Modelling fires in the terrestrial carbon balance by incorporating SPITFIRE into the global vegetation model ORCHIDEE – Part 1: Simulating historical global burned area and fire regime

Abstract. Fire is an important global ecological process that determines the distribution of biomes, with consequences for carbon, water, and energy budgets. The modelling of fire is critical for understanding its role in both historical and future changes in terrestrial ecosystems and the climate system. This study incorporates the process-based prognostic fire module SPITFIRE into the global vegetation model ORCHIDEE, which was then used to simulate the historical burned area and the fire regime for the 20th century. For 2001–2006, the simulated global spatial extent of fire occurrence agrees well with that given by the satellite-derived burned area datasets (L3JRC, GLOBCARBON, GFED3.1) and captures 78–92% of global total burned area depending on which dataset is used for comparison. The simulated global annual burned area is 329 Mha yr−1, which falls within the range of 287–384 Mha yr−1 given by the three global observation datasets and is close to the 344 Mha yr−1 given by GFED3.1 data when crop fires are excluded. The simulated long-term trends of burned area agree best with the observation data in regions where fire is mainly driven by the climate variation, such as boreal Russia (1920–2009), and the US state of Alaska and Canada (1950–2009). At the global scale, the simulated decadal fire trend over the 20th century is in moderate agreement with the historical reconstruction, possibly because of the uncertainties of past estimates, and because land-use change fires and fire suppression are not explicitly included in the model. Over the globe, the size of large fires (the 95th quantile fire size) is systematically underestimated by the model compared with the fire patch data as reconstructed from MODIS 500 m burned area data. Two case studies of fire size distribution in boreal North America and southern Africa indicate that both the number and the size of big fires are underestimated, which could be related with too low fire spread rate (in the case of static vegetation) and fire duration time. Future efforts should be directed towards building consistent spatial observation datasets for key parameters of the model in order to constrain the model error at each key step of the fire modelling.

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