The Wildland Fire Emission Inventory: emission estimates and an evaluation of uncertainty

Abstract

We present the Wildland Fire Emission Inventory (WFEI), a high resolution model for non-agricultural open biomass burning (hereafter referred to as wildland fires) in the contiguous United States (CONUS). WFEI was used to estimate emissions of CO and PM<sub>2.5</sub> for the western United States from 2003–2008. The estimated annual CO emitted ranged from 436 Gg yr<sup>−1</sup> in 2004 to 3107 Gg yr<sup>−1</sup> in 2007. The extremes in estimated annual PM<sub>2.5</sub> emitted were 65 Gg yr<sup>−1</sup> in 2004 and 454 Gg yr<sup>−1</sup> in 2007. Annual wildland fire emissions were significant compared to other emission sources in the western United States as estimated in a national emission inventory. In the peak fire year of 2007, fire emissions were ~20 % of total CO emissions and ~39 % of total PM<sub>2.5</sub> emissions. During the months with the greatest fire activity, wildland fires accounted for the majority of CO and PM<sub>2.5</sub> emitted across the study region. <br><br> The uncertainty in the inventory estimates of CO and PM<sub>2.5</sub> emissions (ECO and EPM<sub>2.5</sub>, respectively) have been quantified across spatial and temporal scales relevant to regional and global modeling applications. The uncertainty in annual, domain wide emissions was 28 % to 51 % for CO and 40 % to 65 % for PM<sub>2.5</sub>. Sensitivity of the uncertainty in ECO and EPM<sub>2.5</sub> to the emission model components depended on scale. At scales relevant to regional modeling applications (Δ<i>x</i> = 10 km, Δ<i>t</i> = 1 day) WFEI estimates 50 % of total ECO with an uncertainty <133 % and half of total EPM<sub>2.5</sub> with an uncertainty <146 %. The uncertainty in ECO and EPM<sub>2.5</sub> is significantly reduced at the scale of global modeling applications (Δ<i>x</i> = 100 km, Δ<i>t</i> = 30 day). Fifty percent of total emissions are estimated with an uncertainty <50 % for CO and <64 % for PM<sub>2.5</sub>. Uncertainty in the burned area drives the emission uncertainties at regional scales. At global scales the uncertainty in ECO is most sensitive to uncertainties in the fuel load consumed while the uncertainty in the emission factor for PM<sub>2.5</sub> drives the EPM<sub>2.5</sub> uncertainty. Our uncertainty analysis indicates that the large scale aggregate uncertainties (e.g. annual, CONUS) that are typically reported for biomass burning emission inventories may not be appropriate for evaluating and interpreting results of modeling applications that employ the emission estimates. When feasible, biomass burning emission inventories should be evaluated and reported across the scales for which they are intended to be used