# Extreme Ground Snow Loads in Europe from 1951 to 2100
## 3 Results and Discussion  
### Climate Simulations for the Historical Period  
First, the capability of RCM ensemble to reproduce the observed climate was assessed, comparing the outcomes of historical simulations for the period 1951-2005, which are based on the measured atmospheric composition changes, with the available observations. Characteristic ground snow load maps were developed with this aim and illustrated in Figure 2.  
The maps in Figure 2 were developed extracting, for each considered GCM/RCM and for each cell in the EUR11 domain, the annual maxima in the period 1951-1990, by means of an extreme value analysis, fitting an EVI distribution. White areas in the maps in Figure 2 represent cells characterized by unrealistic snow accumulation (SWE \(>\) 70 kN/m\({}^{2}\)), which were disregarded in the analysis according to the already mentioned criterion. The reference ground snow load map, derived for each climate region from the analysis of observations covering the same period [33] during the ESLRP, and reported in EN1991-1-3 Annex C (Figure 3a) was compared with the snow load map derived according  
Figure 2: Characteristic ground snow load maps, showing load values associated to 0.02 probability of exceedance in one year, elaborated from the analysis of the nine climate models, described in Table 1, considering the period 1951–1990. to the Climate Models Ensemble mean (Figure 3b), in terms of differences of characteristic values (Figure 3c). To be consistent with EN1991-1-3 provisions, cells characterized by altitude higher than 1500 m are not represented in Figure 3.  
The reference map in Figure 3a was obtained considering the altitude of the cell of the climate model and the snow load-altitude relationship provided in EN1991-1-3:2003 [34] for the corresponding climatic regions. Error statistics (root-mean-square error, RMSE, and mean absolute error, MAE) were evaluated considering characteristic load values obtained from RCMs simulations over the period 1951-1990 (ensemble mean) and the reference values obtained from the analysis of measurements in ESRRP for the different climatic regions defined in [33; 34], shown in Figure 1. Error statistics were thus computed for each cell in the climatic region (Alpine, Mediterranean, Central East, Central West, Iberian Peninsula, UK and Eire) comparing snow loads given by the RCMs and EN provisions. Error values were then averaged for cells belonging to the same region and reported in Table 2.  
RMSE values range from 0.32 (Central West) to 2.43 \(\mathrm{kN/m^{2}}\) (Alpine) and highlight significant errors especially for the Alpine, Mediterranean and Iberian regions. Differences in error magnitude can be motivated by the different orographic features. In fact, the considerable smoothing of surface orography in RCMs, associated to cell dimension, leads to higher errors in orographically structured terrain [27], while regions characterized with small orography variability show a better performance. Moreover, higher snow loads are \begin{table}
\begin{tabular}{c c c} \hline \hline
**Climatic Region** & **RMSE (\(\mathrm{kN/m^{2}}\))** & **MAE (\(\mathrm{kN/m^{2}}\))** \\ \hline Alpine & 2.43 & 1.44 \\ Mediterranean & 1.10 & 0.73 \\ Central East & 0.39 & 0.30 \\ Central West & 0.32 & 0.16 \\ Iberian Peninsula & 0.83 & 0.34 \\ UK–Eire & 0.36 & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSE and MAE statistics between RCMs ensemble mean and EN1991-1-3:2003 ground snow load map. Figure 3: Characteristic ground snow load maps as provided in EN1991-1-3:2003 (**a**) and derived according to the Climate Models Ensemble mean (**b**), difference map between CMs ensemble and EN1991-1-3:2003 (**c**) for the period 1951–1990. associated to high altitude regions and consequently higher absolute errors are obtained for regions such as the Alps. The detected errors suggest the adoption of a calibration strategy to analyze climate projections of snow water equivalent. The FC approach is an efficient methodology, commonly used in climate impact research [20].