Providing timely information on urban greenhouse gas (GHG) emissions and
their trends to stakeholders relies on reliable measurements of atmospheric
concentrations and the understanding of how local emissions and atmospheric
transport influence these observations.
Portable Fourier transform infrared (FTIR) spectrometers were deployed at five stations
in the Paris metropolitan area to provide column-averaged
concentrations of CO2 (XCO2) during a field campaign in spring of
2015, as part of the Collaborative Carbon Column Observing Network (COCCON).
Here, we describe and analyze the variations of XCO2 observed at
different sites and how they changed over time. We find that observations
upwind and downwind of the city centre differ significantly in their
XCO2 concentrations, while the overall variability of the daily cycle
is similar, i.e. increasing during night-time with a strong decrease
(typically 2–3 ppm) during the afternoon.
An atmospheric transport model framework (CHIMERE-CAMS) was used to simulate
XCO2 and predict the same behaviour seen in the observations, which
supports key findings, e.g. that even in a densely populated region like
Paris (over 12 million people), biospheric uptake of CO2 can be of
major influence on daily XCO2 variations. Despite a general offset
between modelled and observed XCO2, the model correctly predicts the
impact of the meteorological parameters (e.g. wind direction and speed) on
the concentration gradients between different stations. When analyzing local
gradients of XCO2 for upwind and downwind station pairs, those local gradients are found to
be less sensitive to changes in XCO2 boundary conditions and biogenic
fluxes within the domain and we find the model–data agreement further
improves. Our modelling framework indicates that the local XCO2
gradient between the stations is dominated by the fossil fuel CO2
signal of the Paris metropolitan area. This further highlights the potential
usefulness of XCO2 observations to help optimize future urban GHG
emission estimates.
Atmospheric background concentrations of CO2 measured since 1958 in
Mauna Loa, USA, have passed the symbolic milestone of 400 ppm (monthly mean)
as of 2013 (Jones, 2013). Properly quantifying fossil fuel CO2
emissions (FFCO2) can contribute to defining effective climate
mitigation strategies. We focus our attention on cities, which are a critical part of
this endeavour as emissions from
urban areas are currently estimated to represent from 53 % to 87 % of
global FFCO2, depending on the accounting method considered, and are
predicted to increase further (IPCC, 2013; IEA, 2008;
Dhakal, 2009). As stated in the IPCC Fifth assessment report, “current
and future urbanization trends are significantly different from the past”
and “no single factor explains variations in per capita emissions across
cities and there are significant differences in per capita greenhouse gas
(GHG) emissions between cities within a single country” (IPCC, 2014).
Therefore, findings in one city can often not be simply extrapolated to other
urban regions. Furthermore, the large uncertainty of the global contribution
of urban areas to CO2 emissions today and in the future is why a new
generation of city-scale observing and modelling systems is needed.
In recent years, more and more atmospheric networks have emerged that
observe GHG concentrations using the atmosphere as a large-scale integrator,
for example in Paris, France (e.g. Bréon et al., 2015; Xueref-Remy et
al., 2018), Indianapolis, USA (e.g. Turnbull et al., 2015; Lauvaux et al.,
2016); Salt Lake City, USA (Strong et al., 2011; Mitchell et al., 2018);
Heidelberg, Germany (e.g. Levin et al., 2011; Vogel et al., 2013); and Toronto,
Canada (e.g. Vogel et al., 2012). The air measured at in situ ground-based
stations is considered to be representative of surface CO2 fluxes of a
larger surrounding area (1–10 000 km2), i.e. the emissions of
the greater Paris area dominate the airshed of Île-de-France
(ca. 12 000 km2) (Staufer et al., 2016). If CO2 measurements are performed both
upwind and downwind of a city, the concentration gradient between the two
locations is influenced by the local net flux strength between both sites
and atmospheric mixing (Bréon et al., 2015;
Turnbull et al., 2015; Xueref-Remy et al., 2018). To derive quantitative flux estimates, measured
concentration data are typically assimilated into numerical atmospheric
transport models which calculate the impact of atmospheric mixing on
concentration gradients for a given flux space–time distribution. Such a
data assimilation framework implemented for Paris with three atmospheric
CO2 measurement sites (Xueref-Remy et al., 2018) previously allowed
the
derivation of quantitative estimates of monthly emissions and their uncertainties
over 1 year (Staufer et al., 2016).
Space-borne measurements of the column-average dry air mole fraction of
CO2 (XCO2) are increasingly considered for the monitoring of urban
CO2. This potential was shown with OCO-2 and GOSAT XCO2
measurements, even though the spatial coverage and temporal sampling
frequency of these two instruments were not optimized for FFCO2 (Kort
et al., 2012; Janardanan et al., 2016; Schwandner et al., 2017), while other
space-borne sensors dedicated to FFCO2 and with an imaging capability
are in preparation (O'Brien et al., 2016; Broquet et al., 2018). Important
challenges of satellite measurements are that they are not as accurate as
in situ ones, having larger systematic errors, while the XCO2 gradients in the column are typically 7–8 times smaller than in the
boundary layer. Another difficulty of space-borne imagery with passive
instruments is that they will only sample city XCO2 plumes during clear-sky conditions for geostationary satellites and with an additional
constraint to observations at around midday for low-Earth-orbiting
satellites.
The recent development of a robust portable ground-based FTIR (Fourier
transform infrared) spectrometer as described in Gisi et al. (2012) and Hase
et al. (2015, 2016) (EM27/SUN, Bruker Optik, Germany) greatly facilitates the
measurement of XCO2 from the surface, with better accuracy than from
space and with the possibility of continuous daytime observation during
clear-sky conditions. Typical compatibility (uncorrected bias) of the
EM27/SUN retrievals of the different instruments in a local network is
better than 0.01 % (i.e. 0.04 ppm) after a careful calibration
procedure and a harmonized processing scheme for all spectrometers (Frey et al., 2015).
The Collaborative Carbon Column Observing Network (COCCON) (Frey
et al., 2018) intends to offer such a framework for operating the EM27/SUN.
This type of spectrometer therefore represents a remarkable opportunity to
document XCO2 variability in cities as a direct way to estimate
FFCO2 (Hase et al., 2015) or in preparation of satellite missions.
When future low-Earth-orbit operational satellites with passive imaging
spectrometers of suitable capabilities to invert FFCO2 sample
different cities, this will likely be limited to clear-sky conditions and at
a time of the day close to local noon. Increasing the density of the COCCON
network stations around cities will allow us to evaluate those XCO2
measurements and to monitor XCO2 during the early morning and
afternoon periods, which will not be sampled with low-Earth-orbit satellites.
From geostationary orbit, which can also have other benefits, those
time periods can however be observed and could be compared to ground-based
measurements (e.g. Butz et al., 2015; O'Brien et al., 2016).
This study focuses on the measurements of XCO2 from ground-based
EM27/SUN spectrometers deployed within the Paris metropolitan area during a
field campaign in the spring of 2015 and modelling results. This campaign
can be seen as a demonstration of the COCCON network concept applied to the
quantification of an urban FFCO2 source. Several spectrometers were
operated by different research groups, while closely following the common
procedures suggested by Frey et al. (2015). The paper is organized as
follows. After the instrumental and modelling setup descriptions of Sect. 2,
the observations of the field campaign and the modelling results will be
presented in Sect. 3. Results are discussed in Sect. 4 together with the
study conclusions.
Methods and materialsDescription of study area and field campaign design
During the COCCON field campaign (28 April to 13 May 2015) five
portable FTIR spectrometers (EM27/SUN, Bruker Optik, Karlsruhe, Germany)
were deployed in the Parisian region (administratively known as
Île-de-France) and within the city of Paris. The campaign was conducted in early spring
as the cloud cover is typically low in April and May and the time between
sunrise and sunset is more than 14 h.
The Paris metropolitan area houses over 12 million people, with about
2.2 million inhabiting the city of Paris. This urban region is the most
densely populated in France with ∼1000 inhabitants/km2 and over
21 000 inhabitants/km2 for the city of Paris itself (INSEE, 2016). The estimated CO2 emissions from the metropolitan
region are 39 Mt yr-1, according to the air quality association AIRPARIF
(Association de surveillance de la qualité de l'air en
Île-de-France), which monitors the airshed of greater Paris. On-road
traffic emissions and the residential and tertiary (i.e. commercial) sectors are
the main sources (accounting for over 75 %), and there are minor contributions from
other sectors such as industrial sources and airports
(https://www.airparif.asso.fr/en/, AIRPARIF, 2016). It was crucial to
understand the spatial distribution of these CO2 sources to optimally
deploy the COCCON spectrometers. To this end a 1 km emission model for
France by IER (Institut für Energiewirtschaft und Rationelle
Energieanwendung, University of Stuttgart, Germany) was used as a starting
point (Latoska, 2009). This emission inventory is based on the available
activity data such as, for example, traffic counts, housing statistics, or energy
use, and the temporal disaggregation was implemented according to Vogel et
al. (2013). In brief, the total emissions of the IER model were rescaled to
match the temporal factors for the different emission sectors according to
known national temporal emission profiles.
To quantify the impact of urban emissions on XCO2, the FTIR instruments
were deployed along the dominant wind directions in this region in spring,
i.e. southwesterly (Staufer et al., 2016), in order to maximize the
likelihood to capture upwind and downwind air masses (see Fig. 1). The two
southwesterly sites (GIF and RES; see Table 2 for site abbreviations) are located in a less densely populated
area, where emissions are typically lower than in the city centre, where the
station JUS is located. The data in Fig. 1 show that the densest FFCO2
emission area extends northwards and eastwards. The two northwesterly sites
(PIS and MIT) were placed downwind of this area. All instruments were
operated manually and typically started operation at around 07:00–08:00 local time
from which they continuously observe XCO2 until 17:00–18:00 LT.
CO2 emissions in the Île-de-France region according to
the IER emission inventory. Measurement sites are indicated by red crosses.
Instrumentation, calibration, and data processing
The EM27/SUN is a portable FTIR spectrometer which has been described in
detail in Gisi et al. (2012) and Frey et al. (2015), for example. Here, only a
short overview is given. The centrepiece of the instrument is a Michelson
interferometer which splits up the incoming solar radiation into two beams.
After inserting a path difference between the beams, the partial beams are
recombined. The modulated signal is detected by an InGaAs detector covering
the spectral domain from 5000 to 11000 cm-1 and is called an
interferogram. As the EM27/SUN analyzes solar radiation, it can only operate
in sunny daylight conditions. A Fourier transform of the interferogram
generates the spectrum and a DC correction is applied to remove the
background signal and only keep the AC signal (see Keppel-Aleks et al., 2007).
A numerical fitting procedure (PROFFIT code) (Schneider and Hase, 2009)
then retrieves column abundances of the concentrations of the
observed gases from the spectrum. The single-channel EM27/SUN is able to
measure total columns of O2, CO2, CH4 and H2O. The ratio
over the observed O2 column, assumed to be known and constant, delivers
the column-averaged trace gas concentrations of XCO2 and XCH4 in
µmol mol-1 dry air, with a temporal resolution of 1 min.
XCO2 is the dry air mole fraction of CO2, defined as XCO2= column[CO2]/column[dry air]. Applying the ratio over the observed
oxygen (O2) column reduces the effect of various possible systematic
errors; see Wunch et al. (2011).
In order to correctly quantify small differences in XCO2 columns
between Paris upstream and downstream locations, measurements were
performed with the five FTIR instruments side by side before and after the
campaign, as we expect small calibration differences between the different
instruments due to slightly different alignment for each individual
spectrometer. These differences are constant over time and can be easily
accounted for by applying a calibration factor for each instrument. Previous
studies showed that the instrument-specific corrections are well below 0.1 %
for XCO2 (Frey et al., 2015; Chen et al., 2016) and are stable for
individual devices. The 1σ precision for XCO2 is on the order of
0.01 %–0.02 % (<0.08 ppm) (e.g. Gisi et al., 2012; Chen et al., 2016; Hedelius et al.,
2016; Klappenbach et al., 2015). The calibration
measurements for this campaign were performed in Karlsruhe using the Total
Carbon Column Observing Network (TCCON) (Wunch et al., 2011) spectrometer at
the Karlsruhe Institute of Technology (KIT), Germany, for 7 days before the
Paris campaign between 9 and 23 April and after the campaign
on 18 until 21 May.
Figure S1 (left panel) shows the XCO2 time series of the calibration
campaign, in which small offsets between the instruments' raw data are visible.
As these offsets are constant over time, a calibration factor for each
instrument can be easily applied; actually these are the calibration factors
previously found for the Berlin campaign (Frey et al., 2015). These factors
are given in Table 1, for which all EM27/SUN instruments are scaled to match
instrument no. 1. The calibrated XCO2 values for 15 April are
shown in Fig. S1. None of the five instruments that participated in the
Berlin campaign show any significant drift; in other words, the calibration
factors found 1 year before were still applicable. This is a good
demonstration of the instrument stability stated in Sect. 2.2, especially
as several instruments (nos. 1, 3, 5) were used in another campaign in
northern Germany in the meantime. The EM27/SUN XCO2 measurements can
also be made traceable to the WMO international scale for in situ
measurements by comparison with measurements of a collocated TCCON
spectrometer, which are calibrated against in situ standards by aircraft and
air-core measurements (Wunch et al., 2010; Messerschmidt et al., 2011)
performed using the WMO scale.
Normalization factors for the five EM27/SUN instruments derived
during measurements before and after the Paris field campaign. Values in
parentheses are standard deviations. Measurements of instrument 1 were
arbitrarily chosen as the reference from which the others were scaled. The
calibration factors from a previous field campaign in Berlin (Hase et al.,
2015) are also shown. Calibration factors between the two field campaigns
agree well within 0.02 % (∼0.08 ppm) for all instruments.
During the campaign and for the calibration measurements we recorded
double-sided interferograms with 0.5 cm-1 spectral resolution. Each
measurement of 58 s duration consisted of 10 scans using a scanner velocity
of 10 kHz. For precise timekeeping, we used GPS sensors for each
spectrometer.
In situ surface pressure data used for the analysis of the calibration
measurements performed at KIT were recorded at the co-located
meteorological tall tower. During the campaign, a MHD-382SD data logger
recorded local pressure, temperature and relative humidity at each station.
The analysis of the trace gases from the measured spectra for the
calibration measurements has been performed as described by Frey et al. (2015).
For the campaign measurements we assume a common vertical
pressure–temperature profile for all sites, provided by the model, so that
the surface pressure at each spectrometer only differs due to different site
altitudes. The 3-hourly temperature profile from the European Centre for
Medium-Range Weather Forecasts (ECMWF) operational analyses interpolated for
site JUS located in the centre of the array was used for the spectra
analysis at all sites. The individual ground pressure was derived from site
altitudes and pressure measurements performed at each site.
Before and after the Paris campaign, side-by-side comparison measurements
were performed with all five EM27/SUN spectrometers and the TCCON spectrometer
operated in Karlsruhe at KIT. All spectrometers were placed on the top of
the IMK office building north of Karlsruhe. The altitude is 133 m above sea
level (a.s.l.); coordinates are 49.09∘ N and 8.43∘ E.
The processing of the Paris raw observations (measured interferograms) was
performed as described by Gisi et al. (2012) and Frey et al. (2015) for the
Berlin campaign: spectra were generated applying a DC correction, a
Norton–Beer medium apodization function and a spectral resampling of the
sampling grid resulting from the FFT on a minimally sampled spectral grid.
PROFFWD was used as the radiative transfer model and PROFFIT as the
retrieval code.
Atmospheric transport modelling framework
We used the chemistry transport model CHIMERE (Menut et al., 2013) to
simulate CO2 concentrations in the Paris area. More specifically, we
used the CHIMERE configuration over which the inversion system of Bréon
et al. (2015) and Staufer et al. (2016) was built to derive monthly to
6 h mean estimates of the CO2 Paris emissions. Its horizontal grid,
and thus its domain and its spatial resolution, is illustrated in Fig. S2.
It has a 2×2 km2 spatial resolution for the Paris
region, and 2×10 and 10×10 km2 spatial
resolutions for the surroundings. It has 20 vertical hybrid pressure-sigma
(terrain-following) layers that range from the surface to the
mid-troposphere, up to 500 hPa. It is driven by operational meteorological
analyses of the ECMWF Integrated Forecasting System, available at an
approximately 15×15 km2 spatial resolution and 3 h temporal
resolution.
In this study the CO2 simulations are based on a forward run over 25 April–12 May 2015
with this model configuration; we do not
assimilate atmospheric CO2 data and so no inversion for surface
fluxes was conducted. In the Paris area (the Île-de-France administrative
region), hourly anthropogenic emissions are given by the IER inventory; see
Sect. 2.1. The anthropogenic emissions in the rest of the domain are
prescribed from the EDGAR V4.2 database for the year 2010 at 0.1∘
resolution (Olivier and Janssens-Maenhout et al., 2012). In the whole
simulation domain, the natural fluxes (the net ecosystem exchange, NEE) are
prescribed using simulations of CTESSEL, which is the land-surface component
of the ECMWF forecasting system (Boussetta et al., 2013), at a 3 hourly and
15×15 km2 resolution. Finally, the CO2 boundary
conditions at the lateral and top boundaries of the simulation domain and the
simulation CO2 initial conditions on 25 April 2015 are prescribed
using the CO2 forecast issued by the Copernicus Atmosphere Monitoring
Service (CAMS, http://atmosphere.copernicus.eu/, last access: last
access: 1 March 2019) at a ∼15 km global resolution
(Agustí-Panareda et al., 2014).
The CHIMERE transport model is used to simulate the XCO2 data. However,
since the model does not cover the atmosphere up to its top, the CO2
fields from CHIMERE are complemented with those of the CAMS CO2
forecasts from 500 hPa to the top of the atmosphere to derive total column
concentrations. The derivation of modelled XCO2 at the sites involves
obtaining a kernel-smoothed CO2 profile of CHIMERE and CAMS and
vertical integration of these smoothed profiles, weighted by the pressure at
the horizontal location of the sites.
The parametrization used to smooth modelled CO2 profiles approximates
the sensitivity of the EM27/SUN CO2 retrieval as a function of
pressure and sun elevation. Between 1000 and 480 hPa, a linear dependency of the
instrument averaging kernels on solar zenith angle (Θ) is assumed with
boundary values following Frey et al. (2015):
k480hPa=1.125,k1000hPa=1.0+0.45s3,
where s=Θ/90∘
Approximate averaging kernels are obtained
by linear interpolation to the
pressure levels of CHIMERE and CAMS. If p>1000 hPa, k is
linearly extrapolated. Above 480 hPa (p<480 hPa), the
averaging kernels can be approximated by
ku,s=1.125-0.6u3-0.4us3,
where u is (480 hPa-p)/480. The kernel-smoothed CO2 profile,
CO2_models, is obtained by
CO2_models=KCO2_model+I-KCO2a,
where CO2_model is the modelled CO2 profile by CHIMERE or
CAMS, I the identity matrix and K is a diagonal matrix
containing the averaging kernels k. The a priori CO2 profile,
CO2a, is provided by the Whole Atmosphere Community Climate
Model (WACCM) model (version 6) and interpolated to the pressure levels of
CHIMERE and CAMS. CO2_models is the appropriate CO2
profile to calculate modelled XCO2 at the location of the sites.
For a given site, the simulated XCO2 data are thus computed from the
vertical profile of this site as
XCO2_CHIMERE=1psurf∫psurfptop_CHIMCO2_CHIMsdp+∫ptop_CHIMp=0hPaCO2_CAMSsdp,
where psurf is the surface pressure, ptop_CHIM=500 hPa the pressure corresponding to the top boundary of the CHIMERE
model, and CO2_CHIMs and CO2_CAMSs are the
smoothed CO2 concentrations of CHIMERE and CAMS, respectively. For
comparison we also calculated XCO2 at a lower spatial resolution with
the CAMS data alone as
XCO2_CAMS=∫psurfp=0hPaCO2_CAMSsdp.
Results and discussionObservationsMeteorological conditions and data coverage/instrument
performance
During the measurement campaign (28 April until 13 May 2015),
meteorological conditions were a major limitation for the availability of
XCO2 observations. Useful EM27/SUN measurements require direct
sunlight,
and low wind speeds typically yield higher local XCO2. Most of the time
during the campaign, conditions were partly cloudy and turbid, and so
successful measurements at a high solar zenith angle (SZA) were rare.
Therefore, the data coverage between 28 April and 3 May is
limited (see Table 2). As is typical for spring periods in Paris, the
temperature and the wind direction vary and display less synoptic variations
than in winter. The dominant wind directions were mostly northeasterly at
the beginning of the campaign and mostly southeasterly during the second
half of the campaign. We find that the wind speeds during daytime nearly
always surpass 3 m s-1, which has been identified by Bréon et al. (2015)
and Staufer et al. (2016) as the cut-off wind speed above which the
atmospheric transport model CHIMERE performs best in modelling CO2
concentration gradients in the mixed layer.
Summary of all measurement days with the number of observations at
each of the sites, Mitry-Mory (MIT), Gif-sur-Yvette (GIF), Piscop (PIS),
Saulx-les-Chartreux (RES) and Jussieu (JUS), the overall quality ranking of
each day according to the number of available observations and temporal
coverage (with classification from poor to great: +, ++, +++,
++++), and the ground-level wind speed and direction.
DateNo. of observations QualityWind speed (m s-1)Wind directionMITGIFPISRESJUS28 Apr 2015 (Tu)179102178199234++4W29 Apr 2015 (We)110124016153+5W-SW4 May 2015 (Mo)194859616383+6S-SE5 May 2015 (Tu)77278518592+8S-SW6 May 2015 (We)8188871390+8SW7 May 2015 (Th)169313252286238+++3SW9 May 2015 (Sa)1790181289149++6W10 May 2015 (Su)325478362542282++++3S11 May 2015 (Mo)410431251298413++++3S-SW12 May 2015 (Tu)324222230326203+++4N-NW13 May 2015 (We)159181822856+4NE
Despite some periods with unfavourable conditions, more than 10 000 spectra
were retrieved among the five deployed instruments. The quality of the
spectra for each day was rated according to the overall data availability
and to be consistent with Hase et al. (2015). The best measurement conditions
prevailed for the period between 7 and 12 May.
Observations of <inline-formula><mml:math id="M162" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">XCO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> in Paris
The observed XCO2 in the Paris region for all sites (10 415 observations)
ranges from 397.27 to 404.66 ppm with a mean of 401.26 ppm (a
median of 401.15 ppm). The strong atmospheric variability of XCO2
across Paris and within the campaign period is reflected in the standard
deviation of 1.04 ppm for 1 min averages. We find that all sites exhibit
very similar diurnal behaviours with a clear decrease in XCO2 during
the
daytime and a noticeable day-to-day variability as seen in Fig. 2. This is
to be expected as they are all subject to very similar atmospheric transport
in the boundary layer height and to similar large-scale influences, i.e.
surrounded by stronger natural fluxes or air mass exchange with other
regions at synoptic timescales. However, observed XCO2 concentrations
at the downwind sites for our network remain clearly higher from sites that
are upwind of Paris (see Fig. 2). The shifting dominant wind conditions
also explain why the sites RES and GIF are lowest in the beginning of the
campaign and higher on 12 and 13 May after meteorological
conditions changed. This indicates that the influence of urban emissions is
detectable with this network configuration under favourable meteorological
conditions. By comparing the different daily variations in Fig. 3, it is
apparent that the day-to-day variations observed at the two southwesterly
(typically upwind) sites GIF and RES are approximately 1 ppm, with both
sites exhibiting similar diurnal variations throughout the campaign period.
This can be expected as their close vicinity would suggest that they are
sensitive to emissions from similar areas and to concentrations of air
masses arriving from the southwest.
Time series of
observed XCO2 in the Parisian region for all five sites (all valid
data of 1 min averages).
The typical decrease in XCO2 found over the course of a day is about 2
to 3 ppm. This decrease could be driven by (natural) sinks of CO2,
which can be expected to be very strong as our campaign took place after the
start of the growing season in Europe for most of southern and central
Europe (Rötzer and Chmielewski, 2001).
The observations at the site located in Paris (JUS) display similarly low
day-to-day variations and a clear decrease in XCO2 over the course of
the day. The latter feature indicates that even in the dense city centre,
XCO2 is primarily representative of a large footprint like in other
areas of the globe (Keppel-Aleks, 2011) and supports the findings of Belikov
et al. (2017) concerning the footprints for the Paris and Orleans TCCON
sites. Thus, our total column observations are less critically affected by
local emissions than in situ measurements (Bréon et al., 2015; Ammoura et al.,
2016). It is also apparent that the decrease in XCO2 (the slope) during
the afternoon for 28 and 29 April as well as 7 and
10 May is noticeably smaller than on other days during this campaign. As
XCO2 is not sensitive to vertical mixing, this has to be caused by
different CO2 sources and sinks acting upon the total column arriving
at JUS.
Time series of
observed XCO2 in the Parisian region sorted by station.
The two (typically downwind) sites PIS and MIT northeast of Paris show a
markedly larger day-to-day spread in their general XCO2 levels as
well as strongly changing slopes for the diurnal XCO2 decrease. For
these sites the exact wind direction is critical as they can be downwind of
the city centre that has a much higher emission density or less dense suburbs
(see Fig. 1).
Observed spatial gradients of XCO2 for 7 May
(southwesterly winds) and 10 May (southerly winds).
Gradients in observed <inline-formula><mml:math id="M179" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">XCO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
In order to focus more on the impact of local emissions on atmospheric
conditions and less on that of CO2 fluxes from outside of our urban
domain in our analysis of XCO2, we choose to study the spatial
gradients (Δ) among different sites. Fundamentally, this approach
assumes that regional- and large-scale fluxes have a similar impact on
XCO2 for the sites within our network due to the close proximity of
sites and the smoothing of remote emission signals due to atmospheric
transport by the time the air mass arrives in our domain. Ideal conditions
were sampled on 7 May, with predominantly southwesterly winds,
and on 10 May with southerly winds. We can see in Fig. 4 that all
sites were, on average, elevated compared to RES, chosen as reference here
as it was upwind of Paris during those days. The hodographs for both days
also indicate that the wind fields were consistent across Paris (see Fig. S3).
The observations from GIF showed only minimal differences with RES,
while the rest of the sites (PIS, JUS and MIT) had Δ values of 1 to
1.5 ppm. During southwesterly winds, MIT is downwind of the densest part of
the Paris urban area, and JUS is impacted by emissions of neighbourhoods to
the southwest. The site of PIS is still noticeably influenced by the city
centre but, as can be seen in Fig. 1, we likely do not catch the plume of
the most intense emissions but rather from the suburbs. On 10 May,
with its dominant southerly winds, the situation was markedly different.
While GIF was still only slightly elevated, the XCO2 enhancement at MIT
was significantly lower and quite similar to JUS for large parts of the day.
The highest ΔXCO2 can be observed at PIS, again typically
ranging from 1 to 1.5 ppm. As seen in Fig. 1, PIS is then directly downwind
of the densest emission area, while MIT is only exposed to CO2
emissions from the eastern outskirts of Paris.
It is also important to note that the impact of the local biosphere that is
assumed to cause the strong decrease in XCO2 during the day is not seen
on both days for these spatial gradients. For a more comprehensive
interpretation of these observations the use of a transport model (as
described in Sect. 2.3) is necessary.
ModellingModel performance
Before interpreting the modelled XCO2 we need to evaluate the
performance of the chosen atmospheric transport model framework as described
in Sect. 2.3. Comparing it to meteorological observations (wind speed and
wind direction) at GIF, we find that CHIMERE predicts these variables well
throughout the duration of the campaign (see Fig. S4). Changes in wind
speed direction and speed are reproduced with a slight overestimation at low
wind speeds (>1 m s-1). In addition to the meteorological forcing, the
model performance can also be expected to depend on the chosen model
resolution. Therefore, we compared XCO2 at JUS calculated based on the
coarser-resolution atmospheric transport and flux framework CAMS (15 km)
and the higher-resolution emission modelling input for the framework based
on CHIMERE (2 km) for the inner domain and based on CAMS boundary conditions (see
Fig. S2). We find that the coarser model displays similar inter-daily
variations, but that the high-resolution model modifies the modelling
results on shorter timescales. We find that the afternoon XCO2
decreases are often more pronounced in CHIMERE. Only the high resolution
will be considered and referred to in the following. The impact of using
different flux maps (fossil fuel CO2) on the modelled XCO2 can
unfortunately not be explicitly investigated here as only one
high-resolution (1 km) emission product available for fossil fuel CO2
was available for this study region (see Sect. 2.3), and other global
emission products are usually not intended for urban-scale studies.
Modelled XCO2 for all stations.
Modelled <inline-formula><mml:math id="M198" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">XCO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and its components
The modelled XCO2 for the five sites (Fig. 5) co-evolves over the
period of the campaign with occurrences of significant differences. This was
already seen with the measurements, but the model allows us to look at the full
time series. The model reveals clear daily cycles of XCO2, with an
accumulation during the night-time and a decrease during the daytime. Despite a good
general agreement of modelled XCO2 at all sites for the timing
of daily minima and their synoptic changes, for example, differences in XCO2 are
observed between the sites for many days. Typically the northeasterly sites
(PIS, MIT) show an enhancement in modelled XCO2 compared to the
southwesterly sites (GIF, RES).
To understand the synoptic and diurnal variations of the modelled
XCO2, we analyzed the contribution of different sources (and sinks)
of CO2, namely the NEE, the fossil fuel
CO2 emissions (FFCO2) and the boundary conditions (BCs),
i.e. the variations of CO2 not caused by fluxes within our domain
(the example of JUS is given in Fig. 6). The day-to-day variability of
modelled XCO2 is dominated by changing boundary conditions and
coincides with synoptic weather changes. As the CO2 emitted from the
different sources is transported in the model as independent tracers, the
strong daily decrease in XCO2 can be directly linked to NEE, which
leads to a decrease of ∼1 ppm (but up to 4 ppm) during the day, but
can also cause positive enhancements during the night-time driven by biogenic
respiration. The XCO2 from fossil fuel emissions causes significant
enhancements compared to the background but is often compensated by NEE.
During short periods, fossil fuel emissions can however lead to enhancements
of up to 4 ppm.
Time series of XCO2 and related fluxes for JUS.
Panel (a) provides a comparison of modelled total XCO2 and
XCO2 variations due to changes in boundary conditions (BC only).
Panel (b) shows the contribution of the different flux components,
namely fossil fuel CO2 emissions and biogenic fluxes.
Modelled <inline-formula><mml:math id="M218" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula><inline-formula><mml:math id="M219" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">XCO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> gradients and its components
To be able to assess the impact of local sources and reduce the influence of
NEE and BC on the modelled signals, we analyze the XCO2 gradient (i.e.
station-to-station difference) with RES being taken as reference. In Fig. 7a
we compare Δ and its components, i.e. fossil fuel
CO2, biogenic CO2 and CO2 transported across the boundary of
the domain (BCs), along a south–north direction. For the
modelled Δ we can see that MIT shows a positive value during the
campaign period whenever the predominant wind direction was southwesterly.
We also find that Δ between JUS and RES was both negative and
positive during the campaign and predominantly negative between MIT and
JUS. When split into FFCO2, BC and NEE components, we can clearly see that
the total Δ is dominated by FF causing XCO2 offsets of up to 4 ppm, but more typically 1 ppm gradients are observed. Gradients can also
change rapidly (within a few hours) if the wind direction changes, for
example on 1 and 12 May. This highlights the fact that,
during such conditions, we cannot assume a simple upwind–downwind
interpretation of our sites. As expected, the contributions from BC and NEE
are generally greatly reduced when analyzing ΔXCO2. The most
important impact of NEE on the XCO2 gradients of -1 and +1 ppm can
be seen on 8 and 11 May, respectively. This means that,
despite greatly reducing the impact of NEE on average, the contribution of
NEE cannot be fully ignored. BC is an overall negligible contribution to
ΔXCO2, even though it reaches -0.4 ppm on 11 May.
Modelled XCO2 gradients for each station relative to RES are
given in (a) with its contributing components in the panels below.
Total ΔXCO2(a), the fossil fuel contribution to
ΔXCO2,ff(b), the biogenic contribution to
ΔXCO2,bio(c) and the influence of the boundary
conditions ΔXCO2,BC(d). The dominant wind
conditions for each day given at the top of the figure and days without
observations due to precipitation are in red.
Model data and observation comparison
<inline-formula><mml:math id="M241" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">XCO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
A comparison of modelled and observed XCO2 is of course limited to the
relatively short periods when observations are available. Over these periods
we can see a general issue in reproducing the general XCO2 for each day
in the model as observed XCO2 is significantly lower, revealing a
fairly stable bias between 1 and 2 ppm. As our CO2 boundary conditions
were from a forecast product, this is not unexpected, as already small
issues in estimating carbon uptake (or emissions) at the European scale can
have such an impact on the boundary conditions. However, we observe that the
main features, like daily cycles and synoptic changes of the modelled and
observed XCO2, are comparable as seen in Fig. 8. The daytime
variations are well reproduced by the model and the general relative
concentrations among sites are preserved, e.g. the highest values for
XCO2 at MIT are on 9 May and the highest XCO2 values for PIS are later
on 10 and 11 May. We also see that the timing of the daily
minima is not fully covered in the observed data as it typically happens
after sunset and cessation of biosphere uptake. To reduce the impact of
uncertainties of the boundary conditions on our analysis, a gradient approach
was tested.
Comparison of modelled (solid lines) and observed hourly averaged
XCO2 (symbols) with standard deviations as error bars.
Due to the prevailing southeasterly wind conditions, we can compare
XCO2 at the typical downwind sites (PIS, MIT) relative to the mostly
upwind sites (RES, GIF) and expect elevated XCO2 downwind.
Furthermore, we can expect to see negative gradients for opposing wind
conditions, i.e. northwesterly. For other wind conditions, the concentration
difference is not determined by emissions between the station pairs but
rather by the areas upwind of the sites (see Fig. 1). We find that the model
versus observed ΔXCO2 of PIS relative to RES generally falls
along the 1:1 line with a slope of 1.07±0.09 with a Pearson's R of
0.8. Negative ΔXCO2 values, seen in Fig. 9, are associated with
meteorological conditions when winds come from northerly directions; i.e. the
roles of normal upwind and downwind sites are reversed. For wind
perpendicular to the direct line of sight for (PIS, RES) the concentration
enhancements are small and harder to interpret. The gradient of XCO2
MIT relative to RES has a significantly lower range for modelled XCO2
while the observed range of XCO2 is similar to PIS. The slope of
observed to modelled ΔXCO2 for upwind–downwind (or
downwind–upwind conditions) is 1.72±0.06 with a Pearson's R of 0.96.
This points to a significant underestimation of the impact of urban sources
on the MIT–RES gradient, which is especially visible in the more negative
ΔXCO2 during northerly wind conditions. This could indicate
that the spatial distribution of our emissions prior should be improved; i.e.
emissions in the eastern outskirts/suburbs are likely underestimated in the
IER emissions model. The low modelled ΔXCO2 could also be due
to overestimated horizontal dispersion in the model, which seems less likely.
Again the model does not predict concentration differences well for
perpendicular wind conditions. When comparing the mean modelled daily cycle
of the days with southwesterly wind conditions and when observations exist
with the mean diurnal cycle for all days within the field campaign period
when MIT and PIS can be considered downwind of RES, we find that the days
with observations do not significantly differ from those without observations
(see Fig. 10). An investigation of typical diurnal variations of modelled
ΔXCO2 can only be performed to a limited degree with the
observational data available for suitable wind conditions. Within the large
uncertainties, the modelled and observed ΔXCO2 agree throughout
the day. When analyzing the modelled ΔXCO2 components we also
find that the observed daytime increases in ΔXCO2 are driven by
CO2 added by urban FFCO2 burning and that the impact of FF is
significantly higher at the PIS (up to 1 ppm) than at the MIT site
(0.5 ppm) in the model, when both sites are downwind of Parisian emissions.
Our observations indicate that both sites have strong diurnal variations.
Given that the most important biogenic sinks, in our domain, can be expected
to be found in the rural parts surrounding Paris, we would expect the
biogenic contribution to be similar at both sites (as predicted by the
model). This would further point towards the impact of FF emissions on the
MIT site being larger than predicted by our modelling framework.
Comparison of modelled and observed hourly averaged ΔXCO2
for gradients between PIS and RES (a) and MIT and RES (b),
with standard deviations of the minute values of the hourly mean as vertical
bars and the points colour coded by wind direction from 0 to 359∘.
Comparison of modelled (black) and observed mean daily cycles (blue)
of hourly averaged ΔXCO2 of PIS (a) and of MIT
(b) during the campaign when RES can be considered an upwind site.
Labels at the top of (a) and (b) denote the number of days
contributing to the mean. The mean daily cycle for all days within the
campaign period when PIS and MIT are downwind of RES is given in light grey.
The modelled contribution of different CO2 sources–sinks to the mean
daily cycle for days with observations for the two sites is given in
(c) and (d).
Different ΔXCO2 diurnal variations can be found for other
upwind–downwind site pairs, but they are all systematically driven by the
locally added CO2 from FFCO2.
Conclusion and outlook
For the 2-week field campaign we demonstrated the ability of a network of
five EM27/SUN spectrometers, placed on the outskirts of Paris, to track the
XCO2 changes due to the urban plume of the city. However, we also found
that XCO2 cannot be simply interpreted in the context of local
emissions as, even in such a densely populated area, XCO2 is still
significantly influenced by natural CO2 uptake during the growing
season. Understanding the area influencing XCO2 and/or the use of
suitable atmospheric transport models seems indispensable to correctly
interpret atmospheric XCO2 variations. Using a gradient approach, i.e.
analyzing the difference between XCO2 measured at upwind and downwind
stations, greatly reduced the impact of the CO2 boundary condition, which
reflects fluxes outside the domain and biogenic fluxes within the domain.
Overall, the XCO2 variability modelled using our ECMWF CHIMERE system
with IER (1×1 km2) emissions data was found to be comparable with the
observed variability and diurnal evolution of XCO2, despite a higher
background for modelled XCO2. Our modelling framework, run at a 2×2 km2 resolution over Paris also predicts that biogenic fluxes and
boundary conditions (i.e. the influence of CO2 being transported into
our domain) have only a very small impact on ΔXCO2 during a few situations, specifically when
meteorological condition changes made the concept of “upwind” and
“downwind” not applicable. When comparing modelled and measured
ΔXCO2, we find strong correlations (Pearson's R) of 0.8 and 0.96 for
PIS–RES and MIT–RES, respectively. The offset between model and observations
also diminished for ΔXCO2 and the slope found between the observed
and modelled PIS–RES gradients is statistically in accordance with a 1:1
relationship (1.07±0.09). However, the slope of the MIT–RES XCO2
gradient of 1.72±0.06 suggests that the emission model could
potentially be improved, as it seems unlikely that the general atmospheric
transport in the model is the key issue as both site pairs would be subject
to very similar winds. Another potential source of error that needs to be
investigated is if such an underestimation of ΔXCO2 could be
caused by the limited model resolution. It also seems rather likely that a
2×2 km2 model would cause a general spreading of point source emissions
and not systematically underestimate emissions impacts from less densely
populated parts of Île-de-France. The data also confirm previous
results by models that XCO2 gradients caused by a megacity do not
exceed 2 ppm, which supports the previous requirement for satellite
observations of less than 1 ppm precision on individual soundings and
biases lower than 0.5 ppm (Ciais et al., 2015). The gradients are mainly
caused by the transport of FFCO2 emissions but, interestingly, during
specific episodes, a noticeable contribution comes from biogenic fluxes,
suggesting that these fluxes cannot always be neglected even when using
gradients.
Unfortunately, the duration of the campaign was relatively short, so that an
in-depth analysis of mean daily cycles or the impact of ambient conditions
(traffic conditions, temperature, solar insolation, etc.) on the observed
gradient and underlying fluxes could not be investigated here. Hence, future
studies in Paris and elsewhere should aim to perform longer-term
observations during different seasons, which will allow better understanding
of
changes in biogenic and anthropogenic CO2 fluxes. A
remotely controllable shelter for the EM27/SUN instrument is currently under
development (Heinle and Chen, 2018). This will considerably facilitate the
establishment of permanent spectrometer arrays around cities and other
sources of interest. Nevertheless, our study already indicates that such
observations of urban XCO2 and ΔXCO2 contain original
information to understand local sources and sinks and that the modelling
framework used here is a step forward to support their detailed
interpretation in the future. An improved model will also be able to adjust
or better model the background conditions and potentially use this type of
observations to estimate local CO2 fluxes using a Bayesian inversion
scheme similar to the existing system based on in situ observations for
Paris (Staufer et al., 2016).
We expect that the previous successful collaboration in the framework of the
Paris campaign will mark the permanent implementation of COCCON as a common
framework for a French–Canadian–German collaboration on the EM27/SUN
instrument. The acquisition of additional spectrometers is planned by
several partners.
Data availability
The data are available from the corresponding author upon
request. The CHIMERE modelling system source code and documentation is freely
available from the Laboratoire de Météorologie Dynamique, France,
http://www.lmd.polytechnique.fr/chimere/ (last access: 4 March 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-3271-2019-supplement.
Author contributions
FRV, MF, FH, IXR, MKS, PCh, PJ, YT, CJ, TB, QT and JO supported the field
campaign and contributed data to this study.
MF, FH, FRV, JS, GB and PCi planned the fieldwork and modelling activities
for this study.
JS, GB, FC, and FRV performed the CHIMERE modelling, provided modelling data
input and/or analyzed the output data.
MF, FH and FRV processed and analyzed the EM27/SUN data.
FRV, MF, JS, FH and PCi wrote sections of the paper and created figures
and tables.
All authors reviewed, edited and approved the paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
All authors would like to thank the three anonymous reviewers for their
comments that helped to significantly improve this paper. ECCC would like to
thank Ray Nasser (CRD) and Yves Rochon (AQRD) for their internal review. The
authors from LSCE acknowledge the support of the SATINV group of Frederic
Chevallier. The authors from KIT acknowledge support from the Helmholtz
Research Infrastructure ACROSS. The authors from LISA acknowledge support
from the OSU-EFLUVE (Observatoire des Sciences de l'Univers-Enveloppes
Fluides de la Ville à l'Exobiologie).
Edited by: Ronald Cohen Reviewed by: three anonymous referees
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