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LLM - Digital Humanities 16

ERROR: type should be string, got "https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Long-term observations of black carbon and carbon monoxide in the \n1 \nPoker Flat Research Range, central Alaska, with a focus on forest \n2 \nwildfire emissions \n3 \nTakeshi Kinase1, Fumikazu Taketani1,2, Masayuki Takigawa1, Chunmao Zhu2, Yongwon Kim3, Petr \n4 \nMordovskoi1, and Yugo Kanaya1,2 \n5 \n1Institute of Arctic Climate and Environment Research, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), \n6 \nYokohama 2360001, Japan \n7 \n2Earth Surface System Research Center, Research Institute for Global Change, Japan Agency for Marine-Earth Science and \n8 \nTechnology (JAMSTEC), Yokohama 2360001, Japan \n9 \n3International Arctic Research Center, University of Alaska Fairbanks (UAF), Fairbanks 757340, U.S.A. 10 \n \n11 \nCorrespondence to: Takeshi Kinase (tkinase@jamstec.go.jp) \n12 \nAbstract \n13 \nForest wildfires in interior Alaska represent an important black carbon (BC) source for the Arctic and sub-Arctic. However, \n14 \nBC observations in interior Alaska have not been sufficient to constrain the range of existing emissions. Here, we show our \n15 \nobservations of BC mass concentrations and carbon monoxide (CO) mixing ratios in the Poker Flat Research Range (65.12° \n16 \nN, 147.43° W), located in central Alaska, since April 2016. The medians of the hourly BC mass concentration and CO mixing \n17 \nratio throughout the observation period were 13 ng m-3 and 124.7 ppb, respectively. Significant peaks in the BC mass \n18 \nconcentration and CO mixing ratio were observed at the same time, indicating influences from common sources. These BC \n19 \npeaks coincided with peaks at other comparative sites in Alaska, indicating large BC emissions in interior Alaska. Source \n20 \nestimation by FLEXPART-WRF confirmed a contribution of forest wildfires in Alaska when high BC mass concentrations \n21 \nwere observed. For these cases, we found a positive correlation (r = 0.44) between the observed BC/∆CO ratio and fire \n22 \nradiative power (FRP) observed in Alaska and Canada. This finding indicates that the BC and CO emission ratio is controlled \n23 \nby the intensity and time progress of forest wildfires and suggests the BC emission factor or/and inventory could be potentially \n24 \nimproved by FRP. We recommend that FRP be integrated into future bottom-up emission inventories to achieve a better \n25 \nunderstanding of the dynamics of pollutants from frequently occurred forest wildfires under the rapidly changing climate in \n26 \nthe Arctic. 27 \n28 Long-term observations of black carbon and carbon\n1 Takeshi Kinase1, Fumikazu Taketani1,2, Masayuki Takigawa1, Chunmao Zhu2, Yongwon Kim3, Petr \n4 \nMordovskoi1, and Yugo Kanaya1,2 \n5 1 Introduction \n29 Climate change in the Arctic region has been strongly accelerated compared to the global average (Box et al., 2019; Bonfils et \n30 \nal., 2020). The near-surface air temperature increased between 1.8 and 3.1 °C in the period between 1971 and 2017 (Box et \n31 \nal., 2019). This rapid temperature increase in the Arctic region caused significant decreases in the extent of sea ice (Aizawa et \n32 \nal., 2021), resulting in the acceleration of Arctic warming (Cohen et al., 2014; Thackeray and Hall, 2019). Even if net CO2 \n33 \nemission is controlled to zero until the end of the 21st century (SSP1-2.6 scenario), modelling studies predicted a more than \n34 \n3.5 °C temperature increase (Cai et al., 2021; Xie et al., 2022). However, there are still some difficulties associated with climate \n35 \npredictions based on global climate models because of the widespread use of different model hindcasts and forecasts (Overland \n36 \net al., 2014). Specifically, it is known that the Arctic amplification process causes a significant acceleration in Arctic warming, \n37 \nbut the process is highly complicated and is not sufficiently understood; this includes processes involved in aerosol \n38 \nconcentration changes and the deposition of black carbon (BC) on snow and ice surfaces (Cohen et al., 2014). Thus, more \n39 \nresearch is required to understand Arctic climatic processes. 40 BC aerosols, which are formed by various incomplete combustion processes, such as fossil fuel and biomass burning (Bond et \n41 \nal., 2013), strongly contribute to warming by absorbing solar radiation (Bond et al., 2013; IPCC, 2021). In addition, BC \n42 \ndeposited on snow and ice surfaces decreases surface albedo and contributes to snow melting and warming (Aoki et al., 2011; \n43 \nBond et al., 2013; Oshima et al., 2020; IPCC, 2021). BC can be transported over long distances (estimated lifetimes are 3–6 \n44 \ndays globally (Wang et al., 2014; Lund et al., 2018)) and affect the climate and environment of remote regions, such as the \n45 \nArctic (Wang et al., 2011; Matsui et al., 2022). However, large discrepancies among model estimations for BC climate effects \n46 \non the Arctic remain (Gliß et al., 2021) because of a lack of observation data (IPCC, 2021) to constrain the models in terms of \n47 \ndependence on emission inventories (Pan et al., 2020; Matsui et al., 2022) and/or removal rates (Ikeda et al., 2017; Lund et al., \n48 \n2018). https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Abstract \n13 Abstract \n13 \nForest wildfires in interior Alaska represent an important black carbon (BC) source for the Arctic and sub-Arctic. However, \n14 \nBC observations in interior Alaska have not been sufficient to constrain the range of existing emissions. Here, we show our \n15 \nobservations of BC mass concentrations and carbon monoxide (CO) mixing ratios in the Poker Flat Research Range (65.12° \n16 \nN, 147.43° W), located in central Alaska, since April 2016. The medians of the hourly BC mass concentration and CO mixing \n17 \nratio throughout the observation period were 13 ng m-3 and 124.7 ppb, respectively. Significant peaks in the BC mass \n18 \nconcentration and CO mixing ratio were observed at the same time, indicating influences from common sources. These BC \n19 \npeaks coincided with peaks at other comparative sites in Alaska, indicating large BC emissions in interior Alaska. Source \n20 \nestimation by FLEXPART-WRF confirmed a contribution of forest wildfires in Alaska when high BC mass concentrations \n21 \nwere observed. For these cases, we found a positive correlation (r = 0.44) between the observed BC/∆CO ratio and fire \n22 \nradiative power (FRP) observed in Alaska and Canada. This finding indicates that the BC and CO emission ratio is controlled \n23 \nby the intensity and time progress of forest wildfires and suggests the BC emission factor or/and inventory could be potentially \n24 \nimproved by FRP. We recommend that FRP be integrated into future bottom-up emission inventories to achieve a better \n25 \nunderstanding of the dynamics of pollutants from frequently occurred forest wildfires under the rapidly changing climate in \n26 \nthe Arctic. 27 28 1 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. BC mass concentrations have long been observed in the atmosphere and snow at Utqiagvik (Barrow) (Eck et al., 2009; Garrett \n61 \net al., 2011; Mori et al., 2020), which is a high Arctic coastal tundra site. Campaign studies on atmospheric BC mass \n62 \nconcentrations were also conducted in interior Alaska using aircrafts (Kondo et al., 2011b; Bian et al., 2013; Creamean et al., \n63 \n2018). These campaign observations have provided an in-depth understanding of aerosol parameters related to wildfires. 64 \nHowever, separate long-term observations of BC mass concentrations are required to characterize annual trends and seasonality. 65 \nFewer studies have reported atmospheric BC mass concentrations in interior and coastal Alaska (Polissar et al., 1996, 1998; \n66 \nEck et al., 2009; Mouteva et al., 2015) and the high Arctic coastal site (Alert, Canada) (Garrett et al., 2011). To understand the \n67 \nlong-term variations in BC mass concentration and their impacts on the climate and environment, more BC observation data \n68 \nfrom interior Alaska are needed (AMAP, 2011). In this study, we aimed to investigate detailed variations in BC mass \n69 \nconcentration and its sources, with a focus on forest wildfires in interior Alaska, based on our monitoring of BC and CO at the \n70 \nPoker Flat Research Range (PFRR), which is a University of Alaska Fairbanks (UAF) observational site in interior Alaska. 71 \n \n72 \n2 Method \n73 \n2.1 Observation site \n74 \nWe conducted BC and CO monitoring at the PFRR (65.12° N, 147.43° W, 500 m a.s.l.) starting in April 2016. The PFRR is \n75 \nlocated in the centre of interior Alaska (Figure 1), approximately 35 km northeast of Fairbanks. The PFRR is surrounded by a \n76 \npredominant evergreen needled-leaved (black spruce; Picea mariana) forest with shrubland and herbaceous vegetation \n77 \n(Buchhorn et al., 2020). In this study, BC and CO monitoring results were analysed between April 2016 and December 2020. 78 \n \n79 BC mass concentrations have long been observed in the atmosphere and snow at Utqiagvik (Barrow) (Eck et al., 2009; Garrett \n61 \net al., 2011; Mori et al., 2020), which is a high Arctic coastal tundra site. 1 Introduction \n29 For long-range transport from Asia to the Arctic, constraints on the major BC emissions from East Asia (Choi et al., \n49 \n2020; Kanaya et al., 2020), ship-based observations for BC transport to the Arctic (Taketani et al., 2016, 2022), evaluation of \n50 \nthe multimodel bias using these datasets (Whaley et al., 2022) and an improved understanding of transport mechanisms and \n51 \nsource attributions (Ikeda et al., 2017; Zhu et al., 2020) have been achieved. However, more observational constraints are \n52 \nrequired for the characterization of BC emissions from boreal forest wildfires (Pan et al., 2020; AMAP, 2021). 53 Forest wildfires in the northern American region, especially those that occur in Alaska every summer (Picotte et al., 2020), are \n54 \none of the important BC emission sources in the Arctic and subarctic troposphere, and they result in depositional fluxes on \n55 \nsnow and ice over the Arctic and surrounding regions (Xu et al., 2017; AMAP, 2021; Williamson and Menounos, 2021; Matsui \n56 \net al., 2022). The occurrences of these forest wildfires in interior Alaska have increased since the 1980s (Sierra-Hernández et \n57 \nal., 2022), and this increasing trend is predicted to continue (Hu et al., 2015; Box et al., 2019; AMAP, 2021); the emission of \n58 \naerosols, including BC from forest wildfires, is projected to severely affect the environment (Halofsky et al., 2020) and climate \n59 \n(Schmale et al., 2021) in the future. 60 2 Campaign studies on atmospheric BC mass \n62 \nconcentrations were also conducted in interior Alaska using aircrafts (Kondo et al., 2011b; Bian et al., 2013; Creamean et al., \n63 \n2018). These campaign observations have provided an in-depth understanding of aerosol parameters related to wildfires. 64 \nHowever, separate long-term observations of BC mass concentrations are required to characterize annual trends and seasonality. 65 \nFewer studies have reported atmospheric BC mass concentrations in interior and coastal Alaska (Polissar et al., 1996, 1998; \n66 \nEck et al., 2009; Mouteva et al., 2015) and the high Arctic coastal site (Alert, Canada) (Garrett et al., 2011). To understand the \n67 \nlong-term variations in BC mass concentration and their impacts on the climate and environment, more BC observation data \n68 \nfrom interior Alaska are needed (AMAP, 2011). In this study, we aimed to investigate detailed variations in BC mass \n69 \nconcentration and its sources, with a focus on forest wildfires in interior Alaska, based on our monitoring of BC and CO at the \n70 \nPoker Flat Research Range (PFRR), which is a University of Alaska Fairbanks (UAF) observational site in interior Alaska. 71 \n \n72 2 Method \n73 \n2.1 Observation site \n74 \nWe conducted BC and CO monitoring at the PFRR (65.12° N, 147.43° W, 500 m a.s.l.) starting in April 2016. The PFRR \n75 \nlocated in the centre of interior Alaska (Figure 1), approximately 35 km northeast of Fairbanks. The PFRR is surrounded by\n76 \npredominant evergreen needled-leaved (black spruce; Picea mariana) forest with shrubland and herbaceous vegetatio\n77 \n(Buchhorn et al., 2020). In this study, BC and CO monitoring results were analysed between April 2016 and December 2020\n78 \n \n79 We conducted BC and CO monitoring at the PFRR (65.12° N, 147.43° W, 500 m a.s.l.) starting in April 2016. The PFRR is \n75 \nlocated in the centre of interior Alaska (Figure 1), approximately 35 km northeast of Fairbanks. The PFRR is surrounded by a \n76 \npredominant evergreen needled-leaved (black spruce; Picea mariana) forest with shrubland and herbaceous vegetation \n77 \n(Buchhorn et al., 2020). In this study, BC and CO monitoring results were analysed between April 2016 and December 2020. 78 \n \n79 We conducted BC and CO monitoring at the PFRR (65.12° N, 147.43° W, 500 m a.s.l.) starting in April 2016. The PFRR is \n75 \nlocated in the centre of interior Alaska (Figure 1), approximately 35 km northeast of Fairbanks. The PFRR is surrounded by a \n76 \npredominant evergreen needled-leaved (black spruce; Picea mariana) forest with shrubland and herbaceous vegetation \n77 \n(Buchhorn et al., 2020). In this study, BC and CO monitoring results were analysed between April 2016 and December 2020. 78 \n \n79 3 0 \nFigure 1. A map that shows the location of the PFRR and other sites compared in section 3.2 (Trapper Creek, Denali, and \n1 \nhttps://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 80 \nFigure 1. A map that shows the location of the PFRR and other sites compared in section 3.2 (Trapper Creek, Denali, and \n81 \nToolik Lake Field Station). All hot spots (larger than 0.3 (MW) in fire radiative power (FRP)) observed in the USA and Canada \n82 \nby the Visible Infrared Imaging Radiometer Suite (VIIRS) between 2016 and 2020 are shown in red colour. 83 \n84 80 \nFi\n1 A\nth t h\nth l\nti\nf th PFRR\nd\nth\nit\nd i\nti\n3 2 (T\nC\nk D\nli\nd\n81 Figure 1. A map that shows the location of the PFRR and other sites compared in section 3.2 (Trapper Creek, Denali, and \n81 \nToolik Lake Field Station). All hot spots (larger than 0.3 (MW) in fire radiative power (FRP)) observed in the USA and Canada \n82 \nby the Visible Infrared Imaging Radiometer Suite (VIIRS) between 2016 and 2020 are shown in red colour. 83 \n84 Cases with hourly ∆CO larger than 3-σ (13.9 ppb in median, 1-σ was \n101 \nderived from zero mode measurements before and after the hourly ambient air observations) were only used for analysis. 102 \n \n103 2.2 Measurements \n85 4 \nBC was measured by a Continuous Soot Monitoring System (BCM3130, Kanomax, Japan) with a flow rate of 0.78 L min-1 at \n86 \nstandard temperature and pressure (STP; 273 K and 1013 hPa). Sample air was introduced using an approximately 10 m \n87 \nconductive silicone tube (1/2” i.d.) from a height of 5.5 m above the ground. To minimize interferences from scattering particles, \n88 \ncoarse mode particles (approximately >1.0 μm), such as mineral dust, were removed by a PM1.0 cyclone (URG-2000-30ED, \n89 \nURG, USA) operated with a small flow regulation pump (~4.5 L min-1 at STP). In addition, to remove nonrefractory particles, \n90 \nsuch as sulfate and organics, the sample air was heated to approximately 300 °C using a heated inlet before it was introduced \n91 \ninto the instrument. More details of the instrument are described elsewhere (Miyazaki et al., 2008; Kondo et al., 2009, 2011a). 92 4 \nBC was measured by a Continuous Soot Monitoring System (BCM3130, Kanomax, Japan) with a flow rate of 0.78 L min-1 at \n86 \nstandard temperature and pressure (STP; 273 K and 1013 hPa). Sample air was introduced using an approximately 10 m \n87 \nconductive silicone tube (1/2” i.d.) from a height of 5.5 m above the ground. To minimize interferences from scattering particles, \n88 \ncoarse mode particles (approximately >1.0 μm), such as mineral dust, were removed by a PM1.0 cyclone (URG-2000-30ED, \n89 \nURG, USA) operated with a small flow regulation pump (~4.5 L min-1 at STP). In addition, to remove nonrefractory particles, \n90 \nsuch as sulfate and organics, the sample air was heated to approximately 300 °C using a heated inlet before it was introduced \n91 \ninto the instrument. More details of the instrument are described elsewhere (Miyazaki et al., 2008; Kondo et al., 2009, 2011a). 92 4 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. One-minute observation data were averaged to hourly data as the primary data. The limit of detection value (LOD) for hourly \n93 \nBC mass concentration was estimated to be 2 ng m-3, which is the sum of average hourly data and 3-σ values using 18 hours \n94 \nof particle-free air measurements. 95 \nThe CO mixing ratio was measured by an infrared absorption photometer (48iTLE, Thermo Fisher Scientific, USA) with a \n96 \nflow rate of 0.5 L min-1. Sample air was introduced using an approximately 10 m PFA tube from a height of 5.5 m above the \n97 \nground. Internal zero measurements were carried out for 20 minutes every hour, and the CO mixing ratio was estimated from \n98 \nthe difference in absorption between the sample and the zero measurements. Span gas (0.99 ppm CO/N2, Taiyo-Nissan, Tokyo, \n99 \nJapan) calibration was performed in April 2016. We calculated ∆CO as the enhancement in CO from background levels (14 \n100 \ndays moving 5-percentile values of observation results). Cases with hourly ∆CO larger than 3-σ (13.9 ppb in median, 1-σ was \n101 \nderived from zero mode measurements before and after the hourly ambient air observations) were only used for analysis. 102 \n \n103 One-minute observation data were averaged to hourly data as the primary data. The limit of detection value (LOD) for hourly \n93 \nBC mass concentration was estimated to be 2 ng m-3, which is the sum of average hourly data and 3-σ values using 18 hours \n94 \nof particle-free air measurements. 95 \nThe CO mixing ratio was measured by an infrared absorption photometer (48iTLE, Thermo Fisher Scientific, USA) with a \n96 \nflow rate of 0.5 L min-1. Sample air was introduced using an approximately 10 m PFA tube from a height of 5.5 m above the \n97 \nground. Internal zero measurements were carried out for 20 minutes every hour, and the CO mixing ratio was estimated from \n98 \nthe difference in absorption between the sample and the zero measurements. Span gas (0.99 ppm CO/N2, Taiyo-Nissan, Tokyo, \n99 \nJapan) calibration was performed in April 2016. We calculated ∆CO as the enhancement in CO from background levels (14 \n100 \ndays moving 5-percentile values of observation results). https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. with downwind atmospheric BC observations (Kanaya et al., 2020), while other bottom-up inventories might result in a factor \n124 \nof ~2 overestimation. The PES fields were calculated with a horizontal resolution of 0.5 × 0.5 degrees. The contribution of \n125 \nparticles within 100 m from the surface was considered for the calculation of PES for anthropogenic emissions. The plume \n126 \nheight of the GFAS (Global Fire Assimilation System) (Di Giuseppe et al., 2017) was also used for the estimation of the \n127 \ninjection height for biomass burning emissions. The fractional contribution of anthropogenic emissions was considered using \n128 \neight sectors in the ECLIPSE emission, and the anthropogenic and biomass burning emissions were divided into eight regions. 129 \nThe mean age of BC was also estimated by the mean lag time between release and observed time weighted by the amount of \n130 \nemission at each time period within the 20-day backward calculations. 131 \n \n132 2.3 Model calculation \n104 The FLEXPART (FLEXible PARTicle dispersion model)-WRF (Weather Research & Forecast) model was used in backward \n105 \nmode to characterize the source areas and sectors for the sampled air masses at the PFRR. FLEXPART-WRF version 3.3 \n106 \n(Brioude et al., 2013) and WRF version 4.4 (Skamarock et al., 2019) were employed for this study. The FLEXPART-WRF \n107 \nmodel was driven by mass weighted wind fields and perturbation within the PBL calculated by WRF, which covers the \n108 \nNorthern Hemisphere with a 45-km horizontal resolution. The ERA5 global reanalysis (Hersbach et al., 2020) was used as the \n109 \ninitial and lateral boundary conditions of WRF, and the meteorological field of WRF was also nudged to ERA5 with e-folding \n110 \ntimes of 3 hours and 12 hours for wind fields and temperature, respectively. Wet deposition is the major removal process for \n111 \nBC, and the deposition process in FLEXPART version 10 (Grythe et al., 2017) was applied to the FLEXPART-WRF model \n112 \nand was used in this study, with values of 10.0, 1.0, 0.9, and 0.1 employed as the collection efficiencies for wet deposition by \n113 \nrain and snow and the activation efficiencies of cloud condensation nuclei (CCN) and ice nuclei (IN) (Crain, Csnow, CCNeff, and \n114 \nINeff), respectively. The FLEXPART-WRF calculation was conducted every 6 hours from April 2016 to December 2020. For \n115 \neach simulation, 40000 particles were released at 0.5 × 0.5 degrees (horizontally) and 200 m AGL (vertically) centred at the \n116 \nPFRR. The particles were tracked for 20 days at 6-hour intervals. The primary output of the FLEXPART-WRF backward \n117 \ncalculations was the potential emission sensitivity (PES), which expresses the residence time of particles at a given location \n118 \nand is used to characterize the transport pathways of the sampled air masses. The concentration of BC was estimated by \n119 \nmultiplying PES and emissions based on a procedure reported by Sauvage et al. (2017). ECLIPSE (Evaluating the Climate \n120 \nand Air Quality Impacts of Short-Lived Pollutants) version 6b (Klimont et al., 2017) and GFED (Global Fire Emission \n121 \nDatabase) version 4.1 (Daily) (van der Werf et al., 2014) were used as the anthropogenic and biomass burning emissions, \n122 \nrespectively. Note that the Chinese BC emissions from ECLIPSE version 6b with the monthly profile of version 5 are certified \n123 5 3.1 Time series of observed BC and CO concentrations \n158 The time series of BC mass concentration and CO mixing ratio are shown in Figure 2, and those of annual median, 10th, and \n159 \n90th percentile values are summarized in Table 1. The hourly median BC mass concentration and 10th and 90th percentile \n160 \nvalues throughout the observation period were 13, 3, and 56 ng m-3, respectively. No significant increase in annual median BC \n161 \nmass concentration was observed (Table 1). Abrupt peaks (up to 5540 ng m-3) were occasionally observed during summer. 162 \nThis seasonality differed from BC observational reports at Utqiagvik (Barrow), which showed BC mass concentration over \n163 \nthe long term using the same instrument (BCM3130) employed in this study (Sinha et al., 2017; Mori et al., 2020). The previous \n164 \nreport showed that the BC mass concentration increases in winter and early spring and decreases in summer. These differences \n165 \nare possibly caused by a difference in location. The PFRR is located in interior Alaska, while Barrow is located on the \n166 \nnorthernmost coast of Alaska, suggesting that large BC emissions occurred around the PFRR. 167 The median, 10th, and 90th percentiles of hourly CO mixing ratios throughout the observation period were 124.7, 99.0, and \n168 \n148.2 ppb, respectively. Similar to BC, significant increases in the annual median CO mixing ratio were not observed, but \n169 \ncontrary to the BC mass concentration, the CO mixing ratio showed clear seasonal variation, high in spring (between February \n170 \nand April, 143.5 ppb in the median) and low in summer (July and August, 103.3 ppb in the median) (Figure 2(b)). These \n171 \nobserved CO mixing ratios and seasonal variations were consistent with the aircraft observation results (less than 500 m AGL \n172 \nabove the PFRR) provided by the NOAA Global Monitoring Laboratory (https://doi.org/10.15138/39HR-9N34; accessed on \n173 \n2 November 2023) (Figure S2) and previous studies that reported the CO mixing ratio at the PFRR (Kasai et al., 2005; \n174 \nYurganov et al., 1998). In summer, CO peaks coincident with BC mass concentration were found, suggesting a common \n175 \nemission source for both BC and CO. 176 The median, 10th, and 90th percentiles of hourly CO mixing ratios throughout the observation period were 124.7, 99.0, and \n168 \n148.2 ppb, respectively. No significant increase in annual median B\n161 \nmass concentration was observed (Table 1). Abrupt peaks (up to 5540 ng m-3) were occasionally observed during summ\n162 \nThis seasonality differed from BC observational reports at Utqiagvik (Barrow), which showed BC mass concentration o\n163 \nthe long term using the same instrument (BCM3130) employed in this study (Sinha et al., 2017; Mori et al., 2020). The previo\n164 \nreport showed that the BC mass concentration increases in winter and early spring and decreases in summer. These differen\n165 \nare possibly caused by a difference in location. The PFRR is located in interior Alaska, while Barrow is located on \n166 \nnorthernmost coast of Alaska, suggesting that large BC emissions occurred around the PFRR. 167 \nThe median, 10th, and 90th percentiles of hourly CO mixing ratios throughout the observation period were 124.7, 99.0, a\n168 \n148.2 ppb, respectively. Similar to BC, significant increases in the annual median CO mixing ratio were not observed, b\n169 \ncontrary to the BC mass concentration, the CO mixing ratio showed clear seasonal variation, high in spring (between Febru\n170 \nand April, 143.5 ppb in the median) and low in summer (July and August, 103.3 ppb in the median) (Figure 2(b)). Th\n171 \nobserved CO mixing ratios and seasonal variations were consistent with the aircraft observation results (less than 500 m AG\n172 \nabove the PFRR) provided by the NOAA Global Monitoring Laboratory (https://doi.org/10.15138/39HR-9N34; accessed\n173 \n2 November 2023) (Figure S2) and previous studies that reported the CO mixing ratio at the PFRR (Kasai et al., 20\n174 \nYurganov et al., 1998). In summer, CO peaks coincident with BC mass concentration were found, suggesting a comm\n175 addition, we used FRP values greater than 0.3 MW because hot spots smaller than 0.3 MW included outliers (Figure S1). Only \n154 \nhot spots that were observed within the previous 24 hours were considered. 155 addition, we used FRP values greater than 0.3 MW because hot spots smaller than 0.3 MW included outliers (Figure S1). Only \n154 \nhot spots that were observed within the previous 24 hours were considered. 155 \n \n156 2.4 Analysis of the effect of forest wildfire on the BC mass concentration at the PFRR\n133 We characterized the observed BC/∆CO ratios, which are known to be valuable indicators of emission sources and combustion \n134 \nconditions (Kondo et al., 2011b; Pan et al., 2017; Selimovic et al., 2019), in terms of fire radiative power (FRP), which accounts \n135 \nfor forest fire intensity. To do this, we compared the BC/∆CO ratio in high BC mass concentration cases observed in summer \n136 \nand FRP observed by the Visible Infrared Imaging Radiometer Suite (VIIR) on the Suomi NPP satellite. Airmasses were traced \n137 \nfor 4 days at the most using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT; Stein et al., 2015)) \n138 \nwith GDAS1 meteorological datasets (3 h archived 1° × 1° Global Data Assimilation System) from the National Centers for \n139 \nEnvironmental Prediction (http://ready.arl.noaa.gov/gdas1.php; accessed on 2 November 2023). The calculation started from \n140 \n500 m AGL at the PFRR site, and fire spots were searched along with the trajectories. 141 The BC/∆CO ratio is also affected by atmospheric processes (Kanaya et al., 2016; Choi et al., 2020), as only BC is lost via \n142 \nwet removal processes. To extract observation results that were not affected by wet removal processes, we used accumulated \n143 \nprecipitation along the trajectory (APT) as an indicator of wet removal processes. Previous studies showed that the BC/∆CO \n144 \nratio can be significantly changed when APT is larger than 1 mm (Choi et al., 2020; Kanaya et al., 2016; Kondo et al., 2011b). 145 \nTherefore, the duration for the accumulation of fire spots was shortened when APT reached 1 mm or when the trajectory \n146 \nreached ground level. Rectangles were defined with ±0.5° in the longitudinal direction and ±0.25° in the latitudinal direction \n147 \ncentring around hourly air mass positions. Then, the FRP and the number of hot spots were accumulated for individual \n148 \nrectangles over the duration of the trajectories. Finally, the total accumulated FRP (∑FRP) was divided by the detected total \n149 \nspot number to yield an index describing the conditions of fires affecting the observed airmasses. As hot spot datasets, VIIRS \n150 \n375 m (VNP14IMGTML_NRT) archived datasets from the Fire Information for Resource Management System (FIRMS) \n151 \nwebsite (https://earthdata.nasa.gov/firms; accessed on 2 November 2023) were used in this study. The selected confidence \n152 \nlevels were ‘nominal’ or ‘high’, and the selected type attributed to thermal anomalies was ‘presumed vegetation fire’. In \n153 6 6 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. addition, we used FRP values greater than 0.3 MW because hot spots smaller than 0.3 MW included outliers (Figure S1). Only \n154 \nhot spots that were observed within the previous 24 hours were considered. 155 \n \n156 \n3 Results and discussion \n157 \n3.1 Time series of observed BC and CO concentrations \n158 \nThe time series of BC mass concentration and CO mixing ratio are shown in Figure 2, and those of annual median, 10th, and \n159 \n90th percentile values are summarized in Table 1. The hourly median BC mass concentration and 10th and 90th percentile \n160 \nvalues throughout the observation period were 13, 3, and 56 ng m-3, respectively. No significant increase in annual median BC \n161 \nmass concentration was observed (Table 1). Abrupt peaks (up to 5540 ng m-3) were occasionally observed during summer. 162 \nThis seasonality differed from BC observational reports at Utqiagvik (Barrow), which showed BC mass concentration over \n163 \nthe long term using the same instrument (BCM3130) employed in this study (Sinha et al., 2017; Mori et al., 2020). The previous \n164 \nreport showed that the BC mass concentration increases in winter and early spring and decreases in summer. These differences \n165 \nare possibly caused by a difference in location. The PFRR is located in interior Alaska, while Barrow is located on the \n166 \nnorthernmost coast of Alaska, suggesting that large BC emissions occurred around the PFRR. 167 \nThe median, 10th, and 90th percentiles of hourly CO mixing ratios throughout the observation period were 124.7, 99.0, and \n168 \n148 2\nb\ni\nl\nSi il\nBC\ni\nifi\ni\ni\nh\nl\ndi\nCO\ni i\ni\nb\nd b\n169 addition, we used FRP values greater than 0.3 MW because hot spots smaller than 0.3 MW included outliers (Figure S1). O\n154 \nhot spots that were observed within the previous 24 hours were considered. 155 \n \n156 \n3 Results and discussion \n157 \n3.1 Time series of observed BC and CO concentrations \n158 \nThe time series of BC mass concentration and CO mixing ratio are shown in Figure 2, and those of annual median, 10th, a\n159 \n90th percentile values are summarized in Table 1. The hourly median BC mass concentration and 10th and 90th percent\n160 \nvalues throughout the observation period were 13, 3, and 56 ng m-3, respectively. addition, we used FRP values greater than 0.3 MW because hot spots smaller than 0.3 MW included outliers (Figure S1). Only \n154 \nhot spots that were observed within the previous 24 hours were considered. \n155 \n \n156 3.1 Time series of observed BC and CO concentrations \n158 Similar to BC, significant increases in the annual median CO mixing ratio were not observed, but \n169 \ncontrary to the BC mass concentration, the CO mixing ratio showed clear seasonal variation, high in spring (between February \n170 \nand April, 143.5 ppb in the median) and low in summer (July and August, 103.3 ppb in the median) (Figure 2(b)). These \n171 \nobserved CO mixing ratios and seasonal variations were consistent with the aircraft observation results (less than 500 m AGL \n172 \nabove the PFRR) provided by the NOAA Global Monitoring Laboratory (https://doi.org/10.15138/39HR-9N34; accessed on \n173 \n2 November 2023) (Figure S2) and previous studies that reported the CO mixing ratio at the PFRR (Kasai et al., 2005; \n174 \nYurganov et al., 1998). In summer, CO peaks coincident with BC mass concentration were found, suggesting a common \n175 \nemission source for both BC and CO. 176 177 7 3.2 Comparisons with other observation sites \n185 Note that our BC observation, which had a better LOD (2 ng m-3) and higher temporal resolution (1 hour), \n199 \ncould provide more reliable data in this low range. On the other hand, the maximum BC mass concentrations were higher at \n200 \nthe PFRR within the period with common BC peaks than at TRCR and TOOL but similar at DENA (Table 2). This indicates \n201 \nthat significant BC emissions in central Alaska were better captured at the PFRR than at other observation sites. We will \n202 \ndiscuss source and emission ratio characterization in Sections 3.4 and 3.5 by fully utilizing the superior temporal resolution \n203 \nand accuracy of our observations. 204 \n205 The BC mass concentration peaks were nearly coincided for the PFRR, DENA, TRCR, and TOOL (Figure S3). The median \n194 \nand maximum daily BC mass concentrations observed at each site are summarized in Table 2. The median BC mass \n195 \nconcentrations at DENA, TRCR, and TOOL were larger than those at the PFRR by 6–19 ng m-3 (Table 2), but the significance \n196 \nof the difference is unclear considering methodological differences and associated uncertainties (precision). Here, the \n197 \nuncertainties of the thermal/optical reflectance method varied between 12 and 14 ng m-3 in median values during the whole \n198 \nobservation period. Note that our BC observation, which had a better LOD (2 ng m-3) and higher temporal resolution (1 hour), \n199 \ncould provide more reliable data in this low range. On the other hand, the maximum BC mass concentrations were higher at \n200 \nthe PFRR within the period with common BC peaks than at TRCR and TOOL but similar at DENA (Table 2). This indicates \n201 \nthat significant BC emissions in central Alaska were better captured at the PFRR than at other observation sites. We will \n202 \ndiscuss source and emission ratio characterization in Sections 3.4 and 3.5 by fully utilizing the superior temporal resolution \n203 \nand accuracy of our observations. 204 \n205 Table 2. Summary of site locations and measurement results at PFRR (this study), TRCR, DENA, and TOOL. 206 Table 2. Summary of site locations and measurement results at PFRR (this study), TRCR, DENA, and TOOL. 206 \na Data were selected from the same date at PFRR, TRCR, and DENA. 3.2 Comparisons with other observation sites \n185 p\nWe compared the BC observation results from the PFRR to those from other Alaskan sites (Table 2 and Figure S3), i.e., \n186 \nTrapper Creek (TRCR), Denali (DENA), and Toolik Lake Field Station (TOOL), using datasets for 24-hour filter samples \n187 \ncollected every three days. The datasets were from the thermal/optical reflectance method at DENA, TRCR, and TOOL \n188 \n(http://views.cira.colostate.edu/fed/QueryWizard/; accessed on 2 November 2023). A systematic bias might be present in terms \n189 \nof the methods used, but it is most likely within a factor of 2 from the actual conditions based on comparisons with recent data \n190 \nat various sites (Miyazaki et al., 2008; Kondo et al., 2009; Kanaya et al., 2008; Kondo et al., 2011a; Ohata et al., 2021; Sinha \n191 \net al., 2017). For the BC mass concentration observed at TRCR, DENA, and TOOL, datasets flagged V0 (valid value) were \n192 \nselected. 193 We compared the BC observation results from the PFRR to those from other Alaskan sites (Table 2 and Figure S3), i.e., \n186 \nTrapper Creek (TRCR), Denali (DENA), and Toolik Lake Field Station (TOOL), using datasets for 24-hour filter samples \n187 \ncollected every three days. The datasets were from the thermal/optical reflectance method at DENA, TRCR, and TOOL \n188 \n(http://views.cira.colostate.edu/fed/QueryWizard/; accessed on 2 November 2023). A systematic bias might be present in terms \n189 \nof the methods used, but it is most likely within a factor of 2 from the actual conditions based on comparisons with recent data \n190 \nat various sites (Miyazaki et al., 2008; Kondo et al., 2009; Kanaya et al., 2008; Kondo et al., 2011a; Ohata et al., 2021; Sinha \n191 \net al., 2017). For the BC mass concentration observed at TRCR, DENA, and TOOL, datasets flagged V0 (valid value) were \n192 \nselected. 193 We compared the BC observation results from the PFRR to those from other Alaskan sites (Table 2 and Figure S3), i.e., \n186 \nTrapper Creek (TRCR), Denali (DENA), and Toolik Lake Field Station (TOOL), using datasets for 24-hour filter samples \n187 \ncollected every three days. The datasets were from the thermal/optical reflectance method at DENA, TRCR, and TOOL \n188 \n(http://views.cira.colostate.edu/fed/QueryWizard/; accessed on 2 November 2023). 3.2 Comparisons with other observation sites \n185 A systematic bias might be present in terms \n189 \nof the methods used, but it is most likely within a factor of 2 from the actual conditions based on comparisons with recent data \n190 \nat various sites (Miyazaki et al., 2008; Kondo et al., 2009; Kanaya et al., 2008; Kondo et al., 2011a; Ohata et al., 2021; Sinha \n191 \net al., 2017). For the BC mass concentration observed at TRCR, DENA, and TOOL, datasets flagged V0 (valid value) were \n192 \nselected. 193 The BC mass concentration peaks were nearly coincided for the PFRR, DENA, TRCR, and TOOL (Figure S3). The median \n194 \nand maximum daily BC mass concentrations observed at each site are summarized in Table 2. The median BC mass \n195 \nconcentrations at DENA, TRCR, and TOOL were larger than those at the PFRR by 6–19 ng m-3 (Table 2), but the significance \n196 \nof the difference is unclear considering methodological differences and associated uncertainties (precision). Here, the \n197 \nuncertainties of the thermal/optical reflectance method varied between 12 and 14 ng m-3 in median values during the whole \n198 \nobservation period. Note that our BC observation, which had a better LOD (2 ng m-3) and higher temporal resolution (1 hour), \n199 \ncould provide more reliable data in this low range. On the other hand, the maximum BC mass concentrations were higher at \n200 \nthe PFRR within the period with common BC peaks than at TRCR and TOOL but similar at DENA (Table 2). This indicates \n201 \nthat significant BC emissions in central Alaska were better captured at the PFRR than at other observation sites. We will \n202 \ndiscuss source and emission ratio characterization in Sections 3.4 and 3.5 by fully utilizing the superior temporal resolution \n203 \nand accuracy of our observations. 204 \n \n205 The BC mass concentration peaks were nearly coincided for the PFRR, DENA, TRCR, and TOOL (Figure S3). The median \n194 \nand maximum daily BC mass concentrations observed at each site are summarized in Table 2. The median BC mass \n195 \nconcentrations at DENA, TRCR, and TOOL were larger than those at the PFRR by 6–19 ng m-3 (Table 2), but the significance \n196 \nof the difference is unclear considering methodological differences and associated uncertainties (precision). Here, the \n197 \nuncertainties of the thermal/optical reflectance method varied between 12 and 14 ng m-3 in median values during the whole \n198 \nobservation period. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Table 1. Annual summary of the observed hourly BC mass concentration and CO mixing ratio at the PFRR. 178 \n \nBC (ng m-3) \nCO (ppb) \nYear \nMedian \n10th \npercentile \n90th \npercentile \nMedian \n10th \npercentile \n90th \npercentile \n2016a \n \n11 \n2 \n49 \n109.7 \n93.1 \n130.3 \n2017 \n15 \n3 \n65 \n128.2 \n100.5 \n148.8 \n2018 \n14 \n3 \n53 \n118.2 \n93.3 \n149.4 \n2019 \n15 \n3 \n63 \n128.4 \n113.1 \n150.8 \n2020 \n13 \n3 \n50 \n131.3 \n107.5 \n150.6 \na Observations started on 28 April 2016. 179 \n \n180 \n \n181 \nFigure 2. Time series of (a) BC mass concentration and (b) CO mixing ratio. Grey lines, blue points, and red triangles show \n182 \nhourly, daily, and monthly averages, respectively. 183 Table 1. Annual summary of the observed hourly BC mass concentration and CO mixing ratio at the PFRR. 178 \n \nBC (ng m-3) \nCO (ppb) \nYear \nMedian \n10th \npercentile \n90th \npercentile \nMedian \n10th \npercentile \n90th \npercentile \n2016a \n \n11 \n2 \n49 \n109.7 \n93.1 \n130.3 \n2017 \n15 \n3 \n65 \n128.2 \n100.5 \n148.8 \n2018 \n14 \n3 \n53 \n118.2 \n93.3 \n149.4 \n2019 \n15 \n3 \n63 \n128.4 \n113.1 \n150.8 \n2020 \n13 \n3 \n50 \n131.3 \n107.5 \n150.6 \na Observations started on 28 April 2016. 179 \n \n180 Table 1. Annual summary of the observed hourly BC mass concentration and CO mixing ratio at the PFRR. 8 Table 1. Annual summary of the observed hourly BC mass concentration and CO mixing ra\n178 Annual summary of the observed hourly BC mass concentration and CO mixing ratio at the PFRR 180 180 \n \n181 \nFigure 2. Time series of (a) BC mass concentration and (b) CO mixing ratio. Grey lines, blue points, and red triangles show \n182 Figure 2. Time series of (a) BC mass concentration and (b) CO mixing ratio. Grey lines, blue points, and red triangles show \n182 \nhourly, daily, and monthly averages, respectively. 183 Figure 2. Time series of (a) BC mass concentration and (b) CO mixing ratio. Grey lines, blue points, and red triangles show \n182 \nhourly, daily, and monthly averages, respectively. 183 Figure 2. Time series of (a) BC mass concentration and (b) CO mixing ratio. Grey lines, b\n182 8 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 184 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. b Observations started on 13 November 2018. 208 \n \n209 \n3.3 Comparison of observation and model simulations and possible BC sources \n210 \nFigure 3(a) shows a time series of 6-hour averages of the observation data and 6-hourly BC mass concentrations estimated by \n211 \nFLEXPART-WRF simulations. FLEXPART-WRF could capture the high BC mass concentration peaks (Figure 3(a)) with a \n212 \ncorrelation coefficient of 0.7 (Figure S4). The median of the simulated/observed ratio (observation data > LOD in this case) \n213 \nwas 1.0 for the whole observation period, indicating good agreement between the model simulation and observations. 214 \nThe source region and source sector contributions derived from the FLEXPART-WRF simulation are shown in Figure 3(b) \n215 \nand (c). Source regions were classified into 8 categories, i.e., Europe, Central Asia, Russia, East Asia, Canada, Alaska, USA \n216 \n(excluding Alaska), and Others (Figure 3(b)). Source sectors were classified into 9 categories, i.e., biomass burning, ship, gas \n217 \nflaring, waste incineration, transport, industry, energy, domestic, and agriculture (Figure 3(c)). The BC source sectors and \n218 \nregions varied significantly according to the season (Figure 3(b) and (c)). In the warm season (between May and September), \n219 \nthe possible BC source regions were Russia (3.6–74% in the 10–90 percentile) and Alaska (12–85% in the 10–90 percentile) \n220 \n(Figure 3(b)), and the possible source sector was estimated to be biomass burning (8.1–88% in the 10–90 percentile) (Figure \n221 \n3(c)), especially when BC mass concentration was high, suggesting that BC contributions from biomass burning that occurred \n222 \nin Russia and Alaska are both significant for BC mass concentrations at the PFRR. As snow cover disappears from the ground \n223 \nand the atmospheric conditions become drier, forest wildfires caused by lightning increase in these warm seasons (Reap, 1991; \n224 \nKaplan and Lau, 2021), resulting in increases in BC emissions from biomass burning (AMAP, 2021). We will focus on these \n225 \nhigh BC mass concentration cases from Alaska and discuss the relationship between forest wildfire intensity and the BC/∆CO \n226 \nratio in the following section. 227 \nOn the other hand, in the cold seasons (between October and April), the domestic (24–48% in the 10–90 percentile) and \n228 \ntransport sectors (25–48% in the 10–90 percentile) were estimated to be possible dominant BC source sectors (Figure 3(c)). 229 \nThe dominant source region was Alaska (19–88% in the 10–90 percentile), and occasionally, Russia (0.89–31% in the 10–90 \n230 \npercentile) and East Asia (1.2–41% in the 10–90 percentile) were significant (Figure 3(b)). 231 \n \n232 b Observations started on 13 November 2018. 208 \n \n209 \n3.3 Comparison of observation and model simulati\n210 \nFigure 3(a) shows a time series of 6-hour averages of \n211 \nFLEXPART-WRF simulations. FLEXPART-WRF co\n212 \ncorrelation coefficient of 0.7 (Figure S4). The median\n213 \nwas 1.0 for the whole observation period, indicating g\n214 \nThe source region and source sector contributions de\n215 \nand (c). Source regions were classified into 8 categor\n216 \n(excluding Alaska), and Others (Figure 3(b)). Source \n217 \nflaring, waste incineration, transport, industry, energ\n218 \nregions varied significantly according to the season (F\n219 \nthe possible BC source regions were Russia (3.6–74%\n220 \n(Figure 3(b)), and the possible source sector was estim\n221 \n3(c)), especially when BC mass concentration was hig\n222 \nin Russia and Alaska are both significant for BC mass\n223 \nand the atmospheric conditions become drier, forest w\n224 \nKaplan and Lau, 2021), resulting in increases in BC e\n225 \nhigh BC mass concentration cases from Alaska and di\n226 \nratio in the following section. 227 \nOn the other hand, in the cold seasons (between Oc\n228 \ntransport sectors (25–48% in the 10–90 percentile) w\n229 \nThe dominant source region was Alaska (19–88% in t\n230 \npercentile) and East Asia (1.2–41% in the 10–90 perc\n231 \n \n232 b Observations started on 13 November 2018. 208 \n \n209 \n3.3 Comparison of observation and model simulations and possible BC sources \n210 \nFigure 3(a) shows a time series of 6-hour averages of the observation data and 6-hourly BC mass concentrations estimated by \n211 \nFLEXPART-WRF simulations. FLEXPART-WRF could capture the high BC mass concentration peaks (Figure 3(a)) with a \n212 \ncorrelation coefficient of 0.7 (Figure S4). The median of the simulated/observed ratio (observation data > LOD in this case) \n213 \nwas 1.0 for the whole observation period, indicating good agreement between the model simulation and observations. 214 \nThe source region and source sector contributions derived from the FLEXPART-WRF simulation are shown in Figure 3(b) \n215 \nand (c). Source regions were classified into 8 categories, i.e., Europe, Central Asia, Russia, East Asia, Canada, Alaska, USA \n216 \n(excluding Alaska), and Others (Figure 3(b)). Source sectors were classified into 9 categories, i.e., biomass burning, ship, gas \n217 \nflaring, waste incineration, transport, industry, energy, domestic, and agriculture (Figure 3(c)). The BC source sectors and \n218 \nregions varied significantly according to the season (Figure 3(b) and (c)). In the warm season (between May and September), \n219 \nthe possible BC source regions were Russia (3.6–74% in the 10–90 percentile) and Alaska (12–85% in the 10–90 percentile) \n220 \n(Figure 3(b)), and the possible source sector was estimated to be biomass burning (8.1–88% in the 10–90 percentile) (Figure \n221 \n3(c)), especially when BC mass concentration was high, suggesting that BC contributions from biomass burning that occurred \n222 \nin Russia and Alaska are both significant for BC mass concentrations at the PFRR. As snow cover disappears from the ground \n223 \nand the atmospheric conditions become drier, forest wildfires caused by lightning increase in these warm seasons (Reap, 1991; \n224 \nKaplan and Lau, 2021), resulting in increases in BC emissions from biomass burning (AMAP, 2021). We will focus on these \n225 \nhigh BC mass concentration cases from Alaska and discuss the relationship between forest wildfire intensity and the BC/∆CO \n226 \nratio in the following section. 227 \nOn the other hand, in the cold seasons (between October and April), the domestic (24–48% in the 10–90 percentile) and \n228 \ntransport sectors (25 48% in the 10 90 percentile) were estimated to be possible dominant BC source sectors (Figure 3(c))\n229 3.2 Comparisons with other observation sites \n185 207 \n \n \n \n \n Daily BC mass concentration (ng m-3) \n \nSite \nLatitude (°N) \nLongitude (°W) \nAltitude (m a.s.l) \nMedian \nMaximum \nPFRRa \n65.12 \n147.43 \n500 \n18 \n920 \nTRCR \n62.32 \n150.32 \n155 \n37 \n570 \n \nDENA \n63.73 \n148.97 \n658 \n24 \n1044 \n \nTOOLb \n68.64 \n149.61 \n740 \n28 \n643 9 233 \nFigure 3. Time series of (a) BC mass concentrations from observations at the PFRR (6-hour average) and FLEXPART-WRF \n234 \nestimates (6 hours). Black circles and red triangles show the observed and simulated BC mass concentrations, respectively. 235 \nTime series of simulated 6-hourly (b) contributions from BC source regions and (c) contributions from BC source sectors \n236 \nestimated by FLEXPART-WRF simulation. Individual colours show the source regions and sectors. 237 Figure 3. Time series of (a) BC mass concentrations from observations at the PFRR (6-hou\n234 Figure 3. Time series of (a) BC mass concentrations from observations at the PFRR (6-hour average) and FLEXPART-WRF \n234 estimates (6 hours). Black circles and red triangles show the observed and simulated BC mass concentrations, respectively. 235 \nTime series of simulated 6-hourly (b) contributions from BC source regions and (c) contributions from BC source sectors \n236 \nestimated by FLEXPART-WRF simulation. Individual colours show the source regions and sectors. 237 \n \n238 3.3 Comparison of observation and model simulations and possible BC sources \n210 g\nwas 1.0 for the whole observation period, indicating good agreement between the model simulation and observations. 214 \nThe source region and source sector contributions derived from the FLEXPART-WRF simulation are shown in Figure 3(b) \n215 \nand (c). Source regions were classified into 8 categories, i.e., Europe, Central Asia, Russia, East Asia, Canada, Alaska, USA \n216 \n(excluding Alaska), and Others (Figure 3(b)). Source sectors were classified into 9 categories, i.e., biomass burning, ship, gas \n217 \nflaring, waste incineration, transport, industry, energy, domestic, and agriculture (Figure 3(c)). The BC source sectors and \n218 \nregions varied significantly according to the season (Figure 3(b) and (c)). In the warm season (between May and September), \n219 \nthe possible BC source regions were Russia (3.6–74% in the 10–90 percentile) and Alaska (12–85% in the 10–90 percentile) \n220 \n(Figure 3(b)), and the possible source sector was estimated to be biomass burning (8.1–88% in the 10–90 percentile) (Figure \n221 \n3(c)), especially when BC mass concentration was high, suggesting that BC contributions from biomass burning that occurred \n222 \nin Russia and Alaska are both significant for BC mass concentrations at the PFRR. As snow cover disappears from the ground \n223 \nand the atmospheric conditions become drier, forest wildfires caused by lightning increase in these warm seasons (Reap, 1991; \n224 \nKaplan and Lau, 2021), resulting in increases in BC emissions from biomass burning (AMAP, 2021). We will focus on these \n225 \nhigh BC mass concentration cases from Alaska and discuss the relationship between forest wildfire intensity and the BC/∆CO \n226 \nratio in the following section. 227 On the other hand, in the cold seasons (between October and April), the domestic (24–48% in the 10–90 percentile) and \n228 \ntransport sectors (25–48% in the 10–90 percentile) were estimated to be possible dominant BC source sectors (Figure 3(c)). 229 \nThe dominant source region was Alaska (19–88% in the 10–90 percentile), and occasionally, Russia (0.89–31% in the 10–90 \n230 \npercentile) and East Asia (1.2–41% in the 10–90 percentile) were significant (Figure 3(b)). 231 10 233 \nhttps://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 233 \nhttps://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. The normalized frequency distribution of the BC/∆CO ratio for the high BC mass concentration cases is shown in Figure 4(a). 247 \nThe median, 10th, and 90th percentile values of the BC/∆CO ratio during these periods were 4.7, 1.8, and 18 ng m-3 ppb-1, \n248 \nrespectively. These observed BC/∆CO ratios in the high BC mass concentration cases were in the same range or sometimes \n249 \nhigher than those in previous studies that reported the BC/∆CO ratios from boreal forest wildfire emissions in Canada (Kondo \n250 \net al., 2011b) and Siberia (Paris et al., 2009; Chi et al., 2013; Vasileva et al., 2017). Increases in biomass burning derived \n251 \nBC/∆CO ratios with combustion efficiency were suggested from a boreal forest wildfire study (Kondo et al., 2011b) and from \n252 \nlaboratory-scale burning experiments of crop residues (Pan et al., 2017); however, in-depth studies examining variabilities in \n253 \nBC/∆CO ratios based on long-term, near-forest observations have not been conducted. To consider the possibility that \n254 \ncombustion conditions (flaming and smouldering) primarily control the BC/∆CO ratio, we will investigate the relationship \n255 \nbetween the BC/∆CO ratio and forest wildfire intensity in the following section. 256 The medians of the contributions of biomass burning and the mean age of BC estimated by the FLEXPART-WRF simulation \n257 \nin these high BC mass concentration cases were higher and shorter (95.5% and 2.6 days) than those in other periods (7.6% and \n258 \n6.9 days) (Figure 4(b) and (c)), indicating a strong contribution of BC from neighbouring forest wildfires. We also calculated \n259 \nthe 6-hourly mass-weighted biomass burning contributions from individual source regions (5 categories based on Figure 3(c), \n260 \nCanada and USA are categorized as North America and East Asia, Central Asia, and Europe are included in Others) to the BC \n261 \nmass concentrations at the PFRR (Figure 5). As a result, we found that large peaks, such as those observed between June and \n262 \nAugust in 2017, 2018, and 2019, coincided well with the peaks of BC contributions from forest wildfires in Alaska. These \n263 \nresults confirmed that the observed high BC mass concentration cases were primarily affected by local forest wildfires in \n264 \nAlaska. 3.4 Biomass burning contribution for high BC concentration cases \n239 Hereafter, we focus on high BC mass concentration cases at the PFRR (647 hours in total), which were selected with the 98 \n240 \npercentile value (171 ng m-3) as the threshold for the hourly BC mass concentration. The cumulative BC mass concentration \n241 \nobserved in these high BC mass concentration cases accounted for 5.7–43% of the annual BC mass concentration, although \n242 \nthe duration of these periods was very short (17–187 hours in a year). Most of these high BC concentration cases \n243 \n(approximately 90%) were observed in warm seasons (between June and September) and were related to forest wildfires in \n244 \nAlaska. The median CO mixing ratio for the high BC concentration cases (174.7 ppb) was also significantly higher than that \n245 \nin other periods (124.7 ppb), suggesting that both BC and CO were emitted from forest wildfires (see Section 3.3). 246 Hereafter, we focus on high BC mass concentration cases at the PFRR (647 hours in total), which were selected with the 98 \n240 \npercentile value (171 ng m-3) as the threshold for the hourly BC mass concentration. The cumulative BC mass concentration \n241 \nobserved in these high BC mass concentration cases accounted for 5.7–43% of the annual BC mass concentration, although \n242 \nthe duration of these periods was very short (17–187 hours in a year). Most of these high BC concentration cases \n243 \n(approximately 90%) were observed in warm seasons (between June and September) and were related to forest wildfires in \n244 \nAlaska. The median CO mixing ratio for the high BC concentration cases (174.7 ppb) was also significantly higher than that \n245 \nin other periods (124.7 ppb), suggesting that both BC and CO were emitted from forest wildfires (see Section 3.3). 246 11 These peaks were widely observed in Alaska (Section 3.2) and imply a large impact of local forest wildfires on BC \n265 \nmass concentration in this region. However, when these high BC mass concentration cases were selected, the median of the \n266 \nsimulated/observed ratio was 0.30, indicating underestimation in the model simulation (possibly due to insufficient spatial \n267 \nresolution for neighbouring forest wildfires and difficulties in representing the vertical profiles of BC emissions) or/and in \n268 \nemission inventories in the high BC mass concentration cases. Several studies have indicated that differences in different \n269 \ninventories cause large uncertainties in model estimates of BC emissions, atmospheric concentrations, and radiative impacts, \n270 \nespecially in boreal North America (Carter et al., 2020; Pan et al., 2020). The impact of different inventories on model estimates \n271 \nwill be discussed in the future. 272 12 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 13 \n \n \n \n274 274 13 3.5 Relationship between the BC/∆CO ratio and FRP \n286 In the previous section, we showed that most high BC mass concentration cases were related to forest wildfires in Alaska. We \n287 \nselected 406 hourly cases between June and September from the data selected in Section 3.4 as high BC cases from forest \n288 \nwildfires and chose 184 cases of hourly BC observations results from forest wildfires detected in Alaska and western Canada \n289 \nby back trajectory analysis. Note that we also confirmed that no back trajectories could suggest forest wildfires in other seasons. 290 \nWe found a positive correlation (r = 0.44, p < 0.0001, n = 184, Figure 6) between the BC/∆CO ratio and ∑FRP/point values, \n291 \nand its slope and intercept with standard errors were 0.11 (±0.02) and 2.5 (±0.22), respectively (Figure 6). This positive \n292 \ncorrelation between the BC/∆CO ratio and ∑FRP/point values, represented for the first time to our knowledge, is qualitatively \n293 \nconsistent with previous studies that showed that high combustion efficiency (larger than 0.9 in modified combustion \n294 \nefficiency value (MCE)) increased BC/∆CO ratios (Selimovic et al., 2019; Kondo et al., 2011b; Pan et al., 2017), which is \n295 \nrelated to the fact that the BC production process is mostly related to the flaming process (high MCE), while that of CO is \n296 \nrelated to the smouldering process (low MCE). Although MCE and FRP are different parameters, both parameters indicate \n297 \ncombustion conditions and have a strong correlation (Wiggins et al., 2020). Therefore, for the first time, we report a robust \n298 \ncorrelation between the BC/∆CO ratio and FRP as a combustion condition indicator. The wide range of BC/∆CO ratios reported \n299 \nfrom boreal forest wildfires, from 1.7–3.4 ng m-3 ppb-1 (Kondo et al., 2011b) to 6.1–6.3 ng m-3 ppb-1 (Vasileva et al., 2017), \n300 \ncould be better explained when the index introduced here (∑FRP/point) is considered. This clear relationship should be taken \n301 \ninto account when constructing future emission inventories from boreal forest wildfires. 302 A positive correlation was found after optimizing the spatial window size (±0.5° in the longitudinal direction and ±0.25° in \n303 \nthe latitudinal direction), in which hot spots were taken into account for each hour along the trajectory (from -96 to 0 hours), \n304 \nand the associated time window was used to determine coincident fires that affected the observations (from -24 to 0 hours). https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Figure 4. (a) A histogram of the observed BC/∆CO ratio at the PFRR in high BC mass concentration cases. Histograms of the \n275 \n(b) contributions of biomass burning and (c) mean age of BC simulated with the FLEXPART-WRF model in high BC \n276 \nconcentration cases and other cases. Black and red bars used in (b) and (c) indicate high BC concentration cases and other \n277 \ncases, respectively. Hourly results are shown for the observed BC/∆CO ratio (a), and 6-hourly results are shown for the \n278 \nFLEXPART-WRF simulation ((b) and (c)). 279 \n280 280 \n \n281 \nFigure 5. A stacked graph of the 6-hourly mass-weighted biomass burning contributions for BC mass concentration at the \n282 \nPFRR estimated with FLEXPART-WRF simulations. Grey bars indicate the total BC mass concentration for all sources, and \n283 \nthe colours show the individual source regions. 284 \n285 280 \n \n281 Figure 5. A stacked graph of the 6-hourly mass-weighted biomass burning contributions for BC mass concentration at the \n282 \nPFRR estimated with FLEXPART-WRF simulations. Grey bars indicate the total BC mass concentration for all sources, and \n283 \nthe colours show the individual source regions. 284 Figure 5. A stacked graph of the 6-hourly mass-weighted biomass burning contributions for BC mass concentration at the \n282 \nPFRR estimated with FLEXPART-WRF simulations. Grey bars indicate the total BC mass concentration for all sources, and \n283 \nthe colours show the individual source regions. 284 14 14 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 3.5 Relationship between the BC/∆CO ratio and FRP \n286 305 \nThe remaining scatter might have arisen from differences in the detection of hot spots in the presence of clouds (Li et al., 2018). 306 \nTo overcome shortcomings in hot spot detection, improvements in the frequency of hot spot scanning should be made, for \n307 \nexample, via the use of MODerate Resolution Imaging Spectroradiometer (MODIS) combined with VIIRS observations; it \n308 \nshould be noted, however, that there is a significant bias in FRP observations between MODIS and VIIRS, especially for boreal \n309 \nforests (Li et al., 2018). Improvements in the accuracy and consistency of FRP analysis between multiple satellite observations \n310 \ncan facilitate a more in-depth understanding of the relationship between FRP and the BC/dCO ratio. 311 The simulated/observed ratios in high BC mass concentration cases were significantly low (0.30, Section 3.3), contrary to the \n312 \ngood agreement observed in overall cases (1.0, Section 3.3). The BC/∆CO ratios in commonly used emission inventories are \n313 \n4.4 ng m-3 ppb-1 for GFED4s (van der Werf et al., 2017) and 4.9 ng m-3 ppb-1 for Andreae (2019) and are in a range similar to \n314 \nthat of our median BC/∆CO ratio. However, our observed BC/∆CO ratios in high BC concentration cases for forest wildfires \n315 \nhad a broad range between 1.7 and 7.3 ng m-3 ppb-1 at the 10 and 90 percentiles (median was 4.2 ng m-3 ppb-1), respectively, \n316 \nrelated to the ∑FRP/point values. This indicates that the BC emission factors from biomass burning could vary depending on \n317 15 A positive \n355 \ncorrelation was found between these parameters (r = 0.44), with a slope and intercept of 0.11 (±0.02) and 2.5 (±0.22), \n356 \nrespectively. For the first time, the properties of the BC/∆CO ratio from boreal forest wildfires were systematically \n357 4 Conclusion \n328 \nWe showed key features of the BC and CO concentrations observed at the PFRR in interior Alaska since 2016 in this paper. 329 \nThe annual medians of the BC mass concentration and CO mixing ratio were 11–15 ng m-3 and 109.7–131.3 ppb, respectively. 330 \nLarge and short-term increases in BC mass concentrations were sometimes observed between June and September. A clear \n331 \nseasonal variation was observed in the CO mixing ratio, which was high in spring (between February and April, 143.5 ppb in \n332 \nthe median) and low in summer (July and August, 103.3 ppb in the median). The CO mixing ratio coincided with the high BC \n333 \nmass concentration peaks, suggesting a strong contribution from forest wildfires to BC and CO concentrations. 334 \nThe BC mass concentrations observed at other sites in Alaska, i.e., DENA, TRCR, and TOOL, were compared with our results. 335 \nThe annual median BC mass concentrations at the PFRR were lower than those at TRCR, DENA, and TOOL, but coinciding \n336 \nBC mass concentration peaks were found at these observation sites. In these high BC mass concentration cases, BC mass \n337 \nconcentrations at the PFRR were larger than those at TRCR and TOOL but similar at DENA, indicating that significant BC \n338 \nemissions from forest wildfires occurred in interior Alaska and affected broad areas in Alaska. 339 The dominance of forest wildfires in Alaska as a major cause of high BC mass concentration was also supported by the model \n340 \nsimulations. We simulated BC mass concentration using the FLEXPART-WRF model and compared the simulations with the \n341 \nobservation results. The model simulation could capture observational results (r = 0.70) in which the median \n342 \nsimulated/observed ratio was 1.0. The estimated BC source sectors and regions were biomass burning from Russia and Alaska \n343 \nbetween June and September, while those for other periods were domestic sources and transport and were mainly from Alaska. 344 \nWhen we focused on high BC mass concentration cases (greater than 98 percentile values), we found that forest wildfires \n345 \noccurring in Alaska were the dominant source of BC in those cases from the model simulation results. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. the FRP. Our findings suggest the potential for improving BC emission inventories and/or emission factors by using FRP. The \n318 \nfrequency of boreal forest fires may increase in the future (Box et al., 2019; Hu et al., 2015); as a result, their impact on climate \n319 \nand air quality might become more severe in Alaska and the Arctic (Kim et al., 2005; Schmale et al., 2018; Stohl et al., 2006). 320 \nOur long-term observations of BC and CO at an hourly temporal resolution in the interior of Alaska provide unique information \n321 \nto test model simulations and emission inventories relevant to the climate and air quality of the Arctic. 322 \n323 324 \nFigure 6. Correlation between the BC/∆CO ratio in the high BC mass concentration cases observed at the PFRR and \n325 \n∑FRP/point values. The linear regression curve is shown by a red line. 326 \n327 324 Figure 6. Correlation between the BC/∆CO ratio in the high BC mass concentration cases observed at the PFRR and \n325 \n∑FRP/point values. The linear regression curve is shown by a red line. 326 Figure 6. Correlation between the BC/∆CO ratio in the high BC mass concentration cases observed at the PFRR and \n325 \n∑FRP/point values. The linear regression curve is shown by a red line. 326 16 16 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4 Conclusion \n328 \nWe showed key features of the BC and CO concentrations observed at the PFRR in interior Alaska since 2016 in this paper. 329 \nThe annual medians of the BC mass concentration and CO mixing ratio were 11–15 ng m-3 and 109.7–131.3 ppb, respectively. 330 \nLarge and short-term increases in BC mass concentrations were sometimes observed between June and September. A clear \n331 \nseasonal variation was observed in the CO mixing ratio, which was high in spring (between February and April, 143.5 ppb in \n332 \nthe median) and low in summer (July and August, 103.3 ppb in the median). The CO mixing ratio coincided with the high BC \n333 \nmass concentration peaks, suggesting a strong contribution from forest wildfires to BC and CO concentrations. 334 \nThe BC mass concentrations observed at other sites in Alaska, i.e., DENA, TRCR, and TOOL, were compared with our results. 335 \nThe annual median BC mass concentrations at the PFRR were lower than those at TRCR, DENA, and TOOL, but coinciding \n336 \nBC mass concentration peaks were found at these observation sites. In these high BC mass concentration cases, BC mass \n337 \nconcentrations at the PFRR were larger than those at TRCR and TOOL but similar at DENA, indicating that significant BC \n338 \nemissions from forest wildfires occurred in interior Alaska and affected broad areas in Alaska. 339 4 Conclusion \n328 \nWe showed key features of the BC and CO concentrations observed at the PFRR in interior Alaska since 2016 in this paper. 329 \nThe annual medians of the BC mass concentration and CO mixing ratio were 11–15 ng m-3 and 109.7–131.3 ppb, respectively. 330 \nLarge and short-term increases in BC mass concentrations were sometimes observed between June and September. A clear \n331 \nseasonal variation was observed in the CO mixing ratio, which was high in spring (between February and April, 143.5 ppb in \n332 \nthe median) and low in summer (July and August, 103.3 ppb in the median). The CO mixing ratio coincided with the high BC \n333 \nmass concentration peaks, suggesting a strong contribution from forest wildfires to BC and CO concentrations. 334 \nThe BC mass concentrations observed at other sites in Alaska, i.e., DENA, TRCR, and TOOL, were compared with our results. 335 \nThe annual median BC mass concentrations at the PFRR were lower than those at TRCR, DENA, and TOOL, but coinciding \n336 \nBC mass concentration peaks were found at these observation sites. In these high BC mass concentration cases, BC mass \n337 \nconcentrations at the PFRR were larger than those at TRCR and TOOL but similar at DENA, indicating that significant BC \n338 \nemissions from forest wildfires occurred in interior Alaska and affected broad areas in Alaska. 339 \nThe dominance of forest wildfires in Alaska as a major cause of high BC mass concentration was also supported by the model \n340 \nsimulations. We simulated BC mass concentration using the FLEXPART-WRF model and compared the simulations with the \n341 \nobservation results. The model simulation could capture observational results (r = 0.70) in which the median \n342 \nsimulated/observed ratio was 1.0. The estimated BC source sectors and regions were biomass burning from Russia and Alaska \n343 \nbetween June and September, while those for other periods were domestic sources and transport and were mainly from Alaska. 344 \nWhen we focused on high BC mass concentration cases (greater than 98 percentile values), we found that forest wildfires \n345 \noccurring in Alaska were the dominant source of BC in those cases from the model simulation results. The mean ages of BC \n346 \nand biomass burning contributions in these high BC mass concentration cases estimated by FLEXPART-WRF were 2.6 days \n347 \nand 95.5%, respectively, relatively shorter and higher than those in other cases (6.9 days and 7.6%, respectively). The peaks \n348 \nof the calculated biomass burning contributions from Alaska to BC mass concentrations at the PFRR coincided well with \n349 \nobserved and simulated peaks in high BC mass concentration cases, suggesting that the forest wildfires that occurred around \n350 \nthe PFRR are significant. 351 \nThe median observed BC/∆CO ratio in high BC mass concentration cases related to forest wildfires was 4.2 ng m-3 ppb-1 and \n352 \nwas in the same range as that in previous studies reporting the BC/∆CO ratio of boreal forest wildfire emissions. Finally, we \n353 \ntracked airmass origin for 4 days using the HYSPLIT model in these cases and investigated the relationship between the \n354 \nobserved BC/∆CO ratio and FRP, which was normalized by the number of hot spots (points) observed by VIIRS. https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Data availability \n360 \nThe BC and CO observation results at the PFRR site are available from the corresponding author upon request. We used public \n361 \ndata \nfor \nBC \nobservation \nresults \nat \nDenali, \nTrapper \nCreek, \nand \nToolik \nLake \nField \nStation \n362 \n(http://views.cira.colostate.edu/fed/QueryWizard/). 363 \n \n364 \nSupplement \n365 \nThe supplement related to this article is available online at https://doi.org/xxxxxxxx. 366 \n \n367 \nAuthor contributions \n368 \nTK, FT, CZ, YK, and YK conducted and recorded observations for BC and CO at the PFRR site. MT conducted the \n369 \nFLEXPART-WRF model simulations. YK assisted in the fieldwork at the PFRR site. TK, FT, MP, and YK summarized the \n370 \nobservation results, and TK wrote the first draft with MT. All authors contributed to the discussion and writing of the \n371 \nmanuscript. 372 \n \n373 \nCompeting interests \n374 \nAt least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. 375 \n \n376 \nAcknowledgement \n377 \nThe authors acknowledge technical support from Dr. Takuma Miyakawa, a researcher at JAMSTEC and help with field work \n378 \nfrom Dr. Hideki Kobayashi, a researcher at JAMSTEC. The authors also thank all the supporting members at JAMSTEC. The \n379 \nauthors thank NOAA ARL for providing the HYSPLIT model and GDAS1 meteorological data. We also thank the IMPROVE \n380 \nnetwork. IMPROVE is a collaborative association of state, tribal, and federal agencies and international partners. The US \n381 \nEnvironmental Protection Agency is the primary funding source, with contracting and research support from the National Park \n382 \nService. The Air Quality Group at the University of California, Davis, was the central analytical laboratory, and carbon analysis \n383 \nwas carried out by the Desert Research Institute. We also thank the anonymous reviewers for their precise and valuable \n384 \ncomments that greatly improved the paper. 385 \n \n386 Data availability \n360 \nThe BC and CO observation results at the PFRR site are av\n361 \ndata \nfor \nBC \nobservation \nresults \nat \nDenali\n362 \n(http://views.cira.colostate.edu/fed/QueryWizard/). 363 \n \n364 \nSupplement \n365 \nThe supplement related to this article is available online at\n366 \n \n367 \nAuthor contributions \n368 \nTK, FT, CZ, YK, and YK conducted and recorded ob\n369 \nFLEXPART-WRF model simulations. YK assisted in the\n370 \nobservation results, and TK wrote the first draft with M\n371 \nmanuscript. The mean ages of BC \n346 \nand biomass burning contributions in these high BC mass concentration cases estimated by FLEXPART-WRF were 2.6 days \n347 \nand 95.5%, respectively, relatively shorter and higher than those in other cases (6.9 days and 7.6%, respectively). The peaks \n348 \nof the calculated biomass burning contributions from Alaska to BC mass concentrations at the PFRR coincided well with \n349 \nobserved and simulated peaks in high BC mass concentration cases, suggesting that the forest wildfires that occurred around \n350 \nthe PFRR are significant. 351 The median observed BC/∆CO ratio in high BC mass concentration cases related to forest wildfires was 4.2 ng m-3 ppb-1 and \n352 \nwas in the same range as that in previous studies reporting the BC/∆CO ratio of boreal forest wildfire emissions. Finally, we \n353 \ntracked airmass origin for 4 days using the HYSPLIT model in these cases and investigated the relationship between the \n354 \nobserved BC/∆CO ratio and FRP, which was normalized by the number of hot spots (points) observed by VIIRS. A positive \n355 \ncorrelation was found between these parameters (r = 0.44), with a slope and intercept of 0.11 (±0.02) and 2.5 (±0.22), \n356 \nrespectively. For the first time, the properties of the BC/∆CO ratio from boreal forest wildfires were systematically \n357 \ncharacterized in terms of FRP, suggesting the potential to improve emission inventories and/or emission factors by using FRP. 358 \n \n359 17 Brioude, J., Arnold, D., Stohl, A., Cassiani, M., Morton, D., Seibert, P., Angevine, W., Evan, S., Dingwell, A., Fast, J. D., \n417 \nEaster, R. C., Pisso, I., Burkhart, J., and Wotawa, G.: The Lagrangian particle dispersion model FLEXPART-WRF version \n418 \n3.1, Geosci. Model Dev., 6, 1889–1904, https://doi.org/10.5194/gmd-6-1889-2013, 2013. \n419 References \n393 Aizawa, T., Ishii, M., Oshima, N., Yukimoto, S., and Hasumi, H.: Arctic warming and associated sea ice reduction in the early \n394 \n20th century induced by natural forcings in MRI‐ESM2.0 climate simulations and multimodel analyses, Geophys. Res. Lett., \n395 \n48, https://doi.org/10.1029/2020gl092336, 2021. 396 Aizawa, T., Ishii, M., Oshima, N., Yukimoto, S., and Hasumi, H.: Arctic warming and associat\n94 Aizawa, T., Ishii, M., Oshima, N., Yukimoto, S., and Hasumi, H.: Arctic warming and associated sea ice reduction in the early \n394 \n20th century induced by natural forcings in MRI‐ESM2.0 climate simulations and multimodel analyses, Geophys. Res. 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We al\n384 \ncomments that greatly improved the paper. 385 \n \n386 18 18 g\ng\nEaster, R. C., Pisso, I., Burkhart, J., and Wotawa, G.: The Lagrangian particle dispersion model FLEXPART-WRF version \n418 \n3.1, Geosci. Model Dev., 6, 1889–1904, https://doi.org/10.5194/gmd-6-1889-2013, 2013. \n419 https://doi.org/10.5194/egusphere-2023-2764\nPreprint. Discussion started: 21 December 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Financial support \n387 \nThis work was funded by the Arctic Challenge for Sustainability II (ArCS II), Program Grant Number JPMXD1420318865, \n388 \nthe Arctic Challenge for Sustainability (ArCS), Program Grant Number JPMXD1300000000, and a National Research \n389 \nFoundation of Korea Grant from the Korean Government (MSIT; the Ministry of Science and ICT, NRF-\n390 \n2021M1A5A1065425) (KOPRI-PN23011). 391 \n \n392 Financial support \n387 \nThis work was funded by the Arctic Challenge for Sustainability II (ArCS II), Program Grant Number JPMXD1420318865, \n388 \nthe Arctic Challenge for Sustainability (ArCS), Program Grant Number JPMXD1300000000, and a National Research \n389 \nFoundation of Korea Grant from the Korean Government (MSIT; the Ministry of Science and ICT, NRF-\n390 \n2021M1A5A1065425) (KOPRI-PN23011). 391 \n \n392 References \n393 P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. 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