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Biogeosciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Oct 2018

Research article | 18 Oct 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models

Matthias Forkel1, Niels Andela2, Sandy P. Harrison3, Gitta Lasslop4, Margreet van Marle5, Emilio Chuvieco6, Wouter Dorigo1, Matthew Forrest4, Stijn Hantson7, Angelika Heil8, Fang Li9, Joe Melton10, Stephen Sitch11, Chao Yue12, and Almut Arneth13 Matthias Forkel et al.
  • 1Climate and Environmental Remote Sensing Group, Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria
  • 2Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 3Department of Geography and Environmental Science, University of Reading, Reading, UK
  • 4Senckenberg Biodiversity and Climate Research Institute, Frankfurt am Main, Germany
  • 5Deltares, Delft, the Netherlands
  • 6Environmental Remote Sensing Research Group, Department of Geology, Geography and the Environment, Universidad de Alcalá, Alcalá de Henares, Spain
  • 7Geospatial Data Solutions Center, University of California, Irvine, CA, USA
  • 8Department for Atmospheric Chemistry, Max Planck Institute for Chemistry, Mainz, Germany
  • 9International Center for Climate and Environmental Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 10Climate Research Division, Environment Canada, Victoria, BC, Canada
  • 11College of Life and Environmental Sciences, University of Exeter, Exeter, UK
  • 12Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
  • 13Atmospheric Environmental Research, Institute of Meteorology and Climate research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

Abstract. Recent climate changes increases fire-prone weather conditions and likely affects fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled Dynamic Global Vegetation Models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socioeconomic predictor variables and burned area. We applied this approach to monthly burned area time series for the period 2005–2011 from satellite observations and from DGVMs from the Fire Model Inter-comparison Project (FireMIP) that were run using a common protocol and forcing datasets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socioeconomic variables (e.g. human population density). DGVMs broadly reproduce the relationships to climate variables and some models to population density. Interestingly, satellite-derived responses show a strong increase of burned area with previous-season leaf area index and plant productivity in most fire-prone ecosystems which was largely underestimated by most DGVMs. Hence our pattern-oriented model evaluation approach allowed to diagnose that current fire-enabled DGVMs represent some controls on fire to a large extent but processes linking vegetation productivity and fire occurrence need to be improved to accurately simulate the role of fire under global environmental change.

Matthias Forkel et al.
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Status: open (until 29 Nov 2018)
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Matthias Forkel et al.
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Short summary
Weather conditions, humans and the type and structure of vegetation control the occurrence of fires. Here we used satellite observations, global vegetation models, and a machine-learning algorithm to compare the relationships between burned area and controls in observations and models, respectively. Climate is the most important control on burned area in observations and models. However, models underestimate the strong increase of burned area with higher previous-season plant productivity.
Weather conditions, humans and the type and structure of vegetation control the occurrence of...