A statistical method to model non-stationarity in precipitation records changes

• Main Authors: Gonzalez, Paula, Naveau, Philippe, Thao, Soulivanh, Worms, Julien
• Other Authors: Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mathématiques de Versailles (LMV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), FRAISE-LEFE/INSU, 80 PRIME CNRS-INSU, ANR-20-CE40-0025,T-REX,nouveaux challenges pour la prédiction des extremes et sa validation(2020), ANR-19-CE46-0011,MeLODy,Bridging geohysics and MachinE Learning for the modeling, simulation and reconstruction of Ocean DYnamics(2019), European Project: 101003469,XAIDA
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: Extreme event attribution; Climate extreme events; Detection & attribution; Precipitation; [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]; [SDU.STU]Sciences of the Universe [physics]/Earth Sciences; North Atlantic
• Online Access: https://hal.science/hal-04006516; https://hal.science/hal-04006516/document; https://hal.science/hal-04006516/file/Paula_article_Hal.pdf

International audience In the context of climate change, assessing how likely a particular change or event has been caused by human influence is important for mitigation and adaptation policies. In this work we propose an extreme event attribution (EEA) methodology to analyze yearly maxima records, key indicators of climate change that spark media attention and research in the EEA community. Although they deserve a specific statistical treatment, algorithms tailored to record analysis are lacking. This is particularly true in a non-stationarity context. This work aims at filling this methodological gap by focusing on records in transient climate simulations. We apply our methodology to study records of yearly maxima of daily precipitation issued from numerical climate model IPSL-CM6A-LR. Illustrating our approach with decadal records, we detect in 2023 a clear human induced signal in half of the globe, with probability mostly increasing, but decreasing in the south and north Atlantic oceans