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4 results on '"FRAISE-LEFE/INSU"'

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

Author

Gonzalez, Paula, Naveau, Philippe, Thao, Soulivanh, Worms, Julien, 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), and European Project: 101003469,XAIDA

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Climate extreme events, Detection & attribution, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Precipitation, Extreme event attribution
Abstract

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

Published
2023

2. Multimodel Errors and Emergence Times in Climate Attribution Studies

Author

Naveau, Philippe, Thao, Soulivanh, 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), Part of this work was supported by the DAMOCLES-COST-ACTION on compound events, French national program (FRAISE-LEFE/INSU and 80 PRIME CNRS-INSU) Institut national des sciences de l'Univers, INSU,CNRS, ANR-19-CE46-0011,MeLODy,Bridging geohysics and MachinE Learning for the modeling, simulation and reconstruction of Ocean DYnamics(2019), ANR-20-CE40-0025,T-REX,nouveaux challenges pour la prédiction des extremes et sa validation(2020), and European Project: 101003469,XAIDA

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Atmospheric Science, Bias, General circulation models, Statistics, Uncertainty, Model comparison, Model evaluation/performance, Model errors
Abstract

Global climate models, like any in silico numerical experiments, are affected by different types of bias. Uncertainty quantification remains a challenge in any climate detection and attribution analysis. A fundamental methodological question is to determine which statistical summaries, while bringing relevant signals, can be robust with respect to multimodel errors. In this paper, we propose a simple statistical framework that significantly improves signal detection in climate attribution studies. We show that the complex bias correction step can be entirely bypassed for models for which bias between the simulated and unobserved counterfactual worlds is the same as between the simulated and unobserved factual worlds. To illustrate our approach, we infer emergence times in precipitation from the CMIP5 and CMIP6 archives. The detected anthropogenic signal in yearly maxima of daily precipitation clearly emerges at the beginning of the twenty-first century. In addition, no CMIP model seems to outperform the others and a weighted linear combination of all improves the estimation of emergence times. Significance Statement We show that the bias in multimodel global climate simulations can be efficiently handled when the appropriate metric is chosen. This metric leads to an easy-to-implement statistical procedure based on a checkable assumption. This allows us to demonstrate that optimal convex combinations of CMIP outputs can improve the signal strength in finding emergence times. Our data analysis procedure is applied to yearly maximum of precipitation from CMIP5 and CMIP6 databases. The attribution of the anthropogenic forcing clearly emerges in extreme precipitation at the beginning of the twenty-first century.

Published
2022
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3. Evaluation of binary classifiers for asymptotically dependent and independent extremes

Author

Legrand, Juliette, Naveau, Philippe, Oesting, Marco, 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), University of Stuttgart, Part of this work was supported by the DAMOCLES-COST-ACTION on compound events, Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016, French national program (FRAISE-LEFE/INSU and 80 PRIME CNRSINSU), ANR-21-RHUS-0016,EpCART,Epigenetic re-programming of CAR T cells for solid tumors(2021), ANR-19-CE46-0011,MeLODy,Bridging geohysics and MachinE Learning for the modeling, simulation and reconstruction of Ocean DYnamics(2019), and European Project: 101003469,XAIDA

Subjects
Methodology (stat.ME), FOS: Computer and information sciences, Multivariate Extreme Value Theory, Multivariate Reg- ular Variation, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Hidden Regular Variation, River Discharges, Binary Classification, Multivariate Regular Variation, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Statistics - Methodology
Abstract

Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this paper, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic of scoring rules used in forecasting literature. In this context, we propose and study a risk function adapted to extremal classifiers. The inferential properties of our empirical risk estimator are derived under the framework of multivariate regular variation and hidden regular variation. A simulation study compares different classifiers and indicates their performance with respect to our risk function. To conclude, we apply our framework to the analysis of extreme river discharges in the Danube river basin. The application compares different predictive algorithms and test their capacity at forecasting river discharges from other river stations. As a byproduct, we study the special class of linear classifiers, show that the optimisation of our risk function leads to a consistent solution and we identify the explanatory variables that contribute the most to extremal behavior.

Published
2021

4. Improved regional frequency analysis of rainfall data

Author

Philomène Le Gall, Anne-Catherine Favre, Philippe Naveau, Clémentine Prieur, Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Mathematics and computing applied to oceanic and atmospheric flows (AIRSEA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Grenoble Alpes (UGA)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), 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), Within the CDP-Trajectories framework, this work is supported by the French National Research Agency in the framework of the 'Investissements d’avenir' program (ANR-15-IDEX-02). We gratefully acknowledge financial support for this study provided by the Swiss Federal Office for Environment (FOEN), the Swiss Federal Nuclear Safety Inspectorate (ENSI), the Federal Office for Civil Protection (FOCP), and the Federal Office of Meteorology and Climatology, MeteoSwiss, through the project EXAR ('Evaluation of extreme Flooding Events within the Aare-Rhine hydrological system in Switzerland'). Part of this work was also supported by the French national programs (FRAISE-LEFE/INSU and 80 PRIME CNRS-INSU), and the European H2020 XAIDA (Grant agreement ID: 101003469). Philippe Naveau also acknowledge the support of the French Agence Nationale de la Recherche (ANR) under reference ANR-20-CE40-0025-01 (T-REX project), and the ANR-Melody (ANR-19-CE46-0011)., 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), ANR-15-IDEX-0002,UGA,IDEX UGA(2015), and Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
spatial clustering, [STAT]Statistics [stat], Atmospheric Science, precipitation extremes, Geography, Planning and Development, [SDE]Environmental Sciences, Management, Monitoring, Policy and Law, [MATH]Mathematics [math], probability weighted moments, Physics::Atmospheric and Oceanic Physics, extended generalized Pareto distribution
Abstract

International audience; Rainfall are subject to local orography features and their intensities can be highly variable. In this context, identifying climatically coherent regions can greatly help interpreting rainfall patterns and improve the inference of return levels. In practice, partitioning a region of interest into homogeneous sub-regions is a delicate statistical task, especially in regards to modeling heavy rainfall features. In this work, our main goal is to propose and study a fast and efficient clustering algorithm. Compared to classical regional frequency analysis techniques, a key aspect is that our algorithm does not rely on the a priori choice of covariates. The proposed numerical scheme is only based on the precipitation dataset at hand, including low, moderate and heavy rainfall. In terms of inference, our approach builds on the easy-to-compute and reliable probability weighted moments commonly used in hydrology. While being in compliance with extreme value theory, we do not impose a parametric form on rainfall distributions and, neither thresholding nor block maxima steps are required in our proposed approach. By construction, our clustering method preserves the scale invariance principle of any classical regional frequency analysis. The performance of our clustering algorithm is assessed on a detailed experimental design based on the extended Generalized Pareto distribution. Sensitivity to the number of clusters is carefully analyzed. We apply our clustering algorithm on Switzerland daily precipitation measured at 191 sites. The found homogeneous regions are consistent with local orography and our approach outperforms the classical regional frequency analysis based on normalized elevation and coordinates as covariates. To complete our analysis of Swiss rainfall, we propose three models based on our clustering outputs. A comparison between our local, semi-regional and regional models indicates that a relatively simple model with two clusters and a spatially varying scale parameter can compete very well against complex models.

Published
2021
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