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Snow research fills gap in Arctic climate understanding

Seasonal statistical modeling of Alaska’s snow distribution field surveys leads to a better understanding of the changing hydrology, topography and vegetation dynamics in the Arctic and Sub-Arctic. Credit: Los Alamos National Laboratory

Comprehensive data from several seasons of field research in Alaska’s Arctic will address uncertainties in the Earth system and climate change models about snow cover in the region and its effects on water and the environment.

“Snow cover and its distribution affect not only the Arctic, but also the global energy balance, so the way it changes is critical to understanding how the future global climate will shift,” said Katrina Bennett, lead author of the article in The cryosphere. Bennett is principal investigator at Los Alamos National Laboratory for the Department of Energy’s Next Generation Ecosystem Experiment Arctic project. “Our statistical model fills the gap in understanding the spatial distribution of snow.”

The study found that spatial distribution depends most strongly on vegetation, elevation and landscape features, such as stream banks and banks – areas of topographical variability where shrubs grow and snow accumulates.

The statistical model is based on random-forest machine learning and characterizes the spatial pattern of the snow distribution at the end of winter and identifies the main factors that determine the spatial distribution. The model also predicts the snow distribution for the local survey sites and can be generalized over the entire region.

Bennett said the analysis will be useful in validating physically-based hydrology models of permafrost, such as the Advanced Terrestrial Simulator developed at Los Alamos. The work will also help validate and improve snow redistribution in the land surface model within the Department of Energy’s Energy Exascale Earth System Model.

“Ultimately, it will advance our understanding of the changing hydrology, topography and vegetation dynamics in the Arctic and subarctic,” Bennett said.

Seasons in the snow

The multi-institutional research team, made up of members from Los Alamos, University of Alaska Fairbanks, Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory, and University of Wisconsin-Madison, conducted snow surveys at two small sites in the spring of 2017-2019. Seward Peninsula.

“We would like to gratefully acknowledge Mary’s Igloo, Sitnasuak and Council Native Corporation for their guidance and for allowing our research on their traditional grounds,” Bennett said.

The fieldwork focused on collecting snow depth and density measurements at the end of winter to calculate the amount of water in the snowpack. Those measurements characterize the effects of snow cover on water and temperature better than snow depth measurements.

To create a model of snow distribution, the team estimated landscape factors for topography, vegetation and wind, then quantified their impact on snow distribution using three statistical models.

Exploring the dynamics that reshape permafrost environments

More information:
Katrina E. Bennett et al, Spatial patterns of snow distribution in the subarctic region, The cryosphere (2022). DOI: 10.5194/tc-16-3269-2022

Provided by Los Alamos National Laboratory

Quote: Snow research fills gap in Arctic climate understanding (2022, Aug 17) retrieved Aug 17, 2022 from https://phys.org/news/2022-08-gap-arctic-climate.html

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