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Modeling fire spread as an infection process is intuitive: An ignition lights a patch of fuel, which infects its neighbor, and so on. Infection models produce nonlinear thresholds, whereby fire spreads only when fuel connectivity and infection probability are sufficiently high. These thresholds are fundamental both to managing fire and to theoretical models of fire spread, whereas applied fire models more often apply quasi-empirical approaches. Here, we resolve this tension by quantifying thresholds in fire spread locally, using field data from individual fires ( n = 1,131) in grassy ecosystems across a precipitation gradient (496 to 1,442 mm mean annual precipitation) and evaluating how these scaled regionally (across 533 sites) and across time (1989 to 2012 and 2016 to 2018) using data from Kruger National Park in South Africa. An infection model captured observed patterns in individual fire spread better than competing models. The proportion of the landscape that burned was well described by measurements of grass biomass, fuel moisture, and vapor pressure deficit. Regionally, averaging across variability resulted in quasi-linear patterns. Altogether, results suggest that models aiming to capture fire responses to global change should incorporate nonlinear fire spread thresholds but that linear approximations may sufficiently capture medium-term trends under a stationary climate.

Wildfires and land use play a central role in the long‐term carbon (C) dynamics of forested ecosystems of the United States. Understanding their linkages with changes in biomass, resource use, and consumption in the context of climate change mitigation is crucial. We reconstruct a long‐term C balance of forests in the contiguous U.S. using historical reports, satellite data, and other sources at multiple scales (national scale 1926–2017, regional level 1941–2017) to disentangle the drivers of biomass C stock change. The balance includes removals of forest biomass by fire, by extraction of woody biomass, by forest grazing, and by biomass stock change, their sum representing the net ecosystem productivity (NEP). Nationally, the total forest NEP increased for most of the 20th century, while fire, harvest and grazing reduced total forest stocks on average by 14%, 51%, and 6%, respectively, resulting in a net increase in C stock density of nearly 40%. Recovery from past land‐use, plus reductions in wildfires and forest grazing coincide with consistent forest regrowth in the eastern U.S. but associated C stock increases were offset by increased wood harvest. C stock changes across the western U.S. fluctuated, with fire, harvest, and other disturbances (e.g., insects, droughts) reducing stocks on average by 14%, 81%, and 7%, respectively, resulting in a net growth in C stock density of 14%. Although wildfire activities increased in recent decades, harvest was the key driver in the forest C balance in all regions for most of the observed timeframe. Plain Language Summary: We estimate past forest fires in four regions of the contiguous United States from 1926 to 2017 using historical statistics and satellite data. We compare the biomass removed from forests by wildfires, wood harvest, and forest grazing to identify which of these indicators had the strongest impact on forest biomass. Fire suppression and biomass recovery from past harvest in the twentieth century led to biomass regrowth. Forest regrowth was stronger in the East of the United States than in the West. Recently, wildfires have increased in the West. Higher tree mortality, drought, windthrow, and insects counteracted forest growth in the West. We conclude that fire suppression contributed to forest regrowth in the past, but harvest had the strongest impact in all regions. We show that the connection between wildfires, harvest, and grazing is an important factor for biomass change in forests and needs to be investigated over long time periods. Key Points: We present a new data set, reconstructing biomass removals by fire, harvest, and grazing in U.S. forests on a regional scale from 1941 to 2017Harvest and fire were the most intensive removals of biomass over the 20th century. Regional trends diverge from national trendsIn the context of natural climate solutions, long‐term and regional dynamics of interconnected drivers of forest biomass change need to be considered [ABSTRACT FROM AUTHOR]

In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems. [ABSTRACT FROM AUTHOR]

Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends. [ABSTRACT FROM AUTHOR]

Recent climate changes increases fire-prone weather conditions and likely affects 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 fire-enabled 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 socioeconomic predictor variables and burned area. We applied this approach to monthly burned area time series for the period 2005-2011 from satellite observations and from DGVMs from the Fire Model Inter-comparison Project (FireMIP) that were run using a common protocol and forcing datasets. 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 socioeconomic variables (e.g. human population density). DGVMs broadly reproduce the relationships to climate variables and some models to population density. Interestingly, satellite-derived responses show a strong increase of burned area with 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 to diagnose that current fire-enabled DGVMs represent some controls on fire to a large extent but processes linking vegetation productivity and fire occurrence need to be improved to accurately simulate the role of fire under global environmental change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim Large ungulate grazers have been a fundamental component of African savannas since the spread of the savanna ecosystem in the Miocene. The magnitude of the impact of ungulates on vegetation has been debated for a long time, but quantifying such effects at the continental scale has been difficult. This study presents an attempt to estimate for the first time the potential impact of large natural ungulate grazer herds on African ecosystems. Location The African continent (excluding Madagascar). Method The potential impacts of grazing on grass biomass, competition between grasses and trees, the occurrence and effects of wildfire and biome distribution were simulated with a model that couples a physiological grazer population model with a physiological dynamic vegetation one (not including the effects of browsing). This model has previously been shown to reproduce grazer densities across African wildlife reserves. Results Modelled grazer densities corresponded reasonably well with the continental distribution of African grazers represented by the herbivore functional types in the model. The coupled model predicted a hump-shaped relationship between annual precipitation and grazer biomass densities within a range of estimates from independent studies. In accordance with other studies, net primary productivity and the length of the dry season were the strongest predictors of grazer densities in the model. The inclusion of grazers in the model did not substantially alter an already (without grazers) reasonable fit between simulated vegetation biomass and burned area and estimates of these derived from remote sensing data. Nevertheless, the grazer-vegetation model predicted substantial impacts on grass biomass, tree biomass and burned area, particularly in areas with high grazer densities. The biome distribution at the continental scale, however, was similar with and without grazers. Main conclusion The results suggest that natural large ungulate grazers have been important drivers of ecosystem functioning in some savanna ecosystems, but also that they do not have a large effect on the continental-scale biome distribution and carbon stocks. [ABSTRACT FROM AUTHOR]