Evaluation of annual maximum snow depth data estimation from the European-wide reanalysis C3S MTMSI (Copernicus Climate Change Service – Mountain Tourism Meteorological and Snow Indicators) against in-situ observations

Abstract. Large snow load events are a major hazard for both human societies, in particular buildings and transport safety, and natural ecosystems. National and European frameworks provide guidelines and standards in order to take into account extreme snow load hazard in infrastructure design. However, there is a lack of reference data for their implementation. This is even more challenging in the context of climate change, which modifies the frequency and intensity of major snow load events. In the context of the Framework Partnership Agreement on Copernicus User Uptake, we have developed a pan-European extreme value analysis of annual snow load maxima based on the Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset available from the Copernicus Climate Change Service. This dataset includes reanalysis data for the period 1962–2015, based on the UERRA (Uncertainties in Ensembles of Regional Reanalyses) reanalysis and snow cover simulations, and past and future climate projections based on regional climate simulations for the period 1951–2100. Here, we describe the evaluation of the MTMSI reanalysis component in terms of annual snow depth maxima against multiple in-situ observation datasets. Results are provided at the NUTS-3 (Nomenclature des unités territoriales statistiques) scale used in MTMSI, for multiple elevations over a large area stretching from the European Alps to the Scandinavian countries. For 75 % of the comparisons between observed and simulated snow depth maxima, we report absolute bias scores between −0.23 and 0.15 m, correlations above 0.59, and a Kling–Gupta Efficiency metric above 0.29. We identify some areas where MTMSI does not adequately portray in-situ observations of snow depth maxima, located in the Alps and coastal areas of the Netherlands, Norway, Sweden, and Croatia. This study thus provides background information for assessing the relevance of this pan-European dataset in terms of annual snow depth maxima, relevant for annual snow mass and snow load maxima based on complementary information derived from snow cover model output. The MTMSI annual maximum snow depth reanalysis dataset is available through the following link: https://doi.org/10.5281/zenodo.15181401 (Kamir et al., 2025).

Authors

Institutions

Publication Details

Journal
Earth system science data
Published
2026-01-05
DOI
https://doi.org/10.5194/essd-18-17-2026
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of annual maximum snow depth data estimation from the European-wide reanalysis C3S MTMSI (Copernicus Climate Change Service – Mountain Tourism Meteorological and Snow Indicators) against in-situ observations

Penelope Gehring, Ali Nadir Arslan, Bodo Wichura, Guillaume Évin et al.
Earth system science data
Cryospheric studies and observations
article

Evaluation of annual maximum snow depth data estimation from the European-wide reanalysis C3S MTMSI (Copernicus Climate Change Service – Mountain Tourism Meteorological and Snow Indicators) against in-situ observations

Penelope Gehring, Ali Nadir Arslan, Bodo Wichura, Guillaume Évin, Elisa Kamir, Samuel MORIN
article en

Abstract

Abstract. Large snow load events are a major hazard for both human societies, in particular buildings and transport safety, and natural ecosystems. National and European frameworks provide guidelines and standards in order to take into account extreme snow load hazard in infrastructure design. However, there is a lack of reference data for their implementation. This is even more challenging in the context of climate change, which modifies the frequency and intensity of major snow load events. In the context of the Framework Partnership Agreement on Copernicus User Uptake, we have developed a pan-European extreme value analysis of annual snow load maxima based on the Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset available from the Copernicus Climate Change Service. This dataset includes reanalysis data for the period 1962–2015, based on the UERRA (Uncertainties in Ensembles of Regional Reanalyses) reanalysis and snow cover simulations, and past and future climate projections based on regional climate simulations for the period 1951–2100. Here, we describe the evaluation of the MTMSI reanalysis component in terms of annual snow depth maxima against multiple in-situ observation datasets. Results are provided at the NUTS-3 (Nomenclature des unités territoriales statistiques) scale used in MTMSI, for multiple elevations over a large area stretching from the European Alps to the Scandinavian countries. For 75 % of the comparisons between observed and simulated snow depth maxima, we report absolute bias scores between −0.23 and 0.15 m, correlations above 0.59, and a Kling–Gupta Efficiency metric above 0.29. We identify some areas where MTMSI does not adequately portray in-situ observations of snow depth maxima, located in the Alps and coastal areas of the Netherlands, Norway, Sweden, and Croatia. This study thus provides background information for assessing the relevance of this pan-European dataset in terms of annual snow depth maxima, relevant for annual snow mass and snow load maxima based on complementary information derived from snow cover model output. The MTMSI annual maximum snow depth reanalysis dataset is available through the following link: https://doi.org/10.5281/zenodo.15181401 (Kamir et al., 2025).

Earth system science dataVol. 18(1)
Institut polytechnique de Grenoble (FR), Finnish Meteorological Institute (FI), Centre National de la Recherche Scientifique (FR), Deutscher Wetterdienst (DE), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Météo-France (FR), Institut des Géosciences de l'Environnement (FR), Institut de Recherche pour le Développement (FR), University of Vaasa (FI), Université Grenoble Alpes (FR)
European Commission, Centre National de la Recherche Scientifique, Center for Neuroscience and Regenerative Medicine
Openalex Percentile: Top 98%
Cryospheric studies and observations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.