Fire-driven deforestation is the major source of carbon emissions from Amazonia. Recent expansion of mechanized agriculture in forested regions of Amazonia has increased the average size of deforested areas, but related changes in fire dynamics remain poorly characterized. We estimated the contribution of fires from the deforestation process to total fire activity based on the local frequency of active fire detections from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. High-confidence fire detections at the same ground location on 2 or more days per year are most common in areas of active deforestation, where trunks, branches, and stumps can be piled and burned many times before woody fuels are depleted. Across Amazonia, high-frequency fires typical of deforestation accounted for more than 40% of the MODIS fire detections during 2003-2007. Active deforestation frontiers in Bolivia and the Brazilian states of Mato Grosso, Pará, and Rondônia contributed 84% of these high-frequency fires during this period. Among deforested areas, the frequency and timing of fire activity vary according to postclearing land use. Fire usage for expansion of mechanized crop production in Mato Grosso is more intense and more evenly distributed throughout the dry season than forest clearing for cattle ranching (4.6 vs. 1.7 fire days per deforested area, respectively), even for clearings >200 ha in size. Fires for deforestation may continue for several years, increasing the combustion completeness of cropland deforestation to nearly 100% and pasture deforestation to 50-90% over 1-3-year timescales typical of forest conversion. Our results demonstrate that there is no uniform relation between satellite-based fire detections and carbon emissions. Improved understanding of deforestation carbon losses in Amazonia will require models that capture interannual variation in the deforested area that contributes to fire activity and variable combustion completeness of individual clearings as a function of fire frequency or other evidence of postclearing land use. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing.

Climate regulates fire activity through the buildup and drying of fuels and the conditions for fire ignition and spread. Understanding the dynamics of contemporary climate–fire relationships at national and sub-national scales is critical to assess the likelihood of changes in future fire activity and the potential options for mitigation and adaptation. Here, we conducted the first national assessment of climate controls on US fire activity using two satellite-based estimates of monthly burned area (BA), the Global Fire Emissions Database (GFED, 1997–2010) and Monitoring Trends in Burn Severity (MTBS, 1984–2009) BA products. For each US National Climate Assessment (NCA) region, we analyzed the relationships between monthly BA and potential evaporation (PE) derived from reanalysis climate data at 0.5° resolution. US fire activity increased over the past 25 yr, with statistically significant increases in MTBS BA for the entire US and the Southeast and Southwest NCA regions. Monthly PE was strongly correlated with US fire activity, yet the climate driver of PE varied regionally. Fire season temperature and shortwave radiation were the primary controls on PE and fire activity in Alaska, while water deficit (precipitation – PE) was strongly correlated with fire activity in the Plains regions and Northwest US. BA and precipitation anomalies were negatively correlated in all regions, although fuel-limited ecosystems in the Southern Plains and Southwest exhibited positive correlations with longer lead times (6–12 months). Fire season PE increased from the 1980's–2000's, enhancing climate-driven fire risk in the southern and western US where PE–BA correlations were strongest. Spatial and temporal patterns of increasing fire season PE and BA during the 1990's–2000's highlight the potential sensitivity of US fire activity to climate change in coming decades. However, climate-fire relationships at the national scale are complex, based on the diversity of fire types, ecosystems, and ignition sources within each NCA region. Changes in the seasonality or magnitude of climate anomalies are therefore unlikely to result in uniform changes in US fire activity.

We present a method for estimating monthly burned area globally at 1° spatial resolution using Terra MODIS data and ancillary vegetation cover information. Using regression trees constructed for 14 different global regions, MODIS active fire observations were calibrated to burned area estimates derived from 500-m MODIS imagery based on the assumption that burned area is proportional to counts of fire pixels. Unlike earlier methods, we allow the constant of proportionality to vary as a function of tree and herbaceous vegetation cover, and the mean size of monthly cumulative fire-pixel clusters. In areas undergoing active deforestation, we implemented a subsequent correction based on tree cover information and a simple measure of fire persistence. Regions showing good agreement between predicted and observed burned area included Boreal Asia, Central Asia, Europe, and Temperate North America, where the estimates produced by the regression trees were relatively accurate and precise. Poorest agreement was found for southern-hemisphere South America, where predicted values of burned area are both inaccurate and imprecise; this is most likely a consequence of multiple factors that include extremely persistent cloud cover, and lower quality of the 500-m burned area maps used for calibration. Application of our approach to the nine remaining regions yielded comparatively accurate, but less precise, estimates of monthly burned area. We applied the regional regression trees to the entire archive of Terra MODIS fire data to produce a monthly global burned area data set spanning late 2000 through mid-2005. Annual totals derived from this approach showed good agreement with independent annual estimates available for nine Canadian provinces, the United States, and Russia. With our data set we estimate the global annual burned area for the years 2001-2004 to vary between 2.97 million and 3.74 million km², with the maximum occurring in 2001. These coarse-resolution burned area estimates may serve as a useful interim product until long-term burned area data sets from multiple sensors and retrieval approaches become available.

A new one-decade (1997ĝ€"2006) dataset of formaldehyde (HCHO) columns retrieved from GOME and SCIAMACHY is compared with HCHO columns simulated by an updated version of the IMAGES global chemical transport model. This model version includes an optimized chemical scheme with respect to HCHO production, where the short-term and final HCHO yields from pyrogenically emitted non-methane volatile organic compounds (NMVOCs) are estimated from the Master Chemical Mechanism (MCM) and an explicit speciation profile of pyrogenic emissions. The model is driven by the Global Fire Emissions Database (GFED) version 1 or 2 for biomass burning, whereas biogenic emissions are provided either by the Global Emissions Inventory Activity (GEIA), or by a newly developed inventory based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) algorithms driven by meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF). The comparisons focus on tropical ecosystems, North America and China, which experience strong biogenic and biomass burning NMVOC emissions reflected in the enhanced measured HCHO columns. These comparisons aim at testing the ability of the model to reproduce the observed features of the HCHO distribution on the global scale and at providing a first assessment of the performance of the current emission inventories. The high correlation coefficients (r>0.7) between the observed and simulated columns over most regions indicate a good consistency between the model, the implemented inventories and the HCHO dataset. The use of the MEGAN-ECMWF inventory improves the model/data agreement in almost all regions, but biases persist over parts of Africa and Australia. Although neither GFED version is consistent with the data over all regions, a better agreement is achieved over Indonesia and Southern Africa when GFEDv2 is used, but GFEDv1 succeeds better in getting the correct seasonal patterns and intensities of the fire episodes over the Amazon basin, as reflected in the significantly higher correlations calculated in this region. Although the uncertainties in the HCHO retrievals, especially over fire scenes, can be quite large, this study provides a first assessment about whether the improved methodologies and input data implemented in GFEDv2 and MEGAN-ECMWF lead to better results in the comparisons of modelled with observed HCHO column measurements.

Biomass burning represents an important source of atmospheric aerosols and greenhouse gases, yet little is known about its interannual variability or the underlying mechanisms regulating this variability at continental to global scales. Here we investigated fire emissions during the 8 year period from 1997 to 2004 using satellite data and the CASA biogeochemical model. Burned area from 2001–2004 was derived using newly available active fire and 500 m. burned area datasets from MODIS following the approach described by Giglio et al. (2006). ATSR and VIRS satellite data were used to extend the burned area time series back in time through 1997. In our analysis we estimated fuel loads, including organic soil layer and peatland fuels, and the net flux from terrestrial ecosystems as the balance between net primary production (NPP), heterotrophic respiration (R_h), and biomass burning, using time varying inputs of precipitation (PPT), temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation (fAPAR). For the 1997–2004 period, we found that on average approximately 58 Pg C year⁻¹ was fixed by plants as NPP, and approximately 95% of this was returned back to the atmosphere via R_h. Another 4%, or 2.5 Pg C year⁻¹ was emitted by biomass burning; the remainder consisted of losses from fuel wood collection and subsequent burning. At a global scale, burned area and total fire emissions were largely decoupled from year to year. Total carbon emissions tracked burning in forested areas (including deforestation fires in the tropics), whereas burned area was largely controlled by savanna fires that responded to different environmental and human factors. Biomass burning emissions showed large interannual variability with a range of more than 1 Pg C year⁻¹, with a maximum in 1998 (3.2 Pg C year⁻¹) and a minimum in 2000 (2.0 Pg C year⁻¹).

Formaldehyde columns retrieved from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography/Chemistry (SCIAMACHY) instrument onboard ENVISAT satellite through 2003 to 2006 are used as top-down constraints to derive updated global biogenic and biomass burning flux estimates for the non-methane volatile organic compounds (NMVOCs) precursors of formaldehyde. Our interest is centered over regions experiencing strong emissions, and hence exhibiting a high signal-to-noise ratio and lower measurement uncertainties. The formaldehyde dataset used in this study has been recently made available to the community and complements the long record of formaldehyde measurements from the Global Ozone Monitoring Experiment (GOME). We use the IMAGESv2 global chemistry-transport model driven by the Global Fire Emissions Database (GFED) version 1 or 2 for biomass burning, and from the newly developed MEGAN-ECMWF isoprene emission database. The adjoint of the model is implemented in a grid-based framework within which emission fluxes are derived at the model resolution, together with a differentiation of the sources in a grid cell. Two inversion studies are conducted using either the GFEDv1 or GFEDv2 as a priori for the pyrogenic fluxes. Although on the global scale the inferred emissions from the two categories exhibit only weak deviations from the corresponding a priori estimates, the regional updates often present large departures from their a priori values. The posterior isoprene emissions over North America, amounting to about 34 Tg C/yr, are estimated to be on average by 25% lower than the a priori over 2003-2006, whereas a strong increase (55%) is deduced over the south African continent, the optimized emission being estimated at 57 Tg C/yr. Over Indonesia the biogenic emissions appear to be overestimated by 20-30%, whereas over Indochina and the Amazon basin during the wet season the a priori inventory captures both the seasonality and the magnitude of the observed columns. Although neither biomass burning inventory seems to be consistent with the data over all regions, pyrogenic estimates inferred from the two inversions are reasonably similar, despite their a priori deviations. A number of sensitivity experiments are conducted in order to assess the impact of uncertainties related to the inversion setup and the chemical mechanism. Whereas changes in the background error covariance matrix have only a limited impact on the posterior fluxes, the use of an alternative isoprene mechanism characterized by lower HCHO yields (the GEOS-Chem mechanism) increases the posterior isoprene source estimate by 11% over northern America, and by up to 40% in tropical regions.

Long term, high quality estimates of burned area are needed for improving both prognostic and diagnostic fire emissions models and for assessing feedbacks between fire and the climate system. We developed global, monthly burned area estimates aggregated to 0.5° spatial resolution for the time period July 1996 through mid-2009 using four satellite data sets. From 2001–2009, our primary data source was 500-m burned area maps produced using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance imagery; more than 90% of the global area burned during this time period was mapped in this fashion. During times when the 500-m MODIS data were not available, we used a combination of local regression and regional regression trees developed over periods when burned area and Terra MODIS active fire data were available to indirectly estimate burned area. Cross-calibration with fire observations from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) and the Along-Track Scanning Radiometer (ATSR) allowed the data set to be extended prior to the MODIS era. With our data set we estimated that the global annual area burned for the years 1997–2008 varied between 330 and 431 Mha, with the maximum occurring in 1998. We compared our data set to the recent GFED2, L3JRC, GLOBCARBON, and MODIS MCD45A1 global burned area products and found substantial differences in many regions. Lastly, we assessed the interannual variability and long-term trends in global burned area over the past 13 years. This burned area time series serves as the basis for the third version of the Global Fire Emissions Database (GFED3) estimates of trace gas and aerosol emissions.

Tropical deforestation contributes to the build-up of atmospheric carbon dioxide in the atmosphere. Within the deforestation process, fire is frequently used to eliminate biomass in preparation for agricultural use. Quantifying these deforestation-induced fire emissions represents a challenge, and current estimates are only available at coarse spatial resolution with large uncertainty. Here we developed a biogeochemical model using remote sensing observations of plant productivity, fire activity, and deforestation rates to estimate emissions for the Brazilian state of Mato Grosso during 2001–2005. Our model of DEforestation CArbon Fluxes (DECAF) runs at 250-m spatial resolution with a monthly time step to capture spatial and temporal heterogeneity in fire dynamics in our study area within the ''arc of deforestation'', the southern and eastern fringe of the Amazon tropical forest where agricultural expansion is most concentrated. Fire emissions estimates from our modelling framework were on average 90 Tg C year⁻¹, mostly stemming from fires associated with deforestation (74%) with smaller contributions from fires from conversions of Cerrado or pastures to cropland (19%) and pasture fires (7%). In terms of carbon dynamics, about 80% of the aboveground living biomass and litter was combusted when forests were converted to pasture, and 89% when converted to cropland because of the highly mechanized nature of the deforestation process in Mato Grosso. The trajectory of land use change from forest to other land uses often takes more than one year, and part of the biomass that was not burned in the dry season following deforestation burned in consecutive years. This led to a partial decoupling of annual deforestation rates and fire emissions, and lowered interannual variability in fire emissions. Interannual variability in the region was somewhat dampened as well because annual emissions from fires following deforestation and from maintenance fires did not covary, although the effect was small due to the minor contribution of maintenance fires. Our results demonstrate how the DECAF model can be used to model deforestation fire emissions at relatively high spatial and temporal resolutions. Detailed model output is suitable for policy applications concerned with annual emissions estimates distributed among post-clearing land uses and science applications in combination with atmospheric emissions modelling to provide constrained global deforestation fire emissions estimates. DECAF currently estimates emissions from fire; future efforts can incorporate other aspects of net carbon emissions from deforestation including soil respiration and regrowth.