Long term record of atmospheric CO 2 and

GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 20, GB2001, doi:10.1029/2004GB002431, 2006

Long-term record of atmospheric CO2 and stable isotopic ratios at
Waliguan Observatory: Seasonally averaged 1991–2002 source/sink
signals, and a comparison of 1998–2002 record to the 11 selected sites
in the Northern Hemisphere
Lingxi Zhou,1 James W. C. White,2 Thomas J. Conway,3 Hitoshi Mukai,4
Kenneth MacClune,2 Xiaochun Zhang,1 Yupu Wen,1 and Jinlong Li5
Received 11 December 2004; revised 4 December 2005; accepted 16 January 2006; published 5 April 2006.

[1] This paper investigates seasonally averaged atmospheric CO2 source or sink signals
observed at Waliguan Baseline Observatory (WLG, 36
170N, 100
540E, 3816 m asl) in
western China from 1991 to 2002. Both linear and geometric mean regressions as
well as statistical significance test were performed between detrended CO2 and stable
isotope monthly data by means of Keeling Model and Miller-Tans Model. The estimated
slope Dd13C/DCO2 due to the seasonality in CO2 and d13C is (0.050 ± 0.004)% ppm1,
the mean source/sink isotopic signatures ds (i.e., d13Cs and d18Os) are (26.159 ±
1.924)% and (7.662 ± 2.113)%, respectively, and the mean atmospheric d13C
discrimination ddis (i.e., ds minus dbg) where the dbg is the isotopic value of the background
atmosphere was (18.174 ± 1.959)% by the Keeling Model, in agreement with results
from other continental background sites in the Northern Hemisphere. We suggest that

exchange with terrestrial biosphere dominates the observed CO2, d13C and d18O seasonal
cycles at WLG. In addition, atmospheric CO2 and d13C data from 11 selected NH sites in
the NOAA ESRL air sampling network from 1998 to 2002 were analyzed and compared
to the WLG data for the same period to better address common and specific features
observed in this region. The annual cycle amplitude differences, secular and seasonal
Dd13C/DCO2 discrepancies among sites will be useful to better understand carbon uptake
and release especially on the Eurasian continent. The estimated ds during certain times at
each specific site could possibly provide useful information on CO2 fluxes.
Citation: Zhou, L., J. W. C. White, T. J. Conway, H. Mukai, K. MacClune, X. Zhang, Y. Wen, and J. Li (2006), Long-term record of
atmospheric CO2 and stable isotopic ratios at Waliguan Observatory: Seasonally averaged 1991 – 2002 source/sink signals, and a
comparison of 1998 – 2002 record to the 11 selected sites in the Northern Hemisphere, Global Biogeochem. Cycles, 20, GB2001,
doi:10.1029/2004GB002431.

1. Introduction
[2] Stable CO2 isotope measurements combined with
CO2 mixing ratio measurements in the atmosphere, can
potentially be used to discriminate between different CO2
source and sink mechanisms [Bakwin et al., 1998; Miller
and Tans, 2003; Nakazawa et al., 1993, 1997a, 1997b]. The
1

Key Laboratory for Atmospheric Chemistry, Centre for Atmosphere
Watch and Services, Chinese Academy of Meteorological Sciences, China
Meteorological Administration, Beijing, China.
2
Institute for Arctic and Alpine Research, University of Colorado,
Boulder, Colorado, USA.
3
Global Monitoring Division, Earth System Research Laboratory,
National Oceanic and Atmospheric Administration, Boulder, Colorado,
USA.
4
Center for Global Environmental Research, National Institute for
Environmental Studies, Tsukuba, Japan.
5
School for Environmental Sciences, Peking University, Beijing, China.

Copyright 2006 by the American Geophysical Union.
0886-6236/06/2004GB002431

Keeling Model (i.e., ‘‘Keeling Plot’’ approach) [Keeling,

1958, 1961], based on conservation of mass, is a method for
relating changes in CO2 and d13C to the isotopic signature
of a source or sink adding or removing CO2 from the
background CO2 mixing ratio and isotopic ratio. When
analyzing atmospheric CO2 and d13C measurements, the
most prominent feature is the negative correlation of d13C
with the CO2 mixing ratio. This feature is often called the
‘‘Keeling relationship’’ and is commonly used to determine
the 13C signature of the source or sink (ds) causing the CO2
variability [Inoue and Sugimura, 1985; Pataki et al., 2003;
Trolier et al., 1996].
[3] As recorded in the literature [Friedli et al., 1987;
Miller et al., 2003; Miller and Tans, 2003; Randerson et al.,
2002], in the Keeling Model linear regression approach
known traditionally as Keeling Model-I, the independent
variable, x, (in this case the inverse of the atmospheric CO2
mixing ratio) is assumed to have no error and the errors in
the dependent variable, y, (in this case d13C of atmospheric

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CO2) are unrelated to the independent variable. This stands
in contrast to the Keeling Model-II approach (geometric
mean regression) which does account for errors in both x
and y parameters [Pataki et al., 2003]. The Keeling Model-I
generally gives smaller slope values and more positive y
intercepts than does Keeling Model-II. In a Keeling ModelII regression the intercept is that of a Keeling Model-I
regression divided by the correlation coefficient (R) of both
the x and y variables. As the R value of the x and y variables
declines, the Keeling Model-II intercept is systematically
more negative than the Keeling Model-I intercept.
[4] A Miller-Tans Model, i.e., the ‘‘New Model’’ described in [Miller and Tans, 2003] for calculating isotopic
discrimination from atmospheric measurements of CO2 and
d13C allows for variable background values of both CO2 and

d13C and is particularly well suited for evaluating a long
time series. The Miller-Tans Model, also based on the
conservation of mass, plots the detrended CO2 versus
d13C*CO2 data sets, with an equation derived from mass
balances for atmospheric CO2 and d13C*CO2 [Miller and
Tans, 2003],



dobs Cobs ¼ ds Cobs þ Cbg dbg  ds :

ð1Þ

Here, C and d refer to atmospheric CO2 and d13C, and the
subscripts obs, bg and s refer to observed, background and
source values. The background values used in equation (1)
can take different forms and could be constant or vary with
time. Thus the ds is determined by the slope of the curve
derived from the linear regression fitting technique, as
opposed to the y intercept of the Keeling Model. For
background values, dbg, the Miller-Tans model uses the

secular (5 years in this paper) trend curve to obtain isotopic
discriminations by source or sink (ddis, equals to ds minus
dbg). Since a Keeling Model requires a nonvarying background value for both CO2 and d13C, a requirement that is
commonly violated in time series studies, the Miller-Tans
Model could be used for an improved examination of the
correlation between CO2 and d13C.
[5] Several laboratories operate sampling networks to
monitor the trace-gas and stable isotopes composition of
the global atmosphere. Among them, the NOAA ESRL
(formerly CMDL) Cooperative Global Air Sampling Network is the largest. In all the networks, however, the
sampling site locations are heavily biased toward windy
marine locations, which minimize the influence of local
anthropogenic and terrestrial biospheric emissions [Conway
et al., 1994; Francey et al., 1998; Keeling et al., 1995;
Nakazawa et al., 1993, 1997a; World Meteorological
Organization (WMO), 2003a, 2003b].
[6] Waliguan Baseline Observatory (WLG, 36
170N,
100
540E, 3816m asl), situated in remote western China,
is one of the WMO’s 23 Global Atmosphere Watch (GAW)
baseline stations scattered around the globe. This site

provides essential information on sources and sinks from
within the Eurasian continent [Zhou et al., 2003, 2004,
2005]. In this study, seasonally averaged atmospheric CO2
source or sink signals observed at WLG for the period 1991
to 2002 are investigated by the Keeling Model and the
Miller-Tans Model approaches. We also present a compar-

GB2001

ison of WLG to atmospheric CO2 and d13C data sets for the
period 1998– 2002 from 11 ESRL network sites in the
Northern Hemisphere (NH). The main purpose is to provide
a basic understanding of the CO2 and d13C common features
in Eurasian continental regions as well as provide additional
constraints for the carbon budget of the Asian inland
plateau, and to supply a long-term and accurate observational record for use in carbon cycle and global climate
research.

2. Methods
2.1. Measurement Scale and WLG Data

[7] In this paper, the CO2 mixing ratios are reported on
the NOAA ESRL measurement scale [Climate Monitoring
and Diagnostics Laboratory (CMDL), 2004; GlobalviewCO2, 2004] in units of mmol mol1 (parts per million
abbreviated ppm, 106 mol CO2 per mol of dry air). The
isotopic ratios are expressed in per mil (%) relative to the
standard isotopic ratio Vienna Pee Dee belemnite (VPDB)CO2 [Trolier et al., 1996] for both the d13C and d18O. A total
of 118 monthly CO2 versus d13C and 110 monthly CO2
versus d18O data points simultaneously available from
NOAA ESRL discrete measurements over the entire
period May 1991 to December 2002 at WLG were used
in the Keeling Model and the Miller-Tans Model regression
analysis.
2.2. Detrended Monthly Mean Data Sets and
Background Atmospheric Values at WLG
[8] The secular (5 years in this paper) trends in the
atmospheric CO2 and d13C monthly mean time series could
seriously affect the seasonal Keeling Model results [Miller
and Tans, 2003; Pataki et al., 2003; Randerson et al., 2002;
Trolier et al., 1996]. To examine the relationship between
CO2 mixing ratio and d13C through the seasonal cycle, we

constructed time series of ‘‘detrended’’ monthly data sets by
first subtracting a linear secular trend from each of the time
series; then a single mean value for each time series,
obtained from the average value of its linear fitting curve,
was added back to each data point in the time series for use
with regression analysis [see Randerson et al., 2002]. There
are several choices of the relative dbg, and this choice
determines relevant value of the ds. In this paper, we simply
employ dbg the mean values of each data set of atmospheric
CO2 mixing ratio and d13C time series. We calculate here
the Keeling Model slopes (dd13C/d[CO2]1) and intercepts
(ds) and the Miller-Tans Model slopes (ds). We use the
detrended monthly data sets for the entire time span as well
as for the different seasons in this period, and also calculate
the ddis at the site.
[9] To investigate potential differences of the source or
sink signal among seasons, the detrended monthly mean
CO2 mixing ratio and d13C data sets were then partitioned
according to season with the months of March, April and
May as spring, June, July and August as summer, September, October and November as autumn and December,

January and February as winter. An alternative approach
is to separate the data sets into the ‘‘winter-half-year’’
(November through April) and ‘‘summer-half-year’’ (May

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through October) according to typical climatic and environmental features observed at WLG [Zhou et al., 2003, 2004].
Both approaches are tested in our analysis.
2.3. Selected NH Sites and Time Span of the Data
[10] Details for the 12 selected NH sites (WLG included)
in the NOAA ESRL Flask Air Sampling Network are given
later in the results and discussion section of this paper.
These sites range in latitude from 19.52
N to 82.45
N and
in altitude from 3 to 3810 m asl, and are generally defined
as inland, coastal or island locations in this study, with a
focus on the 35
N–45
N Eurasia inland region. Besides

latitudinal and geographical effects, we also use 2000 m as
the dividing line to test whether there is a difference in the
CO2 and d13C between the high- and low-altitude sites. The
comparison of WLG to other NH sites has been restricted to
the period between January 1998 and December 2002
because the monthly mean atmospheric CO2 and d13C data
were simultaneously available from the 12 stations (except
for IZO, missing a few monthly data in 1998 and 2002).
The CO2 and d13C monthly mean time series, data detrending, annual cycle amplitudes, annual means and growth
rates, Keeling Model and Miller-Tans Model, and the ds, the
ddis from each the other NOAA ESRL sites are derived
using the same methodology as performed on the WLG
1991 – 2002 data sets.

3. Results and Discussion
3.1. Seasonal Relationship Between the CO2 Mixing
Ratio and Isotopes at WLG
3.1.1. Test Keeling Model and Miller-Tans Model
Approach Using WLG Data Sets
[11] To test and discuss effect of different regression
methods on the Miller-Tans Model and the Keeling Model
we calculated ds and slopes of the WLG 1991– 2002 data
sets using both Model-I (linear regression) and Model-II
(geometric mean regression) techniques. We found that the
Keeling Model-I produced similar ds values (e.g.,
25.265%, R2 = 0.9328 for the entire period) to the
Miller-Tans Model-I (25.266%, R2 = 0.9669), even when
their R2 values are quite different. With the Model-II results,
however, the ds values, which tend to be more negative than
Model-I results, are quite different (26.159% by the Keeling Model-II and 25.695% by the Miller-Tans Model-II) if
R2 values are different (in this study, the Miller-Tans Model
generated higher R2 values than that of Keeling Model for
both the annual and seasonal analyses. This information is
presented in following paragraphs). The Dd13C/DCO2 in the
Keeling Model-II approach (0.050% ppm1) tends to be
lower than that found using Keeling Model-I (0.048%
ppm1), where Dd13C is equal to dobs minus dbg, and DCO2 is
equal to Cobs minus Cbg. The ds values shift significantly in
both methods if the R2 < 0.90 thus they are hard to interpret
and have limited usage. We use the standard error estimate
from the Model-I regression parameters (in this case slope
and y intercept) to approximate the error estimate for the
Model-II regression parameters as suggested by Pataki et al.
[2003]. In this study, the Miller-Tans Model approach gives
higher ‘‘Student T’’ statistical significances and lower uncer-

GB2001

tainties than that of the Keeling Model. While the MillerTans Model approach is more flexible and thus applicable to
more situations, the most common approach within the
community remains the Keeling Model. To make our study
comparable with others, both model results are displayed,
however, only the Keeling Model results are discussed in the
following sections.
3.1.2. Relationship Between Atmospheric CO2 Mixing
Ratio and D13C in the Seasonal Cycle
[12] The relationship between the detrended atmospheric
CO2 mixing ratios and d13C monthly means (118 data sets)
at WLG for the entire period May 1991 to December 2002
are shown in Figure 1 for the Keeling Model and MillerTans Model approaches. The good fit of the least squares
method to the Keeling Model (R2 = 0.9328) indicates that
the relationship between the seasonal variations of CO2 and
d13C at WLG can be approximated well using a linear
function. The ddis is estimated to be (18.174 ± 1.959)% by
the Keeling Model-II and (17.710 ± 0.958)% by the
Miller-Tans Model-II, where we simply employ the dbg
(7.985 ± 0.035)% the mean values of atmospheric d13C
time series at WLG from May 1991 to December 2002.
[ 13 ] To examine the seasonality of the ds, relevant
detrended data sets are partitioned and calculated by season,
and also by winter-half-year and summer-half-year. Listed in
Table 1 are the Keeling Model-II slopes (dd13C/d[CO2]1),
Keeling Model-II y-intercepts and Miller-Tans Model-II
slopes (ds), dbg, ddis at WLG for the period May 1991 to
December 2002. In addition, Table 1 lists the statistical
significant
test results for both models (‘‘Student T’’, T =
p
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ð N  2ÞR2 =ð1  R2 Þ, where R is correlation coefficient of
the regressions between detrended monthly means of atmospheric CO2 and d13C, N is number of the data pairs).
[14] Using Keeling Model-II, we found that CO2 and d13C
showed higher correlation in summer-half-year (R2 =
0.9604) than in winter-half-year (R2 = 0.9174). Among
seasons, the greatest correlation between CO2 and d13C
occurred during summer (R2 = 0.9362) and the least correlation during spring (R2 = 0.5636). The high correlation in
the summer can be attributed to anthropogenic sources and
sinks that are less complicated during the summer months at
WLG and thus provide relatively simplified fluxes. The poor
correlation during spring is likely due to the mixing of local
air masses with transported remote air masses since spring at
the sampling site has the greatest range of surface winds and
long-range transport among the seasons. Winter (R2 =
0.8498) has relatively uniform surface winds and long-range
air mass transport patterns. These winds come mainly from
the western (desert) direction. Autumn (R2 = 0.8133) has
less uniform winds than winter [Zhou et al., 2003, 2004].
Moreover, since the change in CO2 and d13C is not as great in
the other seasons relative to summer, any analytical error
will have a greater effect on the spring, winter and autumn
data sets.
[15] The slope Dd13C/DCO2 in the seasonal cycle varies
in range from 0.050 to 0.045% ppm1, the ds varies in
range from 26.354 to 24.636%, and the ddis varies in
range from 18.283 to 16.854% by Keeling Model-II
(for spring data sets with poor R2, exceptional ds and thus
ddis are obtained by both Keeling and Miller-Tans Models).

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Figure 1. Correlations between atmospheric CO2 mixing ratios and d13C detrended monthly means (n =
118) at WLG for the period May 1991 to December 2002. (top) Keeling Model results (d13C versus
inverse of CO2 mixing ratio, and Dd13C versus DCO2) with least squares fitting passed through each of
the data sets. (bottom) Miller-Tans Model results, i.e., d13C* CO2 versus CO2 mixing ratio with a least
squares fitting.
In general, the estimated ds as well as the ddis are more
negative in summer-half-year (dominant surface winds and
long-range transport from the eastern direction, with relatively more population and vegetation) than that in winterhalf-year, with the least negative values during autumn.
However, the estimated ds especially during spring is very
uncertain owing to the relatively low correlation coefficients
(R2 < 0.90) in the relationship of CO2 and d13C (for
instance, R2spring = 0.5636, R2autumn = 0.8133, R2winter =
0.8498). Furthermore, researchers [Miller and Tans, 2003;
Pataki et al., 2003; Randerson et al., 2002; Zondervan and
Meijer, 1996] have indicated that ds almost never represents
the isotopic signature of a single source but rather the fluxweighted average of more than one source/sink.
[16] According to literature [Ciais et al., 1995a, 1995b;
Denning et al., 1995; Francey et al., 1990, 1995; Mook et
al., 1983; Nakazawa et al., 1993, 1997a, 1997b; Trolier et
al., 1996], by assuming the respective values of 365.0 ppm,
8.0% and 25.0% for the present levels of the atmo-

spheric CO2 mixing ratio, the d13C in atmospheric CO2 and
the d13C in the biosphere, respectively, the resultant Dd13C/
DCO2 is 0.047 % ppm1 for the CO2 exchange between
the atmosphere and the biosphere. Complicating this relationship would be fossil fuel CO2 which typically shows
d13C 28.5% but with regard to a detrended season-toseason comparison its influence can be neglected. Other
deviations from the Keeling relationship can come from airsea CO2 exchange. CO2 release from and uptake to the
oceans account for 0.002% ppm1 and 0.005% ppm1,
respectively. The observed seasonality of the Dd13C/DCO2
of 0.050 to 0.045 % ppm1 at WLG is in good
agreement with the value calculated by assuming that the
exchange of CO2 between the atmosphere and the biosphere
is the dominant process. We thus suggest that seasonally
varying CO2 exchange between the atmosphere and terrestrial biosphere is primarily responsible for the observed
seasonal variations in atmospheric CO2 and d13C at WLG.
The variation of the CO2 source or sink signature between

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a
Keeling slope unit is % ppm1, rate of change in d13C with respect to the CO2 mixing ratio. Miller-Tans slope
is denoted by ds, %, d13C ‘‘signature’’ of the CO2 source or sink. Estimated mean d13C
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
discriminations are denoted by ddis, which equals ds – dbg, %. Statistical significance test is ‘‘Student T,’’ with T = ð N  2ÞR2 =ð1  R2 Þ, where R is correlation coefficient of the regressions between detrended
monthly means of atmospheric CO2 and d13C, N is number of the data pairs.

40.127
3.198
13.406
7.104
7.743
16.938
26.706
1.980
2.048
2.042
2.042
2.048
2.000
2.000
17.710 ± 0.958
15.955 ± 12.980
15.921 ± 1.708
14.925 ± 4.866
15.818 ± 4.062
16.824 ± 2.147
17.202 ± 1.066
18.174 ± 1.959
25.896 ± 34.407
16.854 ± 3.757
18.130 ± 11.701
18.283 ± 9.392
17.996 ± 4.674
17.729 ± 2.228
0.035
0.035
0.055
0.041
0.045
0.036
0.047
±
±
±
±
±
±
±
7.985
8.191
7.782
7.921
8.071
8.097
7.884
26.159 ± 1.924
34.087 ± 34.372
24.636 ± 3.702
26.051 ± 11.660
26.354 ± 9.346
26.093 ± 4.638
25.613 ± 2.181
0.050 ± 0.004
0.051 ± 0.052
0.045 ± 0.007
0.045 ± 0.020
0.046 ± 0.016
0.048 ± 0.008
0.048 ± 0.004
Total
Spring
Summer
Autumn
Winter
W-H-yr
S-H-yr

118
28
30
32
28
56
62

25.695 ± 0.923
24.146 ± 12.949
23.703 ± 1.654
22.846 ± 4.825
23.889 ± 4.017
24.921 ± 2.111
25.086 ± 1.019

Miller-Tans
T
Keeling
T
Ta,
a = 0.05
Miller-Tans
ddis, %
Keeling
ddis, %
dbg, %
Miller-Tans
Slope ds, %
Keeling
Intercept ds, %
Keeling
Slope, % ppm1
N
CO2 d13C
Data Sets

Table 1. Keeling Model-II and Miller-Tans Model-II Results (±2s), With Keeling Slopes, Keeling y Intercepts and Miller-Tans Slopes and Estimated Mean d13C Discriminations by
Source or Sink Versus Background Atmospheric Values at WLG for the Period May 1991 to December 2002, as Well as a Statistical Significance Testa

58.211
5.795
20.270
11.432
12.129
25.406
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ZHOU ET AL.: WALIGUAN CO2 AND ISOTOPES

GB2001

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seasons as well as between the winter-half-year and summer-half-year are likely due to the existence of numerous
sources and sinks with different isotopic signatures, such as
C3 versus C4 plants. Different air mass transport during
these time periods, bringing in air from different regions,
may also contribute. To better understand and address a
specific source or sink and its exchanging processes in the
WLG region, more intensive observational and modeling
studies are needed.
3.1.3. Relationship Between Atmospheric CO2 Mixing
Ratio and D18O in the Seasonal Cycle
[17] As cited [Flanagan et al., 1997; Friedli et al., 1987;
Ishizawa et al., 2002; Keeling, 1961; Mook et al., 1983;
Nakazawa et al., 1997b; Trolier et al., 1996] and discussed
above, d18O in atmospheric CO2 is determined by exchange
with vegetation, leafwater, soils and the ocean. It is the
exchange of CO2 with the terrestrial biosphere (through
ecosystem photosynthesis and respiration) that drives much
of the seasonal variation in the d18O of atmospheric CO2,
especially over the continental NH. In general, CO2 that is
in equilibrium with leafwater generates a d18O signature that
varies between +2 to +6%, and CO2 that is in equilibrium
with soil water generates a d18O signature that varies
between 9 to 2%. CO2 exchange with the oceans
generates a minor d18O signature.
[18] The d18O monthly mean time series observed at
WLG varies seasonally but with relatively large scatter.
However, it is expected there might be a relationship
between d18O and CO2 that is similar to that of d13C and
CO2 found in a ‘‘Keeling-Plot’’. We constructed a Keeling
Model-I relationship (by substituting d18O for d13C in the
Keeling-Plot) between the detrended atmospheric CO2 mixing ratios and d18O monthly means (110 data sets) at WLG
for the entire period of May 1991 to December 2002.
Although a negative linear relationship is found, the CO2
mixing ratios and d18O are not strongly correlated, particularly when compared with the good correlation found
between the CO2 mixing ratios and d13C for the same
period, and thus we do no show the plots here. The
estimated ds for 18O is 8.853% (110 data sets, R2 =
0.19, P < 0.01), for summer-half-year it is 7.574% (55
data sets, R2 = 0.11, P < 0.02) and for winter-half-year it is
6.558% (55 data sets, R2 = 0.18, P < 0.01). The estimated
ds for 18O (7.662 ± 2.113)% at WLG is in agreement with
a mean source or sink where the 18O of CO 2 is in
equilibrium with soil water (varying from 9 to 2%)
[Ishizawa et al., 2002]. Thus, though subject to a large
degree of uncertainty due to relatively poor linear relationship (R2 < 0.2) compared to that of the d13C and CO2
relationship, the observed d18O seasonal cycle is most likely
a result of the exchange of atmospheric CO2 with soil water
throughout the year. It is clear that we have much to learn
about 18O values of atmospheric CO2, but it is also clear
that there are large signals and great potential in the data, as
demonstrated by the d18O data at WLG.
3.2. Annual Cycle and Interannual Variation at WLG
and the 11 Selected NH Sites
[19] We now compare the WLG results with those from
other middle- to high-latitude NH sites in the NOAA ESRL
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Table 2. Station Information and 5-Year-Average (±2s) Mean Annual Cycle Amplitudes of the Atmospheric CO2 Mixing Ratios and
d13C Derived From Detrended CO2 and d13C Monthly Mean Time Series Obtained From Each of the Selected Northern Hemisphere Sites,
1998 – 2002a
Site

Latitude, deg

ALT
BRW
MHD
UUM
KZD
KZM
NWR
TAP
WLG
IZO
MLO
KUM

82.45N
71.32N
53.33N
44.45N
44.45N
43.25N
40.05N
36.73N
36.29N
28.30N
19.53N
19.52N

Longitude, deg

Altitude, m asl

Site General Description

Annual
Amplitude CO2, ppm

210
11
25
914
412
2519
3475
20
3810
2360
3397
3

high NH tundra
high NH tundra
mid-high NH coastal
Eurasian inland
Eurasian inland
Eurasian inland/high site
America inland/high site
Asian coastal
Eurasian inland/high site
low NH coastal/high site
low NH island/high site
low NH island

14.90 ± 1.16
16.34 ± 0.79
15.03 ± 1.53
15.60 ± 2.24
15.85 ± 2.49
14.07 ± 1.88
7.99 ± 1.47
13.82 ± 3.14
10.69 ± 0.58
7.70 ± 1.24
6.15 ± 0.47
8.28 ± 0.60

62.52W
156.60W
9.90W
111.10E
75.57E
77.88E
105.58W
126.13E
100.90E
16.48W
155.58W
154.82W

Annual
Amplitude d13C, %
0.760
0.803
0.779
0.773
0.818
0.753
0.420
0.613
0.518
0.397
0.315
0.430

±
±
±
±
±
±
±
±
±
±
±
±

0.056
0.044
0.053
0.093
0.127
0.075
0.115
0.184
0.034
0.040
0.026
0.044

a

Except using 3-year average, 1999 – 2001, for IZO.

Air Sampling Network. Station information and 5-yearaverage (±2s) annual cycle amplitudes for CO2 and d13C
for the 12 sites (WLG included) are listed in Table 2.
Figure 2 shows the geographic location of the five Eurasian
sites. The CO2 and d13C annual cycle amplitudes for each
year for the 12 NH sites are plotted in Figure 3. Sites are
plotted by site code and are ranked by latitude (ALT is the
highest latitude and KUM is the lowest) to avoid data
overlap and to make the ranking clear. The mean CO2
amplitudes vary from 6.15 ppm at MLO to 16.34 ppm at
BRW. The mean d13C amplitudes vary from 0.315% at
MLO to 0.818% at KZD. In general, CO2 and d13C mean
annual amplitudes at the sites located at latitudes >40
N are
systematically larger (CO2 amplitude >14 ppm and d13C
amplitude >0.7%, respectively) than those at the lowlatitude sites, reflecting the importance of the seasonal cycle
of photosynthesis and respiration at higher latitudes. There
are two apparent exceptions. The Asian coastal site TAP has
high amplitudes for its latitude, presumably because it is
downwind from China. The North America high-altitude
site NWR has low amplitude for its latitude, presumably as
it is a high-altitude site and plant productivity upwind in the
western United States is low. Comparison of two sets of
sites (1) Eurasian inland sites KZD, UUM, and KZM and
(2) low-latitude island sites MLO and KUM showed that
CO2 and d13C mean annual amplitudes are negatively
correlated with altitude. This is because most of the fossil
fuel source and biospheric CO2 sources and sinks are near
the surface. The CO2 and d13C mean annual amplitudes
(10.69 ± 0.58) ppm and (0.518 ± 0.034)% at WLG are in
accord with its latitude and altitude. However, they are
lower than the amplitudes obtained from the other Eurasian
inland sites (including the high-altitude site KZM) located
between 35
N and 45
N suggesting less proximal biospheric
influence.
[20] The phases of the CO2 (maximum in April, minimum
in August) and d13C (maximum in August, minimum in
April) seasonal cycles at WLG are similar to the other sites
located between 82.45
N and 36.29
N. The phases
observed at middle- to high-latitude NH sites are about 1
month earlier than at the lower latitude sites IZO, MLO and
KUM. According to the literature [CMDL, 2004; Conway et

al., 1994; Denning et al., 1995; Mook et al., 1983; WMO,
2003a], the phase delay of the CO2 and d13C seasonal cycles
at low latitudes results from the transport of air from higher
latitudes, as well as the enhanced seasonality of CO2
sources and sinks at high latitudes relative to low latitudes.
[21] Among the 12 sites, those located between 35
N and
45
N have consistently larger interannual fluctuations; however, no obvious altitude effect or temporal trend was
detected. At WLG the CO2 annual amplitude varies from
9.84 ppm (in 2000) to 11.8 ppm (in 1999), and the d13C
amplitude varies from 0.479% (in 2000) to 0.562% (in
1998). The CO2 and d13C interannual amplitude fluctuations
were least at WLG and greatest at TAP among the sites in
between 35
N and 45
N. We attribute this difference to the
specific sources/sinks and transport at each site: WLG is far
removed from large fossil fuel sources and vegetation, while
TAP, downwind from China, is affected by large local and
regional fossil fuel sources and biospheric sources and sinks.
3.3. Annual Means and Growth Rates at WLG and
the 11 Selected NH Sites
[22] The CO2 and d13C annual means for each year for the
12 sites are shown in Figure 4. At WLG the CO2 annual

Figure 2. Geographic location of the selected Eurasian
sites WLG, KZD, KZM, UUM, and TAP of the NOAA
ESRL Flask Air Sampling Network.

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GB2001

ZHOU ET AL.: WALIGUAN CO2 AND ISOTOPES

GB2001

d13C, likely owing to strong anthropogenic and biogenic
sources from the Asian continent and Eurasian inland,
respectively. The CO2 and d13C mean annual values at
WLG are among the lowest CO2 and heaviest d13C, but
are comparable to the measurements obtained from the
high-altitude sites KZM and MLO.
[24] The CO2 mean growth rates at the 12 sites (displayed
in Table 3) range from 1.61 to 2.18 ppm yr1 and the d13C
range from 0.052 to 0.010% yr1 (except for IZO). The
CO2 mean growth rates are similar at all sites except for
KZD. The d13C mean growth rates however, show more
variability among sites, with the largest decreases at KZD
and NWR, and the smallest decreases at KUM, MLO, BRW
and ALT. The d13C mean decrease rate at WLG was close to
those at UUM and KZM as expected (within ±0.002% yr1),
also located in the Eurasian inland. The (Dd13C/DCO2)

Figure 3. Annual cycle amplitudes of atmospheric CO2
mixing ratio and d13C derived from detrended monthly data
for each year (1998– 2002) at each of the selected Northern
Hemisphere sites (WLG included) from the NOAA ESRL
flask air sampling program. Sites are plotted by site code
and are ranked by latitude (ALT is the highest latitude and
KUM is the lowest) to avoid data overlap and to make the
ranking clear.
means vary from 365.7 ppm in 1998 to 372.7 ppm in 2002.
The d13C at WLG varies from 8.115% in 2000 to
8.009% in 1998. The interannual variations in the CO2
annual data at the 12 sites are slightly different from each
other. The d13C annual data at the 12 sites however, showed
more complicated interannual fluctuations among the years
and sites. The differences among sites, especially among
Eurasian inland sites, reflect variations in carbon uptake and
release on the Eurasian Continent.
[ 23 ] In general, the sites located at high altitudes
(>2000 m asl) as well as at low latitudes (45
N) are lower in CO2 and heavier in d13C.
Among the sites, TAP (Asian costal) and KZD (Eurasian
inland) are relatively high in CO2 and more negative in

Figure 4. Annual mean atmospheric CO2 mixing ratios
and d13C derived from monthly data for each year (1998–
2002) at each of the selected Northern Hemisphere sites
(WLG included) from the NOAA ESRL flask air sampling
program. Sites are plotted by site code and are ranked by
latitude (ALT is the highest latitude and KUM is the lowest)
to avoid data overlap and to make the ranking clear.

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ZHOU ET AL.: WALIGUAN CO2 AND ISOTOPES

GB2001

Table 3. Five-Year-Average (±2s) Mean Growth Rates of the
Atmospheric CO2 Mixing Ratios and d13C as Well as Rate of
Secular Changes in d13C With Respect to the CO2 Mixing Ratios
(Dd13C/DCO2) at Each of the Selected Northern Hemisphere Sites,
1998 – 2002a
Sites
ALT
BRW
MHD
UUM
KZD
KZM
NWR
TAP
WLG
IZO
MLO
KUM

Growth Rate
CO2, ppm yr1
1.68
1.72
1.80
1.70
2.18
1.87
1.92
1.79
1.73
1.61
1.61
1.66

±
±
±
±
±
±
±
±
±
±
±
±

0.55
0.91
0.47
0.67
2.36
0.78
0.15
0.78
0.96
0.57
0.35
0.35

Growth rate
d13C, % yr1
0.014
0.013
0.026
0.020
0.052
0.022
0.030
0.018
0.021
0.004
0.010
0.010

±
±
±
±
±
±
±
±
±
±
±
±

0.018
0.039
0.041
0.033
0.118
0.020
0.017
0.058
0.066
0.018
0.020
0.016

Dd13C/DCO2,
% ppm1
0.008
0.008
0.014
0.012
0.024
0.012
0.016
0.010
0.012
0.002
0.006
0.006

±
±
±
±
±
±
±
±
±
±
±
±

0.033
0.043
0.087
0.049
0.050
0.026
0.113
0.074
0.069
0.032
0.057
0.046

a

Except using 3-year average, 1999 – 2001, for IZO.

ratio in the secular trends at the 11 sites (except for IZO)
vary from (0.006 ± 0.046) to (0.024 ± 0.050)% ppm1.
The ratio at WLG is in good agreement with UUM and
KZM, probably because these three sites typically sample
well mixed air masses over the Eurasian inland.
3.4. Seasonally Averaged Isotopic Signature of the
CO2 Source or Sink at WLG and the 11 Selected NH
Sites
[25] For the period 1998 to 2002, for all 12 NH sites, we
calculate the seasonal Dd13C/DCO2, ds, dbg and ddis as well
as the ‘‘Student T’’ test by both Keeling Model and MillerTans Model in the same manner as performed on the 1991 –
2002 WLG data. The results are given in Table 4. The
Keeling Model results illustrates that the relationship between seasonal variation of CO2 and d13C at the 12 sites can
be approximated using a linear function. Among the sites,
TAP, NWR, WLG, KUM and IZO showed seasonal correlation coefficients lower than R2 = 0.95 between CO2 and
d13C than the other sites. Thus estimated Dd13C/DCO2 and
ds from these sites are subject to larger uncertainties
and should be carefully interpreted. On the other hand,
ALT, KZM, MHD and BRW show the highest degree of
correlation in seasonally averaged CO2 and d13C data
among the sites (R2 > 0.98). By Keeling Model-II, the
seasonal Dd13C/DCO2 ratios for the period of 1998 – 2002 at
the 12 sites ranged from 0.056 (KUM and NWR) to
0.049% ppm1 (TAP) with most sites having ratios in the
range of 0.053 to 0.050% ppm1. The ds ranged from
28.848 (KUM) to 26.503% (BRW) with most sites
having values in the range of 28 to 27%. The ddis
ranged from 20.765 (KUM) to 18.293% (BRW) with
most sites having values in the range of 20 to 19%.
[26] The estimated Dd13C/DCO2, ds and ddis vary from site
to site. This is because ds obtained at each site represents the
mean isotopic composition of all sources, biogenic and
anthropogenic, contributing to the CO2 seasonal variation
on a regional as well as on a continental scale over the entire
period. Overall, the seasonal Dd13C/DCO2 ratio of (0.050
± 0.003)% ppm1, the ds of (26.578 ± 1.674)% and the

GB2001

ddis of (18.505 ± 1.718)% by Keeling Model-II at WLG
are comparable to the values found at other 11 sites in this
study. The seasonal ds observed at WLG also compares well
with values observed and modeled at other background sites
[Francey et al., 1998; Levin et al., 1995; Randerson et al.,
2002].

4. Conclusions
[27] For the period 1991 to 2002, the relationship between
the seasonal change of CO2 and d13C at WLG is approximated well with a linear function. The Dd13C/DCO2 ratio in
the seasonal cycle is (0.050 ± 0.004)% ppm1, the ds is
(26.159 ± 1.924)%, and the ddis is (18.174 ± 1.959)%
by the Keeling Model-II, averaged over the entire period.
The CO2 and d13C showed higher correlation in the summer-half-year than in the winter-half-year. The strongest
correlation occurred in summer, and the least correlation in
spring. The Dd13C/DCO2 calculated by seasons varied from
0.050 to 0.045% ppm1 in good agreement with the
value expected from CO2 exchange between the atmosphere
and biosphere. We thus suggest that the seasonally dependent CO2 exchange between the atmosphere and the terrestrial biosphere is primarily responsible for the observed CO2
and d13C seasonal variations at WLG. The ds range from
26.354 to 24.636%, and the ddis vary from 18.283 to
16.854%, calculated by seasons. The ds and ddis are more
negative in the summer-half-year because the dominant
surface winds and long-range transport are from the eastern
direction where there are highly populated and vegetated
areas. Though subject to a large degree of uncertainty due to
the relatively poor linear relationship between atmospheric
CO2 and d18O, the observed d18O seasonal cycle at WLG is
most likely a result of the exchange of atmospheric CO2
with soil water throughout the year.
[28] A comparison of WLG to 11 NH sites in the NOAA
ESRL network for the period 1998 to 2002 indicates that the
CO2 and d13C mean annual amplitudes are negatively
correlated with altitude. This is because most of the anthropogenic and biospheric CO2 sources are located at lower
elevations. The temporal phases of CO2 and d13C at WLG
are similar to records from the other sites located at
36.29
N– 82.45
N. The CO2 and d13C mean annual amplitudes at WLG are much lower than records from the other
Eurasian inland sites located between 35
N and 45
N. The
primary reason for the comparably small seasonal amplitude
at WLG is due to its high altitude. WLG is in line with most
other sites in showing a decreasing trend of seasonality with
latitude while NWR and TAP are at odds likely owing to the
special conditions of those sites as described earlier. The
CO2 and d13C amplitude interannual fluctuations were
lowest at WLG and the highest at TAP among the sites
between 35
N and 45
N.
[29] CO2 and d13C mean annual values from the sites
located at high altitudes (>2000 m asl) and those located at
low-NH (45N) latitudes are lower in
CO2 and higher in d13C. The CO2 and d13C mean annual
values at WLG are among the lowest CO2 and the highest
d13C obtained from the high-altitude/low-latitude sites
KZM, MLO and KUM. The CO2 mean growth rate at

8 of 10

a
Keeling slope unit is % ppm1, rate of change in d13C with respect to the CO2 mixing ratio. Miller-Tans slope
is denoted by ds, %, d13C ‘‘signature’’ of the CO2 source or sink. Estimated mean d13C
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
discriminations are denoted by ddis, which equals ds  dbg, %. Statistical significance test is ‘‘Student T,’’ with T = ðN  2ÞR2 =ð1  R2 Þ, where R is correlation coefficient of the regressions between detrended
monthly means of atmospheric CO2 and d13C, N is number of the data pairs.

88.810
61.301
61.714
38.745
39.524
71.883
25.019
18.813
30.777
30.727
41.487
32.001
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
2.000
0.175
0.285
0.299
0.636
0.608
0.233
1.414
2.036
0.858
0.841
0.514
0.899
±
±
±
±
±
±
±
±
±
±
±
±
18.874
18.194
19.367
19.288
19.195
19.321
20.057
18.057
18.109
19.451
19.462
20.390
0.283
0.507
0.515
1.192
1.154
0.394
2.718
4.373
1.718
1.635
1.004
1.724
±
±
±
±
±
±
±
±
±
±
±
±
18.926
18.293
19.480
19.534
19.429
19.394
20.643
19.146
18.505
19.863
19.575
20.765
0.070
0.072
0.068
0.072
0.077
0.065
0.038
0.055
0.044
0.044
0.027
0.038
±
±
±
±
±
±
±
±
±
±
±
±
8.190
8.210
8.138
8.177
8.272
8.066
8.098
8.321
8.073
8.098
8.056
8.083
0.105
0.213
0.231
0.564
0.531
0.168
1.376
1.981
0.814
0.797
0.488
0.861
±
±
±
±
±
±
±
±
±
±
±
±
27.064
26.404
27.506
27.465
27.467
27.387
28.155
26.378
26.183
27.549
27.518
28.473
60
60
60
60
60
60
60
60
60
54
60
60
ALT
BRW
MHD
UUM
KZD
KZM
NWR
TAP
WLG
IZO
MLO
KUM

±
±
±
±
±
±
±
±
±
±
±
±
27.116
26.503
27.618
27.711
27.701
27.460
28.741
27.467
26.578
27.961
27.631
28.848
0.052 ± 0.0004
0.050 ± 0.001
0.053 ± 0.001
0.053 ± 0.002
0.053 ± 0.002
0.053 ± 0.0006
0.056 ± 0.005
0.049 ± 0.008
0.050 ± 0.003
0.053 ± 0.003
0.053 ± 0.002
0.056 ± 0.003

0.213
0.435
0.447
1.119
1.077
0.329
2.680
4.318
1.674
1.591
0.977
1.686

Miller-Tans
T
Keeling
T
Ta a = 0.05
Miller-Tans
ddis (%)
Keeling
ddis (%)
dbg (%)
Miller-Tans
Slope ds (%)
Keeling
Intercept ds (%)
Keeling
Slope (% ppm1)
N
Sites

Table 4. Keeling Model-II and Miller-Tans Model-II Results (±2s), With Keeling Slopes, Keeling y Intercepts and Miller-Tans Slopes, and Estimated Mean d13C Discriminations by
Source or Sink Versus Background Atmospheric Values at Each of the Selected Northern Hemisphere Sites From 1998 to 2002, as Well as a Statistical Significant Testa

126.700
87.609
85.895
54.723
56.411
100.590
35.125
28.062
44.305
43.538
58.980
44.954

ZHOU ET AL.: WALIGUAN CO2 AND ISOTOPES

GB2001

GB2001

WLG was comparable to the other sites. The d13C mean
decrease rate at WLG was close to the measurements at the
Eurasian inland sites UUM and KZM. The Dd13C/DCO2
changing ratio of the secular trends at WLG are in good
agreement with UUM and KZM, probably because they
typically sample well mixed air masses over the Eurasian
inland.
[30] The estimated seasonally Dd13C/DCO2, ds and ddis
vary among the 12 NH sites. The ds derived for each site
represents the mean isotopic composition of all sources
contributing to the CO2 seasonal variation on regional and
continental scales. The seasonally Dd13C/DCO2, ds and ddis
at WLG are comparable to the values at the 11 sites. The ds
at WLG also compares well with values observed and
modeled at other background sites. The estimated ds during
certain times at each specific site could possibly provide
useful information on CO2 fluxes. However, interpretation
of the ds is more complicated when fluxes of opposing
isotopic sign are involved.
[31] Very useful information on the fluxes of CO2 could
be provided by the estimated ds in the relevant time span at
each specific site. However, as we cited and discussed
above, more attention should be paid to the decomposition
and interpretation of the ds when fluxes of opposing isotopic
sign are involved. At large spatial scales, such as with the
remote flask sampling sites, variation in the isotopic composition of CO2 will always have contributions from both
photosynthesis and ecosystem respiration regardless of the
season, along with contributions from ocean exchanges
[Randerson et al., 2002; Tans et al., 1993]. Other
approaches may be used to help isolate multiple isotopic
sources, including the use of the additional isotopic constraint of d18O to separate biogenic fluxes [Yakir and Wang,
1996], the use of CO [Bakwin et al., 1998; Miller et al.,
2003] and the D14C of CO2 [Levin et al., 1995; Zondervan
and Meijer, 1996] to estimate the impact of fossil fuel
combustion, and radon (222Rn) [Schmidt et al., 1996] to
distinguish between air masses with an oceanic versus a
terrestrial origin.

[32] Acknowledgments. This work is supported by a Key Project
Sponsored by the Scientific Research Foundation for the Returned
Overseas Chinese Scholars (State Personnel Ministry [2004]99), a key
project sponsored by the Climate Change Science Foundation (China
Meteorological Administration CCSF-2006-4), a Japan Society for Promotion of Science Post-doctoral Fellowship (PB01736), and a United
Nation’s GEF Fund (GLO/91/G32). We thank the staff of Waliguan
Station for their efforts in collecting the flask air samples. We appreciate
NOAA ESRL and CU-INSTAAR for cooperation on the Waliguan NDIR
and flask air sampling programs. Helpful comments and suggestions of
two anonymous reviewers are gratefully acknowledged. The authors
would like to especially thank one of the reviewers: the annotated
manuscript has contributed to a significant improvement in the resubmission and the further revised version of this paper.

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