Recent climate changes have increased fire-prone weather conditions in many regions and have likely affected 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 firee-nabled 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 socio-economic predictor variables and burned area. We applied this approach to monthly burned area time series for the period from 2005 to 2011 from satellite observations and from DGVMs from the "Fire Modeling Intercomparison Project" (FireMIP) that were run using a common protocol and forcing data sets. 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 socio-economic variables (e.g. human population density). DGVMs broadly reproduce the relationships with climate variables and, for some models, with population density. Interestingly, satellite-derived responses show a strong increase in burned area with an increase in 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 us to diagnose that veg-etation effects on fire are a main deficiency regarding fireenabled dynamic global vegetation models' ability to accurately simulate the role of fire under global environmental change.
1.Tech Univ Wien, Dept Geodesy & Geoinformat, Climate & Environm Remote Sensing Grp, Vienna, Austria 2.NASA, Goddard Space Flight Ctr, Biospher Sci Lab, Greenbelt, MD 20771 USA 3.Univ Reading, Dept Geog & Environm Sci, Reading, Berks, England 4.Senckenberg Biodivers & Climate Res Ctr, Frankfurt, Germany 5.Deltares, Delft, Netherlands 6.Univ Alcala De Henares, Dept Geol Geog & Environm, Environm Remote Sensing Res Grp, Alcala De Henares, Spain 7.Univ Calif Irvine, Geospatial Data Solut Ctr, Irvine, CA USA 8.Max Planck Inst Chem, Dept Atmospher Chem, Mainz, Germany 9.Chinese Acad Sci, Inst Atmospher Phys, Int Ctr Climate & Environm Sci, Beijing, Peoples R China 10.Environm Canada, Climate Res Div, Victoria, BC, Canada 11.Univ Exeter, Coll Life & Environm Sci, Exeter, Devon, England 12.Lab Sci Climat & Environm, Gif Sur Yvette, France 13.Karlsruhe Inst Technol, Inst Meteorol & Climate Res, Atmospher Environm Res, Garmisch Partenkirchen, Germany
Recommended Citation:
Forkel, Matthias,Andela, Niels,Harrison, Sandy P.,et al. Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models[J]. BIOGEOSCIENCES,2019-01-01,16(1):57-76