Publications

You can also find my articles on my Google Scholar profile.

Integrating non-stationarity and uncertainty in design life levels based on climatological time series

Published in Weather and Climate Extremes, 2025

This paper presents some of my thesis methodological developments, the combined predictive equivalent reliability, which aims to keep design-period exceedance risk identical to existing practice in the context of non-stationary climate change, while including stochastic and estimation uncertainty. It is applied to Southern France annual maxima.

Recommended citation: Occitane Barbaux, Philippe Naveau, Nathalie Bertrand, Aurélien Ribes, Integrating non-stationarity and uncertainty in design life levels based on climatological time series, Weather and Climate Extremes, Volume 50, 2025, 100807, ISSN 2212-0947, https://doi.org/10.1016/j.wace.2025.100807. https://www.sciencedirect.com/science/article/pii/S2212094725000659

An automatic procedure for the attribution of extreme events at the global scale: a proof of concept for heatwaves

Published in Cover Bulletin of the American Meteorological Society Bulletin of the American Meteorological Society, 2025

This paper builds upon the technical developments presented in my thesis, including the use of MCMC chains, to propose an automatic and online attribution method for extreme events.

Recommended citation: Qasmi, S., A. Ribes, J. Cattiaux, O. Barbaux, Y. Robin, and W. Dulac, 2025: An automatic procedure for the attribution of extreme events at the global scale: a proof of concept for heatwaves. Bull. Amer. Meteor. Soc., BAMS-D-24-0265.1, https://doi.org/10.1175/BAMS-D-24-0265.1, in press. https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-24-0265.1/BAMS-D-24-0265.1.xml

A Bayesian statistical method to estimate the climatology of extreme temperature under multiple scenarios: the ANKIALE package

Published in Egusphere, 2025

This paper builds upon the technical developments presented in my thesis, including the use of MCMC chains, to propose a method and associated Python package for estimating the statistics of extreme temperatures in a Bayesian framework.

Recommended citation: Robin, Y., Vrac, M., Ribes, A., Barbaux, O., and Naveau, P.: A Bayesian statistical method to estimate the climatology of extreme temperature under multiple scenarios: the ANKIALE package, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-1121, 2025. https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1121/