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1. 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
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2. Sources of Uncertainty in Regional and Global Terrestrial CO 2 Exchange Estimates

Author

Julia E. M. S. Nabel, Anthony P. Walker, Danica Lombardozzi, Naomi E. Smith, B. Poulter, Stephen Sitch, Christian Rödenbeck, Ingrid T. Luijkx, Sönke Zaehle, Frédéric Chevallier, Wei Li, J. G. Canadell, Atul K. Jain, Etsushi Kato, P. Ciais, Wouter Peters, P. Peylin, Sebastian Lienert, Daniel S. Goll, V. Haverd, Pierre Friedlingstein, Prabir K. Patra, Ronny Lauerwald, Hanqin Tian, Julia Pongratz, Michael O'Sullivan, David Makowski, Ana Bastos, College of Engineering, Mathematics and Physical Sciences [Exeter] (EMPS), University of Exeter, LUDWIG MAXIMILIANS UNIVERSITAT MUNCHEN DEPARTMENT OF GEOGRAPHY MUNICH DEU, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), 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), ICOS-ATC (ICOS-ATC), 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), Agronomie, AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), College of Life and Environmental Sciences [Exeter], Modélisation INVerse pour les mesures atmosphériques et SATellitaires (SATINV), Max-Planck-Institut für Biogeochemie (MPI-BGC), Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, WUR - Wageningen University and Research Centre [Wageningen], Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), CSIRO Oceans and Atmosphere, CISRO Oceans and Atmosphere, Department of Geosciences, Environment and Society, Université libre de Bruxelles (ULB), Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Wageningen University and Research [Wageningen] (WUR), Centre for Isotope Research [Groningen] (CIO), University of Groningen [Groningen], Department of Geography, University of Augsburg, Department of Atmospheric Sciences [Urbana], University of Illinois at Urbana-Champaign [Urbana], University of Illinois System-University of Illinois System, Institute of Applied Energy (IAE), Climate and Environmental Physics [Bern] (CEP), Physikalisches Institut [Bern], Universität Bern [Bern]-Universität Bern [Bern], CLIMATE AND GLOBAL DYNAMICS LABORATORY NATIONAL CENTER FOR ATMOSPHERIC RESEARCH BOULDER USA, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Biospheric Sciences Laboratory, NASA Goddard Space Flight Center (GSFC), INTERNATIONAL CENTER FOR CLIMATE AND GLOBAL CHANGE RESEARCH AUBURN UNIVERSITY AUBURN USA, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Max Planck Institute for Biogeochemistry (MPI-BGC), European Comission. Grant Numbers: ASICA (649087), IMBALANCE-P European Space Agency (ESA). Grant Number: ESRIN/ 4000123002/18/I-NB National Science Foundation (NSF). Grant Number: AGS 12-43071 U.S. Department of Energy (DOE). Grant Number: DESC0016323, 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)-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), Universität Bern [Bern] (UNIBE)-Universität Bern [Bern] (UNIBE), 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), ANR-16-CONV-0003,CLAND,CLAND : Changement climatique et usage des terres(2016), and Isotope Research

Subjects
0106 biological sciences, Meteorologie en Luchtkwaliteit, FLUXES, Atmospheric Science, 010504 meteorology & atmospheric sciences, Meteorology and Air Quality, Biome, BURNED AREA PRODUCT, atmospheric inversions, 01 natural sciences, Carbon cycle, DATA ASSIMILATION, Data assimilation, PLANT FUNCTIONAL TYPES, global carbon budget, carbon cycle, Environmental Chemistry, Land use, land-use change and forestry, ATMOSPHERIC CO2, 0105 earth and related environmental sciences, General Environmental Science, ddc:910, LAND-COVER CHANGE, Global and Planetary Change, WIMEK, FOSSIL-FUEL, Land use, business.industry, 010604 marine biology & hydrobiology, VEGETATION MODEL ORCHIDEE, Fossil fuel, Vegetation, 15. Life on land, CARBON-DIOXIDE EMISSIONS, 13. Climate action, Climatology, [SDE]Environmental Sciences, dynamic global vegetation models, Environmental science, business, Scale (map), INCORPORATING SPITFIRE
Abstract

The Global Carbon Budget 2018 (GCB2018) estimated by the atmospheric CO2 growth rate, fossil fuel emissions, and modeled (bottom-up) land and ocean fluxes cannot be fully closed, leading to a “budget imbalance,” highlighting uncertainties in GCB components. However, no systematic analysis has been performed on which regions or processes contribute to this term. To obtain deeper insight on the sources of uncertainty in global and regional carbon budgets, we analyzed differences in Net Biome Productivity (NBP) for all possible combinations of bottom-up and top-down data sets in GCB2018: (i) 16 dynamic global vegetation models (DGVMs), and (ii) 5 atmospheric inversions that match the atmospheric CO2 growth rate. We find that the global mismatch between the two ensembles matches well the GCB2018 budget imbalance, with Brazil, Southeast Asia, and Oceania as the largest contributors. Differences between DGVMs dominate global mismatches, while at regional scale differences between inversions contribute the most to uncertainty. At both global and regional scales, disagreement on NBP interannual variability between the two approaches explains a large fraction of differences. We attribute this mismatch to distinct responses to El Niño–Southern Oscillation variability between DGVMs and inversions and to uncertainties in land use change emissions, especially in South America and Southeast Asia. We identify key needs to reduce uncertainty in carbon budgets: reducing uncertainty in atmospheric inversions (e.g., through more observations in the tropics) and in land use change fluxes, including more land use processes and evaluating land use transitions (e.g., using high-resolution remote-sensing), and, finally, improving tropical hydroecological processes and fire representation within DGVMs.

Published
2020
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3. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions

Author

Rabin, Sam S., Melton, Joe R., Lasslop, Gitta, Bachelet, Dominique, Forrest, Matthew, Hantson, Stijn, Kaplan, Jed O., Li, Fang, Mangeon, Stéphane, Ward, Daniel S., Yue, Chao, Arora, Vivek K., Hickler, Thomas, Kloster, Silvia, Knorr, Wolfgang, Nieradzik, Lars, Spessa, Allan, Folberth, Gerd A., Sheehan, Tim, Voulgarakis, Apostolos, Kelley, Douglas I., Prentice, I. Colin, Sitch, Stephen, Harrison, Sandy, Arneth, Almut, 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), 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
[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], ENVIRONMENT SIMULATOR JULES, Science & Technology, CLIMATE-CHANGE, LAND-USE, BURNED AREA PRODUCTS, lcsh:QE1-996.5, 04 Earth Sciences, EARTH-SYSTEM, Geology, Ecology and Environment, GLOBAL VEGETATION MODEL, lcsh:Geology, TERRESTRIAL CARBON BALANCE, Earth sciences, [SDE]Environmental Sciences, Physical Sciences, ddc:550, Geosciences, Multidisciplinary, ATMOSPHERIC CO2, EMISSIONS, INCORPORATING SPITFIRE
Abstract

International audience; The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model.

Published
2017
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