ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-7413-2015An ecosystem-scale perspective of the net land methanol flux:
synthesis of micrometeorological flux measurementsWohlfahrtG.georg.wohlfahrt@uibk.ac.athttps://orcid.org/0000-0003-3080-6702AmelynckC.AmmannC.ArnethA.https://orcid.org/0000-0001-6616-0822BambergerI.GoldsteinA. H.https://orcid.org/0000-0003-4014-4896GuL.GuentherA.https://orcid.org/0000-0001-6283-8288HanselA.https://orcid.org/0000-0002-1062-2394HeineschB.https://orcid.org/0000-0001-7594-6341HolstT.HörtnaglL.https://orcid.org/0000-0002-5569-0761KarlT.https://orcid.org/0000-0003-2869-9426LaffineurQ.NeftelA.McKinneyK.https://orcid.org/0000-0003-1129-1678MungerJ. W.https://orcid.org/0000-0002-1042-8452PallardyS. G.SchadeG. W.SecoR.https://orcid.org/0000-0002-2078-9956SchoonN.Institute of Ecology, University of Innsbruck, Innsbruck,
AustriaEuropean Academy of Bolzano, Bolzano,
ItalyBelgian Institute for Space Aeronomy, Brussels,
BelgiumResearch Station Agroscope, Climate and Air Pollution
Group, Zurich, SwitzerlandKarlsruhe Institute of Technology, IMK-IFU,
Garmisch-Partenkirchen, GermanyInstitute of Agricultural Sciences, ETH Zurich,
Zurich, SwitzerlandDepartment of Environmental Science, Policy, and
Management, University of California, Berkeley, CA, USAEnvironmental Sciences Division, Oak Ridge National
Laboratory, Oak Ridge, TN, USAAtmospheric Sciences and Global Change Division, Pacific
Northwest National Laboratory, Richland, WA, USAInstitute of Ion Physics and Applied Physics, University
of Innsbruck, Innsbruck, AustriaExchanges Ecosystems-Atmosphere, Department Biosystem
Engineering (BIOSE), University of Liege, Gembloux, BelgiumDepartment of Physical Geography and Ecosystem Science,
Lund University, Lund, SwedenInstitute of Meteorology and Geophysics, University of
Innsbruck, Innsbruck, AustriaRoyal Meteorological Institute, Brussels,
BelgiumSchool of Engineering and Applied Sciences, Harvard
University, Cambridge, MA, USADepartment of Forestry, University of Missouri,
Columbia, MO, USADepartment of Atmospheric Sciences, Texas A&M
University, College Station, TX, USADepartment of Earth System Science, University of
California, Irvine, CA 92697, USAG. Wohlfahrt (georg.wohlfahrt@uibk.ac.at)7413742723December201427January201523May201522June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/7413/2015/acp-15-7413-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/7413/2015/acp-15-7413-2015.pdf
Methanol is the second most abundant volatile organic compound in the
troposphere and plays a significant role in atmospheric chemistry. While
there is consensus about the dominant role of living plants as the major
source and the reaction with OH as the major sink of methanol, global
methanol budgets diverge considerably in terms of source/sink estimates,
reflecting uncertainties in the approaches used to model and the empirical
data used to separately constrain these terms. Here we compiled
micrometeorological methanol flux data from eight different study sites and
reviewed the corresponding literature in order to provide a first cross-site
synthesis of the terrestrial ecosystem-scale methanol exchange and present
an independent data-driven view of the land–atmosphere methanol exchange.
Our study shows that the controls of plant growth on production, and
thus the methanol emission magnitude, as well as stomatal conductance on the hourly
methanol emission variability, established at the leaf level, hold across
sites at the ecosystem level. Unequivocal evidence for bi-directional
methanol exchange at the ecosystem scale is presented. Deposition, which at
some sites even exceeds methanol emissions, represents an emerging feature
of ecosystem-scale measurements and is likely related to environmental
factors favouring the formation of surface wetness. Methanol may adsorb to
or dissolve in this surface water and eventually be chemically or
biologically removed from it. Management activities in agriculture and
forestry are shown to increase local methanol emission by orders of
magnitude; however, they are neglected at present in global budgets. While
contemporary net land methanol budgets are overall consistent with the grand
mean of the micrometeorological methanol flux measurements, we caution that
the present approach of simulating methanol emission and deposition
separately is prone to opposing systematic errors and does not allow for full advantage to be taken of the rich information content of micrometeorological flux
measurements.
Introduction
Methanol (CH3OH) is, on average, the second most abundant volatile
organic compound (VOC) in the troposphere (e.g. Jacob
et al., 2005) and often the most abundant one regionally
(e.g. Seco et al., 2011), with typical mole fractions
in the continental boundary layer of 1–10 nmol mol-1
(Heikes et al., 2002). With an atmospheric lifetime of 5–12 days (Jacob et al., 2005), methanol has been shown to play
a role in modulating the presence of oxidants in the upper troposphere
(Tie et al., 2003). It affects atmospheric chemistry as an
atmospheric source of formaldehyde (Palmer et al., 2003)
and carbon monoxide (Duncan et al., 2007). Model
calculations suggest methanol emissions constitute 10 % of the total
global biogenic non-methane VOC (BVOC) emissions, the second highest single-compound contribution after isoprene (Guenther et
al., 2012).
The primary source of atmospheric methanol is emissions from living plants,
followed by smaller source contributions from the decay of dead plant
matter, biomass burning, and direct emissions from anthropogenic activities, the
ocean and atmospheric production (Seco et al., 2007). On a
regional scale, dairy farming and industrial activities are important
sources as well (e.g.
Gentner et al., 2014). The major sink for methanol is oxidation by OH
radicals, followed by dry and wet deposition to land and ocean. Estimates of
the global land net flux, i.e. the balance between sources and sinks of
methanol on land, vary widely between 75 and 245 Tg yr-1 (Singh et al.,
2000; Galbally and Kirstine, 2002; Heikes et al., 2002; Tie et al., 2003;
von Kuhlmann et al., 2003a, b; Millet et al., 2008; Stavrakou et al.,
2011), although more recent estimates converge to a more narrow range of
75–108 Tg yr-1 (Jacob et al., 2005; Millet et al., 2008; Stavrakou et
al., 2011).
General characterisation of the study sites (see Table S1 for
further details on experimental setup).
Blodgett Forest (BF)Missouri Ozark (MO)Harvard Forest (HF)Vielsalm (VA)Oensingen- INT (OS-INT)Oensingen- EXT (OS-EXT)Neustift (NS)Stordalen Mire (SD)CountryUSAUSAUSABelgiumSwitzerlandSwitzerlandAustriaSwedenLatitude38.89∘ N38.76∘ N42.54∘ N50.30∘ N47.28∘ N47.28∘ N47.12∘ N68.33∘ NLongitude120.63∘ W92.16∘ W72.17∘ W5.98∘ E7.73∘ E7.73∘ E11.32∘ E19.05∘ EElevation (m)1315216340450450450970351MAP (mm)129011101066100011001100852304MAT (∘C)9.013.67.87.59.09.06.5-0.7ClimateMediterraneanTemperate continentalTemperateTemperate maritimeTemperate continentalTemperate continentalTemperate alpineBorealPlant functional typeConiferous evergreen forestDeciduous broadleaf forestMixed forestMixed forestGrasslandGrasslandGrasslandWetlandManagementUnderstory cut–––HarvestHarvestHarvest–LAI (m2 m-2)1–1.71.3–4.04.8–5.42.6–3.80.4–3.50.2–5.10.2–7.8up to 3.5Measurement/avg. canopy height (m)11/532/2230/2352/301.2/0.151.2/0.22.5/< 1.02.95/<0.5Data coverage, days of year (year)142–170 (1999)125–296 (2012)149–248 (2007)182–304 (2009) 60–273 (2010) 91–334 (2011)176–213 (2004)158–175 (2004) 214–249 (2004)143–325 (2008) 78–305 (2009) 77–346 (2011) 87–330 (2012)121–273 (2006) 121–260 (2007)Flux methodREAvDECvDECvDECvDECvDECvDECvDECKey referenceSchade and Goldstein (2001)Seco et al. (2015)McKinney et al. (2011)Laffineur et al.(2012)Brunner et al.(2007)Brunner et al. (2007)Hörtnagl et al.(2011)Holst et al. (2010)
Abbreviations: MAP, mean annual precipitation; MAT, mean annual temperature; LAI, leaf area index.
Much of the knowledge and data embedded into the parameterisation of plant
methanol emissions derives from work at the leaf level (Galbally and
Kirstine, 2002; Guenther et al., 2012). In living plants, methanol is
produced as a byproduct of pectin metabolism during cell wall synthesis
(Fall and Benson, 1996) and thus methanol production and emission
are positively correlated with plant growth (Custer and Schade, 2007;
Hüve et al., 2007) and pectin content (Galbally and
Kirstine, 2002). This circumstance led Galbally and Kirstine
(2002) to simulate global methanol emissions as a function of net primary
productivity (NPP) that consists of pectin and the fraction thereof which is
demethylated during growth, an approach which has since been adopted by
others (Jacob et al., 2005; Millet et al., 2008). Most other global
budgets rely on the MEGAN model (Guenther et al., 1995,
2012) to simulate methanol emissions using light- and temperature-driven
algorithms. While lacking a sound physiological basis, the latter approach
is successful in simulating observed variations in methanol emissions due to
the fact that methanol emissions are strongly controlled by stomatal
conductance, reflecting its low Henry constant (Niinemets and Reichstein,
2003; Harley et al., 2007). Stomatal conductance, in the absence of soil
water limitations, tracks diurnal variations in light and temperature, which
in turn correlate with diurnal methanol emissions
(e.g. Hörtnagl et al., 2011).
The deposition of methanol in global models is typically represented in a
very simplistic fashion using fixed deposition velocities. These vary by up
to a factor of 4 between different studies (e.g. Galbally and
Kirstine, 2002; Millet et al., 2008) and are often, constrained by observed
atmospheric concentrations, tuned to close the atmospheric budget. Recently,
several studies have reported significant methanol deposition to terrestrial
ecosystems and/or clear evidence of bidirectional exchange (Misztal et
al., 2011; Schade et al., 2011; Laffineur et al., 2012). The observed
deposition has been related to high ambient methanol mole fractions downwind
of industrial methanol sources (Laffineur et al., 2012),
the presence of water films in the plant canopy or soil within which
methanol may adsorb/dissolve and can be removed by chemical transformations
(Laffineur et al., 2012) and/or methylotrophic bacteria
(Fall and Benson, 1996; Abanda-Nkpwatt et al., 2006).
In summary, while there is consensus about the dominant role of living
plants as the major source and the reaction with OH radicals as the major
sink of methanol, global methanol budgets diverge considerably in terms of
source/sink estimates (Jacob et al., 2005), reflecting
uncertainties in the approaches used in models and the empirical data used
to separately constrain the source/sink terms.
Micrometeorological methods allow measurements of the net exchange of mass,
energy and momentum between the underlying surface and the atmosphere over
the spatial scale of typically hundreds of metres (Baldocchi et
al., 1988). Thanks to advances in proton-transfer-reaction mass
spectrometry, a fast and sensitive analytical method to determine methanol
mole fractions in ambient air in real time during the past decade (Karl
et al., 2001, 2002; Müller et al., 2010), ecosystem-scale
methanol flux measurements have been reported from multiple sites and in a
few cases over multiple seasons (Tables 1 and 2). Because
micrometeorological flux measurements allow quantification of the net flux
of methanol between ecosystems and the atmosphere quasi-continuously and
over extended periods of time, they are ideal for assessing the performance
of models at the ecosystem scale. Up to now, however, few (if any) studies
have made use of this rich data source in a more holistic fashion.
Literature survey of micrometeorological methanol flux studies and
the net land methanol flux derived from global budget studies compared to
the results of the present study.
Methanol flux (nmol m-2 s-1)VdaVegetation typeMethodAverageSDMedianMaximumMinimum(cm s-1)Ecosystem-scale studies Schade and Custer (2004)Bare agricultural soilEC4.60.00.1–0.4Custer and Schade (2007)Rye grassEC0.220.220.11.5-0.6∼ 0.1Warneke et al. (2002)Alfalfa cropDEC4.734.70.0Schade et al. (2011)Deciduous forestREA5.0-3.61.1Karl et al. (2003)Mixed deciduous forestvDEC6.119.9-1.7Spirig et al. (2005)Mixed deciduous forestvDEC4.0-1.1Baker et al. (2001)Coniferous forestREA56.0-12.0Karl et al. (2005)Coniferous forestvDEC2.80.91.0Rinne et al. (2007)Coniferous forestvDEC1.43.70.1Park et al. (2014)Pine forestvDEC4.2Karl et al. (2004)Tropical rainforestvDEC4.8-0.90.3Langford et al. (2010a)Tropical rainforestvDEC-0.32.6-0.6Davison et al. (2009)Mediterranean macchiavDEC3.7Park et al. (2013)Orange orchardEC1.7Fares et al. (2012)Citrus orchardvDEC0.26-2.7410.0-5.0Brilli et al. (2014)SRC poplar plantationEC1.41.0Misztal et al. (2011)Oil palm plantationvDEC-0.40.9-0.23.0-3.1Velasco et al. (2005)UrbanvDEC9.0Langford et al. (2009)Urban(v)DEC4.76.24.3Velasco et al. (2009)UrbanvDEC12.86.3Langford et al. (2010b)UrbanvDEC8.38.18.2Global average net land fluxbHeikes et al. (2002) 1.80.4Galbally and Kirstine (2002) 0.70.1Tie et al. (2003) 1.3Jacob et al. (2005) 0.80.2Millet et al. (2008) 0.60.4Stavrakou et al. (2011) 0.6This study Blodgett ForestConiferous forestREA23.936.911.3228.7-23.11.8Missouri OzarkDeciduous forestvDEC0.92.10.516.2-9.00.3Harvard ForestMixed deciduous forestvDEC0.71.50.39.5-2.51.0VielsalmMixed deciduous forestvDEC-0.12.2-0.119.3-20.71.9Oensingen-INTcGrasslandvDEC1.7(1.9)2.0(2.6)1.0(1.1)12.4(29.8)-1.5(-1.5)0.1Oensingen-EXTcGrasslandvDEC2.8(4.4)3.1(9.0)1.7(2.0)18.4(110.9)-2.9(-6.3)0.2NeustiftcGrasslandvDEC1.5(1.8)2.1(4.2)0.8(0.8)22.1(155.1)-9.7(-9.7)0.5StordalenWetlandvDEC0.20.60.24.2-1.50.7
a Average night-time deposition velocity. b
The net land flux was derived by summing emissions from
plants, decay of plant matter, biomass burning, anthropogenic activities and
subtracting dry and wet deposition to land, dividing by the land area (133.8×1012 m2) and converting from mass to molar basis using
32 g mol-1.
c Values in parentheses include data
influenced by site management events.
The main objective of this study is thus (i) to compile the available
ecosystem-scale methanol exchange data from micrometeorological flux
measurements, (ii) to conduct a first cross-site synthesis of the magnitude
of and controls on the terrestrial net ecosystem methanol exchange and (iii)
to provide an independent constraint on the land methanol exchange against
which models can be compared.
Methods
In total, growing season data from eight sites in the Northern Hemisphere
were available for the present synthesis (Table 1). Key metrics of
micrometeorological methanol flux measurements from additional sites were
obtained from a literature survey (Table 2). The climate space covered the
Mediterranean to the boreal climate zone, with mean annual temperatures
ranging from -0.7 to +9.0 ∘C; however, most of the
sites (six) were located in the temperate climate zone. The study sites
comprised four forests, three managed grasslands and one wetland.
The net ecosystem methanol exchange was determined by means of the virtual
disjunct eddy covariance (vDEC) method (Karl et al.,
2002) at seven sites and by the relaxed eddy accumulation (REA) method at
one site. With the vDEC method, as with the “true” eddy covariance method
(Baldocchi et al., 1988), measurements of the three-dimensional
wind vector by means of sonic anemometers are made at high temporal
resolution (50–100 ms). Methanol mole fractions are measured at disjunct
time intervals separated typically by 1–3 s with integration times of
100–500 ms (Table S1 in the Supplement). As shown by Hörtnagl et al. (2010), the vDEC method increases random variability compared to the true
eddy covariance method but does not result in a systematic bias. This was
confirmed by a direct comparison between vDEC and true eddy covariance
methanol flux measurements by Müller et al. (2010).
Methanol mole fractions were measured with proton-transfer-reaction mass
spectrometers (PTR-MS) on mass-to-charge ratio (m/z) 33 (see
Hansel et al., 1995; Lindinger et al., 1998; and
Graus et al., 2010, for more details on the PTR-Q-MS and
PTR-TOF-MS technology). The PTR-MS instruments were typically housed in a
sheltered location some distance away from or at the bottom of the instrument
tower supporting the sonic anemometer. Air was pumped from an inlet close to
the sonic anemometer to the PTR-MS through an inlet line, which was designed
to minimise interactions between the tubing material and methanol (i.e.
through use of inert materials and heating). Further details on the study
sites, instrumentation and experimental protocols are given in Tables 1 and
S1 and the references cited therein. In contrast to the eddy covariance
CO2 flux community (Baldocchi, 2003), which has made
considerable progress in standardising flux measurement protocols
(Mauder and Foken, 2006), little effort has been made in the
(much smaller) VOC flux community to standardise measurement protocols. In
the present study we have decided to use the data from the different sites
as they are, with measurements, processing and quality control as
described in the key references in Table 1. We acknowledge that this
approach potentially introduces systematic bias among sites. As shown in
Table S1 in the Supplement, there are necessarily large
differences in the air sampling systems due to different canopy and tower
heights, but the PTR-MS setups were remarkably similar.
At the Blodgett Forest study site, methanol exchange was determined with the
relaxed eddy accumulation (REA) method by sampling up- and downdraughts of
air into separate reservoirs (cooled activated carbon microtraps), which
were analysed immediately after collection using a gas chromatography flame
ionisation detector technique (Schade and Goldstein, 2001).
Even though the REA method is a less direct method than the vDEC
(Hewitt et al., 2011), the data from Blodgett Forest
were included in the present analysis because several studies demonstrated
good correspondence between VOC fluxes measured concurrently by the REA and
the eddy covariance method (e.g. Westberg et al., 2001; Lee et al.,
2005).
Additional auxiliary data included concurrent measurements of the major
environmental drivers, including air temperature and humidity, horizontal
wind speed, incident photosynthetically active radiation and precipitation
above the canopy and soil temperature and water content in the near-surface
soil. In addition we collected above-canopy net ecosystem carbon dioxide
exchange (NEE), which was measured at each site within the framework of the
FLUXNET project (Baldocchi et al., 2001; Baldocchi, 2003), and derived
therefrom gross photosynthesis (GPP) and ecosystem respiration
(Reichstein et al., 2005).
Data were combined into a common format and analysed with SPSS version 19.
Statistical analysis was performed, unless stated otherwise, on the quality-filtered half-hourly data.
Results and discussionMagnitude of methanol exchange
Hourly bin-averaged diurnal variation of methanol fluxes
(circles; left y axis) and mole fractions (squares; right y axis) at the
eight study sites (error bars represent ±1 standard deviation).
Note the differing scaling on the y axis. Data from Oensingen-INT,
Oensingen-EXT and Neustift are exclusive of periods influenced by management
practices.
Box plots of methanol fluxes at the eight study sites. The left
y axis refers to sites/measurements not influenced by site management
events, while the right y axis (note differing scaling) shows data for
Blodgett Forest and the grassland sites inclusive of measurements
during/after management (MO: Missouri Ozark; HF:
Harvard Forest; VA: Vielsalm; OS-INT:
Oensingen – intensive; OS-EXT: Oensingen – extensive; NS: Neustift; SD: Stordalen; BF:
Blodgett Forest). Box plots show minima/maxima (circles), 5 and 95 %
quartiles (whiskers), the interquartile range (box) and the median
(horizontal line).
The eight investigated study sites, as shown in Figs. 1 and 2 and Table 2,
showed quite contrasting methanol exchange rates; however, they also exhibited
common features: all study sites showed both net emission and net deposition
of methanol (Fig. 2) and methanol fluxes exhibited a more or less pronounced
average diurnal pattern (Fig. 1), in phase with the diurnal course of
incident radiation and air temperature (Fig. S1 in the Supplement). Flux magnitudes
were, however, quite different: by far the largest net emissions were observed at
Blodgett Forest, whose average methanol emissions (23.9 nmol m-2 s-1) exceeded those of the other sites by a factor of 10 and more
(Table 2). The three grasslands, excluding periods following management
activities, were characterised by average net emission rates of 1.5–2.8 nmol m-2 s-1. Management, harvesting and the application of organic
fertiliser caused methanol emissions from the grasslands to increase by an
order of magnitude during the day of the management intervention and remain
elevated a few days thereafter, before fluxes returned back to previous
values (Fig. 3). These were followed by the Missouri Ozark and Harvard
Forest mixed forest sites (0.7–0.9 nmol m-2 s-1). The lowest
average methanol fluxes were measured at the wetland site of Stordalen
(0.2 nmol m-2 s-1) and the mixed forest of Vielsalm. The latter was in
fact characterised by a negative average flux (-0.1 nmol m-2 s-1) – i.e. methanol deposition exceeded emissions at this site.
From a comparison with the other seven study sites (Fig. 2) and the
literature (Table 2) it becomes clear that the emissions observed at
Blodgett Forest are exceptionally high, even compared to elevated emissions
observed over agricultural crops and grasslands after harvesting or the
application of organic fertiliser (e.g. Brunner et al., 2007; Davison et
al., 2008; Hörtnagl et al., 2011; Ruuskanen et al., 2011; Brilli et al.,
2012). Schade and Goldstein (2001) attributed these high
emissions to the cutting of shrubs in the understory, such as manzanita, of
the site prior to the measurements, as part of a regular forest plantation
management intervention. The cut plant material was left at the site and may
have caused the elevated methanol emissions, similar to what was observed at
the grassland sites after harvesting (Fig. 3). In contrast to the grassland
sites, where these emissions were confined to less than 3 days after
harvesting (Fig. 3) and cuttings were removed later, elevated emissions at
Blodgett Forest were sustained. Bouvier-Brown et al. (2012)
noted that measurements in subsequent years showed lower fluxes by a factor
of 2–3. Park et al. (2014), who measured BVOC fluxes at Blodgett
Forest 10 years later with the vDEC method, reported an average methanol
flux of 4.2 nmol m-2 s-1, which is comparable in magnitude with
the results from the other sites of this study and non-urban sites in the
literature (Table 2). Park et al. (2014) also measured vDEC
2-methyl-3-butene-2-ol (MBO) fluxes, which agreed with the corresponding REA
flux estimates measured in 1999 concurrently with the methanol fluxes by
Schade and Goldstein (2001). We are thus confident that the
observed large emissions at Blodgett forest likely reflected the recent
disturbance of the site.
Effect of management (harvest and manure application) on methanol
fluxes of grassland study sites Neustift (NS), Oensingen-INT (OS-INT) and
Oensingen-EXT (OS-EXT) within indication of study year and, where
applicable, number of harvest.
Large net deposition fluxes of methanol, and even sites that represent net
methanol sinks over extended periods of time, have not been reported in the
literature until very recently (Langford et al., 2010a; Misztal et al.,
2011; Schade et al., 2011; Laffineur et al., 2012). The present study
confirms that net deposition of methanol is a common phenomenon (Table 2),
and it is observed at half of the study sites for more than 25 % of the
time (Fig. 2). Laffineur et al. (2012) developed a
theoretical framework to simulate methanol exchange at Vielsalm and showed
that the bi-directional nature of methanol exchange can be explained by
adsorption/desorption of methanol in water films within the ecosystem (aided
by the low Henry constant of methanol) and a postulated sink process. While
the latter had to be invoked in order to make the model match the sustained
deposition fluxes, it is well established that methylotrophic bacteria
inhabit plant surfaces and soils (Conrad, 1996; Fall and Benson, 1996;
Conrad and Claus, 2005; Kolb, 2009; Stacheter et al., 2013) and may
significantly reduce net leaf and ecosystem methanol emissions
(Abanda-Nkpwatt et al., 2006).
After excluding data from Blodgett Forest and the grassland data influenced
by management activities, we calculate a “grand mean” of 1 nmol m-2 s-1 as the average of the methanol fluxes of all sites in this study.
Assuming the Earth's ice-free land area (133.8×1012 m2) to emit
methanol at this average rate year-round, which is an overestimation due to
off-season fluxes being typically much lower than the growing season data
compiled in this study (Bamberger et al., 2014), a global net land methanol flux of 135 Tg yr-1 can be extrapolated. This value falls into
the middle of the range of available global budget studies (75–245 Tg yr-1; Table 2) and is quite close to the 75–108 Tg yr-1 range of
budgets published after 2005 (Jacob et al., 2005; Millet et al., 2008;
Stavrakou et al., 2011). In addition to a likely warm-season bias, globally
important ecosystems, such as tropical forests, are under-represented in our
study, and included sites are likely not representative of pectin contents
elsewhere (Custer and Schade, 2007). We thus stress the large
uncertainties associated with this simplistic upscaling.
Box plots of night-time methanol deposition velocities at the eight
study sites. Horizontal dashed lines indicate the range of deposition
velocities (0.1–0.4 cm s-1) used in global budgets (see also Table 2).
Box plots show minima/maxima (circles), 5 and 95 % quartiles
(whiskers), the interquartile range (box) and the median (horizontal line).
Observed night-time net deposition velocities (medians) ranged between 0.02
and 1.0 cm s-1, with five of the eight sites bracketing the range of
0.1–0.45 cm s-1 (Fig. 4). Including daytime deposition flux
measurements did not substantially change these ranges (compare Fig. 4 with
Fig. S2). These values are consistent with night-time deposition velocities
reported in the literature (Table 2) and overlap with the range of fixed
deposition velocities of 0.1–0.4 cm s-1 used in global methanol
budgets (Singh et al., 2000; Galbally and Kirstine, 2002; Heikes et al.,
2002; von Kuhlmann et al., 2003a, b; Jacob et al., 2005; Millet et al.,
2008). Due to the concurrent emission and deposition of methanol, these
observed deposition velocities represent “net” deposition velocities, while
values used in global budget studies are “gross” deposition velocities.
Because the former are lower than the latter if there is any concurrent
emission of methanol, this suggests that global models may be
underestimating land deposition velocities and thus, provided that models
correctly reproduce atmospheric concentrations, may be underestimating
methanol sources to a similar degree.
Methanol mole fractions at the height of the flux measurements (Table 1)
exhibited relatively little diurnal variability, with a tendency towards
minima during daylight periods and the afternoon (Fig. 1). The highest
(median) mole fractions were found at Blodgett Forest (11.6 nmol mol-1), whereas the lowest were found at Stordalen (1.4 nmol mol-1), consistent with
the range of 1–10 nmol mol-1 reported by Heikes et al. (2002) for the continental boundary layer. Overall, mole fractions
correlated positively with methanol fluxes across sites (r2=0.69,
p=0.011), i.e. higher ambient mole fractions were associated with larger
net emissions.
Controls on methanol exchange
In order to investigate the controls on methanol exchange, a multiple linear
regression analysis was conducted for each site, separating the flux data by
their sign, i.e. into net deposition and net emission (Table 3).
Pearson correlation coefficients of multiple linear regressions of
half-hourly methanol emission and deposition fluxes as a function of several
independent variables (PAR: photosynthetic photon flux density;
RH: relative air humidity; TA: air temperature;
SWC soil water content; u∗: friction velocity; ET:
evapotranspiration; GPP: gross primary
productivity; TSEOP: time since end of precipitation; n: number of measurements).
a Excluding data
influenced by site management; * p<0.05; ** p<0.01; *** p<0.001; ns: not significant; NA: not available.
Methanol emission scaled positively with incident photosynthetically active
radiation and evapotranspiration, and these two independent variables
explained the highest fraction of the variance (0.17<r2<0.62; p<0.001) at most sites. We interpret
this to indicate the strong stomatal control of methanol exchange, owing to
the low Henry constant which favours leaf-internal partitioning of methanol
to the liquid phase (Niinemets and Reichstein, 2003), rather than
a light effect, since Oikawa et al. (2011b) have shown that
methanol emissions are not directly affected by light.
GPP and air temperature, which explained 7 to 43 % (p<0.001)
of the variability at the individual sites (Table 3), were positively
related to methanol emissions, which we interpret to indicate a general
relationship of these two variables with plant growth and thus methanol
production. GPP provides assimilates for growth, and temperature tightly
controls cell division and enzyme reaction rates. While this results in
correlations between methanol emission and these factors, actual methanol
production has been shown to be more complex (Harley et al., 2007; Oikawa
et al., 2011a), and these relationships should thus be viewed as
phenomenological. Galbally and Kirstine (2002) were the
first to link plant growth and methanol emissions in a global budget by
assuming proportionality with NPP. Here we use GPP, which equals NPP plus
autotrophic respiration, as an alternative proxy for plant growth that was
generally available in the present data set, and the corresponding
relationships with net methanol fluxes are shown in Fig. 5 (Fig. S3 in
the Supplement shows the relationships with the net ecosystem
CO2 exchange). Slopes of linear regressions (forced through the origin,
excluding Blodgett Forest and grassland data affected by management
activities) ranged between 3.5 × 10-5 g C-CH3OH g C-GPP-1 (Vielsalm) and
2.5 × 10-4 g C-CH3OH g C-GPP-1 (Oesingen-EXT), with an
average of 1.25 × 10-4 g C-CH3OH g C-GPP-1.
Relationship between gross photosynthesis (GPP) and methanol flux.
Small grey symbols represent half-hourly flux measurements; black
symbols represent 10 bin averages with equal numbers of data. Error bars refer to
1 standard deviation. Note different x and y scales in different panels.
Taking the most recent global GPP value (123 Pg C yr-1) from
Beer et al. (2010), this yields a net land methanol flux of 41 Tg yr-1, which is about half of the lowest estimates available from
global budgets (Millet et al., 2008; Stavrakou et al., 2011). Accounting
for the positive y offset (i.e. not forcing the regression through the
origin) observed at most sites (Fig. 5) or filtering data for positive
methanol fluxes increases the above number by only 20 % (data not shown).
Making the assumption that NPP amounts to around 50 % of GPP (Waring et
al., 1998; Zhang et al., 2009) approximately doubles the average number
quoted above. Compared to the range of 3.5–5.3 × 10-4 g C-CH3OH g C-NPP-1 deduced from the literature
(Galbally and Kirstine, 2002; Millet et al., 2008; Stavrakou et al.,
2011), our values of NPP lost as net land methanol flux are thus lower by
about a factor of 2. As shown in Fig. 6, an inverse relationship between
the fraction of GPP that was lost as net methanol emission and the median
night-time deposition velocities was observed, with an exponential fit
explaining 77 % of the variability between sites (excluding data from
Blodgett Forest). In contrast, no significant correlation between the ratio of net
methanol flux to GPP was found with GPP itself (data not shown),
suggesting no relationship between site productivity and the fraction of GPP
that is lost as net methanol emission. The magnitude of methanol deposition
thus clearly influences the observed fraction of GPP that is lost as
methanol emission and limits the usefulness of GPP for upscaling the net
methanol exchange. In addition, it should be stressed that, on short timescales, GPP may be poorly correlated with NPP and even less with growth and
the associated demethylation of pectin (Galbally and
Kirstine, 2002).
Methanol flux to GPP ratio as a function of the median night-time
deposition velocity. The solid line represents an exponential fit
(r2=0.77).
Friction velocity and relative humidity explained slightly lower fractions
of the variance compared to air temperature and GPP (Table 3). The positive
relationship between friction velocity and methanol emission likely reflects
the high degree of covariation between friction velocity and air
temperature and photosynthetically active radiation (data not shown).
Relative humidity was inversely related to methanol emission at all sites
(Table 3), which may result from canopy water films developing during
periods of high relative humidity (Burkhardt et al.,
2009) within which methanol may adsorb/dissolve, effectively resulting in a
reduction of the net emission. Alternatively, this may reflect the inverse
relationship of relative humidity with temperature and photosynthetically
active radiation and their relationship with methanol exchange discussed
above. The time since the end of the last precipitation event (TSEOP), which
was introduced as a surrogate for the presence of canopy water films
(Laffineur et al., 2012), and soil water content
explained less than 8 % of the variability in methanol emissions
(Table 3). In the case of TSEOP, this likely indicates that a more
process-based approach would be required to properly capture the effect of
wetting and subsequent drying on methanol exchange (Warneke et al., 1999;
Laffineur et al., 2012).
The investigated independent variables generally explained a smaller
fraction of the variability in observed deposition compared to emission
fluxes and half of the relationships were statistically not significant
(Table 3). Relative humidity and friction velocity were the independent
variables explaining the highest fraction (up to 21 %) of the variance at
most sites. Except for one site, friction velocity was negatively correlated
with methanol deposition, suggesting more efficient downward transport of
methanol as mechanical turbulence increases. In contrast to methanol
emissions, which were inversely related to relative humidity, a positive
correlation with methanol deposition was found at half of the sites,
indicating that relative humidity plays a more variable role among sites in
modulating deposition than emission. The remaining variables explained less
than 10 % of the variability in observed methanol deposition fluxes
(except for the intensive grassland of Oensingen).
In an attempt to investigate the common and site-specific controls on
methanol emission and deposition, all data (except for Blodgett forest and
those from the grassland sites influenced by management activities) were
subjected to a univariate analysis of variance (Table 4). For methanol
emissions, site identity and photosynthetically active radiation were the
most important main effects. The largest fraction of variance was, however,
explained by the interaction terms of site with relative humidity (η2=1.45 %) and GPP (η2=0.98 %), and to a lesser
degree with photosynthetically active radiation and air temperature
(Table 4). For methanol deposition, site identity was the only significant
main factor (η2=2.96 %) and also contributed the largest
fraction of explained variance, followed by the interaction terms between
site and relative humidity and air temperature (Table 4). Overall this
suggests that controls on methanol exchange are strongly site-specific
and/or that factors not accounted for, such as soil type and microbial
activity, play a substantial, possibly interactive, role in governing the
ecosystem–atmosphere methanol exchange.
Conclusions
By compiling micrometeorological methanol flux data from eight different
sites and by reviewing the corresponding literature, this study provides a
first cross-site synthesis of the terrestrial ecosystem-scale methanol
exchange and presents an independent, data-driven view of the
land–atmosphere methanol exchange. Below we summarise the major findings,
draw conclusions and make recommendations for future work.
It is now unequivocal that, at the ecosystem scale, methanol exchange is
bi-directional (Figs. 1 and 2, Table 2) and, at some sites, deposition can
even prevail over emission during extended periods of time (Langford et
al., 2010a; Misztal et al., 2011; Laffineur et al., 2012). This finding is
not new from the perspective of global methanol budgets, which do account
for deposition to land and the oceans in addition to the OH sink, but
emission and deposition are treated separately, which likely results in
inconsistencies (Singh et al., 2000; Galbally and Kirstine, 2002; Heikes
et al., 2002; Tie et al., 2003; von Kuhlmann et al., 2003a, b; Jacob et
al., 2005; Millet et al., 2008; Stavrakou et al., 2011). The prominent role
of deposition is an emerging feature of ecosystem-scale measurements and is
in contrast to leaf-level work that has almost exclusively reported methanol
emissions and focussed on describing the corresponding controls (e.g.
Niinemets and Reichstein, 2003; Harley et al., 2007; Hüve et al., 2007).
Variance explained (partial eta-squared, η2) in methanol
emission and deposition based on univariate analysis of variance (UNIANOVA)
using all data exclusive of Blodgett Forest and the grassland site data
influenced by management activities. See Table 3 for abbreviations.
The bi-directional nature of the terrestrial methanol flux makes it
difficult for the present generation of models, which simulate emission and
deposition separately, to fully capitalise on the rich information of
micrometeorological measurements for calibration/validation.
Guenther et al. (2012) proposed adding an estimate
of the deposition flux to the net flux measured by micrometeorological
methods to be used for calibrating the primary emission in MEGAN. While
correct in principle, the emerging picture of methanol deposition being more
difficult to predict than emission (Tables 3 and 4) makes it difficult in
practice to “estimate” the magnitude of the deposition flux with confidence.
We argue that these difficulties should be addressed by a new generation of
models which reflect the available process knowledge about the controls on
both emission and deposition of methanol and merge it into a unified
modelling framework. For the strong stomatal control on methanol emissions
(Niinemets and Reichstein, 2003; Harley et al., 2007) and the role of
water in adsorption/desorption of methanol (Laffineur et
al., 2012), the corresponding theory is available. Land surface models which
include a description of the ecosystem water budget, i.e. stomatal
conductance, leaf energy balance, interception of precipitation
(e.g. Berry et al., 1997), would provide most of
the interfaces to this end. Further work is required in order to better
understand the controls on leaf methanol production (Harley et al., 2007;
Oikawa et al., 2011a), the role of chemical and/or biological (in particular
microbial) removal of methanol on (wet) surfaces (Fall and Benson, 1996;
Abanda-Nkpwatt et al., 2006; Laffineur et al., 2012) and the importance of
soils as sources/sinks of methanol (Asensio et al., 2008; Greenberg et
al., 2012; Stacheter et al., 2013; Peñuelas et al., 2014). Doing so is
likely to require a combination of laboratory experiments under controlled
conditions in order to better understand processes and in situ studies in order to
confirm the relevance of these processes under real-world field conditions.
Assessing the role of surface moisture for methanol exchange would clearly
profit from direct measurements, distributed vertically within the plant
canopy, of surface wetness in order to better quantify dew formation,
interception of precipitation and the associated drying dynamics
(e.g. Bregaglio et al., 2011).
Earlier work (Karl et al., 2001; Brunner et al., 2007;
Davison et al., 2008; Hörtnagl et al., 2011; Ruuskanen et al., 2011;
Brilli et al., 2012) and Fig. 3 conclusively show that management of agricultural
ecosystems (biomass harvesting, grazing or application of organic
fertiliser) results in short-term increases of methanol emissions by an
order of magnitude. Despite being relatively short-lived, these bursts of
BVOC emissions make a substantial contribution to the total BVOC budget of
these agricultural ecosystems (Hörtnagl et al., 2011; Bamberger et
al., 2014). Much less information is available for the effects of various
forest management activities (pruning, thinning, clear cutting, residue
management, etc.) on BVOC and methanol fluxes. Data from Blodgett Forest
(Figs. 1 and 2) and the studies by Haapanala et al. (2012) and
Schade and Goldstein (2003) suggest that forest management
activities may cause longer-term perturbations of BVOC emissions compared to
agricultural ecosystems. Given that the human appropriation of NPP has
increased from 13 % of the NPP of potential vegetation in 1910 to 25 %
in 2005 (Krausmann et al., 2013), we suggest that the
effects of management on methanol emissions should be quantified for a
larger range of ecosystems (in particular for managed forests) and be
included in global budgets. As shown by Brilli et al. (2012) for
grasslands, the magnitude of post-harvesting BVOC emissions scales with the
amount of harvested biomass, suggesting that these emissions could be
modelled based on agricultural/forestry census data (Schade and
Goldstein, 2003), possibly in combination with remote sensing (for hindcast
applications).
This study relied on data from eight study sites and reviewed an additional 21 published studies; thus it represents only a first step towards a
data-driven assessment of the global land methanol flux. Data from
additional sites in under-represented ecosystem types and climates are
required to better constrain differences between different ecosystem types
which are embedded in model parameters of different plant functional types
(PFTs); for example, at present 10 of the 11 woody PFTs in MEGAN have one common
methanol emission factor and the remaining 5 PFTs another one
(Guenther et al., 2012). In a next step, methanol
flux measurements need to be conducted over multiple years
(including off-season periods; Bamberger et al., 2014) in order
to be able to quantify and explain interannual variability in atmospheric
methanol mole fractions. Doing so will also increase the likelihood of
observing extremes in methanol exchange caused by weather extremes and/or
biotic interference. For example, laboratory leaf-scale work has shown that
herbivory by insects may elicit large methanol emissions
(Von Dahl et al., 2006). However, at present we largely
lack the data necessary for devising and testing models simulating
herbivory-related perturbations of the methanol exchange at ecosystem scale
(Arneth and Niinemets, 2010).
Building upon the experiences gathered in the FLUXNET project
(Baldocchi et al., 2001), the BVOC flux community also should
make a concerted effort towards standardising flux data acquisition and
processing so that data are more readily comparable and models can be
calibrated and validated based on harmonised data sets. Finally, we
emphasise that micrometeorological methanol flux measurements are important,
but not sufficient, for a better understanding and quantification of the
global land methanol exchange. To this end, a multi-disciplinary and
multi-scale approach which bridges detailed process studies at the
molecular level (e.g. Abanda-Nkpwatt et al., 2006; Oikawa et al., 2011a;
Oikawa et al., 2011b) and remote sensing at the global scale
(e.g. Stavrakou et al., 2011) is required.
The Supplement related to this article is available online at doi:10.5194/acp-15-1-2015-supplement.
Acknowledgements
The work presented in this study received financial support from the
following sources: the Austrian National Science Fund (FWF; P19849-B16,
P23267-B16 and L518-N20), the Tyrolean Science Fund (TWF; Uni-404/486 and
Uni-404/1083), the EU Industry-Academia Partnerships and Pathways Programme
(IAPP; 218065), the Belgian Science Policy Office (BELSPO) (SD/TE/03A)
through the IMPECVOC (Impact of Phenology and Environmental Conditions on
BVOC Emissions from Forest Ecosystems) research project, the Fundación
Ramón Areces through a postdoctoral fellowship awarded to Roger Seco,
and the PNNL Laboratory Directed Research and Development programme support
for Alex Guenther. Flux measurements at Harvard Forest are a component of
the Harvard Forest Long-term Ecological Research (LTER) site and are
additionally supported by the Office of Science (BER), US Department of
Energy. The authors would like to acknowledge the support of this work by
Martin Graus, Markus Müller, Taina Ruuskanen, Ralf Schnitzhofer, Mario
Walser, Alfred Unterberger, and Kevin P.
Hosman.Edited by: J. Rinne
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