Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

Moon, Wonkyu; Nandan, Vishnu; Scharien, Randall K.; Wilkinson, Jeremy; Yackel, John J.; Barrett, A. P.; Lawrence, Isobel; Segal, Rebecca A.; Stroeve, Julienne; Mahmud, Mallik; Duke, Patrick J.; Else, Brent

doi:10.1088/1748-9326/ab3b8d

Cited by 18 publications

(21 citation statements)

References 46 publications

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“…Given the variable nature of snow over sea ice, especially over deformed sea ice, the IMB and snow buoy snow depths will not be representative of the coarse 25-km SnowModel-LG grid cell. Even over nondeformed areas, or over smooth landfast ice, there can be several meters' length scale related to the formation of snow dunes (Moon et al, 2019). Considering that the dominant errors between buoy data and model outputs are the initial conditions, rather than the snow depth evolution, we set the first buoy observation equal to the average of the MERRA-2 and ERA5 snow depth values LG simulated snow depth evolution during N-ICE2015, using MERRA-2 (blue) and ERA-5 (green) atmospheric forcing.…”

Section: 1029/2019jc015900mentioning

confidence: 99%

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

et al. 2020

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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.

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Section: 1029/2019jc015900mentioning

confidence: 99%

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

et al. 2020

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“…The observed variations showed a periodicity common to length scales associated with snow dunes or interactions between drifted elements (Sturm et. al, 2002;Moon et al, 2019). In the future it would be instructive to evaluate how information on surface https://doi.org/10.5194/tc-2019-305 Preprint.…”

Section: Discussionmentioning

confidence: 99%

Local-scale variability of snow density on Arctic sea ice

King¹,

Howell²,

Brady³

et al. 2020

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Abstract. Local-scale variations in snow density and layering on Arctic sea ice were characterized using a combination of traditional snow pit and SnowMicroPen (SMP) measurements. In total, 14 sites were evaluated within the Canadian Arctic Archipelago and Arctic Ocean on both first (FYI) and multi-year (MYI) sea ice. Sites contained multiple snow pits with coincident SMP profiles as well as unidirectional SMP transects. An existing SMP density model was recalibrated using manual density cutter measurements (n = 186) to identify best-fit parameters for the observed conditions. Cross-validation of the revised SMP model showed errors comparable to the expected baseline for manual density measurements (RMSE = 34 kg m−3 or 10.9 %) and strong retrieval skill (R2 = 0.78). The density model was then applied to SMP transect measurements to characterize variations at spatial scales of up to 100 m. A supervised classification trained on snow pit stratigraphy allowed separation of the SMP density estimates by layer-type. The resulting dataset contains 58,882 layer-classified estimates of snow density on sea ice representing 147 m of vertical variation and equivalent to more than 600 individual snow pits. An average bulk density of 310 kg m−3 was estimated with clear separation between FYI and MYI environments. Lower densities on MYI (277 kg m−3) corresponded with increased depth hoar composition (49.2 %), in strong contrast to composition of the thin FYI snowpack (19.8 %). Spatial auto-correlation analysis showed layered composition on FYI snowpack to persist over long distances while composition on MYI rapidly decorrelated at distances less than 16 m. Application of the SMP profiles to determine propagation bias in radar altimetry showed the potential errors of 0.5 cm when climatology is used over known snow density.

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“…While these data were routinely collected to support interpretation of the radar backscatter, snow on sea ice is spatially variable at a variety of scales as wind redistribution results in the formation of snow dunes and bedforms (Moon et al, 2019;Filhol and Sturm, 2015). Further, different ice types (i.e., FYI vs. MYI) have different temporal evolutions of snow depth.…”

Section: Kuka Radar Setup and Deploymentmentioning

confidence: 99%

“…While younger ice tends to be thinner and more dynamic, much less is known about how thickness and volume are changing. Accurate ice thickness monitoring is essential for heat and momentum budgets, ocean properties, and the timing of sea ice algae and phytoplankton blooms (Bluhm et al, 2017;Mundy et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Abstract. To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as to investigate the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC international Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and as a scatterometer (scan mode), provided the first in situ Ku- and Ka-band dual-frequency radar observations from autumn freeze-up through midwinter and covering newly formed ice in leads and first-year and second-year ice floes. Data gathered in the altimeter mode will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angles, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar, illustrate examples of its data and demonstrate their potential for these investigations.

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Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

Cited by 18 publications

References 46 publications

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

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

Local-scale variability of snow density on Arctic sea ice

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

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