Introduction
Fossil fuel CO2 (CO2ff) is the fundamental contributor to the
increase in atmospheric CO2; hence its precise quantification is
crucial to better understand the global carbon budget. One of the major
uncertainties in the projections of climate change is the uncertainty in the
future carbon budget due to feedbacks between terrestrial ecosystems and
climate (Heimann and Reichstein, 2008). Information on the response of
the biosphere to climate variations can be obtained from atmospheric
CO2 observations, but isolating the biospheric signal in the measured
CO2 mixing ratios requires an accurate quantification of the fossil
fuel component. Several methods have therefore been proposed for quantifying
CO2ff, which are based on observations or models. A widely employed
approach is to determine CO2ff with an atmospheric transport model that
incorporates CO2ff emissions from a bottom-up emission inventory.
Emission inventories are based on statistics of the energy use by different
sectors and the quantification of CO2ff emissions by accounting for the
carbon content of each fuel and its corresponding oxidation ratios
(Friedlingstein et al., 2010; Le Quéré et al., 2016). When
compared to other greenhouse gases, national emission inventories for
CO2 are quite accurate, but the computation of these inventories is
laborious, and the quality depends on the energy statistics and reporting
methods that vary greatly between countries (Marland, 2008; Marland et
al., 2009). A recent study evaluating different energy statistics and cement
production data estimated an uncertainty of about 5 % for the global
fossil fuel emissions of the past decade (2006–2015) (Le Quéré
et al., 2016). At country level the uncertainties are usually below 5 %
in developed countries but often exceed 10 % in developing countries
(Ballantyne et al., 2015).
Additional uncertainties arise from the spatial and temporal disaggregation
of national annual total emissions to the grid of the atmospheric transport
model. At sub-country scales (less than 150 km), the uncertainty from
bottom-up estimates can reach up to 50 % (Ciais et al., 2010).
Finally, errors in the transport model and the inability to correctly
represent point observations in the model may contribute substantially to
the uncertainty of model-simulated CO2ff mixing ratios (Tolk et al.,
2008; Peylin et al., 2011).
Radiocarbon measurements can be used to directly quantify CO2ff
in atmospheric CO2 observations. Radiocarbon is produced in the upper
atmosphere during the reaction of neutrons with nitrogen induced by cosmic
rays (Currie, 2004). In addition, nuclear bomb tests in the 1960s led to
large radiocarbon input into the atmosphere, which thereafter decreased due
to gradual uptake by the oceans and the terrestrial biosphere (Manning et
al., 1990; Levin et al., 2010). Nowadays, the decline in atmospheric
14CO2 is mainly driven by input from 14C-free fossil fuel
CO2 (Levin et al., 2010). This decline is well detectable at background
sites such as Jungfraujoch, Switzerland, and Schauinsland, Germany (Levin et
al., 2013). While all reservoirs exchanging carbon with the atmosphere are
relatively rich in 14C, fossil fuels (millions of years old) are devoid
of 14C due to its radioactive decay with a half-life of 5370 years.
Hence, any fossil fuel CO2 emitted to the atmosphere will dilute the
background 14C signal, the so-called Suess effect, which can then be
used to unravel recently added fossil fuel CO2 to the atmosphere
(Zondervan and Meijer, 1996; Levin et al., 2003; Gamnitzer et al., 2006;
Turnbull et al., 2006, 2009, 2011a, 2014, 2015; Levin and Karstens, 2007;
Lopez et al., 2013). However, this depletion can also partially be offset by
CO2 release from the biosphere which has enriched
14C / 12C ratios due to nuclear bomb tests in the 1960s.
14C produced by these tests was absorbed by the land biosphere and is
now gradually being released back to the atmosphere (Naegler and Levin,
2009). Another contribution could be direct 14C emissions from nuclear
industries (Levin et al., 2010). This technique also enables separation
between biospheric and fossil fuel CO2 components in atmospheric
CO2 observations, and it thus better constrains the biospheric CO2
fluxes when coupled with inversion models (Basu et al., 2016). The
uncertainty in CO2ff estimated by the radiocarbon method is
mainly determined by the precision in the 14C measurement, the choice of
background, and the uncertainty in the contribution from other sources of
14C such as nuclear power plants (NPPs) (Turnbull et al., 2009).
Despite its importance as a fossil fuel tracer, measurements of 14C are
still sparse. The measurements are expensive and laborious, which so far has
prevented frequent sampling and has motivated researchers to combine 14C
measurements with additional tracers such as CO to enhance spatial and
temporal coverage (Gamnitzer et al., 2006; Turnbull et al., 2006, 2011a,
2014, 2015; Levin and Karstens, 2007; Vogel et al., 2010; Lopez et al.,
2013). The CO method relies on using high frequency CO measurements and
regular calibration of the temporally changing ΔCO : ΔCO2ff ratios based on weekly or biweekly 14C measurements.
Despite its advantage of providing a proxy for continuous CO2ff
data, the method introduces additional uncertainties due to diurnal and
seasonal variability in the CO sink, and the presence of multiple non-fossil
CO sources such as oxidation of hydrocarbons or wood and biofuel combustion
(Gamnitzer et al., 2006). Spatial variations in the ΔCO : ΔCO2 ratio across Europe due to different source compositions and
environmental regulations, which affect the measured ratios due to changes in
air mass origin (Oney et al., 2017), are the main reason for the temporally
changing ΔCO : ΔCO2ff ratio for a given
measurement site. Additionally, variability in the CO / CO2 emission
ratios of the sources can contribute to its spatial and temporal variability
(Vogel et al., 2010; Turnbull et al., 2015).
In Switzerland, CO2 contributes about 82 % of the total greenhouse
gas emissions according to the Swiss national emission inventory for 2013,
and fossil fuel combustion from the energy sector contributes more than 80 % of the total CO2 emission (FOEN, 2015b). In order to validate
such bottom-up estimates, independent techniques based on atmospheric
measurements are desirable. In addition, as mentioned above, the biospheric
CO2 signals can only be estimated with a good knowledge of CO2ff.
In this study, we present and discuss 14CO2 measurements conducted
biweekly between 2013 and 2015 at the Beromünster tall tower in
Switzerland. From these samples in combination with background CO, CO2,
and 14CO2 measurements at the high-altitude remote location
Jungfraujoch, Switzerland, Δ / ΔCO2ff ratios
(RCO) are derived. These ratios are then combined with the in situ
measured ΔCO mixing ratios to estimate a high-resolution time series
of atmospheric CO2ff mixing ratios and, by difference, of the
biospheric CO2 component. The influence of 14C emissions from
nearby NPPs and correction strategies are also discussed.
The geographical map of Beromünster and Jungfraujoch measurement
sites (blue) as well as the five NPPs in Switzerland (red).
Methods
Site description and continuous measurement of CO and CO2
A detailed description of the Beromünster tall tower measurement system
as well as a characterization of the site with respect to local
meteorological conditions, seasonal and diurnal variations of greenhouse
gases, and regional representativeness can be obtained from previous
publications (Oney et al., 2015; Berhanu et al., 2016; Satar et al.,
2016). In brief, the tower is located near the southern border of the Swiss
Plateau, the comparatively flat part of Switzerland between the Alps in the
south and the Jura Mountains in the northwest (47∘11′23′′ N, 8∘10′32′′ E, 797 m a.s.l.), which is characterized by intense
agriculture and rather high population density (Fig. 1). The tower is 217.5 m tall with access to five sampling heights
(12.5, 44.6, 71.5, 131.6, 212.5 m) for measuring CO, CO2, CH4, and H2O using cavity
ring-down spectroscopy (CRDS) (Picarro Inc., G-2401). By sequentially
switching from the highest to the lowest level, mixing ratios of these trace
gases were recorded continuously for 3 min per height, but only the
last 60 s was retained for data analysis. The calibration procedure
for ambient air includes measurements of reference gases with high and low
mixing ratios traceable to international standards (WMO-X2007 for CO2
and WMO-X2004 for CO and CH4), as well as target gas and more frequent
working gas determinations to ensure the quality of the measurement system.
From 2 years of data a long-term reproducibility of 2.79 ppb, 0.05 ppm,
and 0.29 ppb for CO, CO2, and CH4, respectively, was determined for
this system (Berhanu et al., 2016).
Sampling and CO2 extraction for isotope analysis
Air samples for 14CO2 analysis were collected from the highest
inlet usually between 09:00 to 13:00 UTC. At the beginning we collected one
sample per month, which was eventually changed to sampling every second week
from November 2013 onwards. During each sampling event, three samples were
collected over a 15 min interval in 100 L PE-AL-PE bags (TESSERAUX,
Germany) from the flush pump exhaust line of the 212.5 m sampling inlet,
which has a flow rate of about 9 L min-1 at ambient conditions. The
sampling interval was chosen to ensure radiocarbon sample collection in
parallel with the continuous CO and CO2 measurements by the CRDS
analyzer at the highest level. Each bag was filled at ambient air pressure
for 6 to 8 min, and a total air volume of 50 to 70 L (at STP) was
collected.
CO2 extraction was conducted cryogenically in the laboratory at the
University of Bern usually the day after the sample collection. During the
extraction step, the air sample was first pumped through a stainless-steel
water trap (-75 ∘C), which was filled with glass beads (Raschig
rings, 5 mm, Germany). A flow controller (Analyt-MTC, Aalborg, USA) with
a flow totalizer tool was attached to this trap to maintain a constant flow of
air (1.2 L min-1) towards the second trap (trap 2), a spiral-shaped
stainless-steel tube (1/4 in.) filled with glass beads (∼ 2 mm)
and immersed in liquid nitrogen to freeze out CO2. When the flow
ceased, trap 2 was isolated from the line and evacuated to remove gases
which are non-condensable at this temperature. Then, trap 2 was warmed to
room temperature and eventually immersed in slush at -75 ∘C to
freeze out any remaining water. Finally, the extracted CO2 was expanded
and collected in a 50 mL glass flask immersed in liquid nitrogen.
Sample extraction efficiency was calculated by comparing the amount of the
cryogenically extracted CO2 with the CO2 measured in situ by the
CRDS analyzer during the time of sampling. The amount of CO2 extracted
is determined first by transferring the extracted CO2 cryogenically to
a vacuum line of predetermined volume. Then, based on the pressure reading
of the expanded gas, and the total volume of air collected determined by the
mass flow controller with a totalizer function attached to trap 1, CO2
mixing ratios were calculated.
At the end of 2014 we noticed that there was a leakage from the sampling
line exhaust pumps, which resulted in unrealistically high CO2 mixing
ratios (usually more than 500 ppm). Therefore, we replaced all the exhaust
pumps. To further ensure that the leakage problem during sampling is solved,
we regularly check for leaks before sampling by closing the needle valves
leading to the pumps and monitoring in case there is any flow with the flow
meter attached after the pump. Since the replacement we have not observed
any indication of leakage. Seven samples which were suspected to be
contaminated due to this issue were consequently excluded. The sample
extraction efficiency since then has usually been better than 99 %. We
also conducted a blank test to check the presence of any leaks or contamination
during sample processing but did not observe any of these issues. Five more
samples were excluded in 2014 due to a strong mismatch among triplicates in
the measured CO2 after the sample extraction which indicated
contamination.
Measurement of δ13C, δ18O, and Δ14C
Prior to radiocarbon measurement, the extracted CO2 was analyzed for
the stable isotopes δ13C and δ18O using
an isotope ratio mass spectrometer (IRMS, Finnigan MAT 250) at the Climate and
Environmental Physics Division of the University of Bern, which has an accuracy
and precision of better than 0.1 ‰ for both δ13C and δ18O (Leuenberger et al.,
2003). 14C analysis of the extracted CO2 was performed with an
accelerator mass spectrometer (AMS), MICADAS (MIni CArbon DAting System), at
the Laboratory for the Analysis of Radiocarbon (LARA) at the Department of
Chemistry and Biochemistry of the University of Bern
(Szidat et al., 2014). The Automated Graphitization
Equipment (AGE) was used to prepare solid target gas (Nemec et al.,
2010) from the extracted CO2 stored in 50 mL glass flasks. A
measurement series consisted of up to 15 air samples converted to 30 solid
graphite targets (duplicates), together with four and three targets from
CO2 produced by combustion of the NIST standard oxalic acid II (SRM
4990C) and fossil CO2 (Carbagas, Gümligen), respectively, which
were used for the blank subtraction, standard normalization, and correction
for isotopic fractionations. For the fractionation correction, δ13C values of the AMS were used, which show a long-term standard
uncertainty of ±1.2 ‰ (Szidat et
al., 2014). The AMS δ13C values agree well on average with the
corresponding IRMS results, revealing a statistically insignificant
difference of -0.2 ± 1.2 ‰ with slightly more
depleted AMS results.
Data reduction was performed using the BATS program (Wacker et
al., 2010). The uncertainty of an individual 14C measurement typically
amounts to ∼ 2.1 ‰, including
contributions from counting statistics (∼ 1.1 ‰), corrections of normalization (i.e., blank subtraction,
standard normalization, and correction for isotopic fractionations)
(∼ 1.1 ‰), and an unaccounted-for long-term
variability of sampling and 14C analysis according to Szidat et al. (2014) (1.5 ‰). These contributions are comparable to
previous observations (Graven et al., 2007).
During calculation of weighted averages of the duplicates, the uncertainty
of the mean is determined with the contributions of the counting statistics
and the normalization, whereas the uncertainty of the unaccounted-for long-term
variability is considered fully afterwards, as this contribution cannot be
reduced by averaging of two measurements performed on the same day. This
uncertainty of the weighted average typically amounts to ∼ 1.9 ‰; it is compared with the standard deviation of the
duplicates, and the larger of these values is used as the final uncertainty
of the duplicates. The mean of the three individual samples from the same
day, which is used below in Sect. 2.4.1 as Δ14Cmeas, is
then determined and associated with the average uncertainty of the three
duplicates, as the variability of the three samples is comparable to this
average uncertainty for all cases.
As the 14C / 12C from Beromünster was measured at the LARA
laboratory in Bern, whereas the corresponding background samples from
Jungfraujoch were analyzed at the low-level counting (LLC) facility of the
Institute of Environmental Physics, Heidelberg University, the data sets
needed to be adjusted to each other. A recent interlaboratory compatibility
test between the LARA lab (code no. 2) and Heidelberg (LLC) estimated a
small bias (Hammer et al., 2016). The measurement bias
(i.e., the mean difference of the measured Δ14C minus the consensus
value of the participating laboratories for all investigated CO2
samples) is +1.8 ± 0.1 and -0.3 ± 0.5 ‰ for Bern and Heidelberg, respectively, from which the
bias between both labs of 2.1 ± 0.5 ‰ is
determined with a larger measured Δ14C for Bern. Consequently,
2.1 ± 0.5 ‰ was subtracted from the 14C
measurements of the Beromünster samples.
Determination of the fossil fuel CO2 component
The Δ14C technique
For the determination of the CO2ff component we followed
approaches similar to those in in previous studies (Zondervan and Meijer, 1996; Levin et
al., 2003; Levin and Karstens, 2007; Turnbull et al., 2009). The measured
CO2 is assumed to be composed of three major components: the free
troposphere background (CO2bg), the regional biospheric component
(CO2bio) comprising photosynthesis and respiration components, and the
fossil fuel component (CO2ff):
CO2meas=CO2bg+CO2bio+CO2ff
Each of these components has a specific Δ14C value (i.e., the
deviation in per mill of the 14C / 12C ratio from its primary
standard, and corrected for fractionation and decay using 13C
measurements) described as Δ14Cmeas, Δ14Cbg, Δ14Cbio, and
Δ14Cff. In analogy to Eq. (1), a mass balance
approximation equation can also be formulated for 14C as
CO2measΔ14Cmeas+1000‰=CO2bg(Δ14Cbg+1000‰)+CO2bioΔ14Cbio+1000‰+CO2ffΔ14Cff+1000‰.
Note that non-fossil-fuel components such as biofuels are incorporated into
the biospheric component in Eq. (1). The fossil fuel term in Eq. (2) is zero
as fossil fuels are devoid of radiocarbon (Δ14Cff=-1000 ‰). By replacing the biospheric CO2 component
in Eq. (1) by a formulation derived from Eq. (2), the fossil fuel CO2
component is derived as
CO2ff=CO2bgΔ14Cbg-Δ14Cbio-CO2measΔ14Cmeas-Δ14CbioΔ14Cbio+1000‰.
Equation (3) can be further simplified by assuming that Δ14Cbio is equal to Δ14Cbg (Levin et al.,
2003) as
CO2ff=CO2measΔ14Cbg-Δ14CmeasΔ14Cbg+1000‰.
Hence, the fossil fuel CO2 component can be determined using the
CO2meas and Δ14Cmeas values measured at the site as
well as Δ14Cbg obtained from the Jungfraujoch mountain
background site in the Swiss Alps.
However, the CO2ff determined using Eq. (4) incorporates a small bias
due to the non-negligible disequilibrium contribution of heterotrophic
respiration as well as due to contributions from NPPs. To correct for the
bias from these other contributions, an additional term (CO2other and
Δ14Cother) can be included in Eq. (4) as suggested by
Turnbull et al. (2009):
CO2ff=CO2measΔ14Cbg-Δ14CmeasΔ14Cbg+1000‰+CO2otherΔ14Cother-Δ14CbgΔ14Cbg+1000‰,
where CO2other and Δ14Cother represent the additional
CO2 and radiocarbon contributions from other sources such as NPPS and
biospheric fluxes, respectively.
The contributions from heterotrophic respiration will lead to an
underestimation of CO2ff on average by 0.2 ppm in winter and 0.5 ppm in
summer, estimated for the Northern Hemisphere using a mean
terrestrial carbon residence time of 10 years (Turnbull et al., 2006).
To account for the bias from heterotrophic respiration, a harmonic function
varying seasonally between these values was added to the derived
CO2ff values. However, variation of respiration fluxes on shorter
timescales cannot be accounted for by this simple correction. The correction
strategy for the contribution from NPPs is described in Sect. 2.4.2 below.
Simulation of 14CO2 from nuclear power plants
Radiocarbon is produced by nuclear reactions in NPPs and primarily emitted in
the form of 14CO2 (Yim and Caron, 2006), except for pressurized
water reactors (PWRs), which release 14C mainly in the form of
14CH4. Previous studies have shown that such emissions can lead to
large-scale gradients in atmospheric Δ14C activity and offset the
depletion from fossil fuel emissions (Graven and Gruber, 2011). In
Heidelberg, Germany, an offset of 25 and 10 % of the fossil fuel signal was
observed during summer and winter, respectively, due to emissions from a
nearby plant (Levin et al., 2003). Similarly, Vogel et
al. (2013) determined the influence of NPPs for a measurement site in Canada and
estimated that about 56 % of the total CO2ff component was
masked by the contribution from NPPs, though this large number was obtained
for a site in close vicinity of the CANadian Deutrium Uranium-type reactor
(CANDU), known for producing particularly high 14C emissions. In
Switzerland, there are five NPPs, and the closest plant is located about
30 km to the northwest of Beromünster (Fig. 1). Furthermore, air masses
arriving at Beromünster are frequently advected from France, which is the
largest producer of nuclear power in Europe.
To estimate the influence of Swiss and other European NPPs on Δ14C at Beromünster, we used FLEXPART-COSMO backward Lagrangian
particle dispersion simulations (Henne et al., 2016). FLEXPART-COSMO was
driven by hourly operational analyses of the non-hydrostatic numerical
weather prediction model COSMO provided by the Swiss weather service
MeteoSwiss at approximately 7 × 7 km2 resolution for a domain covering
large parts of western Europe from the southern tip of Spain to the northern
tip of Denmark and from the west coast of Ireland to eastern Poland. For
each 3 h measurement interval during the 3-year period, a source
sensitivity map (footprint) was calculated by tracing the paths of 50 000 particles released from Beromünster at 212 m above ground over 4 days
backward in time. The source sensitivities were then multiplied with the
14CO2 emissions of all NPPs within the model domain. Thereby, the
emission of a given NPP was distributed over the area of the model grid cell
containing the NPP. Source sensitivities were calculated for three different
vertical layers (0–50, 50–200, 200–500 m). Since the height of
ventilation chimneys of the Swiss NPPs is between 99 and 120 m, only the
sensitivity of the middle layer was selected here as it corresponds best to
the effective release height.
The release of 14C both in inorganic (CO2) and organic form
(CH4) is routinely measured at all Swiss NPPs. Annual totals of 14C
emissions are published in the annual reports of the Swiss Federal Nuclear
Safety Inspectorate ENSI
(https://www.ensi.ch/de/dokumente/document-category/strahlenschutzberichte/).
The corresponding data have been kindly provided by the Swiss Federal Nuclear
Safety Inspectorate ENSI and the Berner Kraftwerke (BKW), operating the NPP
Mühleberg at temporal resolutions ranging from annual (Benznau 1 & 2)
to monthly (Leibstadt, Gösgen) and biweekly (Mühleberg), and we
assumed constant emissions over the corresponding periods. For Beznau 1, the
emissions of 2015 were distributed over the first 3 months of the year due to
the shutdown of the plant in March 2015. The largest sources of
14CO2 in Switzerland are the two boiling water
reactors Mühleberg and Leibstadt (Loosli and Oeschger, 1989).
Beznau 1 and 2 and Gösgen are PWRs emitting about 1 order of magnitude
less 14CO2. For NPPs outside Switzerland, the emissions were
estimated from energy production data reported to the International Atomic
Energy Agency (IAEA) and NPP type-specific emission factors following Graven
and Gruber (2011). The difference δΔ14CnucBRM in
Δ14C between the nuclear emission signals at Beromünster
(Δ14CnucBRM) and at Jungfraujcoh (Δ14CnucJFJ) was then computed following Eq. (4) in Levin et
al. (2010) and assuming that the mole fraction (n14) of 14C due to
NPPs at Jungfraujoch is negligible compared to Beromünster. We then
obtain
Δ14Cnuc=fnnpp14nmeasCO2+1000,
with the dimensionless factor f=8.19×1014 and
nnpp14 /nmeasCO2 being the number
of 14C atoms due to NPPs simulated with FLEXPART-COSMO relative to the
total number of C atoms (12C + 13C + 14C) (which is
equal to the total number of CO2 molecules) measured at Beromünster.
Calculation of RCO, ΔCO / ΔCO2, and high-resolution CO2ff
A ΔCO / ΔCO2ff ratio (RCO) was calculated as the
slope of the geometric mean regression (model II), with ΔCO being
the CO enhancement over a background measured at Jungfraujoch, and the
CO2ff values as determined above. The CO measurements at Jungfraujoch
were conducted using a CRDS analyzer (Picarro Inc., G-2401) with a
measurement precision of ±2.5 ppb for 1 min aggregates (Zellweger et al., 2012).
As CO is usually co-emitted with CO2 during incomplete combustion of
fossil and other fuels, we have also computed a tracer ratio designated as
ΔCO / ΔCO2 from the enhancements in the in situ measured
CO and CO2 mixing ratios over the Jungfraujoch background
(Oney et al., 2017). CO2bg and CObg values were
obtained by applying the robust extraction of baseline signal (REBS)
statistical method (Ruckstuhl et al., 2012) to the continuous CO2
and CO measurements at the high-altitude site Jungfraujoch (Schibig et
al., 2016) with a bandwidth of 60 days. Note that, while RCO strictly
refers to the ratio of ΔCO to fossil fuel CO2 emissions, the
ΔCO / ΔCO2 ratio can be influenced by biospheric
contribution as well as CO2 emissions from non-fossil sources such as
biofuels and biomass burning.
CO2 mixing ratios (hourly averages) at Beromünster (black)
from the sample inlet at 212.5 m and from background measurements at
Jungfraujoch (blue) filtered using the REBS function for periods when
14C sampling was conducted (a), Δ14C determined from
the biweekly point samplings at the site before (green) and after (red)
correction for the intercomparison offset (see Sect. 2.3) and the 14C
contribution from NPPs (see Eq. 5) and from 14-day integrated samplings at
Jungfraujoch (blue) (b), CO2ff determined during this
period applying Eq. (4) with a mean CO2ff value of 4.3 ppm
(dashed line) (c), the biospheric CO2 determined by simple
subtraction of CO2bg and CO2ff from the
CO2meas (d), and the temperature record during this
period at the 212.5 m height level (e). Error bars in
panels (b) and (c) indicate the mean uncertainty in
Δ14C measurement (±2.0 ‰) and calculated
CO2ff (±1.2 ppm), averaged for the triplicate samples, while
error bars in panel (d) are obtained from error propagation of the
components in panels (a), (b), and (c). CO2
mixing ratios in the top panel are only shown from times matching the
radiocarbon sampling at Beromünster tower.
In order to construct the high-resolution CO2ff time series, we
combined the in situ measured CO enhancements at the Beromünster tower
with the radiocarbon-derived ratios RCO and estimated
CO2ffCO for the 3-year data set as
CO2ffCO=COobs-CObgRCO,
where COobs is the hourly averaged CO measurements at the tower.
Results and discussions
Δ14CO2 and CO2ff
Figure 2a shows the in situ measured hourly mean CO2 dry-air mole
fractions at Beromünster (black) from the 212.5 m sample inlet matching
at hours when air samples were collected for radiocarbon analysis and the
corresponding background CO2 at Jungfraujoch (blue). During the
measurement period, we recorded CO2 mixing ratios between 389 and
417 ppm. Spikes of CO2 were observed mainly during winter, associated
with weak vertical mixing and enhanced anthropogenic emissions, while lower
CO2 mixing ratios were recorded during summer due to strong vertical
mixing and photosynthetic uptake.
Isotopic analysis of the air samples yielded Δ14Cmeas
between -12.3 and +22.8 ‰, with no clear seasonal trend, after
correction for the model-simulated contribution from NPPs (Fig. 2b). Based on
the simulations described in Sect. 2.4.2, we have calculated a mean
enhancement in Δ14C of +1.6 ‰ and a maximum of
+8.4 ‰ due to NPPs. This agrees qualitatively with the
coarse-resolution simulations of Graven and Gruber (2011), which suggest a mean
enhancement of +1.4 to +2.8 ‰ over this region (Graven and
Gruber, 2011). While about 70 % of this contribution is due to Swiss
NPPs, the remaining contribution is of foreign origin. About 75 % of the
contribution from the Swiss NPPs is due to Mühleberg, which is located
west of Beromünster and hence frequently upstream of the site, due to the
prevailing westerly winds (Oney et al., 2015). Note that each data point
represents a mean value of the triplicate samples collected consecutively
with a standard error of 2 ‰ among triplicates. During this period,
the background Δ14C values measured at Jungfraujoch varied between
15 and 28 ‰. Regional depletions in Δ14C due to
fossil fuel emissions, i.e., differences between Beromünster and the
clean-air reference site Jungfraujoch, were in the range of -0.7 to
-29.9 ‰ with a mean value of -9.9 ‰.
Figure 2c shows the corresponding CO2ff determined after
correcting for radiocarbon emissions from NPPs. The typical uncertainty in
CO2ff is 1.2 ppm calculated by quadratically combining a mean
Δ14C measurement uncertainty of 2.0 ‰ in both the sample
and the background values, 0.3 ‰ from biospheric correction,
0.5 ‰ from interlaboratory offset, and a mean uncertainty of
1.2 ‰ in the estimation of 14C contribution from NPPs. A mean
fossil fuel CO2 contribution of 4.3 ppm was calculated from these
samples. Few cases, notably the sample from 27 March 2014, showed a higher
CO2ff and a strong depletion in Δ14Cmeas,
consistent with the high CO2 mixing ratio shown in the top panel. This
could be due to a strong local fossil fuel contribution or a polluted air mass
transported from other regions of Europe coinciding with the grab samplings.
As this event occurred during a period with moderate temperatures (mean
temperature of 6.8 ∘C measured at the highest level of the
Beromünster tower between March and May), strong fossil fuel CO2
emissions due to heating are not expected. The FLEXPART-COSMO transport
simulations for this event suggest an air mass origin from southeastern
Europe (see Supplement). Periods with winds from the east, colloquially known
as Bise, are well known to be associated with very stable boundary layers and
correspondingly strong accumulation of air pollutants during the cold months
of the year between autumn and spring. Air masses reaching Beromünster
from eastern Europe have recently been reported to contain unusually high
levels of CO during late winter and early spring periods, coinciding with
this sampling period (Oney et al., 2017).
Ratios (RCO) determined using radiocarbon measurements
after correcting for influence from NPPs and applying model II regression,
and ratios derived from continuous CO and CO2 measurements by the CRDS
analyzer as enhancements (ΔCO : ΔCO2) using
Jungfraujoch background measurements. RCO values are given in
mmol mol-1 with standard uncertainties of the slope and r2 values
in brackets, and n represents the number of samples for the radiocarbon
method. Note that, according to the Swiss emission inventory report for
greenhouse gas emissions in 2013, the annual anthropogenic CO / CO2
emission ratio for the national estimate is 7.8 mmol mol-1.
RCO (ΔCO : ΔCO2ff)
Number of
ΔCO : ΔCO2
(radiocarbon)
samples (n)
(CRDS)
Winter (Dec–Feb)
12.5 ± 3.3 (0.6)
8
7.3 (0.9)
Summer (Jun–Aug)
14.1 ± 4.0 (0.3)
14
13.4 (0.02)
All data
13.4 ± 1.3 (0.6)
45
8.3 (0.5)
By subtracting the background and fossil fuel CO2 contributions from
the measured mixing ratios, CO2bio values were also determined, ranging
between +11.2 and -12.4 ppm (Fig. 2d). Even if there is no clear
seasonal trend, the lowest CO2bio values were recorded during
summer, implying net photosynthetic CO2 uptake, while most of the values in
winter are positive or close to zero due to respiration. During summer 2015,
we observed strong variability in both CO2 and CO2bio (Fig. 2a
and d). However, this period was one of the hottest and driest summers in
central Europe (Orth et al., 2016). In Switzerland, it was the
second-hottest summer since the beginning of measurements in 1864 with most
of the extreme dates in July (MeteoSuisse, 2015). Such climate
extremes can lead to enhanced respiration and reduced photosynthesis and, in
turn, higher CO2 and CO2bio in the atmosphere. Looking
specifically at the two data points in June and July 2015, the daily average
temperatures recorded at Beromünster were 24.6 and 26 ∘C at the highest inlet of 212.5 m (Fig. 2e). Based on
measurements at Beromünster and other cities of the CarboCount CH
network in 2013, Oney et al. (2017) reported that for a daily mean
temperature of greater than 20 ∘C the biosphere over the Swiss
plateau tends to become a net CO2 source. The observed positive spikes
in CO2 (Fig. 2a) and CO2bio (Fig. 2d) likely resulted from such
extremes.
RCO values from radiocarbon measurements
From the simultaneous CO and radiocarbon measurements, we calculated an
RCO of 13.4 ± 1.3 mmol CO/mol CO2 with a
correlation coefficient (r2) of 0.7 and a median value of
11.2 mmol CO/mol CO2 (note that change in RCO is
insignificant when we use smoothed 14C background from Jungfraujoch).
When we split the data seasonally, RCO values of 12.5 ± 3.3
and 14.1 ± 4.0 mmol CO/mol CO2 were obtained during winter and
summer, respectively (Table 1). Even if the two values are not significantly
different considering the uncertainties, the very low correlation coefficient
during summer (r2=0.3) implies a larger uncertainty in the derived
RCO. Our wintertime estimate is well within the range of values
from previous studies (10–15 mmol mol-1) observed at other sites in
Europe and North America (Gamnitzer et al., 2006; Vogel et al., 2010;
Turnbull et al., 2011a). To test the sensitivity of this ratio to the
selection of background site, we additionally calculated RCO
using background values estimated with the REBS method from the in situ CO
measurements at Beromünster instead of Jungfraujoch. The value obtained
in this way (12.7 ± 1.2, r2=0.6) is not significantly different
from the value obtained using Jungfraujoch as the background site.
Considering the persistent decrease in CO emissions (Zellweger et al., 2009)
in response to the European emission legislation, our estimated
RCO is surprisingly high. A recent study investigating the
CO / CO2 ratio from road traffic in Islisberg tunnel, Switzerland,
also observed a significant decrease in this ratio compared to previous
estimates, pointing to a substantial reduction in CO emissions from road
traffic, with a CO / CO2 ratio of 4.15 ± 0.34 ppb ppm-1
(Popa et al., 2014). This may indicate a significant contribution from
non-road traffic emissions, which account for more than 70 % of the total
CO2 emissions leading to the high apparent RCO.
The RCO value derived in this study is significantly higher than the
anthropogenic CO / CO2 emission ratio of 7.8 mmol mol-1 calculated from
Switzerland's greenhouse gas inventory report for 2013 (FOEN, 2015b, a).
However, this can be due to enhanced CO emissions transported from other
European cities towards Beromünster. Oney et al. (2017) observed
particularly large CO / CO2 ratios at Beromünster during several
pollution events in late winter and early spring 2013 which were associated
with air mass transport from eastern Europe, where poorly controlled
combustion of biofuels and coal likely results in high ratios.
ΔCO / ΔCO2 from continuous measurements
Figure 3 shows the seasonally resolved correlations of ΔCO with
ΔCO2 derived from in situ measured CO and CO2 enhancements
over the background observed at Jungfraujoch, for which we estimated a tracer
ratio of 8.3 ± 0.1 mmol mol-1 (r2=0.5) for the entire
measurement period. Considering the seasonally resolved ΔCO / ΔCO2 ratios, barely any correlation is observed in
summer, and weak correlations (r2 < 0.4) are observed during
spring and autumn. This can be due to the dominance of biogenic fluxes over
fossil fuel fluxes during these periods of the year. From measurements during
winter, when the two species are most strongly correlated, a ΔCO / ΔCO2 ratio of 7.3 ± 0.1 mmol mol-1
(r2=0.9) is obtained. Recently, Oney et al. (2017) reported a higher
wintertime ratio of 8.3 mmol mol-1 for the same combination of
measurements at Beromünster and Jungfraujoch but for a different time
period. If we consider only winter 2013 as in their data, we obtain
essentially the same value, while much lower ratios of 6.5 and
6.4 mmol mol-1 were calculated for 2014 and 2015, respectively. The
higher ratios in winter 2013 are likely related to the unusually cold
conditions and extended periods of air mass transport from eastern Europe.
Note that, in contrast to RCO, these enhancement ratios also
include emissions from non-fossil sources such as biofuels and biomass
burning as well as the influence of biogenic fluxes. The Swiss national
inventory attributes about 15 % of total CO2 emissions in 2014 to
non-fossil-fuel sources (FOEN, 2015b). If we correct for these sources
assuming a constant contribution throughout the year, the wintertime ΔCO / ΔCO2 ratio for the 3-year data becomes
8.7 mmol mol-1.
The correlation between enhancements in CO and CO2 at
Beromünster over Jungfraujoch background for the different seasons. The
black dots and the black solid line correspond to the individual wintertime
RCO values and the linear fit to these points, respectively.
Time series of hourly mean CO mixing ratios measured at
Jungfraujoch (a) and Beromünster (b) sites, with the red
curve showing the estimated background values using the REBS method with
60-day window. Panel (c) shows the hourly mean CO2ff
time series calculated using the emission ratios determined from radiocarbon
measurements, and the CO enhancements at Beromünster over the
Jungfraujoch background based on Eq. (7). The blue dots in panel (c)
shows the CO2ff values determined using the radiocarbon
measurements.
This ratio of 8.7 mmol mol-1 is still about 30 % lower than the
RCO estimate for the same period of 12.5 mmol mol-1, shown
as a black line in Fig. 3. This suggests that despite the strong correlation
between ΔCO and ΔCO2 in winter the regional CO2
enhancements are not only caused by anthropogenic emissions but include a
significant contribution from biospheric respiration. Miller et al. (2012)
showed that such strong correlations between CO2 and CO during winter
may arise from respiratory fluxes co-located with fossil fuel fluxes trapped
under the wintertime shallow and stable boundary layer but with strongly
biased ratios when compared to RCO. Turnbull et al. (2011b) also
observed a substantial contribution of biospheric CO2 fluxes even during
winter (20–30 % from non-fossil-fuel sources including photosynthesis
and respiration) from samples collected at two sites in East Asia. The
magnitude of these fluxes was roughly similar to the CO2ff flux
when continental background was used (Turnbull et al., 2015). Hence, the
observed correlation between ΔCO and ΔCO2 in this study
is not only due to spatially and temporally correlated sources but is caused
to a large extent by meteorological variability associated with more or less
accumulation of trace gases in the boundary layer irrespective of their
sources. This interpretation is also supported by the fact that a strong
correlation (r2 > 0.7) was also observed between CO and
CH4 during winter at the same tower site (Satar et al., 2016) despite
their sources being vastly distinct. In Switzerland about 80 % of
CH4 emissions are from agriculture (mainly from ruminants), while more
than 85 % of CO emissions are from the transport sector and residential
heating (FOEN, 2015a).
Time series (hourly resolution) of the biospheric CO2 derived
as a residual of the difference between the total CO2,
CO2bg, and CO2ff for all data (a), and only afternoon data from
12:00–15:00 UTC (b). The green lines show negative
CO2bio, implying uptake, while red ones represent positive
CO2bio. The average uncertainty of CO2bio amounts
±1.3 ppm calculated from error propagation.
High-resolution time series of CO2ff and CO2bio
Figure 4 shows the hourly mean CO mixing ratios at Jungfraujoch and
Beromünster between 2013 and 2015. CO mixing ratios as high as 480 ppb
were recorded at Beromünster, while generally lower CO values were
recorded at the more remote site Jungfraujoch. A pronounced seasonality in
CO can be observed at Beromünster with higher values in winter and lower
values during summer due to stronger vertical mixing and chemical depletion
of CO by OH (Satar et al., 2016). The hourly mean
CO2ff time series calculated using these continuous CO measurements and
the seasonally resolved RCO values derived using the radiocarbon
measurements are displayed in Fig. 4c. A seasonal trend in the calculated
CO2ff is observed with frequent spikes of CO2ff during
winter, while summer values show less variability. We calculated a monthly mean
amplitude (peak to trough) of 6.3 ppm with a maximum in February and a
minimum in July. During the measurement period, we observed CO2ff
mixing ratios ranging up to 27 ppm coinciding with cold periods and likely
from enhanced anthropogenic emissions due to heating. Instances of slightly
negative CO2ff contributions, which occurred during less than 5 % of
the time, were associated with negative enhancements in CO (i.e., ΔCO < 0). This could be simply due to an overestimation of background
values by the REBS function during these periods.
Figure 5a shows the hourly averaged residual CO2bio values, which
exhibit not only a clear seasonal cycle but also a considerable scatter in all
seasons, ranging from -13 to +30 ppm. During winter, most values were close to
zero or positive, implying a dominance of respiration fluxes. In summer,
conversely, pronounced negative and positive excursions were observed mostly
due to the diurnal cycle in net CO2 fluxes, which are dominated by
photosynthetic uptake during daytime and respiration at night. Another factor
contributing to such variations may be the application of a constant emission
ratio neglecting any diurnal variability (Vogel et al., 2010).
It should also be noted that any non-fossil-fuel CO2 sources such as
emissions from biofuels would be incorporated into the CO2bio term
since CO2ff in Eq. (1) represents the fossil fuel sources only, adding
more variability to the data set. In order to reduce the influence of these
diurnal factors, we have looked into afternoon CO2bio values (12:00–15:00 UTC), when the CO2 mixing ratios along the tower are uniform
(Satar et al., 2016) and RCO variability is minimal.
Similar to the seasonal pattern in Fig. 5a, a clear seasonal cycle in
biospheric CO2 can be observed (Fig. 5b) in agreement with biospheric
exchange, but both positive and negative extremes are less frequently
observed (-12 to +22 ppm).
The variation in CO2bio during afternoon (12:00–15:00 UTC) was
recently estimated at this site at a range of -20 to +20 ppm by
combining observations and model simulations for the year 2013
(Oney et al., 2017). Our estimates are more positive than those in their study,
due to the higher RCO which results in lower
CO2ff and correspondingly higher CO2bio values.
Biospheric CO2 shows a seasonally dependent diurnal variation as shown
in Fig. 6. During winter (December–February), the biospheric CO2
component remains consistently positive (+2 to +5 ppm) throughout the
day, implying net respiration fluxes. In summer, a clear feature with
increasing CO2bio values during the night peaking between 07:00
and 08:00 UTC (i.e., between 08:00 and 09:00 local time) can be observed.
This buildup during the night can be explained by CO2 from respiration
fluxes accumulating in the stable and shallow nocturnal boundary layer. Then,
after sunrise, the early morning CO2bio peak starts to gradually
decrease due to a combination of onset of photosynthesis and enhanced
vertical mixing due to the growth of the boundary layer. At Beromünster,
a decrease in CO2 mixing ratios from both processes is visible more or
less at the same time at the 212.5 m height level. As reported by Satar et
al. (2016), this decrease in early morning CO2 concentrations at the
212 m inlet lags behind the decrease at the lowest sampling level of 12.5 m
by approximately 1 h. Between 12:00 and 15:00 UTC, when the daytime
convective boundary layer is fully established, the biospheric CO2
continues to become more negative, implying net photosynthetic uptake, which
eventually stabilizes for 3–5 h until nighttime CO2bio
accumulation starts.
Hourly variations of monthly averaged biospheric CO2 during
summer (June–August) and winter (December–February). While winter values
dominated by respiration are constant throughout a day, summer values show a
significant diurnal variation induced by photosynthesis and vertical mixing.
The error bars are the standard deviations of the hourly averaged
CO2bio values for each month.
Conclusions
From continuous measurements of CO and CO2 and biweekly radiocarbon
samples at the Beromünster tall tower, we have estimated a ΔCO / ΔCO2ff ratio (RCO) which was
subsequently used to construct a 2.3-year-long high-resolution
CO2ff time series. We have corrected the ratio for an offset of
about 16 % caused by 14C emissions from nearby NPPs. This bias was
calculated by comparing the simulated mean enhancement in Δ14C
(1.6 ‰) due to NPPs with the measured mean depletion in
Δ14C due to fossil fuel CO2 (9.9 ‰). The
radiocarbon-based RCO derived in this study during winter is
about 30 % higher than the CO:CO2 enhancement ratios estimated from
continuous CO and CO2 measurements during the same period, suggesting a
significant biospheric contribution to regional CO2 enhancements during
this period. This is in agreement with previous studies that observed
20–30 % biospheric contribution during winter (Turnbull et al., 2011b).
The obtained CO2ff time series shows a clear seasonality with frequent
spikes during winter associated with enhanced anthropogenic emissions and
weak vertical mixing, while summer values are mostly stable.
By subtracting the estimated CO2ff and CO2bg from CO2meas, we
have also calculated the biospheric CO2 component, which ranges between
-15 and +30 ppm. Considering only afternoon data (12:00–15:00 UTC),
when the convective boundary layer is fully established, CO2bio showed
its minimum in summer coinciding with net photosynthetic uptake but still
with frequent positive excursions, especially during summer 2015, possibly
driven by the record hot and dry summer during this period. During winter,
CO2bio becomes nearly zero or positive, implying respiration fluxes.
A pronounced diurnal variation in CO2bio was observed during
summer modulated by vertical mixing and biospheric exchange, while this
variation disappears during winter. However, the variation in
CO2bio may also be influenced by the uncertainty of the
CO2ff estimate, especially due to applying a constant emission
ratio while calculating CO2ff. Hence, it will be important in the
future to include seasonally and diurnally resolved RCO values
from high-frequency radiocarbon measurements to better estimate
CO2ff. Detailed analysis of the planetary boundary layer height
may also provide useful information to better clarify such variations, and it
will be the focus of future studies. Additionally, including independent
tracers such as atmospheric potential oxygen estimates
based on concurrent CO2 and O2 measurements will be very useful to
validate fossil fuel emission estimates from the radiocarbon method. This
technique is also advantageous as the fossil fuel CO2 estimate is
unaltered by contribution from NPPs, and it accounts for the contribution
from biofuels.