Global Fire Models Research Articles

Anthropogenic warming is a key climate indicator of rising urban fire activity in China

Abstract China, one of the most populous countries in the world, has suffered the highest number of natural disaster-related deaths from fire. On local scales, the main causes of urban fires are anthropogenic in nature. Yet, on regional to national scales, little is known about the indicators of large-scale co-varying urban fire activity in China. Here, we present the China Fire History Atlas (CFHA), which is based on 19 947 documentary records and represents fires in urban areas of China over the twentieth century (1901–1994). We found that temperature variability is a key indicator of urban fire activity in China, with warmer temperatures are correlated with more urban fires, and that this fire–temperature relationship is seasonally and regionally explicit. In the early twentieth century, however, the fire–temperature relationship was overruled by war-related fires in large urban areas. We further used the fire–temperature relationship and multiple emissions scenarios to project fire activity across China into the twenty-first century. Our projections show a distinct increase in future urban fire activity and fire-related economic loss. Our findings provide insights into fire–climate relationships in China for densely-populated areas and on policy-relevant time scales and they contribute spatial coverage to efforts to improve global fire models.

INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model

INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model

Global fire modelling and control attributions based on the ensemble machine learning and satellite observations

Contemporary fire dynamics is one of the most complex and least understood land surface phenomena. Global fire controls related to climate, vegetation, and anthropogenic activity are usually intertwined, and difficult to disentangle in a quantitative way. Here, we leveraged an ensemble of five machine learning (ML) models and multiple satellite-based observations to conduct global fire modeling for three fire metrics (burned area, fire number, and fire size), and quantified driving mechanisms underlying annual fire changes in a spatially resolved manner for the period 2003–2019. Ensemble learning is a meta-approach that combines multiple ML predictions to improve accuracy, robustness, and generalization performance. We found that the optimized ensemble ML well reproduced annual dynamics of global burned area (R2 = 0.90, P < 0.001), total fire numbers (R2 = 0.86, P < 0.001), and averaged fire size (R2 = 0.70, P < 0.001). Additionally, the ensemble ML captured key spatial patterns of multi-year mean magnitudes, annual variabilities, anomalies, and trends for different fire metrics. Our ML-based fire attributions further highlighted the dominant role of enhanced anthropogenic activity in reducing global burned area (−1.9 Mha/yr, P < 0.01), followed by climate control (−1.3 Mha/yr, P < 0.01) and insignificant positive vegetation control (0.4 Mha/yr, P = 0.60). Spatially, climate dominated a much larger burned area (53.7%) than human (23.4%) or vegetation control (22.9%); however, the counteracting effects from regional wetting and drying trends weakened the net climate impacts on global burned area. The fire number and fire size exhibited similar spatial control patterns with burned area; globally, however, fire number tended to be more affected by climate while fire size more influenced by human activities. Overall, our study confirmed the feasibility and efficiency of ensemble ML in global fire modeling and subsequent control attributions, providing a better understanding of contemporary fire regimes and contributing to robust fire projections in a changing environment.

Reliability of cross-regional applications of global fire danger models: a Peruvian case study

BackgroundFire danger indexes (FDIs) are used as proxies for fire potential and are often developed for specific locations. For practical purposes, the extrapolation of the underlying calculations into novel locations is common, but it is generally uncertain if the relationships between FDIs and fire potential observed in the environment in which the index was developed are equally relevant in others. For example, although a topographically, ecologically, and climatologically complex country, f ire danger forecasts in Peru use a standard set of nationwide thresholds applied to the Fire Weather Index. In this study, we validate the underlying assumption that weather-fire relationships are spatially uniform within Peru by (1) making cross-regional comparisons of the statistical distributions of four FDIs—Burning Index, Energy Release Component, Fire Weather Index, and Keetch-Byram Drought Index, and (2) making cross-regional comparisons of the expected daily MODIS hotspot count percentiles conditioned on FDI values.ResultsSignificant regional differences in the distributions of daily FDI values were observed in every pair of regions within Peru, and with the exception of a pair of regions within the Amazon, little data (<90 days) were necessary to detect these differences. After controlling for FDI values and seasonal and annual effects with regressions, differences in predicted hotspot percentiles were common, differing by as much as 47 percentage points. Across the pairs of regions, the magnitude of these differences tended to decrease as climatic similarity increased, but some counterexamples were also apparent.ConclusionsThe noticeable differences in the distributions of daily FDI values suggest that a standard set of breakpoints may produce unreliable inferences regarding fire potential. We also find that even if the climatic conditions were similar across Peru, the same FDI values in two locations can produce substantially differing predictions of wildfire activity. This suggests that other factors besides FDI values can strongly mediate wildfire activity and that better fire potential predictions could be produced if these factors are accounted for.

Modelling Human-Fire Interactions: Combining Alternative Perspectives and Approaches

Although it has long been recognised that human activities affect fire regimes, the interactions between humans and fire are complex, imperfectly understood, constantly evolving, and lacking any kind of integrative global framework. Many different approaches are used to study human-fire interactions, but in general they have arisen in different disciplinary contexts to address highly specific questions. Models of human-fire interactions range from conceptual local models to numerical global models. However, given that each type of model is highly selective about which aspects of human-fire interactions to include, the insights gained from these models are often limited and contradictory, which can make them a poor basis for developing fire-related policy and management practices. Here, we first review different approaches to modelling human-fire interactions and then discuss ways in which these different approaches could be synthesised to provide a more holistic approach to understanding human-fire interactions. We argue that the theory underpinning many types of models was developed using only limited amounts of data and that, in an increasingly data-rich world, it is important to re-examine model assumptions in a more systematic way. All of the models are designed to have practical outcomes but are necessarily simplifications of reality and as a result of differences in focus, scale and complexity, frequently yield radically different assessments of what might happen. We argue that it should be possible to combine the strengths and benefits of different types of model through enchaining the different models, for example from global down to local scales or vice versa. There are also opportunities for explicit coupling of different kinds of model, for example including agent-based representation of human actions in a global fire model. Finally, we stress the need for co-production of models to ensure that the resulting products serve the widest possible community.

Historical and future global burned area with changing climate and human demography

Summary Wildfires influence terrestrial carbon cycling and represent a safety risk, and yet a process-based understanding of their frequency and spatial distributions remains elusive. We combine satellite-based observations with an enhanced dynamic global vegetation model to make regionally resolved global assessments of burned area (BA) responses to changing climate, derived from 34 Earth system models and human demographics for 1860–2100. Limited by climate and socioeconomics, recent BA has decreased, especially in central South America and mesic African savannas. However, future simulations predict increasing BA due to changing climate, rapid population density growth, and urbanization. BA increases are especially notable at high latitudes, due to accelerated warming, and over the tropics and subtropics, due to drying and human ignitions. Conversely, rapid urbanization also limits BA via enhanced fire suppression in the immediate vicinity of settlements, offsetting the potential for dramatic future increases, depending on warming extent. Our analysis provides further insight into regional and global BA trends, highlighting the importance of including human demographic change in models for wildfire under changing climate.

Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project

Abstract. Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle and to infer relationships between climate, land use and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, in order to improve projections of fire characteristics and fire impacts on ecosystems and human societies in the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39–536 Mha) and global annual fire carbon emission (0.91–4.75 Pg C yr−1) for modern conditions (2002–2012), but most of the range in burnt area is within observational uncertainty (345–468 Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent interannual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the Northern Hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions, and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears to correlate with a better representation of burnt area. The two older fire models included in the FireMIP ensemble (LPJ–GUESS–GlobFIRM, MC2) clearly perform less well globally than other models, but it is difficult to distinguish between the remaining ensemble members; some of these models are better at representing certain aspects of the fire regime; none clearly outperforms all other models across the full range of variables assessed.

Improving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data

Abstract. Vegetation fires influence global vegetation distribution, ecosystem functioning, and global carbon cycling. Specifically in South America, changes in fire occurrence together with land-use change accelerate ecosystem fragmentation and increase the vulnerability of tropical forests and savannas to climate change. Dynamic global vegetation models (DGVMs) are valuable tools to estimate the effects of fire on ecosystem functioning and carbon cycling under future climate changes. However, most fire-enabled DGVMs have problems in capturing the magnitude, spatial patterns, and temporal dynamics of burned area as observed by satellites. As fire is controlled by the interplay of weather conditions, vegetation properties, and human activities, fire modules in DGVMs can be improved in various aspects. In this study we focus on improving the controls of climate and hence fuel moisture content on fire danger in the LPJmL4-SPITFIRE DGVM in South America, especially for the Brazilian fire-prone biomes of Caatinga and Cerrado. We therefore test two alternative model formulations (standard Nesterov Index and a newly implemented water vapor pressure deficit) for climate effects on fire danger within a formal model–data integration setup where we estimate model parameters against satellite datasets of burned area (GFED4) and aboveground biomass of trees. Our results show that the optimized model improves the representation of spatial patterns and the seasonal to interannual dynamics of burned area especially in the Cerrado and Caatinga regions. In addition, the model improves the simulation of aboveground biomass and the spatial distribution of plant functional types (PFTs). We obtained the best results by using the water vapor pressure deficit (VPD) for the calculation of fire danger. The VPD includes, in comparison to the Nesterov Index, a representation of the air humidity and the vegetation density. This work shows the successful application of a systematic model–data integration setup, as well as the integration of a new fire danger formulation, in order to optimize a process-based fire-enabled DGVM. It further highlights the potential of this approach to achieve a new level of accuracy in comprehensive global fire modeling and prediction.

Modelling biomass burning emissions and the effect of spatial resolution: a case study for Africa based on the Global Fire Emissions Database (GFED)

Abstract. Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. We developed a model using MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500 m spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050, 0.125, 0.250∘). We estimated fire emissions of 0.68 Pg C yr−1 at 500 m resolution and 0.82 Pg C yr−1 at 0.25∘ resolution; a difference of 24 %. At 0.25∘ resolution, our model results were relatively similar to GFED4, which also runs at 0.25∘ resolution, whereas our 500 m estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 Pg C yr−1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse-resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500 m and 0.25∘ resolution simulations, besides those stemming from representation errors in the calibration process, namely (1) biome misclassification leading to errors in parameterization, (2) errors due to the averaging of input data and the associated reduction in variability, and (3) a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations of up to a factor 4 and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse-resolution models and suggest the need for increased spatial resolution in global fire emission models.

Global fire season severity analysis and forecasting

Fire activity has a huge impact on human lives. Different models have been proposed to predict fire activity, which can be classified into global and regional ones. Global fire models focus on longer timescale simulations and can be very complex. Regional fire models concentrate on seasonal forecasting but usually require inputs that are not available in many places. Motivated by the possibility of having a simple, fast, and general model, we propose a seasonal fire prediction methodology based on time series forecasting methods. It consists of dividing the studied area into grid cells and extracting time series of fire counts to fit the forecasting models. We apply these models to estimate the fire season severity (FSS) from each cell, here defined as the sum of the fire counts detected in a season. Experimental results using a global fire detection data set show that the proposed approach can predict FSS with a relatively low error in many regions. The proposed approach is reasonably fast and can be applied on a global scale.

Historical (1700–2012) global multi-model estimates of the fire emissions from the Fire Modeling Intercomparison Project (FireMIP)

Abstract. Fire emissions are a critical component of carbon and nutrient cycles and strongly affect climate and air quality. Dynamic global vegetation models (DGVMs) with interactive fire modeling provide important estimates for long-term and large-scale changes in 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 based on field and laboratory studies in various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. The evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products. They can capture the high emissions over the tropical savannas and low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Before the 1850s, all models show only a weak trend in global fire emissions, which is consistent with the multi-source merged historical reconstructions used as input data for CMIP6. On the other hand, the trends are quite different among DGVMs 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 an important dataset for further development of regional and global multi-source merged historical reconstructions, analyses of the historical changes in fire emissions and their uncertainties, and quantification of the role of fire emissions 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.

Emergent freeze and fire disturbance dynamics in temperate rainforests

AbstractThe coastal temperate rainforests of South and North America are part of the most biomass dense forest biome on the planet. They are also subject to rapid climatic shifts and, subsequently, new disturbance processes – snow loss‐driven mortality and the emergence of fire in historically non‐fire‐exposed areas. Here, we compare and contrast Southern and Northern Hemisphere coastal temperate rainforests of the Americas, two of the largest examples of the biome, via synthesis of current literature, future climate expectations and new downscaling of a global fire model. In terms of snow loss, a rapid decline in winter snow is leading to mass mortality of certain conifer species in the Northern Hemisphere rainforests. High‐elevation Southern Hemisphere forests, which are beginning to see similar declines in snow, may be vulnerable in the future, especially bogs and high‐water content soils. Southern Hemisphere forests are seeing the invasion of fire as an ecological force at mid‐to‐high latitudes, a shift not yet observed in the north but which may become more prominent with ongoing climate change. We suggest that research should focus on the flammability of seral vegetation and bogs under future climate scenarios in both regions. By comparing these two drivers of change across similar gradients in the Northern and Southern Hemispheres, this work points to the potential for emerging change in unexpected places in both regions. There is a clear benefit to conceptualising the coastal temperate rainforests of the Americas as two examples of the biome which can inform the other, as change is proceeding in similar directions but at different rates in each region.

Development of a REgion‐Specific Ecosystem Feedback Fire (RESFire) Model in the Community Earth System Model

AbstractFires play a critical role in modulating regional and global climate through disturbances on meteorological, biogeochemical, and hydrological processes, while fires are strongly affected by climate, terrestrial ecosystems, and human activities. The complex climate‐fire‐ecosystem interactions with anthropogenic disturbance are not well understood. We developed a REgion‐Specific ecosystem feedback Fire (RESFire) model in the Community Earth System Model (CESM) that provides modeling capability to reproduce the observed burning patterns and trends and to understand fire related climatic processes. Comparing with the default Community Land Model version 4.5 fire model in CESM, the RESFire model includes heterogeneous natural and anthropogenic constraints on fire ignition and spread, improved fire impact parameterization including online fire emissions and fire induced land cover changes, and modeling bias corrections for online fire weather simulation. Evaluation results based on the International Land Model Benchmarking package show significant improvements in fire simulation performance. The overall modeling score of burned area simulation increases from 0.50 with Community Land Model version 4.5 to 0.62 (RESFire driven by the observation‐reanalysis data) and 0.60 (RESFire driven by the bias‐corrected Community Atmosphere Model version 5 simulation). The attribution analysis of decadal burned area trends suggests distinct contributions of natural and anthropogenic forcing in different regions, which are consistent with previous observations. The model also includes a fire impact module for estimating atmospheric responses to fire emissions as well as fire disturbances on ecosystems, land cover, and surface radiation budget. These results demonstrate the latest progress of global fire model development that enables fully interactive climate‐fire‐ecosystem studies using CESM.

Analysis fire patterns and drivers with a global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations

Abstract. Biomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to dynamic global vegetation models (DGVMs) and Earth system models (ESMs). We present SEVER-FIRE v1.0 (Socio-Economic and natural Vegetation ExpeRimental global fire model version 1.0), which is incorporated into the SEVER DGVM. One of the major focuses of SEVER-FIRE is an implementation of pyrogenic behavior of humans (timing of their activities and their willingness and necessity to ignite or suppress fire), related to socioeconomic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires is compared with regional historical fire statistics. We focus on both the model output accuracy and its assumptions regarding fire drivers and perform (1) an evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and interannual variability; (2) analysis to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation. SEVER reproduces the main features of climate-driven interannual fire variability at a regional scale, for example the large fires associated with the 1997–1998 El Niño event in Indonesia and Central and South America, which had critical ecological and atmospheric impacts. Spatial and seasonal patterns of fire incidence reveal some model inaccuracies, and we discuss the implications of the distribution of vegetation types inferred by the DGVM and of assumed proxies of human fire practices. We further suggest possible development directions to enable such models to better project future fire activity.

Climate seasonality as an essential predictor of global fire activity

AbstractAimFire is a globally important disturbance that affects nearly all vegetated biomes. Previous regional studies have suggested that the predictable seasonal pattern of a climatic time series, or seasonality, might aid in the prediction of average fire activity, but it is not known whether these findings are applicable globally. Here, we investigate how seasonality can be used to explain variations in fire activity on a global scale.LocationGlobal, 60° S–60° N.Time periodData averaged over the period 1999–2015.MethodsWe describe a method to partition a periodic seasonal cycle into two seasons and define conceptually simple temporal metrics that describe spatial variability in seasonality. We explore the usefulness of these metrics in explaining global fire activity using the average monthly time series of precipitation and temperature and a flexible machine learning procedure (random forests).ResultsA simple model that uses only precipitation and temperature amplitude and synchrony between wet and warm seasons correctly predicts 66% of the variability in global fire activity, substantially more than a model with mean annual temperature and precipitation. A more complex model that includes all nine metrics predicts 87% of variability in global fire activity.Main conclusionsThis study shows that seasonality of temperature and precipitation can be used to predict multi‐year average fire activity in a globally relevant way. The mechanisms highlighted in our work could be used to improve global fire models and enhance their ability to represent the spatial patterns of fire activity. Our method might also be useful in hindcasting historical fire using reanalysis or predicting future fire regimes using coarse output from climate models.

A Global Analysis of Hunter-Gatherers, Broadcast Fire Use, and Lightning-Fire-Prone Landscapes

We examined the relationships between lightning-fire-prone environments, socioeconomic metrics, and documented use of broadcast fire by small-scale hunter-gatherer societies. Our approach seeks to re-assess human-fire dynamics in biomes that are susceptible to lightning-triggered fires. We quantify global lightning-fire-prone environments using mean monthly lightning and climatological flammability, and then compare how well those environments and socioeconomic variables (population density, mobility, and subsistence type) serve as predictors of observed broadcast fire use from the ethnographic data. We use a logistic model for all vegetated, forested, and unforested biomes. Our global analysis of human-fire-landscape interaction in three hundred and thirty-nine hunter-gatherer groups demonstrates that lightning-fire-prone environments strongly predict for hunter-gatherer fire use. While we do not maintain that lightning-fire-prone environments determine the use of fire by small societies, they certainly appear to invite its use. Our results further suggest that discounting or ignoring human agency contradicts empirical evidence that hunter-gatherers used fire even in locations where lightning could explain the presence of fire. Paleoecological research on fire and hypothesis testing using global fire modeling should consider insights from human ecology in the interpretation of data and results. More broadly, our results suggest that small-scale societies can provide insight into sustainable fire management in lightning-fire-prone landscapes.

Global patterns of interannual climate-fire relationships.

Climate shapes geographic and seasonal patterns in global fire activity by mediating vegetation composition, productivity, and desiccation in conjunction with land-use and anthropogenic factors. Yet, the degree to which climate variability affects interannual variability in burned area across Earth is less understood. Two decades of satellite-derived burned area records across forested and nonforested areas were used to examine global interannual climate-fire relationships at ecoregion scales. Measures of fuel aridity exhibited strong positive correlations with forested burned area, with weaker relationships in climatologically drier regions. By contrast, cumulative precipitation antecedent to the fire season exhibited positive correlations to nonforested burned area, with stronger relationships in climatologically drier regions. Climate variability explained roughly one-third of the interannual variability in burned area across global ecoregions. These results highlight the importance of climate variability in enabling fire activity globally, but also identify regions where anthropogenic and other influences may facilitate weaker relationships. Empirical fire modeling efforts can complement process-based global fire models to elucidate how fire activity is likely to change amidst complex interactions among climatic, vegetation, and human factors.

A fire model with distinct crop, pasture, and non-agricultural burning: use of new data and a model-fitting algorithm for FINAL.1

Abstract. This study describes and evaluates the Fire Including Natural &amp;amp; Agricultural Lands model (FINAL) which, for the first time, explicitly simulates cropland and pasture management fires separately from non-agricultural fires. The non-agricultural fire module uses empirical relationships to simulate burned area in a quasi-mechanistic framework, similar to past fire modeling efforts, but with a novel optimization method that improves the fidelity of simulated fire patterns to new observational estimates of non-agricultural burning. The agricultural fire components are forced with estimates of cropland and pasture fire seasonality and frequency derived from observational land cover and satellite fire datasets. FINAL accurately simulates the amount, distribution, and seasonal timing of burned cropland and pasture over 2001–2009 (global totals: 0.434×106 and 2.02×106 km2 yr−1 modeled, 0.454×106 and 2.04×106 km2 yr−1 observed), but carbon emissions for cropland and pasture fire are overestimated (global totals: 0.295 and 0.706 PgC yr−1 modeled, 0.194 and 0.538 PgC yr−1 observed). The non-agricultural fire module underestimates global burned area (1.91×106 km2 yr−1 modeled, 2.44×106 km2 yr−1 observed) and carbon emissions (1.14 PgC yr−1 modeled, 1.84 PgC yr−1 observed). The spatial pattern of total burned area and carbon emissions is generally well reproduced across much of sub-Saharan Africa, Brazil, Central Asia, and Australia, whereas the boreal zone sees underestimates. FINAL represents an important step in the development of global fire models, and offers a strategy for fire models to consider human-driven fire regimes on cultivated lands. At the regional scale, simulations would benefit from refinements in the parameterizations and improved optimization datasets. We include an in-depth discussion of the lessons learned from using the Levenberg–Marquardt algorithm in an interactive optimization for a dynamic global vegetation model.

Lightning Forcing in Global Fire Models: The Importance of Temporal Resolution

AbstractIn global fire models, lightning is typically prescribed from observational data with monthly mean temporal resolution while meteorological forcings, such as precipitation or temperature, are prescribed in a daily resolution. In this study, we investigate the importance of the temporal resolution of the lightning forcing for the simulation of burned area by varying from daily to monthly and annual mean forcing. For this, we utilize the vegetation fire model JSBACH‐SPITFIRE to simulate burned area, forced with meteorological and lightning data derived from the general circulation model ECHAM6. On a global scale, differences in burned area caused by lightning forcing applied in coarser temporal resolution stay below 0.55% compared to the use of daily mean forcing. Regionally, however, differences reach up to 100%, depending on the region and season. Monthly averaged lightning forcing as well as the monthly lightning climatology cause differences through an interaction between lightning ignitions and fire prone weather conditions, accounted for by the fire danger index. This interaction leads to decreased burned area in the boreal zone and increased burned area in the Tropics and Subtropics under the coarser temporal resolution. The exclusion of interannual variability, when forced with the lightning climatology, has only a minor impact on the simulated burned area. Annually averaged lightning forcing causes differences as a direct result of the eliminated seasonal characteristics of lightning. Burned area is decreased in summer and increased in winter where fuel is available. Regions with little seasonality, such as the Tropics and Subtropics, experience an increase in burned area.

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.