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. 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.

A Lagrangian Snow‐Evolution System for Sea‐Ice Applications (SnowModel‐LG): Part I—Model Description

“…This date was chosen solely because this OIB observation year covered the most area with its flight lines. Late‐winter snow property distributions are similar for the other 37 years (Stroeve et al, 2020); the details are different, but the general conclusions are the same as those revealed by looking at the 1 April 2014 data. Domain‐average quantities from the Figure 2 panels are provided in Table 2.…”

“…These SnowModel‐LG simulations are validated against snow depth and snow density field observations in Part II of this paper (Stroeve et al, 2020). Part II also compares the model outputs with other Arctic snow‐related data sets, including those from other OIB data sets, passive microwave products, and two climatologies, and analyzes the long‐term trends in the simulated snow properties.…”

“…This assimilation used the OIB waveform fitting QuickLook product (GSFC‐NK) described by Kwok et al (2017) (NSIDC, 2016). See Stroeve et al (2020) for additional details.…”

“…This paper (Part I) describes the model, the modeling approach, the Lagrangian simulation method, the input data sets, and the general model results. A companion paper (Part II; Stroeve et al, 2020) compared the model outputs with snow depth estimates from field campaigns, ice mass balance buoys, passive microwave remote sensing, and various snow climatologies. Part II also evaluated the snow‐related differences between the two reanalyses, the simulated interannual variability, and the 38‐year trends in key snow properties.…”

Surface-based Ku- and Ka-band polarimetric radar for sea ice studies

“…Data availability. Data are available at the NERC data center (https://doi.org/10.5285/5FB5FBDE-7797-44FA-AFA6-4553B122FDEF, Stroeve et al, 2020b). On 1 January 2023, all MOSAiC data will be made publicly available with a citable DOI in a certified data repository following the FAIR Data Principles.…”