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3. Distribution-based pooling for combination and multi-model bias correction of climate simulations.

Author

Vrac, Mathieu, Allard, Denis, Mariéthoz, Grégoire, Thao, Soulivanh, and Schmutz, Lucas

Subjects
CLIMATE change models, ATMOSPHERIC models, CLIMATE change, SENSITIVITY analysis, QUANTILES
Abstract

For investigating, assessing, and anticipating climate change, tens of global climate models (GCMs) have been designed, each modelling the Earth system slightly differently. To extract a robust signal from the diverse simulations and outputs, models are typically gathered into multi-model ensembles (MMEs). Those are then summarized in various ways, including (possibly weighted) multi-model means, medians, or quantiles. In this work, we introduce a new probability aggregation method termed "alpha pooling" which builds an aggregated cumulative distribution function (CDF) designed to be closer to a reference CDF over the calibration (historical) period. The aggregated CDFs can then be used to perform bias adjustment of the raw climate simulations, hence performing a "multi-model bias correction". In practice, each CDF is first transformed according to a non-linear transformation that depends on a parameter α. Then, a weight is assigned to each transformed CDF. This weight is an increasing function of the CDF closeness to the reference transformed CDF. Key to the α pooling is a parameter α that describes the type of transformation and hence the type of aggregation, generalizing both linear and log-linear pooling methods. We first establish that α pooling is a proper aggregation method by verifying some optimal properties. Then, focusing on climate model simulations of temperature and precipitation over western Europe, several experiments are run in order to assess the performance of α pooling against methods currently available, including multi-model means and weighted variants. A reanalysis-based evaluation as well as a perfect model experiment and a sensitivity analysis to the set of climate models are run. Our findings demonstrate the superiority of the proposed pooling method, indicating that α pooling presents a potent way to combine GCM CDFs. The results of this study also show that our unique concept of CDF pooling strategy for multi-model bias correction is a credible alternative to usual GCM-by-GCM bias correction methods by allowing handling and considering several climate models at once. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics

Author

Faranda, Davide, Messori, Gabriele, Bourdin, Stella, Vrac, Mathieu, Thao, Soulivanh, Riboldi, Jacopo, Fromang, Sébastien, Yiou, Pascal, Faranda, Davide, Messori, Gabriele, Bourdin, Stella, Vrac, Mathieu, Thao, Soulivanh, Riboldi, Jacopo, Fromang, Sébastien, and Yiou, Pascal

Abstract

Although the life-cycle of tropical cyclones is relatively well understood, many of the underlying physical processes occur at scales below those resolved by global climate models (GCMs). Projecting future changes in tropical cyclone characteristics thus remains challenging. We propose a methodology, based on dynamical system metrics, to reconstruct the statistics of cyclone intensities in coarse-resolution datasets, where maximum wind speed and minimum sea-level pressure may not be accurately represented. We base our analysis on 411 tropical cyclones occurring between 2010 and 2020, using both ERA5 reanalysis data and observations from the HURDAT2 database, as well as a control simulation of the IPSL-CM6A-ATM-ICO-HR model. For both ERA5 and model data, we compute two dynamical system metrics related to the number of degrees of freedom of the atmospheric flow and to the coupling between different atmospheric variables, namely the local dimension and the co-recurrence ratio. We then use HURDAT2 data to develop three bias-correction approaches for SLP minima: a univariate, unconditional quantile–quantile bias correction, a quantile–quantile bias correction conditioned on the two dynamical systems metrics, and a multivariate correction method. The conditional approach generally outperforms the unconditional approach for ERA5, pointing to the usefulness of the dynamical systems metrics in this context. We then show that the multivariate approach can be used to recover a realistic distribution of cyclone intensities from comparatively coarse-resolution model data.

Published
2023
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6. Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics

Author

Faranda, Davide, Messori, Gabriele, Bourdin, Stella, Vrac, Mathieu, Thao, Soulivanh, Riboldi, Jacopo, Fromang, Sebastien, Yiou, Pascal, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), London Mathematical Laboratory, 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), Uppsala Universitet [Uppsala], Department of Meteorology [Stockholm] (MISU), Stockholm University, Modélisation du climat (CLIM), ANR-19-ERC7-0003,BOREAS,Dynamique et thermodynamique des tempêtes de neige en changement climatique(2019), European Project: 101003469,XAIDA, and European Project: 956396,EDIPI

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Atmospheric Science, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, [NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]
Abstract

International audience Although the life-cycle of tropical cyclones is relatively well understood, many of the underlying physical processes occur at scales below those resolved by global climate models (GCMs). Projecting future changes in tropical cyclone characteristics thus remains challenging. We propose a methodology, based on dynamical system metrics, to reconstruct the statistics of cyclone intensities in coarse-resolution datasets, where maximum wind speed and minimum sea-level pressure may not be accurately represented. We base our analysis on 411 tropical cyclones occurring between 2010 and 2020, using both ERA5 reanalysis data and observations from the HURDAT2 database, as well as a control simulation of the IPSL-CM6A-ATM-ICO-HR model. Using ERA5 data, we compute two dynamical system metrics related to the number of degrees of freedom of the atmospheric flow and to the coupling between different atmospheric variables, namely the local dimension and the co-recurrence ratio. We then use HURDAT2 data to develop a univariate quantile--quantile bias correction conditioned on these two metrics, as well as a multivariate correction method. The conditional approach outperforms a conventional univariate correction of the sea-level pressure data only, pointing to the usefulness of the dynamical systems metrics introduced. We then show that the multivariate approach can be used to recover a realistic distribution of cyclone intensities from comparatively coarse-resolution model data.

Published
2022

9. Detecting intense hurricanes from low resolution datasets via dynamical indicators

Author

Faranda, Davide, Messori, Gabriele, Yiou, Pascal, Thao, Soulivanh, Pons, Flavio, Dubrulle, Berengere, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), London Mathematical Laboratory, 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), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), 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)-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), Uppsala Universitet [Uppsala], Department of Meteorology [Stockholm] (MISU), Stockholm University, Systèmes Physiques Hors-équilibre, hYdrodynamique, éNergie et compleXes (SPHYNX), Service de physique de l'état condensé (SPEC - UMR3680), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut Rayonnement Matière de Saclay (IRAMIS), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], [NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]
Abstract

Although the life-cycle of hurricanes is well understood, many of the underlying physical processes occur at scales below those resolved by globalclimate models (GCMs), so that projecting future changes in hurricance characteristicsremains challenging. We assess the capability of dynamical system metrics to identify intense cyclones even in coarse resolution datasets, where wind speed may be not accurately represented. We compute dynamical indicators, namely the persistence and number of active degrees of freedom, from the horizontal wind field of 146 tropical cyclones occurred between 2010 and 2018 using ERA5 reanalysis data at 0.25° horizontal resolution, and link these to the maximum sustained winds as detected from observational datasets. Our analysis provides a representation of cyclones in phase space and allows to: i) identify different stages of the cyclones’ life cycle as distinct regions of the phase space; and ii) locate regions of the phase space associated with intense cyclones (as detected from observations). Specifically, we find that the most intense cyclones are associated with a strong decrease of the instantaneous dimension and an increase in persistence of tropical cyclones. This relation could be used for detection of intense cyclones in comparativelycoarse resolution datasets, such as those issued from GCM simulations or century-long reanalyses.

Published
2021

10. R2D2: Accounting for temporal dependences in multivariate bias correction via analogue ranks resampling

Author

Vrac, Mathieu, 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), 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), 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), and 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)-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)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [SDE.MCG]Environmental Sciences/Global Changes, [SDE]Environmental Sciences
Abstract

Over the last few years, multivariate bias correction methods have been developed to adjust spatial and/or inter-variable dependence properties of climate simulations. Most of them do not correct – and sometimes even degrade – the associated temporal features. Here, we propose a multivariate method to adjust the spatial and/or inter-variable properties while also accounting for the temporal dependence, such as autocorrelations. Our method consists in an extension of a previously developed approach that relies on an analogue-based method applied to the ranks of the time series to be corrected, rather than applied to their ``raw’’ values. Several configurations are tested and compared on daily temperature and precipitation simulations over Europe from one Earth System Model. Those differ by the conditioning information used to compute the analogues, and can include multiple variables at each given time, a univariate variable lagged over several time steps, or both – multiple variables lagged over time steps. Compared to the initial approach, results of the multivariate corrections show that, while the spatial and inter-variable correlations are still satisfactorily corrected even when increasing the dimension of the conditioning, the temporal autocorrelations are improved with some of the tested configurations of this extension. A major result is also that the choice of the information to condition the analogues is key since it partially drives the capability of the proposed method to reconstruct proper multivariate dependencies.

Published
2020

11. LETTER Conditional and residual trends of singular hot days in Europe

Author

Jézéquel, Aglaé, Bevacqua, Emanuele, D’andrea, Fabio, Thao, Soulivanh, Vautard, Robert, Vrac, Mathieu, Yiou, Pascal, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Department of Meteorology [Reading], University of Reading (UOR), 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), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), 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)-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), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), 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), and 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)

Subjects
extreme events, thermodynamics, climate change, [SDE]Environmental Sciences, dynamics, attribution
Abstract

International audience; The influence of anthropogenic climate change on both mean and extremely hot temperatures in Europe has been demonstrated in a number of studies. There is a growing consensus that high temperature extremes have increased more rapidly than the regional mean in central Europe, while the difference between extreme and mean trends is not significant in other European regions. However, it is less clear how to quantify the changes in different processes leading to heat extremes. Extremely hot temperatures are associated to a large extent with specific types of atmospheric circulation. Here we investigate how the temperature associated with atmospheric patterns leading to extremely hot days in the present could evolve in the future. We propose a methodology to calculate conditional trends tailored to the circulation patterns of specific days by computing the evolution of the temperature for days with a similar circulation to the day of interest. We also introduce the concept of residual trends, which compare the conditional trends to regional mean temperature trends. We compute these trends for two case studies of the hottest days recorded in two different European regions (corresponding to the heatwaves of summer 2003 and 2010). We use the NCEP reanalysis dataset, an ensemble of CMIP5 models, and a large ensemble of a single coupled model (CESM), in order to account for different sources of uncertainty. We also evaluate how bias correction of climate simulations influences the results.

Published
2020

15. Analyses of the Northern European Summer Heatwave of 2018

Author

Yiou, Pascal, primary, Cattiaux, Julien, additional, Faranda, Davide, additional, Kadygrov, Nikolay, additional, Jézéquel, Aglae, additional, Naveau, Philippe, additional, Ribes, Aurelien, additional, Robin, Yoann, additional, Thao, Soulivanh, additional, van Oldenborgh, Geert Jan, additional, and Vrac, Mathieu, additional

Published
2020
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16. Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation.

Author

Krouma, Meriem, Yiou, Pascal, Déandreis, Céline, and Thao, Soulivanh

Subjects
PRECIPITATION forecasting, WEATHER forecasting, GEOPOTENTIAL height, STATISTICAL sampling, LEAD time (Supply chain management)
Abstract

In this study, we aim to assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities of Western Europe. The SWG is based on random sampling of analogs of the geopotential height at 500 hPa. The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) for lead times of 5 and 10 days for different areas in Europe. We found that the low predictability of our model is related to specific weather regimes, depending on the European region. Comparing SWG forecasts to ECMWF forecasts, we found that the SWG shows a good performance for 5 days. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g. region for analogs) must be tuned for each region in order to optimize its performance. [ABSTRACT FROM AUTHOR]

Published
2021
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18. Tendance et variabilité de la vapeur d'eau atmosphérique : un enjeu pour l'étude du niveau moyen océanique

Author

Thao, Soulivanh and Thao, Soulivanh

Abstract

La mesure du niveau de la mer par altimétrie satellitaire est perturbée par la présence de vapeur d'eau dans l'atmosphère. Un radiomètre micro-onde, sur les missions altimétriques, est chargé de corriger les mesures de ces perturbations. Les exigences quant à la qualité de cette correction, appelée correction troposphérique humide, sont particulièrement fortes pour l'étude des changements climatiques. Cette thèse a pour objet l'étude des corrections troposphériques humides utilisées dans le cadre des missions altimétriques Jason-1 et Envisat. L'objectif est de caractériser les incertitudes liées à la correction et d'identifier les potentielles anomalies présentes. L'étude faire ressortir une potentielle dérive dans l'étalonnage du radiomètre de la mission Jason-1 après 2008. Pour la mission Envisat, l'analyse met en avant des biais régionaux à l'approche des côtes. Ces derniers sont probablement liés au traitement de la donnée radiométrique., Measurements of the sea surface height are disturbed by the presence of water vapor in the atmosphere. A microwave radiometer, on altimetric missions, is used to correct the measurements from theses disturbances. Requirements on the quality of this correction, called the wet tropospheric correction, are stringent for the survey of climate changes. This thesis concerns the monitoring of the wet tropospheric correction used in the altimetry missions, Jason-1 and Envisat. The aim is to characterize uncertainties related to this correction and to identify potential anomalies. The analysis brings out a potential drift in the radiometer used on Jason-1, after 2008. For the Envisat missions, the presence of biases near coastlines suggests processing related issues.

Published
2014

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