Assessment of the Biospheric Contribution to Surface Atmospheric CO2Concentrations over East Asia with a Regional Chemical Transport Model

KOU Xingxia,ZHANG Meigen,PENG Zhen,and WANG Yinghong

1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing100029

2University of Chinese Academy of Sciences,Beijing100049

3School of Atmospheric Sciences,Nanjing University,Nanjing210093

Assessment of the Biospheric Contribution to Surface Atmospheric CO2Concentrations over East Asia with a Regional Chemical Transport Model

KOU Xingxia1,2,ZHANG Meigen∗1,PENG Zhen3,and WANG Yinghong1

1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing100029

2University of Chinese Academy of Sciences,Beijing100049

3School of Atmospheric Sciences,Nanjing University,Nanjing210093

A regional chemical transport model,RAMS-CMAQ,was employed to assess the impacts of biosphere–atmosphere CO2exchange on seasonal variations in atmospheric CO2concentrations over East Asia.Simulated CO2concentrations were compared with observations at 12 surface stations and the comparison showed they were generally in good agreement.Both observations and simulations suggested that surface CO2over East Asia features a summertime trough due to biospheric absorption,while in some urban areas surface CO2has a distinct summer peak,which could be attributed to the strong impact from anthropogenic emissions.Analysis of the model results indicated that biospheric fuxes and fossil-fuel emissions are comparably important in shaping spatial distributions of CO2near the surface over East Asia.Biospheric fux plays an important role in the prevailing spatial pattern of CO2enhancement and reduction on the synoptic scale due to the strong seasonality of biospheric CO2fux.The elevation of CO2levels by the biosphere during winter was found to be larger than 5 ppm in North China and Southeast China,and during summertime a signifcant depletion(≥7 ppm)occurred in most areas, except for the Indo-China Peninsula where positive biofux values were found.

CO2regional transport modeling,seasonal variation,biospheric fux

1. Introduction

Carbondioxide(CO2)is oneofthe most importantgreenhouse gases warming our atmosphere.Its temporal and spatial variability in the atmosphere refects the infuence of anthropogenic inputs,including fossil-fuel emissions,cement production,land-use change(mainly deforestation)and other human activities,as well as the removal by two known reservoirs:the ocean and the terrestrial biosphere(Morimoto et al.,2000;Liu et al.,2005;Piao et al.,2009).The remainder that stays in the atmosphere exhibits large diurnal,synoptic,seasonal and interannual variability,especially over land. Previous studies have found that most CO2emissions originate from cities due to human activities,even though urban areas cover only a small fraction of Earth’s land area(Wang et al.,2012;Schneising et al.,2013).From the perspective of the natural carbon cycle,global terrestrial ecosystemsabsorb carbon at a rate of 1–4 Pg yr-1,offsetting 10%–60% of fossil-fuel emissions,while the regional patterns of terrestrial carbon sources and sinks remain uncertain(Solomon et al.,2007;Zhao et al.,2012;Zhang et al.,2013).With increasing scientifc and political interest in regional aspects of the global carbon cycle,there is a strong impetus to resolve fne-scale CO2transport and variability.

Atmospheric CO2is a particularly important climatic issue in East Asia because this area has experienced extensive industrialization in the last three decades with accumulated impacts of anthropogenic emissions and regionally distinct changes of land-use conditions and climate trends(Streets et al.,2003a;Zhang et al.,2009;Liu et al.,2013a;Wang et al.,2013).Previous efforts have shown some promising achievements with high-resolution regional chemical transport models(CTMs)(Chevillard et al.,2002;Sarrat et al., 2007;Ahmadov et al.,2009;Ballav et al.,2012).For example,Ahmadov et al.(2009)coupled the atmospheric Weather Research and Forecasting(WRF)model with a diagnostic biospheric model(Vegetation Photosynthesis and Respira-tion Model;VPRM)to understand the effects that mesoscale transport has on atmospheric CO2distributions.Also,WRFChem has been used for regional transport simulations with prescribed terrestrial biogenic fux input from two biogeochemical model simulations[Carnegie–Ames–Stanford Approach(CASA)and Simple Biosphere Model(SIB3)],and the results indicated that surface fux horizontal distributions and wind directions are the dominant controls for CO2synoptic variations(Ballav et al.,2012).Some key requirements for regional CO2modeling have been noted,such as using realistic initial and lateral boundary conditions,while issues that are of interest for the magnitude and spatial extent of biospheric and anthropogenic roles in atmospheric CO2have not yet been addressed in most regional modeling studies.Considering the unique characteristics(e.g., long atmospheric lifetime,large background concentration, and strong biosphere–atmosphereexchanges)of atmospheric CO2that are distinctlydifferentfromothertraditionallymodeled chemical pollutants,further study is therefore needed to gain insight into the biospheric and anthropogenic contributions to shaping the CO2spatial distribution and seasonal variations in East Asia.

By incorporating a VPRM in the online mode,the comprehensive regional air quality modeling system,RAMSCMAQ (Regional Atmospheric Modeling System and Models-3 Community Multi-scale Air Quality)was developed to simulate atmospheric CO2concentrations over East Asia,and its feasibility in regional CO2modeling was demonstrated(Kou et al.,2013).The CO2volume fraction is transported as a tracer in this model,with prescribed surface CO2fuxes that include fossil-fuel emissions,biomass burning,air–sea CO2exchange,and biosphere–atmosphere CO2exchange.Biosphericfuxis the net fuxbetweenuptake from photosynthesis and release from ecosystem respiration, and has been found to have signifcant impacts on surface CO2concentrations(Ahmadov et al.,2007;Piao et al.,2007; Le Quere et al.,2009;Peters et al.,2010;Liu et al.,2013b). Consideringthe highlevel ofuncertaintyin simulatedbiofux in current terrestrial biosphere models(Canadell et al.,2007; Schaefer et al.,2012;Huntzinger et al.,2012,2013),the widely recognized results from the CarbonTracker-2011 optimized estimation(CT2011 oi)were adopted in this study (Peters et al.,2007).The primary purpose of this study is to further investigate and understand the spatial distributions andseasonalvariationsofatmosphericCO2concentrationsin EastAsia onfnespatialscales;andthentoidentifyandquantify the biospheric contribution with this modeling system.A description of the model and input data is given in section 2,followed by a presentation and discussion of the results in section 3.A summary and conclusions are provided in section 4.

2. Model description

The RAMS-CMAQ modeling system was developed based on the US EnvironmentalProtection Agency’s CMAQ, with RAMS providing the three-dimensional meteorological felds(Zhang et al.,2002),and was extended to include CO2simulation by Kou et al.(2013).The study domain for CMAQ was 6654×5440 km2,with a grid resolution of 64×64 km2on a rotated polar stereographic map projection centered at(35.0°N,116.0°E),and covered the whole area of East Asia(as shown in Fig.1).The model system has 15 vertical layers in theσz-coordinate system,unequally spaced from the ground to approximately 23 km,with nearly half of them concentrated in the lowest 2 km(vertical resolution of 100–200 m from the ground to approximately 1.5 km)to resolve the planetary boundary layer.The output time step is 1 h.CO2is treated in the model as an inert chemical species, whose concentrations are determined by atmospheric transport(horizontalandverticaladvectionanddiffusion)andfour types of prescribed fuxes.

The fossil-fuel emissions were adopted from the Regional Emission Inventory in Asia,with monthly gridded data at a 0.25°×0.25°resolution(REAS v2.1;Kurokawa et al.,2013).The REAS estimates the emissions from fuel combustion sources and non-combustion sources,including power,industry,transport,and other sectors(e.g.,commercial and residential).Biomass-burning emissions from forest wildfres,savanna burning and slash-and-burn agriculture were provided by the Global Fire Emissions Database monthly mean inventory at a spatial resolution of 0.5°× 0.5°(GFED v3;van der Werf et al.,2010).As mentioned above,biosphere–atmosphere exchange and ocean fux were obtained from CT2011 oi[global resolution of 3°(lon) ×2°(lat),3 hourly]. The National Oceanic and Atmospheric Administration’s(NOAA)CarbonTracker,used in this study,is an example of a data assimilation system that provides optimized biosphere and ocean CO2fuxes usingin situCO2observations from a global observational network and prior biospheric fux from the CASA model (Peters et al.,2007).

Figure 2 shows the seasonal mean distribution of biospheric fuxes and ocean fux(Figs.2a–d),and fossil-fuel and biomass-burningemissions(Figs.2e–h)in the model domain.In Figs.2a–d,negative values indicate the removal of CO2from the atmosphere to the biosphere,and positive values indicate the release of CO2to the atmosphere.The seasonal and spatial variation of biospheric CO2fux in East Asia is strongly infuenced by the seasonal growth and decay of plants in terrestrial ecosystems,which is mainly driven by the seasonal variation of precipitation,temperature,photosynthetically active solar radiation,and other meteorological factors(Fu et al.,2009).Generally,the biosphere absorbs CO2in summer,as the uptake of atmospheric CO2by photosynthesis exceeds CO2released by respiration in the growing season.While in winter,the biosphere acts as a source since CO2released by respiration exceeds uptake by photosynthesis(Figs.2a and 2c).It should be noted that South East Asia(the Indo-China Peninsula)displays distinctively different seasonal patterns of CO2exchange,with strong absorption in winter and release in summer,since photosynthesis is taking place under unfavorable conditions(i.e.,the exces-sive precipitation delivered by the southern Asian monsoon and insuffcientphotosyntheticallyactive radiationduringthe rainyseason),while respirationincreases with the rising temperature(Yu et al.,2013).

Figures 2e–h show the seasonality of fossil-fuel and biomass-burning emissions in the model domain for 2010. Compared to biofux,the horizontal distribution patterns of fossil-fuel emissions show considerablespatial heterogeneity and large gradients(Fig.2e).They also display a lower degree of seasonality,but with a peak during wintertime(Figs. 2f–h and Table 1)in most areas due to increased energy consumption to meet the electricity demands for air conditioning and heating,especially in the north of China(Gurney et al., 2005;Wang et al.,2012;Zhao et al.,2012;Liu et al.,2013a). The increase in Southeast Asia(Fig.2f)could be primarily attributable to the rise of biomass burning during springtime(Streets et al.,2003b).Biomass-burning emissions(L¨u et al.,2006)and ocean–atmosphere CO2fuxes(Xu et al., 2013)also show seasonal variation,but are of minor importance compared to the biospheric and fossil-fuel fuxes in the model domain(Table 1 and Fig.2).Fully understanding the effects ofbiomass burningandoceanfux is outsidethe scopeof this work and is not discussed further here.

Table 1.Monthly mean CO2fuxes used in the CMAQ simulation over the model domain(units:Tg C month-1).

The spatial pattern of CO2net fux(i.e.,the sum of these four types of fuxes)shown in Fig.3 retains features from both the biofux and fossil-fuel emissions,which is attributable to their comparable fux magnitudes in the model domain(Fig.2).During wintertime(Fig.3a),the biosphere acts as a source,which further magnifes the CO2net fux on the basis of increased anthropogenic input;while during spring,summer,and autumn(Figs.3b–d),some fossil-fuel emissions are offset by negative biofux,thus resulting in a neutral(zero)or negative net fux in the biospheric sink functional areas(e.g.,Northwest China and Inner Mongolia).The modeling system executed simultaneous simulations of CO2continuously from 26 December 2009 to 31 December 2010,starting at 0000 UTC 26 December.In this study,the initial felds and boundary conditions of atmospheric CO2volume fraction were obtained by interpolation of CT2011 optimized estimation(CT2011 oi;data availableat http://carbontracker.noaa.gov).To understandthe role of biospheric fux,atmospheric CO2was simulated by CMAQ using two set fuxes.The frst was designed as the standard simulation with prescribed fux including fossil-fuel emissions,biomass-burning emissions,biospheric and ocean fuxes to investigate the CO2spatial distribution.The second was performed as the comparison simulation,which was similar to the former but without biospheric fux(i.e.,only fossil-fuel emissions,biomass-burning emissions,and ocean fux included).The biospheric contribution to surface CO2concentration was obtained by subtracting the results of the comparison simulation from the standard one.

3. Results and discussion

3.1.Model evaluation

An evaluation of temperature,precipitation,wind speed, and total solar radiation is frst presented in order to diagnose the strengths andweaknesses in the simulated meteorological conditions.The locations of observation stations are given in Fig.1,andFig.4 showsa comparisonbetweensimulatedand ground-based measurements.Observational surface temperature,precipitation and wind speed were obtained from 838 Chinese stations,among which 99 stations provided total solar radiation measurements.As shown in Fig.4,the simulated and observed results were generally in good agreement (Figs.4a–d).The summertime ridge of temperature,precipitation and total solar radiation was well captured by the model.Majordefcienciesinthe RAMS simulationsincluded an underestimation of precipitation and an overestimation of total solar radiation.This could be attributable to the bias in simulationforthesouthernpartofChina,whichislikelytobe predominantly associated with the cloud–radiation transfer parameterization(Leung et al.,1999;Ge et al.,2011).Wind speedwaswell reproducedbythemodelinmostmonths(Fig. 4c).The comparison led to confdence in the meteorological conditions provided by RAMS and verifed that the confguration of the simulation adopted in this study was suitable for studying the transport of CO2with prescribed fuxes in EastAsia.

ThemodeledCO2mixingratioswerethencomparedwith ground-based in-situ measurements from the World Data Centre for Greenhouse Gases(WDCGG,http://ds.data.jma. go.jp/gmd/wdcgg/)and Chinese Ecosystem Research Network(CERN).The geographical information of 12 East Asian observationstations is listed in Table 2,whichcouldbe classifed generally as coastal,remote ocean,mountain,and inland stations.Figures 5 and 6 show the observed and simulated monthly-averaged CO2concentrations at these stations from January to December of 2010.For the six WDCGG stations[AMY(Lee and Kim,2013),DDR(Muto,2013), MKW(Ohno,2011),UUM(Conway,2013),WLG(Zhou, 2013)and YON(Fukuyama,2013)],the observed data at hourly time intervals were obtained.Thus,the monthly meansofthe simulationandobservationwerebothcalculated from the daily means based on hourly outputs,with the standard deviation of the simulation provided(Fig.5).For the six CERN stations(Changbai Mountains,Changsha,Dinghu Mountains,Fukang,Gongga Mountains,and Xinglong),the observed monthly means were calculated from four weekly measurements(in cases of no missing data),and the weeklymean values were created from instantaneousin-situmeasurements every seven days.The simulated monthly CO2means used to compare with the CERN observations were calculated from daily means with the standard deviation provided(Fig.6).

Acomparisonofthestationswherelandfuxdominatedis discussed in order to further evaluate CMAQ-simulated temporalvariations(onamonthlybasis)andanalyzetheunderlying factors driving the observed seasonal variability of CO2. Examples of monthlymeanCO2concentrationsfromCMAQ and observations are shown in Fig.5 for six stations(AMY, DDR,MKW,UUM,WLGandYON),andthestatistical char-acteristics are listed in Table 3.As shown in Fig.5,simulated CO2concentrations were generally in good agreement with observed ones.The CO2levels in remote sites were similar (especially in winter;～390 ppm)as a result of comparable biofux and relatively low anthropogenic infuence.CO2concentrations were lowest during summer at most sites(except for MKW)owing to the strong biospheric absorption.In addition,AMY,with higher CO2all year round than WLG, which is geographically at a similar latitude(～36.5°N),implies a greaterinfuenceoflarge-scaletransportof localemissions(Ballav et al.,2012).Generally,CMAQ performed better during winter,spring and autumn than in summer,which is associated with the strength of terrestrial biosphere impacts.This implies stronger effects of uncertainty in biofux in the regional CO2simulation during summertime,since the uncertainty of biofux estimation has been found to be seasonally dependent,as refected by a terrestrial model intercomparison(Huntzinger et al.,2013).Possible reasons also include,but are not restricted to,errors in model transport in the boundary layer,the infuence of complex local topography and small-scale system effects,and temporal emissions profles,as well as the lag effect of the biospheric contribution to the atmospheric concentration in regional CTMs due to the long atmospheric lifetime of CO2.

Table 2.Location and general description of the observation sites.

For these six stations,the correlation coeffcient between simulated and observed monthly mean CO2concentrations ranged from 0.34 at MKW to a maximum of 0.98 at UUM (Table3).Themountainstation(DDR)andremoteoceanstation(YON)showed lower seasonal variation.At the mountain site(DDR),we used the 5th-layer(～750 m AGL,to

approach the real height)CMAQ results to compare with the observations.The bias between the simulation and observations infers a diffculty for the model to capture the infuence of such complex local topography.At the remote stations of WLG and UUM,mostly driven by long-range transport and local biospheric fux,CMAQ reasonably reproduced the monthly variations with mean biases of 0.57%and-0.19%, and correlation coeffcients of 0.87 and 0.98,respectively. MKW is unique and of higher interest compared to the other sites,not only because it frequently receives local fossil-fuel emissions from Nagoya,but also because it shows different seasonality of higher CO2levels in summer and lower values inthe otherseasons.As canbe seenin Fig.5,CMAQ demonstrated potential in reproducing the monthly-averaged CO2concentrations at urban sites dominated by anthropogenic emissions,but with a lower bias in summer.Biofux estimation by interpolation of the CT2011 oi results with global resolution of 3°(lon)×2°(lat)tends to be hard for resolving the urban ecosystem conditions.Also,it was diffcult for the modelwithits 64km×64kmhorizontalresolutiontocapture the infuence from complex local topographyand small-scale system effects.Attributing the underestimation at urban sites duringsummertimeto errorsin fossil-fuelemissions,biofux, or model transport would lead to drastically different conclusions(i.e.,upscaling emissions in the frst versus no scaling in the latter two)(McKain et al.,2012).Also,it should be noted that not all urban stations exhibit such a seasonal pattern due to variation in anthropogenic emissions and urban ecosystem conditions.

Table 3.Statistical characteristics of observed and simulated CO2concentrations on a monthly basis in 2010 for stations AMY,DDR, MKW,UUM,WLG and YON.

The seasonality of surface CO2concentrations at six CERN sites is summarized in Fig.6.These six sites,spreading from south to north China with different terrestrial ecosystems,cover both fast-developingregions(e.g.,Dinghu Mountains in the Pearl River Delta region,Changsha Station in Southeast China,and Xinglong in the Beijing–Tianjin–Hebei city cluster),and non-urban regions(e.g.,Changbai Mountains in the forests of the northeast,Fukang Station in the deserts of the northwest,and Gongga Mountains on the TibetanPlateau).Monthly-meanCO2observationsat thesurface sites exhibited maxima in winter and minima in summer due to variation in biofux magnitude as well as regional anthropogenic emissions.The summertime trough of CO2concentrations was well captured by the model.Figure 6 shows that the CO2concentrations of Changbai Mountains, Dinghu Mountains,Xinglong and Fukang exhibited strong seasonal variation,with differences between the maximum and minimum monthly means being as large as～20 ppm. As for the urban site at Changsha,observed monthly CO2concentrations displayed a summertime trough but with a sharp increase in August.Monthly CO2at Gongga Mountains showed little seasonal variation due to the cold wet climatethroughouttheyear,whichis unfavorableforthegrowth of plants.In general,the model reproduced the monthly mean mixing ratios of CO2as well as the seasonal variations, while major defciencies included an underestimationof CO2at Fukang and Xinglong and an overestimation at Changsha. The discrepancy was probably due to the uncertainty in input fuxes and the model’s inability to resolve the complex local topography and small-scale system effects with its 64 km×64 km resolution(van der Molen and Dolman,2007; Kou et al.,2013).In addition,the fact that the model results were not sampled for the specifc time when the measurements were conducted could explain part of the bias between them and the observations.

Although several biases with the model have been identifedbasedontheresultspresentedinFigs.5and6,wecansee that the CO2spatial variation in urban and non-urbanregions was reproduced well by CMAQ,and the evaluation against observations lends confdence to the model’s ability in capturing the general seasonal pattern of surface CO2concentration in East Asia.Therefore,we do not expect the model biases to change the main fndings of the present study on the regionally and monthly-averaged general seasonality of surface CO2over East Asia.

3.2.Impacts of biofux on surface CO2concentrations

The horizontal distributions of simulated seasonal mean CO2concentrations of the standard simulation in winter, spring,summer and autumn are presented in Figs.7a–d. As expected,the CMAQ simulation showed an ability to resolve the fne-scale features with numerous hotspots and stronger spatial heterogeneityof CO2while in general retaining the large-scale spatial patterns.Discernible gradients in CO2could be seen between northwestern and eastern China. CO2concentrations above 425 ppm were found over North China,and the Southeast China coastal regions,where intensive human activities are concentrated.This feature was in agreement with the CO2net fuxes distribution pattern(Fig. 3).Characterized by a high frequency of steady winds,inverse temperature structure(Ge et al.,2011),and considerable emissions(Fig.3),higher levels of CO2were also found in the Sichuan Basin.Careful analyses of the results suggestedthatitis notappropriatetogeneralizeoneseasonalpattern for surface CO2concentrations that fts situations across all parts of East Asia.

Seasonal CO2variations were particularly high in East Asia,with higher concentrations in winter,lower concentrations in spring and autumn,and the lowest concentrations in summer.From Fig.7a it can be seen that the high CO2concentrations were mainly distributed over regions with intensive human activities during wintertime.Following the wind pattern controlled by the Siberian High,the surface winds were initially northeastwards in northern China,Japan,and Korea.Thus,higher emissions as well as transport together contributed to the accumulation of CO2in these areas.In summer(Fig.7c),CO2concentrations of less than 410 ppm (lessthan435ppminurbanregions)werefoundinmostareas ofSoutheastChina.TheCO2valuesintheNorthChinaPlain, Korea and eastern Japan were found to be higher than 420 ppm.In addition to the anthropogenic input of fossil fuels combustion and residential emissions,possible reasons also include the monsoon transition process of prevailing south and southeast winds pushing pollution northwards and contributing to the higher CO2levels in this area.Overall,Figs.7a–ddemonstratethat the much-refneddescriptionsof transport andemissions in CMAQ allows for a moredetailed characterization of the spatial distribution of CO2and can facilitate an interpretation of sparse observational data in a regional context over East Asia.Next,we discuss the role of biosphericfuxinshapingthespatial patternsofCO2bycomparing the results from the standard and comparison simulation.

Figures 7e–h show the results of the CMAQ simulation without prescribed biospheric fux,and Fig.8 provides thedifference between the standard and the comparison simulation.Upon examination of the results of the standard simulation(Figs.7a–d),those of the comparison simulation(Figs. 7e–h)exhibit a similar spatial pattern throughout the year. For example,the steep gradients in CO2between northwestern and eastern China in Figs.7a–d can still be seen in Figs. 7e–h.Numerous domes of CO2(≥435 ppm)formed near large fossil-fuel emissions sources(as shown in Fig.2).The dispersion of CO2from these domes and from smaller emissions sources resulted in an increase of CO2in the surrounding areas.

The biofux,acting as a net source or sink due to the growth or decay of the terrestrial biosphere during various seasons and regions,is identifed as the dominant control for atmospheric CO2seasonal variation(as shown in Fig. 8).This is further superimposed on regional patterns of anthropogenic contribution,rather than at scattered spots.Surface CO2concentrations in the comparison simulation were muchlower thanin the standardsimulation duringwinter and spring,and much higher during summer and autumn,in most areas.A weaker CO2enhancement and reduction could be seen in the northwest of China due to lower biofux absolute values(Figs.2a–d).The decrease of CO2concentrations constrained by biospheric fuxes usually occurred in summer and,to a lesser degree,in autumn.Moreover,Fig.7c shows that summertime CO2greater than 415 ppm often appeared over Korea and Japan,North China,and coastal regions of Southeast China;whereas in the comparison results (Fig.7g),the areas(＞415ppm)appearedover a much larger scale.The different variation identifed here could perhaps be explained by the associated terrestrial biosphere distribution(Figs.2a–d),suggesting that biofux offsetting anthropogenic CO2emissions would lead to distinctly differentsurface CO2conditions.A comparison of Figs.7 and 8 suggests that(1)biofux dominates the distribution pattern in areas far away from large anthropogenic perturbations;and(2) the biospheric contributionin the majority of cities cannot be regardedas negligible accordingto the specifc urban ecosystem conditions.

The sensitivity experimentsalso facilitated the interpretation of CO2distribution and suggested a comparable importance of biospheric fuxes and fossil-fuel emissions in shaping spatial distributions of CO2near the surface over the model domain.For example,Figs.7 and 8 clearly show that anthropogenic inputs and transport processes were the major contributorsin formingthe hotspots of CO2concentration in the east of Japan(as for the situation of MKW,discussed above)during summer(Fig.7g),while biospheric fuxes helped offset the high concentration in this area by～3–7 ppm(Fig.8c).Moreover,the terrestrial biosphere brought about great reduction in surface CO2concentrations (≥7ppm)duringsummerinmostareas ofthe modeldomain, except for the Indo-China Peninsula where positive biofux was found during this period(Fig.2c).As can be seen in Fig.8a,the biosphere played an important role in elevating the CO2levels in most areas during winter(≥3 ppmin North China and Southeast China,and≥5 ppm in the Sichuan Basin),although the anthropogenicinputs and meteorological conditions had already caused an accumulation of CO2in these areas(Fig.7a).In addition,it is interesting that the areas with decreased CO2concentration in autumn(Fig. 8d)were much larger than the negative biofux areas shown in Fig.2d.Such regionally varied situations imply that for conclusions to be drawn regarding carbon sinks and sources, functional areas of terrestrial ecosystems and the associated transportmechanismsbothneed to befurtherexaminedwhen investigating the contribution of the biosphere(i.e.,magnify or shrink the atmospheric CO2levels).

4. Summary and conclusions

In this study,seasonal variations of surface CO2concentrations in East Asia were investigated by applying a comprehensive regional air quality modeling system,RAMSCMAQ,with prescribed CO2fuxes that included fossilfuel emissions,biomass burning,ocean fux,and biosphere–atmosphere exchange.The biospheric contribution to regulatingthe spatiotemporaldistributionand seasonalpatternsof CO2nearthesurfaceoverEastAsia wasassessed.Theresults demonstratedthe potential of regionalCTMs(CMAQ,in this case)to facilitate interpretationsof CO2observations,and resolve fne-scale features.The comparison of the model results with ground-based in-situ measurements indicated that the model reproduces temporal and spatial variations of CO2concentrations reasonably well,but with a higher bias during the growing season(especially summer),implying stronger effects of uncertainty in biofux estimation in regional simulations during summertime.Careful analyses of the results suggested that it is not appropriateto generalize one seasonal pattern for surface CO2concentrations to ft situations across all parts of East Asia.

The results of the sensitivity experiments showed that biospheric fuxes and fossil-fuel emissions are comparably importantin shapingspatial distributionsof surfaceCO2concentrations over East Asia.Fossil-fuel emissions play an important role in shaping the general spatial distribution of CO2near the surface over East Asia,whereas biospheric fux is responsiblefor the prevailingspatial patternof CO2enhancementand reductionon the synopticscale.The contributionof the biospheric CO2component varies signifcantly due to the strong seasonality of biospheric fux.In winter,the increases of CO2levels by the biosphere were found to be larger than 5 ppm in North China and Southeast China,and during summertime the biosphere made a great reduction(≥7 ppm)in most areas,except for the Indo-China Peninsula where positive biofux was found.In areas far away from large anthropogenic perturbations,the biospheric contribution dominates the spatial pattern of surface CO2distribution;while in densely urbanized regions the biospheric contribution cannot be regarded as negligible due to the specifc urban ecosystem conditions.The results presented here also serve as a foundation for future work in which further comprehensive examinations of CO2spatiotemporal variability and the various associated uncertainties are performed.

Acknowledgements.This work was supported by the Strategic Priority Research Program–Climate Change:Carbon Budget and Relevant Issues(Grant No.XDA05040404),the National High Technology Research and Development Program of China(Grant No.2013AA122002),and the National Natural Science Foundation of China(Grant No.41130528).This study used ground-based in-situ CO2concentration observations from the stations of AMY, DDR,MKW,UUM,WLG,YON(WDCGG),Changbai Mt.,Changsha,Dinghu Mt.,Fukang,Gongga Mt.,and Xinglong(CERN).We express deep gratitude to the dedicated principal investigators,research teams and support staff of the stations for providing their CO2observation records on the WDCGG website.Also,we would liketoexpress deep appreciation toSCAS-CERNfor providing CO2measurements.CarbonTracker results used in the model as initial felds,boundary conditions and biofux estimations were provided by NOAA ESRL,Boulder,Colorado,USA,http://carbontracker. noaa.gov.

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(Received 28 March 2014;revised 2 June 2014;accepted 25 June 2014)

∗Corresponding author:ZHANG Meigen

Email:mgzhang@mail.iap.ac.cn