctic Ocean, with statistically significant trends during the cold season months in the marginal ice zones around the Arctic Ocean and slight positive trends north of Greenland and near the pole. Plain Language Summary: This study evaluates a new snow accumulation model to simulate both realistic snow depth and snow density distributions over Arctic sea ice, filling a critical data gap for polar science. Running the model from August 1980 onward, snow depth is found to be declining as the melt season has lengthened, shortening the time over which snow can accumulate on the ice. This new daily snow and density product will be useful for climate studies as well as improving sea ice thickness retrievals from Satellite radar and laser altimeters. Key Points: A new Arctic snow depth and density product is available for sea ice thickness retrievals from August 1980 to July 2018 Snow depth compares well with other snow depth representations Arctic snow depth is declining over time