Interactions between atmospheric warming, local surface conditions, and ground temperatures complicate efforts to predict future permafrost changes in regions such as Labrador, northeastern Canada, where ground temperature monitoring is limited. This study provides the first comprehensive investigation of factors influencing local-to-regional ground temperature variability in Labrador’s highland environments including in areas subject to infrastructure development. Ground surface temperature measurements (n = 100), in situ field characteristics (i.e., vegetation, soil, topography), and remotely derived variables were collected at five sampling areas of varying latitudes and continentalities. Our analysis identified no consistent latitudinal trend in mean annual ground surface temperatures (MAGST) or permafrost occurrence probabilities. Instead, the highest permafrost probabilities were found in dry, wind-blown areas with sparse vegetation cover in Labrador’s southwest interior. Using machine learning, we identified the most important climate variables (i.e., mean annual air temperature, air temperature range) and local site characteristics (i.e. maximum understory vegetation height, snow accumulation indices) for predicting regional variability in MAGSTs. These findings will support future geohazard assessments in similar highlands and aid in the development of fine-resolution permafrost distribution maps. These data also help to establish a critical baseline of thermal conditions for evaluating the impacts of further environmental and climatic changes.
Colyn et al. (Thu,) studied this question.
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