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1. A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with theERA5 reanalysis for climate change impact assessments

Author

Thomas Noël, Mathieu Vrac, Guillaume Levavasseur, Dimitri Defrance, Harilaos Loukos, the Climate Data Factory, 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 Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (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)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Fondation de l'École Polytechnique, FX Centre National de la Recherche Scientifique, CNRS, The activities described in this paper were funded by the Copernicus Climate Change Service. ECMWF implements the Copernicus Climate Change Service and the Copernicus Atmosphere Monitoring Service on behalf of the European Commission. To process the data, this study benefited from the IPSL mesocenter ESPRI facility that is supported by CNRS , SU , and Ecole Polytechnique partly funded by IS-ENES3 project. We also thank the Institut Pierre Simon Laplace for assistance with the Synda software and the Deutsches Klimarechenzentrum for the Quality Assurance tool., The Climate Data Factory, 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)-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 Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (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)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)

Subjects
Science (General), [SDV]Life Sciences [q-bio], Computer applications to medicine. Medical informatics, R858-859.7, Climate change, Wind speed, Q1-390, 03 medical and health sciences, 0302 clinical medicine, Downscaling, CMIP5, Impact modeling, Precipitation, Adaptation, Data Article, 030304 developmental biology, Projections, 0303 health sciences, Multidisciplinary, Impact assessment, 13. Climate action, Greenhouse gas, Climatology, Environmental science, ERA5, Climate model, High-resolution, 030217 neurology & neurosurgery, Quantile
Abstract

International audience; A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modeling community standards and value checking for outlier detection.

Published
2021

2. Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales

Author

Yoshihide Wada, Fang Zhao, Yasushi Honda, Thomas Hickler, Jörg Steinkamp, Nikolay Khabarov, Tobias Stacke, Lila Warszawski, David N. Bresch, Tobias Geiger, Wim Thiery, Kazuya Nishina, Jonas Jägermeyr, Sebastian Ostberg, Hannes Müller Schmied, Manolis Grillakis, Iliusi Vega, Kerry Emanuel, Akihiko Ito, Philippe Ciais, Sonia I. Seneviratne, Stefan Lange, Naota Hanasaki, Jinfeng Chang, Jan Volkholz, Aristeidis Koutroulis, Christian Folberth, Matthias Büchner, Christopher P. O. Reyer, Minoru Yoshikawa, Sven Willner, Christoph Müller, Katja Frieler, Hong Yang, Jacob Schewe, Simon N. Gosling, Alexandra Henrot, Dieter Gerten, Marie Dury, Veronika Huber, Chao Yue, Wenfeng Liu, Ted Veldkamp, Hydrology and Hydraulic Engineering, 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), Environmental Restoration and Conservation Agency, ERCA 821010 641816 SAW‐2016‐PIK‐1, 603864 Bundesministerium für Bildung und Forschung, BMBF: 01LA1829A, 01LS1201A2, 01LS1711F Ministry of Economy, Trade and Industry, METI Eidgenössische Technische Hochschule Zürich, ETH: Fel‐45 15‐1 Ministerio de Economía y Competitividad, MINECO: PCIN‐2017‐046, We thank the editor and two anonymous reviewers for constructive feedback. We thank three other anonymous reviewers for helpful comments on an earlier version of this paper submitted to a different journal. This research was supported in part by the German Federal Ministry of Education and Research (BMBF, grant numbers 01LS1201A2, 01LS1711F, and 01LA1829A) and the EU FP7 HELIX project (grant number 603864). Some authors acknowledge support from the Leibniz Competition projects SAW‐2013‐PIK‐5 (EXPACT) and SAW‐2016‐PIK‐1 (ENGAGE). Some authors acknowledge funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 821010 (CASCADES). N. H., K. N., and Y. H. acknowledge support from the ERTD Funds 2RF‐1802 and S‐14 of the Environmental Restoration and Conservation Agency of Japan. W. T. was supported by an ETH Zurich postdoctoral fellowship (Fel‐45 15‐1). V. H. was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO, grant number PCIN‐2017‐046). P. C. acknowledges support from the CLAND ANR Convergence Institute. S. L. acknowledges funding from the European Union's Horizon 2020 research and innovation program under grant agreement number 641816 (CRESCENDO). Open access funding enabled and organized by Projekt DEAL., 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 Water and Climate Risk

Subjects
010504 meteorology & atmospheric sciences, HYDROLOGICAL MODELS, Population, 0207 environmental engineering, FLOOD RISK, Environmental Sciences & Ecology, 02 engineering and technology, Subtropics, [SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology, 01 natural sciences, Population density, Latitude, Climate-related extreme events, Earth and Planetary Sciences (miscellaneous), SDG 13 - Climate Action, Meteorology & Atmospheric Sciences, BURNED AREA, GLOBAL CROP PRODUCTION, Geosciences, Multidisciplinary, 020701 environmental engineering, education, 0105 earth and related environmental sciences, General Environmental Science, Event (probability theory), education.field_of_study, Science & Technology, Land use, Global warming, VEGETATION MODEL ORCHIDEE, Geology, 15. Life on land, TERRESTRIAL CARBON BALANCE, 13. Climate action, Climatology, Physical Sciences, TROPICAL CYCLONE ACTIVITY, HURRICANE INTENSITY, Environmental science, Tropical cyclone, INTERANNUAL VARIABILITY, Life Sciences & Biomedicine, Environmental Sciences, INCORPORATING SPITFIRE
Abstract

Summarization: The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. Presented on: Earth's Future

Published
2020