An outlook on the Sub-Saharan Africa carbon balance · FCCC. The study area is Sub-Saharan Africa (hereinafter called SSA). In particular, research activities are being con-ducted

Biogeosciences 6 2193ndash2205 2009wwwbiogeosciencesnet621932009copy Author(s) 2009 This work is distributed underthe Creative Commons Attribution 30 License

Biogeosciences

An outlook on the Sub-Saharan Africa carbon balance

A Bombelli1 M Henry123 S Castaldi4 S Adu-Bredu5 A Arneth6 A de Grandcourt78 E Grieco1 W L Kutsch9V Lehsten6 A Rasile4 M Reichstein9 K Tansey10 U Weber9 and R Valentini111

1Department of Forest Environment and Resources (DISAFRI) Univ of Tuscia via S Camillo de Lellis 01100 Viterbo Italy2Institut de Recherche pour le Developpement IRD UR SeqBio SupAgro Bat 12 2 place Viala34060 Montpellier Cedex 1 France3AgroParisTech-ENGREF GEEFT 648 rue Jean-Francois Breton BP 7355 ndash 34086 Montpellier Cedex 4 France4Dipartimento di Scienze Ambientali Seconda Universita di Napoli via Vivaldi 43 81100 Caserta Italy5Forestry Research Institute of Ghana (FORIG) PO Box 63 KNUST Kumasi Ghana6Department of Physical Geography and Ecosystems Analysis (INES) Centre for GeoBiosphere Science Lund UniversitySolvegatan 12 223 62 Lund Sweden7Centre de cooperation Internationale en Recherche Agronomique pour le Developpement (CIRAD) Persyst UPR80 TAB-80D 34398 Montpellier Cedex 5 France8Unite de recherche sur la productivite des plantations industrielles (UR2PI) BP 1291 Pointe Noire Congo9Max-Planck Institute for Biogeochemistry Hans-Knoll Strasse 10 07745 JENA Germany10Department of Geography University of Leicester Leicester LE1 7RH UK11Euro-Mediterranean Centre for Climate Change (CMCC) via Augusto Imperatore 16 73100 Lecce Italy

Received 15 September 2008 ndash Published in Biogeosciences Discuss 20 February 2009Revised 14 September 2009 ndash Accepted 23 September 2009 ndash Published 15 October 2009

Abstract This study gives an outlook on the carbon bal-ance of Sub-Saharan Africa (SSA) by presenting a summaryof currently available results from the project CarboAfrica(namely net ecosystem productivity and emissions from firesdeforestation and forest degradation by field and model es-timates) supplemented by bibliographic data and comparedwith a new synthesis of the data from national communica-tions to UNFCCC According to these preliminary estimatesthe biogenic carbon balance of SSA varies from 016 Pg Cyminus1 to a much higher sink of 100 Pg C yminus1 (depending onthe source data) Models estimates would give an unrealisticsink of 323 Pg C yminus1 confirming their current inadequacywhen applied to Africa The carbon uptake by forests and sa-vannas (034 and 189 Pg C yminus1 respectively) are the maincontributors to the resulting sink Fires (072 Pg C yminus1) anddeforestation (025 Pg C yminus1) are the main contributors to theSSA carbon emissions while the agricultural sector and for-est degradation contributes only with 012 and 008 Pg C yminus1respectively Savannas play a major role in shaping the SSAcarbon balance due to their large extension their fire regimeand their strong interannual NEP variability but they are alsoa major uncertainty in the overall budget Even if fossil fuel

Correspondence toA Bombelli(bombelliunitusit)

emissions from SSA are relative low they can be crucial indefining the sign of the overall SSA carbon balance by reduc-ing the natural sink potential especially in the future Thispaper shows that Africa plays a key role in the global carboncycle system and probably could have a potential for carbonsequestration higher than expected even if still highly uncer-tain Further investigations are needed particularly to betteraddress the role of savannas and tropical forests and to im-prove biogeochemical models The CarboAfrica network ofcarbon measurements could provide future unique data setsfor better estimating the African carbon balance

1 Introduction

The climate system is strongly linked to the global carboncycle CO2 is by far the largest contributor to the an-thropogenically enhanced greenhouse effect (IPCC 2007a)while at the same time a large proportion of the emitted CO2is absorbed by ocean and terrestrial ecosystems Improvingour knowledge of terrestrial sources and sinks of CO2 andother greenhouse gases (GHGs) and their spatial and tem-poral variability is a key action to 1) better understand andpredict the global change and 2) enhance human capacity toundertake mitigation and adaptation actions

Published by Copernicus Publications on behalf of the European Geosciences Union

2194 A Bombelli et al An outlook on the Sub-Saharan Africa carbon balance

The role of the African continent in the global carbon cy-cle and therefore in climate change is increasingly recog-nised (Houghton and Hackler 2006 Williams et al 2007)Even if Africa contributes only less than 4 to the globalanthropogenic fossil fuel emissions (Canadell et al 2009)it is a major source of interannual variability in global at-mospheric CO2 (Williams et al 2007) 20 of both globalnet primary production (NPP) and global land use emis-sions (mainly from forest degradation and deforestation)and around 40 of fire emissions have been attributed tothe African continent significantly affecting the atmosphericchemistry (Andreae 1991 Houghton 2003 Kituyi et al2005 van der Werf et al 2006)

In Sub-Saharan Africa (SSA) the role of land use changein controlling CO2 emissions and annual C budgets at re-gional and global scale may be more critical than in anyother regions (Houghton and Hackler 2006) with low fos-sil fuel emissions Africarsquos current continental scale carbonfluxes are dominated by uptake and release from terrestrialecosystems (which in turn are strongly linked to climatefluctuations) as well as forest degradation and deforestation(Williams et al 2007) African forests contain large car-bon stocks in biomass up to 255 Mg C haminus1 in tropicalrainforests (Palm et al 2000) that appear to be particu-larly vulnerable mainly due to the impact of climate changeland use change population increase and political instabilityAfricarsquos land use pressures will undoubtedly increase in thenext years and the forecasted increase in drought and tem-perature (IPCC 2007a) can lead to a significant decrease inecosystem carbon stocks in tropical forests and savanna atleast without human countermeasures (Tan et al 2009)

Despite the increasingly acknowledged importance ofAfrica in the global carbon cycle and its high vulnerability toclimate change due to both ecological and socio-economicfactors there is still a lack of studies on the carbon cyclein representative African ecosystems in particular tropicalforests Up to now it is not clear whether Africa is a netcarbon absorber or emitter however Williams et al (2007)in their comprehensive recent review on the African carboncycle showed that Africarsquos decadal scale mean carbon bal-ance appears to be neutral whereas latest results by Ciais etal (2009) showed that in the last decade Africa has probablybeen acting as a net carbon sink

Many open questions remain also on soil GHG (CH4 CO2and N2O) fluxes especially in tropical seasonally-dry areas

This lack of data on African carbon budget makes the nu-merical models of the carbon cycle highly uncertain whenapplied to Africa model simulations can provide just an ap-proximate profile of Africa since these models are usuallydeveloped and validated for different latitudes (Chevallier etal 2009 Ciais et al 2009 Weber et al 2009) Thereforethere is a strong need for a denser and more representativenetwork of carbon observation stations over Africa in orderto improve the regional carbon estimates using biogeochem-

ical models (Williams et al 2007 Ciais et al 2009 Weberet al 2009)

In addition a deeper knowledge of the African potentialto mitigate climate change through carbon sequestration andreduction of emissions is highly required by the current inter-national climate policy It is thus important to quantify bothcarbon stocks and fluxes of African forests and other ecosys-tems especially in the context of the UNFCCC (United Na-tions Framework Convention on Climate Change) mecha-nisms such as the Clean Development Mechanisms (CDM)of the Kyoto protocol and the Reducing Emissions fromavoided Deforestation and forest Degradation (REDD)

As a consequences of the above arguments the projectCarboAfrica funded by the European Commission waslaunched in 2006 in order to 1) support a network of con-tinued and enhanced continent-wide observations of Sub-Saharan Africarsquos carbon stocks fluxes atmospheric concen-trations and ecological processes 2) improve biogeochemi-cal models representing the main African ecosystem types3) better understand the role of fire emissions of SSA in theglobal carbon cycle and 4) assess the potential of SSA forcarbon sequestration and emission reduction

In this paper we present an outlook on the carbon balanceof SSA by using first results from the project CarboAfrica(namely net ecosystem productivity and emissions from firesdeforestation and forest degradation by field and model es-timates) supplemented by bibliographic data and comparedwith a new synthesis of the data from national communica-tions to UNFCCC in order to contribute towards 1) a betterquantification of the terrestrial carbon budget of SSA and2) the improvement of our understanding of the role of SSAin the global carbon cycle

2 Methodology

Results of this paper have been obtained using both pub-lished and unpublished data of the CarboAfrica project sup-plemented by bibliographic data and compared with a newsynthesis of the data from national communications to UN-FCCC The study area is Sub-Saharan Africa (hereinaftercalled SSA) In particular research activities are being con-ducted in the following SSA countries Benin BotswanaBurkina Faso Ghana Ivory Coast Mali Niger CongoSouth Africa Sudan and Zambia Other SSA countries maybe considered before the end of the project

21 The CarboAfrica methodology

The CarboAfrica methodology is based on an integratedmultiscale and multitemporal approach The work is di-vided in 6 work packages dealing with long term ob-servation systems and data integration and consolidationprocess understanding of carbon fluxes of SSA ecosys-tems model-data integration for up-scaling to region and

Biogeosciences 6 2193ndash2205 2009 wwwbiogeosciencesnet621932009

A Bombelli et al An outlook on the Sub-Saharan Africa carbon balance 2195

29

Fig 1 Global Ecological Zone map of Africa (FAO FRA 2001

httpwwwfaoorg80geonetworkuuid=baa463d0-88fd-11da-a88f-000d939bc5d8)

showing the location of the sixteen eddy covariance sites that are run by or contribute

data to the CarboAfrica project Overlapped are the borders of the major eco-regions

of Sub-Saharan Africa as defined by Weber et al (2009) on the basis of the broad

distribution of ecosystem types and used to extend models and field data The legend

of the Ecological Zones classes is shown in Figure A1 of Appendix A

CarboAfrica (CA) sites

CA sites showed in Table 3

Non CA sites showed in Table 3

Ecoregions from Weber et al 2009

Country boundaries

Fig 1 Global Ecological Zone map of Africa (FAO FRA 2001 httpwwwfaoorg80geonetworkuuid=baa463d0-88fd-11da-a88f-000d939bc5d8) showing the location of the sixteen eddy covariance sites that are run by or contributedata to the CarboAfrica project Overlapped are the borders of the major eco-regions of Sub-Saharan Africa as defined by Weber etal (2009) on the basis of the broad distribution of ecosystem types and used to extend models and field data The legend of the EcologicalZones classes is shown in Fig A1 of Appendix A

continent fire-climate-carbon cycle interactions on regionaland continental scale evaluation of a sustainable sequestra-tion potential in relation with CDM communications and ca-pacity building More details on the project methodology isavailable atwwwcarboafricanet

211 Carbon stock and fluxes

The observational backbone of the project is constitutedby the experimental eddy covariance flux network (Fig 1)for gas exchange measurements (carbon water and energyfluxes) between ecosystems and the atmosphere at landscapescale1 (see Archibald et al 2009 Merbold et al 2009 fordetails on the eddy covariance methodology) for details onthe eddy covariance methodology) This long-term observa-

1In this paper positive values of carbon fluxes represent carbonsink and negative values represent carbon source

tion system integrates data from at least eighteen intensivestudy sites established within CarboAfrica as well as via anumber of other research projects (sixteen eddy covariancesites of which one mobile station in three different sites andtwo atmospheric stations) covering a range of representativeland cover from tropical forest to desert going through dif-ferent woodland types different savanna types grasslandsEucalyptusplantations and different agricultural crops Thisnetwork includes the first flux station in an African tropicalforest which is providing new information on the Africarsquosrole in the global carbon cycle Biomass samples are beingtaken to provide additional data for carbon estimates valida-tion Above ground biomass data used to produce the figuresof this paper have been derived from the last IPCC report(2007a) We have used the data from Henry and collabo-rators (unpublished) to make an estimation of Soil OrganicCarbon (SOC) for the different classes of FAO forest ecolog-ical classification (FAO 2006)

wwwbiogeosciencesnet621932009 Biogeosciences 6 2193ndash2205 2009


212 Ecosystem processes

Intensive field campaigns at the flux sites of Fig 1 were con-ducted to measures ecosystem processes in soil and vege-tation including soil and plant water relations and gas ex-change (CO2 but also CH4 and N2O) plant phenology andplant ecophysiology (see also Kutsch et al 2008 for moredetails)

213 Modelling

In situ and remote sensing data were integrated for the modelparameterisation and evaluation Different models from dataoriented to process oriented were used to provide a multipleconstraint approach to the continental carbon cycle integra-tion This allows covering the model domain in terms of vari-able temporal scales spatial scales and processes addressedData presented here were extracted from a simulation exper-iment on the interannual variability of Africarsquos ecosystemproductivity (Weber et al 2009) It is noteworthy that themodels were developed mostly for other continents and arenot validated against site data for the annual carbon balanceof SSA See also Chevallier et al (2009) Ciais et al (2009)Seaquist et al (2009) and Sjostrom et al (2009) for moredetails on the models used by CarboAfrica

214 Fires

The CarboAfrica fire approach considers both burned areaextent and fire intensity and emission Remote sensing basedcarbon emission estimates from fires were derived by com-bining the mapping of burned area and the fire radiative en-ergy (FRE) technology (see also Lehsten et al 2009 Robertset al 2009) The interactions of ecosystem productivitywith fire patterns was studied using a combination of pre-scribed burned area (based on the L3JRC analysis) and thedynamic vegetation model (DVM) LPJ-GUESS with the firemodel SPITFIRE included (Smith et al 2001 Thonicke etal 2009) Using a DVM provides a tool to disentangle theinteractions of precipitation NPP and litter production asthe main drivers of fires and to investigate whether globalpatterns emerge that are generally representative for savannaecosystems

215 GHGs emissions

GHGs emission data were extracted from the African coun-tries national communication to the United Nations Frame-work Convention on Climate Change (UNFCCC) The fol-lowing classes were considered anthropogenic emissions(regrouping emissions from the energy industry and wastesectors) agriculture emissions Land Use Change andForestry (LUCF) emissions and removals (ie the carboncapture from the atmosphere and storage in terrestrial ecosys-tems) The base year adopted for most of the countries was1994 except for Sierra Leone and Egypt (1990) Gambia and

Chad (1993) Togo Mali Mauritania and Sudan (1995) andBurundi (1998) All data were converted to CO2 equivalenton the basis of the Global Warming Potential as reported inIPCC (2007b) We have not considered some data becausethey were not reliable South Africarsquos emissions by LUCFsector is zero because of intensive afforestation activities ex-pansion of plantations and conservation policies (as reportedin its National Communication)

National data were regrouped in three different Africanregions Northern Africa Sub-Saharan Africa and SouthAfrica Northern Africa was defined as composed of Al-geria Egypt Libya Morocco Tunisia and Western SaharaThe remaining countries were considered to belong to SSAexcluding South Africa which has been considered individu-ally Per-capita data have been derived by dividing the emis-sion values by the population (UN 2006)

3 Results and discussion

31 The Sub-Saharan carbon balance an overview ofcurrent estimates

According to the National Communications to UNFCCCthere is a great disproportion among emissions from differentAfrican regions (Table 1) South Africa is the main carbonemitter producing about 14 of the global African emis-sions (403 Tg eq CO2) This result is much more evidentif we compare per-capita anthropogenic emissions SouthAfrica emits 80 Mg eq CO2 per person more than four timesof both the northern Africa and the rest of SSA

Considering the low fossil fuel emissions SSA carbon bal-ance is dominated by two large fluxes emissions from landuse change and uptake by terrestrial ecosystems ExcludingSouth Africa emissions from LUCF and agriculture sectorin SSA are 1845 Tg eq CO2 and removalsminus2461 Tg eq CO2Combining all the emissions (including fossil fuel) and re-movals SSA (excluding South Africa) is currently a net sinkof 312 Tg eq CO2 However considering the sources fromNorthern and South Africa by the current available NationalCommunications the whole African continent appears to bea small source of 359 Tg eq CO2

However the SSA carbon balance due to relatively smallnet difference between the two large fluxes of emissions andremovals is subjected to large interannual variability and tohigh uncertainties in the data used to report to UNFCCC na-tional inventories

The overall annual carbon balance of SSA region (CB) canbe summarized into its main components as

CB=NEPminusEFSminusDminusFDminusAminusFFminusLT + R

Where NEP is net ecosystem productivity of terrestrialecosystems EFS emissions from savanna fires D emissionsfrom deforestation (eg change of land use) FD emissionsfrom forest degradation (eg logging) A emissions from



Table 1 Total annual GHG emission estimates (above) and per-capita values (below) for three different African regions Northern Africa(NA) Sub-Saharan Africa ndash excluding South Africa (SSAa) and South Africa (SA) The following sectors were considered anthropogenicemissions (regrouping emissions from the energy industry and waste sectors) agriculture emissions Land Use Change and Forestry (LUCF)emissions and removals Data were processed from the African countries national communication to the United Nations Framework Con-vention on Climate Change (UNFCCC) All data were converted to CO2 equivalent on the base of Global Warming Potential reported inIPCC (2007b) South Africarsquos emissions by LUCF sector is zero because of intensive afforestation activities expansion of plantations andconservation policies See Sect 215 for more details

Region Emissions sources

Anthropogenic Agriculture LUCF Total emissions Removals Net emission[Tg eq CO2] [Tg eq CO2] [Tg eq CO2] [Tg eq CO2] [Tg eq CO2] [Tg eq CO2]

NA 235 51 25 312 25 287SSAa 338 375 1470 2145 2461 minus312SA 364 39 0 403 19 384Total Africa 938 465 1495 2860 2505 359

Anthropogenic Agriculture LUCF Total emissions Removals Net emissionPer-capita [Mg eq CO2] [Mg eq CO2] [Mg eq CO2] [Mg eq CO2] [Mg eq CO2] [Mg eq CO2]


a = excluding South Africa

agriculture FF emissions from fossil fuel LT lateral trans-port of carbon from the region (rivers trade food consump-tion etc) and R precipitation inputs We can disregard in afirst assumption both LT and P (that partially counterbalanceeach other) assume for agriculture the emission statistics ofUNFCCC and try to address more in details the other com-ponents

32 C stocks and NEP of SSA terrestrial ecosystems

A comprehensive analysis of the role of terrestrial ecosys-tem carbon contribution to the SSA carbon balance shouldbe based on inventories of carbon pools and their changesin time direct flux measurements (as from the CarboAfricanetwork see Kutsch et al 2008 Archibald et al 2009 Mer-bold et al 2009) and process-based biogeochemical modelsthat derive NEP from estimates of GPP and ecosystem respi-ration (Ciais et al 2009 Friedlingstein et al 2009 Weberet al 2009) Since CarboAfrica network is currently underoperation and long term data sets are not yet available consis-tently through all the sites we present only few direct flux re-sults and concentrate our analysis on existing inventory dataand models derived outputs

In broadest terms the two dominant vegetation types inSSA are forests (ranging from tropical to mountain regions)and savannas (ranging from grasslands to woodlands)

Most of the forest biomass in Africa is localized inthe Sub-Saharan region and more precisely in the CentralAfrican belt Estimates of above-ground dry biomass in

30

-200

-150

-100

-50

0

50

100

150

200

TAr TAwa TAwb TBSh TM SCs SBSh SM

C s

toc

ks

(M

g h

a-1

)

Aboveground Roots SOC 0-30 SOC 30-100

Fig 2 Distribution of C stocks among different African forest ecosystems Tar =

Tropical rain forests TAwa = Tropical moist deciduous forests TAwb = Tropical dry

forests TBSh = Tropical shrublands TM = Tropical mountains SCs = Subtropical

dry forests SBSh = Subtropical steppes SM = Subtropical mountain systems

Aboveground biomass and root C were obtained from IPCC (2007) and SOC was

obtained from Henry (unpublished)

Fig 2 Distribution of C stocks among different African for-est ecosystems Tar = Tropical rain forests TAwa = Tropical moistdeciduous forests TAwb = Tropical dry forests TBSh = Tropicalshrublands TM = Tropical mountains SCs = Subtropical dryforests SBSh = Subtropical steppes SM = Subtropical mountainsystems Aboveground biomass and root C were obtained fromIPCC (2007) and SOC was obtained from Henry (unpublished)

African tropical forests vary among authors ranging from30 to 255 Mg C haminus1 (Brown 1997 Palm et al 2000Houghton and Hackler 2006 IPCC 2007a) As reportedin Fig 2 mean aboveground C accumulated in humid tropi-cal forests is 155 Mg C haminus1 (IPCC 2007a) Above ground



Table 2 Gross Primary Productivity (GPP) Net Primary Productivity (NPP) and Net Ecosystem Productivity (NEP) in different Africanecoregions (as defined by Weber et al 2009) Data are presented per unit of surface (a) and extended to different African regions (b) Datawere extrapolated from Weber et al (2009) by averaging the estimates from the four different model used

(a) GPP NPP NEPg C mminus2 yminus1 g C mminus2 yminus1 g C mminus2 yminus1

Northern Savanna Belt 1341plusmn474 649plusmn184 248plusmn50Central Africa Tropical Forest 2558plusmn733 1151plusmn431 345plusmn241Horn of Africa 919plusmn386 428plusmn160 155plusmn29Suthern Raingreen woodland 1572plusmn576 702plusmn213 262plusmn64South African Grassland 831plusmn348 367plusmn111 139plusmn32

(b) GPP NPP NEPPg C yminus1 Pg C yminus1 Pg C yminus1

Northern Savanna Belt 883plusmn312 427plusmn121 163plusmn033Central Africa Tropical Forest 731plusmn209 329plusmn123 098plusmn069Horn of Africa 152plusmn064 071plusmn026 026plusmn005Suthern Raingreen woodland 740plusmn271 331plusmn100 123plusmn030South African Grassland 212plusmn089 093plusmn028 035plusmn008Sub-Saharan Savannas 1987plusmn369 922plusmn176 348plusmn067Sub-Saharan Africa 2717plusmn336 1251plusmn159 446plusmn058

31

Fig 3 Carbon stocks in humid tropical ecosystems of Cameroon under different

management F = Forest A = Cucumber based farm B = Banana farm C =

Cropland BF = Bush fallow LF = Long fallow TF = Tree fallow SF = Secondary

forest CP = Cocoa plantation Data were obtained from Kotto-Same et al (1997) and

Kanmegne (2004) = crop mixtures (maize groundnuts cassava banana)

-150

-100

-50

0

50

100

150

200

250

F A B C BF LF TF SF CP

C s

tock

s (

Mg

ha

-1)

ROOT C ABGC SOC 0-50

Fig 3 Carbon stocks in humid tropical ecosystems of Cameroonunder different management F = Forest A = Cucumber based farmB = Banana farm C = Cropland BF = Bush fallow LF = Long fal-low TF = Tree fallow SF = Secondary forest CP = Cocoa planta-tion Data were obtained from Kotto-Same et al (1997) and Kan-megne (2004) = crop mixtures (maize groundnuts cassava ba-nana)

carbon stocks decrease from tropical forest with a minimumin the mountain forests due to limitations of climate and soilon forest productivity (Fig 2) Tropical rain forests show thehighest belowground C both for the root (57 Mg C haminus1)

and the soil components (53 and 55 Mg C haminus1 for 0ndash30and 0ndash100 cm) while minimum values are shown by moun-tain forests Both above ground and below ground C stocks

vary between different forest management types (Fig 3) Theconversion of forest to other land use types involves signifi-cant changes in C stocks particularly SOC While the maxi-mum SOC was observed in logged forest (77 Mg C haminus1) theminimum SOC was observed in recent bush fallow 66 (Mg Chaminus1) Similar trend was observed by Houghton (1999) whoestimated SOC of about 100 and 50 Mg haminus1 in undisturbedclosed and open forests respectively

From FAO statistics (FAO 2006) and IPCC data (IPCC2007a) it is possible to derive an overall assessment of the Cstocks in SSA forests Above ground carbon stock for SSAforests is equal to 48 Pg C Integrating our unpublished datain combination with FAO and IPCC data is possible to esti-mate the contribution of belowground C stock which includeroots and soil organic carbon to be equal to 161 Pg in SSAfor 0ndash100 cm soil layer The total C stocks for SSA can thusbe considered to be 209 Pg C of which about 77 is comingfrom belowground carbon

NEP estimates from forest inventories are difficult to ob-tain We can consider the current UNFCCC national com-munication data as an overall estimate (see Table 1) whichgives a total removal (mainly forests) of 113 Pg C yminus1 forSSA This value is comparable to recent model results fromCarboAfrica that provide a new insight in NEP estimation forforests SSA forest NEP according to several models (Weberet al 2009) can be estimated as 098plusmn069 Pg C yminus1 (seeTable 2) This value can be regarded as a first approxima-tion considering Central Africa tropical forest (as defined byWeber et al 2009) as the main forest region of SSA



Table 3 Net Primary Productivity (NPP) and Net Ecosystem Productivity (NEP) in different savannas When needed original data wereconverted to Mg C haminus1 yminus1 NSB = Northern Savannah Belt SRW = Southern Raingreen Woodlands SAG = South African grasslands = CarboAfrica data

Citation Region NPP NEPMg C haminus1 yminus1 Mg C haminus1 yminus1

Scholes and Walker (1993) SAG 381 123Hanan et al (1998) NSB 331 032Chidumayo (2002) SRW 042Veenendaal et al (2004) SAG 012Grace et al (2006) Africa 58Grace et al (2006) Global 72 014van der Werf et al (2006) Africa 182Grace unpublished in Ciais et al (2009) Africa 59Brummer et al (2008) NSB 453Archibald et al (2009) SAG minus025Lehsten et al (2009) Africa 63Kutsch and Merbold (unpublished) SRW 1627 005

Average 835plusmn565 082plusmn156

Tropical drought-deciduous woodlands savannas shrub-lands thickets and grasslands form a seamless continuumwhich can only be divided into distinct structural types by ap-plying arbitrary limits (Scholes and Hall 1996) In this paperwe use the term savanna to indicate the co-occurrence of treesand grasses in different proportion (Sankaran et al 2004)ranging from grassland to woodland and defined by Scholesand Hall (1996) as ldquotropical tree-grass systemsrdquo This systemcovers the majority of the surface of the following Africanecoregions as defined by Weber et al (2009) Northern Sa-vannah belt Horn of Africa Southern raingreen woodlandsand South African grasslands According to the above defi-nition Sub-Saharan savannas (including grasslands and openwoodlands) cover an area of about 15 million km2 which cor-responds to more than 80 of SSA land area However thisarea and the consequent extrapolations is overestimated be-cause the above African ecoregions contain also other differ-ent ecosystem types

NPP estimates of savanna ecosystems are available froma number of studies but only few estimates are availablefor NEP and they vary from a source value ofminus025 Mg Chaminus1 yminus1 (Archibald et al 2009) to a sink value of 453 MgC haminus1 yminus1 (Brummer et al 2008) (see Table 3) The Car-boAfrica project is trying to fill in this gap and some firstresults are given in Table 3 (Kutsch et al 2008 Archibaldet al 2009 Merbold et al 2009) The field observationsdeviate substantially from the NEP data extrapolated fromthe modelling intercomparison across the different Africanecoregions of Weber et al (2009) (Table 2) This showsthe uncertainty in modelling as well as field estimates thecurrent model runs estimate an overall carbon budget of348plusmn067 Pg C yminus1 Field measurements in Table 3 areregrouped in the different African ecoregions of Weber et

al (2009) as shown in Fig 1 By extending the averageNEP value of each ecoregion on its respective land surfaceand summing the total values of each ecoregion we obtain asmaller sink strength (189 Pg C yminus1) than the models2 Nu-merous processes that can impede the carbon sink are not ornot adequately included in the modelling (eg nutrient limita-tion herbivores indirect effects of fire etc) The model esti-mates consider northern savannas southern rain-green wood-lands and grasslands as well as horn of Africa all together asthe dominant savanna like ecosystems of SSA but do notconsider carbon emissions from fires

According to the preliminary modelling results forestecosystems have much larger productivity per unit of landarea (about 13 Mg haminus1 yminus1) compared to NEP for savannas(2 Mg haminus1 yminus1) However the savannas are extending overa much larger area and thus they determine significantly thecarbon budget of the region As we will see below the an-nual net carbon balance of SSA is dominated by savannasfires which reduce considerably their effect as a large sinkof carbon

33 The role of savanna fires (EFS) in the SSA carbonbalance

Africa GHG emissions are highly affected by vegetationfires contributing substantially to the total carbon emissionsover large parts of the continent (Lehsten et al 2009) Es-timates of biomass burned annually in Africa are very dif-ferent however they are often around thousands of Tg ofbiomass (Andreae 1993 Hao et al 1996 Ito and Penner

2For the ecoregion Horn of Africa where no field sites are avail-able we used the average NEP value of SSA savannas extended onits land surface



Table 4 Data related to deforestation and forest degradation in the Congo Basin African countries Source of data is specified in the footnote

Cameroon Gabon Congo DRC CAR Equatorial Guinea Congo Basin

Land surface (1000 ha)a

Land area 46 540 25 767 34 150 226 705 62 298 2805 398 265Dense forest area 21 436 21 190 25 914 124 566 8227 1843 203 176Production forests 10 500 19 000 12 000 87 000 3300 1250 133 050Area under forest management process 4348 6368 7115 9680 2994 55 30 560

Annual forest cover change ()b

Gross deforestation minus028 minus016 minus012 minus033 minus015 minus035 minus027Net deforestation minus019 minus012 minus003 minus026 minus007 041 minus019Net degradation minus002 minus009 minus001 minus015 minus002 052 minus01FAO deforestation minus09 005 minus01 minus04 minus01 minus06 minus036

Average C stocks (Mg C haminus1)c

SOC (0ndash30 cm) 52 51 75 49 41 70 54Aboveground C 90 167 231 173 123 70 142Root C 17 32 44 33 23 13 23

Impact of forest cover change on C stocks (Tg yminus1)d

Conversion to cropland 438 473 170 6454 215 ndash 7750Conversion to agroforestry 104 142 050 1981 063 ndash 2340Impact of degradation 029 246 039 2611 042 ndash 2968Expected C losses from logging (Tg)e 4435 6496 7257 9873 3054 056 31171

a Data on land area and forest management is obtained from Nasi et al (2006)b Data from deforestation and degradation rate were obtained from FAO (2006) and CBFP (2007)c SOC was obtained from Henry (unpublished) aboveground C was obtained from FAO (2007) and root C was obtained from abovegroundbiomass using rootshoot ration for tropical humid region (Jackson et al 1996)d Impact of deforestation and degradation was obtained using the of C impacted by forest conversione Impact of logging was estimated using a coefficient of 102 Mg C haminus1 obtained from Brown et al (2005) and considering a time rotationof 40 years

2004 van der Werf et al 2004) Latest results published inthis issue (Lehsten et al 2009 Roberts et al 2009) showlower values 723plusmn70 and 855 Tg C yminus1 respectively re-sulting into an average of 079 Pg C yminus1

Wildfires are a prominent feature and intrinsic ecosys-tem component of savannas worldwide Some theoreticalmodels even attribute the existence of savannas ie the co-occurrence of trees and grasses to their high fire frequency(see Sankaran et al 2004) The vast majority of African fireevents occur in Savannas (Tansey et al 2004) While theoverall difference between ldquonaturalrdquo and anthropogenically-driven fire regime is difficult to be determined it seems clearthat currently the vast majority of fires are ignited by humansmostly due to different land management practices (Saarnak2001) There are different theories about the relationship be-tween fire frequency and standing biomass but a better pre-vention of fires could lead to a much higher above and belowground biomass gain (Grace et al 2006)

According to Lehsten et al (2009) fires consume around10 of Savanna NPP annually Savanna systems subjectedto a time-integrated relatively constant fire frequency havean almost neutral carbon balance since the fire induced car-

bon losses are offset by the consequent vegetation re-growth(Saarnak 2001) However when merging the above C lossfrom savanna fires (079 Pg C yminus1) with the estimated NEPof savannas (excluding the fire component) the results show aresidual carbon sink variable from 110 (using the field mea-surements) to 269 Pg C yminus1 (using the model estimates)Clearly improved estimates and a more detailed analysis ofall contributing processes are required to pin-point and re-duce this variability and the major uncertainties

34 The role of deforestation (D) in the SSA carbonbalance

Approximately 16 of the worldrsquos forests are in Africa andaround 4 million ha yrminus1 of forest have been lost in the pe-riod 2000ndash2005 in Africa (FAO 2006) In 2005 Africa hadthe highest rate of deforestation with 062 of annual loss offorest area Most of the forest carbon losses were reported forthe Central Africa particularly in the Congo Basin countriesThe Congo Basin forest is the worldrsquos second largest tropicalforest covering 223 million of hectares in six countries andcontaining a quarter of the worldrsquos remaining tropical forest



(FAO 2006) Even if Africa showed the smallest deforestedarea of all tropical regions (Achard et al 2004) an aver-age size of 0805 and 0611 million hectares was deforestedevery year for the period 1900ndash2000 and 2000ndash2005 respec-tively (Table 4) Thus Africa has been contributing up to 35of total tropical land clearing from deforestation with an an-nual carbon loss from deforestation of 037 Pg C yminus1 in thelast decades (Houghton 2003) Carbon losses through de-forestation tend to be ldquopermanentrdquo in Africa as afforestationand reforestation rates are modest at less than 5 of annualdeforestation

Conversion from forest to cultivation through slash-and-burn activities could decrease the aboveground C stock to220 Mg C haminus1 (the rate of recovery in fallow systems de-pending on biomass at the start of the fallow period) (Kotto-Same et al 1997) and the belowground C stock to 244 MgC haminus1 in humid and sub-humid tropics (Vagen et al 2004)

On the basis of average C stocks in different Cameroonland uses (Fig 3) which can be considered representative forSSA region we have estimated that C losses from the con-version of forest to different croplands range from the highestvalue of 224 Mg C haminus1 for crop mixtures (maize ground-nuts cassava banana) to the lowest value showed by cucum-ber based croplands (59 Mg C haminus1) On the other hand theconversion of forest to cocoa plantations led to C losses ofabout 133 Mg C haminus1

Based on these new values we can provide an approxi-mate estimation of the SSA deforestation related emissionsin Central Africa of 101 Tg C yminus1 (see Table 4) If we as-sume for simplicity similar rates of deforestation and landconversion for Eastern and South Africa forests the overalldeforestation emission losses would be 251 Tg C yminus1 Thisestimate is lower than that by Houghton (2003) of 370 Tg Cyminus1 which was based on the total forest area in Africa Ifwe correct for the total forest area of Africa (635 Mha) ourestimate is 316 Tg C yminus1 which is comparable with the es-timate of Houghton (2003) and thus we can keep 251 Tg Cyminus1 as first approximation of the value of SSA emission bydeforestation in our carbon balance calculation

35 The role of forest degradation (FD) in the SSA car-bon balance

Deforestation and forest degradation are two different pro-cesses with different ecological consequences Deforesta-tion involves cutting damaging and burning of forest andis mainly associated with slash and burn agriculture (FAO2006) in SSA whereas forest degradation is mainly relatedto logging activities and forest encroachment by local popu-lation While logging activities mostly correspond to clear-cutting practices in Asia and America in tropical Africa se-lective logging is practised Degradation is less well docu-mented than deforestation and is also more difficult to mapWhile deforestation due to cropland conversion can be re-ported by satellite imageries forest degradation can not be

easily accounted by remote sensing tools Moreover esti-mating the logging impact on C stocks it is difficult becauseof the missing information on illegal logging practices andthe poorly documented amount of legal logging Howeverwhen considering the impact of selective logging activitieson C stocks Brown et al (2005) reported C losses of about10 Mg C haminus1 of tropical forest in Congo considering a timerotation of 40 years By extrapolating this value using thearea under forest management as reported in Table 4 forthe Congo Basin and scaled to the overall SSA forest areawe obtain an annual emission rate for degradation equal to82 Tg C yminus1

To our knowledge no data were available on the impact ofdegradation due to forest encroachment on C stocks There-fore an alternative method to derive emission rates from en-croachment was also used by assuming that C stocks in de-graded forest were close to those measured in secondary for-est (Fig 3) From the difference between carbon stock of anatural forest and a degraded one it was estimated that theconversion from forest to degraded forest by encroachmentled to C losses of about 98 Mg C haminus1

After logging a natural regeneration of a secondary foresttakes place and can revert carbon losses recovering carbonstocks at a certain point in time Very few studies report thenatural capacity of African ecosystems to regenerate afterperturbations Kotto-Same (1997) reported that about 74of aboveground C was regenerated after a period of fallow of18 years According to the regression between time of fal-low and C stocks the total system C would be equal to theoriginal natural forest after a period of 24 years (Kotto-Same1997)

36 A first synthesis of the CarboAfrica results

Table 5 summarises the first numbers derived from the Car-boAfrica estimates together with bibliographic data and com-pares them with the UNFCCC national reporting estimatesBy this preliminary synthesis the provisional biogenic car-bon balance of SSA varies from around a smaller valueof 016 Pg C yminus1 (using UNFCCC estimates that do notconsider explicitly the net ecosystem productivity of savan-nas) to 100 Pg C yminus1 Other studies such as Williams etal (2007) and Ciais et al (2009) showed a neutral balanceor a small sink of 015 Pg C yminus1 respectively This last valueis very similar to the one derived from UNFCCC estimatesThe inclusion of the fossil fuel emissions to derive the over-all SSA carbon balance reduces the natural sink potential to081 Pg C yminus1 and can even turn it into a small source of003 Pg C yminus1 if we consider the UNFCCC estimates (Ta-ble 5) Model estimates for forest and savanna NEP wouldlead to a larger unrealistic biogenic sink of 323 Pg C yminus1

Therefore the terrestrial ecosystem component of SSAseems to be a carbon sink but there is still a high uncertaintyespecially about the savanna role (depending on the useddata in situ national communications models) that leads



Table 5 Comparison between preliminary carbon balance estimates for SSA derived from CarboAfrica data (supplemented by bibliographicdata as specified below) and the UNFCCC national communication data NEP(F) = Net Ecosystem Productivity of Forests NEP(S) = NetEcosystem Productivity of Savannas D = emissions from Deforestation FD = emissions from Forest Degradation EFS = Emissions fromSavanna Fires A = emissions from Agriculture FF = emissions from Fossil Fuel Biogenic = NEP(F)+NEP(S)+D+FD+EFS+A Data aregiven in Pg C yminus1 NEP(F) is taken from Lewis et al (2009) NEP(S) is derived from CarboAfrica and bibliographic data listed in Table 3FF is taken from Canadell et al (2009)

Sinks Sources SSA C-balanceNEP(F) NEP(S) D FD EFS A FF Biogenic Total

034 189 minus025 minus008 minus079 minus011 minus018 100 081

UNFCCC Removals LUCF emissions A068 minus040 minus011 minus019 016 minus003

in turn to a high uncertainty about this sink value The fewavailable annual NEP values from in situ estimates highlydiffer each other and the average model result also hides alarge variability among the different model outputs rangingfrom 13 to 39 Pg C yminus1 Moreover terrestrial ecosystemmodels have not been used in depth for savanna ecosystemsup to now and it is likely that a number of crucial processesare not yet well represented This highlights a crucial re-search need as the importance of African savannas for dy-namics of the African carbon balance is also seen in theirlarge interannual variability that seems to be strongly relatedto water balance In addition from preliminary CarboAfricaresults (Castaldi De Grandcourt and collaborators unpub-lished) it seems that African savannas might represent a sig-nificant source of CH4 by termite activity thus reducing theirsink potential Further studies are therefore needed to betterquantify the CH4 source value and the role of land charac-teristics and disturbance on this source Hence increasingthe field efforts and improving the terrestrial modelling ca-pacity for these regions shall be a key focus in future Africancarbon-cycle research in particular in the frame of the Car-boAfrica project that is producing new carbon flux data torefine and improve the above estimates

4 Conclusions

The preliminary results of the CarboAfrica project suggestedthat the biogenic SSA carbon balance is a delicate equilib-rium between a number of large fluxes that are both humanand climate related resulting in a probable carbon sink valueThere is still a high uncertainty about this sink value due to1) the current data used which are preliminary and need vali-dation 2) the generalization of the Africarsquos land surface con-sidered as savanna that leads to a consequent overestimationof the total savanna NEP 3) the lack of process representa-tion in terrestrial models and 4) the lack of an optimal cover-age of the most representative ecosystem types (in particulartropical forests) Nevertheless this first analysis shows that

Africa could have a probable potential for carbon sequestra-tion larger than expected which should not be ignored forstudies of global carbon cycle-climate interactions

Savannas play a dominant role in shaping the SSA carbonbalance due to their large land cover extension their fireregime and the strong interannual variability of their plantproductivity

Forests seems to play an important role as carbon sinkand this is confirmed also by the recent study of Lewis etal (2009) showing that in the last 40 years there was anincrease of 034 Pg C yminus1 in the carbon storage in Africantropical forests

Even if fossil fuel emissions from SSA are quite low theyare dramatically increasing and they already play a key roleby reducing the natural sink potential In effect they couldturn the SSA carbon balance from a sink to a source value

By these results a critical and urgent need for better under-standing the main components of the African carbon balancehas emerged in particular for better addressing the role ofsavannas and tropical forests Moreover further studies ondisturbances and plant and soil processes including methaneand nitrogen fluxes should be considered to better under-stand the role of CO2 and other non CO2 greenhouse gasesin the overall SSA GHGs budget This interest is growingfrom the fact that agriculture is rapidly expanding also in re-sponse to biofuels demand This rapid change in the SSAlandscape and the increase in fossil fuel emissions have alsoto be considered in future projection of the carbon cycle ofthe region

CarboAfrica project with its observational network willindeed provide a unique and increased dataset of field mea-surements for improving model parameterization and vali-date African regional estimates The next researches in theframe of the CarboAfrica project will try to reduce the un-certainties in order to clarify the Sub-Saharan African role inthe global carbon cycle



32

Appendix A

Fig A1 Legend of the classes of the Ecological Zones shown in Figure 1

Fig A1 Legend of the classes of the Ecological Zones shown inFig 1

AcknowledgementsFinancial support for this scientific researchcame from the European Commission which has been funding theproject ldquoCarboAfricardquo (GOCE 037132) under the 6 FrameworkProgramme (FP6) The authors thank Gloria D Djagbletey ofFORIG and Ms Marion Chesnes for their contribution during thefield work at Bobiri Forest Reserve (Ghana) and Antonio Martucci(FAO) for his graphical support Thanks to all the CarboAfricapartners and collaborators which have made possible this project

Edited by S Bouillon

References

Achard F Eva H D Mayaux P Stibig H-J and BelwardA Improved estimates of net carbon emissions from land coverchange in the tropics for the 1990s Global Biogeochem Cycles18 GB2008 doi1010292003GB002142 2004

Andreae M O Biomass burning its history use and distribu-tion and its impact on the environmental quality and global cli-mate Global Biomass Burning Atmospheric Climatic and Bio-spheric Implications Levine J S MIT Press Cambridge Mas-sachusetts USA 1991

Andreae M O The influence of tropical biomass burning on cli-mate and the atmospheric environment edited by Oremland RS Biogeochemistry of Global Change Radiatively Active TraceGases 113ndash150 New York Chapman and Hall 1993

Archibald S A Kirton A van der Merwe M R Scholes R JWilliams C A and Hanan N Drivers of inter-annual variabil-

ity in Net Ecosystem Exchange in a semi-arid savanna ecosys-tem South Africa Biogeosciences 6 251ndash266 2009httpwwwbiogeosciencesnet62512009

Baker D F Law R M Gurney K R Rayner P PeylinP Denning A S Bousquet P Bruhwiler L Chen YH Ciais P Fung I Y Heimann M John J Maki TMaksyutov S Masarie K Prather M Pak B Taguchi Sand Zhu Z TransCom 3 inversion inter comparison impactof transport model errors on the interannual variability of re-gional CO2fluxes 1988ndash2003 Global Biogeochem Cycles 20GB1002 doi1010292004GB002439 2006

Brown S Pearson T Moore N Parveen A Ambagis S andShoch D Impact of selective logging on the carbon stocksof tropical forests Republic of Congo as a case study Win-rock International available athttpcarpeumdeduresourcesDocumentsrptcarbon congo 3 2005 winrockpdfview 2005

Brown S Estimating biomass and biomass change of tropicalforests a primer FAO Forestry Paper 134 FAO Rome 1997

Brummer C Falk U Papen H Szarzynski J Wassmann Rand Bruggemann N Diurnal seasonal and interannual vari-ation in carbon dioxide and energy exchange in shrub savannain Burkina Faso (West Africa) J Geophys Res 113 G02030doi1010292007JG000583 2008

Canadell J G Raupach M R and Houghton R A Anthro-pogenic CO2 emissions in Africa Biogeosciences 6 463ndash4682009httpwwwbiogeosciencesnet64632009

Cao M K Zhang Q F and Shugart H H Dynamic responsesof African ecosystem carbon cycling to climate change ClimateRes 17 183ndash193 2001

CBFP (Congo Basin Forest Partnership) The forestsof the Congo Basin State of the Forest 2006 editedby Devers D and vande Weghe J P available athttpwwwcbfporgkeydocshtmlfile=docskeydocsState20of20the20Forest20200620smallpdf 2007

Chevallier F Fortems A Bousquet P Pison I Szopa S De-vaux M and Hauglustaine D A African CO emissions be-tween years 2000 and 2006 as estimated from MOPITT observa-tions Biogeosciences 6 103ndash111 2009httpwwwbiogeosciencesnet61032009

Chidumayo E N Changes in miombo woodland structure underdifferent land tenure and use systems in central Zambia J Bio-geogr 29 1619ndash1626 2002

Ciais P Piao S-L Cadule P Friedlingstein P and Chedin AVariability and recent trends in the African terrestrial carbon bal-ance Biogeosciences 6 1935ndash1948 2009httpwwwbiogeosciencesnet619352009

FAO Global Forest Resources Assessment 2005 Progress towardssustainable forest management FAO Forestry paper 147 Foodand Agriculture Organization of the United Nations Rome2006

FAO State of the Worldrsquos Forests 2007 The Food and AgricultureOrganization of the United Nations Rome 144 pp 2007

Friedlingstein P Cadule P Piao S L Ciais P and Sitch SThe African contribution to the global climate-carbon cycle feed-back of the 21st century Biogeosciences Discuss 5 4847ndash48662008httpwwwbiogeosciences-discussnet548472008

Grace J San Jose J Meir1 P Miranda H S and Montes R AProductivity and carbon fluxes of tropical savannas J Biogeogr33 387ndash400 2006



Hanan N P Kabat P Dolman A J and Elbers J A Photosyn-thesis and carbon balance of a Sahelian fallow savanna GlobalChange Biol 4 523ndash538 1998

Hao W M Ward D E Olbu G and Baker S P emissions ofCO2 Co and hydrocarbons from fires in diverse African savannaecosystems J Geophys Res 101 23577ndash23584 1996

Houghton R A and Hackler J L Emissions of carbon fromland use change in sub-Saharan Africa J Geophys Res 111G02003 doi1010292005JG000076 2006

Houghton R A The annual net flux of carbon to the atmospherefrom changes in land use 1850ndash1990 Tellus B 51 298ndash313doi101034j1600-0889199900013x 1999

Houghton R A Revised estimates of the annual net flux of carbonto the atmosphere from changes in land use and land manage-ment 1850ndash2000 Tellus B 55 378ndash390 2003

IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Reportof the Intergovernmental Panel on Climate Change Core writ-ing Team edited by Pachauri R K and Reisinger A IPCCGeneva Switzerland 104 pp 2007a

IPCC Changes in Atmospheric Constituents and Radiative Forc-ing edited by Solomon S Qin D Manning M Chen ZMarquis M Averyt K B Tignor M and Miller H L Cli-mate Change 2007 The Physical Science Basis Contributionof Working Group I to the Fourth Assessment Cambridge Uni-versity Press Cambridge United Kingdom and New York NYUSA 129ndash234 2007b

Ito A and Penner J E Global estimates of biomass burning emis-sions based on satellite imagery for the year 2000 J GeophysRes 109 D14505 doi1010292003JD004423 2004

Jackson R B Canadell J Ehleringer J R Mooney H A SalaO E and Schulze E D A global analysis of root distributionsfor terrestrial biomes Oecologia 108 389ndash411 1996

Kanmegne J Slash and Burn Agriculture in the Humid For-est Zone of Southern Cameroon Soil Quality Dynamics Im-proved Fallow Management and Farmersrsquo Perceptions Wagenin-gen University Wageningen 180 pp 2004

Kituyi E Wandiga S O Andreae M O and Helas G Biomassburning in Africa role in atmospheric change and opportuni-ties for emission mitigation edited by Sum Low P ClimateChange and Africa Cambridge University Press Cambridge79ndash89 2005

Kotto-Same J Woomer P L Appolinaire M and Louis Z Car-bon dynamics in slash-and-burn agriculture and land use alterna-tives of the humid forest zone in Cameroon Agriculture Ecosys-tems and Environment 65 245ndash256 1997

Kutsch W L Hanan N Scholes B McHugh I Kubheka WEckhardt H and Williams C Response of carbon fluxes towater relations in a savanna ecosystem in South Africa Biogeo-sciences 5 1797ndash1808 2008httpwwwbiogeosciencesnet517972008

Lehsten V Tansey K Balzter H Thonicke K Spessa A We-ber U Smith B and Arneth A Estimating carbon emissionsfrom African wildfires Biogeosciences 6 349ndash360 2009httpwwwbiogeosciencesnet63492009

Lewis S L Lopez-Gonzalez G Sonke B Affum-Baffoe KBaker T R Ojo L O Phillips O L Reitsma J MWhite L Comiskey J A Djuikouo K M-N Ewango CE N Feldpausch T R Hamilton A C Gloor M Hart T

Hladik A Lloyd J Lovett J C Makana J-R Malhi YMbago F M Ndangalasi H J Peacock J Peh K S-HSheil D Sunderland T Swaine M D Taplin J Taylor DThomas S C Votere R and Woll H Increasing carbon stor-age in intact African tropical forests Nature 457 1003ndash1007doi101038nature07771 2009

Merbold L Ardo J Arneth A Scholes R J Nouvellon Y deGrandcourt A Archibald S Bonnefond J M Boulain NBrueggemann N Bruemmer C Cappelaere B Ceschia EEl-Khidir H A M El-Tahir B A Falk U Lloyd J KergoatL Le Dantec V Mougin E Muchinda M Mukelabai MM Ramier D Roupsard O Timouk F Veenendaal E Mand Kutsch W L Precipitation as driver of carbon fluxes in 11African ecosystems Biogeosciences 6 1027ndash1041 2009httpwwwbiogeosciencesnet610272009

Mosier A R Gas flux measurements techniques with special ref-erence to techniques suitable for measurements over large eco-logical uniform areas Soils and the greenhouse effect edited byBouwman A F John Wiley and Sons Ltd Chichester 289ndash3011990

Nasi R Cassagne B and Billand A Forest management in Cen-tral Africa where are we Int For Rev 8 14ndash20 2006

Palm C A Woomer P L Alegre J Arevalo L Castilla CCordeiro D G Feigl B Hairiah K Kotto-Same J MendesA Moukam A Murdiyarso D Njomgang R Parton W JRicse A Rodrigues V Sitompul S M and van NoordwijkM Carbon sequestration and trace gas emissions in slash-and-burn and alternative land-uses in the humid tropics Alternativeto Slash-and-Burn Climate ChangeWorking Group Final ReportPhase II ICRAF Nairobi Kenya 36 pp 1999

Roberts G Wooster M J and Lagoudakis E Annual and diur-nal african biomass burning temporal dynamics Biogeosciences6 849ndash866 2009httpwwwbiogeosciencesnet68492009

Rodenbeck C Houweling S Gloor M and Heimann M Time-dependent atmospheric CO2 inversions based on interannuallyvarying tracer transport Tellus B 55 488ndash497 2003

Saarnak C F A shift from natural to human-driven fire regimeimplications for trace-gas emissions Holocene 11 373ndash3752001

Sankaran M Ratnam J and Hanan N P Tree-grass coexistencein savannas revisited ndash insights from an examination of assump-tions and mechanisms invoked in existing models Ecol Lett 7480ndash490 2004

Scholes R J and Walker B H (Eds) An African savanna syn-thesis of the Nylsvley study Cambridge University Press Cam-bridge UK 1993

Scholes R J and Hall D O The Carbon Budget of Tropical Sa-vannas Woodlands and Grasslands edited by Breymeyer AI Hall D O Melillo J M andAgren G I Global ChangeEffects on Coniferous Forests and Grasslands SCOPE 56 JohnWiley amp Sons Chichester 69ndash100 1996

Seaquist J W Hickler T Eklundh L Ardo J and HeumannB W Disentangling the effects of climate and people on Sahelvegetation dynamics Biogeosciences 6 469ndash477 2009httpwwwbiogeosciencesnet64692009

Sjostrom M Ardo J Eklundh L El-Tahir B A El-Khidir HA M Hellstrom M Pilesjo P and Seaquist J Evaluationof satellite based indices for gross primary production estimates



in a sparse savanna in the Sudan Biogeosciences 6 129ndash1382009httpwwwbiogeosciencesnet61292009

Smith B Prentice I C and Sykes M T Representation ofvegetation dynamics in the modelling of terrestrial ecosystemscomparing two contrasting approaches within European climatespace Global Ecol Biogeogr 10 621ndash637 2001

Smith K A Clayton H McTaggart I P Thomson P E ArahJ R M and Scott A The measurement of nitrous oxide emis-sions from soil by using chambers Philosophical TransactionsPhys Sci Eng Lond 351 327ndash338 1995

Tansey K Gregoire J-M Stroppiana D Sousa A Silva JM N Pereira J M C Boschetti L Maggi M Brivio PA Fraser R Flasse S Ershov D Binaghi E Graetz Dand Peduzzi P Vegetation burning in the year 2000 Globalburned area estimates from SPOT VEGETATION data J Geo-phys Res 109 D14S03 doi1010292003JD003598 2004

Tan Z Tieszen L L Tachie-Obeng E Liu S and Dieye A MHistorical and simulated ecosystem carbon dynamics in Ghanaland use management and climate Biogeosciences 6 45ndash582009httpwwwbiogeosciencesnet6452009

Thonicke K Spessa A Prentice I C Harrison S P andCarmona-Moreno C The influence of vegetation fire spreadand fire behaviour on global biomass burning and trace gas emis-sions Global Change Biol submitted 2009

United Nations World Population Prospects The 2006 RevisionPopulation Division of the Department of Economic and SocialAffairs of the United Nations Secretariat available athttpesaunorgunpp 2006

Vagen T G Lal R and Singh B R Soil carbon sequestrationin sub-saharan Africa a review Land Degraded Develop 1654ndash71 2004

van der Werf G R Randerson J T Collatz G J Giglio L Ka-sibhatla P S Arellano A F Olsen S C and Kasischke E SContinental-scale partitioning of fire emissions during the 1997to 2001 El NinoLa Nina period Science 303 73ndash76 2004

van der Werf G R Randerson J T Giglio L Collatz G JKasibhatla P S and Arellano Jr A F Interannual variabilityin global biomass burning emissions from 1997 to 2004 AtmosChem Phys 6 3423ndash3441 2006httpwwwatmos-chem-physnet634232006

Veenendaal E M Kolle O and Lloyd J Seasonal varia-tion in energy fluxes and carbon dioxide exchange for a broad-leaved semi-arid savanna (Mopane woodland) in southern AfricaGlobal Change Biol 10 318ndash328 2004

Weber U Jung M Reichstein M Beer C Braakhekke M CLehsten V Ghent D Kaduk J Viovy N Ciais P GobronN and Rodenbeck C The interannual variability of Africarsquosecosystem productivity a multi-model analysis Biogeosciences6 285ndash295 2009httpwwwbiogeosciencesnet62852009

Williams C A Hanan N P Neff J C Scholes R J BerryJ A Denning A S and Baker D F Africa and the globalcarbon cycle Carb Bal Manag 2 3 doi1011861750-0680-2-3 2007



The role of the African continent in the global carbon cy-cle and therefore in climate change is increasingly recog-nised (Houghton and Hackler 2006 Williams et al 2007)Even if Africa contributes only less than 4 to the globalanthropogenic fossil fuel emissions (Canadell et al 2009)it is a major source of interannual variability in global at-mospheric CO2 (Williams et al 2007) 20 of both globalnet primary production (NPP) and global land use emis-sions (mainly from forest degradation and deforestation)and around 40 of fire emissions have been attributed tothe African continent significantly affecting the atmosphericchemistry (Andreae 1991 Houghton 2003 Kituyi et al2005 van der Werf et al 2006)

In Sub-Saharan Africa (SSA) the role of land use changein controlling CO2 emissions and annual C budgets at re-gional and global scale may be more critical than in anyother regions (Houghton and Hackler 2006) with low fos-sil fuel emissions Africarsquos current continental scale carbonfluxes are dominated by uptake and release from terrestrialecosystems (which in turn are strongly linked to climatefluctuations) as well as forest degradation and deforestation(Williams et al 2007) African forests contain large car-bon stocks in biomass up to 255 Mg C haminus1 in tropicalrainforests (Palm et al 2000) that appear to be particu-larly vulnerable mainly due to the impact of climate changeland use change population increase and political instabilityAfricarsquos land use pressures will undoubtedly increase in thenext years and the forecasted increase in drought and tem-perature (IPCC 2007a) can lead to a significant decrease inecosystem carbon stocks in tropical forests and savanna atleast without human countermeasures (Tan et al 2009)

Despite the increasingly acknowledged importance ofAfrica in the global carbon cycle and its high vulnerability toclimate change due to both ecological and socio-economicfactors there is still a lack of studies on the carbon cyclein representative African ecosystems in particular tropicalforests Up to now it is not clear whether Africa is a netcarbon absorber or emitter however Williams et al (2007)in their comprehensive recent review on the African carboncycle showed that Africarsquos decadal scale mean carbon bal-ance appears to be neutral whereas latest results by Ciais etal (2009) showed that in the last decade Africa has probablybeen acting as a net carbon sink

Many open questions remain also on soil GHG (CH4 CO2and N2O) fluxes especially in tropical seasonally-dry areas

This lack of data on African carbon budget makes the nu-merical models of the carbon cycle highly uncertain whenapplied to Africa model simulations can provide just an ap-proximate profile of Africa since these models are usuallydeveloped and validated for different latitudes (Chevallier etal 2009 Ciais et al 2009 Weber et al 2009) Thereforethere is a strong need for a denser and more representativenetwork of carbon observation stations over Africa in orderto improve the regional carbon estimates using biogeochem-

ical models (Williams et al 2007 Ciais et al 2009 Weberet al 2009)

In addition a deeper knowledge of the African potentialto mitigate climate change through carbon sequestration andreduction of emissions is highly required by the current inter-national climate policy It is thus important to quantify bothcarbon stocks and fluxes of African forests and other ecosys-tems especially in the context of the UNFCCC (United Na-tions Framework Convention on Climate Change) mecha-nisms such as the Clean Development Mechanisms (CDM)of the Kyoto protocol and the Reducing Emissions fromavoided Deforestation and forest Degradation (REDD)

As a consequences of the above arguments the projectCarboAfrica funded by the European Commission waslaunched in 2006 in order to 1) support a network of con-tinued and enhanced continent-wide observations of Sub-Saharan Africarsquos carbon stocks fluxes atmospheric concen-trations and ecological processes 2) improve biogeochemi-cal models representing the main African ecosystem types3) better understand the role of fire emissions of SSA in theglobal carbon cycle and 4) assess the potential of SSA forcarbon sequestration and emission reduction

In this paper we present an outlook on the carbon balanceof SSA by using first results from the project CarboAfrica(namely net ecosystem productivity and emissions from firesdeforestation and forest degradation by field and model es-timates) supplemented by bibliographic data and comparedwith a new synthesis of the data from national communica-tions to UNFCCC in order to contribute towards 1) a betterquantification of the terrestrial carbon budget of SSA and2) the improvement of our understanding of the role of SSAin the global carbon cycle

2 Methodology

Results of this paper have been obtained using both pub-lished and unpublished data of the CarboAfrica project sup-plemented by bibliographic data and compared with a newsynthesis of the data from national communications to UN-FCCC The study area is Sub-Saharan Africa (hereinaftercalled SSA) In particular research activities are being con-ducted in the following SSA countries Benin BotswanaBurkina Faso Ghana Ivory Coast Mali Niger CongoSouth Africa Sudan and Zambia Other SSA countries maybe considered before the end of the project

21 The CarboAfrica methodology

The CarboAfrica methodology is based on an integratedmultiscale and multitemporal approach The work is di-vided in 6 work packages dealing with long term ob-servation systems and data integration and consolidationprocess understanding of carbon fluxes of SSA ecosys-tems model-data integration for up-scaling to region and



29












Country boundaries











213 Modelling


214 Fires


215 GHGs emissions


























30

-200

-150

-100

-50

0

50

100

150

200


C s

toc

ks

(M

g h

a-1

)

















31






-150

-100

-50

0

50

100

150

200

250


C s

tock

s (

Mg

ha

-1)





























































4 Conclusions










32

Appendix A





References



































































29












Country boundaries











213 Modelling


214 Fires


215 GHGs emissions


























30

-200

-150

-100

-50

0

50

100

150

200


C s

toc

ks

(M

g h

a-1

)

















31






-150

-100

-50

0

50

100

150

200

250


C s

tock

s (

Mg

ha

-1)





























































4 Conclusions










32

Appendix A





References





































































213 Modelling


214 Fires


215 GHGs emissions


























30

-200

-150

-100

-50

0

50

100

150

200


C s

toc

ks

(M

g h

a-1

)

















31






-150

-100

-50

0

50

100

150

200

250


C s

tock

s (

Mg

ha

-1)





























































4 Conclusions










32

Appendix A





References















































































30

-200

-150

-100

-50

0

50

100

150

200


C s

toc

ks

(M

g h

a-1

)

















31






-150

-100

-50

0

50

100

150

200

250


C s

tock

s (

Mg

ha

-1)





























































4 Conclusions










32

Appendix A





References








































































31






-150

-100

-50

0

50

100

150

200

250


C s

tock

s (

Mg

ha

-1)





























































4 Conclusions










32

Appendix A





References






















































































































4 Conclusions










32

Appendix A





References









































































































4 Conclusions










32

Appendix A





References






















































































4 Conclusions










32

Appendix A





References








































































4 Conclusions










32

Appendix A





References



































































32

Appendix A





References




























































































































