In this study, we
investigated the regional contributions of carbon dioxide (CO2) at the
location of the high Alpine observatory Jungfraujoch (JFJ, Switzerland,
3580 m a.s.l.). To this purpose, we combined receptor-oriented atmospheric
transport simulations for CO2 concentration in the period 2009–2017
with stable carbon isotope (δ13C–CO2) information. We
applied two Lagrangian particle dispersion models driven by output from two
different numerical weather prediction systems (FLEXPART–COSMO and
STILT-ECMWF) in order to simulate CO2 concentration at JFJ based on
regional CO2 fluxes, to estimate atmospheric δ13C–CO2, and to obtain model-based estimates of the mixed source
signatures (δ13Cm). Anthropogenic fluxes were taken from a
fuel-type-specific version of the EDGAR v4.3 inventory, while ecosystem
fluxes were based on the Vegetation Photosynthesis and Respiration Model
(VPRM). The simulations of CO2, δ13C–CO2, and δ13Cm were then compared to observations performed by quantum
cascade laser absorption spectroscopy. The models captured around 40 % of
the regional CO2 variability above or below the large-scale background
and up to 35 % of the regional variability in δ13C–CO2.
This is according to expectations considering the complex Alpine topography,
the low intensity of regional signals at JFJ, and the challenging
measurements. Best agreement between simulations and observations in terms
of short-term variability and intensity of the signals for CO2 and
δ13C–CO2 was found between late autumn and early spring.
The agreement was inferior in the early autumn periods and during summer.
This may be associated with the atmospheric transport representation in the
models. In addition, the net ecosystem exchange fluxes are a possible source
of error, either through inaccuracies in their representation in VPRM for
the (Alpine) vegetation or through a day (uptake) vs. night (respiration)
transport discrimination to JFJ. Furthermore, the simulations suggest that
JFJ is subject to relatively small regional anthropogenic contributions due
to its remote location (elevated and far from major anthropogenic sources)
and the limited planetary boundary layer influence during winter. Instead,
the station is primarily exposed to summertime ecosystem CO2
contributions, which are dominated by rather nearby sources (within 100 km).
Even during winter, simulated gross ecosystem respiration accounted for
approximately 50 % of all contributions to the CO2 concentrations
above the large-scale background. The model-based monthly mean δ13Cm ranged from - 22 ‰ in winter to - 28 ‰ in summer and reached the most depleted values of - 35 ‰ at higher fractions of natural gas combustion, as well as the
most enriched values of - 17 ‰ to - 12 ‰ when impacted by
cement production emissions. Observation-based δ13Cm
values were derived independently from the simulations by a moving
Keeling-plot approach. While model-based estimates spread in a narrow range,
observation-based δ13Cm values exhibited a larger scatter and
were limited to a smaller number of data points due to the stringent
analysis prerequisites.
Introduction
Reliable regional quantification of greenhouse gas (GHG) emissions into the
atmosphere is a prerequisite to determine the effectiveness of mitigation
strategies to limit global warming. Carbon dioxide (CO2) is the prime
player in this regard. Its atmospheric concentrations are altered by both
anthropogenic and natural (terrestrial ecosystem and oceanic) fluxes
(Friedlingstein
et al., 2020). Remote sites are ideal to study large-scale and global
emissions but make it more challenging to characterize individual sources
and sinks as during transport of air masses the signals and signatures
become increasingly diluted and mixed. Thus, remote atmospheric sites
typically focus on long-term trends, and therefore sporadic events are
often discarded in the time series analyses. This leads to loss of
potentially insightful information.
In this study, we focus on the information contained in the regional-scale
signals at the remote high-altitude observatory Jungfraujoch (JFJ), situated
in the Swiss Alps. Owing to its particular location in central western
Europe and its altitude of 3580 m above sea level (a.s.l.), JFJ allows for
studying background concentrations of air pollutants and GHGs in the lower
free troposphere (Herrmann et al., 2015).
These background conditions are representative of large spatial- or temporal-scale variations and not influenced by regional sources or sinks.
Furthermore, regional signals transported from different regions within
western Europe and beyond reach the monitoring station intermittently
(Henne et al., 2010). Thus JFJ offers
both aspects: (i) insight into the atmospheric background and (ii) an
opportunity for studying GHGs and pollutant sources and sinks in the
planetary boundary layer (PBL) on a regional scale. The latter is
challenged, however, by low signal-to-background ratios and requires
high-precision instrumentation. In comparison to a typical low-altitude
site, the regional signal measured at JFJ is integrated over a larger
concentration footprint (source area). This allows for greater coverage
per measurement but also leads to a higher degree of mixing of various
sources and sinks. Atmospheric backward transport simulations can provide
information about the history (location backward in time) of the sampled air
mass and a quantitative relationship between atmospheric concentrations and
sources or sinks (source–sink–receptor relationships) to combat this
challenge. Although atmospheric transport and concentration simulations are
particularly demanding for complex topography, observations at JFJ have been
successfully combined with high-resolution transport simulations in previous
inverse modelling studies to allocate and quantify emissions of CH4
(Henne et al., 2016) and halocarbons (Keller et al., 2011; Brunner
et al., 2017; Vollmer et al., 2021).
The same task, however, is more challenging for CO2 because of the
strong contribution of natural processes in addition to anthropogenic
sources, the interplay between signals from sources and sinks, and the large
temporal variability and broad distribution, especially of the natural
fluxes. In this case, multi-tracer approaches are useful tools, as they
allow for separation of different processes based on composition
characteristics. Some of their benefits and limitations are briefly revoked
in the following.
Carbon monoxide (CO), which is co-emitted during combustion processes, was
used to identify combustion-related CO2 signals
(Levin and Karstens, 2007; Vogel et al., 2010; Vardag et al., 2015;
or Oney et al., 2017). However, this method suffers from variable
CO/CO2 emission ratios and atmospheric production and loss of CO. The
approach is most promising when all sources and sinks in the footprint area are
well characterized, yet it remains challenging for sites with low
signal-to-background ratios.
Other promising tracers are isotopes, as isotope composition measurements
can provide valuable information on the sources and sinks contributing to
the regional signal. Today, sufficiently precise instrumentation is
available that allows measuring the stable isotope composition at high
precision and temporal resolution for several natural GHGs (see Tuzson et al., 2008b, for CO2; Eyer et al., 2016, for CH4; and Waechter et al., 2008, for N2O).
Applying these or similar techniques, for instance, Röckmann et al. (2016), Hoheisel et al. (2019), Menoud et al. (2020), Xueref-Remy et al. (2020), and
Zazzeri et al. (2015, 2017) derived observation-based isotope source
signature estimates from measurements conducted to study near-source or
regional-scale CH4 plumes.
Harris et al. (2017a, b) and Yu et al. (2020) presented similar analyses for N2O. These
studies took advantage of double-isotope constraints, i.e. δ13C–CH4 and δ2H–CH4 for CH4, and δ15N–N2O and δ18O–N2O for N2O and provided
promising results; however, the availability of long-term data sets is
still very limited.
The stable carbon isotope of CO2, δ13C–CO2, can be an
attractive tracer for CO2 sources and sinks. So far it has been largely
employed for analysis of long-term atmospheric background trends
(Keeling et al., 1979; Graven et al., 2017), in global
ecosystem studies
(Ballantyne et al., 2011; Keeling et al., 2017; Van Der Velde et al., 2018), or
to characterize emissions close to a source. Traditionally, near-source
δ13C–CO2 studies focus on ecosystem processes in areas
with low anthropogenic influence (Pataki et al., 2003) or
on anthropogenic emissions under low ecosystem influences, such as the
vehicle tunnel study by Popa et al. (2014). However, the current instrumental capability of high-precision
δ13C–CO2 observations at high temporal resolution (e.g. Sturm et al., 2013, or Vogel et al., 2013)
opens up new opportunities to disentangle CO2 in a more complex
setting. For instance, Pugliese
et al. (2017) and Vardag et al. (2016) recently studied urban air masses,
and Ghasemifard
et al. (2019) and Tuzson et al. (2011) attempted to characterize specific
regional-scale CO2 signals at remote sites. These studies used hourly
to daily resolution and compared observation-based (mixed) isotope source
signatures (δ13Cm) with literature information on
source-specific signatures (δ13Cs), often, however,
reducing the data to few particular pollution events, as this method is
applicable only under very stringent conditions
(see e.g. Zobitz et al., 2006). These source identification or apportionment
studies may successfully use δ13Cs to discriminate
CO2 emissions from fuel burning, in particular to distinguish gaseous
(- 40 ‰ for thermogenesis gas, - 60 ‰
for microbial gas) from solid (- 20 ‰ to - 25 ‰ for wood and coal) or liquid fuels (- 25 ‰ to - 32 ‰ for heating oil,
gasoline, and diesel)
All δ13Cs values mentioned
here are based on Andres et al. (1994), Vardag et al. (2015, 2016), and
Sherwood et al. (2017) and are presented based on the Vienna Pee Dee Belemnite (VPDB) reference scale.
. However, ecosystem δ13Cs adds
further complexity. Firstly, it is highly dependent on plant growth conditions
(ambient humidity, CO2 concentration) and photosynthetic pathway (C3
vs. C4 plants); see Hare et al. (2018) and Kohn (2010). CO2 from C3 plants (which dominate
ecosystems globally) carries a mean respiration signature of - 27.5 ‰ with a range from - 20 ‰ to - 37 ‰ under arid and humid conditions, respectively. The
smallest 13C uptake relative to 12C, i.e. highest fractionation
and thus the most depleted δ13Cs of - 37 ‰, is observed in tropical forests and is of little
relevance for European ecosystems. C4 plants (which primarily includes a few
particular crops such as maize, sugar cane, sorghum, and various kinds of
millet, selected grasses, e.g. clover, and only a few trees and desert
shrubs) exhibit distinctly smaller 13C fractionation during
photosynthesis and can be distinguished from C3 plants based on their
peculiar δ13Cs of about - 12.5 ‰. In
Europe C4 plants make up only a small fraction and are mainly present in
croplands (maize production). Instead C3 plants dominate the European and
global ecosystems (Ballantyne et al., 2011).
Secondly, it is critical to note that δ13Cs for C3 plant
respiration and some anthropogenic sources overlap, limiting source
apportionment approaches for ecosystem and anthropogenic contributions,
which are based only on δ13Cs. Therefore, a multi-isotope approach needs to be considered. The stable oxygen isotope
ratio of CO2, δ18O–CO2, is, aside from the carbon
cycle, subject to the global water cycle (e.g. Welp
et al., 2011) due to the isotope exchange between water and CO2, and
thus it is ambiguous as a CO2 tracer. Instead, the radiocarbon signature may be
used to quantify fossil fuel contributions to atmospheric CO2, e.g.
Levin et al. (2003), Vogel et al. (2010), Turnbull et al. (2015), Berhanu et al. (2017), or Wenger et al. (2019). The Δ14C primarily allows
for discrimination of fossil versus ecosystem carbon. Once this is
accomplished, δ13C provides further insight into the
partitioning of fuel types among the fossil pool or of contributions from
different photosynthetic pathways among the ecosystem pool. Such dual
carbon-isotope approaches making use of co-located δ13C and
Δ14C measurements have already proven successful for carbon
source apportionment in a few gas- (Meijer et
al., 1996; Zondervan and Meijer, 1996) and particle-phase studies (Winiger et al., 2019; Andersson et al.,
2015). Yet, studies are currently limited to infrequent sampling at a few
locations, since the involved laboratory analyses are costly and high-frequency in situ measurement techniques with sufficient precision for
atmospheric Δ14C–CO2 currently unavailable, despite
first developments (e.g. Genoud et al., 2019; Galli et al., 2011).
Despite these promising multi-tracer (CO2, CO) and multi-isotope
(δ13C and Δ14C) approaches outlined above, the low
signal-to-background ratios at remote sites still remain a challenge as
highlighted in previous work by Vardag et al. (2015).
Thus, combining measurements with atmospheric simulations is
essential for regional CO2 apportionment. Yet, to date, only a few
studies have performed hourly-scale regional simulations of CO2
concentration and/or provided “model-based” atmospheric δ13C–CO2 or mixed isotope source signatures (δ13Cm) for a comparison with observations. The available studies
are limited to two ground-based urban locations
(Pugliese-Domenikos et al., 2019; Vardag et al., 2016) and one rural tall-tower location (Wenger et al., 2019).
Here, we address the situation at the high Alpine observatory JFJ. We aim at
challenging our understanding of the contribution of CO2 sources and
sinks within the European domain to the regional CO2 concentration
variability at JFJ and at evaluating model-based δ13C–CO2
and model-based mixed isotope source signatures (δ13Cm)
against observations. To this end, we employ long-term regional CO2
simulations for JFJ for a 9-year period (2009–2017) at 3-hourly
time resolution using two different atmospheric transport models. We
compare the model-based data to atmospheric observations, making use of the
unique long-term high-frequency observations of CO2 and δ13C–CO2 measured by quantum cascade laser absorption spectroscopy
(QCLAS) since 2008 (Sturm
et al., 2013; Tuzson et al., 2011), and deploy a moving Keeling-plot method
to obtain observation-based δ13Cm.
MethodsSite description
The high-altitude research station Jungfraujoch (JFJ) is located at
7∘59′20′′ E, 46∘32′53′′ N in the Swiss Alps at an
altitude of 3580 m a.s.l. on a mountain saddle between the peaks of Jungfrau
and Mönch (both > 4000 m a.s.l.). As part of the Swiss
long-term national monitoring network (NABEL), regular measurements of air
pollutants and GHGs have been performed at JFJ since the 1970s
(Buchmann et al., 2016). The station contributes to European
(EMEP) and global (Global Atmospheric Watch; GAW) monitoring programmes and
was labelled as a class 1 station within the European Integrated Carbon
Observing System (ICOS) in 2018 (Yver-Kwok et al., 2021).
Atmospheric transport simulations
Atmospheric CO2 concentration simulations were conducted for the
period 2009–2017 with two distinct combinations of Lagrangian particle
dispersion models (LPDMs), meteorological input fields, domain size, and
spatial resolution (Table 1). Both models were run
in a receptor-oriented approach, following sampled air masses backward in
time, and as such providing surface source sensitivities (“footprints”).
Convoluting these with spatially and temporally resolved CO2 fluxes
allows for quantitative simulations of CO2 concentrations at the
receptor site (Seibert and Frank, 2004). Here,
we use the fuel-type-specific version of the Emissions Database for Global
Atmospheric Research (EDGAR v4.3) inventory and the Vegetation
Photosynthesis and Respiration Model (VPRM) to account for anthropogenic and
ecosystem CO2 fluxes, respectively. The simulated CO2 mixing
ratios are reported in parts per million (ppm), and we refer to them as “concentration” for
readability. In order to disentangle the influence of the underlying
CO2 fluxes and the transport dynamics on the simulated CO2
concentrations at JFJ, the influence of various parameters such as the
domain size, the meteorological input fields, and the LPDM implementation was
investigated in dedicated simulations with synthetic CO2 fluxes in
Appendix A1.
Overview of atmospheric transport simulation models and
their associated parameters.
* EU and WEU refer to 33∘–73∘ N,
- 15–35∘ E and 36.06–57.42∘ N, -11.92–21.04∘ E, respectively.
FLEXPART–COSMO
A version of the LPDM FLEXPART (Pisso
et al., 2019; Stohl et al., 2005) coupled to output from the regional
numerical weather prediction model COSMO (Baldauf
et al., 2011) was operated using operational analysis fields generated by
MeteoSwiss (see Henne et al., 2016). The model was run in backward mode to
calculate source sensitivities for JFJ. Within each 3-hourly interval,
50 000 model particles were continuously initialized at the receptor
location and traced back in time for 4 d or until they left the model
domain. FLEXPART considers transport by the mean atmospheric flow as well as
turbulent and sub-grid-scale convective mixing. COSMO analyses were
available hourly at a horizontal resolution of approximately 7 km × 7 km
over western Europe (COSMO-7; 36.06–57.42∘ N, -11.92–21.04∘ E; Fig. S1 in the Supplement). The horizontal resolution of the model does
not resolve the steep topography around JFJ. Hence, a difference between
observatory and model altitude exists. In previous studies (e.g.
Keller et al., 2011), the optimal release height was
determined to be around 3100 m a.s.l. when using COSMO-7 inputs,
which is between the true altitude (3580 m) and the model topography (2650 m) at JFJ. Surface source sensitivities were determined from the location of
model particles below a sampling height of 50 m and stored 3-hourly along
the backward simulation, allowing for a 3-hourly coupling to temporally
variable surface fluxes.
STILT-ECMWF
The Stochastic Time-Inverted Lagrangian Transport (STILT) model, first
described by Lin et al. (2003), was driven by the numerical weather forecast fields from the
European Centre for Medium-Range Weather Forecasts (ECMWF), as previously
presented by Trusilova et al. (2010)
and Kountouris et al. (2018a). The
simulations for JFJ were performed at the same release height as with
FLEXPART–COSMO (3100 m a.s.l.), corresponding to 960 m above the model
topography. STILT-ECMWF simulations are also routinely performed within the
activities of the ICOS Carbon Portal (CP), albeit at a release height of 720 m above model ground (2860 m a.s.l.) for the default products for JFJ
(https://stilt.icos-cp.eu/worker/, last access: 12 May 2022, ICOS Carbon Portal, 2022). The particles are released
instantly on a 3-hourly interval and traced back in time for 10 d or
until they leave the European domain (33∘–73∘ N,
15∘ W–35∘ E; Fig. S1). The STILT calculations were
driven by 3-hourly operational ECMWF-IFS analysis and forecast fields available
at a resolution of 0.25∘× 0.25∘ (approx. 25 km × 25 km), whereas STILT output was generated on a finer grid
(approx. 10 km × 10 km). Surface source sensitivities were
evaluated by using a variable sampling height (0.5 ×hPBL); hPBL is the PBL height diagnosed within STILT. Transport and
fluxes were coupled at hourly time resolution.
CO2 fluxes and boundary conditions for the atmospheric transport simulationsRegional CO2a. Anthropogenic emissions
Regional anthropogenic CO2 concentrations for JFJ (CO2.anthr) were
calculated using emission fluxes based on a pre-release of EDGAR v4.3 (G. Janssens-Maenhout, personal communication, 2013). The inventory was disaggregated into
fuel-type-specific categories (Table S1) and provides annual emissions on a
0.1∘× 0.1∘ grid (∼ 10 km × 10 km) (Janssens-Maenhout et al.,
2019; Karstens, 2019). Here, we use 14 categories, representing 11 different
fossil and biogenic fuel types as well as three non-fuel categories from cement
and other production processes (Table 2). The
CO2.anthr comprises CO2 from fuel-burning CO2 (oil, gas,
coal, liquid biofuels, biogas, solid biomass) and CO2 from cement and
other industrial production (referred to as CO2.cement collectively).
We temporally extrapolated the inventory, which was established for the base
year 2010, using annual scaling factors per country and category based on
data from BP (BP, 2019); see Table S2. Additionally, we
applied seasonal, weekly, and diurnal time factors for different
anthropogenic categories. These are based on MACC-TNO
(Kuenen et al., 2014) and are
available in Table S3.
Fuel-type-specific δ13Cs assigned
to the simulated anthropogenic CO2 categories.
Regional ecosystem CO2 fluxes were based on the VPRM
(Mahadevan et al., 2008).
Underlying parameters are specific for seven vegetation types (VT)
including (1) evergreen forest, (2) deciduous forest, (3) mixed forest, (4)
shrubland, (5) savanna, (6) cropland, and (7) grassland. The VTs are based on the
settings typically used within the ICOS Carbon Portal, although, for
instance, category 5 (savanna) is irrelevant within the domain boundaries
used for JFJ. An additional category, “others”, primarily includes water
bodies and urban spaces for which VPRM does not estimate CO2 fluxes
and was hence excluded from the final analysis. The VT maps underlying
VPRM are based on the synergetic land cover product (SYNMAP, Jung et al., 2006). A map showing
the dominant category per grid as used in our study is provided in Fig. S2. Note that oceanic sources and sinks (including oceanic biomass), as well
as human or animal respiration (see e.g. Ciais et al., 2020) and wildfire-related emissions, were not included and are expected to be a minor
contribution to the regional signal at JFJ. With FLEXPART–COSMO, we use an
offline version of VPRM (Gerbig and Koch, 2021) based on
the same ECMWF meteorological analysis as in STILT-ECMWF. Although the
fluxes are generated based on the individual VTs, ecosystem respiration
(CO2.resp), ecosystem uptake (also referred to as gross ecosystem
exchange and thus abbreviated CO2.gee), and net ecosystem exchange
(CO2.nee = CO2.gee - CO2.resp) are provided only as a
total over all VTs. The STILT-ECMWF is coupled online with VPRM and allows
extracting CO2 concentration contributions at JFJ for CO2.nee,
CO2.gee and CO2.resp for the individual VTs separately. The
online VPRM parameterization initially presented by
Kountouris et al. (2018b) was updated for our
study (Gerbig, 2021). A dedicated evaluation of the online
and offline implementation with STILT-ECMWF for JFJ yielded
comparable results for CO2.nee, CO2.gee, and CO2.resp.
Background CO2
We use the Jena CarboScope (JCS) global atmospheric CO2 product for the
determination of the CO2 boundary conditions. These simulations are
based on optimized fluxes (Rödenbeck, 2005) and are
available at http://www.bgc-jena.mpg.de/CarboScope/ (last access: 12 May 2022, 10.17871/CarboScope-s04oc_v4.3). We used
three-dimensional CarboScope fields (version or experiment:
s04oc_v4.3) with a temporal resolution of 6 h and
interpolated concentrations in space and time to the end points of model
particles. The mean over all model particles of a given release forms the
background concentration (denoted fb herein) at the time of the
release. We observed a higher short-term variability in the simulated
background CO2 concentration for FLEXPART–COSMO compared to
STILT-ECMWF, which is a consequence of the smaller domain size, in
particular towards eastern Europe, and shorter backward integration time (4 d versus 10 d).
Total CO2
The sum of CO2.anthr and CO2.nee concentrations provides the
regional contribution to the CO2 concentration at JFJ (i.e.
CO2.regional). Together with the simulation-specific background for
either FLEXPART–COSMO or STILT-ECMWF this yields the total CO2
concentration (i.e. CO2.total) at JFJ.
Model-based δ13C–CO2 estimation
The stable carbon isotope ratio of CO2 is referred to as δ13C–CO2, or δ13C. The estimation of the
mixed δ13C–CO2 source signature (δ13Cm) and ambient δ13C–CO2 isotope ratios
(δ13Ca) is based on the CO2 concentration
simulations. All δ13C–CO2 estimates are given in per mille
(‰) relative to the Vienna Pee Dee Belemnite (VPDB)
reference standard. Further information on stable isotope expressions and
definitions is available in Coplen (2011).
Mixed source signature (δ13Cm)
The absolute values of simulated CO2 concentrations per source and sink
category i, |fs,i|, were weighted with
category-specific source signatures, δ13Cs,i, to
retrieve a mixed source signature, δ13Cm, according to Eq. (1) using the δ13Cs literature-based assumptions
summarized in Tables 2 and 3. The simulated anthropogenic CO2 data
were disaggregated based on fuel type (Table 2)
rather than sectorial processes because δ13Cs can best be
attributed as a function of fuel type. For ecosystem fluxes, a seasonal
cycle in δ13Cs was assumed (Table 3). Following the reasoning of Vardag et al. (2016),
the CO2.gee was treated as a source, i.e. its absolute value was
considered, along with the δ13Cs, using a reversed sign in
Eq. (1).
Assumptions for ecosystem δ13Cs based on Ballantyne et al. (2010, 2011) and Vardag et al. (2016).
Assumptions for fossil and cement sources are based on
Andres et al. (1994). Gaseous fuels are characterized by a
large range (- 15 ‰ to - 85 ‰) as reviewed by
Sherwood et al. (2017),
with a mean of - 44 ‰. The biogas signature is based on
measurements of δ13C–CH4 released by cows, a biogas
production plant, and wastewater treatment (Hoheisel et
al., 2019; Levin et al., 1993). The values are in line with microbial
δ13C–CH4 reviewed by Sherwood et al. (2017).
CO2.cement includes industrial emissions from cement production (NMM)
alongside two minor contributors (CHE, IRO), as detailed in Table S1.
δ13Cm=∑n=1i|fs,i|×δ13Cs,i∑n=1i|fs,i|
δ13C–CO2 background estimate (δ13Cb)
The Jena CarboScope (JCS) CO2 background concentration simulation for
JFJ serves as fb. The δ13C–CO2 background value,
δ13Cb, is estimated thereof through scaling fb by
using a relationship between observations of CO2 and δ13C–CO2 in background air (flask samples as detailed in Sect. 2.6). The relationship is derived following the strategy of Vardag et al. (2016) by
applying yearly linear regression fits between measurements of CO2
and δ13C–CO2 under free troposphere
conditions at JFJ (method A). The obtained δ13Cb is
provided in Fig. S3. In addition to method A we also obtained estimates
for δ13Cb based on a moving linear regression over a 12-month window (method B). Alternatively, we tested the ratio of δ13C–CO2 and CO2 in background air as a scaling factor using
monthly data averaged over 2009–2017 (method C) and daily ratios (method
D). The daily ratios were obtained from QCLAS measurements at 05:00–06:00 (UTC + 1), as the
early morning is considered to be the background condition for JFJ. Results are
available in Fig. S4.
Atmospheric δ13C–CO2 estimates
(δ13Ca)
The mixed source signature, δ13Cm, derived in Eq. (1) was
combined with the background estimates (fb, δ13Cb) in
order to derive estimates of atmospheric δ13C–CO2 isotope
ratios at JFJ, δ13Ca, following Eq. (2). Note that,
contrary to Eq. (1), CO2.gee is considered to be an effective sink in Eq. (2), which is further detailed in Vardag et al. (2016).
δ13Ca=(fb×δ13Cb)+(∑n=1ifs,i×δ13Cm)fb+∑n=1i(fs,i)
Observation-based δ13C–CO2 estimation
Observation-based mixed source signatures, δ13Cm, were
derived using a moving Keeling-plot approach following the example of
Vardag et al. (2016)
and using JFJ-specific fitting and filtering criteria, as detailed in
Sect. 3.2.3.
Observations
The CO2 concentrations and δ13C–CO2 isotope ratios
were continuously measured at JFJ by quantum cascade laser absorption
spectroscopy (QCLAS) during the period 2009–2017. The custom-built QCLAS
instrument (Nelson et al., 2008; Tuzson et al., 2008a, b, 2011; Sturm et al., 2013)
provides high-precision data for the three main CO2 isotopologues
(12C16O2, 13C16O2, and
12C16O18O), and therefore it allows simultaneous
determination of the CO2 concentration and the δ13C–CO2 and δ18O–CO2 values at 1 s time
resolution. The CO2 dry air mole fractions (µmol mol-1)
are reported in units of parts per million (ppm) on the World Meteorological
Organization (WMO) CO2 X2007 scale, while the isotope ratio values are
given in per mille (‰) relative to the Vienna Pee Dee Belemnite
(VPDB) reference standard. The instrument was configured as described in
Tuzson et al. (2011)
during 2009–2011. The hardware and calibration strategy were revised during an
upgrade in 2012, as described in
Sturm et al. (2013) to improve
long-term precision, stability, and traceability within the International System of Units (SI). Furthermore, the
instrument participated in the WMO/IAEA Round Robin 6 Comparison Experiment
to assess the instrument capability to maintain the link to the WMO
recommended level under field operation (NOAA, 2015). Stable
operating conditions guarantee a precision of 0.02 ‰ for
δ13C–CO2 and 0.01 ppm for CO2 at an optimum averaging
time of 10 min. During 2016–2017, laboratory temperature instabilities
adversely affected instrument performance, causing lower data coverage.
CO2 concentrations have additionally been determined at 1 min time resolution
by a commercial cavity ring-down spectrometer (CRDS, G2401; Picarro Inc.,
USA) since 2010, likewise linked to the WMO CO2 X2007 scale. These data are
available as an ICOS product (Emmenegger et al., 2020).
The mean difference (1σ) between the 1 min averaged CRDS and QCLAS
data is 0.1 ± 0.4 ppm for the entire observation period. Besides the
in situ measurements, air samples were collected in triplicate every second
Friday at around 7:00 local time, i.e. at a time when the JFJ site
predominantly experiences lower free troposphere conditions
(Herrmann et al., 2015). CO2
concentration, δ13C–CO2, and δ18O–CO2 in
the flask samples were analysed at Max Planck Institute for Biogeochemistry
(MPI-BGC) in Jena as described in
Van Der Laan-Luijkx et
al. (2013). The flask data, which defined by the sampling time correspond
primarily to background conditions at JFJ, are used to construct δ13Cb. A comparison of flask sample measurements with the QCLAS
measurements for 2009–2017 indicates very good agreement, typically within
± 0.2 ppm for CO2 and ± 0.1 ‰ for
δ13C–CO2, as well as no apparent systematic bias as
a function of time or signal intensity. It should be noted that the data and
sample collection for in situ measurements (QCLAS) and offline samples
(flasks) were not primarily designed to assess an intercomparison between
the two measurement systems. In particular, uncertainties exist regarding
the accurate matching of time stamps. Therefore, the real agreement of the
measurements is likely even better.
Time series analysis
Time series analysis was performed using R programming language v3.6.1
(R Core Team, 2019) by deploying available R packages
(https://cran.r-project.org, last access: 12 May 2022) as well as custom-developed
scripts. While FLEXPART–COSMO simulations provide 3-hourly averages,
STILT-ECMWF provides instantaneous snapshots every third hour.
STILT-ECMWF simulations were interpolated between the 3-hourly nodes for
comparison with 3-hourly averages of observational data. For comparing the
observations with the LPDM output, we use 3-hourly and monthly
averages of the QCLAS measurements. Furthermore, a common JCS-based
background is subtracted from the measurements. The STILT-ECMWF JCS-based
background is preferred as the common background for this particular assessment
over the FLEXPART–COSMO background owing to the higher short-term variations
in the latter (compare Fig. S3a). The background-subtracted data set is
referred to as “regional observations”.
Results and discussionRegional CO2 simulations at JFJMonthly timescalea. Planetary boundary layer influence at JFJ
Air mass transport dynamics determine the exposure of the receptor site JFJ
to air masses from the planetary boundary layer (PBL). Thus, together with
the source or sink strength in the footprint region, they drive the regional
contributions to the CO2 concentrations and are discussed up front.
Previous analyses of tracers (e.g. radon and CO-to-NOy ratio) by
Herrmann et al. (2015) suggested that,
compared to winter (December–February), the PBL influence at JFJ is
enhanced by 1.5- to 2.5-fold in April and August–September and by 3 to
4-fold from May–July. To isolate the influence of seasonally varying
transport, we performed dedicated simulations wherein CO2 fluxes were
assumed constant in space and time (see Appendix A1). This analysis revealed
a 2- to 3-fold larger simulated PBL influence in summer compared to winter
for both models. Diurnal variations were most pronounced in summer,
indicating a 1.4-fold larger PBL influence during the afternoon and evening
(maximum at ∼ 16:00, UTC + 1) compared to the morning
(minimum at ∼ 10:00, UTC + 1). A larger
PBL influence in May and September for STILT-ECMWF compared to
FLEXPART–COSMO appears to be a peculiarity of using ECMWF fields and may
reflect the less-well resolved transport in complex terrain in the coarser-resolution data from ECMWF. Additional differences appear to be related to the
smaller domain size and shorter backward integration used for
FLEXPART–COSMO, which are directly associated with smaller integrated
surface CO2 fluxes. The findings for STILT-ECMWF and FLEXPART–COSMO
from the transport dynamics analysis (Appendix A1) appear to explain some of
the mismatch in the simulated CO2 observed between the simulations in
Fig. 1 (see Sect. 3.1.1b).
Multi-annual monthly means of 3-hourly regional CO2
simulations compared to observations (2009–2017).
CO2.regional (a), its components CO2.anthr
(c), and net ecosystem exchange (CO2.nee) (d). The
difference between simulations (sim) and observations (obs) is presented in
(b). CO2.nee is composed of (e) gross ecosystem
respiration (CO2.resp) and (f) gross ecosystem exchange
(CO2.gee), i.e. gross uptake. Error bars represent 1 SD of the multi-annual means and reflect the year-to-year variability for 2009–2017.
b. Regional CO2 concentration observations and simulations
Simulated CO2.regional for 2009–2017 is compared with the respective
regional CO2 concentration observations in
Fig. 1 (multi-annual monthly means).
The CO2.regional observations show a minimum in June and a maximum in
October and November, both with an amplitude of about 1.8 ppm. Smaller panels
present the corresponding simulated anthropogenic (CO2.anthr) and
ecosystem components (CO2.nee, CO2.gee, CO2.resp).
While CO2.anthr and CO2.nee together constitute CO2.regional,
the sum of ecosystem components CO2.gee and CO2.resp results in
CO2.nee. The minimum in June as observed in the measurements is well
represented by the models, though the amplitude is overestimated. The
October–November maximum is delayed in both models by about 1 month. A
local minimum in December–January is seen in observations and models. The
winter minimum in the regional signal reflects the limited influence of PBL
air masses at JFJ during this period of the year and coincides with a
minimum in CO2.anthr (Fig. 1c) and ecosystem
CO2 (Fig. 1d–f). The models thus appear to
represent the processes contributing to the seasonal variability of the
regional CO2 signal at JFJ quite realistically. It is noteworthy that the
seasonal trends of the regional signal, in particular the local winter
minimum, differ from those in the large-scale CO2 background
concentrations, which show a minimum in August, 2 months later than the
regional signal, and only one maximum in March–April, as shown by Sturm et al. (2013).
Regarding CO2.anthr (Fig. 1c), we conclude
that the reduced transport of PBL air to JFJ during December–January
outweighs a maximum in anthropogenic surface emission fluxes related to
enhanced fuel use for heating during the cold season. Instead,
CO2.anthr simulations reach a maximum at JFJ in spring (April–May) in
both models, resulting from still relatively large anthropogenic surface
emissions and generally more unstable atmospheric conditions due to rising
surface temperatures and sustained colder temperatures aloft. The
STILT-ECMWF simulations comprise a second CO2.anthr maximum in autumn
(September), which is in line with the simulated PBL influence (Appendix
A1).
Given that ecosystem contributions quantitatively dominate the regional
contributions to CO2 concentrations during summer, we reiterate that
the CO2.nee simulations depend on the parameterization of ecosystem
respiration and uptake fluxes in VPRM. The parameterization accounts for
environmental factors such as temperature, radiation, and, through MODIS-derived enhanced vegetation index (EVI) and land surface water index (LSWI),
also for soil moisture (Mahadevan et al., 2008).
Warmer temperatures generally lead to enhanced gross ecosystem fluxes
(CO2.resp and CO2.gee) in summer compared to winter. These trends
are indeed reflected in the simulations for JFJ
(Fig. 1d–f). The strong negative regional
CO2.nee from March to October is a result of only partial compensation
of uptake (CO2.gee) by respiration (CO2.resp). The CO2.gee
minimum in June does not coincide with the CO2.resp maximum in
July–August. This may be explained by the fact that respiration is strongly
dependent on temperature, and July and August typically show the highest
average temperatures in the relevant footprint region. Ecosystem uptake, on
the other hand, has a more complex relationship with temperature (drops off
when too hot), radiation (actually largest in June), water availability
(usually decreasing during the summer), and plant phenology (e.g. Bonan, 2015; Mahadevan et al., 2008).
The simulations qualitatively satisfy our expectations. However, the
overestimation of the amplitude in summer and early autumn by the two models
merits further discussion of potential contributions to this mismatch, which
includes uncertainties in the transport model or in the spatio-temporal flux
distribution. A quantitative assessment is available in Sect. 3.2.2c.
Transport dynamics. The fluxes computed by VPRM
together with the air mass transport dynamics determine the final
seasonality of the ecosystem-related CO2 contributions at JFJ.
It has been reported by Denning et al. (1999) that
the signal from respiration CO2 is amplified over flat terrain because
respiration dominates at night when the boundary layer is shallow. This
observation is referred to as the rectifier effect. At JFJ, we likely observe the inverse
situation, a fair-weather effect, as warm and sunny afternoons favour PBL influence at JFJ,
while low-irradiation periods (nighttime, winter) limit the PBL influence.
Vertical atmospheric transport and photosynthetic activity (uptake) covary
and are both largest on sunny days. In contrast, ecosystem respiration is
active independently of light condition (day–night) and, to a smaller
degree, during colder periods, when PBL influence is limited at JFJ. Such
fair-weather effects may be inadequately captured in the models, as the vertical export of PBL
air in these situations is driven by thermally induced flow systems in
complex terrain (up-slope, up-valley; see Rotach et al.,
2014) that cannot be adequately resolved at the present model horizontal
resolution.
The simulations for JFJ indicate that a considerable fraction of ecosystem
CO2 originated from fluxes within the last few hours before arrival at
JFJ and at distances shorter than 100 km from the site (predominantly north
of JFJ). We find that this “nearby” contribution is particularly pronounced
in summer, whereas cold season sampled air masses are rather associated with
a much wider concentration footprint and are less dominated by
nearby vegetation fluxes. In addition, the nearby vegetation fluxes seem
artificially enhanced by the limited spatial resolution of the vegetation
maps.
VPRM. An overestimation of the CO2.gee or an
underestimation of CO2.resp may be associated with harvesting
activities and drought stress, which are not well reflected in the current
parameterization of VPRM, as well as the spatial representation of
vegetation maps and temperature profiles.
Harvesting usually results in a change in the enhanced vegetation index
(EVI) derived from the MODIS observations. While the reduced ecosystem
uptake due to harvesting is thus in principle already represented in VPRM,
the agricultural biomass left behind after the harvest may lead to increased
respiration. VPRM is unlikely to capture this latter process with its simple
linear dependence of respiration on temperature.
Water stress (drought) can lead to altered respiration and uptake fluxes
(e.g. Ramonet et al., 2020, or Gharun et al., 2020), but it is not explicitly included in VPRM.
Owing to the smoothed topography and vegetation maps in the models, the
effective temperatures in Alpine vegetation are likely not well represented,
and, moreover, the temperature parameterizations in VPRM are not optimized
for Alpine vegetation. No systematic bias net ecosystem exchange is apparent
for ecosystem simulations with STILT-ECMWF for other observational sites in
Europe (data available at the ICOS Carbon Portal, 2022), suggesting that the
discrepancy is predominantly linked to JFJ's location in complex terrain.
Indeed, summer discrepancies appear to be comparatively large at JFJ (3580 m a.s.l.) even when considering other mountain stations, such as Monte Cimone
(∼ 2000 m a.s.l., Italy) or Puy de Dôme (∼ 1500 m a.s.l., France), which are characterized by lower altitude and less
complex topography compared to JFJ.
Uncertainties in daily ecosystem fluxes are estimated in
Kountouris et al. (2015),
based on a comparison with eddy covariance flux observations, to be 2.5 µmol m-2 s-1 for VPRM, with typical spatial error correlation of
around 100 km and a temporal correlation of 30 d. To
estimate the impact of this uncertainty between eddy covariance data and
simulations using VPRM on the simulated CO2, however, full propagation
of the error would be required, including spatial and temporal correlation.
As VPRM is used in many inversion studies, the corresponding error in
simulated CO2 can alternatively be assessed based on the change from
prior to posterior model–data mismatch. Based on Table 3 in the Technical
Note of Kountouris et al. (2018b), typical
numbers for mountain sites such as JFJ are around 4 ppm (prior), which drop
to about 1.5 ppm for posterior fluxes (the assumed model–data mismatch
error).
EDGAR. A mismatch between CO2.regional
simulations and observations may also result from biases in the
CO2.anthr signal. However, as quantified in Sect. 3.2.2c, an
increase in CO2.anthr by a factor of 3 to 4 would be required in order
to compensate for the summer mismatch. Further, the discrepancy during summer is
much larger than that during winter when CO2.anthr contributes the
largest share, and we consider it thus unlikely that CO2.anthr is the
main driver of the summer mismatch. As JFJ is also a popular destination for
touristic day trips, local emissions from tourists and the JFJ infrastructure
itself cannot be excluded. The recent study by Affolter et
al. (2021), however, showed that this effect is expected to be well below
the discrepancy between observations and simulations found here.
c. Composition of simulated anthropogenic and ecosystem
CO2
Ecosystem contributions to CO2 concentrations outweigh the
anthropogenic ones at JFJ most of the year if we consider the multi-annual
monthly means (Fig. 1). For instance, gross
respiration contributions to CO2 concentrations are at their maximum
3–4-fold the anthropogenic ones during summer. However, gross respiration is
overcompensated for by an up to 2-fold gross uptake in summer. During the
colder period, gross respiration dominates the net ecosystem exchange and
equals roughly the amounts of anthropogenic CO2. While on a global
scale monthly ecosystem fluxes indeed outweigh anthropogenic CO2, this
is not the case for urban areas. For instance,
Vardag et al. (2016)
suggest that on cold winter days, the CO2 share in an urban
environment in Germany (Heidelberg) is 90 %–95 % fuel-related, which is
2-fold the CO2.anthr fraction compared to JFJ. Nevertheless, also in
Heidelberg ecosystem contributions can make up 80 % in summer, similar to
our simulations for JFJ.
In Fig. 2a and b we present the ecosystem
contributions at JFJ split for the considered vegetation types (multi-annual
monthly means for 2009–2017, available for STILT-ECMWF only). For summer,
the largest fractions of simulated CO2.resp are related to cropland
(∼ 50 %), followed by forest (∼ 30 %) and
grassland (∼ 10 %). During winter, the cropland share
increases, while the mixed forest share decreases. This may be a result of
the above-discussed change of footprint area from regional (cropland) in
winter to more local (mixed forests) in summer. For CO2.gee, it is
important to consider that absolute quantities approach zero during the cold
season and relative fractions are most meaningful in summer. The
CO2.gee generally displays a larger forest share in comparison to the
one of CO2.resp, possibly as air masses travel through forest-rich
vegetated areas during the last few hours before reaching JFJ (which
corresponds to daytime, when uptake is active). Furthermore, we observe a
shift in the relative CO2.gee share from cropland to forest from April
to September, which is likely the result of vegetation dynamics, considering
that crops mature earlier in the year, and forests absorb carbon much longer
during the growing season.
Simulated regional contributions to the CO2
concentrations at JFJ (multi-annual monthly means of 3-hourly simulations,
2009–2017, STILT-ECMWF). (a) Gross ecosystem respiration per vegetation type
(CO2.resp), (b) gross ecosystem exchange (uptake) per vegetation type
(CO2.gee), (c) CO2.anthr and CO2.resp, (d) CO2.anthr per
fuel-type. Maps of anthropogenic fluxes and vegetation distribution are
provided in Figs. S1 and S2.
In Fig. 2c and d we present the relative fractions of
CO2.anthr. The contributions associated with fossil sources sum up to
90 % of CO2.anthr. The CO2.anthr is dominated by CO2 from
liquid fuel use, in particular light and heavy oil used for on- and off-road
transport as well as domestic heating (∼ 50 %). A further 25 % of CO2.anthr is related to natural gas, and only 10 % is
attributed to solid fossil fuels, including a larger fraction of hard coal
and a smaller fraction of brown coal. Solid biomass, such as residential
wood burning for domestic heating, contributes 10 % to CO2.anthr.
Non-combustion CO2 from cement and other industry production amounts to
5 % of CO2.anthr at JFJ. Seasonal shifts are observed in the
contribution of solid biomass (higher in winter, lower in summer) as well as
in relative fractions of light oil (higher in summer) and natural gas (lower
in summer). The relative contributions of FLEXPART–COSMO (not shown here)
are very similar to the ones of STILT-ECMWF despite the differences in the
absolute quantities of CO2.anthr between the two models
(Fig. 1), which, as discussed above, are primarily
driven by the model's implementation of transport dynamics.
Regression analysis of hourly-scale CO2
simulations vs. observations
The model performance was further evaluated by comparing the 3-hourly
simulated CO2 concentration time series with observations. In
Fig. 3 we present CO2.total, which includes background (fb) and regional contributions
(CO2.regional, i.e. the sum of fs,i). In order to derive
CO2.total, the simulation-specific background (i.e. either
FLEXPART–COSMO or STILT-ECMWF) was added to the respective CO2.regional
data. Overall, the simulations capture the intensity and timing of
individual regional short-term events at the models' 3-hourly
time resolution to a high degree, in addition to the good representation of
annual and seasonal trends.
Time series of CO2.total simulations with
(a, c) FLEXPART–COSMO and (b, d) STILT-ECMWF compared to
hourly observations. (a, b) 2009–2017 (tick marks indicate January
of each year), (c, d) 2013 (JCS-based background is detailed in
Fig. S3a).
We assess the performance separately for the four seasons winter
(December–February, or DJF), spring (March–May, or MAM), summer
(June–August, or JJA), and autumn (September–November, or SON) for the
CO2.regional signal, as summarized in Fig. 4,
and show a 4-year subset for 2012–2015 in addition to the full 9-year
observation period (2009–2017). The subset is of interest as it comprises a
higher frequency and intensity of regional CO2 at JFJ, in particular
considering the winter of 2012/2013, and in addition, measurements by QCLAS had
the best performance during 2012–2015. We primarily consider the
coefficient of determination, r2, regression slope, and bias-corrected
root mean square error (BRMS) in the assessment of the short-term
variability.
Summary of the regression analysis of
CO2.regional simulations vs. observation (data are based on 3-hourly
time resolution; error bars show the 95 % confidence interval). The
parameters (slope, r2 and bias-corrected RMSE, i.e. BRMS) are presented
for FLEXPART–COSMO (a–c) and STILT-ECMWF (d–f), including
the full observation period, 2009–2017, and a 4-year subset (2012–2015).
The mean bias (labelled Y–X) provided in Fig. 5 is usually smaller than 1 ppm with the exception of summer, when the
models exhibit a negative bias of up to 2.5 ppm. Removing this bias before
calculating the root mean square error (RMSE) focuses on the short-term
variability. The BRMS ranges from 1.8 to 3.1 ppm CO2, with the lowest
errors observed during winter and autumn and the highest errors in summer. For
the 3-hourly data, both models reproduce the regional signal with similar
quality. The r2 is 0.44 for FLEXPART–COSMO and 0.41 for STILT-ECMWF,
meaning that the models explain about 40 % of the observed regional
CO2 variability at JFJ. Considering the complex topography and small
amplitude of the regional signal, this is a very satisfactory result and is in
line with comparable simulations by Henne et al. (2016), which
were able to explain a similar fraction of variability in regional CH4
at JFJ for the year 2012 after simulation optimization with respect to
CH4 emissions.
Heat maps for CO2.regional simulations
(SIM) using FLEXPART–COSMO (a–e) and STILT-ECMWF
(f–j) in comparison to regional components of
observations (OBS) for 2012–2015 (full year and per season) at 3-hourly
time resolution. The STILT-ECMWF-based JCS background is subtracted from the
observations to derive the regional component. The weighted least squares
regression takes into account uncertainties in both data sets (the full-page
version of this figure is available in the Supplement).
When analysing individual seasons, we find that the summer period is
characterized by significantly lower r2 for the 3-hourly data compared
to the other seasons, although, aside from the above-mentioned negative bias,
diurnal profiles in the observations during summer are well represented by
the simulations. The slightly better performance for FLEXPART–COSMO compared
to STILT-ECMWF in terms of mean bias and r2 for 3-hourly data may be
partly attributed to the higher spatial resolution that potentially allows
for a better representation of thermally driven atmospheric transport in
mountainous terrain during summer. Note that when adding model-specific JCS
background values to the regional simulations, r2 values are
substantially higher (∼ 0.6–0.9, not shown) because a
considerable part of variability in CO2.total derives from seasonal
variability and long-term trends.
The regression slopes represent the factors by which simulation and
observation intensities agree with each other. For CO2.regional, the
intensity agreement (slope, ∼ 0.9–1.5) varies as a function
of season and model. Slopes are closest to 1 in autumn–winter, and, as for
other regression parameters, larger discrepancies occur in spring–summer.
The spring–summer discrepancies are driven by negative excursions from the
baseline in analogy to the larger warm season mismatch (discussed in Sect. 3.1.1)
and higher mean bias. Again, note that we find the slopes for CO2.total
to be closer to 1 (∼ 0.9–1.3, not shown) than those for the
CO2.regional, confirming the appropriate assumptions for the background
CO2 concentrations.
Atmospheric δ13C–CO2
Simulating regional signals at a high Alpine background site like JFJ is
challenging, yet JFJ is one of very few stations that offer continuous high-frequency δ13C–CO2 observations over multiple years.
Thus, JFJ uniquely allows for combining model-based estimates of atmospheric
δ13C–CO2 and mixed source signatures (δ13Cm) with atmospheric δ13C–CO2 observations
and (“observation-based”) δ13Cm values derived thereof
using a moving Keeling-plot approach.
Atmospheric δ13C–CO2 estimates vs.
observations
We evaluated the atmospheric δ13C–CO2 isotope ratio
estimates (δ13Ca), which are derived following Eq. (2) on a
3-hourly basis, through comparison with the QCLAS observations during the
period 2012–2015 (Fig. 6,
Table 4). Multi-annual monthly means for 2012–2015
are presented in Fig. 7.
Summary of statistics on atmospheric δ13C–CO2 estimates and observations for the period
2012–2015. Values for min, max, median (P50), 25th and 75th
percentiles (P25 and P75), mean (avg), and 1 SD are provided
(hourly data) (see also Fig. S6).
Time series of model-based and observed atmospheric
δ13C–CO2 for the years 2012–2015 (hourly observations).
(a) FLEXPART–COSMO, (b) STILT-ECMWF; tick marks indicate
January of each year. The background, δ13Cb, is presented
in further detail in the Supplement (Fig. S3b). Data are presented at hourly time
resolution (zoomed versions of this figure for 2012, 2013, 2014, and 2015
are provided in the Supplement; see Figs. S7–S9).
(a) Multi-annual monthly means of 3-hourly model-based and
observed atmospheric δ13C–CO2 for the years 2012–2015.
Error bars represent 1 SD of the multi-annual means and reflect the
year-to-year variability for 2012–2015. (b) Difference between
simulations (sim) and observations (obs).
The simulated δ13Ca time series capture the observed
variability in δ13C–CO2 at JFJ well, in particular during
the transition periods in spring and autumn. For most of the summer,
however, the δ13C–CO2 simulations are isotopically
heavier than the observations; i.e. they appear more enriched in 13C.
Despite an offset of ∼ 0.15 ‰, which
appears to be related to the background (δ13Cb) assumptions, the
diurnal profiles in the observations during summer are well represented by
the simulations. Generally, the discrepancy in δ13C appears to
be larger for STILT-ECMWF compared to FLEXPART–COSMO, and thus the
discrepancy in CO2 concentrations itself likely contributes to the
mismatch in δ13C–CO2, as further assessed in Sect. 3.2.2c–d, aside from uncertainties associated with assumptions for δ13Cs (discussed in Sect. 3.2.2a) and δ13Cb (Sect. 3.2.2b).
Sensitivity of δ13C–CO2 estimates to different model assumptionsa. δ13Cs assumptions
The mixed source signature estimates (δ13Cm) as derived in
Eq. (1) are presented in Fig. 8 on a 3-hourly
timescale (monthly data are provided in Fig. S5). The estimated average
δ13Cm is around - 24 ‰ and varies
seasonally between around - 22 ‰ in summer and - 28 ‰ in winter for both FLEXPART–COSMO and STILT-ECMWF.
Extreme values during particular events at 3-hourly time resolution reach
- 35 ‰ when they are heavily impacted by anthropogenic
fuel emissions, including a larger fraction of natural gas (∼ 50 % of regional CO2), and values between - 17 ‰ and - 12 ‰ when impacted by cement production (∼ 30%). The δ13Cs from cement production originates from
carbonates, which are characterized by a similar isotope composition as the
carbonaceous VPDB reference material itself. Consequently, the δ13Cs for cement-related CO2 is 0 ‰.
Although cement-related CO2 contributions to CO2.regional at JFJ
are about 1 order of magnitude smaller than from fuel burning or ecosystem
processes, the influence of cement on δ13Cm is clearly
visible in the model-based data in Fig. 8. These
cement-related peaks in δ13Cm are, however, absent in
δ13Ca (Fig. 6), simply because
even the most intense cement signals at around 1–2 ppm are much smaller than
other CO2 contributions. Thus, when mixed with the background, the
signal is diluted.
Time series of (a) model-based δ13Cm (Eq. 1) and (b–c) model-based δ13Cm
for ecosystem-, fuel-, and cement-related CO2: (b)
FLEXPART–COSMO, (c) STILT-ECMWF; hourly data are used; tick marks
indicate January of each year (see also Fig. S5).
The δ13Cs values, which are underlying the δ13Cm, represent the best available information in the scientific
literature. However, while we use static assumptions, these values may vary
in reality with air mass source region (footprint) and over time. Further
uncertainties may arise from assumed ecosystem δ13Cs. For
instance, C4 plants are not explicitly represented in our model as a
dedicated vegetation type with known spatial distribution. Yet, their
contribution to average ecosystem δ13Cs is captured in the
data of Ballantyne
et al. (2010, 2011), which are underlying the assumptions in
Table 3, as these are derived from ambient
measurements in mixed C3–C4 ecosystems representative for the Northern
Hemisphere. In the footprint region of JFJ, C4 plants are mainly present in
cropland due to maize production. For the year 2017, EUROSTAT reports that
grain maize production made up around 21 % of the overall grain and
cereal production by weight within the EU-28. Of all cropland, roughly 35 %
on a land surface basis is assigned to grain and cereals. Applying a simple
“back-of-the-envelope” calculation, this equates to ∼ 7 % of
C4-related CO2 fluxes within the European Union as a yearly average.
Because maize production is primarily relevant during the spring and summer,
the fraction would be enhanced for this period of the year. Replacing 7 %
of the C3-related CO2 with C4-related CO2 would marginally change
the source signature of crops (< 1 ‰, and that
of the overall ecosystem signal by even less); however, generally δ13Cm would become more enriched and thus the discrepancy between
models and observations larger. Reducing a potential C4-related CO2
fraction instead would make δ13Cm less enriched and
thus bring the simulation data into slightly better agreement with
observations at JFJ. Indeed, the ecosystem assumptions for the Northern
Hemisphere are based on data collected in the USA and might be characterized
by a higher C4 fraction than the footprint region for JFJ.
Vardag et al. (2016)
report a measurement-based mean source signature (δ13Cm)
of - 26 ‰ in summer and about - 32 ‰ in winter for Heidelberg, which is isotopically
lighter when compared to the simulated δ13Cm for JFJ
(- 22 ‰ in summer, - 28 ‰ in
winter). The winter differences between Heidelberg and JFJ are reasonable as
they may derive from larger ecosystem contributions at JFJ (50 %) compared
to Heidelberg (5 %). The summer differences, however, may, aside from
summer overestimations of CO2.regional at JFJ, result from
uncertainties in the assumption for the ecosystem δ13Cs
including the uncertainty of the C4-related CO2 fraction. Indeed,
Vardag et al. (2016) also
suggest that the assumption of δ13Cs=- 23 ‰ for ecosystem CO2 by Ballantyne et al. (2011)
is too enriched for August and September in Heidelberg, and a more depleted
assumption (through adjusting the seasonality in δ13Cs)
would result in improved agreement between model-based δ13C–CO2 and observations at JFJ.
b. δ13Cb assumptions
The background (δ13Cb; see Figs. 6, 7, and the Supplement), as estimated by the
baseline CO2 taken from the JCS assimilation system and the empirical
δ13C–CO2 relationship based on yearly linear regression
fits (method A), closely tracks the evolution of the observed δ13C–CO2 values outside the peaks and varies
seasonally. Yet, inconsistencies are apparent from the use of the yearly
regression fits. Assuming a more depleted δ13Cb during the
second half of the year, for instance by - 0.15 ‰
during late summer (August) and early autumn (September), and assuming a
more enriched δ13Cb during the first half of the year, for
instance by +0.05 ‰ to +0.10 ‰ from January to March,
would reduce the discrepancies between observations and simulations. Indeed,
the moving fit (method B, see Fig. S4b) improves the transitioning between
years. However, the use of multi-annual monthly ratios in method C
introduces discontinuities when transitioning between months, and the daily
ratios (method D) introduce higher scatter and data gaps (see Fig. S4c–d).
c. Sensitivity to CO2 concentrations
Based on the discussion in Sect. 3.1.1 we defined five scenarios, which
aim to bring the simulated summertime CO2.regional concentrations into
better agreement with the observations. In each scenario, we adjust one or a
combination of CO2 sources and sinks by a single scaling factor for the
whole summer period (JJA) for the years 2012–2015, thereby removing the
model bias.
Scenario 1 (sc1): through increasing CO2.anthr we simulate a bias in
the anthropogenic emission fluxes or a wrong seasonal factor for
CO2.anthr during summer.
Scenario 2 (sc2): through reducing both CO2.resp and CO2.gee we
attempt to represent a general VPRM parameterization or vegetation map
representation issue.
Scenario 3 (sc3): through reducing CO2.gee we consider its potential
overestimation by general VPRM parameterization or vegetation map
representation issue in analogy to sc2; this is specific only to CO2.gee.
Scenario 4 (sc4): through increasing CO2.resp we consider its potential
overestimation by general VPRM parameterization or vegetation map
representation issue in analogy to sc2; this is specific only to CO2.resp.
Scenario 5 (sc5): through modifying all signals at equal amounts
(CO2.anthr, CO2.resp, CO2.gee) we attempt to represent a pure
transport issue (i.e. overrepresentation of PBL influence).
Scaling factors for each scenario were derived by weighted least squares
regression and are presented in Table 5. The largest
scaling factors of ∼ 3–4 are found for CO2.anthr,
followed by CO2.resp (∼ 2), indicating that
CO2.anthr or CO2.resp would need to be substantially increased in
order to reduce the bias between models and observations. Instead, a
reduction (scaling factor ∼ 0.7–0.8) would be required if only
CO2.gee was considered, and likewise a reduction in both CO2.resp
and CO2.gee (scaling factor ∼ 0.7–0.8) in order to
achieve a reduced CO2.nee would lead to a reduced bias between the model
and observations.
Scaling factors based on the weighted least squares
regression fitting slope b and intercept a (in parenthesis) used to minimize
the CO2 model bias for JJA (2012–2015).
We further evaluate the effect of CO2 adjustments
(Table 5) on the estimated regional δ13C–CO2 at JFJ in comparison to the observations. First, however,
we discuss the regression analysis for the base scenario.
To obtain an estimate for regional δ13C–CO2 a δ13C–CO2 background needs to be
subtracted from the total signal. Here, we used background method A,
following the strategy used previously by Vardag et al. (2016). A
higher short-term variability was observed for the δ13Cb
from FLEXPART–COSMO compared to STILT-ECMWF (Fig. S3b). Consequently we
used only the STILT-ECMWF-based δ13Cb for further
calculations of regional components (i.e. for the subtraction of background
values from the total signal).
Based on this particular δ13Cb assumption, the regional
estimates agree with the regional observation intensity within a factor of
0.7–1, depending on season. The BRMS is between 0.12 ‰ and 0.14 ‰. Similar to CO2, for spring, autumn, and winter
the models capture the short-term variability in δ13C–CO2
better than in summer. Overall, the r2 values are lower than for
CO2 (max r2= 0.35 for FLEXPART–COSMO and 0.28 for STILT-ECMWF
compared to about 0.4 for CO2), which is not surprising given the
uncertainties in the measurements as well as in the simulations, wherein, for
instance, fixed source signatures were assumed. Despite the fact that
model-based δ13C–CO2 includes uncertainties of
CO2 simulation (used to construct δ13Cm), δ13Cs, and δ13Cb, the relative performance
decreased by only 20 %–30 %. These results at JFJ were achieved with very
low regional CO2 signals, which, compared to the background (ΔCO2), reached at maximum 30 ppm. Instead, the previously conducted
urban studies benefitted from much more pronounced ΔCO2
reaching up to ∼ 150 ppm for both Heidelberg (Vardag et al., 2016)
and Downsview
(Pugliese-Domenikos et al.,
2019). However, they were limited regarding the length of the
observation period (a few months in Downsview) and/or the stringent data
filtering (e.g. Vardag et al., 2016, discarded 85 % of the data and biased the urban data sets
towards nighttime observations, and Pugliese-Domenikos et al., 2019, discarded
80 % of the data for their isotopic mass balance approach). In contrast, the
tall-tower study in rural England was challenged by a low
signal-to-background ratio (ΔCO2 reaching around 20 ppm), and
isotope measurements were performed at low (weekly) time resolution,
although simulations are provided on an hourly scale (Wenger
et al., 2019). In comparison to the results from JFJ, Pugliese-Domenikos et al. (2019) reported an r=0.58 (r2=0.3), a root mean square error
(RMSE) of 1.05 ‰, and a mean bias of 0.04 ‰ for a single month (January) for δ13C–CO2. Wenger et al. (2019) do not provide
any regression parameters for their model–observation comparisons; however,
they observed large uncertainties in the δ13C–CO2
estimation using a Monte Carlo approach. They related a part of their
uncertainty for the δ13C–CO2 estimates to the influence of
ecosystem processes and the dominance of ecosystem fluxes in the regional
CO2 observations and simulations at the rural tall-tower site. Overall,
the JFJ results are very well in line with previous findings despite the
more remote location and correspondingly smaller magnitudes of regional
signals at JFJ.
A representative set of results of the regression analysis for further
scenarios as defined in Sect. 3.2.2c is summarized in the Supplement in Table S4.
Overall, we find that modifications in sc 1 (CO2.anthr) do not lead to
improvement in the agreement between regional δ13C–CO2
observations and simulations at 3-hourly resolution. Sc 5 (transport)
results only in small improvements with regards to the BRMS. While the other
scenarios do not result in major adjustments, for sc 3 (CO2.gee) and
sc 4 (CO2.resp) we observe small model improvements with slightly
increased r2, slightly reduced BRMS, and a smaller bias (Y–X). Note that
the remaining bias depends on the fitting intercept assumptions of the
scaling factor. These results indicate that the δ13C simulation
can be influenced through reasonable modification of CO2 contributions.
Discrepancies between observed and simulated δ13C–CO2 are
thus not exclusively related to uncertainties in source signature (δ13Cs) or background (δ13Cb) assumptions.
However, an optimization of δ13Cb mentioned in Sect. 3.2.2b
might result in improved agreement between δ13C simulations
and observations for the base scenario itself, as we found indications for
improved performance in the regression analysis when using δ13Cb derived using moving linear fits (background method B)
compared to yearly fits (method A).
Summary of the regression analysis of δ13C–CO2 estimation vs. observation (data are based on 3-hourly
time resolution; error bars show the 95 % confidence interval). Performance
parameters (slope, r2 and bias-corrected RMSE – i.e. BRMS) are
presented for the 4-year subset of the observation period (2012–2015) for
FLEXPART–COSMO (a–c) and STILT-ECMWF (d–f) across all
years (ALL) and per season (DJF, MAM, JJA, SON).
Heat maps of model-based regional δ13C–CO2 (SIM) vs. observation (OBS) (3-hourly data) for
FLEXPART–COSMO (a–e) and STILT-ECMWF (f–j) during
2012–2015, for the full year (grey), and per season (DJF – blue, MAM
– green, JJA – orange, SON – red). Uncertainties in the x and y axes are taken
into account in the weighted least squares regression applied here (a full-page version of this figure is available in the Supplement).
Observation-based source signature estimates
Observation-based δ13Cm values are accessible
independently from simulations through a Keeling- or Miller–Tans-plot
approach. However, this approach can be applied only after strict
pre-selection of conditions under which the underlying hypotheses are
fulfilled. Detailed descriptions of pre-requisites and limitations of this
method are available in detail elsewhere (Keeling, 1958, 1961; Miller and Tans, 2003; Pataki et al., 2003; Zobitz et al., 2006; Ballantyne et
al., 2011; Vardag et al., 2016). In brief, previous δ13Cs studies have been
successful in deriving observation-based δ13Cm primarily
under the following conditions: first, when measurements were taken close to
a well-defined source location and using instrumentation with high precision
(e.g., Pugliese et al., 2017) and second, when a pronounced regional signal (referred to as
ΔCO2 and computed as the difference between the CO2
concentration at the site and background) with stable source composition was
observed during stable background conditions and the regional ecosystem
contribution to the observed ΔCO2 was comparatively low (e.g., Vardag et al., 2016).
Such constraints substantially limit the number of regional events that can
be effectively characterized at a given location. Intensities below ΔCO2= 5 ppm, even at high precisions of 0.03 ‰
for δ13C–CO2 and low CO2 errors of 0.1 ppm, lead to
significant fitting errors as assessed by Zobitz et
al. (2006). Intensity-based filtering criteria have therefore been applied
in previous studies (e.g. ΔCO2≥ 5 ppm by
Vardag et al., 2016,
ΔCO2≥ 20 ppm by Smale et al., 2020,
ΔCO2≥ 30 ppm by Pugliese-Domenikos et al., 2019, or ΔCO2≥ 75 ppm by Pataki et al., 2003),
while at JFJ ΔCO2 reaches 30 ppm only during the most intense
events. Most studies also focus on periods when photosynthetic uptake does
not disturb the analysis, consequently biasing the data set to nighttime.
Since a classical day–night splitting to filter ecosystem uptake is not
applicable at JFJ as the received air masses are composed of integrated
fluxes over day and night, such observation-based approaches are expected to
be valid mainly during the cold period. However, the PBL influence at JFJ is
at a minimum during the cold season. For instance, regional CO2
intensities at JFJ are at maximum 30 ppm above the background for the 10 min averaged QCLAS data and on average occur with an intensity of ≥ 5 ppm on 35 d per year during the cold period (range: 20–50 times). This
includes events reaching ≥ 10 ppm on 10 d per year (range: 2–20) and
events reaching ≥ 15 ppm on only 1–6 d per year. Intensities and
frequencies, however, are even lower when hourly averaged data are
considered. These conditions make Keeling and Miller–Tans methods to derive
observation-based δ13Cm particularly challenging at
JFJ.
The high precision of the δ13C–CO2 measurements and the
high time resolution available from the QCLAS instrument allow compensating
for the low ΔCO2 and limiting fitting uncertainties to some extent.
This enables us to create a moving Keeling plot in analogy to
Vardag et al. (2016).
We used a 5 h window to conduct the fit on hourly averaged δ13C–CO2 observations. Only fits with five data points were
considered (i.e. no data gaps were allowed). In addition, we tested
splitting the data set into warm (April–September) and cold season (October–March), as well as
demanding a minimum change in ΔCO2 of 3 ppm within the 5 h
window (with and without requiring a monotonous increase, or m.i., in concentration
with time, threshold: 0.1 ppm). Finally, we filtered the resulting
observation-based intercept value (δ13Cm) by the fitting
error (4 ‰, 3 ‰, 2 ‰, and 1 ‰).
Figure 11a shows observation-based estimates from
two settings: (i) results obtained without considering any predefined change
in ΔCO2 and without filtering by the intercept error (referred
to as “all”) and (ii) results obtained under more stringent criteria
(minimum ΔCO2 change within a 5 h window of 3 ppm, maximum
intercept error of 2 ‰ or 1 ‰).
Keeling fit intercepts (δ13Cm) obtained without predefined
criteria and without error-based filtering clearly do not provide meaningful
data, as δ13Cm is physically meaningful only between 0 ‰, corresponding to pure cement production plumes, and
- 44 ‰ corresponding to pure gaseous fuel-burning plumes
(in a peculiar event, gaseous fuel-burning CO2 may reach - 85 ‰). Most values are expected to be between - 12 ‰ and - 35 ‰ based on the simulated CO2 composition. Indeed,
using predefined fit criteria and error-based filtering yields physically
meaningful δ13Cm from the observations at JFJ, in line
with previous findings by
Vardag et al. (2016)
and Pugliese-Domenikos et al. (2019). Overall, the observation-based δ13Cm value derived with
a more stringent fitting approach are in good agreement with the trends
found in the independently calculated model-based data, which are also shown
in Fig. 11a and Table 6. Because different combinations of predefined criteria (minimum ΔCO2 or season-based restrictions) and filtering (based on the
intercept error) may be used when deriving observation-based δ13Cm, we display three scenarios in Fig. 11b–d. Figure 11b highlights the effect of only
filtering by intercept errors of 4 ‰, 3 ‰, 2 ‰, and 1 ‰.
Instead, Fig. 11c shows the combined effect of
requiring a change in ΔCO2> 3 ppm and filtering by
intercept errors, and Fig. 11d presents data only
for the cold period (October–March), limiting the disturbance of photosynthetic
uptake, in addition to requiring a monotonous increase in ΔCO2
within the 5 h window (i.e. the most stringent criteria). We may generally
conclude that either more stringent intercept error thresholds (such as 1 ‰ for the settings in Fig. 11b) or, alternatively, limiting photosynthetic uptake (through demanding
monotonous increase, and/or filtering for cold season or nighttime) in
combination with less stringent intercept errors (e.g. 2 ‰–3 ‰ in Fig. 11d) appear to yield
equally good results at JFJ, as all δ13Cm values are ≤ 0 ‰ and ≥- 85 ‰ (and thus
physically meaningful). The latter approach, however, discards more data.
The same conclusion holds true when using 10 min averages instead of hourly
data. Note that we do not expect that model-based δ13Cm and observation-based δ13Cm can be
compared directly with each other, as model-based δ13Cm is calculated for 3-hourly resolution and, most
importantly, not restricted to situations when the underlying CO2
simulations match the CO2 observations.
Observation-based mixed source signatures, δ13Cm, derived from a moving Keeling approach (OBS) in
comparison to model-based estimates (SIM, FLEXPART–COSMO, and STILT-ECMWF).
(a) Time series of δ13Cm (tick marks indicate January of
each year). “All” indicates that a minimum change in ΔCO2 was not required, nor was any filtering applied. Results when requiring a
minimum change of 3 ppm in ΔCO2 within the 5 h window and a
fit intercept error (err) < 2 ‰ and < 1 ‰ are provided as green and black markers (open circles
represent October–March, crosses represent April–September). (b–d)δ13Cm
hourly moving Keeling as a function of ΔCO2 for various
criteria: (b) filtering by intercept err < 4 ‰, 3 ‰, 2 ‰, and 1 ‰, (c) demanding a minimum change in CO2 of 3 ppm
and filtering by intercept err < 4 ‰, 3 ‰, 2 ‰, and 1 ‰, (d) demanding a monotonous increase in ΔCO2 of 3 ppm within
the 5 h window and filtering by intercept err < 4 ‰, 3 ‰, 2 ‰, and 1 ‰.
Conclusions
Greenhouse gas emission source and sink identification and quantification at
remote, high-altitude sites is particularly challenging for broadly
distributed, multi-source, and multi-sink compounds such as CO2. In
addition, atmospheric transport simulations are highly challenged by complex
topography. Despite these difficulties, the CO2 simulations performed
on a 3-hourly basis for JFJ agree well with the observations during the
multi-year period 2009–2017. Using Lagrangian particle dispersion models
(LPDMs), we were able to capture 40 % of the observed regional CO2
variability. The results from the model configurations using two different
LPDMs driven by output from two different numerical weather prediction
systems, FLEXPART–COSMO and STILT-ECMWF, appear to differ primarily as a
function of meteorological inputs and their spatial resolution (COSMO vs.
ECMWF), aside from additional variations related to the domain size and
backward integration time. The LPDM implementation (FLEXPART or STILT)
itself contributes comparatively small differences.
The regional CO2 simulations suggest that JFJ's high-altitude location
predominantly experiences influences from the rather nearby (within 100 km)
ecosystem. This is owing to the enhanced PBL influence in summer, which
overlaps with high ecosystem activity. Instead, the peak in anthropogenic
fluxes during winter overlaps with substantially suppressed PBL influence
and a larger (regional) footprint. Therefore, through most of the year, the
ecosystem CO2 contributions, which are composed mainly of cropland and mixed
forest respiration and uptake, outweigh the anthropogenic ones composed of
90 % fossil emissions and dominated by heavy and light oil as well as natural
gas. While the simulated composition resembles our hypothesis for JFJ, the
extent to which ecosystem contributions outweigh anthropogenic ones is
surprisingly large. Indeed, quantitatively, the models perform the CO2
simulations best during winter and transition periods (spring–autumn). For
the summer, the CO2 simulations poorly reproduce the quantities
despite the good qualitative agreement. The atmospheric transport models
employed apparently suffer from their relatively coarse spatial resolution,
which deteriorates model performance in summer and/or fair-weather situations, when
topography-induced convection is not captured very quantitatively during
daytime. Increased model resolution and improved representation of the
Alpine boundary layer in both the LPDMs and the driving numerical weather
prediction models will be necessary to overcome this shortcoming and to
allow for more quantitative conclusions when interpreting observations
during the above-mentioned conditions. However, the net ecosystem
exchange fluxes themselves are also a likely source of error through inaccurate
spatial distribution and VPRM parameterization of respiration and/or uptake
fluxes for the (Alpine) vegetation following limited spatial resolutions of
vegetation maps and possibly temperature profiles.
The simulations of regional CO2 concentrations allow retrieving
model-based mixed source signatures (δ13Cm) and
atmospheric δ13C–CO2 at JFJ. The latter agree well with
the high-frequency observations. The overall δ13C–CO2
correlation (28 %–35 % of variance explained) remains only slightly lower than for CO2
(41 %–44 %). In analogy to the findings for CO2, δ13C–CO2 also shows the lowest agreement between observations and
simulations during the summer. We relate this primarily to the poorly
reproduced CO2 quantities in summer, although the assumption of source
signatures (δ13Cs) and the estimate of the
background (δ13Cb) provide additional uncertainties. For
instance, our δ13Cs estimates do not consider geographic
variations in fuel-specific δ13Cs and ecosystem values are
not specific to photosynthetic pathways. Dedicated maps that allow
separating C3 and C4 vegetation in the VPRM would allow for even better
representing the forward δ13Cm of CO2. In addition,
the simulations would benefit from further optimizations in deriving the
background δ13Cb.
Observation-based assessments of δ13Cm are challenging at
JFJ owing to the low signal-to-background ratios and the integration of
fluxes over day and night, which substantially limited the data set. Yet,
physically meaningful values were obtained. A further disaggregation of
observation-based δ13Cm using mass balance approaches and
assumptions for the endmembers in order to learn more about the CO2
regional composition for any further comparison to the simulated CO2
regional composition was not attempted here, given the small number of
observation-based δ13Cm values obtained. This may be the
focus in future studies. However, we expect that it will remain challenging
to disentangle fuel and ecosystem respiration signals from observation-based
δ13Cm alone, considering that the simulated regional
CO2 fractions at JFJ indicate approximately equal amounts even during
the winter and that solid and liquid fuel emission δ13Cs endmember assumptions overlap with C3 plant respiration signatures. Thus,
while δ13Cs source apportionment approaches prove
meaningful among either the anthropogenic or the ecosystem carbon pool, they
are of more limited use as a singular tracer when the carbon pools are
mixed.
The simulated regional CO2 composition at JFJ suggests that further
analyses would benefit from a multi-tracer approach, in combination with the
continuous CO2 and δ13C observations presented herein.
Additional parameters may include CO, atmospheric potential oxygen (APO),
and 14C as a combustion or fossil fuel tracer, as well as carbonyl sulfide
(COS) and δ18O–CO2 as ecosystem tracers. Indeed, CO, APO,
COS, and δ18O–CO2 observations are available at high
time resolution at JFJ and may be investigated in future, although
determining their regional and background contributions will still be challenged by the low signal-to-background ratios. The biweekly integrated
14CO2 data currently available for JFJ do not allow
distinguishing regional from background contributions. Highly time-resolved
14CO2 measurements or grab sampling during periods with intense
regional CO2 influences would be highly valuable and are foreseen to be
implemented at JFJ as part of the European-wide flask sampling strategy of
the ICOS Research Infrastructure. Moreover, specific episodes at JFJ that
represent air masses of particular regional CO2 composition may (also) be
identified based on continuous δ13C observations in a
multi-tracer manner in future studies.
Transport dynamics analysis for JFJ
We performed a dedicated set of simulations to characterize the atmospheric
transport in backward LPDM simulations for JFJ as represented by different
models in different configurations for 2009–2017. In order to analyse
source sensitivity dependencies on domain size (western Europe – “small” vs.
Europe – “large”), LPDM implementation (FLEXPART vs. STILT), and
meteorological input fields and associated spatial resolution (COSMO vs.
ECMWF), we used four different combinations of these three parameters (Table A1). The simulations are based on one assumed input field of idealized,
positive CO2 fluxes, which were kept constant in time and space for
seven VTs based on the maps underlying the VPRM. This analysis is
designed to study atmospheric transport of chemically passive tracers
released rather uniformly over the European continent to the high Alpine
site, and the obtained signals serve as a measure of PBL influence of JFJ. It
includes the total of the synthetic CO2 concentration time series from
all seven VTs, alongside sub-groups comprising (a) cropland, (b) mixed forest,
and (c) the total of the remaining five VTs. Studying the VT sub-groups gives
insight into the influence of spatial distributions of the sources within
the domains under the given assumptions of uniform fluxes. This transport
dynamics analysis supports the interpretation of the results presented in
Fig. 1.
Figure A1 provides the multi-annual monthly means of the 3-hourly tracer
concentrations at JFJ and highlights the sensitivity to domain size
(E1 vs. E2), meteorological input fields and spatial resolution (E2 vs. E3),
LPDM implementation (E1 vs. E4), and combinations of these (E3 vs. E4).
Overall, we find that the synthetic CO2 concentrations simulated at JFJ
vary between the different models and configurations, as well as with
seasonality and diurnal cycle. The analyses indicate a significant
seasonality in the PBL influence for all four configurations. Higher tracer
concentrations are observed during the warm period (April–September) and
relatively lower tracer concentrations during the colder period
(October–March). This confirms the generally stronger vertical transport
during warm (and possibly sunny) days. Further, meteorological input fields
and related spatial resolution (ECMWF vs. COSMO, i.e. E2 vs. E3) appear to
have a larger influence compared to the LPDM implementation itself (FLEXPART
vs. STILT, i.e. E1 vs. E4), and intensity discrepancies between the models
used in the main text (E3, E4) are largest in winter, followed by summer,
and smallest during transition periods. Concerning the domain size, we find
differences between different VT classes, which is owing to their
heterogeneous spatial distribution as some VT classes are present
predominantly inside (e.g. mixed forest) or outside (e.g. deciduous forests)
the smaller domain boundaries; compare Figure S2. The smallest discrepancy
was thus found for mixed forest (essentially 0 %), and a larger
discrepancy (on average -15 %) was found for cropland at the
artificially assumed spatially and temporally constant fluxes. The influence
of the LPDM implementation itself (FLEXPART vs. STILT, i.e. E1 vs. E4)
appears to be smaller than that of the meteorological fields and spatial
resolution, generating differences mainly during winter periods, when
FLEXPART-ECMWF yields a higher relative signal compared to STILT-ECMWF. In
Fig. A2, we present the PBL influence on diurnal timescales, with up to
1.4 times higher synthetic CO2 concentrations at JFJ during the
afternoon and evening (maximum around 16:00–20:00, UTC + 1) compared to
the morning (minimum around 10:00, UTC + 1). This is observed for
FLEXPART–COSMO (E3) as well as STILT-ECMWF (E4), and it is particularly
pronounced during summer (June–August).
Mean monthly PBL sensitivity (JFJ, 2009–2017)
towards (i) domain size (E1 vs. E2), (ii) meteorological
input fields and spatial resolution (E2 vs. E3), (iii) LPDM
implementation (E1 vs. E4), and (iv) combinations (E3 vs. E4).
Model combinations for transport dynamics analysis. E3
and E4 are the model configurations as used for the CO2 concentration
simulation in the main text.
* EU and WEU refer to 33∘ N–73∘ N,
-15–35∘ E and 36.06–57.42∘ N, -11.92–21.04∘ E, respectively.
Mean diurnal PBL sensitivity (JFJ; winter, DJF, a, b and
summer, JJA, c, d) for the period 2009–2017 for (a)
FLEXPART–COSMO (E3) and (b) STILT-ECMWF (E4).
Abbreviations and definitions
fbCO2 concentration in the background, expressed in ppmfsRegional contribution to the CO2 concentration per category, expressed in ppmCO2.regionalSum of all regional contributions to the CO2 concentrations (fs)CO2.totalSum of CO2.regional and JCS-based CO2 background (fb)CO2.anthrCO2 concentration associated with all anthropogenic (anthr) categoriesCO2.cementCO2 concentration associated with cement productionCO2.fuelCO2 concentration associated with all fuel categoriesCO2.geeCO2 concentration associated with gross ecosystem exchange (i.e. ecosystem uptake) (gee)CO2.neeCO2 concentration associated with net ecosystem exchange (nee)CO2.respCO2 concentration associated with gross ecosystem respiration (resp)δ13Caδ13C–CO2 estimate for atmospheric CO2 at JFJ ‰δ13Cbδ13C–CO2 estimate for the background CO2, ‰δ13Cmδ13C–CO2 mixed source signature for all δ13Cs weighted with the CO2 concentration (fs), ‰δ13Csδ13C–CO2 source signature, ‰COSMOConsortium for Small Scale ModellingECMWFEuropean Centre for Medium-Range Weather ForecastsEDGAREmissions Database for Global Atmospheric ResearchFLEXPARTFlexible Particle ModelJCSJena-CarboScope-based background estimateLPDMLagrangian particle dispersion modelMACC-TNOMonitoring Atmospheric Composition and Climate (provided by TNO)QCLASQuantum cascade laser absorption spectrometerSTILTStochastic Time-Inverted Lagrangian TransportVPRMVegetation and Photosynthesis Respiration Model
Data availability
References to repositories for data and code are provided in the main text and the Supplement. Data complementary to our study are available at 10.5281/zenodo.6583640 (last access: 1 July 2022, Pieber et al., 2022). Further information may be found at https://www.icos-cp.eu/data-services/about-data-portal (last access: 12 May 2022, ICOS Carbon Portal, 2022) and requested from Lukas.Emmenegger@empa.ch.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-10721-2022-supplement.
Author contributions
SMP and SH wrote paper with contributions from all authors. LE
supervised the project. For the simulations, UK, SMP, SH, and DB prepared the annual scaling
factors for the anthropogenic inventory, and CG and FTK prepared updated VPRM
parameters. SH performed the CO2 simulations with FLEXPART–COSMO. UK
performed the CO2 simulations with STILT-ECMWF. SH, DB, UK, CG, FTK, and
SMP performed the transport dynamics analysis. In terms of observations, BT, MS, and LE provided the
experimental data from QCLAS and CRDS. For data analysis, SMP, SH, MST, and DB prepared the
data processing routines. SMP performed the model- and observation-based
δ13C–CO2 and δ13Cm estimations, as well as
overall data analyses and evaluations.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We acknowledge support by the Global Atmosphere Watch Quality Assurance/Science Activity
Centre Switzerland (QA/SAC-CH), funded by MeteoSwiss and Empa. We thank the International Foundation high-altitude research stations Jungfraujoch and Gornergrat for access to
Jungfraujoch facilities and local support, as well as the Swiss National
Supercomputing Centre (CSCS) under project ID s862 and the ICOS Carbon
Portal for access to computational resources. We thank Greet Janssens-Maenhout
for providing the EDGAR v4.3 pre-release version, Christian Rödenbeck for the
Jena CarboScope Fields, TNO for the anthropogenic time factors, Ugo Molteni
for contributions to data analyses and graphical layout, and Armin Jordan, Heiko Moossen, and Michael Rothe for providing the GC-FID and IRMS measurements (flask
samples).
Financial support
This research has been supported by the Swiss National Science Foundation (SNSF) (grant no. P400P2_194390 and grant no. 20FI20_173691 (ICOS-CH phase II)), the Bundesamt für Umwelt (BAFU-RINGO grant), and the European Commission under Horizon 2020 (RINGO (grant no. 730944)).
Review statement
This paper was edited by Andreas Hofzumahaus and reviewed by two anonymous referees.
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