A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses

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.