Fire is an essential Earth system process that alters ecosystem and atmospheric composition. Here we assessed long-term fire trends using multiple satellite data sets. We found that global burned ...area declined by 24.3 ± 8.8% over the past 18 years. The estimated decrease in burned area remained robust after adjusting for precipitation variability and was largest in savannas. Agricultural expansion and intensification were primary drivers of declining fire activity. Fewer and smaller fires reduced aerosol concentrations, modified vegetation structure, and increased the magnitude of the terrestrial carbon sink. Fire models were unable to reproduce the pattern and magnitude of observed declines, suggesting that they may overestimate fire emissions in future projections. Using economic and demographic variables, we developed a conceptual model for predicting fire in human-dominated landscapes.
Uncertainty in pre-industrial natural aerosol emissions is a major component of the overall uncertainty in the radiative forcing of climate. Improved characterisation of natural emissions and their ...radiative effects can therefore increase the accuracy of global climate model projections. Here we show that revised assumptions about pre-industrial fire activity result in significantly increased aerosol concentrations in the pre-industrial atmosphere. Revised global model simulations predict a 35% reduction in the calculated global mean cloud albedo forcing over the Industrial Era (1750-2000 CE) compared to estimates using emissions data from the Sixth Coupled Model Intercomparison Project. An estimated upper limit to pre-industrial fire emissions results in a much greater (91%) reduction in forcing. When compared to 26 other uncertain parameters or inputs in our model, pre-industrial fire emissions are by far the single largest source of uncertainty in pre-industrial aerosol concentrations, and hence in our understanding of the magnitude of the historical radiative forcing due to anthropogenic aerosol emissions.
Fire is an important global ecological process that influences the distribution of biomes, with consequences for carbon, water, and energy budgets. Therefore it is impossible to appropriately model ...the history and future of the terrestrial ecosystems and the climate system without including fire. This study incorporates the process-based prognostic fire module SPITFIRE into the global vegetation model ORCHIDEE, which was then used to simulate burned area over the 20th century. Special attention was paid to the evaluation of other fire regime indicators such as seasonality, fire size and fire length, next to burned area. For 2001–2006, the simulated global spatial extent of fire agrees well with that given by satellite-derived burned area data sets (L3JRC, GLOBCARBON, GFED3.1), and 76–92% of the global burned area is simulated as collocated between the model and observation, depending on which data set is used for comparison. The simulated global mean annual burned area is 346 Mha yr−1, which falls within the range of 287–384 Mha yr−1 as given by the three observation data sets; and is close to the 344 Mha yr−1 by the GFED3.1 data when crop fires are excluded. The simulated long-term trend and variation of burned area agree best with the observation data in regions where fire is mainly driven by climate variation, such as boreal Russia (1930–2009), along with Canada and US Alaska (1950–2009). At the global scale, the simulated decadal fire variation over the 20th century is only 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 underestimated by the model for the regions of high fire frequency, compared with fire patch data as reconstructed from MODIS 500 m burned area data. Two case studies of fire size distribution in Canada and US Alaska, and southern Africa indicate that both number and size of large fires are underestimated, which could be related with short fire patch length and low daily fire size. Future efforts should be directed towards building consistent spatial observation data sets for key parameters of the model in order to constrain the model error at each key step of the fire modelling.
Fire risk assessment should take into account the most relevant components associated to fire occurrence. To estimate when and where the fire will produce undesired effects, we need to model both (a) ...fire ignition and propagation potential and (b) fire vulnerability. Following these ideas, a comprehensive fire risk assessment system is proposed in this paper, which makes extensive use of geographic information technologies to offer a spatially explicit evaluation of fire risk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approaches to merge those variables into synthetic risk indices and finally the validation of the outputs. The model has been applied at a national level for the whole Spanish Iberian territory at 1-km2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-risk and landscape resilience. Each input variable included a particular accuracy assessment, whereas the synthetic indices were validated using the most recent fire statistics available. Significant relations (P<0.001) with fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.
•We analyse >10 years of fluxes and how they relate to climate and stand structural drivers.•We use wavelet coherence to investigate all time scales (days to multiple years).•Incoming shortwave ...radiation, spring minimum temperatures and NDVI explain most variance of annual net ecosystem exchange of carbon.•The role of precipitation strongly depends on the time scale under consideration.•Reduction of NEE due to cloud overrides effects of increased assimilation due to diffuse radiation on daily and larger time scales.
To understand the dynamics of ecosystem carbon cycling more than 10 years of eddy covariance data, measured over an evergreen, temperate, wet sclerophyll forest, were analysed and related to climate drivers on time scales ranging from hours to years. On hourly timescales we find that incoming shortwave radiation is the major meteorological driver of net ecosystem carbon exchange (NEE). Light use efficiency is higher under diffuse light conditions and carbon uptake is further modulated by the effects of variable and suboptimal temperatures (optimal temperature Topt=18°C) as well as by water demand (critical vapour pressure deficit VPDcrit=12hPa). Incoming shortwave radiation is also the major driver on daily time scales. Effects of increased light use efficiency under diffuse conditions, however, are overcompensated by the increased carbon uptake with larger amounts of total incoming shortwave radiation under clear sky conditions. On synoptic time scales a low ratio of actual to potential incoming shortwave radiation is also related to a reduced carbon uptake, or carbon release, and associated with precipitation events. Overcast conditions during an extended wet period (2010–2011) led to lower than average carbon uptake as did extended dry periods during 2003 and 2006. The drought in 2003 triggered an insect attack which turned the ecosystem into a net source of carbon for almost one year. The annual average normalised difference vegetation index (NDVI) is highly correlated with NEE at this site and multiple linear regression shows that NDVI, incoming solar radiation and air temperature explain most of the variance in NEE (r2=0.87, p<0.001). Replacing air temperature with average spring air temperatures further increases the correlation (r2=0.91, p<0.001). Results demonstrate that carbon uptake in this ecosystem is highly dynamic, that wavelet analysis is a suitable tool to analyse the coherence between the carbon exchange and drivers seamlessly, and that long time series are needed to capture the variability.
Fires have influenced atmospheric composition and climate since the rise of vascular plants, and satellite data has shown the overall global extent of fires. Our knowledge of historic fire emissions ...has progressively improved over the past decades due mostly to the development of new proxies and the improvement of fire models. Currently there is a suite of proxies including sedimentary charcoal records, measurements of fire-emitted trace gases and black carbon stored in ice and firn, and visibility observations. These proxies provide opportunities to extrapolate emissions estimates based on satellite data starting in 1997, but each proxy has strengths and weaknesses regarding, for example, the spatial and temporal extents over which they are representative. We developed a new historic biomass burning emissions dataset starting in 1750 that merges the satellite record with several existing proxies, and uses the average of six models from the Fire Model Intercomparison Project (FireMIP) protocol to estimate emissions when the available proxies had limited coverage. According to our approach, global biomass burning emissions were relatively constant with 10-year averages varying between 1.8 and 2.3 petagrams of carbon per year. Carbon emissions increased only slightly over the full time period and peaked during the 1990's after which they decreased gradually. There is substantial uncertainty in these estimates and patterns varied depending on choices regarding data representation, especially on regional scales. The observed pattern in fire carbon emissions is for a large part driven by African fires, which accounted for 58 percent of global fire carbon emissions. African fire emissions declined since about 1950 due to conversion of savanna to cropland, and this decrease is partially compensated for by increasing emissions in deforestation zones of South America and Asia. These global fire emissions estimates are mostly suited for global analyses and will be used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations.
Recent climate changes have increased fire-prone weather conditions in many
regions and have likely affected 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
socio-economic predictor variables and burned area. We applied this approach
to monthly burned area time series for the period from 2005 to 2011 from satellite
observations and from DGVMs from the “Fire Modeling Intercomparison
Project” (FireMIP) that were run using a common protocol and forcing data sets. 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 socio-economic variables (e.g. human population
density). DGVMs broadly reproduce the relationships with climate variables
and, for some models, with population density. Interestingly, satellite-derived responses
show a strong increase in burned area with an increase in 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 us to diagnose that vegetation effects on fire are a main
deficiency regarding fire-enabled dynamic global vegetation models' ability to accurately
simulate the role of fire under global environmental change.
The length of time that carbon remains in forest biomass
is one of the largest uncertainties in the global carbon cycle, with both
recent historical baselines and future responses to environmental ...change
poorly constrained by available observations. In the absence of large-scale
observations, models used for global assessments tend to fall back on
simplified assumptions of the turnover rates of biomass and soil carbon
pools. In this study, the biomass carbon turnover times calculated by an
ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to
assess their current capability to accurately estimate biomass carbon
turnover times in forests and how these times are anticipated to change in
the future. Modelled baseline 1985–2014 global average forest biomass
turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in
phenological processes, which control allocation to, and turnover rate of,
leaves and fine roots, are as important as tree mortality with regard to
explaining the variation in total turnover among TBMs. The different
governing mechanisms exhibited by each TBM result in a wide range of
plausible turnover time projections for the end of the century. Based on
these simulations, it is not possible to draw robust conclusions regarding
likely future changes in turnover time, and thus biomass change, for
different regions. Both spatial and temporal uncertainty in turnover time
are strongly linked to model assumptions concerning plant functional type
distributions and their controls. Thirteen model-based hypotheses of
controls on turnover time are identified, along with recommendations for
pragmatic steps to test them using existing and novel observations. Efforts
to resolve uncertainty in turnover time, and thus its impacts on the future
evolution of biomass carbon stocks across the world's forests, will need to
address both mortality and establishment components of forest demography, as
well as allocation of carbon to woody versus non-woody biomass growth.
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.
Fire emissions are critical for carbon and nutrient cycles, climate, and air quality. Dynamic Global Vegetation Models (DGVMs) with interactive fire modeling provide important estimates for long-term ...and large-scale changes of fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700-2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-the-art global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most up-to-date fire emission factor table from field and laboratory studies over various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. Evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products, and can capture the high emissions over the tropical savannas, low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most of the models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Historically, all models show only a weak trend in global fire emissions before ~1850s, consistent with multi-source merged historical reconstructions. The long-term trends among DGVMs are quite different for the 20th century, with some models showing an increase and others a decrease in fire emissions, mainly as a result of the discrepancy in their simulated responses to human population density change and land-use and land-cover change (LULCC). Our study provides a basic dataset for developing regional and global multi-source merged historical reconstructions and merging methods, and analyzing historical changes of fire emissions and their uncertainties as well as their role in the Earth system. It also highlights the importance of accurately modeling the responses of fire emissions to LULCC and population density change in reducing uncertainties in historical reconstructions of fire emissions and providing more reliable future projections.
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