Global Multi-Model Estimates of the Fire Emissions from the Fire Modeling

Historical (1700–2012) global multi-model estimates of the fire emissions from the Fire Modeling Intercomparison Project (FireMIP) Fang Li, Maria Val Martin, Meinrat Andreae, Almut Arneth, Stijn Hantson, Johannes Kaiser, Gitta Lasslop, Chao Yue, Dominique Bachelet, Matthew Forrest, et al. To cite this version: Fang Li, Maria Val Martin, Meinrat Andreae, Almut Arneth, Stijn Hantson, et al.. Historical (1700– 2012) global multi-model estimates of the fire emissions from the Fire Modeling Intercomparison Project (FireMIP). Atmospheric Chemistry and Physics, European Geosciences Union, 2019, 19 (19), pp.12545-12567. 10.5194/acp-19-12545-2019. hal-02975096 HAL Id: hal-02975096 https://hal.archives-ouvertes.fr/hal-02975096 Submitted on 26 Oct 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Atmos. Chem. Phys., 19, 12545–12567, 2019 https://doi.org/10.5194/acp-19-12545-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Historical (1700–2012) global multi-model estimates of the fire emissions from the Fire Modeling Intercomparison Project (FireMIP) Fang Li1, Maria Val Martin2, Meinrat O. Andreae3,4, Almut Arneth5, Stijn Hantson6,5, Johannes W. Kaiser7,3, Gitta Lasslop8, Chao Yue9,10, Dominique Bachelet11, Matthew Forrest8, Erik Kluzek12, Xiaohong Liu13, Stephane Mangeon14,a, Joe R. Melton15, Daniel S. Ward16, Anton Darmenov17, Thomas Hickler8,18, Charles Ichoku19, Brian I. Magi20, Stephen Sitch21, Guido R. van der Werf22, Christine Wiedinmyer23, and Sam S. Rabin5 1International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 2Leverhulme Center for Climate Change Mitigation, Department of Animal & Plant Sciences, Sheffield University, Sheffield, UK 3Biogeochemistry Department, Max Planck Institute for Chemistry, Mainz, Germany 4Department of Geology and Geophysics, King Saud University, Riyadh, Saudi Arabia 5Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate research, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany 6Geospatial Data Solutions Center, University of California, Irvine, CA, USA 7Deutscher Wetterdienst, Offenbach, Germany 8Senckenberg Biodiversity and Climate Research Institute (BiK-F), Senckenberganlage, Germany 9State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, Shanxi, China 10Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France 11Biological and Ecological Engineering, Oregon State University, Corvallis, OR, USA 12National Center for Atmospheric Research, Boulder, CO, USA 13Department of Atmospheric Science, University of Wyoming, Laramie, WY, USA 14Department of Physics, Imperial College London, London, UK 15Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada 16Karen Clark and Company, Boston, MA, USA 17Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA 18Department of Physical Geography, Goethe University, Frankfurt am Main, Germany 19Department of Interdisciplinary Studies, Howard University, NW, Washington, DC, USA 20Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA 21College of Life and Environmental Sciences, University of Exeter, Exeter, UK 22Faculty of Science, Vrije Universiteit, Amsterdam, the Netherlands 23Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA anow at: CSIRO, Data61, Brisbane, QLD, Australia Correspondence: Fang Li ([email protected]) Received: 15 January 2019 – Discussion started: 8 March 2019 Revised: 15 August 2019 – Accepted: 9 September 2019 – Published: 9 October 2019 Published by Copernicus Publications on behalf of the European Geosciences Union. 12546 F. Li et al.: Historical global multi-model estimates of fire emissions Abstract. Fire emissions are a critical component of carbon dreae and Rosenfeld, 2008; Jiang et al., 2016). Second, by and nutrient cycles and strongly affect climate and air qual- changing the atmospheric composition, fire emissions affect ity. Dynamic global vegetation models (DGVMs) with inter- the global and regional radiation balance and climate (Ward active fire modeling provide important estimates for long- et al., 2012; Tosca et al., 2013; Jiang et al., 2016; Grandey term and large-scale changes in fire emissions. Here we et al., 2016; McKendry et al., 2019; Hamilton et al., 2018; present the first multi-model estimates of global gridded his- Thornhill et al., 2018). Third, fire emissions change the ter- torical fire emissions for 1700–2012, including carbon and restrial nutrient and carbon cycles by altering the deposition 33 species of trace gases and aerosols. The dataset is based of nutrients (e.g., nitrogen, phosphorus), surface ozone con- on simulations of nine DGVMs with different state-of-the- centration, and meteorological conditions (Mahowald et al., art global fire models that participated in the Fire Model- 2008; Chen et al., 2010; McKendry et al., 2019; Yue and ing Intercomparison Project (FireMIP), using the same and Unger, 2018). In addition, they degrade the air quality (Val standardized protocols and forcing data, and the most up- Martin et al., 2015; Knorr et al., 2017), which poses a signif- to-date fire emission factor table based on field and labo- icant risk to human health and has been estimated to result ratory studies in various land cover types. We evaluate the in at least ∼ 165000, and more likely ∼ 339000, premature simulations of present-day fire emissions by comparing them deaths per year globally (Johnston et al., 2012; Marlier et al., with satellite-based products. The evaluation results show 2013; Lelieveld et al., 2015). that most DGVMs simulate present-day global fire emission To date, only emissions from individual fires or small- totals within the range of satellite-based products. They can scale fire complexes can be directly measured from field capture the high emissions over the tropical savannas and low campaigns and laboratory experiments (Andreae and Mer- emissions over the arid and sparsely vegetated regions, and let, 2001; Yokelson et al., 2013; Stockwell et al., 2016; An- the main features of seasonality. However, most models fail dreae, 2019). Regionally and globally, fire emissions are to simulate the interannual variability, partly due to a lack of often estimated based on satellite observations, fire proxy modeling peat fires and tropical deforestation fires. Before records, and numerical models, even though some attempts the 1850s, all models show only a weak trend in global fire have been made to bridge the gap between local observa- emissions, which is consistent with the multi-source merged tions and regional estimations using combinations of aircraft- historical reconstructions used as input data for CMIP6. On and ground-based measurements from field campaigns (e.g., the other hand, the trends are quite different among DGVMs SAMBBA, ARCTAS), satellite-based inventories, and chem- for the 20th century, with some models showing an increase ical transport models (e.g., Fisher et al., 2010; Reddington et and others a decrease in fire emissions, mainly as a result of al., 2019; Konovalov et al., 2018). Satellite-based fire emis- the discrepancy in their simulated responses to human pop- sion estimates are primarily derived from satellite observa- ulation density change and land use and land cover change tions of burned area, active fire counts, and/or fire radiative (LULCC). Our study provides an important dataset for fur- power, and are sometimes constrained by satellite observa- ther development of regional and global multi-source merged tions of aerosol optical depth (AOD), CO, or CO2 (Wied- historical reconstructions, analyses of the historical changes inmyer et al., 2011; Kaiser et al., 2012; Krol et al., 2013; in fire emissions and their uncertainties, and quantification Konovalov et al., 2014; Ichoku and Ellison, 2014; Darmenov of the role of fire emissions in the Earth system. It also high- and da Silva, 2015; van der Werf et al., 2017; Heymann et lights the importance of accurately modeling the responses al., 2017). Satellite-based fire emission estimates are avail- of fire emissions to LULCC and population density change able globally but cover only the present-day period, i.e., since in reducing uncertainties in historical reconstructions of fire 1997 for the Global Fire Emissions Dataset (GFED) and emissions and providing more reliable future projections. shorter periods for others. Historical change in fire emissions has been inferred from a variety of proxies, such as ice-core records of CH4 (iso- 13 tope δ CH4 from a pyrogenic or biomass burning source), 1 Introduction black carbon, levoglucosan, vanillic acid, ammonium, and CO (Ferretti et al., 2005; McCornnell et al., 2007; Coned- Fire is an intrinsic feature of terrestrial ecosystem

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