An attempt to explain recent changes in European snowfall extremes

Faranda, Davide

doi:10.5194/wcd-1-445-2020

Cited by 27 publications

(21 citation statements)

References 70 publications

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“…Following the block maxima approach from extreme value theory (Coles, 2001), we model annual maxima of daily 4338 E. Le Roux et al: Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019 snowfall with the generalized extreme value (GEV) distribution. Indeed theoretically, as the central limit theorem motivates asymptotically sample means modeling with the normal distribution, the Fisher-Tippett-Gnedenko theorem (Fisher and Tippett, 1928;Gnedenko, 1943) encourages asymptotically sample maxima modeling with the GEV distribution. In practice, if Y is a random variable representing an annual maximum, we can assume that Y ∼ GEV(µ, σ, ξ ).…”

Section: Statistical Distribution For Annual Maximamentioning

confidence: 99%

Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019

et al. 2021

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Abstract. Climate change projections indicate that extreme snowfall is expected to increase in cold areas, i.e., at high latitudes and/or high elevation, and to decrease in warmer areas, i.e., at mid-latitudes and low elevation. However, the magnitude of these contrasting patterns of change and their precise relations to elevation at the scale of a given mountain range remain poorly known. This study analyzes annual maxima of daily snowfall based on the SAFRAN reanalysis spanning the time period 1959–2019 and provided within 23 massifs in the French Alps every 300 m of elevation. We estimate temporal trends in 100-year return levels with non-stationary extreme value models that depend on both elevation and time. Specifically, for each massif and four elevation ranges (below 1000, 1000–2000, 2000–3000, and above 3000 m), temporal trends are estimated with the best extreme value models selected on the basis of the Akaike information criterion. Our results show that a majority of trends are decreasing below 2000 m and increasing above 2000 m. Quantitatively, we find an increase in 100-year return levels between 1959 and 2019 equal to +23 % (+32kgm-2) on average at 3500 m and a decrease of −10 % (-7kgm-2) on average at 500 m. However, for the four elevation ranges, we find both decreasing and increasing trends depending on location. In particular, we observe a spatially contrasting pattern, exemplified at 2500 m: 100-year return levels have decreased in the north of the French Alps while they have increased in the south, which may result from interactions between the overall warming trend and circulation patterns. This study has implications for natural hazard management in mountain regions.

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Section: Statistical Distribution For Annual Maximamentioning

confidence: 99%

Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019

et al. 2021

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“…Since 1979 the Barents Sea has been responsible for 95% of the observed March seaice loss across the entire Arctic 38 . We use satellite observations of sea-ice 10 and ERA5 reanalysis 35 to investigate long-term dynamic links with atmospheric moistening in the Barents Region 2,6 , as well as increasing European extreme snowfall 25 (see Methods).…”

Section: Arctic Sea-ice and European Snowfall Trendsmentioning

confidence: 99%

“…These circulation anomalies manifest as phases of negative Arctic Oscillation (AO-) and North Atlantic Oscillation (NAO-) 19,20 , that can drive cold air advection and heavy snowfall across continental mid-latitudes, such as in winters 2009-10, 2010-11, 2012-13 and 2017-18 [21][22][23][24] . However, a direct link between winter Barents sea-ice loss and the recent extreme snowy European winters 25 has yet to be substantiated.…”

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confidence: 99%

Arctic sea-ice loss fuels extreme European snowfall

et al. 2021

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The accelerated loss of Arctic sea-ice has been implicated with severe cold and snowy mid-latitude winters. However, the mechanisms and a direct link remain elusive due to limited observational evidence. Here we present atmospheric water vapour isotope measurements from Arctic Finland during "the Beast from the East" -a severe anticyclonic outbreak that brought heavy snowfall and freezing across Europe in February 2018. We find that an anomalously warm Barents Sea, with a 60% ice-free surface, supplied up to 29 9.3 mm d -1 moisture flux to this cold north-easterly airflow. We demonstrate that approximately 140 gigatonnes of water was evaporated from the Barents Sea during the event, supplying up to 88% of the corresponding fresh snow over Northern Europe. Reanalysis data show that from 1979 to 2020, net March evaporation across the Barents Sea increased by approximately 70 kg per square metre of sea-ice lost (r 2 =0.73, p<0.01), concurrent with a 1.6 mm (water equivalent) per year increase in Europe's maximum snowfall. Our analysis directly links Arctic sea-ice loss with increased evaporation and extreme snowfall, and signifies that by 2080, an Atlantified ice-free Barents Sea will be a major source of winter moisture for continental Europe.Arctic sea-ice plays a critical role in the hydrological cycle and global climate system 1 . In particular, the areal extent and concentration of sea ice controls thermodynamic and radiative processes driving water vapour, clouds, and aerosol feedbacks 2 . Accelerated sea ice loss over recent decades 3 has been linked to increased surface evaporation and latent heat flux 4,5 , as well as localised increases in cloud formation, precipitation, and radiation absorption that further amplifies Arctic warming [6][7][8] .

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“…to model the form of the tail for almost any probability distribution. Asymptotically, as the central limit theorem motivates sample means modelling with the normal distribution, the Fisher-Tippett-Gnedenko theorem (Fisher and Tippett, 1928;Gnedenko, 1943) encourages sample maxima modelling with the generalized extreme value (GEV) distribution. This theorem justifies that the maximum of finitesized blocks with a large enough block size can be modelled with the GEV distribution.…”

Section: Stationary and Non-stationary Models Based On Extreme Value mentioning

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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An attempt to explain recent changes in European snowfall extremes

Cited by 27 publications

References 70 publications

Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019

Elevation-dependent trends in extreme snowfall in the French Alps from 1959 to 2019

Arctic sea-ice loss fuels extreme European snowfall

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

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