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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: [email protected]

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.


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.


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


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


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


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.


(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.


(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


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


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


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.


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.


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


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.


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.


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.


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.


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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