Articles | Volume 17, issue 8
https://doi.org/10.5194/tc-17-3617-2023
© Author(s) 2023. 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-17-3617-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Aug 2023
Research article |
| 28 Aug 2023
| 28 Aug 2023
Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, CEN, 38000 Grenoble, France
Samuel Morin
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, CEN, 38000 Grenoble, France
CNRM, Météo-France, CNRS, Université de Toulouse, 31057 Toulouse, France
Related authors
Zacharie Barrou Dumont, Simon Gascoin, Jordi Inglada, Andreas Dietz, Jonas Köhler, Matthieu Lafaysse, Diego Monteiro, Carlo Carmagnola, Arthur Bayle, Jean-Pierre Dedieu, Olivier Hagolle, and Philippe Choler
The Cryosphere, 19, 2407–2429, https://doi.org/10.5194/tc-19-2407-2025, https://doi.org/10.5194/tc-19-2407-2025, 2025
Short summary
Short summary
We generated annual maps of snow melt-out days at 20 m resolution over a period of 38 years from 10 different satellites. This study fills a knowledge gap regarding the evolution of mountain snow in Europe by covering a much longer period and characterizing trends at much higher resolutions than previous studies. We found a trend for earlier melt-out with average reductions of 5.51 d per decade over the French Alps and 4.04 d per decade over the Pyrenees for the period 1986–2023.
Léon Roussel, Marie Dumont, Marion Réveillet, Delphine Six, Marin Kneib, Pierre Nabat, Kevin Fourteau, Diego Monteiro, Simon Gascoin, Emmanuel Thibert, Antoine Rabatel, Jean-Emmanuel Sicart, Mylène Bonnefoy, Luc Piard, Olivier Laarman, Bruno Jourdain, Mathieu Fructus, Matthieu Vernay, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2025-1741, https://doi.org/10.5194/egusphere-2025-1741, 2025
Short summary
Short summary
Saharan dust deposits frequently color alpine glaciers orange. Mineral dust reduces snow albedo and increases snow and glaciers melt rate. Using physical modeling, we quantified the impact of dust on the Argentière Glacier over the period 2019–2022. We found that that the contribution of mineral dust to the melt represents between 6 and 12 % of Argentière Glacier summer melt. At specific locations, the impact of dust over one year can rise to an equivalent of 1 meter of melted ice.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
Short summary
Short summary
Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
Short summary
Short summary
This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Zacharie Barrou Dumont, Simon Gascoin, Jordi Inglada, Andreas Dietz, Jonas Köhler, Matthieu Lafaysse, Diego Monteiro, Carlo Carmagnola, Arthur Bayle, Jean-Pierre Dedieu, Olivier Hagolle, and Philippe Choler
The Cryosphere, 19, 2407–2429, https://doi.org/10.5194/tc-19-2407-2025, https://doi.org/10.5194/tc-19-2407-2025, 2025
Short summary
Short summary
We generated annual maps of snow melt-out days at 20 m resolution over a period of 38 years from 10 different satellites. This study fills a knowledge gap regarding the evolution of mountain snow in Europe by covering a much longer period and characterizing trends at much higher resolutions than previous studies. We found a trend for earlier melt-out with average reductions of 5.51 d per decade over the French Alps and 4.04 d per decade over the Pyrenees for the period 1986–2023.
Elisa Kamir, Samuel Morin, Guillaume Evin, Penelope Gehring, Bodo Wichura, and Ali Nadir Arslan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-225, https://doi.org/10.5194/essd-2025-225, 2025
Preprint under review for ESSD
Short summary
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.
Léon Roussel, Marie Dumont, Marion Réveillet, Delphine Six, Marin Kneib, Pierre Nabat, Kevin Fourteau, Diego Monteiro, Simon Gascoin, Emmanuel Thibert, Antoine Rabatel, Jean-Emmanuel Sicart, Mylène Bonnefoy, Luc Piard, Olivier Laarman, Bruno Jourdain, Mathieu Fructus, Matthieu Vernay, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2025-1741, https://doi.org/10.5194/egusphere-2025-1741, 2025
Short summary
Short summary
Saharan dust deposits frequently color alpine glaciers orange. Mineral dust reduces snow albedo and increases snow and glaciers melt rate. Using physical modeling, we quantified the impact of dust on the Argentière Glacier over the period 2019–2022. We found that that the contribution of mineral dust to the melt represents between 6 and 12 % of Argentière Glacier summer melt. At specific locations, the impact of dust over one year can rise to an equivalent of 1 meter of melted ice.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
Short summary
Short summary
Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Samuel Morin, Hugues François, Marion Réveillet, Eric Sauquet, Louise Crochemore, Flora Branger, Étienne Leblois, and Marie Dumont
Hydrol. Earth Syst. Sci., 27, 4257–4277, https://doi.org/10.5194/hess-27-4257-2023, https://doi.org/10.5194/hess-27-4257-2023, 2023
Short summary
Short summary
Ski resorts are a key socio-economic asset of several mountain areas. Grooming and snowmaking are routinely used to manage the snow cover on ski pistes, but despite vivid debate, little is known about their impact on water resources downstream. This study quantifies, for the pilot ski resort La Plagne in the French Alps, the impact of grooming and snowmaking on downstream river flow. Hydrological impacts are mostly apparent at the seasonal scale and rather neutral on the annual scale.
Erwan Le Roux, Guillaume Evin, Raphaëlle Samacoïts, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 17, 4691–4704, https://doi.org/10.5194/tc-17-4691-2023, https://doi.org/10.5194/tc-17-4691-2023, 2023
Short summary
Short summary
We assess projected changes in snowfall extremes in the French Alps as a function of elevation and global warming level for a high-emission scenario. On average, heavy snowfall is projected to decrease below 3000 m and increase above 3600 m, while extreme snowfall is projected to decrease below 2400 m and increase above 3300 m. At elevations in between, an increase is projected until +3 °C of global warming and then a decrease. These results have implications for the management of risks.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
Short summary
Short summary
Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
Earth Syst. Dynam., 13, 1059–1075, https://doi.org/10.5194/esd-13-1059-2022, https://doi.org/10.5194/esd-13-1059-2022, 2022
Short summary
Short summary
Anticipating risks related to climate extremes is critical for societal adaptation to climate change. In this study, we propose a statistical method in order to estimate future climate extremes from past observations and an ensemble of climate change simulations. We apply this approach to snow load data available in the French Alps at 1500 m elevation and find that extreme snow load is projected to decrease by −2.9 kN m−2 (−50 %) between 1986–2005 and 2080–2099 for a high-emission scenario.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
Short summary
Short summary
This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Lucas Berard-Chenu, Hugues François, Emmanuelle George, and Samuel Morin
The Cryosphere, 16, 863–881, https://doi.org/10.5194/tc-16-863-2022, https://doi.org/10.5194/tc-16-863-2022, 2022
Short summary
Short summary
This study investigates the past snow reliability (1961–2019) of 16 ski resorts in the French Alps using state-of-the-art snowpack modelling. We used snowmaking investment figures to infer the evolution of snowmaking coverage at the individual ski resort level. Snowmaking improved snow reliability for the core of the winter season for the highest-elevation ski resorts. However it did not counterbalance the decreasing trend in snow cover reliability for lower-elevation ski resorts and in spring.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
Short summary
Short summary
Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 15, 4335–4356, https://doi.org/10.5194/tc-15-4335-2021, https://doi.org/10.5194/tc-15-4335-2021, 2021
Short summary
Short summary
Extreme snowfall can cause major natural hazards (avalanches, winter storms) that can generate casualties and economic damage. In the French Alps, we show that between 1959 and 2019 extreme snowfall mainly decreased below 2000 m of elevation and increased above 2000 m. At 2500 m, we find a contrasting pattern: extreme snowfall decreased in the north, while it increased in the south. This pattern might be related to increasing trends in extreme snowfall observed near the Mediterranean Sea.
Pirmin Philipp Ebner, Franziska Koch, Valentina Premier, Carlo Marin, Florian Hanzer, Carlo Maria Carmagnola, Hugues François, Daniel Günther, Fabiano Monti, Olivier Hargoaa, Ulrich Strasser, Samuel Morin, and Michael Lehning
The Cryosphere, 15, 3949–3973, https://doi.org/10.5194/tc-15-3949-2021, https://doi.org/10.5194/tc-15-3949-2021, 2021
Short summary
Short summary
A service to enable real-time optimization of grooming and snow-making at ski resorts was developed and evaluated using both GNSS-measured snow depth and spaceborne snow maps derived from Copernicus Sentinel-2. The correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly the impact of snow redistribution by skiers, compensation of uneven terrain, or spontaneous local adaptions of the snow management.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
Short summary
Short summary
The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Martin Ménégoz, Evgenia Valla, Nicolas C. Jourdain, Juliette Blanchet, Julien Beaumet, Bruno Wilhelm, Hubert Gallée, Xavier Fettweis, Samuel Morin, and Sandrine Anquetin
Hydrol. Earth Syst. Sci., 24, 5355–5377, https://doi.org/10.5194/hess-24-5355-2020, https://doi.org/10.5194/hess-24-5355-2020, 2020
Short summary
Short summary
The study investigates precipitation changes in the Alps, using observations and a 7 km resolution climate simulation over 1900–2010. An increase in mean precipitation is found in winter over the Alps, whereas a drying occurred in summer in the surrounding plains. A general increase in the daily annual maximum of precipitation is evidenced (20 to 40 % per century), suggesting an increase in extreme events that is significant only when considering long time series, typically 50 to 80 years.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
Nat. Hazards Earth Syst. Sci., 20, 2961–2977, https://doi.org/10.5194/nhess-20-2961-2020, https://doi.org/10.5194/nhess-20-2961-2020, 2020
Short summary
Short summary
To minimize the risk of structure collapse due to extreme snow loads, structure standards rely on 50-year return levels of ground snow load (GSL), i.e. levels exceeded once every 50 years on average, that do not account for climate change. We study GSL data in the French Alps massifs from 1959 and 2019 and find that these 50-year return levels are decreasing with time between 900 and 4800 m of altitude, but they still exceed return levels of structure standards for half of the massifs at 1800 m.
Cited articles
Arnould, G., Dombrowski-Etchevers, I., Gouttevin, I., and Seity, Y.:
Améliorer la prévision de température en montagne par des
descentes d'échelle, La Météorologie, 115, 37–44,
https://doi.org/10.37053/lameteorologie-2021-0091, 2021. a
Auer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R.,
Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M., Müller-Westermeier, G., Kveton, V., Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar, T., Dolinar, M., Gajic-Capka, M., Zaninovic, K., Majstorovic, Z., and Nieplova, E.:
HISTALP – historical instrumental climatological surface time series of the
Greater Alpine Region, Int. J. Climatol., 27, 17–46, https://doi.org/10.1002/joc.1377, 2007. a, b, c
Bandhauer, M., Isotta, F., Lakatos, M., Lussana, C., Båserud, L.,
Izsák, B., Szentes, O., Tveito, O. E., and Frei, C.: Evaluation of daily
precipitation analyses in E-OBS (v19.0e) and ERA5 by comparison to regional
high-resolution datasets in European regions, Int. J.
Climatol., 42, 727–747, https://doi.org/10.1002/joc.7269, 2022. a, b, c, d, e, f, g
Bazile, E., Abida, R., Szczypta, C., Verelle, A., Soci, C., and Le Moigne, P.:
UERRA report 2.9,
https://uerra.eu/publications/deliverable-reports.html (last access: 16 August 2023), 2017. a
Beniston, M., Farinotti, D., Stoffel, M., Andreassen, L. M., Coppola, E., Eckert, N., Fantini, A., Giacona, F., Hauck, C., Huss, M., Huwald, H., Lehning, M., López-Moreno, J.-I., Magnusson, J., Marty, C., Morán-Tejéda, E., Morin, S., Naaim, M., Provenzale, A., Rabatel, A., Six, D., Stötter, J., Strasser, U., Terzago, S., and Vincent, C.: The European mountain cryosphere: a review of its current state, trends, and future challenges, The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, 2018. a
Boone, A. and Etchevers, P.: An intercomparison of three snow schemes of
varying complexity coupled to the same land surface model: Local-scale
evaluation at an Alpine site, J. Hydrometeorol., 2, 374–394,
https://doi.org/10.1175/1525-7541(2001)002<0374:AIOTSS>2.0.CO;2, 2001. a
Brugnara, Y. and Maugeri, M.: Daily precipitation variability in the southern
Alps since the late 19th century, Int. J. Climatol., 39,
3492–3504, https://doi.org/10.1002/joc.6034, 2019. a
Caillaud, C., Somot, S., Alias, A., Bernard-Bouissières, I., Fumière,
Q., Laurantin, O., Seity, Y., and Ducrocq, V.: Modelling Mediterranean heavy
precipitation events at climate scale: an object-oriented evaluation of the
CNRM-AROME convection-permitting regional climate model, Clim. Dynam.,
56, 1717–1752, https://doi.org/10.1007/s00382-020-05558-y, 2021 (data available at: https://esgf-node.ipsl.upmc.fr/projects/esgf-ipsl/, last access: 1 February 2023). a, b, c
Cassou, C., Cherchi, A., and Kosaka, Y.: IPCC, 2021: Annex IV: Modes of
Variability, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2153–2192, https://doi.org/10.1017/9781009157896.018, 2021. a
Copernicus Climate Change Service, Climate Data Store: UERRA regional reanalysis for Europe on single levels from 1961 to 2019, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.32b04ec5, 2019. a
Copernicus Climate Change Service, Climate Data Store: Alpine gridded monthly precipitation data since 1871 derived from in-situ observations, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.6a6d1bc3, 2021. a
Coppola, E., Sobolowski, S., Pichelli, E., et al.: A first-of-its-kind
multi-model convection permitting ensemble for investigating convective
phenomena over Europe and the Mediterranean, Clim. Dynam., 55, 3–34,
2020. a
Cornes, R., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Datasets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018 (data available at: https://surfobs.climate.copernicus.eu/dataaccess/access_eobs_months.php, last access: 16 August 2023). a, b, c, d, e, f
Decharme, B., Brun, E., Boone, A., Delire, C., Le Moigne, P., and Morin, S.: Impacts of snow and organic soils parameterization on northern Eurasian soil temperature profiles simulated by the ISBA land surface model, The Cryosphere, 10, 853–877, https://doi.org/10.5194/tc-10-853-2016, 2016. a, b
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M. van de Berg,, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The
ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, 2011. a
Derksen, C. and Mudryk, L.: Assessment of Arctic seasonal snow cover rates of change, The Cryosphere, 17, 1431–1443, https://doi.org/10.5194/tc-17-1431-2023, 2023. a
Durand, Y., Giraud, G., Laternser, M., Etchevers, P., Mérindol, L., and
Lesaffre, B.: Reanalysis of 47 years of climate in the French Alps
(1958–2005): climatology and trends for snow cover, J. Appl.
Meteorol. Clim., 48, 2487–2512, https://doi.org/10.1175/2009JAMC1810.1,
2009. a
Dutra, E., Balsamo, G., Viterbo, P., Miranda, P. M., Beljaars, A., Schär,
C., and Elder, K.: An improved snow scheme for the ECMWF land surface model:
Description and offline validation, J. Hydrometeorol., 11,
899–916, https://doi.org/10.1175/2010JHM1249.1, 2010. a, b
Dutra, E., Muñoz-Sabater, J., Boussetta, S., Komori, T., Hirahara, S., and
Balsamo, G.: Environmental lapse rate for high-resolution land surface
downscaling: An application to ERA5, Earth and Space Science, 7,
e2019EA000984, https://doi.org/10.1029/2019EA000984, 2020. a, b, c
European Environmental Agency (EEA): European Digital Elevation Model (EU-DEM), version 1.1, EEA [data set],
https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1 (last access: 16 August 2023), 2016. a
Essery, R., Kim, H., Wang, L., Bartlett, P., Boone, A., Brutel-Vuilmet, C., Burke, E., Cuntz, M., Decharme, B., Dutra, E., Fang, X., Gusev, Y., Hagemann, S., Haverd, V., Kontu, A., Krinner, G., Lafaysse, M., Lejeune, Y., Marke, T., Marks, D., Marty, C., Menard, C. B., Nasonova, O., Nitta, T., Pomeroy, J., Schädler, G., Semenov, V., Smirnova, T., Swenson, S., Turkov, D., Wever, N., and Yuan, H.: Snow cover duration trends observed at sites and predicted by multiple models, The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, 2020. a
Fontrodona Bach, A., Van der Schrier, G., Melsen, L., Klein Tank, A., and
Teuling, A.: Widespread and accelerated decrease of observed mean and extreme
snow depth over Europe, Geophys. Res. Lett., 45, 12–312,
https://doi.org/10.1029/2018GL079799, 2018. a
Frei, C.: Interpolation of temperature in a mountainous region using nonlinear
profiles and non-Euclidean distances, Int. J. Climatol.,
34, 1585–1605, https://doi.org/10.1002/joc.3786, 2014. a
Frei, C. and Isotta, F. A.: Ensemble spatial precipitation analysis from rain
gauge data: Methodology and application in the European Alps, J.
Geophys. Res.-Atmos., 124, 5757–5778,
https://doi.org/10.1029/2018JD030004, 2019. a, b
Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337–2351, https://doi.org/10.5194/hess-19-2337-2015, 2015. a
Gascoin, S., Monteiro, D., and Morin, S.: Reanalysis-based contextualization of
real-time snow cover monitoring from space, Environ. Res. Lett.,
17, 114044, https://doi.org/10.1088/1748-9326/ac9e6a, 2022. a
Guérémy, J.-F.: A continuous buoyancy based convection scheme: one- and three dimensional validation, Tellus, 63A, 687–706, https://doi.org/10.1111/j.1600-0870.2011.00521.x, 2011. a
Hall, D. K. and Riggs., G. A.: MODIS/Terra CGF Snow Cover Daily L3 Global 500m
SIN Grid, Version 61, NSIDC [data set], https://doi.org/10.5067/MODIS/MOD10A1F.061, 2020. a, b, c
Hall, D. K., Riggs, G. A., Foster, J. L., and Kumar, S. V.: Development and
evaluation of a cloud-gap-filled MODIS daily snow-cover product, Remote
Sens. Environ., 114, 496–503, https://doi.org/10.1016/j.rse.2009.10.007, 2010. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
et al.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Hock, R., Rasul, R., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y.,
Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Milner, A., Molau, U.,
Morin, S., Orlove, B., and Steltzer, H.: High Mountain Areas, in:
IPCC Special Report on the Ocean and Cryosphere in a
Changing Climate, edited by Pörtner, H.-O., Roberts, D.,
Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K.,
Alegriáa, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer,
N.: High mountain areas, in: IPCC special report on the ocean and cryosphere in a changing climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., et al., 131–202, 2019. a, b, c, d
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, in press, https://doi.org/10.1017/9781009157896, 2021. a
Isotta, F. A., Vogel, R., and Frei, C.: Evaluation of European regional
reanalyses and downscalings for precipitation in the Alpine region,
Meteorol. Z., 24, 15–37, https://doi.org/10.1127/metz/2014/0584, 2015. a, b, c, d
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Deque, M., Georgievski, G., Georgopoulou, E.,
Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C.,
Keuler, K., Kovats, S., Kroner, N., Kotlarski, S., Kriegsmann, A., Martin,
E., Meijgaard, E. V., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher,
C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S.,
Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and
Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for
European impact research, Reg. Environ. Change, 14, 563–578,
https://doi.org/10.1007/s10113-013-0499-2, 2014. a
Justice, C. O., Vermote, E., Townshend, J. R., Defries, R., Roy, D. P., Hall,
D. K., Salomonson, V. V., Privette, J. L., Riggs, G., Strahler, A., Lucht, W., Myneni, R. B., Knyazikhin, Y., Running, S. W., Nemani, R. R., Zhengming, W., Huete, A. R., van Leeuwen, W., Wolfe, R. E., Giglio, L., Muller, J., Lewis, P., and Barnsley, M. J.:
The Moderate Resolution Imaging Spectroradiometer (MODIS): Land remote
sensing for global change research, IEEE T. Geosci.
Remote, 36, 1228–1249, https://doi.org/10.1109/36.701075, 1998. a
Kaiser-Weiss, A. K., Borsche, M., Niermann, D., Kaspar, F., Lussana, C.,
Isotta, F. A., van den Besselaar, E., van der Schrier, G., and Undén, P.:
Added value of regional reanalyses for climatological applications,
Environmental Research Communications, 1, 071004,
https://doi.org/10.1088/2515-7620/ab2ec3, 2019. a, b, c
Klein, G., Vitasse, Y., Rixen, C., Marty, C., and Rebetez, M.: Shorter snow
cover duration since 1970 in the Swiss Alps due to earlier snowmelt more than
to later snow onset, Climatic Change, 139, 637–649,
https://doi.org/10.1007/s10584-016-1806-y, 2016. a
Klein Tank, A., Wijngaard, J., Können, G., Böhm, R., Demarée, G.,
Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C.,
Heino, R., Bessemoulin, P., Müller-Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A. F. V., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., Antonio López, J., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L. V., and Petrovic, P.: Daily dataset of 20th-century surface air temperature and
precipitation series for the European Climate Assessment, Int.
J. Climatol., 22,
1441–1453, https://doi.org/10.1002/joc.773, 2002. a
Klok, E. and Klein Tank, A.: Updated and extended European dataset of daily
climate observations, Int. J. Climatol., 29, 1182–1191, https://doi.org/10.1002/joc.1779, 2009. a
Kochendorfer, J., Nitu, R., Wolff, M., Mekis, E., Rasmussen, R., Baker, B., Earle, M. E., Reverdin, A., Wong, K., Smith, C. D., Yang, D., Roulet, Y.-A., Meyers, T., Buisan, S., Isaksen, K., Brækkan, R., Landolt, S., and Jachcik, A.: Testing and development of transfer functions for weighing precipitation gauges in WMO-SPICE, Hydrol. Earth Syst. Sci., 22, 1437–1452, https://doi.org/10.5194/hess-22-1437-2018, 2018. a
Kochendorfer, J., Earle, M., Rasmussen, R., Smith, C., Yang, D., Morin, S.,
Mekis, E., Buisan, S., Roulet, Y.-A., Landolt, S., Wolff, M., Hoover, J., Thériault, J. M., Lee, G., Baker, B., Nitu, R., Lanza, L., Colli, M., and Meyers, T.: How well are we
measuring snow post-SPICE?, B. Am. Meteorol. Soc.,
103, E370–E388, https://doi.org/10.1175/BAMS-D-20-0228.1, 2021. a
Kotlarski, S., Gobiet, A., Morin, S., Olefs, M., Rajczak, J., and
Samacoïts, R.: 21st Century alpine climate change, Clim. Dynam., 60,
65–86, https://doi.org/10.1007/s00382-022-06303-3, 2023. a
Kuhn, M. and Olefs, M.: Elevation-dependent climate change in the European
Alps, in: Oxford Research Encyclopedia of Climate Science,
https://doi.org/10.1093/acrefore/9780190228620.013.762, 2020. a
Lafaysse, M., Cluzet, B., Dumont, M., Lejeune, Y., Vionnet, V., and Morin, S.: A multiphysical ensemble system of numerical snow modelling, The Cryosphere, 11, 1173–1198, https://doi.org/10.5194/tc-11-1173-2017, 2017. a
Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S.,
Cosme, E., Jonas, T., Winstral, A., and Margulis, S. A.: Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.00325,
2020. a
Li, Q., Yang, T., and Li, L.: Evaluation of snow depth and snow cover
represented by multiple datasets over the Tianshan Mountains: Remote sensing,
reanalysis, and simulation, Int. J. Climatol., 42,
4223–4239, https://doi.org/10.1002/joc.7459, 2022. a
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our skill in modeling
mountain rain and snow is bypassing the skill of our observational networks,
B. Am. Meteorol. Soc., 100, 2473–2490,
https://doi.org/10.1175/BAMS-D-19-0001.1, 2019. a
Lüthi, S., Ban, N., Kotlarski, S., Steger, C. R., Jonas, T., and Schär,
C.: Projections of Alpine Snow-Cover in a High-Resolution Climate
Simulation, Atmosphere, 10, 463, https://doi.org/10.3390/atmos10080463, 2019. a
Masson, D. and Frei, C.: Long-term variations and trends of mesoscale
precipitation in the Alps: recalculation and update for 1901–2008,
Int. J. Climatol., 36, 492–500, https://doi.org/10.1002/joc.4343,
2016. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a
Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., Schöner, W., Cat Berro, D., Chiogna, G., De Gregorio, L., Kotlarski, S., Majone, B., Resch, G., Terzago, S., Valt, M., Beozzo, W., Cianfarra, P., Gouttevin, I., Marcolini, G., Notarnicola, C., Petitta, M., Scherrer, S. C., Strasser, U., Winkler, M., Zebisch, M., Cicogna, A., Cremonini, R., Debernardi, A., Faletto, M., Gaddo, M., Giovannini, L., Mercalli, L., Soubeyroux, J.-M., Sušnik, A., Trenti, A., Urbani, S., and Weilguni, V.: Observed snow depth trends in the European Alps: 1971 to 2019, The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, 2021a. a, b, c, d, e, f
Matiu, M.,
Crespi, A.,
Bertoldi, G.,
Carmagnola, C. M.,
Marty, C.,
Morin, S.,
Schöner, W.,
Cat Berro, D.,
Chiogna, G.,
De Gregorio, L.,
Kotlarski, S.,
Majone, B.,
Resch, G.,
Terzago, S.,
Valt, M.,
Beozzo, W.,
Cianfarra, P.,
Gouttevin, I.,
Marcolini, G.,
Notarnicola, C.,
Petitta, M.,
Scherrer, S. C.,
Strasser, U.,
Winkler, M.,
Zebisch, M.,
Cicogna, A.,
Cremonini, R.,
Debernardi, A.,
Faletto, M.,
Gaddo, M.,
Giovannini, L.,
Mercalli, L.,
Soubeyroux, J.-M.,
Sušnik, A.,
Trenti, A.,
Urbani, S.,
Weilguni, V.: Snow cover in the European Alps: Station observations of snow depth and depth of snowfall (v1.3), Zenodo [data set], https://doi.org/10.5281/zenodo.5109574, 2021b. a
Menard, C. B., Essery, R., Krinner, G., et al.: Scientific and
human errors in a snow model intercomparison, B. Am.
Meteorol. Soc., 102, E61–E79, https://doi.org/10.1175/BAMS-D-19-0329.1, 2020. a
Ménégoz, M., Valla, E., Jourdain, N. C., Blanchet, J., Beaumet, J., Wilhelm, B., Gallée, H., Fettweis, X., Morin, S., and Anquetin, S.: Contrasting seasonal changes in total and intense precipitation in the European Alps from 1903 to 2010, Hydrol. Earth Syst. Sci., 24, 5355–5377, https://doi.org/10.5194/hess-24-5355-2020, 2020. a
Monteiro, D. and
Morin, S.: Multi-decadal analysis of past winter temperature,
precipitation and snow cover data in the European
Alps from reanalyses, climate models and
observational datasets, Zenodo [code], https://doi.org/10.5281/zenodo.8252180, 2023. a
Morin, S., Samacoïts, R., François, H., et al.: Pan-European meteorological and snow indicators of climate change impact on ski tourism, Climate Services, 22, 100215, https://doi.org/10.1016/j.cliser.2021.100215, 2021. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b, c
Nabat, P., Somot, S., Cassou, C., Mallet, M., Michou, M., Bouniol, D., Decharme, B., Drugé, T., Roehrig, R., and Saint-Martin, D.: Modulation of radiative aerosols effects by atmospheric circulation over the Euro-Mediterranean region, Atmos. Chem. Phys., 20, 8315–8349, https://doi.org/10.5194/acp-20-8315-2020, 2020 (data available at: https://esgf-node.ipsl.upmc.fr/projects/esgf-ipsl/, last access: 1 February 2023). a, b
Napoly, A., Boone, A., and Welfringer, T.: ISBA-MEB (SURFEX v8.1): model snow evaluation for local-scale forest sites, Geosci. Model Dev., 13, 6523–6545, https://doi.org/10.5194/gmd-13-6523-2020, 2020. a
Olefs, M., Koch, R., Schöner, W., and Marke, T.: Changes in snow depth,
snow cover duration, and potential snowmaking conditions in Austria,
1961–2020 – a model based approach, Atmosphere, 11, 1330, https://doi.org/10.3390/atmos11121330, 2020. a
Orsolini, Y., Wegmann, M., Dutra, E., Liu, B., Balsamo, G., Yang, K., de Rosnay, P., Zhu, C., Wang, W., Senan, R., and Arduini, G.: Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations, The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, 2019. a, b
Pepin, N., Bradley, R. S., Diaz, H., Baraër, M., Caceres, E., Forsythe, N.,
Fowler, H., Greenwood, G., Hashmi, M., Liu, X., et al.: Elevation-dependent
warming in mountain regions of the world, Nat. Clim. Change, 5, 424–430,
2015. a
Pepin, N., Arnone, E., Gobiet, A., Haslinger, K., Kotlarski, S., Notarnicola,
C., Palazzi, E., Seibert, P., Serafin, S., Schöner, W., Terzago, S., Thornton, J. M., Vuille, M., and Adler, C.: Climate
changes and their elevational patterns in the mountains of the world, Rev. Geophys., 60, e2020RG000730, https://doi.org/10.1029/2020RG000730, 2022. a, b
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P.,
Alias, A., Belušić, D., Berthou, S., Caillaud, C., Cardoso, R. M., Chan, S., Christensen, O. B., Dobler, A., de Vries, H., Goergen, K., Kendon, E. J., Keuler, K., Lenderink, G., Lorenz, T., Mishra, A. N., Panitz, H.-J., Schär, C., Soares, P. M. M., Truhetz, H., and Vergara-Temprado, J.: The
first multi-model ensemble of regional climate simulations at kilometer-scale
resolution part 2: historical and future simulations of precipitation,
Clim. Dynam., 56, 3581–3602, https://doi.org/10.1007/s00382-021-05657-4, 2021. a
Piva Aureliano (PASC): Alpine Convention perimeter,
https://www.atlas.alpconv.org/layers/geonode_data:geonode:Alpine_Convention_Perimeter_2018_v2#more (last access: 16 August 2023),
2020. a
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F.,
Laloyaux, P., Tan, D. G., Peubey, C., Thépaut, J.-N., Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher, M.: ERA-20C: An
atmospheric reanalysis of the twentieth century, J. Climate, 29,
4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1, 2016. a
Ribes, A., Corre, L., Gibelin, A.-L., and Dubuisson, B.: Issues in estimating
observed change at the local scale–a case study: the recent warming over
France, Int. J. Climatol., 36, 3794–3806,
https://doi.org/10.1002/joc.4593, 2016. a
Ridal, M., Körnich, H., Olsson, E., and Andrae, U.: UERRA report 2.5,
https://uerra.eu/publications/deliverable-reports.html (last access: 16 August 2023), 2016. a
Rottler, E., Kormann, C., Francke, T., and Bronstert, A.: Elevation-dependent
warming in the Swiss Alps 1981–2017: Features, forcings and feedbacks,
Int. J. Climatol., 39, 2556–2568, https://doi.org/10.1002/joc.5970,
2019. a
Salomonson, V. V. and Appel, I.: Estimating fractional snow cover from MODIS
using the normalized difference snow index, Remote Sens. Environ.,
89, 351–360, https://doi.org/10.1016/j.rse.2003.10.016, 2004. a, b
Scherrer, S. C.: Temperature monitoring in mountain regions using reanalyses:
Lessons from the Alps, Environ. Res. Lett., 15, 044005, https://doi.org/10.1088/1748-9326/ab702d,
2020. a, b, c
Scherrer, S. C. and Appenzeller, C.: Swiss Alpine snow pack variability: major
patterns and links to local climate and large-scale flow, Clim. Res.,
32, 187–199, https://doi.org/10.3354/cr032187, 2006. a
Schimanke, S., Isaksson, L., and Edvinsson, L.: CERRA Land user guide,
https://confluence.ecmwf.int/display/CKB/Copernicus+European+Regional+ReAnalysis+%28CERRA%29%3A+product+user+guide (last access: 16 August 2023),
2022. a
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau,
J. Am. Stat. Assoc., 63, 1379–1389,
https://doi.org/10.1080/01621459.1968.10480934, 1968. a
Soci, C., Bazile, E., Besson, F., and Landelius, T.: High-resolution
precipitation re-analysis system for climatological purposes, Tellus A, 68, 29879,
https://doi.org/10.3402/tellusa.v68.29879, 2016. a, b
Squintu, A. A., van der Schrier, G., Brugnara, Y., and Klein Tank, A.:
Homogenization of daily temperature series in the European Climate Assessment
& Dataset, Int. J. Climatol., 39, 1243–1261,
https://doi.org/10.1002/joc.5874, 2019.
a
Taillefer, F.: CANARI: Technical documentation, CNRM/GMAP Internal Rep.,
Météo-France, 2002. a
Termonia, P., Fischer, C., Bazile, E., Bouyssel, F., Brožková, R., Bénard, P., Bochenek, B., Degrauwe, D., Derková, M., El Khatib, R., Hamdi, R., Mašek, J., Pottier, P., Pristov, N., Seity, Y., Smolíková, P., Španiel, O., Tudor, M., Wang, Y., Wittmann, C., and Joly, A.: The ALADIN System and its canonical model configurations AROME CY41T1 and ALARO CY40T1, Geosci. Model Dev., 11, 257–281, https://doi.org/10.5194/gmd-11-257-2018, 2018. a
Terzago, S., Fratianni, S., and Cremonini, R.: Winter precipitation in Western
Italian Alps (1926–2010) trends and connections with the North
Atlantic/Arctic Oscillation, Meteorol. Atmos. Phys., 119,
125–136, https://doi.org/10.1007/s00703-012-0231-7, 2013. a
Terzago, S., von Hardenberg, J., Palazzi, E., and Provenzale, A.: Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models, The Cryosphere, 11, 1625–1645, https://doi.org/10.5194/tc-11-1625-2017, 2017. a, b
Terzago, S., Andreoli, V., Arduini, G., Balsamo, G., Campo, L., Cassardo, C., Cremonese, E., Dolia, D., Gabellani, S., von Hardenberg, J., Morra di Cella, U., Palazzi, E., Piazzi, G., Pogliotti, P., and Provenzale, A.: Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments, Hydrol. Earth Syst. Sci., 24, 4061–4090, https://doi.org/10.5194/hess-24-4061-2020, 2020. a
Thorne, P. and Vose, R.: Reanalyses suitable for characterizing long-term
trends, B. Am. Meteorol. Soc., 91, 353–362,
https://doi.org/10.1175/2009BAMS2858.1, 2010. a
Uppala, S. M., Kållberg, P., Simmons, A., et al.: The
ERA-40 re-analysis, Q. J. Roy. Meteor.
Soc., 131, 2961–3012, 2005. a
Vernay, M., Lafaysse, M., Monteiro, D., Hagenmuller, P., Nheili, R., Samacoïts, R., Verfaillie, D., and Morin, S.: The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021), Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, 2022. a
Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux,
J.-M.: A 50-year high-resolution atmospheric reanalysis over France with the
Safran system, Int. J. Climatol., 30, 1627–1644,
https://doi.org/10.1002/joc.2003, 2010. a
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a
Vionnet, V., Six, D., Auger, L., Dumont, M., Lafaysse, M., Quéno, L.,
Réveillet, M., Dombrowski Etchevers, I., Thibert, E., and Vincent, C.:
Sub-kilometer precipitation datasets for snowpack and glacier modeling in
alpine terrain, Front. Earth Sci., 7,
https://doi.org/10.3389/feart.2019.00182, 2019. a
Wrzesien, M. L., Pavelsky, T. M., Durand, M. T., Dozier, J., and Lundquist,
J. D.: Characterizing biases in mountain snow accumulation from global data
sets, Water Resour. Res., 55, 9873–9891, https://doi.org/10.1029/2019WR025350,
2019. a
Short summary
Beyond directly using in situ observations, often sparsely available in mountain regions, climate model simulations and so-called reanalyses are increasingly used for climate change impact studies. Here we evaluate such datasets in the European Alps from 1950 to 2020, with a focus on snow cover information and its main drivers: air temperature and precipitation. In terms of variability and trends, we identify several limitations and provide recommendations for future use of these datasets.
Beyond directly using in situ observations, often sparsely available in mountain regions,...
