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25 Oct 2021
25 Oct 2021

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

Erwan Le Roux1, Guillaume Evin1, Nicolas Eckert1, Juliette Blanchet2, and Samuel Morin3 Erwan Le Roux et al.
  • 1Univ. Grenoble Alpes, INRAE, UR ETNA, Grenoble, France
  • 2Univ. Grenoble Alpes, Grenoble INP, CNRS, IRD, IGE, Grenoble, France
  • 3Univ. Grenoble Alpes, Univ. Toulouse, Météo France, CNRS, CNRM, CEN, Grenoble, France

Abstract. Anticipating risks related to climate extremes often relies on the quantification of large return levels (values exceeded with small probability) from climate projection ensembles. Current approaches based on multi-model ensembles (MMEs) usually estimate return levels separately for each chain of the MME. By contrast, using MME obtained with different combinations of general circulation model (GCM) and regional climate model (RCM), our approach estimates return levels together from the past observations and all GCM-RCM pairs, considering both historical and future periods. The proposed methodology seeks to provide estimates of projected return levels accounting for the variability of individual GCM-RCM trajectories, with a robust quantification of uncertainties. To this aim, we introduce a flexible non-stationary generalized extreme value (GEV) distribution that includes i) piecewise linear functions to model the changes in the three GEV parameters ii) adjustment coefficients for the location and scale parameters to adjust the GEV distributions of the GCM-RCM pairs with respect to the GEV distribution of the past observations. Our application focuses on snow load at 1500 m elevation for the 23 massifs of the French Alps, which is of major interest for the structural design of roofs. Annual maxima are available for 20 adjusted GCM-RCM pairs from the EURO-CORDEX experiment, under the scenario RCP8.5. Our results show with a model-as-truth experiment that at least two linear pieces should be considered for the piecewise linear functions. We also show, with a split-sample experiment, that eight massifs should consider adjustment coefficients. These two experiments help us select the GEV parameterizations for each massif. Finally, using these selected parameterizations, we find that the 50-year return level of snow load is projected to decrease in all massifs, by −2.9 kN m−2 (−50 %) on average between 1986–2005 and 2080–2099 at 1500 m elevation and RCP8.5. This paper extends to climate extremes the recent idea to constrain climate projection ensembles using past observations.

Erwan Le Roux et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esd-2021-79', Anonymous Referee #1, 23 Nov 2021
  • RC2: 'Comment on esd-2021-79', Antonio Speranza, 27 Nov 2021
  • RC3: 'Comment on esd-2021-79', Tamas Bodai, 30 Nov 2021

Erwan Le Roux et al.

Erwan Le Roux et al.


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Short summary
Anticipating risks related to climate extremes is critical for the societal adaptation to climate change. We introduce a novel statistical method, which is flexible and quantifies uncertainties robustly, to estimate future climate extremes from past observations and a set of climate models. We apply it 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.