18 Nov 2021

18 Nov 2021

Review status: this preprint is currently under review for the journal TC.

Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic

Katrina E. Bennett1, Greta Miller1, Robert Busey2, Min Chen1, Emma R. Lathrop1, Julian B. Dann1, Mara Nutt1, Ryan Crumley1, Baptiste Dafflon3, Jitendra Kumar4, W. Robert Bolton2, and Cathy J. Wilson1 Katrina E. Bennett et al.
  • 1Los Alamos National Laboratory, Earth and Environmental Sciences, Los Alamos, NM
  • 2University of Alaska Fairbanks, International Arctic Research Center, Fairbanks, AK
  • 3Lawrence Berkeley National Laboratory, Berkeley, CA
  • 4Oak Ridge National Laboratory, Oak Ridge, TN

Abstract. The spatial distribution of snow plays a vital role in Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, for earth system modelling, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019, ~2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22,000 data points), we developed simple models of spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was the random forest using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, or TPI), while slope, wind, and fine microtopography factors were less important. The models were used to predict SWE at the locations through the study area and for all years. The characterization of the SWE spatial distribution patterns and the statistical relationships developed between SWE and its impacting factors will be used for the improvement of snow distribution modelling in the Department of Energy’s earth system model, and to improve understanding of hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic regions of the globe.

Katrina E. Bennett et al.

Status: open (until 13 Jan 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-341', Anonymous Referee #1, 25 Nov 2021 reply

Katrina E. Bennett et al.

Data sets

End-of-Winter Snow Depth, Temperature, Density, and SWE Measurements at Teller Road Site, Seward Peninsula, Alaska, 2019 Bennett, K. E., Bolton, R., Lathrop, E., Dann, J., Miller, G., Nutt, M., and Wilson, C.

End-of-Winter Snow Depth, Temperature, Density and SWE Measurements at Teller Road Site, Seward Peninsula, Alaska, 2016-2018 Wilson, C., Bolton, R., Busey, R., Lathrop, E., Dann, J., Charsley-Groffman, L., and K. E. Benentt

End-of-Winter Snow Depth, Temperature, Density and SWE Measurements at Kougarok Road Site, Seward Peninsula, Alaska, 2018 Wilson, C., Bolton, R., Busey, R., Lathrop, E., Dann, J. and K. E. Bennett

Katrina E. Bennett et al.


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Short summary
In the Arctic, climate shifts are changing ecosystems, resulting in alterations in snow, shrubs, and permafrost. Thicker snow under shrubs can lead to warmer permafrost because a deeper snow will insulate the ground from the cold winter. In this paper, we examine how snow distribution is changing and leading to deeper snow, thawing permafrost, and changing Arctic landscapes. Eventually, this work will be used to improve models of the earth used to study future changes in Arctic snow patterns.