The status and challenge of global fire modelling

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.

The pyrogeography of eastern boreal Canada from 1901 to 2012 simulated with the LPJ-LMfire model

“…The improvements that were made here reinforce the idea that aside from "top-down" climate control on fire activity, other factors such as lightning, fuel availability, and composition can influence fire statistics (Podur et al, 2002). This highlights the necessity of reconstructing fire history in a complex system that is related to climate and vegetation by taking into account several feedbacks (Hantson et al, 2016).…”

“…The use of a high-quality lightning strike data set instead of the low-resolution LIS/OTD global data set that was used by Pfeiffer et al (2013) allowed us to capture the spatial gradient of fire activity in a substantially better manner (Baker et al, 2016). The results confirmed that fire in the study area is strongly ignition limited, while most fire models have simply assumed that fire would always occur under appropriate weather and fuel conditions, e.g., SIMFIRE (Hantson et al, 2016). LPJ-LMfire simulations confirmed the necessity of simulating fire in a model as the product of the probabilities that are associated with fuel, moisture and ignition.…”

“…It allows fires to burn over multiple days and simulates fire extinction from changes in weather and fuel (Pfeiffer et al, 2013). As in the original version of SPITFIRE and nearly all other large-scale fire models, LMfire does not simulate the cell-to-cell spread of fire (Hantson et al, 2016;Pfeiffer et al, 2013;Rabin et al, 2017).…”

“…The intensity, severity, and size of fires are further influenced by species composition within the landscape, with needleleaf species being more fire prone than broadleaf species owing to their high flammability (Hély et al, 2001). Physical variables such as slope, surficial deposits, and soil moisture can also have significant effects on the rate at which fires spread by influencing fuel moisture or creating natural fire breaks (Hély et al, 2001;Mansuy et al, 2011;Hantson et al, 2016). Climate change scenarios for Canada indicate an increase in both temperature and precipitation in the coming decades.…”

“…Simulations using dynamic global vegetation models (DGVMs) make it possible to estimate the spatiotemporal distribution of fires relative to climate and vegetation Hantson et al, 2016). Indeed, these models simulate shifts in potential vegetation composition and related fire activity in response to changes in climate or environmental constraints (Smith et al, 2001).…”