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Asia-Pac. J. Atmos. Sci., 50(3), 327-344, 2014 pISSN 1976-7633 / eISSN 1976-7951
DOI:10.1007/s13143-014-0020-y
The Effect of Optimization and the Nesting Domain on Carbon Flux Analyses in
Asia Using a Carbon Tracking System Based on the Ensemble Kalman Filter
Jinwoong Kim1, Hyun Mee Kim
1, and Chun-Ho Cho
2
1Atmospheric Predictability and Data Assimilation Laboratory, Department of Atmospheric Sciences, Yonsei University, Seoul, Korea2National Institute of Meteorological Research, Jeju, Korea
(Manuscript received 18 July 2013; accepted 29 November 2013)© The Korean Meteorological Society and Springer 2014
Abstract: To estimate the surface carbon flux in Asia and investigate
the effect of the nesting domain on carbon flux analyses in Asia, two
experiments with different nesting domains were conducted using
the CarbonTracker developed by the National Oceanic and Atmos-
pheric Administration. CarbonTracker is an inverse modeling system
that uses an ensemble Kalman filter (EnKF) to estimate surface
carbon fluxes from surface CO2 observations. One experiment was
conducted with a nesting domain centered in Asia and the other with
a nesting domain centered in North America. Both experiments
analyzed the surface carbon fluxes in Asia from 2001 to 2006. The
results showed that prior surface carbon fluxes were underestimated in
Asia compared with the optimized fluxes. The optimized biosphere
fluxes of the two experiments exhibited roughly similar spatial
patterns but different magnitudes. Weekly cumulative optimized
fluxes showed more diverse patterns than the prior fluxes, indicating
that more detailed flux analyses were conducted during the optimiza-
tion. The nesting domain in Asia produced a detailed estimate of the
surface carbon fluxes in Asia and exhibited better agreement with the
CO2 observations. Finally, the simulated background atmospheric
CO2 concentrations in the experiment with the nesting domain in
Asia were more consistent with the observed CO2 concentrations than
those in the experiment with the nesting domain in North America.
The results of this study suggest that surface carbon fluxes in Asia
can be estimated more accurately using an EnKF when the nesting
domain is centered in Asian regions.
Key words: Carbon flux, carbon tracking method, ensemble Kalman
filter
1. Introduction
Anthropogenic climate changes are primarily induced by
CO2 emissions. The global CO
2 emissions from fossil fuels
have been steadily increasing (Friedlingstein et al., 2010), and
the amount of CO2 accumulated in the atmosphere is at its
highest level in 800,000 years (Lüthi et al., 2008). However,
the land and ocean take up approximately half of the CO2
emissions; the other half remains in the atmosphere. Thus, the
changes in the land and ocean CO2 sinks are still uncertain (Le
Quéré et al., 2009). In Asia, the largest continent in the Northern
Hemisphere, there are considerable anthropogenic CO2 emis-
sions due to the continent’s high population density (Houghton
and Hackler, 2003) and the many industrial areas in developed
and developing countries, including China, which is the largest
CO2 emitter in the world (Gregg et al., 2008). Asia also
contains regions of large CO2 uptake, such as boreal forests in
Asian Russia (Schulze et al., 1999) and temperate forests in
China, Japan, and Korea, which play an important role in the
system of surface carbon sinks (Pan et al., 2011). Therefore,
because surface carbon sources and sinks in Asia affect the
global carbon cycle, it is important to accurately estimate the
surface carbon flux in Asia.
Several inversion studies have endeavored to estimate the
global surface carbon flux using atmospheric CO2 measure-
ments with atmospheric transport models and state-of-the-art
data assimilation methods, such as four-dimensional variational
data assimilation (4DVAR) (Baker et al., 2006a; Chevallier et
al., 2009a, b; Engelen et al., 2009), the ensemble Kalman filter
(EnKF) (Peters et al., 2005, 2007, 2010; Feng et al., 2009;
Kang et al., 2011, 2012), and the ensemble-based 4D data
assimilation system (Miyazaki et al., 2011).
Although the aforementioned studies used state-of-the-art
data assimilation methods, most were observing system
simulation experiments (OSSEs) (Baker et al., 2006a; Chevallier
et al., 2009b; Feng et al., 2009; Kang et al., 2011, 2012;
Miyazaki et al., 2011). OSSEs use simulated observations to
estimate the performance of inversion systems or to verify the
effect of a new observational system on the inversion systems.
Large computational costs have forced some studies that use
real observations to estimate the surface carbon flux to use
transport models with low spatial and temporal resolutions or
short simulation periods (Chevallier et al., 2009a; Engelen et
al., 2009). Although some inversion studies use relatively high-
horizontal-resolution transport models (Baker et al., 2006b),
these studies use the Bayesian synthesis method (Enting, 2002),
which is deficient in calculating huge matrices in current
computing systems. Due to the time-independent analysis of
the Bayesian method, the size of the matrix increases with the
length of the estimation period. Therefore, in these studies, the
surface carbon flux is usually analyzed with monthly or longer
temporal resolution and a small number of regional classifi-
cation types.
Corresponding Author: Hyun Mee Kim, Department of Atmos-pheric Sciences, Yonsei University, 50 Yonsei-ro, Seodaemun-gu,Seoul 120-749, Korea. E-mail: khm@yonsei.ac.kr
328 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
Peters et al. (2005) developed CarbonTracker, which uses
EnKF (Evensen, 1994; Whitaker and Hamill, 2002) to estimate
surface carbon fluxes from surface CO2
measurements. In
contrast to other carbon tracking systems, this system uses an
atmospheric transport model with nesting domains posed on
specific regions, such as North America (Peters et al., 2007) or
Europe (Peters et al., 2010), thereby providing a detailed
analysis of the carbon fluxes in these regions. Compared with
Peters et al. (2007), CarbonTracker Europe adopted more
specific ecosystems to accommodate the heterogeneity in the
complex land-use system and added new observations (Peters
et al., 2010). Because of the small number of observations and
large uncertainties in the prior surface carbon flux inventory in
Asia, CarbonTracker must be applied to examine the surface
carbon flux on this continent. Kim et al. (2012) demonstrated
that CarbonTracker produces realistic results for the Asian
region, displaying an appropriate agreement between the
optimized fluxes and observations. Although Kim et al. (2012)
demonstrated the potential of CarbonTracker for the Asian
region, they did not investigate the effect of optimization on
the surface carbon flux in detail. In addition, CarbonTracker
must be optimized for the Asian region to estimate the surface
carbon flux accurately. As a first step in optimizing Carbon-
Tracker for Asia, a nesting domain is applied to the Asian
region, and the effects of the nesting domain on the carbon
flux analyses are investigated in the present study.
Patra et al. (2008) demonstrated that model simulations at
higher horizontal resolutions correspond more closely with the
observed variations in CO2. In this sense, establishing the
nesting domain over Asia may increase the performance of
simulated CO2 concentrations over Asia, which would lead to
a more accurate analysis of the surface carbon flux in Asia. In
the present study, two experiments were performed with two
different nesting domains using CarbonTracker 2008 (CT2008)
for the period from 2000 to 2006. Section 2 presents the
inversion methodologies used to estimate surface carbon flux,
a description of the observations, and the experimental frame-
work. Section 3 presents the results of the experiments, and
Section 4 provides a summary and discussion.
2. Methodology
a. Ensemble Kalman filter
The data assimilation method used in CarbonTracker is the
ensemble square root filter (EnSRF) proposed by Whitaker
and Hamill (2002), which is a serial, square-root version of the
deterministic algorithms of the EnKF (Evensen, 1994; Tippett
et al., 2003). In the EnSRF, the mean and perturbations of the
state vector are updated independently to prevent the under-
estimation of the analysis error covariance matrix:
, (1)
, (2)
where , and , are the analysis and background of
the mean state vector and of the perturbation state vector,
respectively. In CarbonTracker, the state vector corresponds to
the scaling factor explained in Section 2b. Because the
independent update of the mean and perturbations of the state
vector does not guarantee the proper estimation of the analysis
error covariance matrix, many inflation techniques have been
developed to improve the performance of EnKF data assimi-
lation (e.g., Wang and Bishop, 2003; Bowler et al., 2008;
Whitaker et al., 2008; Anderson, 2009; Li et al., 2009;
Miyoshi, 2011; Kang et al., 2012). Although the EnSRF in
CarbonTracker does not use the inflation method, Kim et al.
(2012) demonstrated that the ensemble spread measured by
rank histograms is maintained properly in the same frame-
work. yo is the observation and H is the observation operator
that is the TM5 transport model (Krol et al., 2005) to calculate
the atmospheric CO2 concentration using flux information. K
is the Kalman gain matrix, defined as
, (3)
where is the background error covariance and R is the
observation error covariance. The observation error is pre-
defined subjectively to reflect the expectation of how well the
transport model can calculate the atmospheric CO2 concen-
tration at each observation site. is the reduced Kalman gain
matrix, defined as
, (4)
where α is a scalar quantity multiplied by the Kalman gain,
calculated as
. (5)
and in Eqs. (3) and (4) can be calculated as
, (6)
, (7)
where N is the number of ensembles.After an observation is used to update the state vector, the
new model CO2 concentrations [ and ] are calcu-
lated using the transport model with the analyzed state vector
to serve as the background information to calculate the
Kalman gain for subsequent observations (Peters et al., 2005).
However, to reduce unnecessary computing time, the following
equations [Eqs. (8) and (9)] are used to update the mean and
perturbation of the model CO2 concentrations, respectively,
rather than calculating new model CO2 concentrations using
xta xt
b K yo
H xtb( )–( )+=
xi′a xi
′b k̃H xi′b( )–=
xta xt
b xi′a xi
′b
K PtbH
T( ) HPtbH
TR+( )
1–
=
Ptb
k̃
k̃ K α⋅=
α 1R
HPtbH
TR+
--------------------------+⎝ ⎠⎛ ⎞
1–
=
PtbH
THPt
bHT
PHT 1
N 1–----------- x1
′ x2′ … xN
′, , ,( ) H x1′( ) H x2
′( ) … H xN′( ), , ,( )T⋅≈
HPHT
≈1
N 1–----------- H x1
′( ) H x2
′( ) … H xN
′( ), , ,( ) H x1
′( ) H x2
′( ) … H xN
′( ), , ,( )T
⋅
H xtb( ) H xi
′b( )
31 May 2014 Jinwoong Kim et al. 329
the transport model:
, (8)
, (9)
where m is a subscript for each observation and Hm is a
pseudomatrix used to update the Kalman gain and reduced
Kalman gain. HmK is calculated using Eqs. (3) and (7). Then,
is calculated from HmK by multiplying by α.
The state vector includes information from not only the
current analysis time but also the previous analysis time due to
the relationship over prolonged periods of time between the
surface carbon flux and atmospheric CO2 concentration. In the
current system, the state vector includes five weeks of infor-
mation, as in Peters et al. (2007, 2010). The state vector in
CarbonTracker does not correspond to the surface carbon flux
on the model grid. Instead, the scaling factor in Section 2b is
adopted to represent the characteristics of the regional surface.
To eliminate spurious noise in the covariance matrix in this
framework, covariance localization is applied in the data
assimilation process. First, a statistical significance test is
performed between the ensemble of the scaling factor and that
of the model CO2 concentration. Then, elements of the Kalman
gain that have a value under the predefined statistical criteria
are set to zero. The exception in the covariance localization
occurs when observations at marine boundary layer (MBL)
sites are used. These observations are taken as having large
footprints and a strong capacity to integrate flux signals (Peters
et al., 2007). Therefore, localization is only applied for obser-
vations in non-MBL sites.
b. Carbon tracking algorithm
To assimilate observed CO2 concentrations, corresponding
model CO2 concentrations must be calculated using a transport
model with a prior surface carbon flux as input. The prior and
optimized flux at 1o × 1o resolution is calculated as
F(x,y,t) = λr·Fbio(x,y,t) + λr·Focn(x,y,t) + Fff(x,y,t) + Ffire(x,y,t), (10)
where Fbio and Focn are prior biosphere and ocean fluxes with
3-h intervals, respectively, and Fff and Ffire are fossil-fuel and
fire-emission fluxes with monthly time intervals, respectively.
The biosphere flux, or net ecosystem exchange (NEE), is
derived from monthly mean net primary production (NPP) and
ecosystem respiration from the Carnegie Ames Stanford
Approach Global Fire Emissions Database version 2 (CASA
GFED2) biosphere model calculation (van der Werf et al.,
2006). The ocean flux is derived from Jacobson et al. (2007).
The fossil-fuel flux is derived from emissions from the Carbon
Dioxide Information and Analysis Center (CDIAC) and Emis-
H xtb( )m H xt
b( )m HmK yo
H xtb( )–( )+=
H xi′b( )m H xi
′b( )m Hmk̃ yo
H xi′b( )–( )–=
Hmk̃
Fig. 1. Ecoregions of Eurasian Boreal (EB; light colors) and Eurasian Temperate (ET; dark colors) regions.
330 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
sion Database for Global Atmospheric Research (EDGAR)
inventories, and the fire flux is derived from CASA GFED2
(van der Werf et al., 2006). The λr is a prior or posterior
scaling factor that is multiplied to the biosphere and ocean
fluxes to be optimized. Each λr corresponds to an ecoregion
over a one-week time interval. The scaling factor acts as a state
vector in the analysis step, as explained in Section 2a. A prior
(posterior) flux is calculated by multiplying a prior (posterior)
scaling factor as indicated in Eq. (10). Therefore, only bio-
sphere and ocean fluxes are optimized in the analysis step; the
fossil fuel and fire fluxes are not optimized.
Ecoregions are defined as the combination of TransCom
regions (Gurney et al., 2002) and ecosystem types from Olson
et al. (1985). By adding 30 ocean inversion regions, a total of
156 ecoregions are used once inappropriate combinations of
TransCom regions and Olson types are excluded. Figure 1
illustrates the ecoregions in the Eurasian Boreal (EB) and
Eurasian Temperate (ET) regions.
TM5 (Krol et al., 2005) is an offline global transport model
with a two-way nested domain. This model is used as an
observation operator in the analysis step. TM5 calculates the
model CO2 concentrations corresponding to the temporal and
spatial distributions of the observed CO2 concentrations. Euro-
pean Centre for Medium Weather Forecast (ECMWF) forecast
data are used as meteorological variables to transport CO2 in
TM5.
After the posterior mean scaling factor is calculated in the
analysis step, the mean scaling factor is predicted using
, (11)
where is a prior mean scaling factor of the current analysis
cycle, and are posterior mean scaling factors of
previous cycles, and λp is a predetermined prior value fixed at
1, which adjusts the magnitude of the optimized flux in Eq.
(10) to the magnitude of the original prior flux when there are
no observations (Peters et al., 2007). If there are no observa-
tions in several analysis steps, the mean scaling factor ap-
proaches λp, and prior flux information without any modifi-
cation of the magnitude of the biosphere and ocean fluxes is
used in the TM5 model. In contrast to the mean scaling factor,
the perturbation scaling factor for the current analysis step is
not predicted, but rather, is generated with a predefined
covariance matrix with Gaussian random noise.
c. Experimental framework
Two experiments with different nesting domains in the TM5
model are conducted using CT2008. The TM5 is run globally
with a 6o × 4o horizontal resolution with two nested inner
domains of 3o × 2o and 1o × 1o horizontal resolutions and with
25 hybrid sigma-pressure levels. Figure 2 presents the nesting
domain of the two experiments. One experiment, the ASI
experiment, has a nesting domain centered in Asia (Fig. 2a).
Domain 2 in the ASI experiment is sufficiently large to include
Asia, and domain 3 is located in Northeast Asia. The second
experiment, the noASI experiment, uses the same nested
domain as Peters et al. (2007), which is centered in North
America (Fig. 2b). Both experiments run globally at 6o × 4o
resolutions from January 2000 to December 2006. Other
model configurations of the two experiments are the same as
in Peters et al. (2007): 150 ensemble members are used (Peters
et al., 2007, 2010), which is adequate to estimate the surface
carbon fluxes over North America and Europe. The optimized
surface carbon flux is analyzed with a 1o × 1o horizontal re-
solution using the posterior scaling factors for any TM5 model
domains with three different resolutions (i.e., 6o × 4o, 3o × 2o,
and 1o × 1o).
d. Observations
Figure 3 illustrates the observation network in the globe and
in Asia. The observations are from the National Oceanic and
Atmospheric Administration (NOAA), Commonwealth Scien-
tific and Industrial Research Organization (CSIRO), Environ-
ment Canada (EC), National Center for Atmospheric Research
(NCAR), and Lawrence Berkeley National Laboratory (LBNL)
(Masarie et al., 2011). The observation number assimilated at
each cycle is approximately 80-140 (Kim et al., 2012), which
is smaller than the number of scaling factors (i.e., 780). The
λtb
λt 2–a λt 1–
a λp+ +( )
3-------------------------------------=
λtb
λt 2–a λt 1–
a
Fig. 2. Nesting domain centered in (a) Asia and (b) North America.The outer box (solid line) is resolved with 3o
× 2o resolution, and the
inner box (dotted line) is resolved with 1o× 1o resolution.
31 May 2014 Jinwoong Kim et al. 331
mid-latitudes and the area near the southern boundary of the
nesting domain in Asia contain a relatively small number of
observation sites compared with the high density of sites in
North America and Europe (Fig. 3a). In addition, the obser-
vation sites in Asia are generally located in mid- or high-
elevation areas, except TAP_01D0 and GMI_01D0, which are
located near sea level. Only surface observations are used in
the assimilation, and aircraft data are used for verification. The
observational data contain discrete or continuous measurement
information. The daily averaged discrete data or the continuous
data averaged between 1200 - 1600 LST, 0000 - 0400 LST, or
1400 - 1800 LST, depending on the observation sites, are used
in the assimilation because the insufficient ability of the
transport model to calculate the vertical mixing of tracers
causes a poor representation of CO2 concentrations (Peters et
al., 2010; NOAA ESRL, 2013).
The observation error (model-data mismatch) is predefined
depending on the characteristics of each observation site. The
predefined observation error is determined by matching
innovation χ2 close to 1 as described in Peters et al. (2007).
The formula to calculate χ is as,
. (12)
The observation error is small (e.g., 0.75 ppm) at observation
sites where the background atmospheric CO2 concentrations
can be observed but large (e.g., 7.5 ppm) at observation sites
located near polluted areas with high anthropogenic CO2
emissions. Because CarbonTracker does not update the emis-
sions from fossil fuel and fire in the analysis process, the
influence of anthropogenic emissions should be diluted during
the optimization. Using large observation errors for the obser-
vation sites near a polluted area is a way to address this
problem because it is difficult to locate observation sites far
from polluted areas in highly populated Asian regions. For
example, the observation error in the TAP_01D0 is currently
7.5 ppm in the CarbonTracker to avoid any detrimental effect
from the fossil fuel emission. However, Turnbull et al. (2011)
demonstrated that biospheric CO2 contributes substantially to
total CO2 variability at the TAP_01D0 by investigating trace
gases including CO2, CO, and fossil fuel CO
2 from flask
samples from Tae-Ahn Peninsula. In addition, the six-year
average of the background uncertainty at the TAP_01D0 is 3.3
ppm, whereas the observation error is 7.5 ppm. As a result, the
innovation χ2 statistics is much less than 1 (i.e., 0.3) in the ASI
experiment, which implies that the observations at TAP_01D0
are not assimilated well because of large observation error
setting in CarbonTracker. Because the CO2 observations in
TAP_01D0 are originated from mostly biospheric sources, it is
necessary to modify the observation error setting for TAP_
01D0 by using the innovation χ2 statistics. Updating the flux
associated with fossil fuel emissions in CarbonTracker may be
another way to address CO2
observations in highly polluted
areas even though the fossil fuel and fire inventories used for a
priori information are quite accurate compared with the bio
and ocean fluxes.
If the difference between the observations and model CO2
calculated using a prior flux is greater than three times the sum
of the model spread and the predefined observation error, those
observations are not assimilated unless they are measured at
MBL sites.
3. Results
a. Characteristics of carbon flux
(1) Average characteristics for six years
In this section, the characteristics of prior and optimized
fluxes from the two experiments are examined. The optimized
flux in 2000 is excluded from this analysis because this period
is considered a spin-up year, similar to Peters et al. (2010).
Only the biosphere flux optimized in the assimilation process is
presented here because the ocean flux has large uncertainties.
The annual cumulative optimized biosphere fluxes and aver-
χy
0H xt
b( )–
HPtbH
TR+
------------------------------=
Fig. 3. Observation network over (a) the globe and (b) Asia. Thedotted line in (b) represents the boundary of the 1o
× 1o resolution
domain. Blue dots represent observation sites used in the assimilation,and green dots indicate aircraft observation sites. The names of theobservation sites appear in red. Information about observation sites in(b) is presented below (b).
Site code Lat Lon Height Site code Lat Lon Height
BKT_01D0 100.32o
E 0.20o
S 864 m GMI_01D0 144.78o
E 13.43o
N 2 m
MKN_01D0 37.30o
E 0.05o
S 3897 m OBN_01D0 36.60o
E 55.11o
N 183 m
WIS_01D0 31.13o
E 34.88o
N 400 m KZD_01D0 75.57o
E 44.45o
N 412 m
KZM_01D0 77.88o
E 43.25o
N 2519 m TAP_01D0 126.13o
E 35.73o
N 20 m
UUM_01D0 111.10o
E 44.45o
N 914 m WLG_01D0 100.90o
E 36.29o
N 3810 m
ULB_01D2 106.20o
E 47.40o
N varies
332 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
aged optimized biosphere fluxes for six years (2001-2006) in
each ecoregion are shown in Table 1. Some of the 19 Olson
types are not present in EB and ET in the TransCom regions
(e.g., the Tropical Forest, Scrub/Woods, Deserts, Shrub/Tree/
Succulents, Conifer Snowy/Coastal, Mangrove, and Ice and
Polar Desert types are missing from EB, and the Northern
Taiga, Conifer Snowy/Coastal, Wooded Tundra, Mangrove,
and Ice and Polar Desert types are missing from ET). The
biosphere fluxes that occupy only small areas or represent
relatively small sources and sinks in other ecoregions (e.g.,
Crops and Water in EB and Shrub/Woods, Tropical Forest,
Wetland, Shrub/Tree/Succulents, and Water in ET) are added
together and are shown under "Others" in Table 1. Conse-
quently, a total of 18 ecoregions are presented in Table 1.
Negative values denote atmospheric carbon fluxes to the
surface (i.e., carbon sinks or carbon uptake), whereas positive
values indicate surface carbon flux released to the atmosphere
(i.e., carbon sources). More than half of the total carbon uptake
in the EB and ET regions occurred in the Conifer Forest type
in EB. The Grass/Shrub type in the ET regions has the second
largest CO2 uptake in EB and ET. These two types occupy
approximately one third of the total area in EB and ET. The
total average optimized biosphere flux is approximately −1.2
Pg C yr−1 in the ASI experiment and −1.3 Pg C yr−1 in the
noASI experiment. The noASI experiment exhibits a greater
carbon uptake than the ASI experiment.
Figure 4 presents the spatial distribution of the averaged
prior and optimized biosphere fluxes of the two experiments
and the difference between the ASI and noASI experiments.
The optimized biosphere flux uptakes of the ASI (Fig. 4d) and
noASI (Fig. 4c) are greater than the prior flux uptake, which
implies that the prior flux uptake calculated by the CASA
GFED2 model is underestimated in Asia, as in North America
(Peters et al., 2007) and Eastern Europe (Peters et al., 2010).
Although the patterns of the two optimized fluxes are similar,
the optimized flux magnitudes are different due to the different
nesting domains of the transport model (Fig. 4b). The differ-
ences between the two optimized biosphere fluxes are large in
the Mixed Forest in EB (Siberia) and the Crops in ET (North
China plain and India), indicating that the carbon sink in the
ASI is smaller than that in the noASI in those regions. In
Crops in India, the optimized biosphere flux of the ASI is a
Table 1. Annual and averaged optimized biosphere fluxes (Tg C yr−1
) from 2001 to 2006 for each ecoregion for the ASI and noASI experiments."Others" denotes the total biosphere fluxes in ecoregions with small areas or small flux magnitudes.
Transcomregion
Olson typeArea Average 2001 2002 2003 2004 2005 2006
(%) ASI noASI ASI noASI ASI noASI ASI noASI ASI noASI ASI noASI ASI noASI
EurasianBoreal
Conifer Forest 10.9 −674.9 −692.5 −611.0 −555.5 −982.7 −961.1 −410.7 −519.5 −811.4 −825.2 −698.0 −654.9 −535.7 −638.8
Broadleaf Forest 0.6 −9.8 −9.1 −5.9 −13.1 6.0 11.3 −2.0 −2.7 −15.4 −22.1 −12.1 −12.8 −29.6 −15.4
Mixed Forest 2.7 −18.1 −34.4 −56.1 −72.4 −28.9 −61.6 17.7 6.8 −20.5 −57.8 −31.2 −34.3 10.2 12.6
Grass/Shrub 2.2 −30.6 −34.8 −43.7 −48.2 −81.9 −89.6 12.3 1.6 7.1 4.7 −44.4 −37.5 −33.4 −39.8
Semitundra 7.6 −95.2 −89.4 −114.4 −120.0 −85.1 −152.3 −173.8 −97.4 −48.1 −46.1 −44.6 −53.5 −105.1 −67.1
Fields/Woods/Savanna
0.8 −7.0 −7.5 −18.8 −7.2 −20.8 −22.8 −5.1 1.7 9.2 4.9 −9.5 −15.3 2.8 −6.6
Northern Taiga 5.6 −56.6 −58.4 −88.0 −102.3 64.9 76.0 −100.0 −106.0 13.8 −48.9 −165.4 −167.9 −65.0 −1.5
Forest/Field 0.7 −7.6 −6.6 −20.9 −14.8 9.1 8.5 −0.6 −2.1 −13.8 −10.2 −0.5 −3.9 −19.0 −17.0
Wetland 0.7 −0.7 2.6 11.4 12.9 −2.0 −2.4 −9.6 −3.2 −11.4 2.1 −0.3 0.1 7.9 6.3
Wooded tundra 1.2 −2.8 −7.8 −0.4 −6.5 3.6 −4.8 −16.0 −15.4 −1.4 −0.8 −0.4 −5.5 −2.4 −13.6
Conifer Forest 0.9 −7.8 −9.5 −11.4 −5.0 −12.0 −13.4 0.3 0.2 −3.4 −9.9 −2.1 −9.9 −18.3 −18.8
Others 1.7 −2.1 −1.6 8.3 8.1 −4.4 −6.6 −5.5 0.3 −15.9 −9.4 −0.1 −1.3 4.8 −0.6
EurasianTemperate
Broadleaf Forest 2.0 −12.6 −10.9 −10.9 −9.9 −17.3 −10.5 −10.0 −8.0 −16.5 −12.5 −5.3 −11.1 −15.6 −13.4
Mixed Forest 3.4 −39.3 −38.2 −56.2 −39.2 −54.1 −31.0 5.6 −9.0 −85.5 −54.4 −6.4 −7.6 −39.2 −88.1
Grass/Shrub 24.9 −187.6 −201.9 −190.4 −206.8 −317.1 −292.9 −29.3 −161.0 −210.0 −120.7 −134.9 −156.9 −244.0 −273.4
Semitundra 8.4 −17.0 −17.1 −14.7 −25.6 −53.4 −61.3 −21.0 2.8 −26.1 −11.3 2.2 6.8 10.8 −14.2
Fields/Woods/Savanna
2.2 −14.8 −12.8 −12.7 −13.3 −20.5 −13.8 −8.7 −7.2 −29.0 −18.3 0.8 −3.7 −19.0 −20.7
Forest/Field 1.6 −11.7 −13.0 −10.9 −9.1 −7.1 −12.1 −10.1 −12.7 −23.7 −26.2 −2.1 −9.2 −16.1 −8.5
Crops 7.9 −33.8 −48.7 −84.0 −75.1 −55.4 −56.1 8.0 14.3 −50.0 −114.7 13.6 4.0 −34.8 −64.3
Others 15.7 −16.9 −17.9 −18.1 −8.4 −28.3 −28.2 −6.4 −7.9 −20.2 −28.7 −9.2 −12.8 −19.1 −21.6
Total 100.0 −1238.6 −1302.8 −1348.8 −1319.2 −1673.2 −1708.9 −755.5 −921.4 −1357.5 −1397.8 −1147.2 −1177.3 −1149.4 −1292.1
31 May 2014 Jinwoong Kim et al. 333
slight sink, whereas that in the noASI is a slight source, which
makes the difference between the two optimized biosphere
fluxes negative.
To verify the effect of moving the nesting domain from Asia
to North America, the spatial distribution of the averaged prior
and optimized biosphere fluxes of the two experiments and the
difference between the ASI and noASI experiments in the
North American domain were additionally investigated (not
shown). The differences between the ASI and noASI experi-
ments in Asia and North America have opposite signs, but the
difference in North America is close to twice that in Asia. If
the observation numbers are considered, the difference per
observation becomes much smaller in North America than in
Asia, which implies that the effect per observation using the
nesting domain is larger in Asia and that the total effect using
the nesting domain may lead a larger effect if more obser-
vations are assimilated in the Asian region.
(2) Annual characteristics
The annual optimized biosphere fluxes exhibited large
interannual variations (Table 1). The greatest carbon flux
uptake occurred in 2002, and the smallest uptake occurred in
2003. The magnitudes of the carbon flux uptakes in 2001 and
2004 were greater than the six-year average, whereas those in
2005 and 2006 were smaller than the six-year average. The
Conifer Forest in EB and Grass/Shrub in ET have major
effects on the variability of the surface carbon flux uptake. The
2002 maximum carbon uptake was caused by large uptakes in
these ecoregions, whereas the 2003 minimum carbon uptake
was caused by small uptakes in these ecoregions. The inter-
annual variation patterns of the optimized biosphere fluxes are
similar in the ASI and noASI, but the total carbon uptakes in
the two experiments are different by approximately 64 Tg C
yr−1 on average for six years (Table 1). Especially in 2003, the
uptake in the ASI is 164 Tg C less than that in the noASI due
to small uptakes in the Conifer Forest in EB and Grass/Shrub
in ET.
Figure 5 presents the annual average biosphere fluxes of a
prior, optimized flux of the ASI and the flux difference
between the ASI and noASI. Overall, the spatial patterns of the
optimized biosphere fluxes are different from those of the prior
biosphere fluxes in the EB, whereas the spatial patterns of the
optimized biosphere fluxes in the EB and ET are similar in the
ASI. The prior biosphere fluxes are positive in western Siberia
in 2001 and 2002 (Figs. 5a, d), but after optimization, these
source ecoregions (i.e., Conifer Forest in EB) change to large
sink ecoregions during those years (Figs. 5b, e), and the
Northern Taiga changes to a large source ecoregion in 2002
Fig. 4. Average biosphere fluxes (g C m−2 yr−1) from 2001 to 2006 of (a) the prior flux, (b) the difference between the optimizedfluxes in the ASI and noASI experiments, (c) the optimized flux in the noASI experiment, and (d) the optimized flux in the ASIexperiment.
334 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
(Fig. 5e). The differences in the optimized fluxes between the
ASI and noASI are not significant in 2001 and 2002. Because
intense fires occurred in southern Siberia (from 110o 16’E to
131oE and from 49o 53’N to 55o 16’N) from March to August
Fig. 5. Annual average biosphere fluxes (g C m−2
yr−1
) from 2001 to 2006 of (a, d, q, j, m, and p) prior fluxes, (b, e, h, k, n, and q)optimized fluxes in the ASI experiment, and (c, f, i, l, o, and r) optimized flux differences between the ASI and noASI experiments.
31 May 2014 Jinwoong Kim et al. 335
(Huang et al., 2009) and extreme drought conditions occurred
in the northern mid-latitudes (Zeng et al., 2005; Knorr et al.,
2007), including Europe (Ciais et al., 2005; Zhao and Running,
2010), in 2003, the prior biosphere flux in eastern Siberia is
positive (Fig. 5g).
After optimization, the Conifer Forest in EB becomes a large
sink, whereas the Mixed Forest in EB remains a source (Fig.
5h). The magnitude of the flux differences over the EB in 2003
is greater than that in the other years, and the uptake in the ASI
is smaller than that in the noASI (Fig. 5i). The spatial distribu-
tions of the optimized biosphere fluxes of 2003 are similar to
those reported by Quegan et al. (2011), which used bottom-up
approaches over the central Siberian regions (52-78oN, 80-
110oE). In 2004 and 2005, large areas of the prior flux in EB
exhibit positive values (Figs. 5j, m), but these regions become
large sinks after optimization (Figs. 5k, n), as they typically are
in previous years. In 2004, the carbon uptake in the Crops in
China in the ASI is smaller than that in the noASI (Fig. 5l). In
2005, the differences between the ASI and noASI are small
(Fig. 5o). In 2006, the source regions of the prior flux are
located in Siberia (Fig. 5p) rather than in the Conifer Forest in
EB, as in previous years; these Siberian areas remain sources
after optimization (Fig. 5q). The flux uptake in the ASI is
smaller than that in the noASI (Fig. 5r).
In contrast to those in the EB, the spatial patterns of the
optimized biosphere fluxes in the ET are similar to those of the
prior biosphere fluxes for all six years. However, the magni-
tudes of the sources and sinks do change after optimization.
(3) Seasonal characteristics
Table 2 presents the seasonally averaged optimized biosphere
fluxes. Carbon uptake primarily occurs in boreal summer due
to the growth of vegetation in the Northern Hemisphere. The
total summer uptake in the ASI is greater than that in the
noASI due to a large peak in the uptake in summer in the ASI.
In the other seasons, the surface carbon release in the ASI is
greater than that in the noASI. These characteristics imply that
the magnitudes of the surface carbon uptake and release show
larger seasonal variations when using the higher resolution
transport model.
Figure 6 shows the weekly cumulative biosphere fluxes for
each year and their anomalies from the six-year average over
EB. The cumulative biosphere fluxes are calculated by
integrating the fluxes from the beginning of the year to the
corresponding week, as in Peters et al. (2010). The prior fluxes
(Fig. 6a) and optimized fluxes of the ASI (Fig. 6b) exhibit
seasonal variations in their sources and sinks. The seasonal
patterns of the biosphere fluxes in EB do not change after
Table 2. Same as Table 1, but seasonally averaged optimized biosphere fluxes (Tg C) from 2001 to 2006 for the ASI and noASI experiments.
Transcomtype
Olson typeArea Winter Spring Summer Autumn
(%) ASI noASI ASI noASI ASI noASI ASI noASI
EB
Conifer Forest 10.9 106.8 81.7 −41.2 −55.2 −998.0 −976.1 251.2 248.7
Broadleaf Forest 0.6 13.5 13.6 8.6 9.2 −45.9 −46.6 14.0 14.7
Mixed Forest 2.7 46.4 44.0 27.8 21.0 −146.2 −152.8 52.4 52.0
Grass/Shrub 2.2 29.2 27.3 17.0 15.4 −119.0 −119.1 40.3 40.1
Semitundra 7.6 39.4 37.1 27.6 29.4 −235.8 −233.3 71.1 75.6
Fields/Woods/Savanna
0.8 13.4 12.8 10.8 9.9 −46.1 −44.1 14.6 13.7
Northern Taiga 5.6 35.6 33.6 36.1 32.5 −222.1 −212.4 89.3 83.1
Forest/Field 0.7 12.7 12.7 7.6 6.9 −43.6 −41.1 15.4 14.8
Wetland 0.7 13.4 13.5 5.9 6.2 −38.0 −35.7 17.3 18.0
Wooded tundra 1.2 9.8 9.9 13.6 13.4 −38.8 −43.7 12.6 12.6
Conifer Forest 0.9 9.9 9.8 4.6 4.6 −25.6 −27.0 3.6 3.6
Others 1.7 21.4 21.2 10.1 10.2 −61.1 −61.0 26.6 27.1
ET
Broadleaf Forest 2.0 13.1 12.8 4.9 4.7 −28.1 −26.1 −1.0 −0.6
Mixed Forest 3.4 48.0 46.6 19.7 18.0 −120.9 −117.0 15.6 15.5
Grass/Shrub 24.9 98.7 45.2 −27.0 −21.1 −307.7 −267.6 53.9 44.5
Semitundra 8.4 40.7 42.5 10.4 13.6 −85.6 −91.2 18.9 19.9
Fields/Woods/Savanna
2.2 13.9 14.1 4.9 5.1 −28.5 −26.9 −3.9 −4.1
Forest/Field 1.6 12.7 12.2 −0.2 −0.7 −22.6 −22.9 −1.1 −1.0
Crops 7.9 34.1 31.9 4.8 3.6 −58.4 −67.5 −9.2 −10.2
Others 15.7 13.9 14.8 0.7 0.0 −25.1 −27.2 −5.1 −4.3
Total 100.0 603.3 513.9 136.3 115.8 −2633.6 −2576.5 655.7 642.3
336 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
optimization, but the magnitudes of the optimized fluxes do
change, displaying less release in the spring and more uptake
in the summer than the prior fluxes (Figs. 6a, b). Because of
the lower springtime release and greater summertime uptake in
the optimized flux, the average maximum cumulative carbon
value in the ASI is smaller than that of the prior flux. The
times when the weekly cumulative fluxes change from positive
to negative are earlier for the optimized fluxes than for the
prior flux (Figs. 6a, b), indicating that the sink of weekly
cumulative fluxes occurs earlier when the optimization process
is applied. As in Yang et al. (2007) and Randerson et al.
(2009), the magnitude of the seasonal carbon cycle predicted by
CASA GFED2 is underestimated, and the seasonal variation
predicted by CASA GFED2 is delayed relative to the carbon
observations.
The interannual variations of the prior flux anomalies (Fig.
6d) are smaller than those of the optimized flux anomalies in
the ASI (Fig. 6e), indicating that the optimization process
increases the magnitude of the interannual variations. In 2002,
the optimized flux anomalies of the ASI and noASI are at their
minimum values because of a large carbon uptake during the
boreal summer (Fig. 6e). Although the optimized flux anom-
alies in the spring of 2003 are lower than those of other years,
the smallest uptake occurs in 2003 because of a lower carbon
Fig. 6. Weekly cumulative biosphere fluxes (Pg C) of each year over Eurasian Boreal regions: (a) prior fluxes, (b)optimized fluxes in the ASI experiment, and (c) optimized flux differences between the ASI and noASI experiments.Weekly cumulative biosphere flux anomalies (Pg C) over Eurasian Boreal regions: (d) prior fluxes, (e) optimized fluxes inthe ASI experiment, and (f) optimized flux differences between the ASI and noASI experiments for 2001 to 2006.
31 May 2014 Jinwoong Kim et al. 337
uptake in the summer, as shown in Table 1. In contrast,
although the optimized flux release in the spring of 2004 is
greater than those of other years, the large uptake in the
summer of 2004 reduces the positive anomaly of that year. The
optimized flux differences increase until summer because the
springtime release in the ASI is greater than that in the noASI,
whereas the optimized flux differences decrease in summer
because the summertime uptake in the ASI is greater than that
in the noASI (Fig. 6c). At the end of all years except 2001, the
total annual uptake in the ASI is less than that in the noASI
(Fig. 6c). Nonetheless, the seasonal variation pattern of the
optimized flux differences in 2001 is similar to that of other
years (Fig. 6c). The anomaly differences indicate that the
absolute values of positive and negative anomalies of the ASI
are greater than those of the noASI, implying that even more
diverse biosphere fluxes can be simulated in the ASI (Fig. 6f)
due to the higher spatial resolution of the transport model in
the ASI.
Figure 7 presents the weekly cumulative biosphere fluxes of
each year and their anomalies from the six-year average over
ET. The seasonal patterns of ET are similar to those of EB, but
the magnitudes of the carbon fluxes of ET are smaller than
those of EB (compare Figs. 7a, b to Figs. 6a, b). In 2003 and
2005, the optimized flux uptakes are smaller than the prior
fluxes, whereas in other years, the optimized flux uptakes are
greater than the prior fluxes (Figs. 7a, b). As in the EB, the
seasonal patterns of the optimized flux differences between the
ASI and noASI are similar in the ET (Fig. 7c). The signs of the
Fig. 7. Same as Fig. 6 but for Eurasian Temperate regions.
338 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
anomalies of the prior fluxes do not change after optimization
(Figs. 7d, e), remaining positive in 2003 and 2005 and negative
in 2001, 2002, and 2004 (Figs. 7d, e). The large positive
anomalies of the optimized fluxes in 2003 and 2005 are caused
by a net release from the Crops in China, as shown in Figs. 5h,
n (Fig. 7e). As in the EB, the absolute values of positive and
negative anomalies in the ET in the ASI are greater than those
in the noASI (Fig. 7f).
The 2001 to 2006 summer uptakes in the prior fluxes are
underestimated compared with those of the optimized fluxes.
This underestimation is caused by large uptakes in boreal
summer and small releases during the non-growing seasons in
the Conifer Forest in Asia. Peters et al. (2007, 2010) found that
croplands in North America and forests in Eastern Europe
(including Russia, Belarus, and the Scandinavian countries)
caused a similar effect.
b. Comparison with observations
In this section, the model CO2 concentrations calculated by
the prior and optimized fluxes of the ASI and noASI are
compared with the observed CO2 concentrations.
Table 3 presents the average bias of the model CO2 concen-
trations simulated using the prior flux at each observation site
located in the nesting domain of the ASI. The bias is calculated
by subtracting the observed CO2 concentrations from the
model CO2 concentrations. The biases in boreal winter are
small at some observation sites (e.g., GMI_01D0, MKN_01D0,
WIS_01D0, KZM_01D0, TAP_01D0, and UUM_01D0) due
to the small carbon uptake by the biosphere during this season.
In spring and summer, the model CO2 concentrations are
greater than the observed CO2 concentrations at most obser-
vation sites. The biases are positive in boreal summer because
of the underestimation of the prior flux uptake, as discussed in
Section 3a. The biases in the noASI are greater than those in
the ASI due to the greater summertime maximum uptake in the
ASI than in the noASI. The BKT_01D0 site in the Tropics
ecosystem type does not exhibit the seasonal variation of the
model CO2; instead, it exhibits a positive bias throughout the
year due to the overestimated prior flux in that ecosystem.
Table 3. Average difference between model CO2 concentrations (ppm) simulated using the prior surface carbon flux and the observed CO
2
concentrations (ppm) at observation sites located in the nested domain of the ASI experiment.
Site NameObs. All Winter Spring Summer Autumn
No. ASI noASI ASI noASI ASI noASI ASI noASI ASI noASI
BKT_01D0 105 8.13 ± 3.52 8.13 ± 3.61 9.11 ± 3.45 9.44 ± 3.36 7.17 ± 4.66 7.01 ± 5.12 8.27 ± 3.24 8.35 ± 2.94 7.77 ± 2.29 8.11 ± 2.37
GMI_01D0 447 2.65 ± 1.06 2.86 ± 1.12 1.87 ± 0.61 1.91 ± 0.59 2.26 ± 0.79 2.45 ± 0.87 2.67 ± 1.01 2.90 ± 1.03 3.33 ± 1.15 3.55 ± 1.20
MKN_01D0 75 4.59 ± 1.80 4.71 ± 1.83 3.41 ± 1.50 3.50 ± 1.49 4.94 ± 1.72 4.72 ± 1.68 5.51 ± 1.66 5.98 ± 1.77 4.49 ± 1.05 4.73 ± 1.05
OBN_01D0 104 3.33 ± 11.99 2.68 ± 11.89 3.86 ± 2.97 2.72 ± 2.79 6.90 ± 4.32 6.44 ± 4.14 −0.07 ± 20.42 −0.75 ± 20.24 2.53 ± 4.25 1.81 ± 4.36
WIS_01D0 289 3.54 ± 2.43 3.85 ± 2.47 3.01 ± 2.17 3.06 ± 2.10 4.31 ± 2.53 4.48 ± 2.41 4.43 ± 2.09 5.13 ± 2.18 2.21 ± 2.65 2.49 ± 2.64
KZD_01D0 268 6.01 ± 4.44 8.68 ± 2.47 5.53 ± 4.43 7.25 ± 5.14 7.02 ± 3.43 8.98 ± 4.84 4.58 ± 3.67 6.82 ± 5.42 4.95 ± 4.22 7.04 ± 7.37
KZM_01D0 237 4.27 ± 3.32 4.09 ± 3.30 2.79 ± 2.42 2.05 ± 1.59 5.13 ± 3.48 5.05 ± 3.63 6.10 ± 3.13 6.52 ± 3.24 3.12 ± 2.91 2.76 ± 2.40
TAP_01D0 194 4.50 ± 4.54 5.76 ± 5.48 1.63 ± 1.32 2.36 ± 0.98 5.87 ± 5.16 8.03 ± 5.89 6.11 ± 5.59 7.31 ± 6.57 2.57 ± 2.75 2.92 ± 4.00
UUM_01D0 273 3.88 ± 2.89 4.23 ± 2.79 2.18 ± 2.95 2.55 ± 2.99 4.41 ± 2.36 4.53 ± 2.20 4.49 ± 3.25 4.68 ± 3.03 3.29 ± 3.02 3.73 ± 3.08
WLG_01D0 183 3.05 ± 2.16 3.50 ± 2.15 1.81 ± 1.25 2.16 ± 1.20 3.10 ± 1.92 3.21 ± 1.91 3.75 ± 3.01 4.82 ± 2.69 2.88 ± 1.75 2.99 ± 2.02
Table 4. RMSE, R2, and bias of the model CO2 concentrations (ppm) simulated using the optimized surface carbon flux from the observed CO
2
concentrations (ppm) at observation sites located in the nested domain of the ASI experiment.
Site Name Obs. No.RMSE R2 Bias
ASI noASI ASI noASI ASI noASI
BKT_01D0 105 6.23 6.21 0.35 0.29 5.23 ± 3.40 5.15 ± 3.49
GMI_01D0 447 0.91 0.91 0.96 0.96 −0.05 ± 0.91 −0.05 ± 0.91
MKN_01D0 75 2.72 2.65 0.64 0.65 1.92 ± 1.94 1.83 ± 1.94
OBN_01D0 104 4.77 5.21 0.71 0.66 0.45 ± 4.77 −0.59 ± 5.20
WIS_01D0 289 2.11 2.09 0.85 0.85 −0.05 ± 2.11 0.02 ± 2.10
KZD_01D0 268 3.91 4.28 0.78 0.73 1.27 ± 3.71 1.64 ± 3.96
KZM_01D0 237 2.43 2.80 0.84 0.78 0.30 ± 2.42 0.08 ± 2.80
TAP_01D0 194 4.59 5.09 0.63 0.59 0.51 ± 4.57 1.45 ± 4.89
UUM_01D0 273 2.68 2.46 0.84 0.86 −0.43 ± 2.65 −0.29 ± 2.45
WLG_01D0 183 1.36 1.43 0.94 0.93 −0.29 ± 1.33 −0.25 ± 1.41
ULB_01D2 210 1.87 2.01 0.84 0.82 0.11 ± 1.87 0.31 ± 1.99
31 May 2014 Jinwoong Kim et al. 339
Table 4 presents the RMSE, R2, and bias of the model CO2
concentrations calculated using the optimized flux at each
observation site. Compared with the model CO2 calculated
from the prior flux, biases are reduced at most sites except
BKT_01D0 and UUM_01D0, which indicates that the surface
carbon flux optimization using the scaling factor works well
over Asia, as shown by Kim et al. (2012). The RMSE, R2, and
bias vary over the observation sites. For example, the
BKT_01D0 site has a high RMSE and bias and a low R2 due
to uncertain prior fluxes in tropical regions. As denoted in Pan
et al. (2011), the estimate of the surface carbon flux at
BKT_01D0 has large uncertainties due to insufficient infor-
mation on deforestation, reforestation, and afforestation. The
model and observed CO2 concentrations at the GMI_01D0 site
display good agreement and small bias. In addition, the two
model CO2 concentrations at GMI_01D0 from the ASI and
noASI display similar results because the location of the
observation site is in the middle of the ocean, far from anthro-
pogenic CO2 sources, indicating that the model CO
2 con-
centration at GMI_01D0 represents the background CO2 con-
centration. The KZD_01D0, KZM_01D0, TAP_01D0, WLG_
01D0, and ULB_01D2 sites, located in the 1o× 1o nested
domain of the ASI experiment, exhibit consistent statistical
values, although the UUM_01D0 site does not. The indepen-
dent validation at the aircraft observation site ULB_01D2,
whose observations are not used in the assimilation, illustrates
that the model CO2 concentrations in the ASI agree with the
observations more closely than do the results of the noASI.
Overall, the results of all observation sites in the nesting
domain in the ASI exhibit better results than those in the
noASI, which indicates that the optimized biosphere flux in the
ASI is more consistent with CO2 observations than that in the
noASI. Some observational data are rejected in the assimilation
process because their CO2 concentrations are much higher than
the model CO2
concentrations. These large differences are
caused by the poor performance of the transport model in
simulating high-CO2-concentration events by anthropogenic
emissions, particularly at the OBN_01D0 site.
Figure 8 presents the linear regressions between the model
CO2 concentrations calculated using the optimized flux and the
Fig. 8. Scatter plots of the model and observed CO2 concentrations (ppm) at (a, b) BKT_01D0, (c, d) GMI_01D0, (e, f)
MKN_01D0, (g, h) OBN-01D0, and (i, j) WIS_01D0, all located inside the 3o× 2o nested domain but outside the inner 1o
× 1o
nested domain of the ASI experiment. The first and third columns present the results of the ASI experiment, and the second andfourth columns present the results of the noASI experiment. The black line denotes a one-to-one match between the model andobserved CO
2 concentrations, whereas the red line indicates the regression line between the model and observed CO
2
concentrations.
340 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
observed CO2
concentrations at observation sites inside the
3o× 2o nested domain but outside the inner 1o× 1o nested
domain of the ASI. As shown in Table 4, the model CO2 con-
centrations at the BKT_01D0, MKN_01D0, and OBN_01D0
sites are not simulated accurately by the transport model and
exhibit low R2 (Figs. 8a, b, e, f, g, and h), whereas the
regression slopes of the other sites are close to 1 (Figs. 8c, d, i,
and j) and exhibit small differences between the ASI and
noASI. Figure 9 presents the linear regression results inside the
1o× 1o nested domain of the ASI. At most sites, the regression
slopes are near 1, with those in the ASI closer to 1 than those
in the noASI.
Figure 10 presents the time series of the observed CO2 con-
centrations and the model CO2 concentrations calculated by
the optimized fluxes. For comparison, both the model and
observed CO2 concentration data from 1200 to 1600 LST are
averaged. As shown in Table 4, the variations in the CO2
concentrations depend on the location of the observation sites.
The model CO2 concentrations are lower than the observed
CO2 concentrations at the KZD_01D0 site in the summer (Fig.
10f), whereas those at the KZM_01D0 site exhibit time-
delayed seasonal variations (Fig. 10g). These differences in the
CO2 concentrations at two close observation sites are caused
by the weak vertical mixing in the boundary layer in the
transport model, as reported by Stephens et al. (2007). The
reduced model CO2 concentrations caused by the large surface
carbon uptake in the summer are not transported properly in
the vertical direction over these regions. At the TAP_01D0
site, the bias in the ASI is smaller than that in the noASI, as
shown in Table 4 (Fig. 10h). The difference between the ASI
and noASI in TAP_01D0 in Table 3 (approximately 0.9 ppm)
is maintained in that way in Table 4, which implies that before
the optimization there is already the difference between the
ASI and noASI due to the nesting domain. The difference
between the ASI and noASI is maintained after the optimi-
zation. Therefore that difference is due to the higher spatial
resolution of the transport model in the ASI.
c. Effect of the observations on the model CO2 concentrations
Because there is no information on the true surface carbon
flux, it is difficult to calculate the flux error accurately. However,
Fig. 9. Scatter plots of the model and observed CO2 concentration (ppm) at (a, b) KZD_01D0, (c, d) KZM_01D0, (e, f) TAP_01D0,
(g, h) UUM-01D0, (i, j) WLG_01D0, and (k, l) ULB_01D2, all located inside the 1o× 1o nested domain of the ASI experiment. The
first and third columns display the results of the ASI experiment, and the second and fourth columns display the results of thenoASI experiment. The black line denotes a one-to-one match between the model and observed CO
2 concentrations, whereas the
red line indicates the regression line between the model and observed CO2 concentrations.
31 May 2014 Jinwoong Kim et al. 341
the effect of observations on the optimization of fluxes can be
indirectly estimated by comparing the model CO2 concen-
trations calculated by the prior and optimized fluxes.
The spatial distributions of the averaged model CO2 con-
centrations calculated by the prior and optimized fluxes in the
two experiments are shown in Fig. 11. The model CO2
concentrations calculated by the optimized fluxes are lower
than those calculated by the prior fluxes, corresponding to the
greater flux uptakes of the optimized fluxes in Fig. 4. The
differences between the model CO2 concentrations calculated
by the prior and the optimized fluxes are large, especially in
the Conifer Forest and Mixed Forest in EB and in the Crops in
ET, implying that the large CO2 uptakes and the small model
CO2 concentrations over these ecoregions are caused by CO
2
observation information reflecting to the optimized fluxes. The
differences between the model CO2 concentrations calculated
by the optimized fluxes and the observed CO2
concentrations
are much smaller than those between the model CO2 concen-
trations calculated by the prior fluxes and the observed CO2
concentrations discussed in Section 3b (compare Tables 3 and
4); this improved accuracy confirms the positive effect of CO2
observation information in optimizing the surface CO2
fluxes
and model concentrations. Overall, the model CO2 concen-
trations calculated by the optimized fluxes in the ASI show
Fig. 10. Model CO2 concentrations (ppm, red dot: ASI experiment, blue dot: noASI experiment) and observed CO
2 concentrations
(ppm, green dot) from 2001 to 2006 at (a) BKT_01D0, (b) GMI_01D0, (c) MKN_01D0, (d) OBN_01D0, (e) WIS_01D0, (f)KZD_01D0, (g) KZM_01D0, (h) TAP_01D0, (i) UUM_01D0, and (j) WLG_01D0, all located inside the 3o
× 2o nested domain ofthe ASI experiment. Model and observed CO
2 concentrations were 1200 to 1600 LST.
342 ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
more detailed features than those in the noASI, indicating that
the observational CO2 information is carried into the posterior
state vector more in the ASI than in the noASI over Asia due
to the high-resolution transport model used in the ASI.
4. Summary and discussion
To investigate the surface carbon flux in Asia and estimate
the effect of different nesting domains on surface carbon flux
analysis over Asia, two experiments, named ASI and noASI,
with different nesting domains were conducted from 2000 to
2006 using CT2008, which uses the EnKF as a data assimi-
lation system. CT2008 combines the background surface flux
information and atmospheric CO2 concentrations measured at
the surface to estimate the surface carbon flux. The ASI and
noASI experiments have nesting domains centered in Asia and
North America, respectively. These experiments have the same
model configurations but different nesting domains and can
thus evaluate the effect of nesting domains on surface flux
analyses over Asia. Because the results are evaluated in Asia,
the ASI and noASI experiments are high- and low-resolution
experiments over Asia, respectively. Various aspects of the
differences in the results between the two experiments were
evaluated: the characteristics of the surface carbon flux, the
effect of the nesting domains on the flux inversion results over
Asia, a comparison of the model and observed CO2 concen-
trations, and the effect of observations on flux optimization.
The prior biosphere fluxes over Eurasian Boreal regions are
underestimated compared with the optimized fluxes in terms
of magnitude, and the times of the sign change of the
cumulative fluxes are delayed. Although these features in the
prior fluxes are corrected by the optimization, the model CO2
concentrations at some observation sites are still delayed due
to weak vertical mixing from the surface to the free atmos-
phere in the model. Peters et al. (2010) noted that the
differences between the prior flux calculated by a “bottom-up”
approach and the optimized biosphere flux calculated by a
“top-down” approach for Europe illustrate the robustness of
the inverse modeling system based on EnKF. However, in the
present study, determining which method is superior is difficult
because of insufficient observations over Asia, particularly in
Siberia. Nevertheless, in the present study, the CO2 obser-
vations constrain the surface carbon flux to have an increased
uptake in Siberia and improve the estimate of the optimized
biosphere flux uptake over Siberia, causing the optimized
characteristics of this flux to be more consistent with the
biosphere fluxes estimated by Quegan et al. (2011) than those
of the prior fluxes.
The spatial and temporal patterns of the optimized fluxes in
the two experiments are roughly similar, but the magnitudes of
Fig. 11. Average model CO2 concentration (ppm) from 2001 to 2006, calculated using the (a) prior flux and (b) optimized flux in
the ASI experiment and the (c) prior flux and (d) optimized flux in the noASI experiment.
31 May 2014 Jinwoong Kim et al. 343
the optimized fluxes of the two experiments differ. The
differences between the two experiments originate from their
different nesting domains with different horizontal resolutions
over Asia.
The model CO2 concentrations using the optimized flux in
the ASI experiment are more consistent with the CO2 observa-
tions than those in the noASI experiment, showing low RMSE
and bias and high R2. In addition, the model CO2 concen-
trations using the optimized flux in the ASI experiment show
more detailed and variable features than those in the noASI
experiment in the Eurasian Boreal and Eurasian Temperate
regions. The high spatial resolution of the transport model in
the ASI experiment produces more accurate CO2 concen-
trations, as suggested by Patra et al. (2008), because coarser
model resolution spreads tracers out over large regions
(Hujinen et al., 2011).
Using Bayesian synthesis methods, Maksyutov et al. (2003)
showed that additional observations over Asia can increase
carbon uptake, decrease carbon release in flux estimates, and
reduce flux uncertainties over Asia (i.e., in the Boreal, Tem-
perate, and Tropical regions in Asia). In the Conifer Forest
types in Eurasian Boreal regions, the largest uptake ecoregions
in Asia, there are no observation sites. The scaling factor in
this region is indirectly optimized by observations located in
other ecoregions nearby. Therefore, additional CO2 observa-
tions could improve estimations of the surface carbon flux
over Asia. For example, Engelen et al. (2011) demonstrated
that satellite radiance observations can affect the quality of the
optimized CO2 concentrations. Observations from the Japanese
Greenhouse gases Observing SATellite (GOSAT) (Yokota et
al., 2009) may also improve the accuracy of the estimate of the
surface carbon flux. In addition, more precise prior flux
information can improve the simulated CO2 concentrations.
Large fossil fuel emissions are produced and appear in North-
east Asia (Gregg et al., 2008). Because the fossil fuel flux is
not optimized in CarbonTracker, improper information (e.g.,
seasonal and monthly variations) regarding the fossil fuel flux
can result in improperly optimized biosphere fluxes. The
prescribed background error covariance in CarbonTracker may
also be relaxed by adopting inflation methods.
Therefore, although this study used CarbonTracker to pro-
perly estimate the surface CO2 flux in Asia with a nesting
domain, further research is necessary to more accurately
estimate the surface CO2 flux in Asia.
Acknowledgments. The authors thank the two anonymous re-
viewers for their valuable comments. This study was supported
by the Korea Meteorological Administration Research and
Development Program under Grant CATER 2012-3032.
Edited by: Song-You Hong, Kim and Yeh
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