The NASA Eulerian Snow on Sea Ice Model (NESOSIM): Initial model
development and analysis

Petty, Alek; Webster, Melinda; Boisvert, Linette N.; Markus, T.

doi:10.5194/gmd-2018-84

Cited by 7 publications

(18 citation statements)

References 34 publications

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“…Further, they run their model at much coarser spatial resolution (75 versus 25 km). Interestingly, both our snow depth and those from Petty et al (2018) show a region of less snow northeast of Greenland and less snow in the Laptev Sea compared to Blanchard‐Wrigglesworth et al (2018).…”

Section: Discussionsupporting

confidence: 57%

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: Discussionsupporting

confidence: 57%

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

et al. 2020

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“…These have produced more realistic snow depth estimates than those defined by the climatologies alone. The first physically based modeling approaches (e.g., Blanchard‐Wrigglesworth et al, 2018; Kwok & Cunningham, 2008; Petty et al, 2018) provided an attractive alternative to using climatologies, particularly in a rapidly changing climate system.…”

Section: Introductionmentioning

confidence: 99%

“…These initial modeling approaches took two paths: (1) they used a single atmospheric reanalysis data set and performed a simple snow accumulation using a temperature threshold to define snowfall from water‐equivalent precipitation inputs, prescribed the snow density using Warren et al (1999) monthly climatologies, and used a Lagrangian parcel‐tracking framework (Blanchard‐Wrigglesworth et al, 2018; Kwok & Cunningham, 2008; Webster et al, 2019), or (2) they implemented a simple two‐layer, winter‐only (no snow melt), snow‐budget model in a Eulerian framework with multiple reanalysis data sets (Petty et al, 2018). Although undeniably superior to using the Warren et al (1999) climatology, these past attempts were performed at relatively coarse resolutions of 75 (Blanchard‐Wrigglesworth et al, 2018) and 100 km (Petty et al, 2018). While these resolutions are likely adequate to capture synoptic‐scale meteorological forcing conditions, they are insufficient to represent ice dynamical processes that also play an important role in snow evolution in sea ice environments.…”

Section: Introductionmentioning

confidence: 99%

“…While these resolutions are likely adequate to capture synoptic‐scale meteorological forcing conditions, they are insufficient to represent ice dynamical processes that also play an important role in snow evolution in sea ice environments. Petty et al (2018) used several reanalyses to account for important biases in precipitation and temperature that exist among the different reanalysis data sets (Barrett et al, 2020; Boisvert et al, 2018). Kwok et al (2017) and Boisvert et al (2018) used airborne data to analyze snow depth information over specific flight lines and parcel trajectories.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution.

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“…Despite recent and significant developments in pan-Arctic scale snow density modelling (e.g. Stroeve et al, In Revision;Petty et al, 2018b), the Arctic snow density distribution remains poorly constrained in time and space. Because of this, representative values for pan-Arctic average snow density are often combined with the snow depth distributions from W99 to calculate the radar wave propagation correction (Kurtz et al, 2014;Hendricks et al, 2016;Tilling et al, 2018).…”

mentioning

confidence: 99%

Brief Communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

Mallett¹,

Lawrence²,

Stroeve³

et al. 2019

Preprint

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Abstract. Pan-Arctic sea ice thickness has been monitored over recent decades by satellite radar altimeters such as CryoSat-2, which emit Ku-band radar waves that are conventionally assumed to penetrate overlying snow and scatter from the ice-snow interface. Here we examine two expressions for the time delay caused by slower radar wave propagation through the snow layer and related assumptions concerning the time-evolution of overlying snow density. Two conventional treatments lead to systematic underestimates of winter ice thickness and thermodynamic growth rate of up to 15 cm over multiyear ice. Correcting these biases would improve the accuracy of sea ice thickness products, which feed a wide variety of model projections, calibrations, validations and reanalyses.

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The NASA Eulerian Snow on Sea Ice Model (NESOSIM): Initial model development and analysis

Cited by 7 publications

References 34 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

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

Brief Communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates

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