Introduction
With annual CO2 emissions from fossil-fuel burning and cement production
having soared in recent decades and approaching 10 Pg C yr-1
, these fluxes
have reached the same order of magnitude as the natural exchange fluxes
between the atmosphere and land surface and between the atmosphere and the
ocean, respectively . Thus, the fossil-fuel
emissions have become a key driver for the spatiotemporal dynamics of
atmospheric CO2, not only close to major sites of emissions but also
far downstream . This
represents simultaneously a challenge and an opportunity. It is an
opportunity since the substantial and growing size of this fossil-fuel
CO2 signal facilitates the use of variations in atmospheric CO2 to
monitor and verify changes in fossil-fuel emissions .
At the same time, the large fossil-fuel CO2 signal complicates the use of atmospheric CO2
observations to determine sources and sinks of CO2 driven by the land
biosphere through atmospheric inverse modeling methods. This requires the
separation of the biospheric signal in atmospheric CO2 from the total
signal, which is usually accomplished by subtracting an estimate of the
fossil-fuel component from the measured atmospheric CO2 concentration.
This implies that any error in the fossil-fuel component tends to be
projected directly onto the inversely estimated biospheric fluxes
. Thus, in order to benefit from the monitoring
and verification opportunity as well as to minimize the magnitude of the
challenge associated with atmospheric inversions, it is paramount to
characterize the fossil-fuel component in atmospheric CO2 well in time and space.
Two sets of approaches have been developed to determine this fossil-fuel
component in atmospheric CO2. A first set of approaches relies on
concurrent observations of carbon monoxide (CO) and/or radiocarbon to
determine the fossil-fuel component in the observed atmospheric CO2
variations .
A major advantage of these observation-based methods is that
they do not require any atmospheric transport modeling and thus are not
sensitive to any errors in the modeled atmospheric transport. A major
disadvantage is that these observation-based estimates are available only at
a relatively small set of observing sites, providing a very limited picture
of the spatiotemporal dynamics of the fossil-fuel signal for larger areas.
Further complications may arise from, for example, poorly known and varying ratios of
the emissions of CO and CO2 in the case of CO-based methods
or the emission of radiocarbon from nuclear power and
reprocessing plants in the case of radiocarbon-based methods .
In the second set of approaches the fossil-fuel CO2 signal is modeled,
starting from the specification of fossil-fuel emissions as a bottom boundary
condition in an atmospheric transport model and then running this model
forward in time . A key advantage of this set of
approaches is that the spatiotemporal dynamics is resolved to the limit
provided by the resolution of the transport model. But this comes at the
disadvantage that the resulting accuracy of the modeled fossil-fuel CO2
signal depends not only on the quality of the fossil-fuel emissions data but
also on that of the transport model. The latter disadvantage is well
illustrated by the results of a recent model intercomparison study, where
inter-model differences in the simulated spatiotemporal pattern of the fossil-fuel CO2 were two to three times larger than the differences resulting from the
use of different emission inventories . Of particular
relevance is the resolution of the atmospheric model, as this is key to
better resolve the topography and land surface contrasts that govern much of
the atmospheric circulation and mixing in the lower atmosphere.
The challenge associated with the modeling of atmospheric transport is
particularly acute for the fossil-fuel component, since fossil-fuel emissions
are distributed in time and space in a highly heterogeneous and non-Gaussian
manner . This reflects the nature of the processes underlying
these emissions, ranging from the point-source nature of the emissions from
coal-fired power plants, whose emissions vary in response to changing needs
for electricity, to the strong diurnal fluctuations of the dispersed
emissions associated with road transportation . This strong
spatial and temporal patterning of the fossil-fuel emissions interacts with
the spatiotemporal variability of atmospheric transport, forming distinct
patterns of the fossil-fuel signal in atmospheric CO2
. Of particular relevance are the diurnal and the seasonal
changes in emissions, since they tend to covary with atmospheric transport,
which can lead to annual mean atmospheric CO2 concentration gradients
that differ from those attained if the emissions were held constant. This
difference, which arises solely from the covariation between fluxes and
transport, is called a “rectification effect” analogous to the rectification
of an AC voltage in an electrical circuit by a diode .
Such unaccounted-for variations in the fossil-fuel signal would
bias the biospheric signal in atmospheric inversion frameworks, hindering us
from developing a better understanding of the role of the land biosphere as a
carbon sink. At the same time, this strong temporal patterning of the
emissions also creates distinct signals that might be used to detect or track
the fossil-fuel signal.
In fact, several studies already explored the possibilities to detect the
fossil-fuel signal and the emissions driving them .
These include a range of methods and systems, including bottom-up methods based on surface observation systems ,
CO and radiocarbon-based methods ,
airborne measurements , satellite constraints
and top-down approaches on the basis of atmospheric inversions
. Spatially, the focus ranges from point-scale
or urban-scale
to regional and global emissions
.
A necessity to successfully deploy any of these different detection
approaches is a good understanding of the spatiotemporal dynamics of the
fossil-fuel signal on a scale that is sufficiently large in order to avoid
an unacceptably high sensitivity to the lateral boundary conditions, i.e.,
on scales exceeding a few 100 km. A successful detection also requires a
good understanding of the contribution of the other processes affecting
atmospheric CO2 variations, namely the exchange fluxes with the land
biosphere and with the ocean, respectively. Further, often it would be quite
useful to know the source processes responsible for the fossil-fuel CO2
signature, i.e., what fraction of the signal stems from emissions from a
coal-fired power plant and what part from road transportation. This helps, for example, with the assessment of how the implementation of a particular policy
affects the fossil-fuel signature, such as the shutting down of coal-fired power plants.
Few studies have taken a continental to global perspective on the fossil-fuel
signal , as the focus in the last few years had been
on urban areas , or just whether the
emissions in the city can be detected or not . In
addition, comparatively less work has been carried out in Europe
, and the majority of it used relatively coarse-resolution atmospheric transport models, resulting in relatively washed-out
gradients of the fossil-fuel signal over Europe ; a few of them focused on whether the potentially reduced
emissions could be discerned by current observation methods in this region . Furthermore, little consideration has been given to the
temporal variations of the emissions.
The main objective of this work is to fill these gaps and to develop a
quantitative understanding of the fossil-fuel CO2 signal in Europe. To
this end, we employ a forward modeling approach using a high-resolution
atmospheric transport model for Europe, forced with finely resolved fossil-fuel emission fluxes in time and space. In this paper, we will
(i) investigate the magnitude of the contribution of the fossil-fuel CO2
signal to the variations in total CO2; (ii) understand how the high
temporal resolution considered in the fossil-fuel emissions affect the fossil
CO2 signal; and (iii) determine the detectability of a reduction of
fossil-fuel emissions from different sources through changes in the column
mean CO2 as seen, for example, by a satellite-based observing system. We first
describe the model and methods, followed by the evaluation of the model. We then present the results, followed by a discussion of each
of the aforementioned three main topics, and then conclude with a summary and
an outlook.
Methods and data
To simulate the fossil-fuel CO2 over central and southern Europe in the
context of the variations in total atmospheric CO2, we employ a regional
high-resolution atmospheric transport model for the European domain and
prescribe lateral and surface boundary conditions for the various components
that constitute atmospheric CO2. These include the fossil-fuel
emissions, the CO2 exchange fluxes with the land and ocean surfaces and
the lateral atmospheric CO2 boundary conditions. The simulations cover
the period 27 March 2008 until 26 March 2009. The following subsections
describe the methods and data in more detail.
Atmospheric transport model
The simulations were undertaken with the limited-area atmospheric prediction model
COSMO (Consortium for Small-scale Modeling)
version 4.23. We employed the COSMO-7 setup developed by the Swiss Federal Office for
Meteorology and Climatology (MeteoSwiss) for the purpose of providing boundary
conditions for the inner COSMO-2 grid used for forecasting the weather in
Switzerland. The COSMO-7 setup has a grid spacing of 6.6 km and its domain covers
central and southern Europe (35.16∘ N, 9.80∘ E (lower left) to
56.84∘ N, 23.02∘ E (upper right); see Fig. ).
The COSMO model is based on the primitive hydro-thermodynamical equations
describing compressible nonhydrostatic flow in a moist atmosphere without
any scale approximations. The model equations are solved numerically on a
rotated latitude–longitude grid, with terrain-following coordinates in the
vertical (60 vertical levels, and lowest level at 10 m), using an
Eulerian finite difference method. Parameterization schemes are used to
resolve the sub-grid-scale physical processes such as vertical diffusion
(turbulence), convection, radiation and soil processes. A tracer transport
module was recently added to the COSMO model, permitting the online transport
of passive tracers in a manner that is fully consistent with the physics of
the model . In our setup, advective transport was
accomplished with a three-dimensional semi-Lagrangian scheme. The tracers are
transported in the model as moist air mass mixing ratios qCO2. Values
reported here are provided as dry air mole fractions χCO2, calculated
as χCO2 = qCO2/(1 - qH2O)
Mdry/MCO2, where qH2O is
the specific humidity and Mdry and MCO2 are the
molar masses of dry air and CO2, respectively. The
column-averaged dry air
mixing ratio is calculated as follows:
XCO2=∑k=1K(p(k+1/2)-p(k-1/2))qCO2(k)/∑k=1K(p(k+1/2)-p(k-1/2))1-qH2O(k)⋅Mdry/MCO2,
where k is the total number of vertical model levels (K = 60) and p is
pressure. We refer to for more details.
Map of the fossil-fuel emissions used in this study. Also depicted
is the domain of the COSMO-7 setup employed here. Shown in transparent color
are the fossil-fuel CO2 emissions for different sectors in units of
gC m-2 yr-1. The colors from the different sector blend to a darker
color when they are co-located as shown by the color mixing star at the
bottom right.
Fossil-fuel emissions
The fossil-fuel emissions for CO2 were generated by merging a relatively
coarse emission inventory for the regions outside Switzerland (EDGAR v4.2_FT2010,
approx. 10 km; ) with a
high-resolution (0.5 km) emission inventory for Switzerland. The latter was
produced by the company MeteoTest specifically for the CarboCount CH project.
The annual emissions from this merged product for the year 2008 amount to
2.54 Pg CO2 over the domain, representing about 10 % of the global
emissions of that year . We merged the emission categories
from the two inventories to five large emission categories: power
generation, residential heating, road transportation, industrial processes,
and others. Even though each of these different categories have a distinct
emission pattern, many of them co-occur in the large metropolitan areas,
leading to a very patchy emission pattern with strong emission hotspots and
extensive regions with relatively low emission densities (Fig. ).
Time dependence of fossil-fuel CO2 emissions for different sectors
and countries. (a) Time functions for the diurnal and weekly emissions for
four sectors. (b) Annual evolution of the CO2 emission intensity for
Germany, Switzerland and southwest Europe (Portugal and Spain). Panel (c) is the same as panel (b)
but for the domain total. Panel (d) is as panel (b) but for United Kingdom, Italy and
eastern Europe (Poland, Czech Republic, Slovakia and Hungary). The range
shown in panels (b)–(d) are the daily minima and maxima for each
country or group of countries.
These emission inventories are given for each emission category as annual
totals for each grid cell, i.e., Eann, requiring us to multiply them with
time functions to generate hourly time series of the fossil-fuel emissions at
each location . The time functions we employed were
originally generated by the University of Stuttgart (Institute für
Energiewirtschaft und Rationelle Energieanwendung, IER) for the GENEMIS
project and have been used since in several air quality
modeling studies. The time functions are comprised of diurnal, weekly and
seasonal components and are specific to each of the main economic sectors
(activities collected in the Selected Nomenclature for Air Pollution (SNAP)
codes) . When constructing these time functions, it is
ensured that their annual mean is equal to unity so that the annual totals
remain unchanged. The scaled emission flux, E(t), is then computed by
modifying the annual total fossil-fuel emission, i.e., Eann:
E(t)=Eann⋅fdiurnal(t)⋅fweek(t)⋅fseason(t),
where t is time (hour of the year) and fdiurnal, fweek and
fseason are the diurnal, weekly and seasonal scaling factors,
respectively. The time function factor fdiurnal depends on the hour of
the day (local time (LT), thour = t modulo 24 h) and is different for
weekdays and weekends to reflect the different level of activities on
weekdays and weekends. The factor fweek depends on the day of the week,
with one value for weekdays (Monday–Friday) and a lower one for Saturdays and
Sundays. The factor fseason depends on the month, but in order to avoid
discontinuities between subsequent months it is linearly interpolated to a
given day between the centers (day 15) of 2 adjacent months. The time
functions (except for the daily one) vary also by country and are locally
adjusted to reflect local time. Some reassignments were necessary to align
the categories used in EDGAR v4.2 and the CarboCount CH inventory (both
following IPCC guidelines) with the SNAP categories as described in the
Supplement.
The time functions differ greatly between the various categories, reflecting
their very different time course of activities over the average day, week or
year (see Fig. a and b). Among all diurnal time
functions, road transportation has the largest diurnal variability and is
characterized by two peaks during the day that reflect the rush hour periods
(08:00–09:00 and 17:00–18:00 LT). Also residential and commercial
combustion have a distinct diurnal cycle with two peaks. In contrast, the
emissions from industrial processes and fossil-fuel-fired power plants vary
less over the course of the day and also have only one peak. The time
functions for the day-of-week reflect primarily the lower industrial
activities and traffic during the weekend, while most other sectors continue
to emit at only slightly smaller rates (see Fig. a).
Combining all the sectors together, emissions during the weekend are 15–20 %
lower than during the week. The seasonal time functions depend primarily on
the local climatic conditions (see Fig. b and d), with
northern, eastern and central European countries having a maximum in winter,
likely due to their need for residential heating, while there is little
seasonality in emissions in the southern European countries. The seasonality
of the total emission (Fig. c) follows that of the
northern, eastern and central European countries with a wintertime maximum,
although somewhat less pronounced due to leveraging effect from the southern
European countries.
In order to be able to trace the fossil-fuel signature in atmospheric
CO2 back to the emitters, we consider separate fossil-fuel tracers for
10 different countries (or groups of countries) in our atmospheric transport
model (see Fig. ). Each of these tracers receives only the
emissions from its respective country or group of countries, while elsewhere
the emissions are set to zero. Due to the linearity of atmospheric transport
and the absence of any transformation of CO2 in the atmosphere, the
individual country-based tracers can then be summed to obtain the total
fossil-fuel CO2 signal. In addition, in order to determine the
contribution of the different CO2 emission categories to the total
fossil-fuel CO2, we also included five additional fossil-fuel tracers,
one each for the five categories we consider: power generation,
residential heating, road transportation, industrial processes, and others.
For these five tracers, we used time-invariant emissions, permitting us to
assess also the role of the time variations in emissions on the fossil-fuel
CO2 signal. In total, we included 17 fossil-fuel tracers (10 countries,
5 sectors, total fossil-fuel CO2 with time-varying emission and
total fossil-fuel CO2 with time-constant emission) in our high-resolution simulation study.
At the lateral boundaries, we employ a partial relaxation boundary condition
for these 17 tracers. In such a partial relaxation, the tracer is relaxed to
the boundary concentration only at the outermost grid cells of the domain and
only when the wind is directed toward the inside of the domain (in COSMO,
this option is provided by the switch “T_RELAX_INFLOW”). Since we are
interested in the fossil-fuel signal emanating from emissions in Europe only,
the lateral boundary concentration was set to zero. Through this option, we
avoid creating a situation where the zero concentration boundary condition is
propagated (erroneously) against the flow back into our domain.
Other CO2 component fluxes
In order to simulate the distribution of total atmospheric CO2, we also
include in our model three other CO2 components, namely background
CO2, the terrestrial biospheric CO2 and the oceanic CO2
components. The background CO2 represents that part of the atmospheric
CO2 that enters the domain through its boundaries. These boundary
concentrations are provided by the post-assimilation results of
CarbonTracker Europe . For this tracer, we use a “full”
relaxation boundary condition. This means that we are restoring the modeled
mixing ratio toward the value provided by CarbonTracker across a transition
zone consisting of 13 grid cells, with the relaxation increasing in strength
from the inner to the outer border of this zone. In COSMO, this option is
provided by the “T_RELAX_FULL” switch.
For the terrestrial biospheric CO2 component, we used the hourly
terrestrial biospheric fluxes from the Vegetation Photosynthesis and
Respiration Model (VPRM) . For the oceanic CO2
component, we combined the monthly air–sea CO2 flux estimates for the
Atlantic from with the annual mean flux estimates
for the Mediterranean by . As the oceanic flux
contribution is small, no attempt was made to add higher-frequency
variability. The lateral boundary conditions for these two tracers were
handled the same way as those for the fossil-fuel signal, i.e., a partial
relaxation toward a zero concentration at the boundary.
Evaluation of COSMO-7-based simulations of the atmospheric CO2
concentration at four European sites. The comparison (observations minus model)
are computed using the daily means for the period of 27 March 2008 through
26 March 2009.
Station
Characteristics
Relative
SD
SD
Correlation
Bias
height (m)
obs
mod
(ppm)
(ppm)
(ppm)
Cabauw (CBW, Netherlands)
tower
20
17.11
12.05
0.57
-5.66
Cabauw (CBW, Netherlands)
tower
60
12.94
11.26
0.61
-2.32
Cabauw (CBW, Netherlands)
tower
200
9.82
7.95
0.63
-1.45
Puy de Dôme (PUY, France)
mountain top
10
7.14
6.82
0.72
-0.8
Hegyhatsal (HUN, Hungary)
continental
10
19.18
9.02
0.52
-12.37
Hegyhatsal (HUN, Hungary)
continental
48
12.99
8.78
0.6
-7.31
Hegyhatsal (HUN, Hungary)
continental
115
10.54
8.09
0.68
-4.14
Mace Head (MHD, Ireland)
coastal
15
6.8
3.92
0.80
0.33
Simulations
The hindcast simulation started on 1 March 2008, with the initial and
boundary conditions for meteorology taken from the operational hourly COSMO-7
analyses of MeteoSwiss and the initial and boundary conditions for
atmospheric CO2 provided by CarbonTracker Europe . The
model was then run for 13 months until 26 April 2009. No assimilation of any
meteorological data was performed. The lateral and surface boundary
conditions for the total of 21 CO2 tracers considered (17 fossil fuel,
4 other components) were prescribed as described above. We consider the first
26 days as a spinup and use the subsequent 12 months for our analyses.
Evaluation
Total atmospheric CO2
We evaluate our COSMO-based results for the total atmospheric CO2
concentration (computed by summing the fossil-fuel component with the three
others) by comparing them to the measurements from four sites in central
Europe, namely Mace Head (MHD; 3.33∘ N, 9.90∘ W; 5 m above
ground, 15 m a.s.l.; coastal site), Cabauw (CBW; 51.97∘ N,
4.92∘ E; 20, 60, 200 m above ground,
0 m a.s.l.; flatland, near urban site), Hegyhatsal (HUN; 6.95∘ N, 16.65∘ E; 10, 48,
and 115 m above ground, 248 m a.s.l.; continental site)
and
Puy de Dôme (PUY; 45.46∘ N, 2.58∘ E, 1480 m a.s.l.; mountain site).
The modeled daily mean atmospheric CO2 at these four sites agrees
generally well with the corresponding observations, although the agreement
deteriorates with proximity to the ground (see Table ). This
deterioration is likely a consequence of the stronger influence of local
processes closer to the ground, which are more difficult to capture by the
model. At the highest measurement level, the correlation between the modeled
and observed values exceed 0.6 at all sites and are statistically significant
at the p < 0.05 level. The highest correlation is found at MHD
(> 0.80). This is due to the relatively steady conditions that characterize
this relatively clean coastal site. Influence from air pollution is only
observed during episodes of transport from the UK and continental
Europe, which are very well captured by the model. The correlations are
somewhat lower at the more polluted or more continental sites, i.e., between
0.57 (lowest level) and 0.63 (highest level) at the coastal tall tower
station CBW in the Netherlands and between 0.52 (lowest level) and
0.68 (highest level) at the continental tall tower station HUN
in Hungary. The atmospheric CO2 variations at the mountain top site PUY in France are well captured (r = 0.72). COSMO tends to systematically
underestimate the observed CO2 concentration at most of the stations and
levels, except at the coast of Ireland (MHD), where it is overestimated by
0.3 ppm (Table ). The biases tend to get larger with
increasing continentality of the sites and the associated higher complexity
of the surrounding terrain and other influencing factors. At CBW,
the biases amount to between -1.4 and -5.7 ppm, while in central
Hungary (HUN) the biases exceed -4 ppm at even the highest vertical level.
The general underestimation of the total CO2 can stem from biases in any
of the components that make up the total CO2 plus biases in atmospheric
transport and mixing. The low and positive bias at MHD, where
the contribution of the background CO2 component dominates, suggests
that this component is overall well modeled and likely not responsible for
the bias at the other sites. Since the contribution of the oceanic fluxes is
very small, this component can be excluded as an explanation as well. Thus,
the general underestimation is thus likely due to the superposition of biases
in atmospheric transport and biases in the underlying boundary conditions for
the fossil-fuel emissions and/or terrestrial fluxes. COSMO is known for
ventilating the planetary boundary layer (PBL) too strongly, particularly in wintertime under weakly stratified conditions. This may be especially acute for the
HUN site, because the air in the lowest atmospheric levels tends to get
trapped at this site owing to the wintertime prevalence of anticyclonic
conditions in the Carpathian Basin . An alternative
explanation is that the biospheric sink simulated by VPRM is too strong, as
discussed later. Unfortunately, we do not have observationally based
estimates of the fossil-fuel or terrestrial biosphere components at the four
sites discussed so far, requiring us to use data from other sites for further evaluation.
Even though COSMO exhibits some biases in the mean, it captures the observed
variability generally well (Table ). In particular, COSMO
reproduces the strong gradient in variability between the coastal site MHD (obs: ∼ 7 ppm; mod: ∼ 4 ppm) and the continental site in central
Hungary (obs: ∼ 11 ppm; mod: ∼ 8 ppm), reflecting primarily differing
contributions of synoptic variations on atmospheric CO2. However, the
absolute magnitude of the variability is not well captured by our
simulations, with COSMO consistently underestimating the observed
variability. Overall, the evaluation of the total atmospheric CO2
concentration reveals an agreement with the observations at the four sites
in terms of both mean and variability. The agreement is clearly better
further away from the ground, i.e., at heights of at least 50 m above the ground.
Fossil-fuel CO2 component
Estimates of the fossil-fuel component in atmospheric CO2 are available
for our model simulation period from Lutjewad in the Netherlands (LUT;
6.35∘ E, 53.4∘ N; 1 m a.s.l.)
and from Heidelberg in Germany (HEI; 49.417∘ N, 8.675∘ E; 116 m a.s.l.)
. Both estimates are based on a combination
of concurrent CO and 14CO2 measurements and represent the fossil-fuel-induced offset relative to a regional background. They are thus
comparable to our modeled fossil-fuel component, as this reflects the offset
relative to the domain-wide background induced by the lateral boundary
conditions. Lutjewad is located on the Wadden Sea dike in the north of the
Netherlands, influenced by the highly populated and industrialized areas in
the Netherlands and in northwestern Germany (Ruhr area). The Heidelberg
station is located near an urban center with considerable fossil-fuel emissions.
Comparison between modeled and observation-based estimates of the fossil-fuel CO2 component for the years 2008–2009. (a) Comparison at the
Lutjewad site in the Netherlands (LUT; 6∘21′ E,
53∘24′ N;
1 m a.s.l.) . (b) Comparison at
Heidelberg (HEI; 49.417∘ N, 8.675∘ E; 116 m a.s.l.)
. The observational estimates are based on concurrent
observations of CO and 14CO2.
At the Dutch site LUT, the daily-averaged fossil-fuel CO2 component
simulated by our model compares well with the observations (r = 0.73, mean
bias -4 ppm) (see Fig. a). Generally, the model
reproduces the observed variability, especially in summer, when the fossil-fuel CO2 component is low due to the deep mixing in the atmosphere.
But the model underestimates the estimated fossil-fuel CO2 component
substantially in winter. This may be due to several reasons. First, the model
may transport signals too quickly out of the PBL, which
is a known problem of many atmospheric transport models under stratified
conditions typical of wintertime (see also above) .
Second, our wintertime emission inventory in the region might be too small
due to, for example, our underestimating the strength of the seasonal
signal in the time functions. Third, the observations might be biased high.
One reason is that these reconstruction rely on a constant ratio between CO
and 14CO2, which may lead to an underestimation of the 14C–CO
ratio compared to real values at some time of the year and subsequent
overestimation of the inferred fossil-fuel CO2 .
At Heidelberg, our model captures the fossil-fuel CO2 component even
better, particularly since the model has a very small mean bias of 0.75 ppm.
Also the day-to-day and the seasonal variations are well represented with a
correlation coefficient of 0.72. The model's (small) overestimation of the
fossil-fuel component may be due to our prescribing all emissions at the
surface, while the fossil-fuel-fired power plants that contribute
substantially to the fossil-fuel CO2 at this site tend to have an
effective emission height quite some distance above the ground due to the
height of the stacks and the additional rise of the buoyant plumes
. Another reason might be an overestimation of the emissions
in our emission inventory EDGAR – an explanation furthered by EDGAR's
emission being higher than those of IER . Especially
assuring, and particularly so in comparison to the situation at LUT, is the
COSMO model's ability at HEI to capture most of the variability and amplitude
of the fossil-fuel component in winter. An exception are the observations
from late December and early January, where the data include a number of
exceptionally high peaks. These peaks may be the result of very strong local
trapping of the emitted fossil-fuel CO2 by, for example, a local inversion
situation, i.e., a process that our model cannot properly resolve.
Maps of the model simulated annual mean components of atmospheric CO2
in the surface layer (10 m above ground). (a) Fossil-fuel component,
(b) total atmospheric CO2, (c) terrestrial biosphere
component and (d) background CO2 component. The results are
shown as dry air mole fraction with units of ppm. The annual mean correspond
to the period 27 March 2008 through 26 March 2009.
Despite these discrepancies, the evaluation results provide us with good
confidence to use our COSMO-7-based system to investigate the spatiotemporal variability of the fossil-fuel CO2 in central and southern
Europe. The presence of an overall negative bias in the total atmospheric
CO2 in the absence of such a bias in the fossil-fuel component suggests
that the bias comes from the terrestrial biospheric component. This could be
due to our VPRM-based estimates of the net fluxes being too negative, as
suggested by , i.e., suggesting a too strong sink for central
and southern Europe, or for our model simulating a too small diurnal and/or
seasonal rectification effect , i.e., a too small
correlation between the time variations in the terrestrial exchange fluxes
and atmospheric transport and mixing. This deficiency does not impact our results
much, since our focus will be on the spatiotemporal variability of the
fossil-fuel CO2 signal.
The spatiotemporal pattern of the fossil-fuel CO2
The spatial pattern
In the annual mean, the fossil-fuel component of atmospheric CO2 in the
surface layer (∼ 10 m above ground) amounts to more than 10 ppm across
wide swaths of central Europe (Fig. a). We computed this
mean using data from all times of the day in order to fully reflect the
annual mean. In large metropolitan areas, such as in western Germany
(Ruhr region), Berlin, London, Paris and Milan, the annual mean fossil-fuel
component exceeds even 30 ppm. To first order, the distribution of the
surface fossil-fuel CO2 reflects the distribution of the emissions (see
Fig. ), suggesting a somewhat limited effectiveness of
atmospheric transport and mixing to disperse the signal aloft and in lateral
directions. In mountainous regions this is clearly a consequence of
topographic constraints; elsewhere this is largely a result of the strong
spatial gradients in emissions, which remain conserved in the annual mean due
to the overall diffusive nature of the dispersion. Nevertheless, a
substantial amount of the emitted CO2 is being transported away, leading
to a sizeable fossil-fuel CO2 signal extending far into the oceans
surrounding Europe, especially the North Sea.
Despite this lateral transport, the relatively good conservation of the
spatial gradients in emissions sets our results distinctly apart from
previous studies, where the fossil-fuel CO2 signal was modeled to be
very smooth in space and on average also substantially smaller. For example,
compared to the results obtained with the medium-resolution (0.5∘)
Regional Model (REMO) , one can detect in our simulations
nearly all major metropolitan regions and other fine-scale features, such as
individual fossil-fuel-fired power plants (e.g., in eastern Germany). This is
primarily the result of the high horizontal and vertical resolution of COSMO
permitting this model to conserve the spatial gradients well. This good
conservation is particularly well illustrated when considering snapshot
distributions of the fossil-fuel CO2 for individual seasons (Fig. ).
This figure also shows the strong impact of the
transport and dilution by the diurnal variations of the PBL, whose impact is particularly strong in summer.
For much of Europe, the fossil-fuel component is the dominant contributor to
the spatial gradients in annual mean atmospheric CO2 (Fig. b–d).
In many places it accounts for nearly all of the
spatial gradients, with the contribution of the background and the
terrestrial biospheric component being substantially smaller. The latter
shows gradients up to 10 ppm (Fig. c), while the
background signal does not exceed a few ppm (Fig. d). In
the big cities, the fossil-fuel CO2 component represents even a sizeable
fraction (10 %) of the total CO2 concentration. This dominance of the
fossil-fuel component together with its highly patterned nature owing to the
many point sources leads to a hotspot pattern in the near-surface map of
total atmospheric CO2 over much of Europe (Fig. b).
However, due to lower emissions in southwestern Europe, the fossil-fuel
CO2 signal is less strikingly visible there compared to central Europe.
At the same time, the sign of the biospheric signal changes in the south and
becomes positive. This compensates for the smaller fossil-fuel signal there
and results in a relatively uniform spatial pattern of atmospheric CO2
across Europe (Fig. b). Also the relatively low CO2
concentrations in the mountain regions, such as the Alps, Apennines, Pyrenees
and central France, reflect the much lower contribution from the fossil-fuel component.
Instantaneous snapshot of the model simulated fossil-fuel CO2
in the surface layer. (a) Snapshot on 1 July 2008 at 06:00 GMT.
Panel (b) is as panel (a) but at 18:00 GMT. (c) Snapshot on
1 January 2009 at 06:00 GMT. Panel (d) is as panel (c) but at 18:00 GMT.
As Fig. but for whole air column-averaged dry air
mole fraction in units of ppm.
Naturally, when investigating the column-averaged dry air mole fractions (XCO2),
i.e., the property typically measured by remote sensing from a
satellite, the annual mean gradients of the fossil-fuel component are much
smaller than those seen at the surface (see Fig. a). This is
a consequence of the lateral gradients being much weaker aloft due to a
more effective horizontal transport and mixing. An additional reason is a
much stronger influence of the lateral boundary conditions, which results in
a dilution of the fossil-fuel components. As a result, most of the hotspot
nature seen in the surface concentration pattern is blurred in XCO2.
Also the magnitude of the signal is much weaker. While the surface signal of
the fossil-fuel CO2 signal amounted to more than 30 ppm in strong
emissions regions, the signal in the column-averaged annual mean XCO2
hardly exceeds 2 ppm. The impact of the predominant westerly air flow becomes
much more obvious in the column-averaged dry air mole fraction XCO2,
with the fossil-fuel component revealing a clear eastward increase that is
substantially stronger than the gradient in the underlying emissions.
The relative dominance of the fossil-fuel component over the other components
of atmospheric CO2 is much weaker when considering the column-averaged
dry air mole fraction of CO2 (see Fig. b–d). As a
result, the total XCO2 is made up of three relatively equally sized
contributions, with the fossil-fuel CO2 signal continuing to dominate
the XCO2 variations in the major metropolitan areas. Contrary to the
annual surface pattern, where CO2 tends to increase eastward, the
highest XCO2 are found in southwestern Europe with a trend toward
lower values going eastward. This is partly a consequence of the lateral
boundary conditions for atmospheric CO2, which tend to lead to the
advection of elevated background CO2 into the domain from the southwest.
But the most important reason is the strong negative terrestrial biosphere
signal over Europe, reflecting the sizeable carbon sink in European forests
in the last decade . Interestingly, the relatively uniform
negative distribution for XCO2 in Fig. c contrasts
with a more patterned biospheric signal in the lowest layer of the atmosphere
(Fig. c). There, the strong negative signal is
restricted to central Europe, while much of southern Europe has a positive
annual mean biospheric signal. The likely reason for this difference is the
biospheric rectification effect , which tends to lead a
vertical redistribution of CO2, i.e., positive values in the lower
atmosphere and negative ones aloft. In most of Europe, this rectification
signal is relatively small in comparison to the annual mean biospheric
component, so that the latter determines the overall signal. But in southern
Europe, where the biospheric fluxes tend to be smaller in magnitude and in
the annual mean to be near zero, the rectifier effect can dominate,
explaining the positive signals in the surface layer (Fig. c)
and simultaneously the negative signals when the biospheric signal is integrated
vertically (Fig. c).
Maps of the annual standard deviation of (a) the fossil-fuel
component and (b) atmospheric CO2 in the surface layer. Shown are
the results for the period 27 March 2008 through 26 March 2009.
Maps of the contribution of fossil-fuel CO2 variability to total
atmospheric CO2 variability within the lowest model layer (0–20 m,
centered at 10 m) on various timescales in percent: (a) contribution
on all timescales; (b) contribution for the seasonal timescale only;
(c) contribution for the synoptic timescale only; (d) contribution
for the diurnal timescale only. Note that the contributions from panels (b)
through (d) do not add up to the numbers shown in panel (a). This
is a result of a partial compensation between the different temporal components
due to the temporal covariations in fossil-fuel and total atmospheric CO2.
The temporal variability
The temporal variability of the fossil-fuel CO2 signal at the surface is
very large, leading to a standard deviation around the annual mean of 30 ppm
or more in the hotspot regions (Fig. a). These
hotspots correspond largely to the regions of highest emissions
(Fig. ). This high variability is a result not only of the
temporal variability of the emissions but also of the interaction
of variability in atmospheric transport and mixing with the strong lateral
gradients seen in the snapshot figures (see Fig. ).
A similar pattern of variability is seen in surface atmospheric CO2
(Fig. b), suggesting that the fossil-fuel CO2
is a major determinant not only of the annual mean spatial distribution of
atmospheric CO2 but also of its temporal variability. This is confirmed
by Fig. a, which shows the relative contribution of
the fossil-fuel CO2 signal to the temporal standard deviations of
atmospheric CO2. In many places, particularly in Europe's major
metropolitan areas but also in many urban areas across Europe, the fossil-fuel signal dominates the variability in atmospheric CO2. But the high
fossil-fuel contribution is not limited to the urban areas. In fact, the
region delineated by having a 50 % contribution or more extends over much of
northern central Europe, including the North Sea (see Fig. a).
In order to better understand the origin of the strong variability, we
decomposed the variability into seasonal, synoptic and diurnal contributions.
The seasonal variation component was derived by averaging the data on a
monthly basis and by subtracting the annual mean. The synoptic component was
then computed by subtracting from the data the time series of the monthly
means and then forming daily averages of these deseasonalized data. Finally,
the diurnal variability was derived by subtracting the seasonal and synoptic
components from the data. To determine the fractional contribution, we then
computed the fractional variance of each component relative to the total
variance. Since the different temporal components can compensate for each
other, the sum of the fractional variance can actually exceed unity.
This decomposition reveals that the contribution of the fossil-fuel CO2
to the total variability of atmospheric CO2 varies greatly depending on
the temporal scale considered (Fig. ). While the
fossil-fuel contribution is comparably small on seasonal timescales
(Fig. b), the contribution on synoptic and particularly on
diurnal timescales is actually very large, exceeding 60 % across nearly the
entire northern part of central Europe (Fig. c and d).
The small contribution on the seasonal timescales is the result of the
dominance of the seasonal cycle of the biospheric fluxes in most of Europe.
An exception are a few places in northern Europe and in the very south of our
European domain. We interpret this to be caused primarily by the relatively
strong seasonality of the fossil-fuel emissions in these regions due to
the strong summertime requirement for cooling in the south and the strong
wintertime demand for heating in the north.
The pattern of the fossil-fuel contribution on synoptic timescales is very
similar to that of the total contribution, meaning its contribution dominates
the total temporal variability. This is consistent with synoptic variations
also being among the strongest contributors to atmospheric variability due
to baroclinic waves and frontal systems being formed out of the strong
baroclinicity that characterize the mid-latitudes. These synoptic weather
events transport the emitted CO2 also quite efficiently outside the main
metropolitan areas, explaining the widespread signal of the fossil-fuel
contribution to the total variance of atmospheric CO2. Even larger than
the fossil contribution to synoptic variability is the contribution on the
diurnal timescale, with the fossil-fuel CO2 contributing more than half
of the variability over most of Europe. This high variability comes from the
interaction of the diurnal variability of the fossil-fuel emissions, with the
strong diurnal variability of atmospheric transport, particularly the diurnal
mixing of the PBL. This covariability between fossil-fuel emissions and atmospheric transport exceeds that between the biospheric
fluxes and atmospheric transport over the entire year due to the latter
fluxes being large and relevant only during the spring–summer period, while
the fossil-fuel emissions are relatively high during most of the months of
the year, particularly close to the sources.
Discussion
The analyses of the results raise a number of questions that we would like to
discuss next. First, why is the diurnal variability so high and, in
particular, what is the contribution of the diurnal (and seasonal) variations
in CO2 emissions on the simulated fossil-fuel CO2 signal? Further,
is there an impact beyond the variability, e.g., on the mean fossil-fuel
CO2 signal? Second, what is the contribution of the various sectors on
the fossil-fuel CO2 signal and in what way do emissions from one country
influence the fossil-fuel CO2 signal in another country? Third, how can
we use the insights gained from the study of the fossil-fuel CO2 signal
to develop optimal strategies for detecting changes in fossil-fuel CO2
emissions? We discuss each of these three questions next.
Maps of the impact of the consideration of time-varying fossil-fuel
emissions. (a) Difference in annual mean surface CO2 between the case
with time-varying and time-constant fossil-fuel emissions. This difference
represents the fossil-fuel rectification effect. (b) Linear correlation
between the fossil-fuel emissions and the height of the planetary boundary
layer height in the COSMO-7 model. Pixels with emissions smaller than
0.06 gC m-2 yr-1 are not plotted. The positive correlation implies high
emissions when the PBL is deep, and vice versa. Most of this correlation
stems from the diurnal timescale, but the correlation is enhanced through
the (mostly) positive correlation also on seasonal timescales (see main
text). The negative correlations over the ocean stem from the fossil-fuel
emissions by ships.
The impact of variations in fossil-fuel emissions on atmospheric CO2
In order to elucidate the role of the temporal variations in fossil-fuel
emissions on the fossil-fuel CO2, we contrast the results of our
standard simulation with time-varying emissions with those where the fossil-fuel emissions were kept constant over time. The annual emissions are
identical for the two cases, but the time-constant case has, on average,
considerably higher emissions in summer and at night.
The contrast between these two cases shows only a small change in the high
diurnal variability of atmospheric CO2 seen in Fig. 8d (results not
shown). The largest changes are found around some of the large metropolitan
areas (e.g., London, Paris, Milan), but they do not exceed 10%. Thus the
majority of the diurnal variability in the fossil-fuel CO2 stems from
the diurnal variations in atmospheric transport and mixing acting on the
strong horizontal gradients in emissions.
While not contributing much to the diurnal variability in the fossil-fuel
CO2, the consideration of the time-varying emission matters quite
substantially for the annual mean distribution of the fossil CO2 signal.
Figure a reveals that the annual mean fossil CO2 signal in
the simulation with time-varying emissions is substantially lower over wide
swaths of Spain, Italy, Benelux, (western) Germany and the UK
compared to the simulation where fossil-fuel emissions were kept constant.
The strongest negative signals are found close to the strongest emitters in
these countries, with magnitudes exceeding several ppm. But the magnitude of
the signal does not correspond to the magnitude of emissions, since regions
with comparably low emissions such as Spain have signals that are as large
as those in high emission regions of the Netherlands. The relatively large
signals in southern Europe are likely due to the stronger PBL dynamics in
these regions throughout the year in comparison to central and northern
Europe. Some regions also have a positive signal from the time-varying
emissions, such as parts of France and northeastern Germany. Thus the
interaction between the variations in fossil-fuel emissions and the
variations in atmospheric transport and mixing leads to a substantial net
signal in atmospheric CO2, even though the total emissions in both cases
are identical.
This net signal represents a fossil-fuel-driven rectification effect
in analogy to the rectification effect associated with the
terrestrial biosphere , i.e., a signal that is
due to the covariance of emissions and atmospheric transport and mixing. Its
(mostly) negative sign emerges from the fact that when the emissions are
large, e.g., during the day, the transport and mixing away from the surface
is strong, diluting the fossil-fuel signal in atmospheric CO2. In
contrast, when the emissions are small, e.g., during the night, the transport
and mixing tends to be weak. Taken together, this results in a more efficient
dilution of the emissions in the time-varying emission case compared to the
time-invariant case, thus explaining the mostly negative sign of the fossil-fuel rectification effect.
This explanation is supported by the mostly positive correlation between the height
of the PBL and the fossil-fuel emissions, since the
height of the PBL is a good proxy for the magnitude of the mixing and transport in the
lowest levels of the atmosphere (Fig. b).
But there are a number of notable exceptions. For example, wide swaths of
northeastern Germany and Poland and some places in central France have a positive
rectification signal. Further, there are places where the covariation of fossil-fuel
emission and the PBL is negative, yet the fossil-fuel rectification effect is
still negative (e.g., the Ruhr valley region in western Germany), suggesting that our
explanation does not cover all aspects. In response, one first needs to recognize
that not only PBL but also other temporally varying phenomena, such as local
atmospheric circulation patterns (e.g., mountain winds, sea breezes), can lead to
covariability between emissions and transport/mixing, creating a rectification signal
that can differ in sign. The contribution of the sea breeze can be identified quite
clearly by the strong negative sign along most of the coastline between southern
Europe and the Mediterranean. Second, the local timing between the growth and decay
of the PBL and the emissions can be quite different due, in part, to the
substantially different time functions for the different emission categories and
their different local contributions (Fig. ). For example, in
regions with a large contribution from road transportation, the local emissions
have a strong peak in the early morning hours, when the PBL is still shallow,
leading to a high signal there, while emissions are lower when the PBL is at its
maximum in the early afternoon. This would create a positive rectification signal.
Finally, in certain places, also the seasonal rectification appears to play a role,
i.e., the seasonal covariations of the emissions with the PBL height. In fact, in
many places the magnitude of the correlation between emission and PBL height on
seasonal timescales exceeds that on diurnal timescales. This seasonal variation is
particularly large for residential heating, which is maximum in winter when the PBL
is low, leading to a positive seasonal rectification. This effect likely
contributes to the negative correlations between emissions and PBL height in large
urban centers such as Paris (Fig. b). We suspect that such
seasonal effects are also the primary reason for the positive rectification signal
in northeastern Germany and northern Poland. In southern Europe, these seasonal
covariations tend to lead to a negative fossil-fuel rectification effect because the
emissions peak in summer (Fig. b), when the PBL height is at
its seasonal maximum.
Maps of the annual mean fossil-fuel CO2 signal generated by
different countries and regions. (a) Surface pattern created by the emissions
from Germany. Panel (b) is as panel (a) but for the France.
(c) Column-averaged pattern created by the emissions from Germany. Panel (d) is as panel (c)
but for France. Shown are the results for the period 27 March 2008 through
26 March 2009.
The magnitude of the fossil-fuel rectification effect is smaller than the
rectifier effect induced by the exchange fluxes with the terrestrial
biosphere but still quite substantial. Thus, the fossil-fuel rectification effect clearly needs to be taken into consideration when
modeling the atmospheric fossil-fuel CO2 signal, highlighting the need
to use and apply accurate time functions. Our results thus clearly support
the results of , who demonstrated the substantial impact of
the consideration of time-varying emissions on atmospheric CO2. We
extend their result by demonstrating an effect on the annual mean fossil-fuel
CO2, suggesting that special attention needs to be given to the relative
timing of variations in atmospheric transport and mixing and fossil-fuel
emissions. Our results confirm the recent findings by , who
demonstrated the fossil-fuel rectification effect for the first time in a
global model. Their signal is locally smaller than ours due to their use of
a much coarser-resolution model, but they also show that the sign of the
fossil-fuel rectification effect tends to vary between timescales, with the
diurnal being primarily negative, while the seasonal rectification effect
being positive. This supports our explanation for the positive signals in
northeastern Germany and northern Poland.
Fossil-fuel CO2 signal from different sources
Near the surface, the fossil-fuel emissions from a particular region create a
distribution that stays mostly within the region of origin (see
Fig. a and b). The fossil-fuel CO2 is highly concentrated near
the localized areas of high emissions and then drops off quickly by distance
with an e-folding spatial scale of a few hundred kilometers. As a result, the
fossil-fuel signal tends to be relatively small outside the region of origin,
rarely exceeding 1 ppm in contrast to the > 20 ppm signal close to the
sources. The different magnitudes of the fossil-fuel CO2 signals from
different regions largely reflects the total emissions, but also the emission
intensity, i.e., the emission per unit area. For example, with a total
emission of 0.59 Pg CO2 yr-1, Germany is the biggest source of
fossil-fuel CO2 within Europe, nearly double that of the second-biggest
emitter (i.e., France), yet Germany is almost half the size of France,
resulting in a considerably higher emission intensity over Germany.
Pie charts depicting the origin of the fossil-fuel CO2 signal for each
country/region for the period 27 March 2008 through 26 March 2009. The
percentages represent the contribution of each country or region of origin to
the total fossil-fuel signal averaged over the air column. The pie
chart for Switzerland reveals, for example, that only 20 % of the fossil-fuel
CO2 signal over its territory stems from its territorial emission. Here,
CH is Switzerland; DE is Germany; FR is France; IT is Italy; AT is Austria; NL is Netherlands;
SW is countries in southwest of the domain; UK is United Kingdom; EA is countries in
eastern domain; OT is the rest of countries.
Maps of the annual mean surface fossil-fuel CO2 stemming
from different sectors in units of ppm: (a) fossil-fuel-fired power
plants, (b) residential heating, (c) industrial processes
and (d) road transportation. Shown are the results for the period
27 March 2008 through 26 March 2009.
A different picture emerges when considering XCO2, i.e., the column-averaged dry air mole fraction CO2.
After having been transported aloft, where the fossil-fuel signal can be much more readily dispersed, the imprint of the emissions
of any particular region to the fossil-fuel CO2 within another region is
actually quite large (Fig. c and d). In a small country,
such as Switzerland, only 20% of the fossil-fuel signature in XCO2
above its territory stems from emissions within, while the contribution of
Germany alone is 21 % and that of France 18 % (Fig. ).
A similar distribution of sources is found for
other small countries, such as Austria. In contrast, the fraction of the
territorial emissions to the total fossil-fuel signal is quite a bit larger
for large countries and regions, such as France or Germany. In the latter case,
more than 50 % of its total fossil-fuel CO2 signal stems from emissions
within, with four countries contributing most of the remainder. The
countries and regions with high overall emissions also contribute, of course,
most strongly to the fossil-fuel CO2 signal in other countries, with
Germany contributing 17 % to the signal in France, 11 % to that in Italy and
28% to that in the Netherlands. Owing to its lower total emissions, France
just contributes 10 % to the signal in Germany and 9 % to that in Italy.
Thus, as is the case with classical air pollution, the fossil-fuel CO2
does not stop at the national borders but extends to continental scales (see Fig. ).
Among all the processes, the CO2 emissions from power plants dominate
the fossil-fuel distribution, with concentrations reaching up to 16 ppm in
the northern part of the domain (see Fig. ). The
point-source nature of this emission sector is clearly visible in the surface
distribution, as is the spatially distinct distribution owing to the large
differences in power production in the different countries of central Europe.
While France has very few fossil-fuel-fired power plants as a result of its
high reliance on nuclear and hydroelectric power plants, Germany, Italy, the
Netherlands and Poland rely strongly on coal- and gas-fired power plants for
their electricity production. This leads to a highly heterogeneous fossil-fuel CO2 signals of the power plant sector. In total, this sector
contributes 32 % to the total fossil-fuel CO2 signal in central Europe,
which is slightly smaller than its contribution to emissions (33 %). This
small difference emerges from the somewhat stronger loss of the signal across
the lateral boundaries from this sector relative to the signal from the other sectors.
The second-largest fossil-fuel CO2 signal is generated by the emissions
from the road transportation sector (22 %) (Fig. d), with
this share actually being somewhat larger than its share in total emissions
(21 %). The transportation sector signal is very smooth due to the
distributed nature of the emissions from this sector (see also Fig. ).
The CO2 signal from the industrial and residential sectors are more
granular than that from the transportation sector but still not as distinct
as the power plant sector, as there are less country-specific policies
impacting the CO2 emissions from these sectors. The emissions and
consequently the CO2 signal largely follow population density. The
residential sector (mostly heating) contributes 18 % to the total fossil-fuel
signal in atmospheric CO2, slightly larger than the emissions from the
industrial sector (17 %). These two shares in the signal very nearly reflect
their shares in total emissions. The emissions from the “other” sectors
(e.g., shipping, waste incineration) is smaller, in comparison (11 %),
but not negligible.
The relative contribution of the emissions from the different sectors to the
fossil-fuel CO2 vary strongly by region (Fig. ). Clearly, close to major fossil-fuel-fired power
plants, this sector dominates. Owing to the dominance of this mode of
electricity production in northern Europe, this signal is particularly strong
there. This is most evident over the North Sea, where the advection of the
emitted CO2 from the power plants in the UK and the Netherlands creates
a particularly visible plume over the ocean. But elsewhere, any of the four
major sources can take the leading role. For example, in Switzerland, Paris,
and London, the emissions from the residential sector dominate the signal,
while over much of southern and western Europe, the transportation sector
dominates. The industrial sector dominates the signal in a few hotspot areas,
where its emissions are high but no major fossil-fuel-fired
power plants are nearby.
Maps of the annual mean relative contribution of each sector to the
total surface fossil-fuel CO2: (a) fossil-fuel-fired power plants,
(b) residential heating, (c) industrial processes and
(d) road transportation.
Changes in annual mean atmospheric CO2 and its standard deviations
resulting from a 30 % reduction in the fossil-fuel emissions from all
sectors:
(a) change in surface mean CO2; (b) change in the column-averaged CO2, i.e.,
XCO2;
(c) change in the standard deviation
of surface CO2 (all seasons); (d) change in the standard deviation
of the column-averaged CO2, i.e., XCO2. Shown are the changes taken
at 13:00 UTC, corresponding to the typical observing times for satellites.
These high spatial variations in the relative contribution puts the findings of
into a spatial context, as they reported for the Heidelberg site a
dominance for emissions from power plants (28 %), while the transportation sector
contributed only 15%. This is a typical value for much of western Germany,
reflecting the relative contribution of the different emission sectors (see also
Fig. ). But the contributions are very different, for example, for
the CarboCount CH sites in Switzerland . At Beromünster, the
transportation sector dominates over the other sectors, with nearly 70 % stemming
from this sector alone, while the contribution from power plant emissions is very
low at this site, since Switzerland does not operate any fossil-fuel power plants.
These large differences in the relative contribution from the different
emission sectors have major implications for the analysis of the fossil-fuel
CO2 and how it may change in response to mitigation measures. For
example, these large differences will lead to substantial spatial gradients
in the CO to CO2 ratio in the fossil-fuel signal, as the different
emission sectors have very different CO to CO2 emission ratios. Since CO
is often used to identify the fossil-fuel component from atmospheric CO2
observations, these variations need to be carefully disentangled in order to
properly diagnose the fossil-fuel component. The strong variations in the
contributions from the different sectors thus add a substantial amount of
uncertainty to the CO method . A second
consequence concerns the detection of changes in emissions from the different
sectors. Thus, with the transportation sector contributing little to the very
large fossil-fuel signal in much of the northeastern part of our domain,
reductions in this sector will be difficult to discern in that region. In
contrast, the high relative contribution of the transportation sector to the
total signal in southwestern Europe makes it actually quite feasible to
detect mitigation measures in this sector in that part of Europe, even though
the overall signal might not be that high.
An important caveat of our simulations is the fact that the effective height
of the emissions above surface was not considered, but rather all CO2
was released into the lowest model level. As a consequence, the surface
CO2 signals from elevated stack emissions from power plants and
residential heating are likely biased high relative to those from the
transportation sector. Given the large contribution from power plant
emissions, it will be important to accurately consider the effective emission
height (including plume rise) in future simulations, a point that was also
raised by .
The response of atmospheric CO2 to an emission reduction
According to their intended nationally determined contributions filed with
the United Nations Framework Convention on Climate Change (UNFCCC) in late 2015,
the European Union and its member states have agreed to a binding
target of a domestic reduction in greenhouse gas emissions of at least 40 %
by 2030 compared to 1990 (http://www4.unfccc.int/Submissions/INDC/Published Documents/Latvia/1/LV-03-06-EU INDC.pdf).
A major question driving international policy making is to what degree such a
reduction can be verified through independent means, such as through the
monitoring of atmospheric CO2 . To address
this question, we conducted several sensitivity experiments to investigate
how various reductions in the magnitude and types of emissions affect not
only the annual mean fossil-fuel CO2 signal but also its variability.
The goal is to determine whether reduced fossil-fuel emissions might be
detectable by current and future observing systems, especially satellites. As
the satellites have a typical overpass time of 13:00 UTC, we
conducted all subsequent analyses using the model data only from this time slot.
Impact of a reduction in power plant emissions on the mean and standard
deviation of the fossil-fuel CO2 signal at 13:00 UTC. (a) Probability
density distribution of the surface atmospheric CO2 for the present and
for a case when the power plant emissions were reduced by 50 % at a site in
eastern Germany (50.32∘ N, 13.19∘ E). (b) Relationship
between the changes in the mean and the standard deviation of the column-averaged
CO2 for a given reduction in power plant emissions, with different color
representing different sites with different characteristics in
their response to this reduction in emission: blue (50.32∘ N,
13.19∘ E), cyan (50.32∘ N, 6.59∘ E), red (42.48∘ N,
6.51∘ W) and orange (49.28∘ N, 6.14∘ E) (locations shown
in Fig. 14b with green circles).
Since CO2 is a conservative tracer in the atmosphere on the timescales
considered here, a uniform reduction in the emissions leads to a uniform and
directly proportional reduction of its current distribution; i.e., a 30 %
reduction of total fossil-fuel emission would simply lead to a 30 % reduction
of the fossil-fuel CO2 signal at the surface (Fig. a)
and throughout the atmospheric column (Fig. a). Concretely, the fossil-fuel CO2 would be reduced by
more than 4 ppm near the surface for vast stretches of central and northern
Europe, with maximum reductions of 10 ppm or more in the emission hotspots
(Fig. a). This contrasts with the reduction in the
column-averaged annual mean XCO2, amounting to just over 0.2 ppm in the
regions where the surface decreases by 4 ppm or more (Fig. b).
A reduction of 0.5 ppm is reached in just a few
isolated locations, generally characterized by a high density of point
sources, primarily fossil-fuel-fired power plants. Thus, given current
measurement accuracies of better than 0.1 ppm for a ground-based observing
network , a 30 % reduction in the fossil-fuel emissions
is fundamentally easily detectable for such a system, although one needs to
bear in mind the nontrivial task of separating the signal from the background
variability. In contrast, such a reduction in the fossil-fuel emissions is
not trivial to detect by satellite observations for most regions (except
around the big power plants) as it is very challenging to obtain and maintain
accuracies better than 0.5 ppm by current space-based observing systems
. Furthermore, such high accuracies are only achieved
when the data are averaged over large scales, i.e., on the order of 1000 km or more.
Nevertheless, taking 0.5 ppm as the threshold for detection within a single
pixel, a 30 % reduction in fossil-fuel emissions thus appears to be beyond
the detectability except for a few hotspot regions (Fig. b).
Even a 50 % reduction would not be trivial to detect
for a satellite-based system on the basis of changes in the column-averaged
dry air mole fraction.
Given these challenges, a potentially attractive second avenue for
determining changes in fossil-fuel emissions is the reduction in temporal
variability of atmospheric CO2 that goes alongside the reduction in the
mean signal. This is particularly promising given the very high contribution
of the fossil-fuel CO2 signal to the variability in atmospheric CO2
(see Fig. ). As is the case for the mean, the
conservative nature of atmospheric CO2 implies that a uniform reduction
of the emissions will lead to a uniform and proportional reduction of the
variability of the fossil-fuel signal as well. However, this is not the case
for the variability in total atmospheric CO2, since covariations
between the fossil-fuel signal and the signal from, for example, the terrestrial
biosphere can lead to nonlinear effects. For example, a negative correlation
between the two components would lead to a situation where the variability of
atmospheric CO2 was smaller than that of the individual components. In
such a case, a reduction of the fossil-fuel emission would lead to a smaller
decrease in variability than expected. If the two components were positively
correlated, the opposite would occur, i.e., the variability in atmospheric
CO2 would decrease more than expected.
Near the surface, the reduction in the temporal standard deviation and in the
mean have nearly the same amplitude for most places (Fig. c).
This makes the analysis of changes in the temporal
variability indeed an attractive option to enhance the detectability of
changes in fossil-fuel emissions. This is much less the case for the annual
mean XCO2, where the standard deviation changes are in general much
smaller than the changes in the mean, with just a few isolated places
revealing changes in the standard deviation of 0.5 ppm or more that might be
discerned by the current generation of satellites.
But in these isolated places, the analysis of the temporal variability might
be an interesting option even for satellite-based measurement systems
(Fig. ). In those places, indicated by the green circles in
(Fig. c), the changes in the temporal standard deviation
are very large. Even for changes in emissions of around only 30 %, the
changes would be detectable for current satellites (Fig. ).
But the number of such sites is very low across
Europe, making this a specialized, rather than general, option.
The detection challenge is not simpler for other potential emission reduction
scenarios, as outlined, for example, in the EU road map (http://ec.europa.eu/clima/policies/strategies/2050/index_en.htm). A 50 %
reduction in the emissions from power plants alone (representing a reduction
of the overall emissions by 16 %) results in the mean surface concentration
of atmospheric CO2 going down by more than 2 ppm over large parts of
northwestern Europe, following the pattern of the surface signal of this
sector (see Fig. a). In addition, we find a substantial
reduction of the standard deviation of surface atmospheric CO2 by more
than 2 ppm in these regions, with the hotspots of power plant emissions
showing a reduction in the standard deviation of atmospheric CO2 of 5 ppm
or more. The reduction of the average annual mean column XCO2 is much
smaller than that of atmospheric CO2 at the surface, amounting to little
more than 0.2 ppm over wide swaths of northern Europe. The maximum reductions
are of the order of 0.5 ppm in the proximity of large clusters of fossil-fuel-fired power plants, i.e., generally too small to detect. But, in these
regions, the changes in the variability in XCO2 is quite high, making
this method again potentially attractive for detecting changes. In fact, in
several regions, including some major cities, a 19 % reduction of the fossil-fuel emissions would result in a change of more than 0.5 ppm in the standard
deviation, i.e., above detection level. This thus supports the findings of
that changes in fossil-fuel emissions are fundamentally
detectable over major cities or major point sources, but it also shows that
this detection is very challenging.
The signals get even more difficult to discern if the emission reductions
occur in individual sectors other than the power plants. For example,
detectable signals by current generation satellites occur only if industrial
emissions are cut by more than 80 % or if residential emissions are cut by
more than 90 %. Also country-level emissions are not trivial to be clearly
detected. A reduction in Germany by 50 % is potentially detectable by current
satellites, with a maximum reduction of XCO2 by 0.95 ppm. For most
other countries, however, a 50 % reduction in emissions is difficult to detect.
All the analyses here relied on using the model output on all available days;
i.e., we assumed perfect temporal coverage. This is overly optimistic, since
cloud cover and other complicating factors (e.g., aerosol layers) will cause
the coverage to decrease considerably, complicating the detection. We assumed
here also “perfect transport”, i.e., no errors in how the emission reductions
manifest themselves in a change in the concentration field. In fact, errors
in this transport are perhaps, besides the lack of observations, the largest
impediment to detect changes in fossil-fuel emissions.
But regardless of this additional challenge, there is much additional
information contained in high-frequency observations of atmospheric CO2.
As we demonstrated above, the temporal variations are potentially highly
useful for detecting fossil-fuel emissions changes from various sources,
especially those with a strong spatial granularity such as power plants or
individual cities. For a routine monitoring of strong point sources,
therefore proposed a constellation of five satellites that combine imaging capability with a relatively wide swath
. Such a constellation would offer daily global
coverage, though the presence of clouds would reduce the effective coverage
considerably. As the precision and accuracy of satellite retrieved XCO2
will improve in the future, that minimum change will go down as well.
Summary and conclusions
We have investigated the fossil-fuel signal in atmospheric CO2 over
southern and central Europe using a regional high-resolution atmospheric
model forced with temporally and spatially highly resolved variations in the
fossil-fuel emissions. The assessment of the modeled atmospheric CO2
with in situ measurements on the highest level across multiple sites across
Europe reveals good agreement on all timescales considered with biases of
less than 1.5 ppm, with the exception of the tall tower site Hegyhatsal in
central Hungary. The model is also able to capture the reconstructed fossil-fuel component at two sites quite successfully. Although the model tends to
underestimate the amplitude of the daily-averaged fossil-fuel CO2 in
winter, the simulation matches fossil-fuel CO2 from both sites very well
most of the time, revealing the high quality of the transport model and
reasonable time profiles of the fossil-fuel emissions used as input.
Over much of Europe, the fossil-fuel CO2 is a dominant component of the
spatial variability of atmospheric CO2, particularly near the surface.
In some places, it even contributes significantly to the total (including
background) CO2, particularly in large urban centers and along power
plant plumes. Also the contribution to the temporal variability is very
substantial. Fossil-fuel CO2 makes a particularly large contribution on
synoptic and diurnal timescales whereas the seasonal variability is
dominated by biospheric activity. The influence is large not only over the
hotspot regions of fossil-fuel emissions but also over large areas
downstream. In case of diurnal variability, fossil-fuel CO2 is the
dominant component over wide areas of northern and western Europe.
Temporal variability of the emissions has a non-negligible influence on
annual mean fossil-fuel CO2 mole fractions near the surface due to
diurnal and seasonal rectifier effects. Differences between annual mean
values with temporally variable and constant emissions can be up to a few ppm
in the hotspot regions but are mostly below 1 ppm elsewhere. This implies
that temporal variability of fossil-fuel emissions needs to be accurately
represented for realistic simulations, confirming the results of
. It is also important for reliably detecting fossil-fuel
emission changes from specific sources since different sources have different
temporal profiles.
Simulating fossil-fuel emissions from different countries and sectors
suggests that the major part of the signal near the surface remains in the
country of origin. Ground-based in situ observations are thus most sensitive
to fossil-fuel emissions from the country where they are located. A different
picture emerges for column-averaged dry air mole fractions (XCO2) as
measured by satellites, for which the signal is much more dispersed. Only
over Germany is the contribution from emissions within the country larger
than 50 %, whereas over France the signal from neighboring countries
dominates (66 %). An important reason for these contrasting results seems to
be the differences in electricity production, which mostly relies on nuclear
power in France but on fossil fuels in its neighboring countries including
Germany, UK and Italy. Over small countries such as Switzerland or the
Netherlands, the contribution from abroad is typically the dominating
component. Among all the processes, fossil-fuel emissions from power plants
contributes the most (approx. one-third) to the total fossil-fuel signal of
CO2 both at the surface and in the column. However, the power plant
signal at the surface is likely overestimated in our simulations, since all
emissions were released into the lowest model level without considering the
true elevation of the source. The signal from power plant emissions has a
pronounced and distinct spatial pattern that provides us an opportunity to
discern changes in from power plant emissions from changes in other sources.
Based on a number of sensitivity studies, we show that reductions in fossil-fuel emissions leave a distinct signal not only in the time mean distribution
of atmospheric CO2 but also in its temporal variability. This opens
potentially additional ways to detect and verify emission reductions. But
this opportunity exists primarily for surface-based measurement networks,
while the satellite-based systems that measure the column-averaged
XCO2 will see too small changes, in general, relative to their current
measurement capabilities. An important exception are a few hotspot sites,
where the satellites will be able to detect fairly modest changes of about
30 % when assuming an accuracy of the satellite observations of 0.5 ppm.
As both satellite and surface measurements have advantages and disadvantages,
combining surface measurements with satellite data and increasing the
frequency and coverage of the latter will be the optimal path forward to
enhance the possibility of detecting future changes in fossil-fuel emissions.