# Extreme Ground Snow Loads in Europe from 1951 to 2100
## 1 Introduction  
Impacts of climate change in several relevant societal and political sectors will become more severe in coming decades [1]. Looking at the available climate observations, a clear consequence of global warming is that the probability of occurrence of extreme weather and climate events has generally increased [2, 3, 4, 5]. Aiming to make key economic and policy sectors more resilient to climate change, reduction of disaster risks and development of adaptation strategies have become global and European priorities [5]. Focusing on civil engineering structures and infrastructures, the challenge is the assessment of projected changes in extremes of climatic actions over time [6, 7, 8, 9]. In structural engineering, variable actions connected to climate, such as wind, snow, precipitation, temperature and so on, are commonly referred to as \"climatic actions\" [10, 11]. The representative values of such actions are associated with specified probabilities of being exceeded during a given reference period: e.g., the characteristic value in EN1990 corresponds to \(p=2\%\) in one year [10]. The representative values are usually determined by elaborating extreme measurements covering a period of 40-50 years, assuming the climate as stationary over time. To consider the influence of changing climate, suitably extended data seriesshould be handled, including available past observations as well as future projections provided by climate models [12]. Trends of extreme temperature, precipitation and snow loads in Europe based on observations and reanalysis data have been recently discussed by the authors in [13]. In this paper, the study is extended to ground snow loads considering high resolution climate projections. Impacts of climate changes on temperature and precipitation are commonly assessed using climate projections [2; 3; 4; 5], while little has yet been done for snow [14], although it represents a significant climate variable. In addition to the complexity of the snow accumulation phenomena, it must be remarked that, on the one hand, changes in snow parameters cannot be appreciated by using only general or global circulation models (GCMs), having low grid resolution (>100 km) [14]. On the other hand, regional climate models (RCMs) with adequate grid resolutions (10-50 km) have been developed only in recent time. In any case, it is often necessary to rely on sophisticated snow models forced by climate projections of temperature and precipitation [6; 7; 15]. Recent collapses of large-span lightweight roofs [6; 7; 16; 17; 18] confirmed that extreme snow loads can cause significant economic damages and causalities [19]. A discussion about the reliability and failure of roofs subjected to snow loads can be found in [20] where a total of 249 roof collapses during the winter 2005/2006 is investigated identifying main observed causes into human errors and insufficient code provisions. Aiming to assess trends of extreme values of snow loads, a general methodology is presented in [6] and [7]. This procedure, combining available projections of daily minimum and maximum temperature and precipitation with site specific information on snowfall accumulation and melting processes, has been successfully applied to some specific regions in [8], and for the evaluation of long-term structural reliability too [21]. In climate impact studies for engineering applications, a common way to represent climate change induced variations is the \"factor of change\" (FC) or \"change factor\" approach [22; 23; 24; 25], which \"_employs the (climate) model's response to climate change to modify observations_\" [23]. Although in other contexts, like specific climatology studies FC can be associated to change of other relevant physical variables such as solar radiation and land use or land cover and so on, in the following factors of change (FCs) are used consistently with their definition in [22; 23; 24; 25], for assessing impact on climate variables relevant in engineering applications.