Introduction
Carbon dioxide (CO2) is the most important greenhouse gas in the
atmosphere. It contributes more than 60 % of total radiative forcing (RF)
of the long-lived greenhouse gases (AGGI, 2014). The large increase of
atmospheric CO2 of nearly 120 ppm above preindustrial levels has been
unequivocally attributed to human-caused emissions (Keeling, 1993; WMO, 2015).
Using atmospheric CO2 observations, the source/sink estimations can be
constrained through inverse models, which is an important way to understand
the carbon cycle in the land biosphere (Chevallier et al., 2011; Thompson et
al., 2009). For this purpose, lots of ground-based stations have been set up
to monitor the CO2 mole fractions around the world. So far, there are
more than 150 sites worldwide where greenhouse gas mole fractions are
measured (Artuso et al., 2009; Dlugokencky et al., 1995; Necki et al., 2003;
Sirignano et al., 2010; Tans et al., 1990; WMO, 2015). Due to technical and
logistical constraints like access to the measurement site, power supply or
internet connection, very few monitoring stations are sufficiently remote to
be permanently exposed to pristine air masses, while many Global Atmosphere Watch (GAW) stations are
occasionally too frequently affected by local sources or sinks (Tsutsumi et
al., 2006; Riley et al., 2005). The measurements at the majority of sites
cannot fully represent the well-mixed CO2 conditions in the regions.
Hence data filtering is an essential part for the analysis of data from
those sites when trying to retrieve representative trends, for the estimations of sources and
sinks, or for the modeling of long-range transport of trace gases
(Greally et al., 2007; Novelli et al., 2003; Prinn et al., 2001; Ryall et
al., 1998).
Several methods have been applied in the past for the extraction of
background mole fractions (or regionally representative) from ground-based
measurements: (1) filters based on specific trace gases or ratios of trace
gas : trace gas (Brunke et al., 2004; Tsutsumi et al., 2006; Zanis et al.,
2007). For example, Tsutsumi et al. (2006) used carbon monoxide (CO) as an
indicator to filter the observed CO2 mole fractions at the station
Yonagunijima located in east Asia. Zanis et al. (2007) used the total
reactive nitrogen (NOy) to CO ratio to distinguish different regimes at
the high-altitude station Jungfraujoch in central Europe. Brunke et al. (2004)
used radon and CO to classify their observations at Cape Point, South
Africa. (2) Meteorological filters are the most commonly used method (Artuso
et al., 2009; Chmura et al., 2008; Collaud Coen et al., 2011; Zellweger et
al., 2003). This method considers various factors such as the local wind
speed, wind direction, boundary layer height, information on the atmospheric
stability, solar input, or general weather conditions and others. Sometimes
it further considers the diurnal CO2 variation as it can be closely
linked to the above parameters (Zhou et al., 2005). (3) Statistical methods is
an approach that generally uses the variations (e.g., a low standard deviation)
of observed data in certain time windows as a threshold to select the
regional values (Cunnold et al., 2002; Morimoto et al., 2003; Pickers and Manning, 2015; Zhang et al., 2007). (4) Numerical transport methods use
atmospheric dispersion modeling (e.g., air mass back trajectories) to study
the advection regimes with subsequent distinction in periods with potential
influence of local or regional source/sink and uninfluenced conditions (Cape
et al., 2000; Manning et al., 2011; Ryall et al., 2001). Some of the studies
combined two or more methods above to select the best well-mixed CO2
mole fractions (e.g., Thoning et al., 1989). However, due to the different
characteristics of each station such as location (e.g., continental sites,
coastal sites), proximity to biosphere and diffuse or point sources etc.,
the best data filtering approach has to be carefully selected for each
station (Ruckstuhl et al., 2012). One method can be also more useful than
another at the same station depending on the parameter of interest. In
brief, there is not a standard method for selecting the background mole
fractions from a continuous data series.
With the rapid development of its economy, China became the largest
fossil fuel CO2 emitter in 2006 and emitted 1.8 Pg carbon in 2011
(LeQuéré et al., 2013; Marland, 2012). The Yangtze Delta area is one
of the most developed regions in China and is one of the largest global
CO2 emission regions (Gregg et al., 2008). The total population in
this area was ∼ 159 million in 2010 (National Bureau of
Statistics, 2011). Moreover, this area is a highly productive region for
paddy rice and winter wheat in China, with a potentially large influence on
the atmospheric CO2 concentrations in this region. For example, winter
wheat and rice production in this region represent 20 % of the total
Chinese wheat harvest and 5 % of the entire Chinese grain production
(Colby et al., 1992; Yan et al., 2003). To understand the character and the
abundance of greenhouse gases in this region, the Chinese Meteorological
Administration (CMA) built the Lin'an (LAN) station in the center of
the Yangtze Delta area in 1983. The station has been included in the
World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) as a
regional station and is named after the small town of Lin'an, which is
approximately 6 km southwest of the station. There was no in situ CO2 measuring system
until January 2009, when a cavity ring-down spectrometer (G1301, Picarro
Inc.) was installed to continuously monitor atmospheric CO2 and
CH4 mole fractions. Since then we have been acquiring the first-hand
greenhouse data at this station (Fang et al., 2013; Pu et al., 2014).
We have found that the CO2 mole fractions at LAN were the highest out
of the four WMO/GAW stations in China (Liu et al., 2009; Fang et al.,
2011). Based on the meteorological methods, we filtered the CO2 records
from 2009 to 2011 and estimated that 16.6 % of the data are likely to be
regionally representative (Fang et al., 2014). However, in a different
study, Pu et al. (2014) used black carbon (BC) as a chemical tracer to identify
the influence of anthropogenic emissions and found that 27.3 % of the data
were regionally representative. In a previous study (Fang et al., 2011), we
also applied a purely statistical method to filter the data which resulted
in regionally representative conditions during 63.5 % of the time in 2009.
The different data filtering approaches have produced different results in
terms of the regional CO2 mole fractions. Our previous study (Fang et
al., 2014) obtained an average regional CO2 mole fraction of 404.2 ± 3.9 ppm
in 2011 at LAN by using a meteorological filter, while Pu et al. (2014)
estimated a corresponding average value of 407 ± 5.3 ppm during
the same period by using a different filtering approach. The difference
(∼ 3 ppm) between these two methods can induce biases in the
estimation of CO2 abundances at the regional scale as well as in the
calculation of sources/sinks by inverse models. In this paper, we applied
four approaches to filter the observed data from 2009 to 2011 at LAN
station, and studied the applicability of them. The four methods use black
carbon as a tracer, a statistical method, methane (CH4) as a tracer, and
meteorological parameters.
Geographic map of the LAN station. The red stars denote cities or
towns near the station. The yellow star indicates the Qingshan Lake nearby.
Experiment
Measurement system
The LAN station (119∘44′ E, 30∘18′ N; 138.6 m a.s.l.)
is about 50 km from Hangzhou (capital of the province of Zhejiang) and 150 km from Shanghai (the largest economic center and the second largest
population city in China) (Fig. 1). North of the station (1.4 km away) is a
small factory where charcoal is manufactured from bamboo wood. The town of Lin'an (with a
population of ∼ 100 000) is approximately 6 km
southwest of the station. The observatory is built on the top of a small hill
(160 m a.s.l.) and is surrounded with hilly lands and farming areas, with dense
vegetation coverage. The site is located in a humid subtropical monsoon
climate zone, with a mean annual precipitation of 1480 mm and a mean
temperature of 15.3∘.
A cavity ring-down spectrometer (CRDS; Picarro Inc., model G1301) is used
for continuous measurements of atmospheric CO2 and CH4. This type
of instrument has been proven suitable for making precise measurements of
CO2 and CH4 mole fractions, since its response is both highly
linear and very stable (Chen et al., 2010; Crosson, 2008). The factory
reported that the precision of the instrument is 50 ppb for CO2 and 0.7 ppb for
CH4 (1 standard deviation, 1σ) in 5 min. Sample air is drawn regularly from about
10 m a.g.l (above ground level). At the end of 2010, a new sampling tower (50 m a.g.l.) was built and
another sampling port was installed at 50 m. The Picarro system then
switched the air sample stream between the 10 m and the 50 m intake every 5 min.
The sample air is filtered and temperature stabilized and dried to
meet the high-quality target of the WMO/GAW network. Details of the system
are described in Fang et al. (2013). Two standard gases are used to
calibrate the measurements and a target gas is used to check the precision
of the system routinely. All of the standards are linked to the WMO X2007
scale (Zhao and Tans, 2006). The CRDS system responds quickly to the sample
and reports data with a frequency of 0.3 Hz. For the long-term time series,
ambient air data are recorded as 5 min averages. Excluding the periods of
system maintenance and calibration, more than 97 % of the total 5 min
average data points were retained. After computing the CO2 mole
fractions, the data were manually inspected to flag any analytical or
sampling problems. More than 97 % of the 5 min data remained after this
filtering step. Then the data were aggregated to hourly averages for further
study. Except when noted differently, the averaged values in this study are
reported with 95 % confidence intervals (CI). The CO2 concentrations
are atmospheric CO2 dry air mole fractions. The black carbon particles
in this study were measured by an aethalometer (AE-31, American Magee
Scientific, USA). Seven wavelengths are used for the observations, with
values of 370, 470, 520, 660, 880, and 950 nm.
Data filtering approach
There are significant CO2 diurnal variations at LAN in all seasons,
indicating the strong influence of biological activity near the site
including absorption by photosynthesis and emissions by plants and soil, and
the variation of boundary layer height. The CO2 differences between the
parallel measurements (10 and 50 m a.g.l.) at LAN also showed distinct diurnal
variations with the most stable and lowest differences (less than 0.2 ± 0.2 ppm)
occurring from 10:00 to 16:00 (local time, LT). At remote sites
where the local sources and sinks are negligible, the CO2 diurnal
variations are generally weak (e.g., Keeling et al., 1976; Zhou et al.,
2005). Thus, in this study, the CO2 data from 10:00 to 16:00 LT were first
selected to represent better mixed conditions. Then the selected data were
used for the study of different filtering approaches. We adopted four data
selection methods to filter the selected CO2 mole fractions. They are
described as follows.
Black carbon (BC) tracer: we adopt a similar routine as used
by Pu et al. (2014). The observed CO2 was filtered based on the
observation of both black carbon concentration and meteorological
parameters. Pu et al. (2014) found a correlation coefficient of 0.53 (R)
between black carbon concentrations and CO2 mole fractions and
concluded that both CO2 and black carbon have some common sources such
as fossil fuel combustion and biomass burning within this area. Thus, first
we excluded the episodes when the black carbon concentration exceeded
5000 ng m-3. During the wet season precipitations when the black carbon
concentration was very low, we used air mass back trajectory analysis to
further flag the data which were likely influenced by anthropogenic
emissions from cities nearby. Finally, we studied the average standard
deviations (σ) of hourly CO2 mole fractions as a function of
wind speed using all data from 10:00 to 16:00 LT from 2009 to 2011. As shown in
Fig. 2, the average σ decreased sharply when local surface wind
speed was faster than 1.5 m s-1. Clearly, higher local surface wind
caused better mixed conditions and consequently more stable CO2 mole
fractions. Thus the remaining data were further flagged when surface wind
speed was below 1.5 m s-1 to minimize the influence of very local
sources and sinks.
Average standard deviations of hourly CO2 mole fractions versus
wind speed based on all data from 10:00 to 16:00 LT from 2009 to 2011. The
standard deviations of hourly CO2 data were calculated based on the
5 min segments.
Statistical method (REBS): here we applied the robust
extraction of baseline signal (REBS) to extract the regional CO2 mole
fractions, which was similar to those used in the Global Atmospheric Gases
Experiment/Advanced Global Atmospheric Gases Experiment (GAGE/AGAGE)
network (Ruckstuhl et al., 2001) to filter halocarbons and other
non-CO2 gases. Ruckstuhl et al. (2012) suggested that a meteorological
filtering of the data should be applied prior to the application of the REBS
method, as the polluted conditions might induce a bias on the background
classification. In this study, this was taken into consideration by using
data from 10:00 to 16:00 LT. The REBS method is a purely nonparametric technique
and assumes that the background signal varies very slowly relative to
contributions of the regional signal. The observed concentrations
Y(ti) are defined by a regional concentration g(ti), plus a polluted
concentration m(ti), plus the measurement errors Ei. The measurement
errors Ei are assumed to be independent and Gaussian-distributed with
mean 0 and variance σ2. If the regional signal m(ti) is zero
in a time period around t0, the baseline signal g(t0) can be
estimated, even when the form of the curve g is unknown. Hence the curve
g(ti) is approximated as being linear in a sufficiently small neighborhood around
any given time point t0. Details of the method are described by Ruckstuhl
et al. (2012). A bandwidth of 60 days was used in this study, while other
bandwidths of 90, 120, and 180 days were also tested. The bandwidth choice did
not considerably influence the retrieved averages, and trends of the
regionally representative CO2 mole fractions were similar. In
comparison with other methods, this approach did not have to be considerably
adapted to the conditions at the individual measurement site.
Correlations between CH4 and CO2 mole fractions
based on all data from 10:00 to 16:00 LT at LAN station. Spring: March–May;
summer: June–August; autumn: September–November; winter: December–following February. The red
lines show linear fits between the CH4 and CO2 mole fractions.
The blue lines in each chart bracket the CO2 values within ±1σ of the data from 10:00 to 16:00 LT in each season.
Auxiliary (AUX) tracer: this tracer uses CH4 as an auxiliary
indicator to filter the CO2 time series. Many previous studies found
positive correlations (mostly in winter) between the atmospheric CH4
and CO2 mole fractions (Conway et al., 1989; Tohjima et al., 2014; Wong
et al., 2015; Worthy et al., 2009) as well as the respective fluxes from
ecosystems (Jamali et al., 2013; Repo et al., 2007). For the data series of
CO2 and CH4 at LAN, we also observed an apparent correlation
between them during the observing period (Fig. 3). The correlation
coefficient (R) is higher than 0.5 for all seasons, which indicates that
there are similar patterns of CO2 and CH4 sources. This phenomenon
is more distinct in spring (R= 0.7) and winter (R= 0.8) when
photosynthetic activity of the vegetation, i.e., the CO2 uptake, is weak.
In summer and autumn, the active absorption of CO2 by terrestrial
ecosystems may partly alter the CO2–CH4 correlation. Indeed, the
positive coefficients still suggest that the anthropogenic emissions
dominate the carbon cycle at the LAN station. In remote areas, an
uncorrelated or negatively correlated relationship is generally observed (e.g.,
Necki et al., 2003). As described above, we also used the robust
extraction of baseline signal (REBS) method to filter the CH4 data
because it has proven to be suitable for extracting the background mole
fractions of CH4 at remote sites (Cunnold et al., 2002). By doing so,
we flagged the hourly CO2 records which correspond to the periods of
locally influenced or regionally representative events of CH4 filtered
by the REBS method. Moreover, although there were correlations
between the atmospheric CH4 and CO2 in all seasons at LAN, they
were not perfectly correlated, meaning that some CO2 events could not
be determined by CH4 mole fractions, especially for data points which
were far away from the linear fit in Fig. 3. To reduce this influence, we
calculated the standard deviations (1σ) of the differences between
measured CO2 mole fraction and linear fit in each season. If the
absolute difference was larger than 1σ, the data point was
considered as locally influenced and was flagged as an outlier. This
additional filter excluded most events with poor CH4–CO2
correlation (Fig. 3).
Meteorological (MET) method: as used in previous studies, the
diurnal variation of CO2 mole fractions, local surface wind direction,
local surface wind speed, and information on nearby sources were all
considered for the CO2 data filtering (Fang et al., 2014; Zhou et al.,
2004, 2005). According to the nearby potential contamination sources (nearby
villages, industry etc.), the data when local surface winds were from SSW–SW
and N sectors were excluded. Then the data were further flagged by
discarding the events when the local surface wind speed was lower than 1.5 m s-1
to minimize the influence of very local sources or sinks as
discussed above.
After the multiple steps filter, there were still a few discrete data points
remaining with high/low CO2 mole fractions in the BC, AUX, and MET
methods. These odd outliers unlikely represented regional CO2 conditions
as they should not peak within a few hours. Thus we used a mathematical
method to further flag the remainder of data in the BC, AUX, and MET
methods. The standard deviation (σ) of hourly CO2 data in a
60-days bandwidth (similar to the REBS method) was calculated. The
differences between every data point and the 60-day average were calculated.
Data were flagged and excluded if the difference exceeded 3σ. After
that, the remaining data in the BC, AUX, and MET were considered as the
least influenced by local sources or sinks.
Filtered CO2 mole fractions in the four approaches (BC: black
carbon as tracer; REBS: Robust Extraction of Baseline Signal; AUX: CH4
as auxiliary tracer; MET: meteorological filter). The closed blue circles
represent the filtered regional events. The open gray circles represent
local events which are influenced by very local sources or sinks. The red
lines are results fitted to the filtered regional events using the
curve-fitting method by Thoning et al. (1989).
Results
Filtered regional CO2 mole fractions
Figure 4 illustrates the filtered CO2 results in the four approaches.
From top to bottom are the results of the BC, REBS, AUX, and MET methods,
respectively. The filtered regional mole fractions account for
∼ 12.2 % in BC, 15 % in REBS, 12.8 % in AUX, and 16.5 %
in MET of the total valid hourly data. The low proportions of regional
CO2 in the four methods reflect the strong influences of local sources
and sinks. The overall seasonal patterns of regional CO2 retrieved in
the four approaches are similar with peaks in winter and troughs in summer,
which is consistent with other observations in the Northern Hemisphere
(Nevison et al., 2008).
Table 1 compares the annual regional CO2 mole fractions in the four
methods. The annual mole fractions in the REBS method are apparently higher
than the others with the smallest discrepancy of 1.7 ± 0.2 ppm in
2010. As the REBS method uses a purely mathematical method, the high
CO2 mole fractions induced by local sources may enhance the σ
values and enlarge the regional CO2 band, and subsequently
introduce higher regional CO2 values. The annual mean CO2 mole
fractions all display increasing trends. Although the annual CO2
determined by the BC method increases from 402.0 ± 0.1 in 2010 to
402.7 ± 0.2 ppm in 2011, the absolute increase (∼ 0.7 ppm)
is too small compared to increases from the other methods, as well as the
global means. For example, the global average CO2 increases were
2.3 ppm in 2009–2010 and 2.0 ppm in 2010–2011 based on WMO/GAW's
statistics (WMO, 2011; 2012) and were 2.39 ppm in 2009–2010 and 1.71 ppm
in 2010–2011 based on observations from NOAA's network (Dlugokencky and
Tans, 2015). Considering the global CO2 growth rate and the increasing
emissions of CO2 in China (CDIAC, 2015; Tohjima et al., 2014), it is
unlikely that the regional CO2 mole fractions at LAN almost remained
constant from 2010 to 2011. In fact, the long-term trend in the BC method
(polynomial part of the curve-fitting function) decreases after February in
2011 until end of the year (data not shown), which is opposite to the
variations of total CO2 emissions (or black carbon) expected from
fossil fuel emissions in China with increasing value from 0.15 Pg C in 2009–2010
to 0.21 Pg C in 2010–2011 (CDIAC, 2015). As the BC method uses a
fixed black carbon concentration (5000 ng m-3 in this study) as a threshold to filter the CO2 record, a large proportion of high regional
CO2 mole fractions in 2011 may be flagged as local events, and
consequently, a decreasing long-term trend was acquired. The absolute
CO2 increases from the other three methods indicate smaller increases
from the 2009 to 2010 period and larger increases from 2010 to 2011, which better
corresponds with the trend in total CO2 emissions in China.
The annual regional CO2 mole fractions by the four
methods from 2009 to 2011.
Year
BC (ppm)
REBS (ppm)
AUX (ppm)
MET (ppm)
2009
398.9 ± 0.1
401.5 ± 0.2
398.6 ± 0.2
398.6 ± 0.2
2010
402.0 ± 0.1
403.7 ± 0.2
400.8 ± 0.2
401.7 ± 0.2
2011
402.7 ± 0.2
407.4 ± 0.2
405.1 ± 0.2
404.9 ± 0.2
Mean seasonal cycles and trends
We used the curve-fitting method described by Thoning et al. (1989) to
derive the seasonal CO2 trends based on the filtered regional mole
fractions from 2009 to 2011 (Figs. 4 and 5). The data were fitted into a
function with three polynomial terms for the long-term trend and four annual
harmonic terms. To minimize the influence of inconsistent records in the
four methods, the data were fitted and interpolated with the same time
interval (2 h). For comparison, surface CO2 mole fractions at similar
latitudes to the marine boundary layer (MBL) reference computed by NOAA/GMD
(sine of 0.5, i.e. 30∘ N) are also presented (NOAA, 2015). The monthly
CO2 mole fractions in the four methods are apparently higher than the
MBL values during the whole year, with an average difference of 10.9 ± 0.1
for BC, 13.9 ± 0.2 for REBS, 11.2 ± 0.2 for AUX, and 11.5 ± 0.2 ppm for MET. The large difference indicates that the
Greater Yangtze Delta area is an important net source of atmospheric
CO2. The monthly CO2 variations in the four approaches show
similar patterns, with minimum values in August and maximum values in
December. The appearance of the lowest values matches with the minimum of
the MBL reference. As reported in a previous study (Fang et al., 2014), the
highest CO2 difference between the Northern Hemisphere and the LAN was in
December, and was due to the lower boundary layer in winter, as well as the
increase in fossil fuel consumption (partly for domestic heating) and cement
burning, as well as plant respiration in this season. In general, these four
approaches do well in capturing the seasonal CO2 cycles at the LAN
station.
However, there are also differences between the monthly CO2 mole
fractions. The monthly CO2 values in the REBS method are always higher
than in the other methods (Fig. 5a). This is because the REBS method uses
variations of the raw data (standard deviation) as a threshold to flag the
locally influenced CO2 mole fractions. The “noisy” CO2 mole
fractions (mostly high outliers) may draw the trend of regional events
upward and subsequently induce higher regional values. This result also
indicates that the REBS method may be less suitable for CO2 data
filtering for a site without a well-defined background as in the case of
LAN. In fact, this method is mostly used at remote sites with few local
sources and sinks (e.g., Zhang et al., 2013).
(a) Variations of monthly CO2 mole fractions in the four
methods, also compared to the surface values at similar latitudes
(30∘ N) from the MBL reference (MBL, 2015). The data in this
figure are values smoothed by the curve-fitting method of Thoning et al. (1989).
(b) The detrended seasonal CO2 cycles in the four methods. This is
the smoothed curve minus the trend. Error bars indicate confidence intervals
of 95 %.
During the winter–spring period, the regional CO2 mole fractions
retrieved with the BC method are apparently lower than those retrieved with the other
methods. This is because the BC method mainly refers to the measured black
carbon concentrations. Emissions of black carbon and CO2 from fossil
fuel and biomass burning occur at both the local and regional scale
(Baumgardner et al., 2002), and the BC method robustly flags the CO2
mole fractions when black carbon concentrations exceed the threshold value
(5000 ng m-3). However, it is difficult to distinguish the local
emissions of black carbon from the regional contents. Especially during the
winter–spring seasons, the regional black carbon and CO2 concentrations
are both high due to the increase of fossil fuel consumption and cement
burning (Feng et al., 2014). These high concentrations should still
represent the volumes at regional scale and should not be flagged. Thus in
the BC method, the flagging of higher CO2 mole fractions as local
representatives is probably the reason for the lower regional CO2
values during the winter–spring period. It should also be mentioned that the
use of a constant threshold in the BC method may lead to slight errors in
the estimation of regional CO2 mole fractions. Secondly, the BC method
is mainly geared towards polluted air masses altered by anthropogenic
sources, whereas the influence of the land biosphere during daytime remains
unconsidered. Moreover, the large difference of the lifetime, which is 4–12
days for black carbon (Cape et al., 2012) and more than decades for CO2
(Moore and Braswell, 1994), may also contribute to the bias of this method.
The monthly CO2 mole fractions in the AUX method are lower than in the
other methods in summer. During this time of the year, a large amount of
CH4 is emitted from wetlands (e.g., from rice paddy fields) on the eastern
China Plain (Lu et al., 2000; Zhang et al., 2010). Thus, these high values
should represent regional conditions rather than local events (Fang et al.,
2013). On the other hand, the active processes of photosynthesis by local
and regional vegetation in summer reduce the observed CO2 mole
fractions, especially by local vegetation, which has a strong negative
influence on the CO2 mole fractions during afternoon. This influence
can also be seen from the frequently lower CO2 mole fractions at 10 m a.g.l.
than at 50 m a.g.l. in the daytime. These different source/sink regimes may
cause some CO2 mole fractions being flagged as regional even though
they are actually influenced by the absorption of local vegetation,
subsequently leading to lower regional values in summer.
Compared with the BC, REBS, and AUX methods, there is no apparent
disadvantage of the MET method, which attempts to eliminate the influence
of local sources and sinks using meteorological information. Figure 5b
illustrates the detrended seasonal cycles of CO2 in the four
approaches. The peak to trough amplitudes of regional CO2 are 14.4 ± 0.1,
18.6 ± 0.1, 22.7 ± 0.1, and 20.4 ± 0.1 ppm for the BC, REBS, AUX, and MET methods, respectively. The amplitude
for the BC method is the lowest, which is ascribed to the lower CO2
mole fractions during the winter–spring period, and the higher values than
AUX and MET method in summer. The higher CO2 mole fractions in the BC
method in summer may be due to the lower black carbon concentrations (Feng
et al., 2014). The highest CO2 amplitude is observed in the AUX method,
which is ascribed to the lowest CO2 mole fractions in summer.
In addition to providing seasonal cycles, the method of Thoning et al. (1989)
also provides an estimate of the trend over the full measurement
period 2009–2011. The regional CO2 mole fractions all show positive
trends with annual growth rates of 1.8 ± 0.01 for BC, 2.8 ± 0.01
for REBS, 3.2 ± 0.01 for AUX, and 3.1 ± 0.01 ppm yr-1
(standard error) for MET. According to the statistics from the WMO
Greenhouse Gas Bulletins (2011, 2012), the global CO2 growth rate
exceeded 2 ppm yr-1 from 2009 to 2012. The growth rate for the BC
method is lower than the global average, which may partly be caused by the
different CO2 to black carbon ratios in the considered years. As the
regional CO2 value in the BC method is based upon the black carbon
concentrations, the increasing fossil fuel standards (upgraded from Chinese
national stage 3 to 4 since 2010) and exhaust efficiency may induce
different ratios between CO2 and black carbon concentrations and hence
extract a smaller CO2 growth rate. Similar to the annual CO2 mole
fractions (Table 1), the annual growth rates of the AUX and MET method are
close. It should be mentioned that only 3 years of data are used to evaluate the
annual CO2 growth rate. The relatively short time series here may
inevitably induce bias on the growth rate estimation, which needs to be
treated with caution.
Comparison of local CO2 events
The benefit of a successful extraction of regional values is twofold. The
identified regional values can be used e.g., for the determination of
regionally representative trends. In addition, the data considered to
be locally influenced can be used to learn more about the sources and sinks
in the vicinity of the station. Fig. 6a displays the seasonal variations of
local CO2 mole fractions in the four approaches. Due to the
considerable proportion of local events below the regional band in REBS
(blue dots Fig. 3), the local CO2 mole fractions were separated into
“REBS-P” events (in or above the regional band) and “REBS-D” events
(below the regional band). The data were also fitted and smoothed by the
method of Thoning et al. (1989). The local CO2 events all reveal a
broad spring maximum peaking in May, and a distinct winter maximum with the
highest value in December. Minimum values are all observed in August. The
peak in December and trough in August agrees with the seasonal pattern of
the regional data. However, there is another distinct peak in May in all the
approaches. Feng et al. (2014) investigated the black carbon measurements in
Shanghai, China (150 km from LAN), and found three peaks in January–February,
April–June, and November–December from 2010 to 2011. As the
anthropogenic emissions of black carbon and CO2 in the Yangtze area
have a similar spatial distribution (Qin and Xie, 2012), the peaks in May and
December are probably due to enhanced anthropogenic emissions in these
months. However, the peak in May is blurred due to the dampening
effect caused by the CO2 uptake with the onset of the growing season.
(a) Variations of locally influenced CO2 mole fractions in four
methods. The data are CO2 mole fractions smoothed by the curve-fitting
method of Thoning et al. (1989). REBS-P denotes local events in or above
regional band in REBS in Fig. 3, and REBS-D denotes local events below the
regional band. (b) Wind-rose distribution of locally influenced CO2
mole fractions by the four approaches at LAN. The REBS-P and REBS-D show
similar distributions. Thus the local events are averaged together as REBS
to avoid a jump in the scale (red dots and line). Error bars indicate confidence
intervals of 95 %.
The local CO2 mole fractions in REBS-P are always higher than for the
other methods, as most of the events identified by REBS-P are observed
during nighttime (00:00–08:00 LT) when local emissions are strong and the boundary
layer becomes lower. Contrarily, the CO2 mole fractions in REBS-D are
mostly observed at midday (12:00–16:00 LT) and are apparently lower than the
other methods, reflecting the strong absorption by local vegetation. Thus it
can be seen that at LAN station, the REBS method tends to define a median
band of the CO2 record as regional representative. Except from December
to January, the local CO2 mole fractions in the BC method are higher
than those in the AUX and MET methods. As discussed above, the tendency of
flagging higher CO2 mole fractions as locally representative is
probably the main reason for the higher CO2 values. This result also
indicates that the BC method induces bias on the local CO2 estimations.
Meteorological data (such as surface wind direction and speed) could help to
understand the greenhouse gas emissions and transport (Dlugokencky et al.,
1995; Massen and Beck, 2011). Figure 6b shows the wind rose distribution
patterns of local CO2 mole fractions in the four methods. The
distributions are similar with the highest CO2 values in the SW–SSW
sectors, including in REBS-P and REBS-D. This is due to the anthropogenic
emissions from the town of Lin'an, located at approximately 6 km southwest of
LAN (Fig. 1). The local CO2 mole fractions in the WSW to SSW sectors in
the BC method are apparently higher than in the other methods. It is also
probably due to the tendency of flagging higher CO2 mole fractions
emitted from the town. The local CO2 mole fractions in these sectors in
the MET method are also higher than those of the REBS and AUX method.
Actually, we studied the wind-rose CO2 distributions in different
seasons and found most of the discrepancies occurred in summer. This
phenomenon can also be seen from Fig. 6a with higher values in the MET
method than in the AUX and REBS methods in August. As discussed above, the
lower local CO2 mole fractions in the AUX and REBS methods in summer
are probably due to the local CO2 mole fractions (below the regional
band in Fig. 3) being flagged as absorption by local sinks (e.g., by
photosynthesis of local vegetation).
The filtered regional and local CO2 mole fractions from
28 to 31 December 2010 (LT). The black dots represent the regional events
and the gray dots denote the local events. The phase 1 and phase 2
represent periods from 06:00 to 16:00 LT in 29 and 30 December 2010,
respectively.
Case analysis
To further investigate the difference of the four data filtering approaches,
we used a period in winter as a case study. In winter, CO2 mole
fractions at LAN are pretty high due to strong emissions and weak absorption
by the regional terrestrial ecosystems. Here we selected two time periods
which were from 06:00 to 16:00 LT on 29 December 2010 (period 1) and 30 December 2010
(period 2), respectively (see Fig. 7). Period 1 features elevated CO2
mole fractions, while period 2 reveals “normal” CO2 values. We compute
the 3-day back trajectories with 500 m a.g.l. for the period with elevated
CO2 (28 December 2010 19:00 to 29 December 2010 06:00) using the Hybrid Single-Particle Lagrangian
Integrated Trajectory (HYSPLIT) dispersion model
(Draxler and Rolph, 2003). The model is based on NCEP/NCAR Reanalysis data
and the trajectories were calculated for every hour (01:00, 02:00, 03:00 LT…). Figure 8
shows all calculated trajectories in period 1. It can be seen that almost
all of the air masses reaching LAN were transported over the cities of
Hangzhou (province of Zhejiang, ∼ 50 km east of the
station) and Nanjing (province of Jiangsu, ∼ 230 km
north of the station). The black carbon during this period also displays an
increased concentration. Since black carbon is mainly emitted by fossil fuel
combustion and biomass burning (Penner et al., 1993; Cooke and Wilson,
1996), the enhanced CO2 mole fractions should be mainly caused by the
transport of emissions from these cities. Both the BC and AUX method flag
all data in period 1 as locally influenced (Fig. 7). In the BC method, due
to the increased black carbon concentration, all the CO2 data are
flagged because the black carbon concentrations are apparently higher than
the yearly average. The meteorological conditions from 10:00 to 16:00 LT
in period 1 favored dilution, i.e., average surface wind speed were 2.6 m s-1;
the σ of the hourly mole fractions was less than 1.3 ppm. Although
the CO2 mole fractions increased in this period, it was more likely
influenced by regional sources (e.g., from Hangzhou and Nanjing) rather than
local sources. Thus, the BC and AUX method may erroneously assign local
conditions. In period 2, some data points from 10:00 to 16:00 LT are flagged as
regional except in the REBS method. As discussed above, this method is a
purely statistical method. The existence of frequently high mole fractions
in winter may enlarge the σ and may consequently deviate the
regional events from the real trend. As a result, some low regional
CO2 mole fractions as in period 2 may not be
identified.
HYSPLIT 72 h back trajectories for every hour from 28 December 2010
19:00 (local time) to 30 December 2010 06:00.
Discussion and conclusions
The main purpose of data filtering at a regional station is to identify the
data which are least influenced by local sources and sinks (Tsutsumi et al.,
2006). However, due to the unique conditions for each station (i.e.
topography, air mass transport, economic development level, etc.) and the
complex influences of local sources and sinks, there is no ultimate way to
rigorously distinguish the locally influenced CO2 from the original
data series. Thus data filtering at this type of regional station is a
relatively empirical work. In this study, four data filtering approaches
are used to flag the observed data from 2009 to 2011 at Lin'an (LAN) station
in the Yangtze Delta area, China. Each of the methods applies multiple steps
to flag the observed CO2 mole fractions. The strong diurnal variations
of observed CO2 mole fractions and the discrepancy between the parallel
measurements (10 and 50 m a.g.l.) indicate that selecting daytime data only is
the first and critical step to study CO2 mole fractions at this kind of
station and this step is therefore applied as a first filtering step in all four
approaches. The four methods in this study are all suitable to capture the
main features of the seasonal cycle of regional CO2 at LAN, but the
different regimes in these methods also induce bias on the regional or local
mole fraction evaluations.
The BC method may be treated with caution, as it is difficult to distinguish
the local emissions of black carbon from the regional contents. Especially
during the winter–spring seasons, this method may underestimate the regional
CO2 mole fractions at LAN. Moreover, it mainly gears to the polluted
air masses altered by anthropogenic sources, and does not consider the
influence of the land biosphere. Additionally, the different lifetime
between atmospheric CO2 and black carbon may also introduce errors on
the estimation, especially during rainy periods. In this study, the annual
mole fractions, the annual growth rate, and the local CO2 values in the
BC method are different from the other three methods. It should be mentioned
that the BC method in Pu et al. (2014) is different from the one in the
present study. Besides using different wind speeds as the filter criterion (2 m s-1)
and excluding outliers, they used all data including both from daytime and
nighttime. The emissions from local vegetation and accumulation in the
shallow boundary layer in the night definitely enhanced the filtered
CO2 mole fractions and induced higher annual values than those in our
study.
The REBS method is based on a purely statistical method. This method is
appealing as it requires no additional information (site specific criteria,
additional observations). However, it may also induce errors when evaluating
the regional CO2 mole fractions, e.g., overestimating the regional
values. In a previous study (Fang et al., 2011), we estimated an annual
average of 405.3 ppm in 2009 by using REBS, which was apparently higher than
the averages in this study (Table 1). Due to the “noisy” CO2 mole fractions
at the regional sites like LAN, the filtered regional trend may be drawn
upward or pulled down from the real variation.
Although there are correlations between CH4 and CO2 at LAN, the
different source/sink regimes may induce bias on the regional CO2
estimation in the AUX method, typically in summer. Further, the
atmospheric CH4 and CO2 at LAN are not perfectly correlated,
meaning that some CO2 events cannot be determined by the CH4 mole
fractions.
In comparison to these approaches, there are fewer disadvantages in the MET
method for the data selection. As this method mainly focuses on the
influence of potential local sources and sinks and considers diurnal
variations and meteorological conditions, it is reasonable to identify the
influence of local sources and sinks and it is suitable to be applied at other
regional stations. We also studied the results at one other station in China
(Longfengshan, 127∘36′ E, 44∘44′ N; 330.5 m a.s.l.)
using different BC, AUX, and REBS, but a similar MET method. The results
also indicated that the MET method was the most favorable (Fang et al.,
2015). However, we have to mention that due to the intake height (only 10 m a.g.l.)
in this study and the complex influence of the land biosphere, the data
selected at LAN may not fully represent the volume at the regional scale.
Although we selected the data from 10:00 to 16:00 LT when the boundary layer was
the highest and the surface wind speed was faster than 1.5 m s-1, the
influence of local land biosphere could not be fully eliminated. This
influence can be seen from the frequently lower CO2 mole fraction at 10 m a.g.l. than 50 m a.g.l. during daytime in summer.
However, it cannot be concluded that the other three methods are not
suitable for the CO2 data selection. For example, the four methods were
also applied to process the observed CO2 record at Mt. Waliguan station
(100∘54′ E, 36∘17′ N; 3816 m a.s.l.) in China
without excluding the nighttime data. As this station is a WMO/GAW global
site and is located in a remote area, we found there were no distinct
differences between the filtered CO2 mole fractions, including the
seasonal cycles, annual growth rates, and background mole fractions (data not
shown); the results agreed very well with the background information in the
Northern Hemisphere. In this study, we selected daytime data only for the
four approaches to exclude the influences of very local sources/sinks (e.g.,
vegetation). However, for sites without strong local sources/sinks and
indistinct diurnal CO2 variations, the nighttime data may also
represent the regional background and can be used. Atmospheric black carbon
is mainly from fossil combustion and biomass burning, Thus the BC method may
be applied at some remote sites to identify the anthropogenic influence on
the observed CO2 records. The theory of REBS assumes that the
background signal varies very slowly relative to contributions of the
regional signal. The results in this study prove that the REBS is not
suitable for the CO2 data filtering at regional stations like LAN.
Instead, it may be applied at some remote stations like Mt. Waliguan. The
AUX can be applied at sites where the atmospheric CH4 and CO2 are
subject to the same sources, and it can also be applied at some remote
stations. Moreover, due to the different characteristics and source/sink
regimes of various gas species, the suitability of a particular filtering
method may even differ when looking at different trace gases at the same
sampling site. This needs to be studied separately.