Determinants and predictability of global wildfire emissions
- 1Physical Geography and Ecosystem Analysis, Lund University, Sölvegatan 12, 22362 Lund, Sweden
- 2KIT/IMK-IFU, Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany
Abstract. Biomass burning is one of the largest sources of atmospheric trace gases and aerosols globally. These emissions have a major impact on the radiative balance of the atmosphere and on air quality, and are thus of significant scientific and societal interest. Several datasets have been developed that quantify those emissions on a global grid and offered to the atmospheric modelling community. However, no study has yet attempted to systematically quantify the dependence of the inferred pyrogenic emissions on underlying assumptions and input data. Such a sensitivity study is needed for understanding how well we can currently model those emissions and what the factors are that contribute to uncertainties in those emission estimates.
Here, we combine various satellite-derived burned area products, a terrestrial ecosystem model to simulate fuel loads and the effect of fire on ecosystem dynamics, a model of fuel combustion, and various emission models that relate combusted biomass to the emission of various trace gases and aerosols. We carry out simulations with varying parameters for combustion completeness and fuel decomposition rates within published estimates, four different emissions models and three different global burned-area products. We find that variations in combustion completeness and simulated fuel loads have the largest impact on simulated global emissions for most species, except for some with highly uncertain emission factors. Variation in burned-area estimates also contribute considerably to emission uncertainties. We conclude that global models urgently need more field-based data for better parameterisation of combustion completeness and validation of simulated fuel loads, and that further validation and improvement of burned area information is necessary for accurately modelling global wildfire emissions. The results are important for chemical transport modelling studies, and for simulations of biomass burning impacts on the atmosphere under future climate change scenarios.