Preprints
https://doi.org/10.5194/essd-2025-225
https://doi.org/10.5194/essd-2025-225
04 Jun 2025
 | 04 Jun 2025
Status: this preprint is currently under review for the journal ESSD.

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

Elisa Kamir, Samuel Morin, Guillaume Evin, Penelope Gehring, Bodo Wichura, and Ali Nadir Arslan

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 maximum based on the Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset available on the Copernicus Climate Change Service. This dataset includes reanalysis data, 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. 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. We highlight satisfactory skills of MTMSI annual snow depth maxima on most NUTS-3, based on the Kling-Gupta Efficiency metric, correlation, and bias scores. We identify some areas where MTMSI does not adequately portray in-situ observation 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 based on 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).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Elisa Kamir, Samuel Morin, Guillaume Evin, Penelope Gehring, Bodo Wichura, and Ali Nadir Arslan

Status: open (until 11 Jul 2025)

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Elisa Kamir, Samuel Morin, Guillaume Evin, Penelope Gehring, Bodo Wichura, and Ali Nadir Arslan

Data sets

The European-wide Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset : annual snow depth maxima E. Kamir et al. https://doi.org/10.5281/zenodo.15181402

Elisa Kamir, Samuel Morin, Guillaume Evin, Penelope Gehring, Bodo Wichura, and Ali Nadir Arslan

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Short summary
This article describes a dataset of annual snow depth maximum across Europe, from 1961 to 2015, based on a regional reanalysis. It evaluates the performance of the dataset, against in-situ snow depth observations. This dataset is found to perform well in most environments, with challenges at high elevation and some coastal areas. Assessing the quality of this dataset is necessary in order to use it as a baseline to infer future changes of extreme snow loads under climate change.
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