# Extreme Ground Snow Loads in Europe from 1951 to 2100
## 2 Material and Methods
### Datasets  
Aiming at evaluating the evolution of snow load on ground in Europe, the climate model data produced by the EURO-CORDEX initiative [25; 26], which are provided by the Earth System Grid Federation (ESGF), are considered in the following, focusing on snow variables [14]. Daily snow water equivalent data were collected from the combination of nine global and regional climate models as described in Table 1. The nine GCMs/RCMs were chosen for the availability of climate projections for both RCP4.5, medium scenario with stabilization of radiative forcing due to climate policy measures to limit emissions, and RCP8.5, representative of the highest emissions and non-climate policy scenarios. These scenarios are generally adopted in impact studies due to the higher availability of climate model runs with respect to RCP2.6 and RCP6. The discussion about the applicability of climate change scenarios is rapidly evolving and at present, RCP4.5 appears to offer more realistic baselines [50] while the use of RCP8.5 is debated within the scientific community [51; 52].  
The selected RCMs provide not only future projections for the period 2006-2100, but also historical simulations for the period 1951-2005. Therefore, the historical run and the RCPs ones are merged to obtain two series of SWE for the period 1951-2100.  
Since the outcome of each climate model can represent an independent sample of future climate, a combination of available models, as large as possible, should be considered to increase the robustness of the assessment. The opportunity to adopt a multi-model ensemble for climate change studies is presented and discussed in [53]. A methodology to enlarge the number of simulations, which are strictly limited by the huge computational time of climate model runs, thus increasing the statistical representatives of the ensemble, has been previously presented by the authors in [54; 55]. Statistical adjustment procedures based on data augmentation and Bayesian inference are presented in [56] and recently applied to large RCM ensemble for the assessment of uncertainty associated to the expected changes in mean temperature and total precipitation in Europe [57].  
Regarding climate projections of snow variables, some inconsistencies were already identified in previous studies for high altitude cells [14], usually above 1500 m. For example, the snow accumulation process may lead to cells characterized by constant snow cover, and unrealistic snow accumulation. This result is not surprising since the resolution and the refinement of the climate model does not allow to capture huge local variations; for this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Acronym** & **Institution Acronym** & **Driving GCM** & **RCM** \\ \hline EC-EARTH-CCLM & CLMcom & ICHEC-EC-EARTH & CCLM4-8-17 \\ HAdGEM2-CCLM & CLMcom & MOHC-HadGEM2-ES & CCLM4-8-17 \\ MPI-CCLM & CLMcom & MPI-M-MPI-ESM-LR & CCLM4-8-17 \\ EC-EARTH- & DMI & ICHEC-EC-EARTH & HIRHAM5 v2 \\ HAdGEM2-HIRHAM & DMI & MOHC-HadGEM2-ES & HIRHAM5 v2 \\ EC-EARTH-RACMO & KNMI & ICHEC-EC-EARTH & RACMO 22E \\ MPI-Remo2009 & MPI-CSC & MPI-M-MPI-ESM-LR & Remo 2009 \\ NCC-Remo2015 & MPI-CSC & NCC-NorESM1-M & Remo 2015 \\ IPSL-WRF381P & IPSL-INERIS & IPSL-IPSL-CM5A-MR & WRF381P \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the main characteristics of the of the members of climate model ensemble used in the study. Data are provided for the historical, RCP4.5 and RCP8.5 experiment by the following Institutions: the Climate Limited-area Modeling Community (CLMcom), the Danish Meteorological Institute (DMI), Royal Netherlands Meteorological Institute (KNMI), the Max Planck Institute (MPI-CSC) and the Laboratoire des Sciences du Climat et de l’Environnement—Institute Pierre Simon Laplace (IPSL-INERIS).  
reason, the grid cells affected by unrealistic snow accumulation, higher than 70 kN/m\({}^{2}\) according to [14], were disregarded in the elaboration of RCMs outputs.