# Extreme Ground Snow Loads in Europe from 1951 to 2100
## 3 Results and Discussion
### Future Climate Projections  
In this section, climate projections provided by the nine climate models described in Table 1 are analyzed to assess future changes in characteristic ground snow loads depending on the RCP4.5 and RCP8.5 scenarios. With this objective, consecutive time windows of 40 years, shifted 10 years from each other, are considered from 1950 to 2090, i.e., (1951-1990, 1961-2000, 1971-2010,..., 2050-2090). Ensemble mean FC maps referring to three time windows (1971-2010, 1991-2030 and 2011-2050) are illustrated in Figures 4 and 5, for two scenarios RCP4.5 and RCP8.5, respectively. These maps show the regional variability of the expected future trends across Europe. A general decreasing trend is obtained for most of Europe, but there are still areas characterized by constant or increasing trend in the future. The statistical significance of the expected changes was evaluated performing a two-sample Kolmogorov-Smirnov (K-S) test, for the distribution of annual maxima in the moving time windows at each cell. A 5% significance level was considered, and dots were added in the maps at cells where changes are statistically significant for the K-S test according to the majority of the investigated climate models. Although in the first time windows statistical significance is observed in few regions, it substantially increases moving far into the future; in fact, almost the whole EUR-11 domain is characterized by statistically significant changes if the time window 2011-2050 is considered (see Figures 4 and 5). In Figures 4 and 5, ensemble mean values of FC computed for each cell in the EUR11 domain are shown, however, an explicit evaluation of uncertainty among the different simulations is needed to ensure robust climate adaptation strategies [61]. A first assessment of the uncertainty in the detected changes was carried out considering the variability of different climate change projections. The agreement among each of the 18 (2 RCPs \(\times\) 9 RCMs) alternative climate projections was evaluated following the procedure illustrated in [61]. FC were classified for all grid cells into four quartiles and an integer value of 1 (lower quartile) to 4 (upper quartile) was assigned to each quartile. The quartile assignment was done separately for each of the 18 simulations, then, for each grid cell, the most frequent quartile value assigned to each grid cell (i.e., the mode) was identified together with the frequency of the mode (i.e., the number of occurrences of the mode). When all 18 simulations are available for the investigated grid cell, the frequency of the mode ranges from 5 (no agreement among simulations) to 18 (perfect agreement). This parameter represents an index of the agreement among different models and scenarios; moreover, its complement to 18 can indirectly provide a rough quantification of the uncertainty of the projections. The results are shown in terms of mode and frequency of the mode for FCs in the time window 2011-2050 with respect to 1951-1990, in Figure 6a,b, respectively. The mode of quartile (Figure 6a) shows the most frequent quartile value associated with each grid cell. For example, in a large part of the Scandinavian region (red areas) the mode is equal to four (fourth quartile), corresponding to higher FC values. In the same areas, a high frequency of the mode value is obtained, therefore a high level of agreement is achieved among the investigated ensemble members (blue areas in Figure 6b). Figure 4: FC maps (ensemble mean) for characteristic ground snow loads, associated to 0.02 probability of exceedance in one year, in the Euro-Cordex domain (first row) and a focus on central Europe (second row). Three moving time windows are considered (1971–2010 (**a**), 1991–2030 (**b**), and 2011–2050 (**c**)) in the elaboration with respect to 1951–1990, based on RCP4.5 scenario. The dots indicate statistically significant changes in the distribution of annual maxima at the 5% level. The level of agreement among climate simulations varies on the investigated territory.