Articles | Volume 20, issue 3
https://doi.org/10.5194/tc-20-1867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-1867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Apr 2026
Research article |
| 01 Apr 2026
| 01 Apr 2026
Past and future changes in avalanche problems in northern Norway estimated with machine-learning models
Department of Physics and Technology, UiT – The Arctic University of Norway, Tromsø, Norway
Rune Grand Graversen
Department of Physics and Technology, UiT – The Arctic University of Norway, Tromsø, Norway
Norwegian Meteorological Institute, Tromsø Office, Tromsø, Norway
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In this study we optimise and train a random forest model to predict avalanche danger in northern Norway based on meteorological reanalysis data. The model performance is at the low end compared to recent similar studies. A hindcast of the frequency of avalanche days (based on the avalanche-danger level) is performed from 1970 to 2024, and a correlation is found with the Arctic Oscillation. This has potential implications for longer-term avalanche predictability.
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Short summary
We train machine-learning models to predict avalanche problems from meteorological and snow-cover data in northern Norway. A major part of the work is the estimation of avalanche-problem changes throughout the 21st century based on future climate projections. We find that while the avalanche danger generally declines towards 2100, the avalanche characteristics will likely change, meaning fewer dry but more wet avalanches, having potential implications for the avalanche-danger forecast quality.
We train machine-learning models to predict avalanche problems from meteorological and...
