DOI: 10.5281/zenodo.15190216
title: Estimating changes in extreme snow load conditionally to global warming levels

description: Codes.zip

The codes process and produce the figures of the paper "G. Evin, E. Le Roux, E. Kamir, and S. Morin. Estimating changes in extreme snow load in Europe as a function of global warming levels. Cold Regions Science and Technology, 231:104424, March 2025. ISSN 0165-232X. doi: 10.1016/j.coldregions.2025.104424. URL https://www.sciencedirect.com/science/article/pii/S0165232X25000072". The results shown in this study rely on the annual maxima of snow water equivalent contained in the data set MTMSI-max-swe.zip (see below).

GlobalTempSmooth.RData

R file containing smoothed global average temperature at the planetary scale for different CMIP5 GCMs, used to provide warming levels under past and future climates as predictors of the statistical models used in Evin et al. (2024). In particular, "globalTempRef" is a list that contains, for each element corresponding to a climate simulation, a vector of 241 warming levels corresponding to the years 1860 to 2100.

NUTS.zip

The different shapefiles contain information about the NUTS-3 for which timeseries are available. 'nuts_id' attribute gives the identifier of each NUTS-3, 'nuts_name' gives its name, 'relief' provides the NUTS-3 type (either mountain called 'montagne', or plain called 'plaine'), finally 'alt_ideal' gives the mean elevation of the NUTS-3 rounded to the hundred.

MTMSI-max-swe.zip and MTMSI-max-sd.zip

These two data sets provide simulations of annual maxima of snow water equivalent (SWE) and snow depth (sd) under past and future climates (EURO-CORDEX climate projections, see below), from 1951 to 2100, that have been calculated on each of the hydrological years from 01/08/Y-1 to 31/07/Y. It covers Europe at NUTS-3 scale (Nomenclature des Unités Territoriales Statistiques, eur, 2015) by steps of 100 m elevation. It relates to the C3S Mountain Tourism Meteorological and Snow Indicators (MTMSI, Morin et al., 2021) data set, consisting of various meteorological and snow indicators at annual scale. Snow conditions are computed by the one-dimensional multi-layer physical snow model Crocus, part of the SURFEX/ISBA land surface model (Vionnet et al., 2012), which takes meteorological forcings as inputs to simulate the state of the snowpack. There is one netcdf file for each climate simulation (one scenario/GCM/RCM).

UERRA: a reanalysis used as a reference data set

The atmospheric reanalysis fields used a reference for adjusting climate projections were extracted from the UERRA MESCAN-SURFEX (UERRA) reanalysis, which spans the time period from 1961 to 2015, at 5.5 km horizontal resolution (Soci et al., 2016), for selected grid points by NUTS-3 areas. In mountainous areas, the data is provided for several elevation steps of 100 m, while for non-mountainous areas the data is provided at the mean elevation of the NUTS-3 region.

EURO-CORDEX GCM/RCM climate projections

The projected atmospheric fields derived from the CMIP5 EURO-CORDEX GCM/RCM pairs which represent 20 future climate change scenarios for historical runs (from 1951 to 2005) and different emission scenarios (from 2005 to 2100): 9 GCM/RCM pairs for RCP4.5 and RCP8.5, including 2 for RCP2.6. The projected atmospheric fields were adjusted by the ADAMONT method (Verfaillie et al., 2017), with the UERRA reanalysis as an observational reference.

References



Regions in the European Union: nomenclature of territorial units for statistics, NUTS 2013/EU-28. Technical report, Publications Office, 2015.

Samuel Morin, Raphaëlle Samacoı̈ts, Hugues François, Carlo M. Carmagnola, Bruno Abegg, O. Cenk Demiroglu, Marc Pons, Jean-Michel Soubeyroux, Matthieu Lafaysse, Sam Franklin, Guy Griffiths, Debbie Kite, Anna Amacher Hoppler, Emmanuelle George, Carlo Buontempo, Samuel Almond, Ghislain Dubois, and Adeline Cauchy. Pan-European meteorological and snow indicators of climate change impact on ski tourism. Climate Services, 22:100215, April 2021. ISSN 2405-8807. doi: 10.1016/j.cliser.2021.100215. URL https://www.sciencedirect.com/science/article/pii/S2405880721000030.

Soci, C., Bazile, E., Besson, F. and Landelius, T. High-resolution precipitation re-analysis system for climatological purposes. Telus A: Dynamic Meteorology and Oceanography, 68(1):29879, December 2016. ISSN null. doi: 10.3402/tellusa.v68.29879. URL https://doi.org/10.3402/tellusa.v68.29879. Publisher: Taylor & Francis eprint: https://doi.org/10.3402/tellusa.v68.29879.

Evin, E. Le Roux, E. Kamir, and S. Morin. Estimating changes in extreme snow load in Europe as a function of global warming levels. Cold Regions Science and Technology, 231:104424, March 2025. ISSN 0165-232X. doi: 10.1016/j.coldregions.2025.104424. URL https://www.sciencedirect.com/science/article/pii/S0165232X25000072



Vionnet, V., E. Brun, S. Morin, A. Boone, S. Faroux, P. Le Moigne, E. Martin, et J.-M. Willemet. « The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2 ». Geosci. Model Dev. 5, no 3 (2012): 773‑91. https://doi.org/10.5194/gmd-5-773-2012.




Verfaillie, D., M. Déqué, S. Morin, et M. Lafaysse. « The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models ». Geosci. Model Dev. 10, no 11 (24 novembre 2017): 4257‑83. https://doi.org/10.5194/gmd-10-4257-2017.