Supervised by:

Alexandre Langlois (Regular member)

Research project description

Weaving traditional and local knowledge with western science to identify drivers of rain-on-snow events and improve snow characterization in the Arctic

IntroductionClimate change is affecting human well-being and health around the globe, but its effect is most pronounced in the Arctic where the observed warming is three times greater than the rest of the planet (Rantanen et al., 2022). The climate changes are increasing the frequency of extreme weather events, including rain-on-snow (ROS) (Il Jeong & Sushama, 2018; Sobota et al., 2020). ROS events have a significant impact on the structure of the snowpack, leading to the formation of ice crusts that have a direct impact on wildlife (Dolant et al., 2018; Kaluskar et al., 2020). Despite recent progress in assessing and measuring snow cover changes in the Arctic (Dolant et al., 2017; Gautier et al., 2022; Ouellet et al., 2016), data at high spatial and temporal resolutions are still missing or incomplete in order to fully understand the impact of climate change on northern snow, wildlife, and communities (Gagnon et al., 2020; Taylor et al., 2020).ObjectivesThe primary objective of the research is to improve knowledge of the drivers of ROS events and the impacts of climate change on the spatial and temporal variability of Arctic snow cover. The study also aims to identify how these changes may affect Peary caribou (Rangifer tarandus pearyi) (Johnson et al., 2016) and Svalbard reindeer (Rangifer tarandus platyrhynchus) (Kaltenborn et al., 2014). The specific objectives are to: 1-identify the environmental and meteorological drivers of Arctic ROS and assess the correlation with the creation of ice crusts in the snowpack; 2-identify the snow conditions that are favorable and unfavorable for caribou and reindeer, and analyze the spatio-temporal trends of snow characteristics in the Arctic. Study sitesThis research focuses on two Arctic regions in particular: the Canadian Arctic Archipelago, which encompasses the range of Peary caribou (Rangifer tarandus pearyi), and Svalbard, a Norwegian archipelago in the Arctic Ocean with populations of Svalbard reindeer (Rangifer tarandus platyrhynchus). Workshops with northern community members and snow measurements will be conducted at two main sites. The first site is at Ikaluktutiak (Cambridge Bay) in Nunavut, which is host to the Canadian High Arctic Research Station. A second field campaign will be conducted in Longyearbyen, Svalbard, in partnership with the University of Svalbard.Material and methodsTo address the first objective, rain-on-snow and ice layer data derived from passive microwave remote sensing data will be used (Dolant et al., 2016, 2017; Langlois et al., 2017). Sea ice maps will be used to assess the correlation between ROS and sea ice conditions (Gautier et al., 2022). Atmospheric reanalysis models (Graham et al., 2019) and weather station data will identify meteorological drivers (Voveris, 2022). To address the second objective, workshops with northern communities will help identify favorable and unfavorable snow conditions for ungulates. The SNOWPACK model (Bartelt & Lehning, 2002) and a model spatialization tool (Ouellet et al., 2016) will be used to analyze spatiotemporal snow trends. Field measurements and local knowledge will be used to validate the results obtained for all objectives. ReferencesDolant, C., Langlois, A., Montpetit, B., Brucker, L., Roy, A., & Royer, A. (2016). Development of a rain-on-snow detection algorithm using passive microwave radiometry. Hydrological Processes, 30(18), 3184‑3196. Johnson, C. A., Neave, E., Blukacz-Richards, E. A., Banks, S. N.,

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