A non-stationary extreme-value approach for  climate projection ensembles: application  to snow loads in the French Alps

Le Roux, Erwan; Evin, Guillaume; Eckert, Nicolas; Blanchet, Juliette; Morin, Samuel

doi:https://doi.org/10.5194/esd-13-1059-2022

Articles | Volume 13, issue 3

https://doi.org/10.5194/esd-13-1059-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-13-1059-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 3

Research article

|

04 Jul 2022

Research article |

| 04 Jul 2022

A non-stationary extreme-value approach for climate projection ensembles: application to snow loads in the French Alps

Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin

Supplement

https://doi.org/10.5194/esd-13-1059-2022-supplement

Data sets

The S2M meteorological and snow cover reanalysis in the French mountainous areas (1958-present) M. Vernay, M. Lafaysse, P. Hagenmuller, R. Nheili, D. Verfaillie, and S. Morin https://doi.org/10.25326/37

2013 Global mean temperature of CMIP5 monthly historical and RCP experiments KNMI https://climexp.knmi.nl/CMIP5/Tglobal/index.cgi

Model code and software

guillaumeevin/pynonstationarygev: pynonstationarygev (v1.0.0) G. Evin https://doi.org/10.5281/zenodo.6769830

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Short summary

Anticipating risks related to climate extremes is critical for societal adaptation to climate change. In this study, we propose a statistical method in order to estimate future climate extremes from past observations and an ensemble of climate change simulations. We apply this approach to snow load data available in the French Alps at 1500 m elevation and find that extreme snow load is projected to decrease by −2.9 kN m⁻² (−50 %) between 1986–2005 and 2080–2099 for a high-emission scenario.