Using five long-running ground-based atmospheric observatories in
Europe, we demonstrate the utility of long-term, stationary, ground-based
measurements of atmospheric total columns for verifying annual methane
emission inventories. Our results indicate that the methane emissions for the
region in Europe between Orléans, Bremen, Białystok, and
Garmisch-Partenkirchen are overestimated by the state-of-the-art inventories
of the Emissions Database for Global Atmospheric Research (EDGAR) v4.2 FT2010
and the high-resolution emissions database developed by the Netherlands Organisation for Applied Scientific Research (TNO) as part of the Monitoring
Atmospheric Composition and Climate project (TNO-MACC_III), possibly due to the
disaggregation of emissions onto a spatial grid. Uncertainties in the carbon
monoxide inventories used to compute the methane emissions contribute to the
discrepancy between our inferred emissions and those from the
inventories.
Introduction
Recent global policy agreements have led to renewed efforts to reduce
greenhouse gas emissions to cap global temperature rise (e.g., Conference of
the Parties 21, COP 21;; ).
This, in turn, has motivated countries to seek methods of reducing their
greenhouse gas emissions. In Europe, methane emissions account for a
significant fraction (about 11 % by mass of CO2 equivalent) of the
total greenhouse gas emissions . The lifetime of
atmospheric methane is significantly shorter than for carbon dioxide, its
100-year global warming potential is significantly larger, and it is at near
steady state in the atmosphere; therefore, significant reductions in methane
emissions are an effective short-term strategy for reducing greenhouse gas
emissions . Emission reduction strategies that include
both methane emission reductions and carbon dioxide reductions are thought to
be among the most effective at slowing the increase in global temperatures
. Thus, it is important to know exactly how
much methane is being emitted and the geographic and temporal source of the
emissions. This requires an approach that combines state-of-the-art emissions
inventories that contain information about the specific point and area
sources of the known emissions and timely and long-term measurements of
greenhouse gases in the atmosphere to verify that the emissions reduction
targets are met.
Because atmospheric methane is well-mixed and has a lifetime of about 12 years , it is transported far from its emission source,
making source attribution efforts challenging from atmospheric measurements
alone. Atmospheric measurements are often assimilated into “flux inversion”
models to locate the sources of the emissions e.g.,
but rely on model wind fields to drive transport, and they also tend to have spatial
resolutions that do not resolve subregional scales. Methane measurement
schemes that constrain emissions on local and regional scales are thus
important to help identify the sources of the emissions and to verify
inventory analyses. Regional- or national-scale emissions are important to
public policy as those emissions are reported annually to the United Nations
Framework Convention on Climate Change (UNFCCC).
The atmospheric measurement techniques that are used to estimate methane
emissions include measurements made in situ, either on the ground, from tall
towers, or from aircraft. Remote sensing techniques are also used, either
from space or from the ground. The spatial scale of the sensitivity to
emissions differs with the measurement technique: surface in situ measurements
provide information about local emissions on urban scales e.g.,, and aircraft in situ measurements can provide
information about regional- and synoptic-scale fluxes e.g.,.
Satellite remote sensing techniques provide information useful for extracting
emission information on larger scales (regional to global) e.g., and for large point or
urban sources e.g.,. Several studies
have shown the importance of simultaneous measurements of co-emitted species
e.g., C2H6 and CH4 or CO and
CO2,
or co-located measurements e.g.,, showing the
added analytical power of the combination of atmospheric tracer information.
Ground-based remote sensing instruments have been used to estimate methane
emissions on urban e.g., and
sub-urban e.g., scales. In ,
, and , the authors have placed mobile
ground-based remote sensing instruments around a particular emitter of
interest (e.g., a city, dairy, or neighborhood) and have designed short-term
campaigns to measure the difference between upwind and downwind atmospheric
methane abundances. From these differences the authors have computed
emission fluxes. However, there is a network of nonmobile ground-based
remote sensing instruments that have been collecting long-term measurements
of atmospheric greenhouse gas abundances. These instruments were not placed
intentionally around an emitter of interest, but collectively they ought to
contain information about nearby emissions. To date, there have been no
studies that have attempted to extract regional methane emission information
from these existing ground-based remote sensing observatories.
In this paper, we will describe our methods for computing the emissions of
methane using five stationary ground-based remote sensing instruments located
in Europe in Sect. . Our results and comparisons to the
state-of-the-art inventories are shown in Sect. , and we summarize
our results in Sect. .
Methods
Our study area is the region between five long-running atmospheric
observatories situated in Europe. Three of the stations are in Germany:
Bremen , Karlsruhe ,
and Garmisch-Partenkirchen . The other two are in Poland
Białystok, and France
Orléans,. Each station measures the
vertical column-averaged dry-air mole fraction of carbon dioxide
(XCO2), carbon monoxide (XCO), methane
(XCH4), and other trace gas species. The locations are shown in
Fig. , overlaid on a nighttime light image from the
National Aeronautics and Space Administration (NASA) to provide a sense of
the population density of the area. These observatories are part of the Total
Carbon Column Observing Network TCCON, and have
been tied to the World Meteorological Organization trace-gas scale through
comparisons with vertically integrated, calibrated in situ profiles over the
observatories .
The locations of the TCCON observatories overlaid on a NASA
nighttime
light image. From west to east, the stations are Orléans (or, pink),
Karlsruhe (ka, green), Bremen (br, blue-green), Garmisch-Partenkirchen (gm,
orange), and Białystok (bi, purple).
Following a similar method to , we estimate
emissions of methane from the data recorded from the TCCON observatories,
coupled with gridded inventories of carbon monoxide within the region. We
compute changes (or “anomalies”) in XCH4 and XCO that
we will refer to as ΔXCH4 and ΔXCO, and we then
compute the slopes relating ΔXCH4 to ΔXCO.
From the computed slopes (α), we can infer emissions of methane
(ECH4) if emissions of carbon monoxide (ECO, in mass per
unit time) are known, using the following relationship:
ECH4=αmCH4mCOECO,
where mCH4mCO is the ratio of the molecular masses of CH4 and CO.
In , measurements from a single
atmospheric observatory were used to infer emissions because the unique
dynamics of the region advected the polluted air mass into and out of the
study area diurnally. In this paper, we rely on several stations to provide
measurements of the boundary of the study region to measure CO and
CH4 emitted between the stations. This analysis relies on a few
assumptions about the nature of the emissions. First, that the lifetimes of
the gases of interest are longer than the transport time within the region.
This is the case both for methane, which has an atmospheric lifetime of 12 years, and for carbon monoxide, which has an atmospheric lifetime of a few
weeks. Second, we assume that typical emissions are consistent over time
periods longer than a few days so that they are advected together. The nature
of the emissions in this region (mostly residential and industrial energy
needs) supports this assumption. Third, we assume that the spatial
distribution of the emissions is similar for CH4 and CO, as
confirmed by the inventory maps (Fig. ). This method
does not require carbon monoxide and methane to be co-emitted (as they
generally do not have the same emissions sources).
To compute anomalies and slopes, we first filter the data to minimize the
impact of data sparsity and air mass differences between stations (Appendix
). Then, for each station, the daily median value is
subtracted from each measurement. This reduces the impact of the station
altitude and any background seasonal cycle from aliasing into the results.
Subsequently, we compute the differences in the XCH4 and
XCO abundances measured at the same solar zenith and solar azimuth
angles on the same day at two TCCON stations. By computing anomalies at the
same solar zenith angles, we minimize any impact that air-mass-dependent
biases could have on the calculated anomalies. This analysis is repeated for
all combinations of pairs of stations within the study area. The vertical
sensitivity of the TCCON measurements is explicitly taken into account by
dividing the anomalies by the surface layer column averaging kernel value, as
we assume that the anomalies are due to emissions near the surface. The
slopes computed for each year and each pair of stations are shown in
Fig. .
The farthest distance between the European TCCON stations included in this
study is between Orléans and Białystok (1580 km).
Climatological annual mean surface wind speeds from the National Centers for
Environmental Prediction (NCEP) and National Center for Atmospheric Research
(NCAR) reanalysis within the study area are about
6 kmh-1 (Fig. ). The air from
Orléans will quickly mix vertically from the surface where the winds
aloft are more rapid than at the surface (see Appendix ).
Thus, air from Orléans would normally reach Białystok in a few days.
To determine whether these anomalies are consistent throughout the transport
time through the study area, we compute anomalies between sites lagged by up
to 14 days. The slopes of the anomalies do not change significantly or
systematically with the lag time (Appendix ; Fig. ), presumably because the atmospheric composition within
the study area is relatively well-mixed or because the emissions are
relatively consistent from day to day within the study area.
The bars show the methane to carbon monoxide anomaly slopes for each
site pair. The method of computing these anomaly slopes is detailed in Sect. of the main text. The black targets indicate the median
value of the slope for that year, when all site pairs are considered
simultaneously, and the 25th and 75th quartiles of the median
value are indicated by the vertical black bars. Outliers are indicated by
open black circles.
Previous papers have used carbon dioxide instead of carbon monoxide to infer
methane emissions. We choose to compute emissions using measurements of
XCO instead of XCO2 in this work because the natural
CO2 fluxes in the region are large compared with the anthropogenic
emissions, and they have a strong diurnal and seasonal cycle. The distance
between the stations is large enough that local (sub-daily) uptake of
CO2 differs from station to station, significantly obscuring the
relationships between methane and carbon dioxide, and thus the anomaly
slopes, especially in the summer months. While the emissions inventory of
anthropogenic CO2 may be more accurate than the CO inventory
in the region, the presence of these large natural fluxes of CO2
precludes its use in the anomaly slope calculation. The accuracy of our
method, therefore, is limited by the accuracy of the carbon monoxide emission
inventory. Fires could provide a large flux of CO without a large
CH4 flux, and this should also be taken into consideration in these
types of analyses. In our study area fluxes from fires are small.
Inventories
To obtain an estimate of carbon monoxide emissions (ECO) within the
study area, we use gridded inventories and sum the emissions within the
study area to compare with our emissions inferred from the TCCON measurements
(see Appendix and Fig.
for details). The two inventories employed here are the Emissions Database for
Global Atmospheric Research (EDGAR) and the Netherlands Organisation for Applied Scientific Research (TNO)
high-resolution emissions database developed as part of the Monitoring Atmospheric
Composition and Climate project (TNO-MACC_III). The EDGAR version
v4.3.1_v2 of January 2016 annual gridded inventory is available at
0.1∘×0.1∘ spatial resolution and reports global emissions
from the year 2000 to 2010 . The
TNO-MACC_III inventory is a Europe-specific air quality emissions inventory,
available on a 0.125∘×0.0625∘ grid, and reports emissions
for 2000–2011 . Both EDGAR and
TNO-MACC_III provide spatially and temporally coincident methane inventories
which we use to compare with our inferred emissions. We use the EDGAR version
v4.2 FT2010 and the TNO-MACC_III methane inventories.
Using country-level emissions reported through 2015 from the European
Environment Agency , we extrapolate the EDGAR and
TNO-MACC_III
gridded inventory CO emissions for the study area through 2015. This
facilitates more direct comparisons with the TCCON measurements, which begin
with sufficient data for our study in 2009. We extrapolate the emissions by
scaling the total emissions from the countries that are intersected by the
area of interest (Germany, Poland, Belgium, France, Luxembourg, and the Czech
Republic) to the last reported year of emissions from the inventory. We then
assume that the same scaling factor applies for each subsequent year. The
details of the extrapolation method are in Appendix
and Figs. and .
The time series of the reported emissions from 2000 to 2015 are shown in
Fig. . The inventories and scaled country-level
reported emissions for this region suggest that emissions of CO and
CH4 have decreased by about 40 % and 20 %, respectively, between 2000
and 2015. The TNO-MACC_III carbon monoxide emissions are on average 15 %
higher than the EDGAR v.4.3.1 emissions in the study area. The total
TNO-MACC_III and EDGAR methane emissions agree to within 2 % in the study
area.
This figure shows the summed EDGAR (green) and TNO-MACC_III
(orange) emissions within the study area for CO (squares) and
CH4 (triangles). The study area is defined in Fig. . All emissions are shown
in units of Tgyr-1. Extrapolation begins after 2010 for EDGAR and 2011 for
TNO-MACC_III.
An earlier version of the EDGAR carbon monoxide inventory was evaluated by
and
, who assimilated satellite
measurements of CO using the EDGAR v3.3FT2000 CO emissions
inventory as the a priori. found that,
over Europe, the a posteriori emissions increase by less than 15 % when
assimilating carbon monoxide from the Measurements of Pollution in the
Troposphere (MOPITT) satellite instrument .
assimilated Infrared Atmospheric
Sounding Interferometer (IASI) CO and MOPITT CO and found that
the a posteriori emissions increase by 16 % and 45 %, respectively.
The more recent EDGAR v4.3.1 CO emissions in our study are 24 % lower
than the EDGAR v3.3FT2000 CO emissions for the year 2000, so it may be
that the EDGAR v4.3.1 CO emissions are significantly underestimated.
However, assimilations of CO are known to be very sensitive to the
chemistry described in the model: most notably the OH chemistry
. Therefore, it is
difficult to determine how much of the discrepancy between versions of the
model is from the inventory or the model chemistry.
The EDGAR methane inventory has been evaluated in several previous studies.
It has been shown to overestimate regional CH4 emissions
e.g., but to underestimate oil and gas
emissions e.g.,.
However, recent methane isotope analysis by has
suggested that the EDGAR inventory overestimates fossil-fuel-related
emissions. The study area of interest here has little oil and gas production,
except for some test sites in Poland , no
commercial shale gas industry, and few pipelines.
Model experiment
To test whether the anomaly method described in Sect. can
accurately infer methane emissions, we conducted a modeling experiment using
version v12.1.0 of the GEOS-Chem model (http://www.geos-chem.org, last access: 4 January 2019) to simulate
methane and carbon monoxide for the year 2010. The model is driven by the
Modern-Era Retrospective analysis for Research and Applications, version 2
(MERRA-2) meteorology from the NASA Global Modeling and Assimilation Office.
The native resolution of the meteorological fields is 0.25∘×0.3125∘, with 72 vertical levels from the surface to
0.01 hPa, which we degraded to 2∘×2.5∘ and 47 vertical levels. We use the linear CO-only and CH4-only
simulations of GEOS-Chem, with prescribed monthly mean OH fields. In
the CO-only simulation, global anthropogenic emissions are from EDGAR
v4.3.1, which are overwritten regionally with the following emissions: the
Cooperative Programme for Monitoring and Evaluation of the Long-range
Transmission of Air Pollutants in Europe (EMEP), the U.S. Environmental
Protection Agency National Emission Inventory for 2011 (NEI2011), the MIX
inventory for Asia, the Visibility Observational (BRAVO) Study Emissions
Inventory for Mexico, and the criteria air contaminants (CAC) inventory for
Canada. The sources of CO from the oxidation of CH4 and
volatile organic compounds (VOCs) are prescribed following
. For the CH4-only simulation, the emissions are as
described in . Global anthropogenic
emissions are from EDGAR v4.3.2, but the US emissions were replaced with
those from , and emissions from
wetlands are from WetCHARTs version 1.0 . For both CO
and CH4 simulations, emissions from biomass burning are from the
Quick Fire Emissions Dataset (QFED) . The biomass
burning in the study area produces less than 2 % of the total anthropogenic
emissions of CO.
This figure compares seasonally averaged modeled total XCO
with the XCO contribution from emissions in Europe. Each season has
two maps: the left map shows the total XCO and the right map shows
the contribution from European emissions (XCO-Eur). The spatial
pattern of the gradients in modeled XCO between the TCCON stations
is reflected in the European contribution.
We used identical OH fields (from version v7-02-03 of GEOS-Chem) for
the CO and CH4 simulations, so that the chemical losses of
methane and carbon monoxide are consistent, and ran tagged CO
experiments so that we could identify the source of the emissions. The model
atmospheric carbon monoxide and methane profiles were integrated to compute
simulated XCO and XCH4. To illustrate the sensitivity of
the modeled fields to European emissions, we show the seasonal means of the modeled XCO
sampled at the five TCCON stations in Fig. . Also plotted is the column contribution
(XCO-Eur) from CO emissions only in Europe (defined as the
broader region between 0–45∘ E and 45–55∘ N). As can be seen, the spatial pattern of the differences in
modeled XCO between the TCCON stations is reflected in
XCO-Eur. We calculated the anomalies in XCO and
XCO-Eur, using the same approach employed with the atmospheric
data, and found that the anomalies in XCO-Eur, which represent the
direct influence of European emissions on atmospheric CO, account for about
35 % of the anomalies in XCO. This confirms that the XCO
anomalies between the TCCON stations are sensitive to European emissions.
This figure shows the results from the modeling experiment using
GEOS-Chem. Panel (a) shows the model ΔXCH4–ΔXCO slopes for each month and pair of stations (indicated by the
colors). The median slopes for each month are overlaid with grey squares. Panel (b) shows the model carbon monoxide emissions (excluding VOC
and methane oxidation) and the model methane emissions. The inferred methane
emissions from our tracer–tracer slope method are plotted in pink squares.
Panel (c) shows the annual methane emissions from the
tracer–tracer slope method and the
model.
To estimate the modeled CH4 emissions using the modeled CO,
the modeled XCO and XCH4 were interpolated to the
locations of the TCCON stations and anomalies and slopes were computed. We
then applied Eq. () to our anomaly slopes to compute
methane emissions from the known CO emissions, accounting for only the
CO emissions from anthropogenic, biomass burning, and biofuel sources.
We neglect sources of CO emissions from the oxidation of CH4
and VOCs because the column enhancements for those emissions are relatively
spatially uniform across this region of Europe, and thus they should not contribute
significantly to the anomalies. The resulting annual CH4 emissions
agree well with the model emissions: the inferred emissions from the anomaly
analysis are higher than the model emissions by less than 2 % (Fig. ).
While the inferred annual emissions agree well with the modeled annual
emissions, the seasonal pattern of the emissions inferred from the anomaly
analysis differs from that of the model. The anomaly analysis overestimates
emissions in the winter and underestimates emissions in the summer. This may
be due to small spatial inhomogeneities in the column enhancements from VOC
(biogenic) emissions that influence the anomaly analysis most in summertime
when VOC emissions are largest. Including the VOC emissions in the total
carbon monoxide emissions leads us to infer annual methane emissions that are
overestimated by 15 %, increasing the inferred summertime emissions without
significantly changing the inferred wintertime emissions.
The seasonal analysis suggests that the 2 % agreement in the annual emission
estimate may reflect the compensating effects of discrepancies over the
seasonal cycle, and improving the seasonal estimate may require a better
treatment of the VOC contribution to atmospheric CO. Nevertheless, the
results here suggest that for this region of Europe, where VOC and methane
oxidation emissions lead to relatively spatially uniform column enhancements
and fire emissions are small, we can successfully use the anomaly method
described in Sect. to infer annual methane emissions.
The black line is the summed EDGAR (green) and TNO-MACC_III
(orange) methane emissions within the study area shown in Fig. . The grey lines indicate the projected emissions
based on scaling the country-level emissions reported by the UNFCCC
to the area emissions in 2010 for EDGAR and 2011 for
TNO-MACC_III. The lower solid lines show the emissions inferred from the
TCCON anomaly analysis using CO emissions from the two models, and the dashed
lines indicate the 5th and 95th percentiles.
Results and discussion
To compute methane emissions, we apply Eq. () to our
anomaly slopes and the inventory-reported carbon monoxide emissions in the
study region (Fig. ). If we choose the mean of the
reported CO emissions from EDGAR v4.3.1 and TNO-MACC_III, the methane
emissions we compute within the study area based on the TCCON measurements
are 1.7±0.3Tgyr-1 in 2009, with a non-monotonic decrease
to 1.2±0.3Tgyr-1 in 2015. The uncertainties quoted here
are from the standard errors on the data slope fitting only; we have not
included uncertainties from the inventories. The magnitude of methane
emissions we compute from the TCCON data are, on average, about 2.3 times
lower than the methane emissions reported by EDGAR and about 2 times lower
than the methane emissions reported by TNO-MACC_III.
Our method of inferring methane emissions depends critically on the carbon
monoxide inventory. The carbon monoxide emissions for 2010 in the study area
from our GEOS-Chem model run, derived from EMEP emissions, were
6.4 Tg, about 35 % higher than the average of the EDGAR and
TNO-MACC_III emissions for that year. This magnitude underestimate has also
been suggested by and
using independent data. Using the
GEOS-Chem carbon monoxide emissions increases the methane emissions inferred
by the anomaly analysis to 2.4±0.3Tg in 2010. This value remains
lower than the EDGAR and TNO-MACC_III methane emissions estimates for 2010,
which are 3 Tg, but by only 20 %. Therefore, we find that the
inventories likely overestimate methane emissions, but the accuracy of our
results relies on the accuracy of the carbon monoxide inventory.
Although the EDGAR and TNO-MACC_III inventories agree to within 15 % in
carbon monoxide emissions and 2 % in methane emissions in the study region,
they spatially distribute these emissions differently. Maps of the spatial
differences between the TNO-MACC_III and EDGAR emissions are shown in Fig. for carbon monoxide and Fig. for methane. EDGAR estimates larger emissions of
carbon monoxide from the main cities in the study region and the surrounding
areas. This is clearly visible from the difference map (Fig. ), where cities such as Hamburg, Berlin, Prague,
Wrocław, Warsaw, Munich, Paris, and Vienna appear in blue. However, the
overall carbon monoxide emissions from TNO-MACC_III in the study area are
higher than EDGAR, and this comes from regions between the main cities,
particularly in Poland and eastern France.
This map shows the difference between the TNO-MACC_III carbon
monoxide emissions and the EDGAR emissions for the year 2010. The black
straight lines delineate the study area from the surrounding region. The
TCCON stations included in this study are marked with black “x”
symbols and labeled in black bold font. The countries intersected by or
contained within the study area are labeled in grey. Warm (red) colors
indicate that the TNO-MACC_III inventory is larger than the EDGAR inventory;
cool (blue) colors indicate that the EDGAR inventory is larger than
TNO-MACC_III.
This map shows the difference between the TNO-MACC_III methane
emissions and the EDGAR emissions for the year 2010. The labeling and
coloring follows that in
Fig. .
The differences between EDGAR and TNO-MACC_III methane emissions also show
that the EDGAR emissions estimates near large cities are significantly larger
(Fig. ). In contrast to the carbon monoxide
spatial distribution, the TNO-MACC_III methane emissions are generally
smaller everywhere, except for discrete point sources.
Comparing country-level carbon monoxide emissions reported in 2010 with the
inventories shows reasonable agreement, which is expected since the
inventories use country-level reports as input. The sum of the carbon
monoxide emissions within the entire countries of Germany, Poland, France,
Luxembourg, Belgium, and the Czech Republic differ between EDGAR and
TNO-MACC_III by 18 %, with EDGAR estimates lower than those from
TNO-MACC_III. Emissions from Germany, most of which are included in the
study area, differ by only 6 % between EDGAR and TNO-MACC_III, again with
EDGAR estimates lower than TNO-MACC_III. The national carbon monoxide
emissions reported to the Convention on Long-range Transboundary Air
Pollution LRTAP Convention,
https://www.eea.europa.eu/ds_resolveuid/0156b7a0ca47485593e7754c52c24afd, last access: 15 November 2017,
agree to within a few percent of the TNO-MACC_III
country-level emissions (e.g., 5.5 % for Germany in 2010).
The differences between 2010 country-level emissions estimates are larger for
methane: EDGAR estimates are larger than TNO-MACC_III estimates by 36 % when
summing all countries intersected by the study area and 8 % when considering
only German emissions. The TNO-MACC_III country-level emissions estimates
agree to within a few percent of the UNFCCC
(http://di.unfccc.int/time_series, last access: 15 November 2017) country-level reported methane
emissions (e.g., 8 % for Germany in 2010).
The differences between the EDGAR and TNO-MACC_III inventories suggest that
the spatial distribution of emissions is less certain than the larger-scale
emissions, since the total carbon monoxide and methane emissions between the
inventories agree to within 15 % and 2 %, respectively, in the study area, but
these estimates can disagree by a factor of 2 on city-level scales.
If we assume that the national-scale methane emissions are correctly reported
in EDGAR and TNO-MACC_III, our results indicate that the methane emissions
in the region are incorrectly spatially distributed in the inventories. It
could be that point or urban sources outside the study area but within the
countries intersected by the study area emit a larger proportion of the
country-level emissions than previously thought.
Conclusions
Using co-located measurements of methane and carbon monoxide from five
long-running ground-based atmospheric observing stations, we have shown that
in the area of Europe between Orléans, Bremen, Białystok, and
Garmisch-Partenkirchen, the inventories likely overestimate methane emissions and point to
a large uncertainty in the spatial distribution (i.e., the spatial
disaggregation) of country-level emissions. However, the magnitude of our
inferred methane emissions relies heavily on the EDGAR v4.3.1 and
TNO-MACC_III carbon monoxide inventories, and thus there is a need for
rigorous validation of the carbon monoxide inventories.
This study demonstrates the potential of clusters of long-term ground-based
stations monitoring total columns of atmospheric greenhouse and tracer
gases. It also shows the potential of having co-located measurements of
multiple pollutants to derive better estimates of emissions. These types of
observing systems can help policy makers verify that greenhouse gas emissions
are reducing at a rate necessary to meet regulatory obligations. The
atmospheric measurements are agnostic to the source (and country of origin)
of the methane, measuring only what is emitted into the atmosphere in a given
area. Thus, they can help validate and reveal inadequacies in the current
inventories, and, in particular, how country-wide emission reports are
disaggregated on a grid. To enhance these results, simultaneous measurements
of complementary atmospheric trace gases, such as ethane, acetylene, nitrous
oxide, nitrogen dioxide, ammonia, and isotopes, would help distinguish between
sources of methane. This would provide additional valuable information that
would likely improve inventory disaggregation.
Data availability
TCCON data are available from the TCCON archive, hosted by
the California Institute of Technology at https://tccondata.org.
Karlsruhe data were obtained
from 10.14291/tccon.ggg2014.karlsruhe01.R1/1182416
(). Bremen data were obtained from
10.14291/tccon.ggg2014.bremen01.R0/1149275
(). Garmisch data were obtained from
10.14291/tccon.ggg2014.garmisch01.R0/1149299
(). Orléans data were obtained from
10.14291/tccon.ggg2014.orleans01.R0/1149276
(). Bialystok data were obtained from
10.14291/tccon.ggg2014.bialystok01.R1/1183984
(). The Emissions Database for Global Atmospheric Research (EDGAR) inventory is
available from the European Commission Joint Research Centre (JRC) and the
Netherlands Environmental Assessment Agency (PBL),
http://edgar.jrc.ec.europa.eu (last access: 7 April 2017). The GEOS-Chem v12.1.0 model is available
from 10.5281/zenodo.1553349 ().
Filtering
The filtering method was designed to remove days of data for which the
atmospheric air mass was inconsistent between sites (e.g., a front was
passing through or there were significant stratospheric incursions into the
troposphere) and for years in which there were too few simultaneous
measurements at a pair of TCCON stations to compute robust
annually representative anomalies.
To address the consistency of the air mass between sites, we retained days on
which the retrievals of hydrogen fluoride (XHF) were between
50 ppt and 100 ppt, and deviated by less
than 10 ppt of the median XHF value for all sites on that
day. HF is a trace gas that exists only in the stratosphere, and thus
it serves as a tracer of tropopause height .
Since the concentration of CH4 decreases significantly above the
tropopause in the midlatitudes, its total column dry-air mole fraction
(XCH4) is sensitive to the tropopause height. Filtering out days
on which XHF varies significantly between sites also ensures that
the anomalies (and thus the slopes) are minimally impacted by stratospheric
variability. This filter removed less than 5% of the data.
To ensure that the anomalies are representative of the full year, we require
that each year has 400 coincident measurements across at least three seasons.
These box plots show the NCEP/NCAR reanalysis long-term climatological monthly mean wind speeds at
the surface (filled black boxes) and at 850 hPa (open red boxes) in the
study area (see Figs. , ,
or for study area maps). The solid black and
dashed red horizontal lines indicate the annual mean wind speed at the
surface and 850 hPa (∼1.5 km), respectively. Wind speeds that are
aloft (on average 17 kmh-1) are significantly swifter than those
at the surface (on average 7.5 kmh-1).
These are the anomaly slopes (ΔCH4/ΔCO)
in ppbppb-1 for each station pair, for the entire time series.
The anomalies are computed by subtracting data within the same solar zenith angle bin
between two TCCON stations. For more detail, see Sect. of the
main text. The x axis indicates the number of days separating the
measurements. The legend identifiers are as follows: br – Bremen, gm – Garmisch-Partenkirchen, bi – Białystok, or – Orléans, ka –
Karlsruhe.
These maps show the inventory emissions for the year 2010 in the
study area (delineated by the solid straight lines) and the surrounding
region. The TCCON stations are marked with black “x” symbols and
labeled in black bold font. The countries intersected by, or contained
within, the study area are labeled in grey. The map in (a) shows
the EDGAR
v4.3.1 emissions inventory for carbon monoxide. The map in (b) shows the
EDGAR FT2010 emissions inventory for methane. The map in (c) shows the
TNO-MACC_III emissions inventory for carbon monoxide. The map in (d) shows the TNO-MACC_III emissions inventory for
methane.
This four-panel plot shows the methodology for scaling the
country-level reported emissions of CO to extrapolate the gridded
inventory emissions to 2015. Panel (a) shows the CO emissions
reported by the European Environment Agency (EEA) for the countries contained
within the study area (Germany, France, Czech Republic, Belgium, Luxembourg,
and Poland). The black stars with a joining line represent the summed total
from the five countries. The EDGAR (green) and TNO-MACC_III (orange)
inventories summed within the study area are plotted with squares joined by
solid lines. Panel (b) shows the ratio between the individual country
totals and the EDGAR area total, normalized to produce an emission ratio of 1
in 2010. The quantity with the least interannual variability in the ratio is
from the country total (black stars with line). Panel (c) shows the
ratio between the individual country totals and the TNO-MACC_III area total,
normalized to produce an emission ratio of 1 in 2011. The quantity with the
least interannual variability in the ratio is, again, from the country total.
Panel (d) shows the scaled country total, normalized to produce the
EDGAR CO emissions for 2010 and the TNO-MACC_III CO emissions
for 2011. This permits us to compute a sensible emission for the study area
through to 2015.
This four-panel plot shows the methodology for scaling the
country-level emissions of CH4 reported to the UNFCCC to extrapolate
the gridded inventory emissions to 2015. The panels and symbols follow the
same description as in Fig. .
Transport time between stations
Figure shows the annual change in monthly mean
climatological wind speeds from the NCEP/NCAR reanalysis . These are interpolated to
surface pressure and 850 hPa pressures (∼1500m
geopotential height) from model (sigma) surfaces and cover from January 1948
through March 2017. Vertical mixing into the boundary layer occurs on the
timescale of a day or two , and thus the relevant wind speed
is between the surface and 850 hPa. The annual mean surface wind
speed is 6kmh-1, which gives a mean transit time between
Orléans and Białystok of 11 days. The annual mean 850 hPa winds are
17kmh-1, which give a shorter mean transit time between
Orléans and Białystok of 4 days.
To test whether the transport time impacts the anomalies, we computed the
slopes for time lags between sites of 0–14 days. Figure shows a small change in anomaly slope as a function of
the lag used to calculate the anomalies. This figure shows that the transport
time between TCCON stations is of negligible importance to the slopes and
lends weight to the decision to compute anomalies from data recorded at two
TCCON stations on the same day.
Computing study area emissions from the inventories
The study area emissions for 2010 are shown in Fig. . We define the study area as the area bounded by
the TCCON stations at (clockwise from the west) Orléans, Bremen,
Białystok, and Garmisch-Partenkirchen, which is marked by the black lines in the figure.
To compute the emissions from the study area, the grid points intersected by
and contained within the solid black lines are summed for each year. The
EDGAR v4.3.1_v2 emissions inventory for CO and FT2010 inventory for
CH4 provide estimates for years 2000–2010. The TNO-MACC_III
inventory provides emissions estimates for both CO and CH4 for
the years 2000–2011.
Projecting inventory emissions beyond 2010
Using data from the European Environment Agency National Database
, we extrapolate the inventory
CO and CH4 emissions for the study area through 2015. This is
done by summing the total emissions for the five countries that are
intersected by the study area (France, Belgium, Germany, Poland, Luxembourg,
Czech Republic), and normalizing the emissions to the last year of the
inventory (2010 for EDGAR, 2011 for TNO-MACC_III). Figures and show the
process for the EDGAR and TNO-MACC_III CO and CH4 emissions,
respectively.
Figure a shows the reported
country-level emissions for the years 1990–2015, their sum (black stars),
and the sum of the inventory emissions for the years available (2000–2010
for EDGAR; 2000–2011 for TNO-MACC_III) in squares. Figure b–c show the ratio of the country-level emissions to the area emissions,
normalized to 1 for the last year available in the inventory. These panels
show that the ratio of the summed country total emissions to the emissions
from the area of interest is less variable from year to year than the
emissions reported for individual countries. Thus, we choose to extrapolate
the area emissions using the country total emissions, scaled to the last year
of the inventory for the study area.
Figure d shows the results of using a single scaling factor to
estimate the study area emissions from the country-level emissions for each
year. We use the summed study area emissions for the years available, and the
extrapolated emissions through 2015 for subsequent analysis (e.g.,
Figs. and ).
Author contributions
DW designed the study, performed the analysis, and wrote the paper. DBAJ ran
the GEOS-Chem model, supported by the CO and CH4 work of JAF
and JDM. JK and HDvdG provided the TNO-MACC_III inventory. GCT helped refine
the data analysis methodology. NMD, FH, JN, RS, and TW provided TCCON data.
All coauthors read and provided feedback on the contents of the paper and
helped interpret the results.
Competing interests
The authors declare no competing interests.
Acknowledgements
The NASA Earth Observatory images were prepared by Joshua Stevens, using
Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging
Radiometer (VIIRS) data from Miguel Román, at
NASA's Goddard Space Flight Center. The
authors would like to thank two anonymous reviewers for thoughtful comments
and suggestions that significantly strengthened the paper.
Review statement
This paper was edited by William Lahoz and reviewed by two
anonymous referees.
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