ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-11453-2017 Canopy uptake dominates nighttime carbonyl sulfide fluxes in a boreal forestKooijmansLinda M. J.https://orcid.org/0000-0002-4758-3368MaseykKadmielhttps://orcid.org/0000-0003-3299-4380SeibtUlliSunWuhttps://orcid.org/0000-0002-2333-6282VesalaTimoMammarellaIvanKolariPasiAaltoJuhoFranchinAlessandroVecchiRobertahttps://orcid.org/0000-0002-1666-1802ValliGianluigiChenHuilinhuilin.chen@rug.nlhttps://orcid.org/0000-0002-1573-6673Centre for Isotope Research, University of Groningen, Groningen, the NetherlandsSchool of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes, UKDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, USADepartment of Physics, University of Helsinki, Helsinki, FinlandDepartment of Forest Sciences, University of Helsinki, Helsinki, FinlandSMEAR II, Hyytiälä Forestry Field Station, University of Helsinki, Korkeakoski, FinlandDepartment of Physics, Università degli Studi di Milano and INFN, Milan, ItalyCooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, USAHuilin Chen (huilin.chen@rug.nl)26September2017171811453114652May201717May201711August201725August2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/11453/2017/acp-17-11453-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/11453/2017/acp-17-11453-2017.pdf
Nighttime vegetative uptake of carbonyl sulfide (COS) can exist due to the
incomplete closure of stomata and the light independence of the enzyme
carbonic anhydrase, which complicates the use of COS as a tracer for gross
primary productivity (GPP). In this study we derived nighttime COS fluxes in
a boreal forest (the SMEAR II station in Hyytiälä, Finland; 61∘51′ N,
24∘17′ E; 181 m a.s.l.) from June to November 2015 using two
different methods: eddy-covariance (EC) measurements (FCOS-EC)
and the radon-tracer method (FCOS-Rn). The total nighttime COS
fluxes averaged over the whole measurement period were -6.8 ± 2.2 and
-7.9 ± 3.8 pmol m-2 s-1 for FCOS-Rn and
FCOS-EC, respectively, which is 33–38 % of the average daytime
fluxes and 21 % of the total daily COS uptake. The correlation of
222Rn (of which the source is the soil) with COS (average
R2= 0.58) was lower than with CO2 (0.70), suggesting that the main sink of COS
is not located at the ground. These observations are supported by soil
chamber measurements that show that soil contributes to only 34–40 % of the
total nighttime COS uptake. We found a decrease in COS uptake with decreasing
nighttime stomatal conductance and increasing vapor-pressure deficit and air temperature,
driven by stomatal closure in response to a warm and dry period in August. We
also discuss the effect that canopy layer mixing can have on the radon-tracer
method and the sensitivity of (FCOS-EC) to atmospheric
turbulence. Our results suggest that the nighttime uptake of COS is mainly
driven by the tree foliage and is significant in a boreal forest, such that
it needs to be taken into account when using COS as a tracer for GPP.
Introduction
The global budget of carbonyl sulfide (COS) is of interest for both
stratospheric and tropospheric chemistry .
COS contributes to the formation of the sulfate
aerosol layer in the stratosphere and
thereby also plays a role in ozone depletion . In the
troposphere COS is linked to the carbon cycle because it follows the same
diffusion pathway into plant stomata as CO2 during photosynthesis. After
COS has entered a plant cell it is hydrolyzed by the enzyme carbonic
anhydrase (CA) to form H2S and CO2. As this
reaction is practically irreversible, COS is not re-emitted by plants, in
contrast to CO2. The close coupling of COS and CO2 uptake fluxes by
vegetation makes COS a potentially powerful tracer for estimates of gross
primary production (GPP) .
Besides the difference in re-emission, the COS and CO2 uptake processes
differ in the sense that the consumption of COS by the CA enzyme is
light-independent. This means that vegetative uptake of COS can continue
during the night if stomata are not completely closed .
showed that nighttime stomatal conductance exists in a wide
variety of plant species and several studies report nighttime depletion of
COS mole fractions . The
measurements presented in , ,
and indicated that nighttime
ecosystem COS fluxes were indeed dominated by the vegetation, and not by the
soil. In these studies, nighttime vegetative fluxes varied between 25 and
50 % of average daytime fluxes. A correlation between nighttime COS fluxes and
stomatal conductance is expected when the nighttime sink of COS is primarily
driven by vegetative uptake. The relation between H2O and COS fluxes shown
by , and
underpins the likely relation between stomatal conductance and COS fluxes.
However, the relation between COS fluxes and stomatal conductance
measurements has not been studied under field conditions. Instead,
used COS ecosystem fluxes to estimate stomatal conductance.
This relation can especially be useful for estimating nighttime stomatal
conductance, which cannot be accurately determined under humid conditions as
the concentration gradient of water vapor in leaf chambers gets too small .
Although COS is not used as a GPP tracer during nighttime conditions (when
GPP is zero), nighttime COS fluxes may interfere with the use of COS for GPP
estimates . To analyze the role of
nighttime COS fluxes on the total COS budget and study correlations with
environmental drivers, it is key to determine nighttime COS fluxes
accurately. Eddy covariance (EC) is a well-established technique to determine
ecosystem fluxes ; however, stable nighttime conditions
complicate the measurements due to non-turbulent processes like canopy-layer
storage and advection . A
method that has been used to derive specifically nighttime fluxes of trace
gases, including COS, is the radon-tracer method .
This method relates the nighttime buildup of
trace gas concentrations to that of 222Rn concentrations
and the 222Rn flux, which is solely driven by the soil. Both the EC and
radon-tracer methods can complement each other to help understand and reduce
uncertainties of nighttime flux measurements.
The aim of this study is to quantify nighttime COS fluxes to determine the
role of these fluxes in the ecosystem COS budget, and to understand the
driving parameters of nighttime COS uptake. In the summer of 2015, we
conducted a field campaign in a Finnish boreal forest using a combination of
COS measurements: atmospheric concentration profiles, and EC and soil chamber
measurements. We use both the EC and radon-based fluxes to quantify nighttime
COS fluxes and infer information about the sink apportionment within the
canopy. We also investigate the correlation of nighttime COS fluxes with
stomatal conductance and environmental parameters and discuss the
implications of nighttime COS fluxes for large-scale GPP estimates.
Field measurements and dataMeasurement site
The field campaign was held from June to November 2015 at the Station for
Measuring Forest Ecosystem-Atmosphere Relations (SMEAR II) in
Hyytiälä, Finland (61∘51′ N, 24∘17′ E; 181 m a.s.l.). The
forest represents boreal coniferous forest and the measurement site is
covered by 50–60-year-old Scots pine (Pinus sylvestris) up to 1 km towards
the north from the measurement site and for about 200 m in all other
directions . The forest outside this area
covers younger pine and spruce. About 700 m southwest of the measurement site
is an oblong lake about 200 m wide. The dominant canopy height is 17 m
with the base at 7 m and the site is characterized by modest height
variation. At this latitude, the daylight duration has a maximum in June with
19 h and 40 min and is 7 h in November.
Instrumentation for measurements of COS, CO2, and H2O
Two quantum cascade laser spectrometers (QCLSs) manufactured by Aerodyne
Research Inc. (Billerica, MA, USA) were deployed in the field for
simultaneous measurements of COS, CO2, CO, and H2O and are described
separately in the following two sections.
QCLS for vertical profile and soil flux measurements
From 1 June until 4 November, one QCLS was operated at 1 Hz for concentration
measurements of sampled air at four heights: 125 m (tall tower), 23, 14, and
4 m (small tower at 30 m distance from the tall tower). An additional height
of 0.5 m was measured as part of the soil chamber measurement routine from
28 June onwards. A multi-position Valco valve (VICI; Valco Instruments Co. Inc.)
was used to switch between the sample tubing from the different profile
heights, soil chambers and calibration cylinder gases. A cycle of 1 h
during the night and during the day is shown in Figs. S1 and S2 in
the Supplement. The sample tubing was continuously flushed. For the
profile measurements, the flow rates were set such that there was a time
delay between 30 and 60 s from the moment that the air enters the inlet at
different heights until it reaches the cell of the QCLS, which is 17 L min-1
for 125 m and 2 L min-1 for 4 m. The flow rate from the Valco
valve through the sample cell was set at 0.15 L min-1, where the sample
cell has a volume of 0.5 L. The following measurements were made during each
hour: 3 min for each of the four heights, 16 min for each of the two
soil chambers, two times 3 min for one calibration cylinder to correct
for instrument drift, and 3 min for each of two other calibration cylinders
to assess the accuracy of the measurements. The first 60 s of each 3 min
measurement were discarded to account for cell flushing time. The three
cylinders were filled with ambient air and calibrated against two NOAA/ESRL
standards for COS (NOAA-2004 scale) and CO2 (WMO-X2007 CO2 scale) at
the University of Groningen. A “zero” air spectrum was measured once every
6 h using high-purity nitrogen (N 5.0). The overall uncertainty
including scale transfer, water vapor corrections, and measurement precision
of this analyzer was determined to be 7.5 ppt for COS and 0.23 ppm for CO2. More detailed information about the calibration and
correction methods can be found in .
QCLS for eddy covariance measurements
A second QCLS was used to measure COS, CO2, CO, and H2O concentrations
at 10 Hz from 28 June onwards. The air is sampled with a flow of 9–10 L min-1
at 23 m height at a small tower that is at 30 m distance from the
125 m tall tower. Wind velocity components were measured by a sonic
anemometer (Solent Research HS1199, Gill Ltd., Lymington, Hampshire, England)
to derive ecosystem fluxes through the EC method. For this analyzer a “zero”
air spectrum was measured once every 30 min. This QCLS was calibrated
against a standard on the same scale as the first QCLS. The CO2 and H2O
fluxes from the QCLS were compared with those obtained at the nearby tall
tower as quality control. The instrumentation in the tall tower is a Gill
Solent 1012R anemometer and a LI-COR LI-6262 gas analyzer .
Soil chambers
Two soil flux chambers (LI8100-104C; LI-COR) modified for analysis of COS
were used in combination with the concentration measurements of the QCLS at
1 Hz to derive soil fluxes. The modifications included operation in an open
flow configuration, replacing the chamber bowl and soil collar with stainless
steel components, and removing or replacing other COS-producing material.
Each chamber was closed once per hour for 9 or 10 min. For supply flow
into the chambers, air was sampled at 0.5 m height in the vicinity of the
soil chambers and was measured for 3 min before and after chamber
closure. The air was pumped into the chambers with flow rates between 1.5 and
2.1 L min-1 through a diaphragm pump (KNF 811) for which we found no
interference with COS. More details on the soil measurements can be found in .
Auxiliary data222Rn
222Rn concentrations were obtained by measurement of its short-lived
decay products attached to aerosol particles (i.e., 214Bi). Detection of
short lived decay products concentration in outdoor air was done by
continuous online alpha spectroscopy during aerosol sampling. Aerosol
particles were collected at 8 m height as part of the ongoing aerosol
monitoring at the site about 50 m away
from the tower where COS and CO2 was sampled. Particles were collected on
a glass micro fibre filter (Whatman GF/A, 47 mm diameter) with an average
flow rate of 17.4 L min-1. Alpha particles emitted by radon decay
products were recorded by a silicon surface barrier detector
(ULTRATM alpha detector by ORTEC, with full width at half maximum of 42 keV) placed a
few millimeters in front of the filter in order to optimize the efficiency
and to allow the detection of alpha particles in air. The hourly alpha energy
spectra were continuously recorded. The concentration of radon daughters is
calculated by taking into account radioactive decay equations, the
accumulation of decay products on the filter during the sampling and the
hypothesis of equilibrium in the progeny after subtraction of the
220Rn daughter contribution. Following ,
222Rn and its decay products were considered in secular radioactive
equilibrium in this work. Further details on the experimental procedure are
reported in and .
Stomatal conductance
The stomatal conductance to water vapor (gsw) was determined from
transpiration measurements obtained through shoot chamber measurements at a
pine shoot at the top of the canopy crown . The
conductance is derived from the vapor pressure deficit at leaf temperature
assuming that the resistance due to the leaf boundary layer is negligible due
to ventilation of the air in the shoot chambers. The leaf temperature is
calculated following a leaf energy balance model that incorporated heating by
incoming shortwave radiation, cooling by transpiration and convection, and
thermal radiation balance. Conductances measured under humid conditions – relative
humidity RH > 80 % – were rejected due to the underestimation of
transpiration at higher RH levels. The stomatal conductance to COS (gsCOS)
is derived based on the relationship between COS and H2O
conductance: gsCOS=gsw/RwCOS,
where RwCOS is the ratio of H2O and COS diffusivities and is
derived by to be 2.0 ± 0.2.
Meteorological data
Meteorological data such as the friction velocity (u*), air temperature (Tair),
relative humidity (RH), soil water content (SWC) and wind
direction were available through the SmartSMEAR database, which contains
continuous data records from the SMEAR sites (available at
http://avaa.tdata.fi). The vapor-pressure deficit (VPD) was calculated from
RH and Tair.
The EC technique is based on turbulence measurements above the canopy and
fluxes are derived from the covariance between a scalar (in this case COS or
CO2) and the vertical wind speed e.g.,. The fluxes derived through this method represent the net
exchange of gases between the canopy layer and the air above. The EC
technique requires turbulent conditions; otherwise gases that accumulate or
get depleted due to sources and/or sinks within the canopy do not reach the
sensors above the canopy. As soon as turbulence is enhanced in the early
morning, these gases are released to levels above the canopy and are only
then being captured by the EC system. This so-called storage change within
the canopy can be significant and should be added to the turbulence flux to
account for the delayed capture of fluxes by the EC system
. In this study we refer to the storage-corrected COS and
CO2 EC flux as FCOS-EC and NEEEC, respectively. The
calculation of storage fluxes is discussed in the next section. In this study
the EC fluxes were calculated using the EddyUH software package developed at
the University of Helsinki . In short, the
high-frequency EC data were despiked according to standard approach
. The spectroscopic correction due to H2O impact on the
absorption line shape was accounted for along with the dilution correction in
the QCLS acquisition software. A 2-D rotation of sonic anemometer wind
components was performed, and 30 min covariances between the scalars and
vertical wind velocity were calculated using linear detrending method.
Short-term drift in the QCLS high-frequency concentration data was negligible
and there was no need to apply more sophisticated approach for detrending the
data, e.g., high-pass recursive filters . The time lag
between the concentration and wind measurements induced by the sampling line
was determined by maximizing the covariance. Due to a better signal-to-noise
ratio, the lag for COS was determined by maximizing the covariance for QCLS
CO2, and the same lag was assigned to COS. Finally, spectral correction
was done according to . Total random uncertainty of the
fluxes was calculated according to the method implemented
in EddyUH, the method proposed by . The
uncertainties of NEEEC and FCOS-EC are estimated from the
standard deviation of data points per night, where night is defined as the
time when the sun elevation angle is below -3∘. A general observation
that is seen with EC measurements is that nighttime NEEEC decreases
with lower u*, whereas respiration is not expected to depend on
atmospheric turbulence. For this reason we filtered out (storage-corrected)
fluxes with u* values below a threshold of 0.3 m s-1. A difference between COS and CO2 fluxes is,
however, that the uptake of COS by leaves is concentration-dependent
and the leaf boundary layer may get depleted in COS under
low-turbulence conditions, slowing uptake rates. It is unknown to what extent
this affects COS fluxes in practice, but it has to be kept in mind that the
u* filtering may be an overstated filtering to COS fluxes. To determine
the fraction that nighttime COS fluxes contribute to total daily COS uptake
we gap-filled COS fluxes with a rectangular hyperbola light response function
that is based on the measured data. Missing COS data under dark conditions
were filled based on the average nighttime flux obtained from this study.
CO2 and H2O ecosystem fluxes from the QCLS were compared with those
from the nearby tall tower. During nighttime, the QCLS CO2 flux is a
factor of 0.73 smaller than the tall-tower fluxes at the same height and the
underestimation has been observed with another EC system at the small tower
as well. found that the tall tower NEEEC agrees
well with upscaled soil and branch chamber measurements. As we rely on the
accuracy of NEEEC in the radon-tracer method (Sect. 3.2) we use
NEEEC from the tall tower instead of the QCLS at the smaller tower
throughout the manuscript. The underestimation is not the same for all gases;
for example, the evapotranspiration flux is only a factor of 0.97 smaller. It is
therefore unknown by how much the FCOS-EC flux is affected by the
general underestimation at the small tower.
Storage fluxes
Storage fluxes (Fstor) are defined as the integral of concentration
changes over height up to the height of the EC measurements (hEC):
Fstor=PRTair∫0hECdC(z)dtdz,
with P the atmospheric pressure, R the molar gas constant and C(z) the COS or
CO2 concentrations (ppt for COS or ppm for CO2) along a profile
. The integral was determined from hourly
measured profile concentrations at 0.5, 4, 14, and 23 m in two ways: (1) by
integrating an exponential fit through the data, and (2) by using trapezoidal
areas . The concentration at ground level that is used
for the second calculation method is estimated by extrapolating the gradient
between 0.5 and 4 m to the ground level. A third calculation was done
assuming a constant profile from the EC measurement height (23 m) to the
ground level, to test the bias in storage fluxes when no profile measurements
are available. The results of the different calculation methods will be
discussed in Sect. 4.1. To reduce the error due to the random noise of COS
concentration measurements, a running average over a 5 h window was
applied to the COS concentration data before the storage fluxes were calculated.
The radon-tracer method
222Rn is a natural radioactive gas that is formed by the decay of
226Ra, which is uniformly distributed in soils
. Once in the atmosphere, 222Rn is affected by
radioactive decay and atmospheric mixing. As the exhalation rate of
222Rn by the soil (FRn) is considered constant and uniformly
distributed, and 222Rn is mixed through the atmosphere in the same way
as other trace gases, the surface fluxes of these trace gases (FC)
can be determined from the concentration change of these gases over time (ΔC)
relative to that of 222Rn (Δ222Rn) :
FC=FRnΔCΔ222Rn.222Rn generally builds up in the boundary layer when it gets shallower
during the night. Figure 1 shows an example of one night during the measurement
campaign where 222Rn concentrations increase in the evening and reach a
maximum in the night, while at the same time CO2 increases and COS
decreases. This nighttime buildup of gases and the constant surface flux of
222Rn make the radon-tracer method appropriate to derive nighttime
fluxes of trace gases. Requirements for this method are that the 222Rn
concentrations are corrected for radioactive decay, that FRn is
known, and that a high correlation exists between the trace gas and
222Rn concentrations. Moreover, when the spatial distribution of sources
and sinks of a trace gas are similar to the source of 222Rn at the
ground, a high correlation between the trace gas and 222Rn can be
expected. Therefore, the correlation between COS and 222Rn
concentrations may give insight into the distribution of sinks of COS within
the ecosystem.
COS, CO2 and 222Rn concentrations, u* and the storage
flux of COS and CO2 (Fstor-COS and
Fstor-CO2) during 12–13 July 2015, where the
data with sun elevation below 0∘ are used to derive nighttime fluxes
of COS and CO2 (black, filled). The bottom figures show the linear
regression between 222Rn and COS (left) and CO2 concentrations
(right) on which FCOS-Rn and NEERn
are based.
Storage fluxes Fstor (green), ecosystem fluxes
NEEEC and FCOS-EC (red) and soil
fluxes Fsoil (blue) of COS (a) and CO2(b)
in summer (July and August) 2015. Thick lines indicate the median values of
the data over the whole measurement period, and the shaded areas specify the
25th–75th percentiles. The median values of NEEEC and
FCOS-EC without storage correction are shown in
gray. The ecosystem fluxes are filtered for low u* values with a
threshold of 0.3 m s-1.
One of the main uncertainties of the radon-tracer method is the magnitude of
FRn. In , FRn was measured at a site
46 km away from the SMEAR II site, which resulted in
FRn= 15.3 mBq m-2 s-1. Model studies have estimated FRn in Europe from
4.0 to 12.4 mBq m-2 s-1 (summarized in Table S1 in the Supplement),
leading to an overall average of 9.6 ±4.1 mBq m-2 s-1.
The exhalation rates depend on the uranium content and soil
properties that affect diffusive transport such as the soil texture and soil
moisture . The FRn values of 4.0 and
11.4 mBq m-2 s-1 that were modeled by for two
different soil moisture maps indicate that the uncertainty of FRn
is in large part caused by different soil moisture.
As the uncertainty of the COS and CO2 ecosystem fluxes derived through the
radon-tracer method (FCOS-Rn and NEERn, respectively) is
in large part determined by the uncertainty of FRn, it is key to
further limit the FRn range between 4.0 and 15.3 mBq m-2 s-1
in Table S1. For that reason we inverted the radon-tracer method to
derive FRn from CO2 and 222Rn concentrations with a known
ecosystem CO2 flux (NEEEC), instead of a known FRn to
derive NEE, which is normally used in the radon-tracer method .
The advantage of this method is that FRn is obtained
from actual measurements at the site, and we will therefore use this
FRn to determine FCOS-Rn. We derived FRn over
the period from July to November and found an average of 5.9 mBq m-2 s-1
with a standard deviation of 3.9 mBq m-2 s-1 and a
standard error of 0.8 mBq m-2 s-1. This value of FRn is
within the range listed in Table S1, but is lower than the average of
9.6 mBq m-2 s-1. We will discuss in Sect. 5.2 what the effect of canopy
layer mixing can be on the derivation of FRn and COS fluxes.
Temporal variation of FRn can be expected due to the changes in SWC
that affect the soil permeability; however, no temporal change or
correlation with SWC was found (R2= 0.07) throughout the season (see Fig. S3).
In Hyytiälä, 222Rn measurements were made at 8 m, and COS and
CO2 concentrations from the same height need to be used to derive their
surface fluxes. We derived concentrations at 8 m from an exponential fit
through the profile concentrations at 0.5, 4, 14 and 23 m. A linear fit
between 4 and 14 m was used in cases where the algorithm for the exponential
fit did not converge. The factor ΔC/Δ222Rn is determined
from a linear regression of concentrations of COS or CO2 against
222Rn for data when the sun elevation is below 0∘ (see Fig. 1 for
an example). Per night, a minimum of five data points need to be available and
R2 between 222Rn and CO2 and COS should be at least 0.5 (for
CO2) and 0.3 (for COS). Uncertainties of NEERn and
FCOS-Rn are determined from the linear regression
as the standard error of the slope.
Soil fluxes
Soil fluxes (Fsoil) were calculated from least squares fits of the
concentrations during chamber closure and by considering mass balance
equations within the chamber . At the start of the campaign we
did blank tests by placing fluorinated ethylene propylene (FEP) foil over the soil and calculated fluxes
through the standard measurement procedure. Soil fluxes were corrected for
blank chamber effects of 0.66 ± 0.48 pmol m-2 s-1 for COS;
blanks for CO2 were negligible (-0.05 ± 0.15 µmol m-2 s-1).
Further details about the soil flux measurements can be found in .
ResultsCOS and CO2 storage fluxes
The storage fluxes of COS (Fig. 2) are slightly negative during nighttime
with an average nighttime value of -0.9 pmol m-2 s-1 in
July–August and -0.5 pmol m-2 s-1 in September–November (Fig. S4).
The average nighttime gradient between 23 and 0.5 m corresponding
to these storage fluxes is 63 ppt for COS and -45 ppm for CO2
(23–0.5 m concentration) in July–August and is 57 ppt and -17 ppm in September–November. Early in
the morning when turbulence is enhanced, the storage fluxes become positive
and have an average maximum of 2.1 (1.8) pmol m-2 s-1 at
09:00 LT (10:00 LT) in July–August (September–November). The storage fluxes of CO2 follow a similar
pattern but have the opposite sign. Storage fluxes of COS calculated from
trapezoidal areas are on average 25 % larger than when an exponential fit
through the profile is integrated. When the concentration profile is assumed
to be constant from the EC measurement height to the ground level, the
storage flux is on average 7 % smaller compared to a profile with an
exponential fit. These differences are small compared to the size of the
ecosystem fluxes. Neglecting storage fluxes would not influence the long-term
budget of COS and CO2, as it only corrects for the delay in release of
accumulated gases from within the canopy ; however, it
does affect the diurnal variability of fluxes, and any attempt at flux
partitioning, particularly if storage fluxes are large. In this dataset,
storage fluxes of both COS and CO2 are small compared to the EC flux,
i.e., storage fluxes are on average 5 % of FCOS-EC and 7 % of
NEEEC, with variation between summer and autumn from 4 %
(July–August) to 6 % (September–November) for FCOS-EC.
COS and CO2 nighttime fluxes through the radon-tracer and EC-based method
The linear correlation between the concentrations of 222Rn and the
scalar (COS or CO2) is key in interpreting the fluxes derived from the
radon-tracer method. Figure 3 shows the distribution of R2 values for the
correlation between 222Rn and COS or CO2. The correlation between
222Rn and CO2 peaks at R2 values in the range 0.9–1.0 and has a
median value of 0.70. The R2 for COS is generally lower with a median of 0.58.
The lower R2 values for COS can partly be explained by the lower
precision of COS measurements compared to those of CO2. However, the
average R2 only slightly increases to 0.64 when the noise of COS is
diminished by taking a running average of a 5 h window over the COS
measurements. This indicates that the lower precision of COS is not the main
aspect influencing the correlation with 222Rn. Another aspect that
influences the correlation with 222Rn is the similarity in vertical
distribution of sources and sinks between the scalar and 222Rn, which
will be further discussed in Sect. 5.1.
Relative frequency of R2 values of the correlation between
concentrations of 222Rn and CO2(a) and COS (b).
The radon-based nighttime fluxes of COS and CO2 are compared with the
EC-based fluxes in Fig. 4. FCOS Rn (NEERn) was determined
for 69 (66) out of 128 nights during the campaign that passed the criteria of
a minimum R2 and a minimum number of available data. Nighttime fluxes
derived with the EC method were determined for 56 nights following removal of
43 % of the data due to u* filtering. FRn was derived from
222Rn concentrations in relation to NEEEC and CO2
concentrations in order to limit the uncertainty of FRn on
FCOS-Rn. This means that the average NEEEC and
NEERn values are close (3.30 ± 0.62 and 3.34 ± 0.82 µmol m-2 s-1, respectively) as they are not independent
of
each other. Both NEEEC and NEERn show a decreasing trend
from summer towards autumn. However, the R2 value between NEEEC
and NEERn is only 0.03, which is likely due to the low
signal-to-noise ratio of both flux techniques.
Both the EC-based and radon-tracer methods show negative nighttime COS fluxes
with an average of -7.9 ± 3.8 pmol m-2 s-1 (FCOS-EC)
and -6.8 ± 2.2 pmol m-2 s-1 (FCOS-Rn). In comparison,
nighttime soil fluxes of COS are on average -2.7 pmol m-2 s-1
(-2.8 ± 1.0 and -2.5 ± 1.2 pmol m-2 s-1 for the two chambers) and
soil fluxes do not show a clear diurnal (Fig. 2) or seasonal cycle. An
overview of the soil fluxes is presented in . Similar to NEE,
a decreasing trend is visible in both FCOS-Rn and FCOS-EC
with an average of -10.9 pmol m-2 s-1 in July and -4.6 pmol m-2 s-1
in October as obtained from FCOS-EC. The
nighttime uptake is 33–38 % of the average daytime fluxes (defined as when
sun elevation is above 20∘) and 21 % of the total daily COS uptake
(obtained from gap-filled data). When the soil flux is subtracted from the
ecosystem flux, the nighttime uptake is 17 % of the total daily uptake.
(a, d) Comparison of EC- and radon-based fluxes for average
nighttime CO2(a) and COS (d) fluxes.
(b, c, e, f) Time series of EC based fluxes (b, e) and
radon-based fluxes (c, f). The uncertainty bars of the EC and
radon-based fluxes are not directly comparable due to the different ways of
determining these uncertainties.
Correlations of FCOS-Rn with
gsCOS, Tair, VPD, and u*. All data (except
FCOS-Rn) are averages over individual nights
(with nighttime defined as sun elevation below -3∘). Data in this
plot largely represent a period in August 2015 with dry conditions
(i.e., decreasing SWC, and increasing Tair and VPD).
FCOS correlation with gsCOS, VPD, Tair and u*
Figure 5 shows FCOS compared to nighttime averaged gsCOS, VPD,
Tair and u* with their respective uncertainties. Soil fluxes did
not show a seasonal or daily cycle and are therefore not
subtracted from the ecosystem-scale fluxes, as this would only add noise to
the fluxes. The nights shown in Fig. 5 only cover summer nights between 28 June
and 25 August 2015, as gsCOS data did not pass the RH filter
criteria after this period due to higher RH. The month August was
characterized by a dry period with SWC decreasing from about 20 to
7 %, the average nighttime temperature increased and RH decreased. Over the
same time period, nighttime gsCOS decreased from 0.02 to
0.006 mol m-2 s-1 (see Fig. S3 for an overview of the meteorological conditions).
Weak correlations are found between FCOS-Rn and gsCOS
(R2= 0.32), Tair (R2= 0.22), VPD (R2= 0.22) and u*
(R2= 0.33), where fluxes decrease under lower gsCOS and u*,
and higher VPD and Tair. The same comparison was made for
FCOS-EC (Fig. S5), which gave
correlations R2= 0.36 (gsCOS), 0.30 (Tair),
0.56 (VPD) and 0.50 (u*) and showed that also FCOS-EC decreased under
lower gsCOS and u*, and higher VPD and Tair. However,
these correlations were only found when no u* filter was applied, as only
a few data points remained after the u* filtering.
gsCOS was on average 0.016 mol m-2 s-1 during nighttime
and 0.117 mol m-2 s-1 during daytime. The average nighttime
gsCOS showed a correlation with the average nighttime VPD
(R2= 0.54, not shown) and gsCOS was negatively correlated with
Tair (R2= 0.60; not shown).
DiscussionVertical distribution of sinks and sources of COS and CO2 compared to that of 222Rn
The benefit of stable conditions within the canopy layer is that the
correlation of COS or CO2 with 222Rn can shed light on the spatial
distribution of sources and sinks of these gases in comparison to the only
source of 222Rn, which is the soil. When the source or sink of COS or
CO2 is focused at the ground level, a high correlation between 222Rn
and these gases can be expected. The fact that CO2 shows a high
correlation with 222Rn indicates that the main source of CO2 is
located near the surface, which is confirmed by the magnitude of nighttime
soil chamber measurements relative to branch chamber measurements in
, who found that respiration of the tree foliage was
1.5–2 µmol m-2 s-1 during summer nights and soil respiration was
5–6 µmol m-2 s-1. In contrast, we find that the correlation between
222Rn and COS is lower, which suggests that the main sink of COS is not
near the surface, but rather at higher levels in the canopy layer. This is
also supported by the soil chamber measurements that were on average
-2.7 pmol m-2 s-1 with only little variation between the two chambers,
which suggests that the soil contributes to 34–40 % of the total nighttime COS uptake.
The effect of canopy layer mixing on flux derivations
When the canopy air is fully mixed, the flux obtained through the
radon-tracer method represents the net exchange flux in that canopy layer,
regardless of the potential difference in the spatial distribution of the
tracer fluxes, e.g., CO2 and 222Rn. In this study, however, the
222Rn concentrations are measured within the canopy layer at 8 m and
decoupling of canopy layers may exist . Fluxes derived
from concentrations within the canopy may therefore not represent the
exchange of these gases in the whole canopy. To discuss the effect of
decoupling on radon-flux calculations we have to distinguish between two
decoupling situations: (1) when the 8 m air is decoupled from the air close
to the ground, and (2) when the 8 m air is decoupled from the canopy layer above:
When the 8 m canopy layer is decoupled from the air close to the ground,
the different flux distribution of CO2 and 222Rn can become apparent. In
the case of decoupling, the respiration of the tree foliage would influence the
8 m concentration, while the CO2 respiration and radon flux at the surface do
not influence the air at 8 m. The 8 m concentration is then not representative
of the canopy layer CO2 flux and would lead to a lower FRn. This
would explain why the FRn that we find (5.9 mBq m-2 s-1) is
lower than the average FRn reported in other literature
(9.6 ± 4.1 mBq m-2 s-1). At the same time, when COS fluxes do not
entirely take place at the surface but within the canopy, this would lead to a
higher FCOS-Rn.
When the 8 m layer is decoupled from the canopy layer above, the air that
is depleted in COS due to the sinks within the canopy may not reach the lower
canopy layers on which FCOS-Rn is based and leads to an underestimation
of FCOS-Rn. Furthermore, the decoupled layer at the surface is more
susceptible to horizontal advection, which may affect the concentration profile as well.
identified decoupling of different canopy levels at
the Hyytiälä site based on changing wind directions at different
heights. They observed a decrease in NEEEC under decoupled
circumstances, which occurred in at least 18.6 % of all nighttime periods.
We did not observe a correlation with FCOS-Rn and the difference in
wind direction between 16.8 and 8.4 m. However, a limitation is that we can
only compare nighttime averages, whereas decoupling does not have to last
throughout the whole night and can also exist during only a fraction of a
night. Furthermore, we do not have wind direction data at other heights
within the canopy to be able to determine if the decoupling takes place below or above 8 m.
Sensitivity of FCOS-EC to u*
It is well accepted that NEEEC underestimates the true NEE under
low u*, as nighttime NEE (respiration only) is not expected to depend on
atmospheric turbulence. By applying a u* filter to COS fluxes, we assume
the same independence of COS uptake to atmospheric turbulence. However, a
negative correlation between FCOS and u* can be expected when
the leaf boundary layer gets depleted in COS under low turbulence conditions
and the uptake of COS gets limited by the COS gradient at the leaf boundary
layer. If this is the case, that means that by applying the u* filtering
to FCOS-EC we bias to higher FCOS-EC data. The
correlation between u* and nighttime COS and CO2 fluxes that is
observed with the EC method (R2= 0.50 for FCOS-EC and 0.30 for
NEEEC, not shown) is also observed with the radon-tracer method for
FCOS-Rn (R2= 0.33) but not for NEERn (R2= 0.003,
not shown). This suggests that nighttime COS uptake by plants is limited by
the reduced COS concentrations at the leaf boundary layer, which is not the
case for CO2. This means that the u* filtering that is applied to
FCOS-EC is possibly an overstated filtering and leads to an
overestimated FCOS-EC, which could explain the difference between
FCOS-EC and FCOS-Rn.
Similar to the limitation on leaf uptake by depleted COS concentrations, soil
COS uptake may also be limited by the depleted COS at the soil–atmosphere
interface. In contrast, soil emissions of CO2 and 222Rn do not depend
on atmospheric concentrations. This may explain the stronger similarity
between CO2 and 222Rn emissions, which is reflected in the higher
correlation between CO2 and 222Rn concentrations than that between
COS and 222Rn (Fig. 3). However, found no correlation
between soil COS fluxes and COS concentrations (R2< 0.001) for ambient
concentrations between 200 and 450 ppt. This implies that the soil COS flux
is not limited by the low ambient concentration at night, and a correlation
between u* and soil COS uptake is not warranted.
Stomatal control of nighttime FCOS
A correlation between nighttime FCOS and gsCOS was
expected due to stomatal diffusion and the light independence of the CA
enzyme. A weak correlation of gsCOS with FCOS was indeed
observed for both the radon-tracer and EC method, although the latter was
only found when no u* filtering was applied to the data, as only a few
data points remained when the u* filtering was included. The decrease in
FCOS when gsCOS decreases and VPD increases is likely
related to the dry and warm period in August to which plants respond by
closing their stomata to prevent excessive water loss. This would also
explain why FCOS is lower under high Tair. In general we
do not find strong correlations between the COS flux and the nighttime
environmental parameters, which can be explained by the low signal-to-noise
ratio of the flux measurements and the fact that FCOS-Rn may not
represent the full canopy layer due to decoupling (see Sect. 5.2).
Moreover, we compare ecosystem fluxes with leaf-level gsCOS within
enclosed chambers, which may not represent the full canopy dynamics.
Nevertheless, the fact that both the radon-tracer and the EC methods confirm
that the COS uptake decreases with decreasing gsCOS indicates that
the nighttime uptake of COS is indeed driven by vegetation. Moreover, soil
fluxes were found to be -2.7 pmol m-2 s-1 on average. With the
total nighttime COS uptake being -6.8 to -8.1 pmol m-2 s-1, soil
fluxes contribute to only 34–40 % of the nighttime COS uptake. Besides
uptake of COS by the soil and leaf stomatal diffusion there is no other
process to our knowledge that would lead to uptake of COS in the ecosystem.
This leads to the conclusion that the nighttime COS uptake is predominantly
driven by vegetative uptake and supports the use of COS to estimate
gsCOS. Assuming that the soil is the only sink
besides the vegetation, we can say that the nighttime vegetative uptake
contributes to 17 % of the total daily COS uptake. Moreover, this study has
confirmed that nighttime stomatal conductance exists at the Hyytiälä site.
Effect of nighttime COS fluxes on GPP derivation
The measurements in this study showed that, unlike the uptake of CO2, the
COS uptake continues during the night, which agrees with the
light independence of the CA enzyme. We showed that the nighttime plant COS
fluxes cover 17 % of the total daily COS plant uptake, which indicates that
nighttime COS uptake is a significant sink in the total COS budget. Including
this nighttime sink is essential in regional COS models and will affect
COS-based GPP model simulations as well. The relationships that we found
between FCOS, gsCOS, VPD and Tair will aid in
implementing nighttime FCOS in models. Furthermore, the
light independence of COS uptake should be taken into account when COS is
being used as tracer for GPP. Besides restricting COS as a GPP tracer to light
conditions, the leaf relative uptake ratio (LRU), which is the normalized
ratio between COS and CO2 fluxes, can be expected to increase when GPP
becomes zero around sunrise and sunset while at the same time COS is
continuously being taken up by vegetation. So far, only
have showed the light dependence of LRU from leaf-scale measurements and
and observed a light dependence in the
ratio of ecosystem fluxes of COS and CO2. Other studies have focused on
LRU values under high-light conditions e.g.,. More leaf-level COS flux measurements should be made to
accurately parameterize the light dependence of LRU in the field.
Conclusions
In this study we quantified nighttime COS fluxes in a boreal forest using
both the EC and the radon-tracer methods, and found that nighttime
FCOS between June and November 2015 was on average -7.9 ± 3.8 and
-6.8 ± 2.2 pmol m-2 s-1 according to
the two different methods, respectively. A high correlation between CO2
and 222Rn indicates that the sources of these gases have a similar
spatial distribution, namely at the soil. A lower correlation of 222Rn
with COS suggests that the main sink of COS is not located at the surface,
but rather at higher levels in the canopy. This is supported by soil chamber
measurements, which show that the soil flux is on average -2.7 pmol m-2 s-1
and only contributes to 34–40 % of the total nighttime COS uptake.
Our estimates for nighttime FCOS are 33–38 % of the size of
daytime average NEEEC fluxes. Based on the EC method, the nighttime
COS uptake is 21 % of the total daily COS uptake and is mostly driven by
aboveground vegetation. Furthermore, we investigated the relation of the
nighttime COS fluxes with stomatal conductance (gsCOS) and
environmental parameters. Measurements of both FCOS-Rn and
FCOS-EC pointed to a decrease in COS uptake with decreasing
gsCOS and increasing VPD and Tair, which is likely
related to a dry and warm period in August to which plants responded by
closing their stomata to prevent excessive water loss. Our results suggest
that the nighttime uptake of COS is mainly driven by the tree foliage and the
relationships that we find between FCOS, gsCOS, VPD and
Tair will aid in implementing nighttime COS uptake in models. Both
the EC and the radon-tracer methods indicate that the nighttime sink of COS
plays an important role in the total COS budget in a boreal forest and needs
to be taken into account when using COS as a tracer for GPP estimates
The nighttime ecosystem fluxes of COS and CO2 obtained
through the radon-tracer and eddy-covariance method can be accessed at
https://doi.org/10.5281/zenodo.858625.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-11453-2017-supplement.
US, KM, HC, TV and LMJK designed the research. LMJK, KM, JA
conducted the field work, WS, IM, PK, AF, RV and GV provided data. LMJK
performed data analysis. LMJK and HC wrote the paper with contributions from
all co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
We greatly appreciate the maintenance and help of the technical staff at
SMEAR II in Hyytiälä, in particular Helmi Keskinen and Janne Levula.
We also thank Bert Kers and Marcel de Vries for their help during
preparations of the campaign at the University of Groningen. We would like to
thank Ute Karstens and Navin Manohar for making FRn simulations
and corresponding data available. We also thank the anonymous reviewers for
their comments on the manuscript. The research leading to these results has
received funding from the European Community's Seventh Framework
Programme (FP7/2007-2013) in the InGOS project (no. 284274), the NOAA
contract NA13OAR4310082, the Academy of Finland Centre of Excellence
(no. 118780), Academy Professor projects (no. 284701 and 282842),
ICOS-Finland (no. 281255) and CARB-ARC (no. 286190).
Edited by: Leiming Zhang
Reviewed by: two anonymous referees
References
Alekseychik, P., Mammarella, I., Launiainen, S., Rannik, Ü., and Vesala,
T.: Evolution of the nocturnal decoupled layer in a pine forest canopy, Agr.
Forest Meteorol., 174–175, 15–27, 2013.Altimir, N., Kolari, P., Tuovinen, J.-P., Vesala, T., Bäck, J., Suni, T.,
Kulmala, M., and Hari, P.: Foliage surface ozone deposition: a role for surface
moisture?, Biogeosciences, 3, 209–228, 10.5194/bg-3-209-2006, 2006.Asaf, D., Rotenberg, E., Tatarinov, F., Dicken, U., Montzka, S. A., and Yakir,
D.: Ecosystem photosynthesis inferred from measurements of carbonyl sulphide
flux, Nat. Geosci., 6, 186–190, 10.1038/ngeo1730, 2013.Aubinet, M., Chermanne, B., Vandenhaute, M., Longdoz, B., Yernaux, M., and
Laitat, E.: Long term carbon dioxide exchange above a mixed forest in the
Belgian Ardennes, Agr. Forest Meteorol., 108, 293–315, 10.1016/S0168-1923(01)00244-1, 2001.
Aubinet, M., Vesala, T., and Papale, D.: Eddy Covariance: A Practical Guide to
Measurement and Data Analysis, Springer, Dordrecht, Heidelberg, London, New York, 2012.Belviso, S., Schmidt, M., Yver, C., Ramonet, M., Gros, V., and Launois, T.:
Strong similarities between night-time deposition velocities of carbonyl
sulphide and molecular hydrogen inferred from semi-continuous atmospheric
observations in Gif-sur-Yvette, Paris region, Tellus B, 65, 20719,
10.3402/tellusb.v65i0.20719, 2013.Berkelhammer, M., Asaf, D., Still, C., Montzka, S., Noone, D., Gupta, M.,
Provencal, R., Chen, H., and Yakir, D.: Constraining surface carbon fluxes
using in situ measurements of carbonyl sulfide and carbon dioxide, Global
Biogeochem. Cy., 28, 161–179, 10.1002/2013GB004644, 2014.Berry, J., Wolf, A., Campbell, J. E., Baker, I., Blake, N., Blake, D., Denning,
A. S., Kawa, S. R., Montzka, S. A., Seibt, U., Stimler, K., Yakir, D., and Zhu,
Z.: A coupled model of the global cycles of carbonyl sulfide and CO2: A
possible new window on the carbon cycle, J. Geophys. Res.-Biogeo., 118, 842–852,
10.1002/jgrg.20068, 2013.Billesbach, D. P., Berry, J. A., Seibt, U., Maseyk, K., Torn, M. S., Fischer,
M. L., Mohammad Abu-Naser, and Campbell, J. E.: Growing season eddy covariance
measurements of carbonyl sulfide and CO2 fluxes: COS and CO2
relationships in Southern Great Plains winter wheat, Agr. Forest Meteorol.,
184, 48–55, 2014.Brühl, C., Lelieveld, J., Crutzen, P. J., and Tost, H.: The role of carbonyl
sulphide as a source of stratospheric sulphate aerosol and its impact on climate,
Atmos. Chem. Phys., 12, 1239–1253, 10.5194/acp-12-1239-2012, 2012.Caird, M. A., Richards, J. H., and Donovan, L. A.: Nighttime Stomatal Conductance
and Transpiration in C3 and C4 Plants, Plant Physiol., 143, 4–10, 10.1104/pp.106.092940, 2007.Campbell, J. E., Carmichael, G. R., Chai, T., Mena-Carrasco, M., Tang, Y., Blake,
D. R., Blake, N. J., Vay, S. A., Collatz, G. J., Baker, I., Berry, J. A., Montzka,
S. A., Sweeney, C., Schnoor, J. L., and Stanier, C. O.: Photosynthetic Control
of Atmospheric Carbonyl Sulfide During the Growing Season, Science, 322, 1085–1088,
10.1126/science.1164015, 2008.Chin, M. and Davis, D.: A Reanalysis of Carbonyl Sulfide as a Source of
Stratospheric Background Sulfur Aerosol, J. Geophys. Res.-Atmos., 100, 8993–9005,
10.1029/95JD00275, 1995.Commane, R., Herndon, S. C., Zahniser, M. S., Lerner, B. M., McManus, J. B.,
Munger, J. W., Nelson, D. D., and Wofsy, S. C.: Carbonyl sulfide in the planetary
boundary layer: Coastal and continental influences, J. Geophys. Res.-Atmos.,
118, 8001–8009, 10.1002/jgrd.50581, 2013.Commane, R., Meredith, L. K., Baker, I. T., Berry, J. A., Munger, J. W., Montzka,
S. A., Templer, P. H., Juice, S. M., Zahniser, M. S., and Wofsy, S. C.: Seasonal
fluxes of carbonyl sulfide in a midlatitude forest, P. Natl. Acad. Sci. USA,
112, 14162–14167, 10.1073/pnas.1504131112, 2015.Crutzen, P. J.: The possible importance of CSO for the sulfate layer of the
stratosphere, Geophys. Res. Lett., 3, 73–76, 10.1029/GL003i002p00073, 1976.
Finkelstein, P. L. and Sims, P. F.: Sampling error in eddy correlation flux
measurements, J. Geophys. Res., 106, 3503–3509, 2001.
Hari, P. and Kulmala, M.: Station for Measuring Ecosystem–Atmosphere Relations
(SMEAR II), Boreal Environ. Res., 10, 315–322, 2005.Karstens, U., Schwingshackl, C., Schmithhüsen, D., and Levin, I.: A process-based
222radon flux map for Europe and its comparison to long-term observations,
Atmos. Chem. Phys., 15, 12845–12865, 10.5194/acp-15-12845-2015, 2015.Kettle, A., Kuhn, U., von Hobe, M., Kesselmeier, J., and Andreae, M.: Global
budget of atmospheric carbonyl sulfide: Temporal and spatial variations of the
dominant sources and sinks, J. Geophys. Res.-Atmos., 107, 4658, 10.1029/2002JD002187, 2002.Kolari, P., Kulmala, L., Pumpanen, J., Launiainen, S., Ilvesniemi, H., Hari,
P., and Nikinmaa, E.,: CO2 exchange and component CO2 fluxes of a
boreal Scots pine forest, Boreal Environ. Res., 14, 761–783, 2009.Kooijmans, L. M. J., Uitslag, N. A. M., Zahniser, M. S., Nelson, D. D., Montzka,
S. A., and Chen, H.: Continuous and high-precision atmospheric concentration
measurements of COS, CO2, CO and H2O using a quantum cascade laser
spectrometer (QCLS), Atmos. Meas. Tech., 9, 5293–5314, 10.5194/amt-9-5293-2016, 2016.Launois, T., Belviso, S., Bopp, L., Fichot, C. G., and Peylin, P.: A new model
for the global biogeochemical cycle of carbonyl sulfide – Part 1: Assessment
of direct marine emissions with an oceanic general circulation and biogeochemistry
model, Atmos. Chem. Phys., 15, 2295–2312, 10.5194/acp-15-2295-2015, 2015.Mammarella, I., Kolari, P., Rinne, J., Keronen, P., Pumpanen, J., and Vesala,
T.: Determining the contribution of vertical advection to the net ecosystem
exchange at Hyytiälä forest, Finland, Tellus B, 59, 900–909,
10.1111/j.1600-0889.2007.00306.x, 2007.
Mammarella, I., Launiainen, S., Gronholm, T., Keronen, P., Pumpanen, J., Rannik,
Ü., and Vesala, T.: Relative humidity effect on the high frequency attenuation
of water vapour flux measured by a closed-path eddy covariance system, J. Atmos.
Ocean. Tech., 26, 1856–1866, 2009.Mammarella, I., Werle, P., Pihlatie, M., Eugster, W., Haapanala, S., Kiese, R.,
Markkanen, T., Rannik, Ü., and Vesala, T.: A case study of eddy covariance
flux of N2O measured within forest ecosystems: quality control and flux
error analysis, Biogeosciences, 7, 427–440, 10.5194/bg-7-427-2010, 2010.Mammarella, I., Peltola, O., Nordbo, A., Järvi, L., and Rannik, Ü.:
Quantifying the uncertainty of eddy covariance fluxes due to the use of different
software packages and combinations of processing steps in two contrasting
ecosystems, Atmos. Meas. Tech., 9, 4915–4933, 10.5194/amt-9-4915-2016, 2016.Marcazzan, G. M., Caprioli, E., Valli, G., and Vecchi, R.: Temporal variation
of 212Pb concentration in outdoor air of Milan and a comparison with 214Bi,
J. Environ. Radioact., 65, 77–90, 10.1016/S0265-931X(02)00089-9, 2003.Maseyk, K., Berry, J. A., Billesbach, D., Campbell, J. E., Torn, M. S., Zahniser,
M., and Seibt, U.: Sources and sinks of carbonyl sulfide in an agricultural field
in the Southern Great Plains, P. Natl. Acad. Sci. USA, 112, 14162–14167,
10.1073/pnas.1319132111, 2014.Montzka, S. A., Calvert, P., Hall, B. D., Elkins, J. W., Conway, T. J., Tans,
P. P., and Sweeney, C.: On the global distribution, seasonality, and budget
of atmospheric carbonyl sulfide (COS) and some similarities to CO2, J.
Geophys. Res.-Atmos., 112, D09302, 10.1029/2006JD007665, 2007.
Nieminen, T., Asmi, A., Dal Maso, M., Aalto, P. P., Keronen, P., Petäjä,
T., Kulmala, M., and Kerminen, V.-M.: Trends in atmospheric new-particle
formation: 16 years of observations in a boreal-forest environment, Boreal
Environ. Res., 19, 191–214, 2014.Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch,
W., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., and Yakir, D.: Towards
a standardized processing of Net Ecosystem Exchange measured with eddy covariance
technique: algorithms and uncertainty estimation, Biogeosciences, 3, 571–583,
10.5194/bg-3-571-2006, 2006.Protoschill-Krebs, G., Wilhelm, C., and Kesselmeier, J.: Consumption of carbonyl
sulphide (COS) by higher plant carbonic anhydrase (CA), Atmos. Environ., 30,
3151–3156, 10.1016/1352-2310(96)00026-X, 1996.Rannik, Ü.: On the surface layer similarity at a complex forest site, J.
Geophys. Res.-Atmos., 103, 8685–8697, 10.1029/98JD00086, 1998.Rannik, Ü., Keronen, P., Hari, P., and Vesala, T.: Estimation of
forest–atmosphere CO2 exchange by eddy covariance and profile techniques,
Agr. Forest Meteorol., 126, 141–155, 2004.Rannik, Ü., Peltola, O., and Mammarella, I.: Random uncertainties of flux
measurements by the eddy covariance technique, Atmos. Meas. Tech., 9, 5163–5181,
10.5194/amt-9-5163-2016, 2016.Sandoval-Soto, L., Stanimirov, M., von Hobe, M., Schmitt, V., Valdes, J., Wild,
A., and Kesselmeier, J.: Global uptake of carbonyl sulfide (COS) by terrestrial
vegetation: Estimates corrected by deposition velocities normalized to the uptake
of carbon dioxide (CO2), Biogeosciences, 2, 125–132, 10.5194/bg-2-125-2005, 2005.
Schmidt, M., Graul, R., Sartorius, H., and Levin, I.: Carbon dioxide and methane
in continental europe: A climatology, and 222Radon-based emission estimates,
Tellus B, 48, 457–473, 1996.Seibt, U., Kesselmeier, J., Sandoval-Soto, L., Kuhn, U., and Berry, J. A.: A
kinetic analysis of leaf uptake of COS and its relation to transpiration,
photosynthesis and carbon isotope fractionation, Biogeosciences, 7, 333–341,
10.5194/bg-7-333-2010, 2010.Sesana, L., Caprioli, E., and Marcazzan, G. M.: Long period study of outdoor
radon concentration in Milan and correlation between its temporal variations
and dispersion properties of atmosphere, J. Environ. Radioact., 65, 147–160,
10.1016/S0265-931X(02)00093-0, 2003.Stimler, K., Berry, J. A., Montzka, S. A., and Yakir, D.: Association between
Carbonyl Sulfide Uptake and 18Δ during Gas Exchange in C-3 and C-4 Leaves,
Plant Physiol., 157, 509–517, 10.1104/pp.111.176578, 2011.Sun, W., Kooijmans, L. M. J., Maseyk, K., Chen, H., Mammarella, I., Vesala, T.,
Levula, J., Keskinen, H., and Seibt, U.: Soil fluxes of carbonyl sulfide (COS),
carbon monoxide, and carbon dioxide in a boreal forest in southern Finland,
Atmos. Chem. Phys. Discuss., 10.5194/acp-2017-180, in review, 2017.Szegvary, T., Leuenberger, M. C., and Conen, F.: Predicting terrestrial 222Rn
flux using gamma dose rate as a proxy, Atmos. Chem. Phys., 7, 2789–2795,
10.5194/acp-7-2789-2007, 2007.Van der Laan, S., Neubert, R. E. M., and Meijer, H. A. J.: Methane and nitrous
oxide emissions in The Netherlands: ambient measurements support the national
inventories, Atmos. Chem. Phys., 9, 9369–9379, 10.5194/acp-9-9369-2009, 2009.van der Laan, S., Manohar, S., Vermeulen, A., Bosveld, F., Meijer, H., Manning,
A., van der Molen, M., and van der Laan-Luijkx, I.: Inferring 222Rn soil
fluxes from ambient 222Rn activity and eddy covariance measurements of
CO2, Atmos. Meas. Tech., 9, 5523–5533, 10.5194/amt-9-5523-2016, 2016.
Vickers, D. and Mahrt, L.: Quality control and flux sampling problems for tower
and aircraft data, J. Atmos. Ocean. Tech., 14, 512–526, 1997.Watts, S. F.: The mass budgets of carbonyl sulfide, dimethyl sulfide, carbon
disulfide and hydrogen sulfide, Atmos. Environ., 34, 761–779, 2000.
Wehr, R., Commane, R., Munger, J. W., McManus, J. B., Nelson, D. D., Zahniser,
M. S., Saleska, S. R., and Wofsy, S. C.: Dynamics of canopy stomatal conductance,
transpiration, and evaporation in a temperate deciduous forest, validated by
carbonyl sulfide uptake, Biogeosciences, 14, 389–401, 10.5194/bg-14-389-2017, 2017.White, M. L., Zhou, Y., Russo, R. S., Mao, H., Talbot, R., Varner, R. K., and
Sive, B. C.: Carbonyl sulfide exchange in a temperate loblolly pine forest grown
under ambient and elevated CO2, Atmos. Chem. Phys., 10, 547–561,
10.5194/acp-10-547-2010, 2010.Winderlich, J., Gerbig, C., Kolle, O., and Heimann, M.: Inferences from CO2
and CH4 concentration profiles at the Zotino Tall Tower Observatory (ZOTTO)
on regional summertime ecosystem fluxes, Biogeosciences, 11, 2055–2068,
10.5194/bg-11-2055-2014, 2014.Wohlfahrt, G., Brilli, F., Hoertnagl, L., Xu, X., Bingemer, H., Hansel, A., and
Loreto, F.: Carbonyl sulfide (COS) as a tracer for canopy photosynthesis,
transpiration and stomatal conductance: potential and limitations, Plant Cell
Environ., 35, 657–667, 10.1111/j.1365-3040.2011.02451.x, 2012.