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Keywords:

  • isotope;
  • methane;
  • wetland;
  • wildfire

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Experimental Methods
  5. 3. Results and Discussions
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[1] To investigate spatial variations of CH4 concentration, δ13CH4, and δD-CH4 over Alaska, aircraft observations were conducted during the summer of 2006. CH4 concentrations elevated above the background level were observed over areas with wetlands and wildfires, important sources of CH4. Several flights showed elevated CH4 values, with corresponding δ13CH4 and δD-CH4 signatures of −63.4‰ ± 3.0‰ and −424‰ ± 79‰, respectively, which are based on the relationship between δ13CH4 (or δD-CH4) and CH4 concentration (single mixing relation), an indication of wetland source. It was also noted that both wetlands and wildfires influenced the CH4 concentrations observed over the wildfire area. Assuming certain emission ratios of CH4 to CO (ERCH4/CO) for the wildfire and certain values of δ13CH4 and δD-CH4 for wetland CH4, we derived δ13CH4 and δD-CH4 of CH4 emitted from the wildfire to be −27.5‰ ± 2.0‰ and −285‰ ± 111‰, respectively, which agreed relatively well with, but was slightly lower than, those obtained by previous studies at lower latitudes. To verify these estimates, bonfire experiments were conducted in the interior of Alaska using the same biomass material burned in the wildfire observed by the aircraft. The result showed that the previously reported ERCH4/CO value was plausible and that δ13CH4 and δD-CH4 obtained by the bonfire experiments agreed with the estimates by the aircraft observations. We also found that δ13CH4 and δD-CH4 values became enriched with increasing combustion efficiency. By using the relationship between δD-CH4 for biomass burning and δD of precipitation, global average of δD-CH4 emitted from biomass burning was estimated to be −204‰ ± 11‰.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Experimental Methods
  5. 3. Results and Discussions
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[2] Although methane (CH4) is an important trace gas in atmospheric chemistry and climate, quantification of the global CH4 budget remains highly uncertain [Forster et al., 2007]. CH4 sources are generally classified into three categories: biogenic, fossil, and biomass burning. But recently, terrestrial plants have been suggested as an additional source [Keppler et al., 2006]. Among them, wetlands (a major biogenic source) and biomass burning comprise about 30% and 10% of the total amount of the global CH4 emission, respectively, with large uncertainty of more than several tens of percent [Forster et al., 2007]. It is very important to reduce uncertainties of such natural CH4 sources for better understanding of the global CH4 budget. While the largest CH4 sources of wetlands [Hein et al., 1997; Walter et al., 2001] and biomass burning [van der Werf et al., 2006] are located in the tropical regions, northern high latitudes also emit substantial amounts of CH4 from wetlands [Zhuang et al., 2004] and biomass burning [Kasischke and Bruhwiler, 2003]. Moreover, it is thought that such emissions in northern high latitudes make a considerable contribution to the interannual and seasonal variations of atmospheric CH4 concentration [Morimoto et al., 2006; Bousquet et al., 2006; Dlugokencky et al., 2001, 2009; Rigby et al., 2008].

[3] Carbon and hydrogen stable isotopic ratios of CH4 (δ13CH4 and δD-CH4) are useful in distinguishing and separating out different sources of CH4 because each source has its own characteristic δ13CH4 and δD-CH4 values [Quay et al., 1999; Whiticar and Schaefer, 2007]. In order to quantify strengths of various CH4 sources using isotopic mass balance equations, it is indispensable to reveal spatial and temporal variations of δ13CH4 and δD-CH4 in the atmosphere as well as to characterize δ13CH4 and δD-CH4 values of each source.

[4] Spatial variations of atmospheric δ13CH4 and δD-CH4 in northern high latitudes have been investigated by several researchers. For instance, Sugawara et al. [1996] reported aircraft measurements of δ13CH4 over Siberia. Yamada et al. [2005] also conducted aircraft observations of δ13CH4 and δD-CH4 over Siberia. Bergamaschi et al. [1998] and Tarasova et al. [2006] measured δ13CH4 and δD-CH4 along the Trans-Siberian Railroad. These studies demonstrated that wetlands and leakages of natural gas were important CH4 sources in Siberia. Meanwhile, long-term temporal variations of δ13CH4 have been measured systematically at the northern high- latitude sites of Barrow, Alaska [Quay et al., 1991, 1999; Miller et al., 2002], Ny Ålesund, Svalbard [Morimoto et al., 2006], and Alert, Canada [Dlugokencky et al., 2009]. Compared with δ13CH4, measurements of δD-CH4 have been relatively sparse in terms of space (both horizontally and vertically) and time. The first objective of this study is to present spatial variations of atmospheric δ13CH4 and δD-CH4 observed over Alaska by an aircraft. The observations extended from the atmospheric boundary layer to the free troposphere over several different kinds of typical CH4 sources, allowing us to discuss various CH4 sources influencing atmospheric CH4.

[5] It is important to know mean δ13CH4 and δD-CH4 values emitted from different sources, their range and variations, and the controlling factors. According to some previous studies, δ13CH4 and δD-CH4 values of wetlands in northern high latitudes range from −50‰ to −80‰ and from −300‰ to −450‰, respectively, and such values are determined by isotopic compositions of the source materials and CH4 production pathways [Martens et al., 1992; Nakagawa et al., 2002; Chanton et al., 2006; Walter et al., 2008]. The δ13CH4 and δD-CH4 values for biomass burning (δ13CH4 (BB) and δD-CH4 (BB)) were reported to be about −25‰ and −200‰, respectively [Chanton et al., 2000; Snover et al., 2000; Yamada et al., 2006]. But these studies on biomass burning focused on tropical and midlatitude regions, and δ13CH4 (BB) and δD-CH4 (BB) in northern high latitudes have not been examined so far. These previous studies suggested that δ13CH4 (BB) and δD-CH4 (BB) depend on biomass fuel being burnt and burning condition. We examine if this findings also holds for biomass burning in high-latitude regions.

[6] We also conducted bonfire experiments in the interior Alaska, using similar vegetation material that was burning in the wildfires observed by the aircraft. Although the experiments were initially designed to provide helpful information for interpreting the aircraft measurements over the wildfires, the experiments also provided the first direct measurements of the variation in δ13CH4 (BB) and δD-CH4 (BB) in northern high latitudes for various burning conditions. This allowed us to examine controlling factors influencing δ13CH4 (BB) and δD-CH4 (BB) variations. In addition, we discuss latitudinal variation of δD-CH4 (BB) using the results obtained in this study.

2. Experimental Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Experimental Methods
  5. 3. Results and Discussions
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

2.1. Aircraft Observations

[7] Air sampling was conducted from 27 July to 4 August 2006 over Alaska using the Cessna Citation Encore aircraft Asuka as part of a global warming campaign project of the Asahi Shimbun Company. Flight details are summarized in Table 1 and the sampling locations are shown in Figure 1. All flights were made during the daytime under fine weather conditions. The air sampling system used on the aircraft consisted of an air intake connected to a fresh air nozzle in the cabin, a small electric diaphragm pump (N86ANDCB, KNF Neuberger GmbH), a dehumidification tube (1 cm (in diameter) × ∼50 cm) filled with magnesium perchlorate (Mg(ClO4)2), and 550 mL Pyrex glass flasks. The sample flasks were of cylindrical shape with Viton O-ring stopcocks at both ends, for which very small amount of vacuum grease was used. Prior to use, the flasks were washed using an ultrasonic cleaner filled with purified water and then evacuated for at least 6 h at 100°C to below 0.13 Pa. To collect an air sample, each flask was sufficiently flushed by fresh air for three minutes with a flow rate of 1.5 L/min through the air-conditioning system of the aircraft, and then the air was compressed to 0.14 MPa above the cabin pressure by using the diaphragm pump. Air sampling was made at eight assigned heights of about 460, 670, 910, 1370, 1980, 2590, 3200, and 5330 m for each location. During the air samplings, the aircraft kept the assigned altitude.

image

Figure 1. Air sampling locations over Alaska in summer 2006. Area codes given in Table 1 are also shown. Air samples were collected at eight assigned heights at each location.

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Table 1. Information of Air Sampling Flights Conducted Over Alaska in 2006
Flight NumberArea CodeSampling Date and Time (LT)Ground SurfaceLatitude (°N)Longitude (°W)Altitude (m)
1Wildfire 127 Jul, 18:11–18:59Wildfire with scattered wetlands64.4148.6460–5330
2Forest 129 Jul, 11:29–12:17Forest67.6143.0610–5330
3Wetland 12 Aug, 12:04–12:39Wetlands66.2148.4460–5330
4Oil Field2 Aug, 17:45–18:20Oil field70.3148.9460–5330
5Oversea3 Aug, 12:20–12:53Sea66.8165.1460–5330
6Wetland 23 Aug, 15:42–16:29Wetlands62.9164.2460–5330
7Wildfire 24 Aug, 9:46–10:23Wildfire with scattered wetlands64.4148.5460–5330
8Forest 24 Aug, 10:50–11:29Forest64.4150.1460–5330

[8] Air samples were collected twice over a wildfire area in the vicinity of a town Nenana, interior Alaska (Flights 1 and 7, denoted as Wildfire 1 and Wildfire 2, respectively, in Table 1). This wildfire was discovered on 7 June and extinguished on 1 November 2006, producing a burned area of 527 km2 (maps, Alaska Interagency Coordination Center, http://fire.ak.blm.gov/). During the air sampling, the wildfires seemed to have entered a smoldering phase. The ground surface in this area was dominantly covered by black spruce forest and scattered wetlands. Viereck et al. [1992] presented four major vegetation zones in Alaska: lowland tundra, upland tundra, boreal forest or taiga, and coastal forest. Our study area belonged to the boreal forest or taiga vegetation zone, which is characterized by evergreen forests (black and white spruce) with mosaics of deciduous forests (birch, aspen, and poplar), shrubs, herbaceous species, and bogs.

[9] Two flights were conducted over wetland dominant areas; one was over wetlands distributed along the meandering Yukon River in the interior Alaska (Flight 3 denoted as Wetland 1 in Table 1) and the other was over the Yukon Delta in the west coast of Alaska (Flight 6 denoted as Wetland 2 in Table 1). According to the Circumpolar Arctic Vegetation Map [Circumpolar Arctic Vegetation Map Team (CAVM Team), 2003], the ground surface of the latter area is classified as wet tundra. The former area belongs to the boreal forest or taiga zone and the latter belongs to lowland tundra, characterized mainly by wet sedge meadow interspersed with many lakes and tussock tundra on drier sites.

[10] One flight was made over Prudhoe Bay oil field on the north coast of Alaska (Flight 4 denoted as Oil Field in Table 1), which is the largest oil field in North America. The surrounding areas were covered with shrubs, tundra, and wetlands [CAVM Team, 2003]. This area belongs to the vegetation zone lowland tundra, and the upland tundra zone extends south of this area [Viereck et al., 1992]. Upland tundra includes moist tundra, dry or alpine tundra, and shrub or high brush tundra.

[11] Two flights were made over boreal forests on the dry ground surface (no wetlands were observable from the aircraft); one was over southern side of Brooks Range (Flight 2 denoted as Forest 1 in Table 1) and the other was over southwestern part of Fairbanks (Flight 8 denoted as Forest 2 in Table 1). Forest 2 is located on the west side of the wildfire area (Flight 1 and 7). In the Forest 1 flight, the lowest air sampling height was constrained to about 610 m because of a flight regulation with regard to the surface topography. Both areas belong to boreal forest or taiga zone [Viereck et al., 1992].

[12] One remaining flight was made over the sea just north of Seward Peninsula (Chukchi Sea) (Flight 5 denoted as Sea in Table 1). The peninsula was dominated by tundra and wetlands [CAVM Team, 2003] and can be classified into lowland tundra or upland tundra [Viereck et al., 1992].

2.2. Bonfire Experiments

[13] Bonfire experiments were conducted on 26 June 2007 at C4 section (65°10′N, 147°30′W) of Caribou–Poker Creeks Research Watershed (CPCRW), which is one of the long-term ecological research (LTER) stations in black spruce forests, located in the Yukon-Tanana Uplands of the Northern Plateaus Physiographic Province near the town Chatanika, interior Alaska. The vegetation consisted mainly of low-productivity black spruce (Picea mariana (Mill.) B.S.P.). The forest floor was covered with moss (Sphagnum spp.), lichen (Ptilum crista-castrensis and Pleurozium schreberii), and shrubs (Vaccinium vitis-idaea and Vaccinium uliginosum). Discontinuous permafrost was widely distributed at depths below 50 cm from the black spruce forest floor. The top 50 cm of the ground consisted of living moss and lichen layers (0–10 cm depth) and dead moss layers (10–50 cm depth). Consult Kim and Tanaka [2003] for more information on this station.

[14] We gathered logs of about 70 year old black spruce, and then burned them in a fireplace. Mosses and shrubs on the forest floor were also collected and ignited in the same way. Smoke samples were collected from the fireplace using an air sampling system, which consisted of an inlet, a water trap, an electric diaphragm pump (N86ANDCB, KNF Neuberger GmbH), and 550 mL Pyrex glass flasks. The inlet was an aluminum conic funnel stuffed with metal mesh for filtering ashes. Water vapor was removed by using dual trap; one was a cryogenic trap immersed into an ethanol bath cooled by liquid nitrogen and the other was chemical trap filled with Mg(ClO4)2. The dual trap was able to lower the dew point of an air sample below −60°C. Aerosols were removed by two membrane filters (pore size; 0.1 μm). After passing the smoke sample through the air sampling system, the sample was pressurized into a glass flask at about +0.2 MPa above ambient pressure. During the bonfire experiments, flaming and smoldering conditions were visually distinguished, i.e., light smoke with bright flames and dense smoke without flames, respectively. Four background air samples were also collected using the same sampling system before and during the bonfire experiments.

2.3. Measurements of CH4, CO, and CO2 Concentrations

[15] CH4 and CO concentrations of the air samples collected by the aircraft were analyzed by using an Agilent 6890 (Agilent Technologies Inc.) gas chromatograph (GC) equipped with a flame ionization detector (FID) and an RGA3 (Trace Analytical Inc.) GC, respectively. Their precisions were 2 ppb for CH4 analysis and 1 ppb for CO analysis. The concentrations were determined relative to the respective working standard gases, which were calibrated against our primary standards prepared by the gravimetric method. Details of our CH4 and CO analytical procedures and standard gases have been described elsewhere [Aoki et al., 1992; Umezawa, 2009; Yashiro et al., 2009].

[16] CH4 and CO concentrations of the background air samples collected during the bonfire experiments were analyzed by the method described above, however, the concentrations of the smoke samples were too high to apply the method. We therefore diluted the smoke samples with pure nitrogen before GC analyses so that their concentrations fell to within concentration ranges of the working standards. Overall uncertainties of CH4 and CO analyses, involving the sample dilution and the GC measurements, were better than 6% and 11%, respectively. CO2 concentration for the background air samples was analyzed by using a GC-9A (Shimadzu Corporation) GC equipped with a methanizer and an FID relative to working standard gases calibrated against our primary standards [Tanaka et al., 1983] with an analytical precision of 0.3 ppm. As CO2 concentration of the smoke samples was also extremely high, special CO2 standard gases prepared by the manometric method (Nippon Sanso Corporation) were used. The precision of the CO2 concentration analysis for smoke samples was about 11 ppm.

2.4. Measurements of Stable Isotopic Ratios of CH4 and CO2

[17] The δ13C and δD are expressed by δ notation as follows:

  • equation image

where δ denotes δ13C or δD and R denotes isotopic ratio 13C/12C or D/H of a sample or an international standard. The respective international standards used for δ13C and δD measurements are usually the Vienna Peedee belemnite (VPDB) and the Vienna standard mean ocean water (VSMOW), and δ13C and δD values in this paper are reported relative to these standards.

[18] As the technical details of our measurement system for δ13CH4 and δD-CH4 were described elsewhere [Umezawa et al., 2009], only a brief description is presented herein. The system consists mainly of a CH4 preconcentration device and a continuous flow gas chromatograph isotope ratio mass spectrometer equipped with a combustion furnace and a pyrolysis furnace for measurements of δ13CH4 and δD-CH4. Ultrahigh-purity helium is used as the carrier gas of the system. Temperature of the preconcentration trap is maintained at −130°C ± 1°C during collection of CH4 from the air sample by passing it through the trap, then at −83°C ± 1°C while the remaining air components such as N2 and O2 (except for CH4) escape, and finally at 100°C ± 1°C for CH4 elusion. The isotopic values are measured on a mass spectrometer (ThermoQuest/Finnigan MAT-Delta Plus XP), relative to respective reference gases. The δ13CH4 and δD-CH4 values of the reference gases were calibrated against our primary standards provided by the International Atomic Energy Association (IAEA): our δ13C primary standard is NBS18, whereas our δD primary standards are VSMOW and SLAP. By analyzing many 100 mL aliquots of an ambient air sample, the precisions were estimated to be 0.08‰ and 2.2‰ for δ13CH4 and δD-CH4 analyses, respectively.

[19] Measurements of stable carbon isotopic ratio of CO2 (δ13CO2) followed the method described previously [Nakazawa et al., 1993, 1997]. CO2 was extracted cryogenically from an air sample and analyzed for δ13CO2 with a precision of 0.02‰ using a mass spectrometer (ThermoQuest/Finnigan MAT-delta S).

[20] In this paper, the isotopic ratios of all the smoke samples obtained from the bonfire experiments were corrected for background CH4 as follows:

  • equation image

where δ denotes δ13CH4, δD-CH4, or δ13CO2, C denotes gas concentration, and subscripts “Meas.” and “BGD” denote measured value of smoke and averaged background samples, respectively.

3. Results and Discussions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Experimental Methods
  5. 3. Results and Discussions
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

3.1. Aircraft Observations

3.1.1. Vertical Profiles of CH4 and CO Concentrations and δ13CH4 and δD-CH4

[21] Figures 2 and 3 show vertical profiles of CH4 and CO concentrations, δ13CH4, and δD-CH4 over Alaska observed from 27 July to 4 August 2006. The CH4 concentrations observed over various locations differ by up to 40 ppb at all heights, indicating that CH4 does not always distribute uniformly even in the free troposphere. In general, except for the profile over Sea (Figure 3), CH4 concentration increases to some extent with decreasing height, suggesting the presence of strong CH4 sources at the surface. The CO concentration observed over Wildfire 2 increases significantly with decreasing height due to CO emissions from wildfires (Figure 2). The other flights show that the CO concentration generally decreases slightly with decreasing height or is nearly constant, although the vertical profile over Wetland 1 exhibits a very slight increase with decreasing height (Figure 2), with spatial variability at all heights less than 50 ppb. These vertical CO profiles suggest absence of strong CO sources in this region, with the exception of wildfires.

image

Figure 2. Vertical profiles of (a) CH4 concentration, (b) CO concentration, (c) δ13CH4, and (d) δD-CH4 measured over Wildfire 1 (open circles with dotted line), Wildfire 2 (open squares with dotted line), Wetland 1 (solid circles with solid line), and Wetland 2 (solid squares with solid line). Error bars represent our measurement precisions of δ13CH4 (0.08‰) or δD-CH4 (2.2‰).

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image

Figure 3. Same as Figure 2 but over Forest 1 (open circles with dotted line), Wildfire 2 (open squares with dotted line), Oil Field (solid circles with solid line), and Sea (solid squares with solid line).

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[22] CH4 and CO concentrations over Alaska have already been measured by Harriss et al. [1992] under the Arctic Boundary Layer Expedition (ABLE 3A) conducted in July–August 1988. Their main research areas were near Barrow and near Bethel in northern and southwestern parts of Alaska, respectively. In the present study, our Oil Field and Wetland 2 sites were located to the east and north of the boundaries of these areas. They showed vertically and horizontally heterogeneous CH4 concentrations, which ranged between 1700 and 1800 ppb in the free troposphere below 6 km altitude to about 2100 ppb at 150 m altitude in the boundary layer of the atmosphere. The baseline levels of the CH4 concentration measured during their flights were lower than those observed in our flights because of the overall global atmospheric CH4 increase of about 80 ppb during the period between the two studies [Dlugokencky et al., 2009; Umezawa, 2009]. Nonetheless, both studies show similar spatial variability, except for their observation of extremely high CH4 concentrations at 150 m altitude over tundra. Our CO measurements are in general agreement with those obtained by Harriss et al. [1992], with relatively constant vertical profiles in unpolluted air (not influenced by wildfires or urban areas). It is difficult to discern a difference in secular CO trend between the two studies, because of the relatively large variability observed during the flights. It should be noted that Novelli et al. [2003] and Yashiro et al. [2009] reported no discernible trend in the atmospheric CO concentration.

[23] Vertical profiles of δ13CH4 and δD-CH4 are also shown in Figures 2 and 3. The spatial variability in the aircraft measurements over different source regions was found to be 0.2‰–0.4‰ for δ13CH4 and 6‰–8‰ for δD-CH4. To our knowledge, this is the first study that shows vertical profiles of δ13CH4 and δD-CH4 over Alaska from near surface to free troposphere. Similar observations were made previously over Siberia by Sugawara et al. [1996] for δ13CH4 and Yamada et al. [2005] for δ13CH4 and δD-CH4. But since the measurements in these studies were made in lower altitudes, they observed larger variations in CH4, δ13CH4, and δD-CH4.

[24] As seen in Figure 2, the CH4 concentrations observed over Wetland 1 and Wetland 2 show relatively height-independent values above around 3000 m, but start to increase below that with decreasing height, indicating a strong influence from the wetlands, particularly on the Wetland 2 profile. Corresponding to this difference between the two locations, no such systematic profile is observed for δ13CH4 and δD-CH4 over Wetland 1, the variability of which is nearly comparable to our measurement precisions. On the other hand, their decreases with decreasing height are clearly seen below 2600 m over Wetland 2. The δ13CH4 values observed over Wetland 2 are mostly lower than those observed over the other areas. It is also interesting to note that the vertical CH4 concentration at Wetland 2 shows uniformly higher values at all altitudes than the Wetland 1 profile (with the exception of a lower value at 2600 m). This difference could be attributable to (1) higher CH4 emission at Wetland 2 compared to Wetland 1 or (2) difference in vertical transport of CH4 emitted from the surface between these two locations during sampling. With regard to the former, it is relevant to note the distinct difference in the vegetation between the two locations; the Wetland 2 area is surrounded by vast areas of poorly drained tundra with numerous lakes, whereas the Wetland 1 area is basically wetland dominated and surrounded by boreal forest with many lakes [CAVM Team, 2003; Viereck et al., 1992]. The CO concentration profiles from both flights are nearly uniform with similar values below 3000 m; above that, however, the Wetland 1 profile shows a gradual increase in concentration with height whereas the Wetland 2 profile shows a sudden increase of about 40 ppb across the 3000 m altitude and remains constant with height thereafter. Such profiles in the free troposphere are characteristic of long-range transport of air mass polluted by wildfires or oil/gas facilities in Siberian region, as suggested by Harriss et al. [1992].

[25] The interesting phenomenon noted above of the low CH4 concentration observed at about 2600 m over Wetland 2 is accompanied by corresponding high δ13CH4 and δD-CH4 values. In this connection, it was found that there was a layer of temperature inversion between about 2400 and 2600 m (not shown here). This inversion layer could have been caused by descending air mass from higher altitudes, producing low CH4 concentration with high δ13CH4 and δD-CH4. This interpretation is consistent with the observation that δ13CH4 in the free troposphere generally increase with height, with a corresponding decrease of CH4 concentration [Tyler et al., 1999]. Alternative possible cause of the anomalous CH4 concentration, δ13CH4, and δD-CH4 at 2600 m is a horizontal intrusion of air mass from a different region.

[26] Two vertical profiles were obtained over the same wildfire (Wildfire 1 on 27 July and Wildfire 2 on 4 August) and are shown in Figure 2. The difference in the profiles below around 3000 m indicates a much stronger influence from surface emission during Wildfire 2 than Wildfire 1, particularly below 1500 m. While the CH4 profiles show a general linear increase in concentration with decreasing height below 3000 m, they differ by a constant value of about 20 ppb in the free troposphere (above 3000 m), showing the magnitude of a temporal variability (likely due to advection) over the time scale of a week. Consistent with the stronger Wildfire 2 surface emission, the Wildfire 2 CO profile shows a dramatic increase from around 2000 m to near surface, while the Wildfire 1 profile shows a much smaller increase of about 30 ppb.

[27] The isotopic profiles also show interesting features related to the discussion above of the CH4 profiles. While the Wildfire 1 δ13CH4 decreases (becoming lighter) linearly with decreasing height below around 3000 m, the Wildfire 2 δ13CH4 remains relatively constant with height. The δD-CH4 profile for Wildfire 1 shows a relatively more rapid decrease with height below 3000 m than the Wildfire 2 δD-CH4 profile, maintaining lower values through the layer. However, at the lowest height level, the Wildfire 1 δD-CH4 value “switches” by increasing to greater than −92‰, while the Wildfire 2 δD-CH4 shows a dramatic decrease to about −98‰.

[28] Another distinctive CH4 profile was obtained over Sea that shows a remarkable increase in CH4 concentration from near surface to around 2000 m (Figure 3) with corresponds to a minimum value of δ13CH4 and δD-CH4. To provide an explanation for this feature, we carried out a back trajectory analysis using the HYSPLIT model (R. R. Draxler and G. D. Rolph, Hybrid Single-Particle Lagrangian Integrated Trajectory Model, 2003, http://www.arl.noaa.gov/ready/hysplit4.html). The trajectory results show that air masses arriving at around the 2000 m height traveled over wetlands of the Seward Peninsula and Yukon Delta region, and were possibly influenced by the surface CH4 emission (Figure 4). Although the HYSPLIT model does not simulate upward motion of air parcels near the surface, it is plausible that CH4 emitted from the surface could be transported upward by the local daytime convection in the atmospheric boundary layer.

image

Figure 4. Five day backward trajectories obtained by using the NOAA HYSPLIT model for air masses at 1500, 2000, and 2500 m levels of an air column of the Sea flight conducted on 3 August 2006. Colors indicate pressure levels of the air mass (see color bar at top right).

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[29] Over the Prudhoe Bay oil field (Oil Field), the CH4 profile shows a minimum at 2600 m, with a slight increase and decrease in the concentration above and below that height, respectively, and displaying minimal variability (<15 ppb) (Figure 3). The CO profile basically shows a relatively constant value with height, but with a slight maximum at 3200 m. These near surface observations of CH4 and CO point to the presence of only a small leakage of these gases from the oil field. The δ13CH4 and δD-CH4 profiles show a general decreasing value with lower altitudes below around 2600 m; overall, there is very little variability with height.

[30] The CO profiles from Forest 1 and Forest 2 are nearly identical in value and show very little dependence with height (Figure 3). On the other hand, the difference in the CH4 profile is quite evident between the two forest locations, with the Forest 2 profile displaying a gradual decrease in the CH4 concentration with height, in contrast to a relatively height-independent profile of Forest 1. This is indicative of a strong CH4 source at Forest 2. Correspondingly, the δ13CH4 profile over Forest 2 shows a linear decrease of about 0.3‰ from around 2000 m to near surface, while a relatively constant value is observed in the same atmospheric layer over Forest 1. A similar behavior can be noted for the δD-CH4 profile, but with a large variability with height. These observed differences in the profiles could be attributable to the difference in the vegetation coverage between the two locations. As described earlier, both areas belong to boreal forest or taiga zone by Viereck et al. [1992], but the difference in the distribution of tree types, shrubs, herbaceous species, and water would be enough to make a noticeable influence on the vertical profiles of CH4 and its isotopes.

3.1.2. Identification of CH4 Emitted From Wetlands and Wildfires

[31] Assuming that CH4 is added from a single source (or multiple sources whose relative flux intensity is constant in time) to a well-mixed background atmosphere (single mixing situation), mass balance equations for CH4 concentration and its isotopes can be written as

  • equation image
  • equation image

where C and δ denote the CH4 concentration and δ13CH4 or δD-CH4, respectively, and subscripts “obs,” “BGD,” and “source” represent the observed value, the background atmospheric value, and the value of the source, respectively. From equations (3) and (4), we obtain the following relationship between the observed CH4 concentration and the isotopic ratios:

  • equation image

Under the single mixing assumption, this equation means that the observed isotopic ratio is linearly correlated with the reciprocal of the observed CH4 concentration, and that the intercept of the linear line gives the isotopic ratio of the CH4 source (or the flux-weighted isotopic ratio of the CH4 sources in a multiple source case). Plots of the isotopic ratio versus the reciprocal of concentration are often referred to as Keeling plots. By applying a linear regression to the observed Keeling plots, δ13CH4 and δD-CH4 values of the CH4 added to the atmosphere can be estimated. Equation (5) could be applied to the real atmospheric variations under the following two assumptions [Pataki et al., 2003]. First, this equation assumes a closed system. Second, it is assumed that δ13CH4 and δD-CH4 values of the CH4 source and the background atmosphere remain unchanged over the period of interest. In addition, a choice of regression methods to estimate the δsource value is also important. Pataki et al. [2003] recommended the usage of geometric mean regression (GMR) [e.g., Ricker, 1973] to the Keeling plots. However, Zobitz et al. [2006] pointed out that the δsource values obtained from GMR tend to have negative biases at low concentration ranges compared to the ordinary least square (OLS) regression. They reported that the calculated δsource is biased from the true value for CO2 variation below 10 ppm, which corresponds to about 50 ppb in the case of CH4 concentration and δ13CH4 or δD-CH4. Considering the observed small variation ranges of the CH4 concentration (Figures 2 and 3), we decided to use OLS regression to obtain δsource values.

[32] In order to apply equation (5) to the data, we assumed that the observed CH4 profiles were produced primarily by the upward CH4 transport from the ground source due to the diurnal mixing of air in the atmospheric boundary layer, and that the influence of horizontal advection on the CH4 profile was not significant. In addition, we assumed that the influence of CH4 destruction by OH on the CH4 concentration and isotopes was negligible. This is a reasonable assumption since only negligible amount of CH4 is consumed by OH over the time of interest, given the near decadal atmospheric lifetime of CH4 [e.g., Forster et al., 2007]. Under these assumptions, we can interpret the observed CH4 profiles as being affected by local CH4 sources on the ground. The Keeling plots for CH4, δ13CH4, and δD-CH4 as well as their OLS regression lines are presented in Figure 5. As shown in Figure 5, clear linear relationships (correlation coefficient R > 0.7, p value <0.05) are found for the respective cases of Wildfire 1, Wildfire 2, Wetland 2, Sea, and Forest 2, except for a slightly lower correlation (R = 0.69) obtained for δD-CH4 over Sea. The Keeling plots over other areas do not show clear high-correlation relationships.

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Figure 5. Keeling plots with linear regression lines for the CH4 concentration and δ13CH4 or δD-CH4: (a) δ13CH4 over Wildfire 1, Wildfire 2, Wetland 1, and Wetland 2; (b) same as Figure 5a but over Forest 1, Forest 2, Oil Field, and Sea; (c) same as Figure 5a but for δD-CH4; and (d) same as Figure 5b but for δD-CH4. Wildfire 1, red open circles with dotted line; Wildfire 2, red solid circles with solid line; Wetland 1, blue open squares with dotted line; Wetland 2, blue solid squares with solid line; Forest 1, green open squares with dotted line; Forest 2, green solid squares with solid line; Oil Field, red solid diamonds with solid line; Sea, blue solid triangles with solid line.

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[33] In the Keeling plot for Wetland 1, no significant correlations were found, pointing to the possibility of weak CH4 emission in Wetland 1 and/or the failure of the assumptions under which equation (5) can be properly used. In this connection, the possibility of low CH4 emission at Wetland 1 has already been mentioned earlier. However, the small isotopic variability observed over Wetland 1 could explain the insignificant correlation. On the other hand, Wetland 2 shows significantly high correlation coefficients for both δ13CH4 (R = 0.91) and δD-CH4 (R = 0.86), suggesting that the single mixing situation is satisfied. The δ13CH4 and δD-CH4 values of the source are estimated to be −63.4‰ ± 3.0‰ and −424‰ ± 79‰, respectively. These values agree with those of CH4 emitted from the wetlands in northern high latitudes, which have been as −50‰ to −80‰ and −300‰ to −450‰ for δ13CH4 and δD-CH4, respectively [Martens et al., 1992; Nakagawa et al., 2002; Walter et al., 2006, 2008]. Among these previous studies, Martens et al. [1992] measured δ13CH4 and δD-CH4 in gas bubbles from lakes located in Yukon-Kuskokwim Delta, close to our observation area, to be −61.4‰ ± 2.5‰ and −342‰ ± 18‰, respectively. Their values are consistent with our estimates.

[34] Plots of Wildfire 1 and Wildfire 2 yield respective correlation coefficients of R = 0.84 and R = 0.88 for δ13CH4 and R = 0.76 and R = 0.93 for δD-CH4. In the case of Wildfire 1, the intercepts of the Keeling plots are −65.5‰ ± 4.8‰ and −326‰ ± 82‰ for δ13CH4 and δD-CH4, respectively. These values agree with those reported for wetland CH4, but disagree with those of CH4 emitted from biomass burning in lower latitudes, which are reported to be about −25‰ and −200‰, respectively [Chanton et al., 2000; Snover et al., 2000; Yamada et al., 2006]. As noted earlier, the effect of the wildfires on the Wildfire 1 profile was not evident. Indeed, the agreement of δ13CH4 and δD-CH4 values with those of the wetland CH4 indicate a strong influence of CH4 emitted from wetlands on Wildfire 1 than wildfire-emitted CH4. On the other hand, the isotopic ratios estimated for CH4 source in Wildfire 2 are −50.1‰ ± 0.7‰ and −272‰ ± 30‰ for δ13CH4 and δD-CH4, respectively. These values are lower than those reported for CH4 from biomass burning in low-latitude regions. As indicated by the rapid increase in the CO concentration below 2000 m (Figure 2b), Wildfire 2 is significantly more affected by the smoke than Wildfire 1; however, the isotopic signature of Wildfire 2 indicates some influence from the wetland emission.

[35] For the Wildfire 2 case, it would be reasonable therefore to include the influence of both wildfires and wetlands, assuming the following mass balance equations:

  • equation image
  • equation image

Here we substituted the values at the height with the lowest CH4 concentration (i.e., 3200 m) for terms with the subscript “BGD.” For δwetland, we substituted the values estimated from Wildfire 1 (−65.5‰ for δ13CH4 and −326‰ for δD-CH4), since Wildfire 1 was influenced significantly by wetland emissions. To solve δwildfire using equations (6) and (7), it was necessary to estimate ΔCwetland and ΔCwildfire separately. For that purpose, we used the observed CO concentration, which shows extremely high values in only the Wildfires 2 case (Figure 2b).

[36] Since CO is a product of incomplete biomass combustion, its concentration was used as a good indicator of wildfire influence. An emission ratio of CH4 to CO (ERCH4/CO) for boreal wildfires was reported by Cofer et al. [1991] to be 0.1. Andreae and Merlet [2001] estimated emission factors for many trace gases and aerosols from biomass burning and ERCH4/CO of 0.077 ± 0.025 was derived for extratropical forests. Both ERCH4/CO values agree with each other within the uncertainty of the latter value. We started our calculation with ERCH4/CO = 0.077, but the range of the value (±0.025) was examined as part of a sensitivity analysis. Assuming that the wildfires caused the CO concentration excess above the background value at Wildfire 2 (Figure 2b), we could estimate ΔCwildfire using ERCH4/CO value, obtaining ΔCwetland and ΔCwildfire separately. We then the rearranged equations (6) and (7) to explicitly eliminate the wetland influence:

  • equation image
  • equation image

where subscript “ap” represents apparent value. Figure 6 shows the vertical profiles of the CH4 concentration, δ13CH4, and δD-CH4 influenced only by the wildfires, along with the observational results. From Figure 6, we can see that a wildfire would increase CH4 concentration and δ13CH4, but would decrease δD-CH4. Since it is unlikely that air above 3000 m was influenced by the wildfires, the δ13CH4 and δD-CH4 emitted from the wildfires were estimated by the Keeling plots using data from the lowest 6 heights to be −27.5‰ ± 2.0‰ (R = −0.98) and −285‰ ± 111‰ (R = 0.66), respectively. Although the measurement uncertainty is nearly the same as the observed variability, we did attempt to extract the wildfire signal from the measurements (Figure 6).

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Figure 6. Vertical profiles of (a) CH4 concentration, (b) δ13CH4, and (c) δD-CH4 over Wildfire 2. The solid circles are observed values. The open circles indicate values subtracting contributions of wetlands and representing contribution only from the wildfires (see text). Error bars represent our measurement precisions of δ13CH4 (0.08‰) or δD-CH4 (2.2‰).

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[37] The estimated δ13CH4 and δD-CH4 values for the wildfires are shown in Figure 7, along with values obtained by some previous studies. By analyzing smoke samples from Brazilian field fires as well as laboratory combustion experiments, Snover et al. [2000] found δ13CH4 and δD-CH4 to be between −19.5‰ and −30.6‰ and between −195‰ and −255‰, respectively. Yamada et al. [2006] conducted bonfire experiments using Japanese agricultural residues and laboratory combustion experiments using rice and maize, and reported δ13CH4 and δD-CH4 to be between −19.9‰ and −34.6‰ and between −196‰ to −262‰, respectively. Chanton et al. [2000] also reported values of −16.6‰ to −30.4‰ for δ13CH4 emitted from control burns in Zambia and North America (not shown). In contrast, our data are from wildfires in northern high latitudes (Alaska). As seen in Figure 7, the estimated δ13CH4 value for the wildfires using ERCH4/CO = 0.077 and δwetland from the Wildfire 1 case (i.e., case S1) agrees with previously reported δ13CH4 for the Brazilian and Japanese biomass burning experiments. On the other hand, the estimated δD-CH4 value for the wildfires is lower than the previously reported values. This difference in δD-CH4 will be discussed later.

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Figure 7. Estimated values of δ13CH4 and δD-CH4 emitted from Wildfire 2 (solid circles). The error bars represent uncertainties expected from the range of the ERCH4/CO value (±0.025). These estimates are dependent on δ13CH4 and δD-CH4 values of wetlands, S1 and S2; S1 employs wetland isotopic signatures obtained from Wildfire 1, while S2 employs signatures from Wetland 2. Also shown are the results obtained from the bonfire experiments (gray circles) and previous field and laboratory experiments (open squares and open triangles).

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[38] Since our isotopic estimates of the CH4 source depend on the ERCH4/CO and δwetland values used, we examined how the calculated δ13CH4 and δD-CH4 values changed with changes in these variables. Assigning a range of values for ERCH4/CO values of ±0.025 around 0.077, we derived the δ13CH4 and δD-CH4 values for the wildfires; the isotopic values became higher when we used smaller ERCH4/CO values. The resulting range of the isotopic values as a function of the ERCH4/CO is presented in Figure 7 as error bars of the estimates. Note that the error bars shown in Figure 7 differ from the regression uncertainty as presented elsewhere in this paper. As for δwetland, we also used the δwetland values derived from the Wetland 2 case (−63.4‰ ± 3.0‰ for δ13CH4 and −424‰ ± 79‰ for δD-CH4) as an alternative example (i.e., case S2). The estimated δD-CH4 value became very high while the estimated δ13CH4 value remained almost the same. This implies that the choice of δwetland values is critical for this estimation method.

[39] We also examined what source affected the high CH4 concentrations around 2000 m over Sea. The δ13CH4 and δD-CH4 values obtained from this flight show a linear relationships against the reciprocal of the CH4 concentration (Figure 5), with the respective correlation coefficients of R = 0.98 and R = 0.69 for δ13CH4 and δD-CH4. The Keeling plots yield the intercepts of −67.8‰ ± 1.6‰ for δ13CH4 and −500‰ ± 187‰ for δD-CH4. The δ13CH4 value is in agreement with the corresponding values of the wetland CH4, but the δD-CH4 value is lower than those of the wetland values, although the estimation error is very large. The lack of δD-CH4 data at the 5300 m altitude due to experimental failure could have caused such underestimation of the source value. In addition to the backward trajectory analysis described earlier, this agreement of the isotopic values with those for wetland CH4 also supports the idea that air masses near 2000 m were substantially affected by the CH4 emissions from wetlands distributed in the western Alaska regions.

[40] The vertical CH4 profile over Oil Field shows a minimum at 2600 m and small increase below that height, accompanied by decrease in δ13CH4 and δD-CH4 (Figure 3). Using the data from the lowest 6 levels in the Keeling plot calculation, the δ13CH4 and δD-CH4 values of CH4 source were estimated to be −82.3‰ ± 11‰ (R = 0.85) and −513‰ ± 149‰ (R = 0.82), respectively. It should be noted that the estimation errors are large compared to those derived for other areas due to small change in the observed CH4 concentration. The estimated values are much lower than those of CH4 originating from fossil fuel, of which typical values are about −40‰ for δ13CH4 and −180‰ for δD-CH4 [Quay et al., 1999], but close to the lower limit of wetland CH4. The small increase in the observed CH4 concentration with decreasing height might be ascribed to CH4 emitted from the wetlands surrounding the oil field. In this connection, however, fossil CH4 possibly has much lower isotopic signatures [Schoell, 1980], meaning that possible contribution from fossil release could not be ruled out.

[41] Since the observed change in the CH4 concentration was very small and the isotopic values did not exhibit systematic variation, the Keeling plots gave no conclusive results for Forest 1. On the other hand, plots for Forest 2 yielded correlation coefficients of R = 0.91 and R = 0.78 for δ13CH4 and δD-CH4, respectively. The intercept values of the Keeling plots are −65.2‰ ± 3.4‰ and −472‰ ± 127‰ for δ13CH4 and δD-CH4, respectively, which are in agreement with those of wetland CH4, although the estimation error is quite large for δD-CH4. Accordingly, increase in the CH4 concentration with decreasing height at Forest 2 could be due to CH4 emission from the wetlands around this area. Interestingly, the Keeling plot intercepts for Forest 2 agree with those deduced for Wildfire 1 where influence from the wildfires was small, but from wetlands was large. The fact that the influence of wetland emission was predominant at these two sites is not surprising, given that they were separated only about 70 km (see Figure 1) and shared similar vegetation type.

3.2. Bonfire Experiments

3.2.1. Measured Gas Concentrations and Isotopic Ratios

[42] As summarized in Table 2, the mean background CO2, CH4, and CO concentrations are 376.4 ± 1.0 ppm, 1856.1 ± 3.5 ppb, and 138 ± 15 ppb, respectively, which are representative of values observed at other northern high-latitude sites, such as Alert, Canada and Barrow, Alaska (World Data Center for Greenhouse Gases (WDCGG), http://gaw.kishou.go.jp/wdcgg/). The δ13CH4 and δD-CH4 values of −47.05‰ ± 0.04‰ and −96.2‰ ± 1.1‰, respectively, are also consistent with the background atmospheric values observed at Ny Ålesund, Svalbard [Morimoto et al., 2006; Umezawa, 2009]. The mean δ13CO2 value of −8.27‰ ± 0.23‰ is also comparable to values at Alert and Barrow reported at the WDCGG. We will discuss the results of our bonfire experiments against these background values inserted into equation (2).

Table 2. CO2, CH4, and CO Concentrations and CH4 and CO2 Isotopic Ratios of the Background Air Collected at Caribou-Poker Creeks Research Watershed, Alaska, on 26 June 2007
SampleCO2 (ppm)CH4 (ppb)CO (ppb)δ13CH4 (‰VPDB)δD-CH4 (‰VSMOW)δ13CO2 (‰VPDB)
BGD-1375.31853.3130−47.08−96.1−8.67
BGD-2376.81853.5129−47.17−94.7−8.27
BGD-3377.61860.6160−47.11−97.4−8.25
BGD-4375.81857.0133−47.11−96.7−8.16
Average376.4 ± 1.01856.1 ± 3.5138 ± 15−47.05 ± 0.04−96.2 ± 1.1−8.27 ± 0.23

[43] Measured concentrations for smoke samples are 2700–30000 ppm for CO2, 60–1700 ppm for CH4, and 650–18000 ppm for CO (Table 3). The CO2 concentration is generally higher during the flaming than in the smoldering stage, while opposite is true for CH4 and CO. To quantify burning condition, we calculated the combustion efficiency (CE) defined as a concentration ratio of CO2/(CO2 + CO), obtaining CE values ranging from 0.61 to 0.92. As mentioned earlier, we assumed ERCH4/CO to be 0.077 when we analyzed the data obtained by the aircraft observations over the wildfires. The bonfire experiments yielded ERCH4/CO of 0.10 ± 0.03, where the uncertainty is the standard deviation of the values obtained in this experiments. This ERCH4/CO is slightly higher than the value we adopted for the aircraft observation, but agree with each other within the uncertainties of both. The present result might suggest that ERCH4/CO of 0.1 by Cofer et al. [1991] is preferable rather than the value of 0.077 by Andreae and Merlet [2001], but our limited number of measurements cannot conclusively favor one value over the other.

Table 3. Results of Bonfire Experiments Made at Caribou-Poker Creeks Research Watershed, Alaska, on 26 June 2007
Sample CodeCombustion StageFuel BiomassCO2 (×103 ppm)CH4 (×103 ppm)CO (×103 ppm)Combustion Efficiencyδ13CH4 (‰VPDB)δD-CH4 (‰VSMOW)δ13CO2 (‰VPDB)
SMK-1flamingblack spruce17.30.1471.880.90−21.27−288.0−26.68
SMK-2smolderingblack spruce8.940.1311.430.86−27.33−291.2−25.49
SMK-3smolderingblack spruce11.70.4413.150.79−28.19−318.9−24.79
SMK-4smolderingblack spruce8.070.3162.140.79−28.67−320.6−25.25
SMK-5intermediateblack spruce21.00.1651.850.92−19.90−299.5−25.72
SMK-6smolderingblack spruce18.50.8316.300.75−27.47−325.5−25.15
SMK-7smolderingblack spruce14.20.2873.530.80−27.45−329.5−25.30
SMK-8flamingblack spruce30.20.7547.030.81−23.01−287.4−28.23
SMK-9smolderingblack spruce2.740.06300.6500.81−28.62−320.3−26.82
SMK-10intermediatemoss/shrub25.30.84510.40.71−27.46−289.0−29.67
SMK-11intermediatemoss/shrub27.81.7118.20.61−30.41−301.4−29.21
SMK-12smolderingmoss/shrub18.50.3744.920.79−32.43−315.0−28.94
3.2.2. Isotopic Ratios of CH4 and CO2 Emitted in the Bonfire Experiments

[44] Based on laboratory and field experiments, previous studies examined some of the controlling factors influencing δ13CH4 (BB) and δD-CH4 (BB) variations during combustion. First, δ13CH4 (BB) is thought to depend directly on δ13C values of burned plant materials [Chanton et al., 2000; Snover et al., 2000; Yamada et al., 2006]. Since C3 and C4 plants have the distinctive δ13C values between −22‰ and −34‰ and between −9‰ and −16‰, respectively [Bender, 1971; Smith and Epstein, 1971; Vogel, 1993], δ13CH4 (BB) would depend on C3–C4 mixing ratio of vegetation being burnt. Likewise, δD-CH4 (BB) would depend on δD of the fuel biomass, which in turn depend on environmental water [Dawson, 1993]. Since δD of environmental water is influenced by δD of precipitation, which is a latitude-dependent variable, it is suggested that δD-CH4 (BB) should vary as a function of latitude [Yamada et al., 2006]. However, measurements of δD-CH4 in smoke from high-latitude biomass burning, where δD of precipitation is highly depleted, have yet to be reported in open literature, so this hypothesis has never been experimentally examined. Second, δ13CH4 (BB) and δD-CH4 (BB) are thought to depend on various stages of the combustion such as flaming and smoldering. Different temperatures in each stage would result in different isotopic fractionation through pyrolysis, subsequent CH4 combustion [Chanton et al., 2000] and methyl radical abstraction of hydrogen from organic materials [Snover et al., 2000].

[45] Table 3 also shows that δ13CH4, δD-CH4, and δ13CO2 range from −19.90‰ to −32.43‰, from −287.4‰ to −329.5‰, and from −24.79‰ to −29.67‰, respectively. Variations of δ13CH4, δ13CO2, and δD-CH4 as a function of CE are shown in Figure 8. As shown in Figure 8, δ13CO2 is nearly constant within the observed range of δ13C values of C3 plants (i.e., −25‰ to −30‰), being independent of CE. Since C3 plants were used as biomass fuel in the bonfire experiments, δ13CO2 should reflect the δ13C value of the biomass fuel. In addition, our results indicate that the burning condition (i.e., CE) of the biomass fuel does not influence δ13CO2; no isotopic fractionation occurs during CO2 production. Schuur et al. [2003] measured δ13CO2 of air samples contaminated with smoke from a forest fire experiment conducted at CPCRW, obtaining a δ13CO2 value of −26.97‰ ± 0.88‰, which corresponds well with those obtained in this study. They also reported that δ13C of the burned vegetation (black spruce and soil surface organic layers) ranged between −25‰ and −31‰. These and our results support δ13CO2 to be δ13C of the biomass fuel. Using laboratory experiments, Turekian et al. [1998] also showed that no CO2 isotopic fractionation occurs during C3 plant combustion.

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Figure 8. Values of (a) δ13CH4 (solid symbols) and δ13CO2 (open symbols) and (b) δD-CH4 against the combustion efficiency for air samples obtained by the bonfire experiments. Solid lines indicate linear regressions for the black spruce combustion; also shown are the regression equations, coefficients of determination, and p values.

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[46] The δ13CH4 values obtained in this study are similar to δ13CO2, giving support to the hypothesis that δ13CH4 (BB) is closely related with δ13C of biomass fuel. Previous investigations have demonstrated that C3 plant combustions would yield δ13CH4 values ranging between −20‰ and −35‰, while C4 plant combustions would result in higher values between −16‰ and −26‰ [Chanton et al., 2000; Snover et al., 2000; Yamada et al., 2006]. At the same time, δ13CH4 has a positive correlation with the CE for the black spruce combustion (Figure 8a), indicating that carbon isotopic fractionation occurs during CH4 production and degree of the fractionation depends on the burning condition. Such positive correlation between δ13CH4 and CE was also found by Chanton et al. [2000] and Yamada et al. [2006]. Chanton et al. [2000] suggested that there are two processes that would cause δ13CH4 variation during biomass burning: pyrolysis of plant materials and subsequent CH4 combustion. The former process would produce 13C-depleted CH4 during incomplete combustion, which is consistent with the depleted δ13CH4 under the low CE, as shown in Figure 8a. On the other hand, the latter process would enrich δ13CH4; CH4 is combusted with a preferential consumption of 12CH4 in the flame and causing 13C enrichment in residual CH4. The enriched δ13CH4 values observed under high CE, which are higher than δ13CO2 in the same smoke samples, may be interpreted in terms of such CH4 combustion process. To examine the correlation in Figure 8a, we compared our results with those of Yamada et al. [2006]. By analyzing δ13CH4 and δD-CH4 from the combustion together with δ13C and δD of the biomass fuel, they examined the influence of CE on apparent carbon and hydrogen isotopic fractionations during combustion (ɛBurn). Based on the equations they used, we rearranged their ɛBurn13C-CE relationship to a δ13CH4-CE relationship. The results indicate that the slope of the δ13CH4-CE relationship obtained by Yamada et al. [2006] is almost identical to that obtained in this study, to within the uncertainties. This suggests that the burning condition governs the degree of δ13CH4 fractionation during the black spruce combustion (and likely other vegetation types).

[47] Since δD-CH4 (BB) is also thought to reflect δD of the biomass fuel [Snover et al., 2000; Yamada et al., 2006], δD-CH4 measured in the present experiments would also reflect δD of the biomass fuel being burnt. However, δD of the biomass fuel in our experiments is not available. We therefore cannot directly examine the link between δD-CH4 and δD of the biomass fuel burned in our experiments. Nonetheless, we discuss the importance of δD of biomass fuel by comparing our results with those reported in other studies. However, we first begin by examining the positive correlation between the δD-CH4 values and CE for the black spruce combustion (Figure 8b). As described earlier, pyrolysis and subsequent CH4 combustion are the important factors that affect CH4 produced from biomass burning. In addition, methyl radical abstraction of hydrogen from organic materials is thought to be another factor controlling the fractionation of δD-CH4 during biomass burning [Snover et al., 2000]. These factors would produce D-depleted CH4 in the smoldering stage under low temperature. The positive correlation between δD-CH4 and CE obtained in our experiments could be interpreted in terms of this explanation. Similar to the δ13CH4 case, we compared slope of the regression line for the black spruce combustion in Figure 8b with that reported by Yamada et al. [2006]. As a result, both slopes agree well with each other to within the uncertainties, suggesting that δD-CH4 variation could be explained by the differences in the burning condition through the processes of the methyl radical abstraction of hydrogen as well as pyrolysis and subsequent CH4 combustion.

[48] In regard to the combustion of mosses and shrubs, it is difficult to find a clear relationship between δ13CH4 or δD-CH4 and CE due to the limited number of samples and their large scatter. It is noteworthy, however, that δ13CO2 and δ13CH4 values for the combustion of mosses and shrubs are relatively lower than those for the black spruce combustion. In this regard, Schuur et al. [2003] found that δ13C of moss was lower than that of black spruce biomass.

[49] δ13CH4 (BB) and δD-CH4 (BB) values of our bonfire experiments and those obtained by Snover et al. [2000] and Yamada et al. [2006] are shown in Figure 7 as δD-CH4 is plotted against δ13CH4. Given the combustion of similar C3 plants, our δ13CH4 values agree with those of the previous studies, including δ13CH4 (BB) obtained by Chanton et al. [2000] for C3 plant combustion. On the other hand, δD-CH4 values obtained in our bonfire experiments, which ranged from −287‰ to −330‰, are considerably lower than −195‰ to −255‰ obtained by Snover et al. [2000] and −196‰ to −262‰ obtained by Yamada et al. [2006]. There might be two reasons for the disagreement: (1) possible differences in the burning condition; however, this is unlikely as the values of CE (0.61 to 0.92) obtained in our experiments are similar to those obtained by Snover et al. [2000] and Yamada et al. [2006]; and (2) a possible difference in the actual composition of the biomass being burnt. Since δD of plant material is significantly affected by δD of the environmental water [Dawson, 1993], latitudinal variation in δD of the environmental water may propagate to δD-CH4 (BB) through δD of biomass fuel [Yamada et al., 2006]. If this is the case, then the fact that the values of δD-CH4 obtained by Snover et al. [2000] and Yamada et al. [2006] came from biomass burning in the tropics and midlatitudes is a significant factor in explaining their higher values. This will be discussed further in section 3.2.3.

[50] In comparing δ13CH4 and δD-CH4 values from the bonfire experiments and those from Wildfire 2 (Figure 7), we note that they agree relatively well, given the similar biomass fuel in both cases. As described earlier, this calculation yielded δ13CH4 and δD-CH4 values of −27.5‰ ± 2.0‰ and −285‰ ± 111‰, respectively, which fell to within the variation ranges observed in the bonfire experiments. It should be also noted that the ERCH4/CO value of 0.077 is acceptable to hold the consistency of isotope signature.

3.2.3. Implications of Biomass Burning for Global δD-CH4 Distribution

[51] As described earlier, the δD-CH4 difference between our study and those of Snover et al. [2000] and Yamada et al. [2006] could be ascribed to the difference in δD of environmental water affected by precipitation. It is known that δD of precipitation shows a quasi-quadratic latitudinal distribution, in which δD decreases poleward, with variability of several tens of ‰ at various latitudes (International Atomic Energy Agency, GNIP maps and animations, 2001, http://isohis.iaea.org, hereinafter IAEA01). In Figure 9, we have plotted the δD-CH4 (BB) distributions we obtained in this study, along with those from Snover et al. [2000] and Yamada et al. [2006] against the δD of precipitation calculated by applying a quadratic regression to all δD data available from low-altitude (<300 m) sites. As seen in Figure 9, δD-CH4 (BB) is correlated well with δD of precipitation, showing that the more δD of precipitation deplete, the more depleted δD-CH4 (BB) becomes. The influence of δD of environmental water on δD-CH4 for biogenic production has been examined globally by Waldron et al. [1999], but such an examination has never been made for biomass burning.

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Figure 9. Relationship between δD-CH4 obtained by the present and previous studies for biomass burning and δD of precipitation. The δD values of precipitation at the respective areas are taken from IAEA01. Solid line and gray shading represent a linear regression line to all data and its 95% confidence level, respectively. Also shown are the regression equation, coefficient of determination, and p value.

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[52] Based on our new findings, we can proceed to evaluate δD-CH4 (BB) over a geographically wide area, by using the latitudinal distribution of δD of precipitation. Given the geographical distribution of CH4 emissions from biomass burning, it is possible to estimate the global average of δD-CH4 (BB). In this calculation, we used the Global Fire Emission Database (GFEDv2) [van der Werf et al., 2006], which includes monthly 1° × 1° grid CH4 emission data estimated by satellite measurements for biomass burning. Using the GFEDv2 data set for the years 1997–2006, the global average of δD-CH4 (BB) was calculated to be −204‰ ± 11‰, with a large amount of CH4 emitted from low-latitude regions making a significant contribution to this value. Based on the GFEDv2 data set for the above years, almost 80%–90% of annual CH4 emissions from biomass burning are located at latitudes between 30°N and 30°S. Snover et al. [2000] estimated the global average of δD-CH4 (BB) to be −210‰ (close to our calculation value), while Yamada et al. [2006] estimated much higher value of −169‰.

4. Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Experimental Methods
  5. 3. Results and Discussions
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[53] To investigate the spatial variations of atmospheric CH4 concentration, δ13CH4, and δD-CH4 over Alaska, aircraft observations were conducted in summer 2006. This is the first observational study to elucidate δ13CH4 and δD-CH4 distributions covering from the surface to midtroposphere over various CH4 source in a high-latitude region. Our findings are summarized as follows.

[54] 1. In Alaska, CH4 concentration, δ13CH4, and δD-CH4 were distributed with large horizontal and vertical variability, indicating substantial influence of local CH4 sources near the ground and of advection in the free troposphere.

[55] 2. CH4 concentration was considerably high over wetlands and wildfires.

[56] 3. Respective values of δ13CH4 and δD-CH4 were estimated to be −63.4‰ ± 3.0‰ and −424‰ ± 79‰ for wetlands and −27.5‰ ± 2.0‰ and −285‰ ± 111‰ for wildfires. Estimated values of δ13CH4 and δD-CH4 for wetlands were in good agreement with the previously reported values of CH4 emitted from wetlands in northern high latitudes, and δ13CH4 and δD-CH4 from the wildfires, respectively, agreed well with and were lower than the previously reported values in lower latitudes, with appropriate values of ERCH4/CO and δwetland.

[57] To examine ERCH4/CO as well as δ13CH4 and δD-CH4 for wildfires in northern high latitudes directly, we conducted bonfire experiments in the interior Alaska. For these experiments, black spruce, mosses and shrubs were used as biomass fuel, which are of similar biomass composition as those burned in the wildfires over which aircraft observations were made. We obtained the following results by the experiments.

[58] 4. ERCH4/CO was found to be 0.10 ± 0.03, which agrees the previously reported value of 0.077 ± 0.025 within the uncertainties of both.

[59] 5. The δ13CH4 and δD-CH4 emitted from the bonfire depended on the combustion efficiency (CE) (or burning condition), i.e., combustion with low CE yields depleted δ13CH4 and δD-CH4.

[60] 6. The δ13CH4 from the bonfire experiments agreed with those reported for combustion of C3 plants in lower latitudes and with the estimated values obtained by our aircraft observations.

[61] 7. The δD-CH4 from the bonfire experiments was significantly lower compared to those from previous field and laboratory experiments in lower latitudes, but consistent with the estimated value by our aircraft observations, implying that δD-CH4 (BB) varies with latitude.

[62] 8. By using the relationship between δD-CH4 (BB) and δD of precipitation, as well as a global data set of biomass burning, a global average of δD-CH4 (BB) was estimated to be −204‰ ± 11‰.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Experimental Methods
  5. 3. Results and Discussions
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[63] We are grateful to the Asahi Shimbun Company for making us part of their current events campaign “Hokkyoku Ihen,” joining with Japan Aerospace Exploration Agency (JAXA) and Hokkaido University. We also appreciate the pilots of the aircraft Asuka and various staff members who assisted us in our measurements. We thank T. Machida for providing air temperature profiles obtained during our observation flights, Tomoyuki Wada for his assistance in the bonfire experiments, and Hisashi Yashiro for his help in analyzing the GFEDv2 data set. We would like to acknowledge Kaz Higuchi and anonymous reviewers for their helpful comments to improve our manuscript.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Experimental Methods
  5. 3. Results and Discussions
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Experimental Methods
  5. 3. Results and Discussions
  6. 4. Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
jgrd17090-sup-0001-t01.txtplain text document1KTab-delimited Table 1.
jgrd17090-sup-0002-t02.txtplain text document1KTab-delimited Table 2.
jgrd17090-sup-0003-t03.txtplain text document1KTab-delimited Table 3.

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