# Extreme Ground Snow Loads in Europe from 1951 to 2100
## 4 Conclusions  
The estimate of the evolution of extreme snow loads in a changing climate is a crucial issue for structural design of roofs. In fact, even in a warmer climate, the relevance of snow induced collapses is confirmed by the negative impacts of recent heavy snowfall events, which took place in various European countries. In this context, the current assumption of stationary climate for the statistical elaboration of historical extreme values of climatic actions, is becoming controversial due to the impacts of climate change, that are calling for the set-up of suitable adaptation strategies for a climate resilient structural design. In the paper, changes in extreme ground snow loads in Europe from 1951 to 2100, were estimated considering an ensemble of high-resolution projections, recently provided by EURO-CORDEX. Maps obtained from climate model simulations for the historical period were compared against maps based on observations, showing a general snow load underestimation by the RCMs. This general underestimation was expected and can be motivated by the gap between the scale of observations and of climate models, even at the finest available resolution. In fact, the smoothing of surface topography in RCMs and the consequent orography mismatch between RCMs grid and weather stations may lead to significant biases in snow loads, especially in orographically structured terrain. In the author's opinion, this issue can be considerably mitigated when the evolution of snow loads is described focusing on factors of change, rather than on absolute load values. In fact, the adoption of a calibration strategy based on the FC approach, seems to be very effective to overcome this problem, allowing a satisfactory estimate of the evolution over time of snow loads at the scale of observations. Consistently with the current techniques adopted to derive climatic load maps based on observations, FC maps are derived considering consecutive time windows of 40 years, shifted 10 years from each other. FC maps are an easy to use representation of climate change signals to adapt load maps in codes and standards [58; 59], as well as to assess variations in structural reliability over time under non-stationary loads [25]. In addition, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Parameter** & **RMSE Regional Average** & **RMSE Single Stations Average** \\ \hline \multirow{3}{*}{RCP4.5} & \(\frac{q_{k}}{Q}\) & 0.021 & 0.113 \\  & \(\overline{Q}\) & 0.073 & 0.150 \\  & \(\text{COV}_{Q}\) & 0.108 & 0.239 \\ \hline \multirow{3}{*}{RCP8.5} & \(\frac{q_{k}}{Q}\) & 0.018 & 0.108 \\  & \(\text{COV}_{Q}\) & 0.055 & 0.139 \\ \cline{1-1}  & \(\text{COV}_{Q}\) & 0.068 & 0.228 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE statistics between FC obtained from the RCMs ensemble and the point measurements for the investigated weather stations. uncertainty in the FC maps can be evaluated considering the variability in climate change projections too. The satisfactory agreement achieved in large areas of the European domain demonstrates that these maps can be a useful tool for planning of conservation and adaptation strategies. Moreover, the comparison of the outcomes of the procedure with the analysis of high-quality point observations at selected weather stations in Switzerland and Germany for the period 1951-2020, confirms the capability of the multi-model ensemble to reproduce historical trends of extreme snow loads on ground in that area. The investigated stations show a general decrease of characteristic values: this is the result of a significant decrease of mean annual maxima, however associated with an increased variability of the action.