Assessing Climate Change Impact on the Spatial Dependence of Extreme Snow Depth Maxima in the French Alps

Nicolet, Gilles; Eckert, Nicolas; Morin, Samuel; Blanchet, Juliette

doi:10.1029/2018wr022763

Cited by 15 publications

(10 citation statements)

References 66 publications

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“…The decrease in average snowfall in northern Italy observed in the last decades has been linked to the increase in temperature due to global climate change (Asnaghi, 2014;Mercalli and Berro, 2003). Similar conclusions also hold for the Alpine region (Serquet et al, 2011;Nicolet et al, 2016Nicolet et al, , 2018, and several studies (Diodato, 1995;Mangianti and Beltrano, 1991) also confirm these trends for central and southern Italy. On a more gen-eral basis, the study by Diodato et al (2019) shows that the variability of average snowfall over Italy during the past millennium can be connected to changes in temperature, with periods of abundant average snowfalls corresponding to generally colder periods (e.g.…”

Section: Introductionsupporting

confidence: 56%

Present and future synoptic circulation patterns associated with cold and snowy spells over Italy

et al. 2022

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Abstract. Cold and snowy spells are compound extreme events with the potential to cause high socioeconomic impacts. Gaining insight into their dynamics in climate change scenarios could help anticipating the need for adaptation efforts. We focus on winter cold and snowy spells over Italy, reconstructing 32 major events in the past 60 years from documentary sources. Despite warmer winter temperatures, very recent cold spells have been associated with abundant and sometimes exceptional snowfall. Our goal is to analyse the dynamical weather patterns associated with these events and understand whether those patterns would be more or less recurrent in different emission scenarios using an intermediate-complexity model (the Planet Simulator, PlaSim). Our results, obtained by considering RCP2.6, RCP4.5 and RCP8.5 end-of-century equivalent CO2 concentrations, suggest that the likelihood of synoptic configurations analogous to those leading to extreme cold spells would grow substantially under increased emissions.

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Section: Introductionsupporting

confidence: 56%

Present and future synoptic circulation patterns associated with cold and snowy spells over Italy

et al. 2022

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“…The characteristic snow load on the ground (s k ) can be calculated by assuming that yearly s k maxima follow a generalized extreme value (GEV) distribution (Coles, 2001). Among many other environmental parameters, this has been already proven to be reasonable for snow depth (e.g., Marty and Blanchet, 2012;Nicolet et al, 2018). Given the strong correlation between snow depth and snow load, the GEV has also successfully been used for the estimation of extreme snow loads (Le Roux et al, 2020).…”

Section: Spatial Extreme Value Statisticsmentioning

confidence: 99%

Towards a reproducible snow load map – an example for Austria

Schellander

Winkler²,

Hell³

2021

Adv. Sci. Res.

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Abstract. The European Committee for Standardization defines zonings and calculation criteria for different European regions to assign snow loads for structural design. In the Alpine region these defaults are quite coarse; countries therefore use their own products, and inconsistencies at national borders are a common problem. A new methodology to derive a snow load map for Austria is presented, which is reproducible and could be used across borders. It is based on (i) modeling snow loads with the specially developed Δsnow model at 897 sophistically quality controlled snow depth series in Austria and neighboring countries and (ii) a generalized additive model where covariates and their combinations are represented by penalized regression splines, fitted to series of yearly snow load maxima derived in the first step. This results in spatially modeled snow load extremes. The new approach outperforms a standard smooth model and is much more accurate than the currently used Austrian snow load map when compared to the RMSE of the 50-year snow load return values through a cross-validation procedure. No zoning is necessary, and the new map's RMSE of station-wise estimated 50-year generalized extreme value (GEV) return levels gradually rises to 2.2 kN m−2 at an elevation of 2000 m. The bias is 0.18 kN m−2 and positive across all elevations. When restricting the range of validity of the new map to 2000 m elevation, negative bias values that significantly underestimate 50-year snow loads at a very small number of stations are the only objective problem that has to be solved before the new map can be proposed as a successor of the current Austrian snow load map.

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“…Extreme snow loads can generate economic damages and casualties. For instance, more than USD 200 million in roof damages occurred during the Great Blizzard of 1993 (O'Rourke and Auren, 1997). In 2006, at the Katowice International Fair, the roof of one of the buildings collapsed under a layer of snow, leading to 65 casualties and 140 in-jured (BBC News, 2006).…”

Section: Introductionmentioning

confidence: 99%

Non-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards

Roux

Evin

Eckert

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. In a context of climate change, trends in extreme snow loads need to be determined to minimize the risk of structure collapse. We study trends in 50-year return levels of ground snow load (GSL) using non-stationary extreme value models. These trends are assessed at a mountain massif scale from GSL data, provided for the French Alps from 1959 to 2019 by a meteorological reanalysis and a snowpack model. Our results indicate a temporal decrease in 50-year return levels from 900 to 4200 m, significant in the northwest of the French Alps up to 2100 m. We detect the most important decrease at 900 m with an average of −30 % for return levels between 1960 and 2010. Despite these decreases, in 2019 return levels still exceed return levels designed for French building standards under a stationary assumption. At worst (i.e. at 1800 m), return levels exceed standards by 15 % on average, and half of the massifs exceed standards. We believe that these exceedances are due to questionable assumptions concerning the computation of standards. For example, these were devised with GSL, estimated from snow depth maxima and constant snow density set to 150 kg m−3, which underestimate typical GSL values for the snowpack.

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Assessing Climate Change Impact on the Spatial Dependence of Extreme Snow Depth Maxima in the French Alps

Cited by 15 publications

References 66 publications

Present and future synoptic circulation patterns associated with cold and snowy spells over Italy

Present and future synoptic circulation patterns associated with cold and snowy spells over Italy

Towards a reproducible snow load map – an example for Austria

Non-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards

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