Boreal forest fire CO and CH<sub>4</sub> emission factors derived from tower observations in Alaska during the extreme fire season of 2015

Abstract

Abstract. Recent increases in boreal forest burned area, which have been linked with climate warming, highlight the need to better understand the composition of wildfire emissions and their atmospheric impacts. Here we quantified emission factors for CO and CH4 from a massive regional fire complex in interior Alaska during the summer of 2015 using continuous high-resolution trace gas observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CRV) tower in Fox, Alaska. Averaged over the 2015 fire season, the mean CO / CO2 emission ratio was 0.142 ± 0.051, and the mean CO emission factor was 127 ± 40 g kg−1 dry biomass burned. The CO / CO2 emission ratio was about 39 % higher than the mean of previous estimates derived from aircraft sampling of wildfires from boreal North America. The mean CH4 / CO2 emission ratio was 0.010 ± 0.004, and the CH4 emission factor was 5.3 ± 1.8 g kg−1 dry biomass burned, which are consistent with the mean of previous reports. CO and CH4 emission ratios varied in synchrony, with higher CH4 emission factors observed during periods with lower modified combustion efficiency (MCE). By coupling a fire emissions inventory with an atmospheric model, we identified at least 34 individual fires that contributed to trace gas variations measured at the CRV tower, representing a sample size that is nearly the same as the total number of boreal fires measured in all previous field campaigns. The model also indicated that typical mean transit times between trace gas emission within a fire perimeter and tower measurement were 1–3 d, indicating that the time series sampled combustion across day and night burning phases. The high CO emission ratio estimates reported here provide evidence for a prominent role of smoldering combustion and illustrate the importance of continuously sampling fires across time-varying environmental conditions that are representative of a fire season.

Reconciling Assumptions in Bottom‐Up and Top‐Down Approaches for Estimating Aerosol Emission Rates From Wildland Fires Using Observations From FIREX‐AQ

“…For example, airborne based measurements tend to be limited to daytime sampling of well‐developed plumes that have risen to an altitude that is accessible by the aircraft. Consequently, these measurements may be biased toward flaming combustion because nighttime and/or smoldering emissions resulting from less energetic fire activity are likely not being sampled (Burling et al., 2011 ; Prichard et al., 2020 ; Wiggins et al., 2021 ). The suggested EF PM for temperate forests from GFED is 17.6 g kg −1 , and the mean EF PM we calculated using FIREX‐AQ in situ airborne measurements is 15.8 ± 4.3 g kg −1 , which is well within range of the suggested EF PM from GFED.…”

Climatic variation drives loss and restructuring of carbon and nitrogen in boreal forest wildfire

“…Therefore, boreal wildfire models often incorporate short-term fire weather variables (e.g. drought indices, temperature, wind speed, relative humidity) and separate soil fuel loads into distinct compartments such as surface litter (influencing ignition and rate of spread) and the more compactly arranged layers below (acting as a heat reservoir that supports extended smoldering over days to weeks) ( de Groot et al, 2003;Van Wagner, 1987;Rabin et al, 2017;Kasischke et al, 2005;Wiggins et al, 2021). Composition of tree species, with their associated fire adaptation strategies, has also been shown to have a strong impact on fire severity and intensity and distinguishes the boreal wildfire regimes of the North American and Eurasian continents (Rogers et al, 2015).…”

A quadcopter unmanned aerial system (UAS)-based methodology for measuring biomass burning emission factors

“…Zhang et al, 2015;Wooster et al, 2018;Reisen et al, 2018) or mounted on masts (e.g. Korontzi et al, 2003;Wiggins et al, 2021) or aircraft (e.g. Liu et al, 2017;May et al, 2014;Yokelson et al, 2007;Barker et al, 2020;Thompson et al, 2020), as well as through ground-based remote sensing (e.g.…”