Estimating changes in extreme snow load in Europe as a function of global warming levels

Most European regions regularly experience large snow loads due to large snow accumulations and/or intense snowfalls. In the future, the magnitude of extreme snow loads is generally expected to decrease due to global warming. However, this decrease depends strongly on the latitude and the elevation. This study aims to provide a flexible statistical framework to estimate changes in extreme snow load (50-year return levels) in Europe as a function of global warming levels, using a multi-model ensemble of snow cover projections. Different sets of nonstationary models are introduced to accommodate the nonzero probability of experiencing years without snow. In particular, parsimonious distributions (exponential, Gamma, Inverse-Gamma) are considered when zero SWE maxima are present. This approach is illustrated by an application to European regions. In a + 3 °C world, extreme snow loads are projected to decrease strongly in all of Europe compared to a warming level of +1 °C, which corresponds roughly to the current climate. • We provide a flexible framework to estimate changes in extreme snow load in Europe as a function of global warming levels. • We introduce nonstationary models dedicated to regions where snowfalls are rare (low-elevation or low-latitude regions). • In a + 3 °C world, extreme snow loads are projected to decrease strongly in all of Europe compared to the current climate.

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Publication Details

Journal
Cold Regions Science and Technology
Published
2025-01-16
DOI
https://doi.org/10.1016/j.coldregions.2025.104424
Citations
4
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
4.42

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article

Estimating changes in extreme snow load in Europe as a function of global warming levels

Samuel Morin, Guillaume Évin, Erwan Le Roux, Elisa Kamir
4 citations
Cold Regions Science and Technology
Climate variability and models
4.42
article

Estimating changes in extreme snow load in Europe as a function of global warming levels

Samuel Morin, Guillaume Évin, Erwan Le Roux, Elisa Kamir
article en
4 citations

Abstract

Most European regions regularly experience large snow loads due to large snow accumulations and/or intense snowfalls. In the future, the magnitude of extreme snow loads is generally expected to decrease due to global warming. However, this decrease depends strongly on the latitude and the elevation. This study aims to provide a flexible statistical framework to estimate changes in extreme snow load (50-year return levels) in Europe as a function of global warming levels, using a multi-model ensemble of snow cover projections. Different sets of nonstationary models are introduced to accommodate the nonzero probability of experiencing years without snow. In particular, parsimonious distributions (exponential, Gamma, Inverse-Gamma) are considered when zero SWE maxima are present. This approach is illustrated by an application to European regions. In a + 3 °C world, extreme snow loads are projected to decrease strongly in all of Europe compared to a warming level of +1 °C, which corresponds roughly to the current climate. • We provide a flexible framework to estimate changes in extreme snow load in Europe as a function of global warming levels. • We introduce nonstationary models dedicated to regions where snowfalls are rare (low-elevation or low-latitude regions). • In a + 3 °C world, extreme snow loads are projected to decrease strongly in all of Europe compared to the current climate.

Cold Regions Science and TechnologyVol. 231
Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), Inserm (FR), Université de Bretagne Occidentale (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), Centre National de Recherches Météorologiques (FR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Evaluation et recherche en services et politiques en santé pour les populations vulnérables (FR), Météo-France (FR), Institut des Géosciences de l'Environnement (FR), Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance (FR), IMT Atlantique (FR), Institut de Recherche pour le Développement (FR), Université Grenoble Alpes (FR)
European Commission, Agence Nationale de la Recherche
Climate action
Openalex Percentile: Top 7%
Climate variability and models
4.42
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