Abstract
Abstract. Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.
Highlights
Each year, about 4 % of the global vegetated area is burnt (Giglio et al, 2013; Randerson et al, 2012)
In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling
We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from Fire Model Intercomparison Project (FireMIP)
Summary
About 4 % of the global vegetated area is burnt (Giglio et al, 2013; Randerson et al, 2012). Changes in fire risk/danger will not necessarily be closely coupled to changes in fire regime in the future given the direct impacts of CO2 on water-use efficiency, productivity, vegetation density, and vegetation composition and distribution This limits the utility of statistically based models for the investigation of feedbacks to climate through fire-driven changes of land-surface properties, vegetation structure or atmospheric composition – feedbacks which have the potential to exacerbate or ameliorate the effects of future climate change on ecosystems as well as influence the security and well-being of people. We give a short overview of the plans for FireMIP and the overall philosophy behind the model benchmarking and evaluation
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