Introduction
Temperatures at high northern latitudes have been observed to be increasing
at a rate of twice the global average over the past two decades (Forster and
Ramaswamy, 2007). It has been suggested that this rise will continue
(Parmentier et al., 2013). This is likely to have significant consequences
for natural greenhouse gas emissions in the region, which contain
potentially large sources that are known to be highly sensitive to changes
in temperature, such as the boreal wetlands and the reservoirs of carbon
that are sequestered in permafrost and as methane hydrates (Smith et al.,
2004; Zimov et al., 2006a, b; Ping et al., 2008). As well
as contributing to radiative forcing, such emissions have the potential to
significantly perturb atmospheric chemistry, including oxidant capacity
(Isaksen et al., 2011). Palaeo-records indicate that strong positive
feedbacks exist between climate and greenhouse gas emissions in the region,
whereby warming causes enhanced emissions that in turn lead to further
warming (Walter et al., 2007; Nisbet and Chappellaz, 2009). Recent studies
have already reported newly identified or growing CH4 emissions from
some of these carbon reservoirs (Westbrook et al., 2009; Shakhova et al.,
2010; Kort et al., 2012; Anthony et al., 2012).
Wetland regions are the single largest source of atmospheric CH4,
accounting for approximately a third of total global CH4 emissions
equivalent to 142–208 Tg yr-1 (Kirschke et al., 2013), for which
Boreal and Arctic regions make a significant contribution (approximately
25 %; Smith et al., 2004; Zhuang et al., 2006). Much of the remainder is
currently suggested to originate from tropical wetlands (Bridgham et al.,
2013). Biogenic CH4 is produced in anoxic soils through the
decomposition of organic matter by methanogenic bacteria (Bridgham et al.,
2013). Emission rates by this process are dependent on soil moisture,
temperature and the availability of organic matter (Pelletier et al., 2007;
Strom and Christensen, 2007). Much of this CH4 does not reach the
atmosphere due to consumption that occurs in oxic soil regions by
methanotrophic bacteria (O'Connor et al., 2010; Parmentier et al., 2011). As
a result of these competing environment-dependant factors, emissions show a
large degree of spatial and temporal variability (Zhuang et al., 2006;
Pickett-Heaps et al., 2011).
CO2 exchange between the surface and the atmosphere in these regions
displays a similar degree of complexity. It is governed by the interplay
between release of CO2 through respiration and uptake by
photosynthesis. At high latitudes, as temperatures rise and the ground thaw
reaches greater soil depths, more organic carbon becomes available for
decomposition, potentially liberating large carbon reservoirs to the
atmosphere (Oelke et al., 2004). However, a simultaneous increase in plant
production and biomass may also occur during the growing season. Rapid
warming at high latitudes is increasing both plant growth and soil
decomposition, making it difficult to determine the overall impact a warmer
climate has on the total net carbon budget of Arctic and Boreal regions
(Zhuang et al., 2006; Davidson and Janssens, 2006; Sitch et al., 2007;
Schuur et al., 2009).
Previously, Arctic wetland emissions have been determined by up-scaling
surface chamber and eddy covariance flux measurements (Pelletier et al.,
2007) or by process-based and inverse models (Petrescu et al. 2010;
Pickett-Heaps et al., 2011; Wania et al., 2010; Bousquet et al., 2011).
However, due to the heterogeneous nature of wetlands, uncertainties exist
when multiple studies are synthesised to determine net emissions for large
areas (Christensen et al., 2007). Currently, there is a lack of flux
measurements at the same spatial scale as the resolution of global land
surface models (typically 0.5∘), which has been identified as a
key reason why models are not able to confidently simulate the wetland
CH4 flux (Melton et al., 2013). Airborne measurements have been shown
to be a powerful tool in reducing these uncertainties (Desjardins et al.,
1997; Miller et al., 2007; Peischl et al., 2012), where the greater spatial
coverage afforded may be an advantage over ground-based measurements under
appropriate conditions, especially when testing the scalability of fluxes
derived for local scales across wider areas.
Ground-based CH4 flux measurements have now been made for multiple
years in several wetland locations within northern Fennoscandia, these
include the Stordalen wetlands, in sub-arctic Sweden (68.33∘ N,
19.05∘ E; Christensen et al., 2012), and both Kaamanen
(69.1∘ N, 27.2∘ E; Maanavilja et al., 2011) and
Lompolojänkkä (68.0∘ N, 24.2∘ E; Aurela et al.,
2009) in Finland. Stordalen summer CH4 emissions have been reported as
4.7 mg CH4 m-2 h-1 (2004–2006) and 6.2 ± 2.6 mg
CH4 m-2 h-1 (2006 and 2007) (Petrescu et al., 2008;
Jackowicz-Korczynski et al., 2010). Mean July CO2 fluxes are
-1152 mg CO2 m-2 h-1, -576 mg CO2 m-2 h-1 and
-504 mg CO2 m-2 h-1 for Lompolojänkkä, Kaamanen and
Siikaneva, respectively (Aurela et al., 2009).
This paper uses in situ measurements collected on board the UK's Facility
for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft to
quantify greenhouse gas net fluxes from the Fennoscandian wetlands during a
dedicated case study. A simple boundary layer mass budget approach
(described in Sect. 3.1) is employed to derive regional fluxes using the
aircraft observations under pseudo-stationary boundary layer flow
assumptions (Sect. 4.1). This estimate is then compared to smaller footprint
ground-based eddy covariance and chamber measurements within the aircraft's
sampling footprint that were made over much of summer 2012 to address
scalability and spatio-temporal heterogeneity (Sect. 4.3). Finally, the
regional-scale aircraft-derived flux is used to assess the skill of two land
surface models (Sect. 4.4).
Methods
The measurements reported in this paper were collected as part of the MAMM
(Methane and other greenhouse gases in the
Arctic: Measurements, process studies and Modelling,
http://arp.arctic.ac.uk/projects/methane-and-other-greenhouse-gases-arctic-measurem/)
project. The aim of the MAMM project is to quantify greenhouse gas fluxes at
high northern latitudes using a combination of measurement, process and
modelling studies. As part of this project, sorties were performed from
Kiruna, Sweden, by the FAAM BAe-146 research aircraft during July 2012 (six
flights), August 2013 (nine flights), September 2013 (seven flights) and July 2014 (eight flights).
This flight has been chosen for this case study due to the favourable
meteorological and flight conditions for applying a mass budget approach
(Sect. 3.1) to derive fluxes (Sect. 4.1). The MAMM campaign is ongoing at
the time of writing and we anticipate that a seasonal analysis will be
addressed in the future.
FAAM BAe-146 research aircraft
CO2 and CH4 dry air mole fractions were determined through
cavity-enhanced absorption spectroscopy on board the FAAM BAe-146 (Model
RMT-200, Los Gatos Research Inc., USA). In-flight CO2 uncertainty was
calculated as ± 0.17 ppm; typical 1 Hz precision is ± 0.70 ppm
(all precisions are 1σ). CH4 uncertainty is calculated at
± 1.31 ppb; 1 Hz precision is ± 2.37 ppb (for a detailed
description of this system see O'Shea et al., 2013b). Separate measurements
of CO2 and CH4 were made by analysing whole-air samples. These
were collected in stainless steel flasks (for a description see Lewis et al., 2013), and analysed post-flight in the laboratory using cavity-ring down
spectroscopy (Model G1301, Picarro Inc., USA). Uncertainty is estimated at
± 0.5 ppb and ± 0.1 ppm for CH4 and CO2, respectively.
During the MAMM flights the mean bias of the whole-air samples (400 samples)
relative to the in situ measurements was 0.16 (±0.46 at 1σ) ppm for CO2 and -0.5 (±4.6 at 1σ) ppb for CH4.
Flask samples were also analysed for δ13C isotopic ratios of
CO2 and CH4, using continuous-flow gas
chromatography / isotope-ratio mass spectrometry, with a precision of 0.1 ‰ (Fisher et al., 2006).
A range of other chemical, tracer and thermodynamic parameters were measured
simultaneously on board the FAAM BAe-146; these include pressure,
temperature and the 3-D wind vector with an estimated uncertainty of 0.3 hPa,
0.1 K and 0.2 m s-1, respectively (Allen et al., 2011). Measurements of
carbon monoxide (CO) and hydrogen cyanide (HCN) are used here to identify
air masses that have been strongly influenced by either biomass burning or
anthropogenic activity using an enhancement-over-background-threshold
technique described by O'Shea et al. (2013a), as such air masses would bias
the calculation of the biogenic flux. Mole fractions of CO were determined
through vacuum ultraviolet fast-fluorescence spectrometry, with an
uncertainty of 2 % (AL5002, Aerolaser GmbH, Germany; Gerbig et al., 1999).
In situ HCN measurements were made using a chemical ionisation mass spectrometer,
with an uncertainty of 10 % (Le Breton et al., 2013).
Surface measurements
CH4 and CO2 eddy covariance and chamber flux measurements were
made in Sodankylä, Finland, from 1 July 2012 to 15 August 2012. The eddy
covariance system used included a USA-1 (METEK GmbH, Germany) three-axis
sonic anemometer/thermometer, a RMT-200 (Los Gatos Research, Inc., USA)
CH4 analyzer and a LI-7200 (Li-Cor, Inc., USA) CO2/H2O gas
analyzer. The measurement height was 6 m a.g.l. (above ground level). The length of
the inlet tubes for both gases was 8 m for CH4 and 1 m for CO2,
with flow rates of 15 and 20 L min-1, respectively. For more details of
the eddy covariance measurement system, see Aurela et al. (2009).
Half-hour flux values were calculated using standard eddy covariance
methods. The original 10 Hz data were block-averaged, and a double rotation
of the coordinate system was performed (McMillen, 1988). The time lag
between the anemometer and gas analyzer signals, resulting from the
transport through the inlet tube, was taken into account in the on-line
calculations. An air density correction related to the latent heat fluxes
was conducted according to Webb et al. (1980). Corrections for the
systematic high-frequency flux loss owing to the imperfect properties and
set-up of the sensors (i.e. insufficient response time, sensor separation,
damping of the signal in the tubing and averaging over the measurement
paths) were carried out off-line using transfer functions with
empirically determined time constants (Aubinet et al., 2000). All data with
wind directions from sector 240 to 290∘ were discarded due to
insufficient fetch. Some data were also discarded due to instrument failures
during weak turbulence (friction velocity < 0.1 m s-1).
CO2 fluxes during the period 14 July 2012 to 1 August 2012 are missing
due to instrumental problems.
Fluxes of CH4 were also measured using the static chamber method, as
follows. These were positioned to cover a range of vegetation types and
water saturations that can be broadly classified into either those situated
in wetlands (39 chambers) and those in the forest (21 chambers). Shallow
frames were installed the day before first sampling to a depth of
∼ 10 cm, and remained in situ for the duration of the study period;
fluxes calculated from the first sampling were not significantly different
from subsequent sampling occasions suggesting that the short settling period
after frame installation had no effect. Fluxes were measured at
∼ 2-day intervals between 12 July and 2 August. For
measurements, chamber lids were attached to the frames and internal air
samples were collected into vials four times over a 45 min incubation period.
Samples were analyzed by gas chromatography and fluxes calculated using
GCFlux, version 2. Reported CH4 fluxes correlate to the best-fit model
for individual chambers (either linear or asymptotic) (for a detailed
description of this approach see Levy et al., 2011, 2012). Fluxes of
N2O and CO2 were also measured by the static chamber method.
However, since static-chamber-measured CO2 fluxes are only a measure of
the ecosystem respiration inside the chambers and do not include uptake by
all plants, they cannot be directly compared with the aircraft-derived flux
estimates; this will be presented in a separate study.
Methane emission models
In Sect. 4.4, we assess the skill of two land surface models: the Joint UK
Land Earth Simulator (JULES; Best et al., 2011; Clark et al., 2011) and
HYBRID8 (Friend, 2010). The JULES model contains a CH4 wetland
emission parameterisation, developed and tested by Gedney et al. (2004) for
use at large spatial scales. The wetland parameterisation is coupled to the
large-scale hydrology scheme of Gedney and Cox (2003), which predicts the
distribution of sub-grid-scale water table depth and wetland fraction
(fw) from the overall soil moisture content and the sub-grid-scale
topography using the approach of Beven and Kirby (1979). The CH4 flux
from wetlands, Fw(CH4), is parameterised as a function of
temperature, wetland fraction and substrate availability, as follows:
Fw(CH4)=fwk(CH4)CsQ10(Tsoil)(Tsoil-T0)/10,
where Tsoil is the soil temperature (in K) averaged over the top 10 cm
and k(CH4) is a global constant which is calibrated to give the required
global CH4 flux. The Q10 is a temperature coefficient to account
for the temperature dependency of the flux. Soil carbon content (Cs)
was used for substrate availability. The default parameter values chosen
were k(CH4)=7.4×10-12 kg m-2 s-1, T0=273.15 K
and Q10(T0)=3.7 (Clark et al., 2011).
The surface physics of the HYBRID8 model are based on the NASA-Goddard Institute for Space Studies (GISS) ModelE
land surface component (Schmidt et al., 2006). This model contains a canopy
representation that has a mechanistic canopy conductance response to various
environmental factors (light, temperature, humidity, CO2 and canopy
height), which has been tested and calibrated using eddy covariance flux
measurements (Friend and Kiang, 2005). Recently, a TOPMODEL (a TOPography based hydrological MODEL) approach has
been implemented to model the hydrology following Niu et al. (2005). Very
similar to the implementation in the JULES land surface model, the TOPMODEL
hydrological module in HYBRID8 uses a topographical index and interactively
computes the wetland fraction in each grid box (fw), and only saturated
soils (determined by fw) contribute to CH4 emissions. The fluxes of
CH4 are also parameterised in a very similar way in HYBRID8 as in
JULES. The governing equation for CH4 production at depth z is
Pw(CH4)=k(CH4)Fps(z)Csom(z)Q10(T(z)-T0)/10,
where k(CH4) is the baseline production rate, Fps(z) is the total pore
space fraction in a specific layer (a function of soil texture),
Csom(z) is the soil organic matter at the depth z, and T(z) is the soil
temperature. For this study, the following representative parameters were
chosen: k(CH4)=1.3×10-11 kg m-2 s-1, Q10=3 and T0=22 ∘C. The CH4 produced is then
transported to adjacent layers via diffusivity, eventually reaching the
atmosphere.
Experiment and analysis methodology
The following section describes the 22 July 2012 flight that was used to
determine regional-scale fluxes using a mass balance approach.
Aircraft mass balance
Mass budget approaches have been employed on several occasions to derive
regional-scale (> 1 km) fluxes of trace species (White et al.,
1976; Gallagher et al., 1994; Choularton et al., 1995; Wratt et al., 2001;
Mays et al., 2009; O'Shea et al., 2014). Observations are typically made in
a background location and then down-wind of a source region to determine the
net enhancement due to this region. The mass budget approach used in this
study is most applicable when measurements are collected parallel to the
prevailing wind vector. If it can be assumed that the non-reactive tracer
species, S, is well mixed from the surface up to the top of the planetary boundary layer (PBL),
Z1, and that entrainment into (and detrainment from) the PBL can be
neglected, then the net flux of S can be determined by
flux=U‾cosϕΔSΔx∫0Z1ndz,
where U‾ (m s-1) is the mean wind speed, and n
(molecules m-3) is the atmospheric number density, which is integrated from the
surface to the top of the boundary layer (m). The ΔS (molecules molecules -1) term
is the enhancement in species S along the transect x
of increment Δx(m). The angle ϕ is between the mean wind
vector and transect x; see Hiller et al. (2014) for further details on the
origin of Eq. (3). In addition to a well-mixed PBL, several other requirements
regarding the PBL structure have to be met for this simple model to be
applicable. First, a single wind vector needs to be assumed. Changes in
either the wind speed or direction will add uncertainty in the calculated
flux. Second, it is assumed that any surface emission is immediately mixed
throughout the PBL column. Third, the PBL height should not vary
significantly while measurements are collected and a strong capping
inversion is needed to prevent significant exchange with the free
troposphere. We examine the uncertainty resulting from each of these
assumptions in Sect. 4.
Flight sampling and study area
On the 22 July 2012 the FAAM BAe-146 surveyed the northern Fennoscandian
landscape in order to quantify emissions from the wetlands in the region.
Four large transects (∼ 340 km) were performed within the
PBL: two east–west (east to west transect 10:42
to 11:46 GMT (Greenwich mean time); west to east 15:26 to 16:04 GMT) and two north–south. Figure 1a shows
the geographic coverage of this flight along with the location of
waypoints: Kiruna (67.9∘ N, 20.2∘ E), Sodankylä
(67.4∘ N, 26.6∘ E) and Kaamanen (69.1∘ N,
27.2∘ E). Figure 2a and b show observations of CH4 and
CO2 collected during longitudinal transects parallel to the prevailing
wind. Figure 2c shows the FAAM BAe-146's altitude when these measurements
were collected, which was varied during transects in order to characterise
both the vertical and horizontal gradients of CH4 and CO2.
To show the prevalent vegetation and land use types within the region, the
flight track is also shown overlaying the land classification (Fig. 1c;
CORINE land cover 2006;
http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster).
As seen, the sampling domain is largely characterised by coniferous forests
(33 %; dark green Fig. 1c), peat bogs (23 %; blue Fig. 1c) and mixed
forests (16 %; green Fig. 1c).
Meteorology overview
Meteorological conditions on the 22 July 2012 were characterised by low
pressure centred over the Barents Sea to the north of the FAAM BAe-146's
sampling domain in this case study. This resulted in a consistent westerly
airflow across northern Scandinavia and shallow cumulus clouds
(∼ 2/8 cover). Surface temperature was ∼ 17 ∘C, as confirmed by infrared radiometers on the aircraft. The
synoptic airflow is illustrated in Fig. 1b, which shows HYSPLIT (Hybrid
Single Particle Lagrangian Integrated Trajectory Model; described by Draxler
and Rolph, 2003) back trajectories calculated along the FAAM BAe-146's
flight track when it was within the PBL (below 1500 m altitude). The
majority of the air mass sampled by the FAAM BAe-146 on 22 July 2012 spent
the previous 5 days at a low level (below 2000 m) within the Arctic region and
over the Arctic Ocean. During the FAAM flight, in situ measurements also showed
winds to be consistently westerly, the mean wind bearing and speed within
the boundary layer was 260 (37 at 1σ)∘ and 6 (2 at
1σ) m s-1, respectively.
(a) FAAM BAe-146 flight track for flight B720 (22 July 2012).
Observations of CH4 in the PBL are coloured according to the legend.
Black diamonds mark Kiruna, Sodankylä and Kaamanen. (b) Five-day HYSPLIT
back trajectories that were started every minute along the FAAM BAe-146's
flight track when it was within the PBL. (c) Flight track where the surface
is coloured using the land use type (CORINE land cover 2006). Numbers
correspond to land types given in Table 1.
Deep vertical profiles of potential temperature (derived here from in situ
measurements of pressure and temperature) from the FAAM BAe-146, performed
over Sodankylä (Fig. 3) at 01:00 and 15:00 GMT and from the two
dropsondes released, show a clear capping inversion was present over the
area during the flight (Fig. 3). Over the run in question, the surface
topography was very flat, 400–500 m a.m.s.l. (above mean sea level) and the infrared
emissivity varied little (∼ 0.98; see Allen et al., 2014).
Therefore, in the absence of significant synoptic meteorological changes,
which were not observed in reanalyses for the area, it is expected that the
PBL depth was relatively uniform over the time and scale of the sampling in
question. This is further examined in Sect. 4.1.
(a) CH4 and (b) CO2 observations along a flight
transect, which was aligned with the prevailing wind direction. The origin
is 20∘ E, 68∘ N and the transect extends in an eastward
direction. The gradients observed in both species were used to determine a
net emission flux for the region using Eq. (3). (c) The aircraft's altitude when
measurements shown in (b) and (c) were collected.
(a) Ascending (01:00 GMT) and (b) descending (15:00 GMT) potential
temperature profiles performed over Sodankylä during flight B720, used
to determine the boundary layer height as described in the text.
Results and discussion
On the 22 July 2012, consistent linear gradients were observed in both
CH4 and CO2 along the longitudinal transects (Fig. 1),
performed parallel to the prevailing wind. CH4 was found to be
approximately 20 ppb higher at the eastern boundary compared to the western,
while CO2 decreased by several ppm over the same interval. No clear
latitudinal trends were observed in either species. However, a region of
significantly enhanced CH4 (up to 20 ppb) was observed to the north of
Sodankylä (Fig. 1), a region with a slightly higher proportion of
wetlands (29 %).
With a mean PBL mole fraction of 89 ppb for CO and 26 ppt for HCN, both
species remained at mole fractions throughout the flight that are
representative of a typical background for the summer at these latitudes
(Vay et al., 2011; O'Shea et al., 2013a). This indicates that any biomass
burning and anthropogenic emissions within the region were small and
well-mixed when sampled by the FAAM BAe-146. To identify the source of the
observed CH4 enhancements we use the measured δ13C
isotopic ratios and a Keeling plot methodology (Pataki et al., 2003). Figure 4 shows a Keeling
plot for all PBL measurements of δ13C–CH4 during the flight on the 22 July 2012 (B720). The
vertical intercept represents the isotopic ratio of the source of the
enhancements. A source of -70.2 ± 3.0 ‰ as seen
here is consistent with wetland CH4 emissions (-71 to -59 ‰; Fisher et al., 2011; Sriskantharajah et al., 2012).
Regional-scale fluxes derived using aircraft observations
Land classification key corresponding to Fig. 1c from CORINE land cover 2006. Also included is the proportion of the aircraft's footprint that
each classification accounted for during the B720 E–W transects.
Number
Land type
Proportion of footprint
during E–W transect (%)
2
Discontinuous urban fabric
0.1
3
Industrial or commercial units
0.0
4
Road and rail networks and associated land
0.0
6
Airports
0.0
7
Mineral extraction sites
0.0
8
Dump sites
0.0
10
Green urban areas
0.0
11
Sport and leisure facilities
0.0
12
Non-irrigated arable land
0.0
18
Pastures
0.1
20
Complex cultivation patterns
0.0
21
Land principally occupied by agriculture
0.1
with significant areas of natural vegetation
23
Broad-leaved forest
10.4
24
Coniferous forest
24.4
25
Mixed forest
16.3
26
Natural grasslands
0.1
27
Moors and heathland
8.3
29
Transitional woodland–shrub
13.8
31
Bare rocks
0.1
32
Sparsely vegetated areas
2.3
33
Burnt areas
0.0
34
Glaciers and perpetual snow
0.0
35
Inland marshes
0.1
36
Peat bogs
19.6
40
Water courses
0.5
41
Water bodies
3.7
Keeling plot showing PBL measurements of δ13C–CH4 during flight on the 22 July 2012 (B720). The intercept
of -70.2 ± 3.0 ‰ is representative of a wetland
source of CH4.
In order to perform a mass budget flux calculation (Eq. 3), we use the fact
that the east–west transect performed during the 22 July 2012 flight was
aligned nearly parallel with the prevailing wind bearing, which was
258∘ during the transects. This gradient (ΔS/Δx)
is determined here by first averaging the data to 500 m intervals
(equivalent to around 4 s of sampling time) along x, before performing an
orthogonal distance regression (Fig. 2a and b). The regression slope is
weighted by the quadrature addition of the analytical uncertainty and the
vertical variability of S throughout the PBL (Fig. 3). The 1σ of the
regression fit is used in the uncertainty propagation to derive a
representative and comprehensive uncertainty on the calculated flux.
In situ measurements on board the FAAM BAe-146 are used here to determine the wind
direction and speed. The transect, x, should ideally be aligned parallel to
the wind vector. However, we note that there was a 12∘ offset
between the mean wind vector and transect x (ϕ, Eq. 3), while the wind
also showed some variation about the mean (24∘ at 1σ). It
then has to be assumed that mole fractions perpendicular to the wind vector
are constant. The mean wind speed was found to be 6 (2 at 1σ) m s-1 for
the longitudinal transects. The 1σ of the wind
direction and speed is used in the uncertainty propagation.
Based on the observed changing vertical gradient in potential temperature, a
PBL height of 1740 m a.g.l. is determined here from both
ascending and descending vertical profiles by the FAAM BAe-146, which show
strong mixing (constant potential temperature profile) between the ground
and the top of the PBL. In addition, above the PBL, both CO2 and
CH4 show abrupt changes in their mole fraction and the vertical wind
speed becomes less variable (variance in the wind speed above the boundary
layer is typically less than 0.2 m2 s-2), supporting the
assumption that entrainment into and out of the boundary layer is relatively
small and so can be neglected for this exercise.
In order to estimate the uncertainty in the determination of the PBL height
we use a simple PBL growth model to estimate the change that could
reasonably be expected in the intervening period between the nearest
vertical profile and the completion of the longitudinal transect used in the
flux calculation (approximately 1 h). The change in PBL height, Δz, over the time period Δt can be estimated using Eq. (4) (Stull, 1988; Cambaliza et al.,
2014):
Δz=2Δtw′θ′‾γ1/2,
where γ is the adiabatic lapse rate and w′θ′‾ is
the surface sensible heat flux, which was measured in Sodankylä. Using
Eq. (4) changes in the PBL depth are estimated to be of the order of 200 m within
1 h, which we use as an estimate of the uncertainty in the PBL height
during the transects.
Within the boundary layer some structure exists in the altitude profile. The
CH4 standard deviation was 4.5 ppb for the ascending profile and 1.7 ppb for the
descending profile, while for CO2 this was 1 ppm for both
the ascending and descending profiles. Some of this variability is likely to
be due to the fact that these profiles are recorded slant-wise in the
horizontal and therefore reflect both variability in vertical mixing and the
existing horizontal gradient. This variability is included in the error
propagation, as mentioned above.
As described in Sect. 3.1, Eq. (3) assumes that emissions are immediately mixed
throughout the PBL column. To estimate the PBL turnover time we calculate
the Deardoff velocity scale,w∗, which corresponds to the mean velocity
of thermals (Stull, 1988):
w∗=gZ1w′θV′‾θV‾1/3,
where g is the acceleration due to gravity, w′θV′‾
is the surface buoyancy flux and θV is the virtual potential
temperature. The minimum time period for an air mass to mix from the surface
to the top of the PBL is calculated to be 19 min. Complete mixing should
occur within approximately three time periods (Karion et al., 2013), in this
case 57 min. This is significantly shorter than the time taken for air
to advect across the transect (up to ∼ 16 h), suggesting
that the assumption of instantaneous vertical mixing is reasonable.
The calculated fluxes are found to be 1.2 ± 0.5 mg CH4 h-1 m-2
and -350 ± 143 mg CO2 h-1 (Table 2 and Fig. 5).
The uncertainty in the total flux is determined by propagating the uncertainties associated with the individual
terms in Eq. (3); these include the uncertainty in the observed (fitted) spatial mole fraction gradient,
known variability in the wind, and boundary layer mixing height, as identified above.
Similar to previous studies (e.g. Ryerson et al., 1998),
the largest known source of uncertainty was found to be the assumption of a
single wind vector for the whole of the transect x. Within the
uncertainties, the fluxes are in agreement whether separately derived
eastward, westward, or combined transects are used in the calculation. The
repeatability of this measured gradient further indicates that both species
were vertically well mixed since the transects were performed at slightly
different altitudes, as shown in Fig. 2c (eastward mean = 507 m,
range = 70 to 1287 m; westward mean =717 m, range =103 to 1382 m). The fluxes
calculated using the 11 whole-air sample measurements, collected along the
east–west transect, are also in excellent agreement (see Table 2) with that
from the continuous in situ measurements. However, in the case of CO2
this is with a large uncertainty.
A comparison between different techniques used to determine
fluxes. The box extents define the 25th and 75th percentiles, and whiskers
are the 10th and 90th percentiles. Note: the eddy covariance percentiles are
for daytime (06:00 to 18:00 GMT) only. Forest and wetland chamber fluxes
represent summer seasonal statistics for 60 chamber measurements (21 in
forest regions and 39 in wetland regions). The scaled chamber (black circle)
is determined by averaging the wetland and forest chamber fluxes as
described in Sect. 4.3. The FAAM BAe-146 and scaled chamber error bar
shows the 1σ uncertainty as described in Sect. 4.1.
Dispersion modelling
The flux derived from the aircraft measurements has also been tested using
forward model runs with the UK Met Office's Numerical Atmospheric-dispersion
Modelling environment (NAME) to diagnose whether the calculated ground flux
might be expected to translate into the observed enhancements seen in
measurements observed aloft when advected. NAME is a 3-D Lagrangian particle
dispersion model (Ryall and Maryon, 1998; Ryall et al., 1998), which is run
here using the UK Met Office's Unified Model meteorological fields (Cullen,
1993). A flux of 1.2 mg CH4 h-1 m-2 was emitted from the
ground in the region bounded by 20 to 28∘ E and
67 to 69.5∘ N continuously for the period from 00:00 GMT on 20 July 2012 to 17:00 GMT on 22 July 2012, and the model was run
forwards to disperse the CH4 through the modelled atmosphere. The
particle motions are calculated based on the large-scale winds, wind meander
and sub-grid-scale stochastic turbulence.
Mean fluxes determined using the FAAM BAe-146, chamber and eddy
covariance techniques. All uncertainties given are as one standard deviation
(1σ). Chamber measurements are separated into the geometric mean of
all seasonally averaged measurements and only those from the 22 July 2012. A
weighted mean of the wetland and forest chamber fluxes is calculated using
the number of occurrences of each land type within the east–west transect
(Fig. 1b).
Flux (mg h-1 m-2)
CH4
CO2
FAAM BAe-146
Eastward transect
1.1 ± 0.6
-375 ± 202
Westward transect
1.6 ± 0.5
-357 ± 135
Both transects
1.2 ± 0.5
-350 ± 143
Whole-air samples
1.0 ± 0.6
-315 ± 368
Eddy covariance
Summer
4.5 ± 1.4
-135 ± 344
Summer day
4.5 ± 1.2
-309 ± 306
Summer night
4.4 ± 1.6
71 ± 264
22 July 2012
4.5 ± 0.9
22 July 2012 day
4.9 ± 0.6
22 July 2012 night
4.4 ± 1.0
Chamber
Wetland summer
4.5 ± 3.7
Wetland 22 July 2012
5.6 ± 5.6
Forest summer
0.05 ± 0.07
Forest 22 July 2012
-0.07 ± 0.05
Weighted average
1.3 ± 1.0
Weighted average 22 July 2012
1.5 ± 1.6
Figure 6 shows a cross section of the atmosphere that is co-incident with
flight B720. The contours show the 1 h average mixing ratio of CH4
averaged over 67.75 to 68.00∘ N (upper panel for 11:00 GMT and lower panel for
16:00 GMT). This shows the modelled increment of CH4 that comes from
the local region, based on the flux calculated by the aircraft observations.
At 11:00 GMT (the time of the eastward transect), the increment in CH4
at the eastern end of the flight is approximately 15 to 20 ppb higher than
the western part of the transect. By 16:00 GMT, the difference in the model
has reduced to 12–15 ppb. This is because the model PBL is well mixed,
and so gradients within it decline as the day progresses and the PBL top
rises. It can be seen in Fig. 6a that the model PBL height is about 2200 m
at 11:00 GMT (corresponding to our eastward transect) and has increased to
about 3000 m by 16:00 GMT (the time of the westward transect). The higher
late afternoon modelled PBL would act to dilute the CH4, which can be
seen in the lower modelled mixing ratio enhancements at 16:00 GMT (Fig. 6b).
However, this dilution was not observed in the late afternoon aircraft
measurements, which also showed a much lower PBL height of 1740 m (Fig. 3),
similar to that observed earlier in the day.
Despite this, the increment to CH4 is comparable for the 11:00 case
(approximately 20 ppb in the observations, and approximately 15–20 ppb in
the dispersion model). The reason for the difference in PBL height between
the model and measurement cannot currently be explained and is beyond the
scope of this study; however, these results confirm that observed
enhancements can be reasonably represented by dispersion modelling when
treating the land as a constant source equal to that derived here, for a PBL
mixing height of ∼ 2200 m (as modelled for the 11:00 GMT
transect).
Dispersion model results from NAME for the mixing ratio of
CH4 originating from the local wetlands in a cross section of the
atmosphere averaged over 67.75 to 68.00∘ N and 1 h surrounding
11:00 GMT on 22 July 2012. The local wetland CH4 source was defined as
a 1.2 mg CH4 h-1 m-2 source emitted from the ground between
20 and 28∘ E and between 67 and 69.5∘ N. Figure 6b shows
the same but for a 1 h average surrounding 16:00 GMT on the same day.
Ground-based flux measurements
In this section, we compare the aircraft-derived flux with seasonally
averaged surface measurements to examine scalability and potential sources
of bias (e.g. spatial heterogeneity). The ground-based measurements during
the MAMM campaign comprised both chamber and eddy covariance flux
measurements, as described in Sect. 2.2. A comparison between these two
techniques and the aircraft-determined flux is complicated by the
differences in their respective footprints. Chambers are the smallest scale
(< 1 m) and are specific to a single land type. While eddy
covariance fluxes are typically representative of 100 to 1000 m and as a
result may average the flux across several land types. The aircraft
represents a regional flux, in this case > 300 km, which
encompasses several ecosystems with air mixed over all.
During the MAMM field campaign, 60 chambers were used to determine CH4
fluxes. Fluxes for the entire measurement period, as well as those for just
22 July 2012, are given in Table 2 and Fig. 5. Forested regions are found to
have negligible net flux, varying between a small source or sink (Ridgwell
et al., 1999), while the wetlands show a wide range of net emissions, which
could be expected since the chambers covered a wide range of soil moisture
saturations.
The aircraft-derived CH4 flux is within the wide range spanned by the
forest and wetland chamber measurements (-0.09 to 11.6 mg CH4 h-1 m-2). This might be expected as both ecosystems are present within the
aircraft's footprint (Fig. 1). For a more direct comparison we perform a
weighted average of the two classes of chamber fluxes. This was done by
first determining the aircraft's surface footprint using the NAME model. The
CORINE land cover map was then used to identify the prevalence of the each
land classification within this footprint (Table 1). Each CORINE
classification was grouped as either a forest (coniferous forest, mixed
forest, transitional woodland, broad-leaved forest) or a wetland (peat bog,
moor and heathland) land type. Using this methodology, during the 22 July
flight's east–west transect, 28 % of the land footprint was classified as
wetland and 65 % was classified as forest. These proportions were then
used to weight the averaging of the two chamber flux categories. The result
of this is 1.3 ± 1.0 mg CH4 h-1 m-2 (mean ± standard deviation) using
the summer mean chambers and 1.5 ± 1.6 mg CH4 h-1 m-2 if just the 22 July 2012 measurements are
used. Though poorly constrained, these are both in good agreement with the
aircraft-derived flux, which is only 0.1 mg CH4 h-1 m-2
and 0.3 mg CH4 h-1 m-2 lower, respectively (Fig. 5).
Uncertainties exist in this comparison since the partitioning is quite broad
and in the assumption of a zero flux for 7 % of the land area. A more
sophisticated comparison would assign measured fluxes for each 2006 CORINE land cover. Nevertheless this simple approach provides a useful validation
of the airborne calculation.
The CH4 and CO2 eddy covariance flux measurements were calculated
for the Sodankylä wetland from 1 July 2012 to 15 August 2012 (Table 2
and Figs. 7–8). CH4 chamber fluxes show a wider range than the eddy
covariance fluxes, which could be expected since they covered the dryer and
wetter parts of the wetland, while the eddy covariance method spatially
integrates these regions and as a consequence is within this range. CH4 fluxes do not
show large variation over diurnal (Fig. 7) or weekly
timescales (Fig. 8). However, CO2 was emitted for several hours around
midnight, while uptake occurred during the day. The mean daytime (06:00 to
18:00 GMT) eddy covariance CO2 measurement of -309 (1σ=306) mg CO2 h-1 m-2 is
only 41 mg CO2 h-1 m-2 higher than the aircraft-derived flux, well within the measurement
uncertainty.
However, the mean daytime eddy covariance CH4 flux of 4.5 ± 1.2 mg CH4 h-1 m-2 for
the summer period is a factor of 4 larger than the aircraft. This is comparable with some other previous
studies in wetlands such as 4.7 mg CH4 m-2 h-1 (2004 to
2006) and 6.2 ± 2.6 mg CH4 m-2 h-1 (2006 and 2007) for
Stordalen (Petrescu et al., 2008; Jackowicz-Korczynski et al., 2010).
Similar to the chamber measurements, this may be because the eddy covariance
footprint is more specific to a single land type than the aircraft in this
instance. To test this, the same scaling was repeated using the CORINE land cover
classification but this time using the Sodankylä wetland eddy covariance
flux instead of that from the wetland chambers, which resulted in a flux of
1.3 ± 0.3 mg CH4 h-1 m-2. This then displays
similarly good agreement with the aircraft-derived flux.
CH4 and CO2 hourly fluxes at Sodankylä wetland site
between 1 July and 15 August 2012 determined using the eddy covariance
technique. CH4 diurnal variation is noted to be small. Net CO2
uptake occurs during the day, with net emission during the night.
Daytime (06:00–18:00) CH4 and CO2 fluxes at Sodankylä
wetland site between 1 July and 15 August 2012 determined using the eddy
covariance technique. Note: CO2 fluxes are not shown for the period 14
July 2012 to 1 August 2012 as it was not possible to calibrate the LI-7200's
CO2 channel in that period.
Comparison against modelled wetland emission estimates
In this section, we compare our measurement-derived CH4 emission fluxes
with those predicted from wetlands in Fennoscandia by two land surface
models: JULES and HYBRID8. The purpose of this comparison is to investigate
how representative the regional snapshot we discuss above is, in the context
of predicted seasonal and interannual variability, and to discuss potential
sources of systematic bias.
For this comparison, runs of the JULES and HYBRID8 models were done on a
0.5∘×0.5∘ terrestrial grid covering Scandinavia, using
the CRU-NCEP (Climatic Research Unit-National Centers for Environmental Prediction) meteorological data set (Viovy and Ciais, 2009). Hourly CH4
emission fluxes from wetlands were derived between January 1980 and December
2012 (the last year currently available in the CRU-NCEP driving
meteorological data set). Table 3 summarises the statistics derived from the
modelled hourly CH4 emission for the domain covered by the aircraft
(20.0–29.0∘ E, 67.5–68.5∘ N) for
July–August 2012 and for all the July–August's between 1980 and 2012. The
modelled fluxes for 2012 are slightly higher but consistent with those
derived from every July and August in the 33-year model run (also shown in
Table 3).
Distribution of the modelled hourly wetland methane emission fluxes
(mg CH4 m-2 h-1) for the domain (20.0–29.0∘ E, 67.5–68.5∘ N) for two periods:
July–August 2012 and the July–August climatology between 1980 and 2012.
Hourly emission flux
JULES
HYBRID8
(mg CH4 m-2 h-1)
July–August
July–August
July–August
July–August
2012
1980–2012
2012
1980–2012
Number of non-zero fluxes
53 568
176 7744
53 568
176 7744
Total number
53 568
176 7744
53 568
176 7744
Minimum
0.0
0.0
0.008
-0.002
Lower quartile
0.057
0.018
0.016
0.013
Median
0.082
0.063
0.023
0.024
Upper quartile
0.11
0.11
0.126
0.097
Maximum
0.21
0.41
1.53
4.62
Mean
0.084
0.073
0.088
0.074
It is evident that the two models significantly underestimate (a factor
∼ 14 for JULES and Hybrid in the mean) the CH4 emission
flux in this region for July–August 2012, when compared to our regionally
representative case study. Furthermore, even the upper quartile maximum
monthly averaged flux in the 31-year climatology (0.11 mg CH4 h-1 m-2 for
JULES and 0.13 mg CH4 h-1 m-2 for HYBRID8) does
not approach the measured aircraft and ground-based results in this case
study. This is possibly because of an under-prediction of wetland extent by
both models in this region, which could be linked to the topographical
data set used and/or the absence of an organic soil type related to
peatlands. Such soils would have very different hydraulic properties to the
mineral soil types currently used in JULES and HYBRID8. Water would be
retained at or close to the surface increasing the area of wetlands. Model
emission fluxes were derived assuming that each grid cell is all wetland.
These results were found to be much closer to the aircraft values for both
JULES (July–August 2012: median 1.6 mg CH4 h-1 m-2,
inter-quartile range 1.4 to 1.8 mg CH4 h-1 m-2) and HYBRID8
(2012: median 1.9 mg CH4 h-1 m-2, inter-quartile range 1.6
to 2.6 mg CH4 h-1 m-2). This suggests that underestimation
of the area of wetlands in both models is probably the major reason for the
under-prediction of the wetland emission fluxes in this region. Petrescu et al. (2010)
investigated the sensitivity to the wetland area and found a wide
variation in methane emission fluxes (37.7 to 157.3 Tg CH4 yr-1)
from wetlands and floodplains above 30∘ N for the years 2001 to
2006 for different estimates of wetland extent. The wetland model
intercomparison (Melton et al., 2013) has further highlighted the major
challenges and uncertainties that exist in modelling wetlands and the
associated CH4 emissions.
Both the JULES and HYBRID8 models have been used to simulate the response of
past and future emissions to climate change (Gedney et al., 2004; Friend, 2010; Quiquet et al., 2014). The results from this comparison suggest
that there are significant uncertainties when emissions are simulated at
regional scales and/or at specific times. Although our snapshot of a
regionally representative flux on a single day should not be directly
extrapolated to demonstrate a systematic under-bias in the climatological
Arctic wetland CH4 flux as predicted by JULES and HYBRID8, these
results do point to the important need for further such case studies from
which to build diagnostic statistics to validate such models. Given that
this study suggests an order of magnitude under-bias in modelled fluxes,
this uncertainty is potentially very important for climate studies that
model CH4 emissions scenarios.
Conclusions
As part of the MAMM field project, airborne measurements of CH4 and
CO2 were collected in the European Arctic in summer 2012. An airborne
mass balance approach was used to derive regional-scale fluxes for the
northern Scandinavian wetlands from one flight on the 22 July 2012. These
were established to be 1.2 ± 0.5 mg CH4 h-1 m-2 and
-350 ± 143 mg CO2 h-1 m-2, which were comparable with
simultaneous seasonally averaged chamber and eddy covariance flux
measurements made in Sodankylä (within 11 % for CO2 and 8 % for
CH4 if the fluxes were scaled using the land type). The internal
consistency of the aircraft-derived fluxes across a wide swath of
Fennoscandia coupled with an excellent statistical comparison with local
seasonally averaged ground-based measurements demonstrates the potential
scalability of such localised measurements to regional-scale representativeness.
Though the fluxes calculated here do not provide information about the wider
temporal variability of fluxes, they do provide a snapshot that can be
compared with the statistical climatology for model fluxes in the region,
which is representative of a spatial scale that is comparable with the
resolution of regional chemical transport and land surface models. This
together with a well-characterised uncertainty mean that these fluxes can
provide a useful constraint for “bottom-up” regional flux calculations. To
this end, a comparison with both the HYBRID8 and JULES land surface model
suggests that they both significantly underestimate the net CH4 flux
from these regions (a factor ∼ 14 for JULES and HYBRID8 in the
mean).
Although our snapshot of a regionally representative flux on a single day
should not be directly extrapolated to demonstrate a systematic under-bias
in the modelled climatological Arctic wetland methane flux, the results
presented here do point to the important need for further such case studies
from which to build diagnostic statistics to validate such models, as this
uncertainty is potentially very important for climate studies that model
CH4 emissions scenarios. Future field campaigns and studies are planned
to exploit the MAMM airborne data set from the 2013 and 2014 flights, to
derive additional regional-scale fluxes of key greenhouse gases either
through mass balance approaches, as illustrated here, or inverse modelling.
These may provide additional information on the consistency of the
disagreement between observations and the JULES/HYBRID8 models at high
northern latitudes.