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1. Quantifying CMIP6 model uncertainties in extreme precipitation projections

Author

John, Amal, Douville, Hervé, Ribes, Aurélien, Yiou, Pascal, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), 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), Horizon 2020 Framework Programme, H2020, H2020 Marie Skłodowska-Curie Actions, MSCA: 813844, Center for Agriculture, Food and the Environment, University of Massachusetts Amherst, CAFE, Horizon 2020, This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, which has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813844. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output to CMIP. We also thank the Institut Pierre-Simon Laplace (ISPL) Mésocentre for Climate Sciences for the CMIP6 data acquisition, storage space, and intensive computing resources for this paper., This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813844 . We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output to CMIP. We also thank the Institut Pierre-Simon Laplace (ISPL) Mésocentre for Climate Sciences for the CMIP6 data acquisition, storage space, and intensive computing resources for this paper., Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), 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)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Atmospheric Science, [SDU]Sciences of the Universe [physics], [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, [SDE.MCG]Environmental Sciences/Global Changes, Geography, Planning and Development, Extremes, Uncertainty, Climate change, Precipitation, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, Management, Monitoring, Policy and Law
Abstract

International audience; Projected changes in precipitation extremes and their uncertainties are evaluated using an ensemble of global climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP). They are scaled by corresponding changes either in global mean surface temperature (ΔGSAT) or in local surface temperature (ΔT) and are expressed in terms of 20-yr return values (RV20) of annual maximum one-day precipitation. Our main objective is to quantify the model response uncertainty and to highlight the regions where changes may not be consistent with the widely used assumption of a Clausius–Clapeyron (CC) rate of ≈7%/K. When using a single realization for each model, as in the latest report from the Intergovernmental Panel on Climate Change (IPCC), the assessed inter-model spread includes both model uncertainty and internal variability, which can be however assessed separately using a large ensemble. Despite the overestimated inter-model spread, our results show a robust enhancement of extreme precipitation with more than 90% of models simulating an increase of RV20. Moreover, this increase is consistent with the CC rate of ≈7%/K over about 83% of the global land domain when scaled by (ΔGSAT). Our results also advocate for producing multiple single model initial condition ensembles in the next CMIP projections, to better filter internal variability out in estimating the response of extreme events.

Published
2022

2. Demand for Information, Macroeconomic Uncertainty, and the Response of U.S. Treasury Securities to News

Author

Hedi Benamar, Thierry Foucault, Clara Vega, Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS), and HEC Research Paper Series

Subjects
Public information, Media Coverage, business.industry, Big data, Measure (physics), Uncertainty, Macroeconomic News, JEL: D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D83 - Search • Learning • Information and Knowledge • Communication • Belief • Unawareness, Investors' Attention, Treasury, Bitly, JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G14 - Information and Market Efficiency • Event Studies • Insider Trading, JEL: G - Financial Economics/G.G2 - Financial Institutions and Services/G.G2.G21 - Banks • Depository Institutions • Micro Finance Institutions • Mortgages, Future interest, Path (graph theory), Econometrics, Economics, [SHS.GESTION]Humanities and Social Sciences/Business administration, Information Demand, U.S. Treasury futures, business, Information demand, Clickstream
Abstract

We use clickstream data to show that investors' demand for information about macroeconomic factors affecting the path of future interest rates is a measure of their uncertainty about this path. In particular, an increase in information demand ahead of influential economic announcements affecting investors' beliefs about future interest rates predicts a stronger reaction of U.S. Treasury note yields to these announcements, as it should if information demand covaries positively with uncertainty. This relationship does not vanish after using standard measures of uncertainty as predictors, suggesting that clickstream data contain unique information about investors' uncertainty.

Published
2018

3. Model selection, identification and validation in anaerobic digestion: a review

Author

Cristina Martin, Jorge Rodríguez, Johan Mailier, Andres Donoso-Bravo, César Arturo Aceves-Lara, Alain Vande Wouwer, Automatic Control Laboratory, Université de Mons (UMons), modelEAU, Département de génie civil et de génie des eaux, Université Laval [Québec] (ULaval), Universidade de Santiago de Compostela [Spain] (USC ), Masdar Institute of Science and Technology [Abu Dhabi], Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control, and Optimization), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office. The scientific responsibility rests with its author(s). This study is also supported by a grant from Belspo (Belgian Science Policy) through its Postdoc fellowships to non-EU researchers program. Johan Mailier is a research fellow supported by the FNRS (Belgian National Fund for Scientific Research)., and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Engineering, Identification, Environmental Engineering, Process (engineering), 020209 energy, engineering, [SDV]Life Sciences [q-bio], 02 engineering and technology, 010501 environmental sciences, water resources, 01 natural sciences, Field (computer science), environmental, Kinetic parameters, Water Purification, Lead (geology), Digestion (alchemy), Biogas, Anaerobic digestion, [SDV.IDA]Life Sciences [q-bio]/Food engineering, 0202 electrical engineering, electronic engineering, information engineering, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, Anaerobiosis, environmental sciences, Waste Management and Disposal, 0105 earth and related environmental sciences, Water Science and Technology, Civil and Structural Engineering, Waste management, business.industry, Ecological Modeling, Model selection, Modeling, Uncertainty, Reproducibility of Results, Pollution, Identification (information), Models, Chemical, 13. Climate action, Biochemical engineering, business, Sensitivity analysis
Abstract

International audience; Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are involved, and whose start-up and operation are delicate issues. In order to better understand the process dynamics and to optimize the operating conditions, the availability of dynamic models is of paramount importance. Such models have to be inferred from prior knowledge and experimental data collected from real plants. Modeling and parameter identification are vast subjects, offering a realm of approaches and methods, which can be difficult to fully understand by scientists and engineers dedicated to the plant operation and improvements. This review article discusses existing modeling frameworks and methodologies for parameter estimation and model validation in the field of anaerobic digestion processes. The point of view is pragmatic, intentionally focusing on simple but efficient methods.

Published
2011