2.1. Estimating Carbon Emissions From Wildland Fires
 Access to a range of geospatial data in the past three decades, including information products derived from satellite remote sensing data, has improved our ability to quantify many factors relevant to the estimation of fire carbon emissions. Remote sensing provides synoptic information from the recent past and present for several important factors that are required to estimate carbon emissions, including the spatial extent of the fire, fuel characterization (fuel type, fuel load, plant physiological and moisture condition), site characteristics before and after the fire event, and environmental conditions during the fire that influence fire intensity and severity. The various approaches in use today overlap conceptually, with most using the basic framework put forth originally by Seiler and Crutzen . Seiler and Crutzen  used this framework to make the first global estimates of contemporary carbon emissions from fire, separately considering emissions from different biomes and different types of land management. Application of geospatial data sets and remote sensing imagery has enhanced this basic concept.
 The Seiler and Crutzen  method for estimation of carbon emissions from wildland fire requires quantification of three parameters: area burned, fuel loading (biomass per unit area), and the proportion of biomass fuel consumed, represented as fuel combustion factors and also known as the combustion completeness. The approach has been refined and emulated for studies at local, regional, and global scales for areas all over the world and a variety of timeframes [Kasischke et al., 1995; Reinhardt et al., 1997; French et al., 2000; Battye and Battye, 2002; French et al., 2003; Kasischke and Bruhwiler, 2003; French et al., 2004; Ito and Penner, 2004; Kasischke et al., 2005; Wiedinmyer et al., 2006; Campbell et al., 2007; Lavoué et al., 2007; Schultz et al., 2008; Joint Fire Science Program, 2009; Ottmar et al., 2009; R. D. Ottmar et al., Consume 3.0, http://www.fs.fed.us/pnw/fera/research/smoke/consume/index.shtml, 2009, accessed 20 October 2010]. The general equation for computing total carbon emissions (Ct) as interpreted by French et al.  and Kasischke and Bruhwiler  is
where A is the area burned (hectares, ha or m2), B is the biomass density or fuel load (t ha−1; kg m−2), fc is the fraction of carbon in the biomass (fuel), and β is the fraction of biomass consumed in the burn.
 Uncertainty in emissions estimates is introduced from all of these inputs [Peterson, 1987; French et al., 2004], and quantification of these uncertainties has been the subject of several studies, especially related to burn area [Fraser et al., 2004; Giglio et al., 2009; van der Werf et al., 2010; Meigs et al., 2011]. Some studies have used general estimates of the preburn biomass and fraction consumed, including the original Seiler and Crutzen  approach and more recent broad-scale studies [Schultz et al., 2008], but fuel consumption models are becoming increasingly more detailed, especially at finer spatial scales where data for refining emissions estimates are becoming available. The biomass or fuel load term represents all organic material at a site and is often divided into fuel components or vegetation strata because of the large differences in structure, composition, and consumption rate between fuel elements, such as trees, shrubs, grasses and sedges, coarse and fine woody debris, and surface organic material [e.g., van der Werf et al., 2006; Ottmar et al., 2007]. Fuel loads vary based on fuel type (a fire science term for vegetation type or ecosystem type) which can be complex in mature forest types or fairly simple in grasslands that have little to no woody debris. While fuel loading is well quantified for some ecosystems, uncertainties for others ecosystems are not well known (e.g., peatland sites and sites dominated by shrubs; see later discussion). While most emissions modeling approaches include surface organic soils as part of the fuel load, the belowground biomass held in plant roots or the organic material associated with mineral soils are not included.
 To derive carbon, the biomass or fuel load (B, mass per unit area) is multiplied by the fraction of carbon in the biomass (fc), usually 0.45 to 0.5 for plant biomass pools and a variable fraction for surface organic materials based on the depth and level of decomposition [French et al., 2003]. The β term is often called the combustion factor, combustion completeness, or burning efficiency (sometimes combustion efficiency, but we reserve this term for emissions partitioning, as explained below). The term is used to capture the variability in the material actually combusted and to determine fuel consumption (the amount of the fuel load removed during a fire). Combustion factors and fuel consumption are known to vary based on fuel type, fuel strata, and fuel condition. In many models combustion factors are determined for each fuel strata and vary due to environmental conditions, especially fuel moisture which is often included as a variable input to emissions models [Hardy et al., 2001; R. D. Ottmar et al., Consume 3.0, 2009].
 Of the six models used in this comparison study, five of them follow the general form of equation 1. Specifics of these models are given in Text S2 and in the references given here. The five models are (1) the First Order Fire Effects Model (FOFEM) 5.7 [Reinhardt et al., 1997], (2) CONSUME 3.0 (R. D. Ottmar et al., CONSUME 3.0, 2009), (3) the newly developed Wildland Fire Emissions Information System (WFEIS), which is based on the CONSUME model [French et al., 2009], (4) the Canadian Forest Service's CanFIRE 2.0 model [de Groot, 2010], and (5) the Global Fire Emissions Database version 3.1 (GFED3) [van der Werf et al., 2010]. A related alternative to equation (1) is represented by the sixth model used in this study, the Canadian Forest Fire Behavior Prediction (FBP) System approach, which uses empirical data from controlled burns and wildfires to statistically relate fuel consumption to fuel dryness through weather parameters [Forestry Canada Fire Danger Group, 1992]. Application of this method is independent of biomass density, but is based on broad fuel classifications, and strong influence by weather conditions that control fuel dryness and the amount of combustion [Amiro et al., 2001] (see Text S2, section S2.2.3, for background on the FBP System method).
 Many emissions calculations include estimation of gas and particulate components in addition to total carbon emissions. Typically, gas and particulate emissions are calculated from total fuel or carbon consumed using experimentally derived emissions factors, the ratio of a particular gas or particulate size class released to total fuel or carbon burned (e.g., g CO/kg fuel) [Cofer et al., 1998; Battye and Battye, 2002; Kasischke and Bruhwiler, 2003]. To estimate the emissions of each gas species, emission factors for flaming versus smoldering (combustion stage) for each fuel component are often used to account for differences in emissions resulting from the combustion type [Cofer et al., 1998; Kasischke and Bruhwiler, 2003]. Most of the carbon released by forest fires is in the form of carbon dioxide (CO2, ∼90% of total emissions), carbon monoxide (CO, ∼9%), and methane (CH4, ∼1%) (for a review, see Andreae and Merlet ). Many pollutants emitted from fire are products of incomplete combustion, including carbon monoxide (CO), particulate matter, and hydrocarbons. Combustion efficiency is defined as the fraction of carbon released from fuel combustion in the form of CO2, with more “efficient” burns releasing proportionally more CO2 than other compounds containing carbon [Cofer et al., 1998]. To summarize, the composition of gaseous emissions from a fire depend not only on the amount of fuel consumed, but also on the chemical composition of the fuel and the combustion efficiency for each fuel component. For United Nations Framework Convention on Climate Change (UNFCC) purposes, CO2 emissions from forest fires on managed lands are incorporated in estimates of ecosystem carbon stock changes, while emissions of CH4, N2O, and greenhouse gas precursors, including CO, are inventoried separately for forests, grasslands, and croplands as a function of the area burned, prefire carbon stocks, and fire seasonality [National Research Council, 2010]. Although modeling greenhouse gas emissions composition is of great interest, and has relevance for greenhouse gas inventories, air quality monitoring, and climate change policies, much of the uncertainty in these estimates arises from limits in our ability to model total biomass emissions. Here we focus our review on total carbon emissions estimates from burning, with the aim of reducing uncertainties associated with this key term.
2.3. Review of Previous Carbon Emissions Work
 Site-based to global-scale approaches to estimating carbon emissions from fire have been conducted in many regions and sites within North America (Table 1). In addition, there are several studies which include estimates of carbon emissions for portions of North America within a global study [e.g., Ito and Penner, 2004; Schultz et al., 2008]. Landscape-scale research studies include a 1994 Alaskan fire [Michalek et al., 2000], an assessment of black spruce in 2004 Alaska fires [Boby et al., 2010], a 2002 fire in the Pacific Northwest [Campbell et al., 2007], and a 2003 fire in boreal Canada [de Groot et al., 2007]; the latter two are used to compare to the new case study results (Table 2). Regional assessments from the literature cover Alaska [Kasischke and Bruhwiler, 2003; French et al., 2004, 2007], Canadian forests [Amiro et al., 2001], the Canadian boreal region [Amiro et al., 2009], the North American boreal region [French et al., 2000; Kasischke et al., 2005], and continental North America [Wiedinmyer et al., 2006]. Three global studies using three different burn area data sets and modeling techniques have produced continental-scale emission estimates as well [Hoelzemann et al., 2004; Reid et al., 2009; van der Werf et al., 2010].
Table 1. Input Sources and Wildland Fire Carbon Emissions Estimates From Specific Studies in North Americaa
|Assessment||Description/Reference(s)||Input Source(s)||Carbon Emissions (kg C m−2)|
|Burn Area (A)||Fuel Loading Method (B)||Fuel Consumption Method (β)|
|1994 Hajdukovich Creek, Alaska||landscape-scaleAlaskan black spruce/Michalek et al. ||remote sensing image (Landsat)||remote sensing vegetation classes with field data||remote sensing with field data||4.0 (2.8 to 8.0) kg C m−2|
|2003 Montreal Lake fire, Saskatchewan (FBP method)||landscape-scale boreal Canada mixed conifer/de Groot et al. ||remote sensing image (Landsat)||Canadian FBP Systemb||Canadian FBP Systemb||1.2c kg C m−2|
|2003 Montreal Lake fire, Saskatchewan (BORFIRE method)||landscape-scale boreal Canada mixed conifer/de Groot et al. ||remote sensing image (Landsat)||Canadian National Forest Inventoryd||BORFIRE model||1.7c kg C m−2|
|2002 Biscuit fire, Oregon||landscape-scale temperate mixed conifer/Campbell et al. ||remote sensing image (Landsat)||field inventory data with remote sensing||field data with remote sensing of severity||1.9 (±0.2) kg C m−2|
|2004 Alaska fires||landscape-scale boreal black spruce/Boby et al. ||n/a (total emissions not calculated)||field inventory data||prefire and postfire soil and stand measures||3.3 (1.5 to 4.6) kg C m−2|
|1950 to 1999 boreal Alaska||regional-scale Alaska boreal forest/French et al. [2003, 2004]||fire records||forest and soil inventory [Kasischke et al., 1995]||ecoregion-level estimates from field measures||2.0 kg C m−2 (average for 50 years)|
|1959 to 1999 boreal Canada||regional-scale Canadian forest region/Amiro et al. ||fire records||Canadian FBP Systemb||Canadian FBP Systemb||1.3 (0.9 to 2.0)c kg C m−2|
|1980 to 1994 boreal North America||regional-scale boreal North America/French et al. ||fire records||vegetation classes with field data||ecoregion-level estimates from field measures||2.1 (0.8 to 3.7) kg C m−2|
|1992 and 1995 to 2003 boreal regions||regional-scale boreal North America/Kasischke et al. ||fire records||forest and soil inventory [Kasischke et al., 1995]||ecoregion-level estimates from field measures||1.0 to 1.8 kg C m−2|
|1998 boreal regions||regional-scale boreal North America/Kasischke and Bruhwiler ||fire records||ecozone-based averages from Bourgeau-Chavez et al. ||ecozone-based averages from French et al. ||1.4 to 2.7 kg C m−2|
|2000 GWEM||global/Hoelzemann et al. ||GLOBSCAR [Simon et al., 2004]||LPJ-DGVM vegetation model [Sitch et al., 2003]||biome mean values [Reid et al., 2005]||2.7 to 4.1 kg m−2 (North America)|
|FLAMBE'||global/Reid et al. ||FLAMBE' modele||FLAMBE' modele||FLAMBE' modele||2.0 to 2.3 kg m−2|
|1997–2009 GFED||global/van der Werf et al. ||GFED v3.1||GFED v3.1||GFED v3.1||0.20 to 9.5 kg C m−2f|
Table 2. Estimates of Carbon Emitted for Case Studiesa
|Model Used (Run)||Burn Area Used in Estimate (ha)||Total Carbon Emissions (Tg C)||Area Normalized Carbon Emissions (kg C m−2)|
|Field-based [Campbell et al., 2007]||200,000||3.80||1.9 ± 0.2|
|Biscuit Fire Original Fuels Map|
|FOFEM 5.7b|| || || |
| Very dryc||199,500d||8.97||4.50|
|CONSUME 3.0b|| || || |
| Very dryc||199,500d||10.62||5.32|
|Biscuit Fire Revised Fuels Map|
|FOFEM 5.7e|| || || |
| Very dryc||199,500d||3.92||1.97|
|CONSUME 3.0e|| || || |
| Very dryc||199,500d||3.44||1.72|
|WFEISe|| || || |
| Landsat burn area||200,444d||6.20||3.10|
| “Daily progression”e||200,154f||6.13||3.06|
| MODIS burn areae||169,916g||5.22||3.07|
|Montreal Lake Firei|
|Canadian FBP System [de Groot et al., 2007]||21,652||0.26||1.20|
|BORFIREj [de Groot et al., 2007]||21,652||0.37||1.70|
|FOFEM 5.7|| || || |
| Very dryc||21,655||1.41||6.51|
|CONSUME 3.0|| || || |
| Very dryc||21,655||0.72||3.32|
|WFEIS Landsat burn area||21,656||0.35||1.60|
|Field-based study (E. S. Kasischke unpublished data, 2010)||184,755m||4.78||2.59|
|FOFEM 5.7|| || || |
| Very dryc||217,232||13.27||6.11|
|CONSUME 3.0|| || || |
| Very dryc||217,780||5.09||2.34|
|WFEIS|| || || |
| Landsat burn area||211,465||5.68||2.68|
| “Daily progression”||211,260||5.30||2.51|
|San Diego County October 2003n|
|FOFEM 5.7|| || || |
| Very dryc||143,757||1.55||1.06|
|CONSUME 3.0|| || || |
| Very dryc||144,657||0.77||0.53|
|WFEIS|| || || |
| Landsat burn area||150,896||1.59||1.05|
| “Daily progression”||150,619||1.61||1.07|
|San Diego County October 2007|
|FOFEM 5.7|| || || |
| Very dryc||119,565||1.26||1.08|
|CONSUME 3.0|| || || |
| Very dryc||122,165||0.58||0.47|
|WFEIS|| || || |
| Landsat burn area||127,381||1.28||1.01|
| “Daily progression”||127,347||1.31||1.03|
 Estimates of carbon emissions from local-scale studies vary, as would be expected, based on vegetation type/biome and the severity of the burn, which determines the proportion consumed. The fire emissions for the Canada and Oregon fires are surprisingly similar given the differences in forest type, structure, and fire severity (1.2 to 1.9 kg C m−2 on average), while the Alaskan fires were somewhat higher, with emissions spanning a range of 2–5 kg C m−2). The higher emissions from the black spruce fires in Alaska are at least partly caused by higher levels of consumption of organic surface fuels which are often extensive and deep within many burn perimeters [Michalek et al., 2000]. Organic soils in boreal regions often can hold as much as 75% of the total fuel loading (20 kg biomass m−2), according to FCCS fuel loading data [Ottmar et al., 2007].
 The regional-scale studies of boreal North America described above, including Alaska and Canada, show a wide range of estimates that reveal key differences in modeling approaches for fuel loads and combustion factors, and thus their combined effect on fuel consumption. All of the regional-scale studies were conducted spatially, which reveals that the variability in results are a function of both the fuel loading (denser fuels in more southerly locations) and fuel consumption (a variety of fire weather conditions) [French et al., 2000]. In these cases, fuel consumption was determined from a few field data sets collected by Canadian researchers studying fire effects and from research conducted in Alaska where attention was paid to improved quantification of consumption of the deep organic surface fuels during fire [Kasischke and Bruhwiler, 2003]. In addition to the regional-scale studies for carbon emissions, there are several activities to monitor and map emissions for air quality, which require much of the same information. In particular, the U.S. Forest Service has developed a framework for smoke modeling [Larkin et al., 2009] and the NOAA Hazard Mapping System (NOAA HMS) is used to monitor and map active fire with the information available for use by NOAA and the U.S. Environmental Protection Agency (EPA) in smoke forecasting for air quality monitoring [Rolph et al., 2009].
 Global studies of carbon emissions have concentrated on quantification of burn area, which at global scales can be the main driver of uncertainty in total emissions estimates. The GWEM study [Hoelzemann et al., 2004] used the GLOBSCAR burned area product as the basis for emission estimates. Results from intercomparison of GLOBSCAR and other products [Boschetti et al., 2004] indicate that this is likely to be one of the largest uncertainties in the GWEM estimates. The FLAMBE' product [Reid et al., 2009] uses active fire detection products, which show a proportional response to fire activity over specific regions that is fairly reliable [Schroeder et al., 2008], but exhibit dramatic variations in detection efficiency between regions. The GFED3 approach uses aggregated map of Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m burned area product (the Direct Broadcast Burn Area Product (DBBAP) [Giglio et al., 2009]) where available, and augments these time series with regressions with active fires to extend the global time series prior to the MODIS record [Giglio et al., 2010]. GFED3 includes separate fuel loads and combustion completeness algorithms for the grass and woody vegetation components of each 0.5° grid cells, although consumption is generalized based on mean carbon pools for each of these vegetation classes at the coarse resolution of a single grid scale. This leads to important scale-dependent uncertainties that can probably only be resolved by higher spatial resolution models.