The European CO 2 , CO , CH 4 and N 2 O balance between 2001 and 2005

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Introduction
Globally, terrestrial ecosystems have absorbed about 30 % of anthropogenic emissions over the period 2000-2007(Canadell et al., 2007;;Le Qu ér é et al., 2009).The fact that the inter-hemispheric gradient of CO 2 , δ 13 C, and O 2 in the atmosphere is smaller than predicted from fossil fuel emissions alone (Ciais et al., 1995;Keeling et al., 1995;Tans et al., 1990) suggests that a significant fraction of terrestrial carbon sequestration must be north of the Equator.Using vertical profiles of atmospheric CO 2 concentrations as a constraint in atmospheric inversions, Stephens et al. (2007) inferred that the magnitude of the total northern land uptake ranges between −900 and −2100 Tg C yr −1 .This range was confirmed through atmospheric inversions (−1100 to −2500 Tg C yr −1 ) and land-based accounting (−1400 and −2000 Tg C yr −1 ) (Ciais et al., 2010a).By assuming that carbon uptake is evenly distributed across the land surface, we obtain a threshold value against which the actual uptake can be compared.Under the assumption of a uniform uptake, the European continent, as defined in this synthesis (5 × 10 6 km 2 ; see below), would absorb about 5 % or equivalently −45 to −105 Tg C yr −1 .
Early estimates indicated that carbon uptake (−135 to −205 Tg C yr −1 ) (Janssens et al., 2003) of the European ecosystems extending to the Ural Mountains (9 × 10 6 km 2 ) was indeed close to the average Northern Hemisphere sink i.e. −90 to −210 Tg C yr −1 for 9 × 10 6 km 2 .More recent estimates, found evidence for a stronger carbon sink of about −270 Tg C yr −1 (Schulze et al., 2009) for the same region.However these new estimates also suggest that the climate mitigation effect of this uptake is being compromised by emissions of other greenhouse gases, leaving little or no greenhouse gas mitigation potential for the European continent.Due to differences in methodology and data products the aforementioned sink estimates cannot be compared against each other and should therefore not be used in support of the hypothesis of an increasing C sink in Europe.The more recent estimates (Schulze et al., 2009) should be simply considered as an update of the earlier estimates (Janssens et al., 2003) because the new estimates made use of new and probably more realistic cropland models (Ciais et  al., 2010c), revised estimates of forest heterotrophic respiration (Luyssaert et al.,2007(Luyssaert et al., , 2010)), incorporated Russian forest inventories (Shvidenko and Nilsson, 2002;Shvidenko et al., 2001) to account for differences in forest management and productivity between EU-25 and Eastern Europe, and accounted for soil carbon losses and gains following land-use change (UNFCCC, 2011).
When estimating the GHG balance of Europe, one has to deal with the small-scale variability of the landscape and of emission sources and simultaneously cover the entire geographic extent of the continent.No single technique spans the range in temporal and spatial scales required to produce reliable regional-scale GHG balances.Nevertheless, we believe the problem can be tackled by using an integrated suite of data and models, based on the philosophy that the continental GHG balance must be estimated by at least two independent approaches, one coming down from a larger scale, and one coming up from a smaller scale.
We present a new compilation of the GHG balance of Europe as defined in Sect.2.1 following this dual constraint approach in which (1) a land-based balance derived mainly from ecosystem carbon inventories and (2) a land-based balance derived partly from eddy-covariance measurements are confronted with (3) the atmospheric-based balance derived from inversion informed by measurements of atmospheric GHG concentrations.
This work builds on earlier compilations by Janssens et al. (2003), Schulze et al. (2009Schulze et al. ( , 2010) ) and Ciais et al. (2010a) but (1) formalizes the accounting framework, (2) better separates the data sources which resulted in two independent rather than a single land-based estimate, (3) increases the number of data products and as such presents a more realistic bias estimate and (4) achieves a higher spatial and temporal consistency of the sink strength through accurate accounting and reporting of the spatial and temporal extent of the estimates.Introduction

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Full 2 Methods and material

Spatial and temporal extent of this study
The area under study (Fig. 1) was limited to Europe defined as the landmass containing the EU-27 plus Albania, Bosnia and Herzegovina, Croatia, Iceland, Kosovo, Macedonia, Norway, Serbia and Montenegro and Switzerland.A geographical rather than a political definition was followed, therefore, overseas territories (e.g.French Guyana) and distant islands (e.g.Spitsbergen, Canary Islands) where excluded from the carbon and GHG budgets whenever possible.It is often not clear whether the fluxes and stock changes from these islands are included in the data products underlying the carbon and GHG budgets.However, this resulted in minor inconsistencies in the spatial extent of the region under study.Also, it is often unclear whether the data for Serbia and Montenegro include Kosovo or not.We assumed they did not and whenever needed applied a bias correction for Kosovo.For each data product, the known anomalies in the spatial extent are indicated in Table 1.Geographical Europe typically extends as far as the Ural Mountains and thus includes Belarus, Ukraine, the Caucasian republics and part of Russia.Under the RE-CAPP initiative these states are subject of a separate synthesis included in this series (Dolman et al., 2012).
Where data availability permitted, carbon and GHG budgets were estimated for two arbitrary time periods i.e. 1996-2000 and 2001-2005.However, especially for the flux-based and inventory-based approaches the time period of several fluxes and stock changes was limited to a period shorter than the study period or ill-defined when based on a compilation of data with different time frames.

Accounting framework for GHG budgets
An accounting framework was developed to infer the C-flux between terrestrial ecosystems and the atmosphere (Fig. 2).The framework is based on a mass balance approach and given that for Europe most of the components have been independently estimated, different accounting schemes may be used to estimate the variable of interest i.e. the net land-atmosphere exchange.In this study we applied three, largely independent, accounting schemes based on: (1) atmospheric inversions, (2) land-based flux measurements and (3) land-based carbon inventories.
In the inversion approach the net land-atmosphere exchange is calculated through optimizing a prior land-atmosphere flux where the prior is adjusted to match the observed atmospheric CO 2 , CO, CH 4 or N 2 O concentrations.Following the notation introduced in Fig. 2 and Table 1 this can be formalized for CO 2 as: Net land − atmosphere flux = 14a (1) Where, flux 14a (Fig. 2) represents the change in atmospheric CO 2 as derived from the inversions.In the land-based approaches the change in C stock can be estimated from flux-based estimates the different components of the budget or alternatively, some of the fluxes can be estimated from repeated C-inventories.These approaches are respectively formalized as: Where 2a + 2b (notation explained in Table 1 and Fig 1a Substitution of Eqs. ( 3) to (6) in Eq. ( 1) results into the following expression for the inventory-based net land-atmosphere flux: Net land − atmosphere flux = 7e + (1c or following elimination of terms Net land − atmosphere flux

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Full Given that several inversions used land surface models to derive the prior fluxes and their errors are and that these models are often calibrated and validated against different subsets of eddy-covariance data (see for example Bousquet et al., 2010), many inversion-based and flux-based accounting schemes are not entirely independent.However, the assumptions and subsequent post-processing of the eddy-covariance observations differed substantially resulting in quasi-independent schemes.Also CO 2 inversion and inventory-based schemes share their data sources for fossil fuel emissions and are therefore not entirely independent for this study.
The same mass balance approach can be applied to estimate the C and GHG sink in ecosystems and biological product pools.Also these sinks could be estimates following three quasi-independent approaches: inversion-based, flux-based and inventorybased.Following the notation introduced in Fig. 2 and Table 1 this can be formalized for C as: Inventory -based C -sink = 9a +9b +9c +9d +9e +9f +9g +9i +9j +10a +10b +11 (10)

Balance closure
The mass balance approach introduced in Sect.2.2 supports internal consistency checks.Stock-based changes in carbon content of inland aquatic ecosystems, land ecosystems, biological products and atmospheric pools obtained from inventories or inversions were confronted with their flux-based equivalents.This approach is formalized for in Eq. ( 3) for inland aquatic ecosystems, Eq. ( 4) for land ecosystems, Eq. ( 5) for the biological product pool and Eq. ( 6) for the atmospheric pool of non-CO2 gasses.Introduction

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Boundaries of the GHG budget
The GHG budget is determined by three boundaries: the spatial, the temporal and the accounting boundary.In this study we used a single spatial boundary (see Sect. 2.1) and two temporal boundaries (see Sect. 2.1).The accounting boundary describes the components that are included in the budget (Fig. 2).However, each of the included components has its own spatial boundaries (e.g.depth to which soil carbon is measured in inventory studies) and its own accounting boundaries.Given that these boundaries are often method-dependent, we choose to specify them in the supplementary material describing the data products (see supplementary material).

Data products
All data products used in this study are described in the supplementary material providing details on the underlying observations, processing done by the data owner, uncertainty estimates and post-processing in this study in addition to literature references.

Uncertainty estimates and error propagation
For data products that were subject of a formal uncertainty analysis, these uncertainty estimates were propagated in the balance computations.However, for the vast majority of the data products, no formal uncertainty analysis was available, for those products we assumed a normal uncertainty distribution with 95 % uncertainty interval amounting to 100 % of the flux estimate (thus 1 standard deviation is 50 % of the flux estimate).This imposed uncertainty was also propagated in the balance computations.
In addition to the uncertainty we quantified the spatial and temporal heterogeneity of the data product for the region (Table 1).Spatial heterogeneity was defined as the between-country heterogeneity of the annual flux of the first year of the sampling period.The country level was the smallest common unit across the different data products.Temporal heterogeneity was calculated as the interannual variability of the Figures

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Full aggregated flux of the region under study.The heterogeneity estimates are simply reported but not used in any of the uncertainty estimates.
For flux components with a single uncertainty component, the probability distribution was assumed to be normal with mean and standard deviation equal to the reported values.The probability distribution of the uncertainty of the CO 2 , CO, CH 4 and N 2 O inversions is often reported by two components.The first component describes a quasi uniform range of likely model outputs and is derived from sensitivity analyses (Table 1).The second component describes a normally distributed uncertainty and is due to the set-up of the inversion model and is typically obtained through the Bayesian approach.Within an inversion these components depend on each other and the former should be contained in the latter, if the latter is well-defined.We used the uncertainty that resulted in the widest uncertainty intervals.
Probability distributions were fully accounted for in the balance sheets by means of simulations based on Monte Carlo techniques.Within each realization of the 6000 Monte Carlo simulations that were performed, (sub)totals were computed from randomly selected realizations of the component fluxes.Mean and standard deviations of the (sub)totals were taken from their probability distribution based on 6000 realizations.

Life cycle analysis
We performed a basic country-based life cycle analysis of the CO 2 cost of land management including the following processes: (a) agricultural activities (ploughing, harrowing, cultivation and planting), (b) production and application of fertilizer, (c) production and application of herbicide (glyphosate), (d) thinning, harvesting and planting of forest, (e) transport of roundwood and (f) transport of fire wood.Emission factors were retrieved from Ecoinvent database (Frischknecht et al., 2007).the time frame of the study, this inconsistency was though to be of minor importance given the assumptions made in the life cycle analysis.
For cropland the following assumptions were made: each cropland is ploughed, harrowed, planted, cultivated, fertilized, sprayed and harvested annually.Grassland is ploughed and harrowed every 10 yr and cultivated and fertilized every year.In the absence of specific data, the CO 2 cost of fertilizer production was distributed over crop and grassland assuming that grasslands received half the dose of croplands.One percent of the forest land was harvested and planted and 10 % was thinned each year.The harvested wood was transported over a distance of 80 km if used as industrial roundwood.Fire wood was transported over a distance of 40 km.

Inversion-derived net land-atmosphere GHG fluxes
A subset of inversions, optimized for Europe was selected to compile the European CO 2 , CH 4 , CO and N 2 O budgets (Tables 1 and 2).Despite the effort in harmonizing the spatial, and temporal extent, the different inversions resulted in largely different estimates of the land-atmosphere flux ranging from 654 Tg C y −1 to 1239 Tg C y −1 for CO 2 (Chevallier et al., 2010;Peters et al., 2010;Peylin et al., 2005;R ödenbeck et al., 2003).For N 2 O the two available inversions converged within 25 % from each other.Although at first this looks very encouraging it should be noticed that both inversions largely used the same observations (Corazza et al., 2011;Thompson et al., 2011) so that the difference between the inversions is most likely due to differences in atmospheric transport and the definitions for prior uncertainties.For CH 4 (Bousquet et al., 2006) and CO (Fortems-Cheiney et al., 2011) just a single inversion was used and therefore inter-model variability could not be estimated.Further, inversions provide only a top-down estimate for regions constrained by the observations.For N 2 O the constrained region was smaller than the region under study (Corazza et al., 2011).Introduction

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Full The land-atmosphere flux of GHG's determines to a large extent the rate of accumulation of GHG's in the atmosphere and its interannual variability modulates the year-to-year growth-rate of GHG's and is thus of special interest to the climate system.Also interannual variability hints at the sensitivity of the land surface to climate variability and therefore in hindsight about future land surface responses to climate change.
The interannual variability of Europe was studied by simultaneously considering two characteristics: (1) the absolute value of the land-atmosphere flux µ j (µ i (| Flux i j |)) and ( 2) the mean interannual variability of the land-atmosphere flux µ j (σ i (| Flux i j |)).Where i indicates the pixel and j indicates the data product.The first characteristic identifies regions where the land-atmosphere flux is potentially important for the climate system whereas the second characteristic identifies regions where the interannual variability is expected to be large.Combining both characteristics in a single variable (Fig. 3) allows the regions to be distinguished that contribute most to the year-to-year variability in atmospheric CO 2 concentration.
For both periods (1996-2000 and 2001-2005), the net land-atmosphere flux in Scandinavia is a small contributor and the central region appears as an important contributor to the interannual variability in atmospheric CO 2 concentration over Europe.A similar latitudinal pattern in interannual variability was observed for deciduous forests but contradicted for evergreen forest based on observations from 39 northern-hemisphere eddy-covariance sites located at latitudes ranging from 29 o N to 64 • N (Yuan et al., 2009).A comparison between two deciduous and one evergreen site suggests that deciduous forests may contribute disproportionately to variability in atmospheric CO 2 concentrations within the northern hemisphere (Welp et al., 2007).Given the higher abundance of deciduous forests in central and southern compared to northern Europe, this finding may help to explain the observed spatial pattern (Fig. 3) in interannual variability.However, it should be noted that the observed higher variability for deciduous trees compared to evergreen is pre-mature given that only three sites were investigated (Welp et al., 2007).Figures

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Full Rather than being an ecosystem property, the interannual variability in ecosystem productivity may be due to differences in weather patterns between central and northern Europe.Such a difference could for example be determined by the North Atlantic Oscillation (NAO) (Hoerling et al., 2001).In some years, the NAO pushes the Mediterranean climate southward resulting in wet weather in the central and Mediterranean Europe, other years the NAO allows a more northern occurrence of the Mediterranean climate resulting in dry weather in central Europe (Hurrell, 1995).Differences in the spatial extent of the summer and winter NAO (Linderholm et al., 2009) may contribute to the observed north-south trend in interannual variability of the land-atmosphere CO 2 flux.
The inversion-derived interannual variability over Europe is sensitive to the lack of observational constraint on fluxes and imperfect knowledge of the prior flux estimates.Atmospheric inversions are forced to achieve mass balance closure.The inversions may achieve mass balance closure by simply attributing the residual fluxes to the least constrained regions.In Europe, the tall tower network that is used to constraint the inversions is less dense in northern, southern and eastern compared to central Europe (Ramonet et al., 2010).Contrary to our observations, this set-up of the inversions is expected assigning the residual fluxes and thus the highest variability to northern and southern Europe.However, in line with the set-up of the inversions, the inversions assigned a high variability to eastern Europe (EST, LVA, LTU and POL), a region poorly constrained by measurements.Therefore, the observed pattern in Eastern Europe could reflect the state of the art in inversion rather than a biological phenomenon.

Land-use and surface area
The study region has surface area of 5 × 10 6 km 2 of which all except Switzerland is being accounted for in the CORINE database.The dominant land cover is forest (35 %) followed by cropland (25 %) and grassland (18 %).Estimates for forest area differ at most 12 % for the EU-25 when FAO and CORINE are compared.This difference is likely explained by CORINE classifying part of the harvested forest as semi-natural vegetation.While different sources (i.e.CORINE versus FAO) agree on the importance of forest and cropland (estimates differ at most 6 % for the EU-25), such convergence is absent for grazing land with estimates diverging by more than 20 % for the EU-25, which contains the best documented nations of Europe.This uncertainty is caused by whether the classification of grazing land includes only permanent grasslands (e.g.permanent pastures) which are intensively and continuously managed (i.e.mowing or grazing) or also natural or semi-natural vegetation that is extensively grazed.
Where the CORINE land cover classes account for wetlands it is important to distinguish between marshes and peatland and more specifically between disturbed and undisturbed peatland.For GHG budgets, distinguishing between these management types is essential because the undisturbed peatland typically acts as a GHG-sink whereas the disturbed peatland under cropland and forests often act as a GHG-source because of enhanced decomposition following harvest, ploughing and or drainage.
The areas for different land uses on peatland have been taken from Joosten (2010).
Although not explicitly reported by Joosten (2010), for this study, the areas were assumed constant between 1996 and 2005.

Eddy covariance based net land-atmosphere flux
The current eddy-covariance tower network is equipped to record the CO 2 exchange between land and atmosphere (this fluxes is also known as NEE in the sense of Chapin et al., 2005).Although an eddy-covariance based CH 4 network is emerging, at present only very few sites report other greenhouse gas fluxes than CO 2 , therefore, our eddycovariance based estimates are limited to CO 2 exchange.At the site scale, the scale for which eddy-covariance measurements are available, CO 2 is exchanged with neighbouring sites (i.e.lateral CO 2 transport, harvest and trade), the underlying soil matrix and the overlying atmosphere.The typical eddy-covariance tower set-up records only small scale vertical exchange between Introduction

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Full ecosystem and atmosphere.Consequently, NEE estimates need to be corrected for lateral transport and leaching from the soil matrix to obtain the ecosystem carbon sink.
Accounting is further complicated by the fact that (Korner, 2003): (1) C, CH 4 and CO 2 are exported to neighbouring ecosystems that are not part of the eddy-covariance network i.e. inland water and product pools (Fig. 2).Therefore, lateral fluxes and the CO 2 exchange between these ecosystems and the atmosphere also need to be accounted for.
(2) For forests and grasslands the network is biased towards uniform established ecosystems, hence, newly established ecosystems following land-use change need to be separately accounted for.
(3) Eddy-covariance measurements are not made during fires to prevent the equipment from being damaged; fires emissions thus need to be separately accounted for.(4) No eddy-covariance network exists over urban and industrial areas, therefore, fossil fuel emissions need to be separately accounted for.(5) The eddy-covariance is not globally representative but given its density over Europe, this is likely a minor issue for estimating mean fluxes over the study region (Sulkava et al., 2011).Equation ( 2) and Fig. 2 show how issues 1 to 4 were accounted for in this study.
Despite being well documented that annual NEE poorly correlates to climate and is more likely driven by site disturbance such as harvest, grazing, thinning, fire, ploughing, etc. (Luyssaert et al., 2007), for a given site, NEE fluxes at high temporal resolutions (i.e.hourly to monthly) are (partly) driven by meteorology (Baldocchi, 2008;Law et al., 2002).Jung et al. (2011) and Papale et al. (2003) used this observation to upscale eddy-covariance measurement to the region.Nevertheless, these authors caution for the lack of spatially explicit disturbance data in their upscaling approach and the substantial uncertainty of their data products (Jung et al., 2009(Jung et al., , 2011)).
For the region and period under consideration both eddy-covariance-based estimates (Table 1), using the same data but different statistical methods, converged at −965 Tg C y −1 within 10 % (one standard deviation).Not surprisingly the estimates indicate that the European land surface consistently takes up CO 2 from the atmosphere.
Interannual variability was estimated at 70 % (one standard deviation) and was tightly Introduction

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Full related to meteorology.Although the spatial variability in NEE is thought to be driven by disturbances, the relationship between the temporal variability in NEE and meteorology may be real (Baldocchi, 2008;Law et al., 2002).Nevertheless, the strength of this relationship is most likely an artefact of the fact that the upscaling makes use of remotely sensed fpar and meteorological data (Jung et al., 2011).Following accounting for fluxes not measured by the eddy-covariance technique (Eq.2), the net landatmosphere flux was estimated at 943 ±540 Tg C y −1 between 2001 and 2005.Several of the flux estimates were temporally unresolved, hence, the interannual variability of the net land-atmosphere flux could not be estimated.

Inventory based net land-atmosphere flux
Alternatively, net land-atmosphere fluxes of CO 2 , CH 4 and CO can be estimated from repeated C-inventories often in conjunction with deterministic models (i.e.Tupek et al., 2010; see also supplementary material) and flux measurements to complete the inventory measurements (see Methods and material).This approach has been formalized in Eq. ( 7).Although this appears as the most straightforward of the three applied approaches to estimate the net land-atmosphere flux, the representativeness of the European estimates may be hampered by data scarcity (see supplementary material).
For example, changes in soil carbon for the entire territory are based on a rather limited number of sampling plots for croplands (Ciais et al., 2010c) and grasslands (Lettens et al., 2005;Goidts and Wesemael, 2007;Soussana et al., 2004;Bellamy et al., 2005) and are based on deterministic modelling for forests (Luyssaert et al., 2010;Tupek et al., 2010).

Uncertainty and consistency of the net land-atmosphere GHG fluxes
Inversion based accounting adjusts a prior set of fluxes for the net ecosystematmosphere exchange with a given uncertainty to better match the observed atmospheric concentrations of the species under study within their uncertainty.Recent inversions come with a formal uncertainty analysis (Table 1).However, the vast majority of the data products other than inversions have not been subjected to formal uncertainty analysis.For these products we assumed a Gaussian uncertainty distribution with 95 % uncertainty interval amounting to 100 % of the flux estimate (thus 1 standard deviation is 50 % of the flux estimate).
The uncertainty of the eddy-covariance and inventory based estimate of the net land-atmosphere flux was estimated from the uncertainty of its components and is thus determined by the assumed uncertainty of 50 %.Despite the shared assumption, the uncertainty of the inventory-based estimate was estimated to be almost one third of that of the flux-based estimate (Table 2).This difference is due to the difference in the magnitude of the fluxes that are used in the balance calculations (i.e.Eq. 2 vs. Eq.7).Given our assumption, the largest component flux comes with the largest uncertainty.Consequently, the total uncertainty is determined by the uncertainty of the upscaled NEE (2ab; Table 1) in the flux-based approach whereas the uncertainty of the inventory-based approach is determined by the uncertainties of fossil fuel burning (5a; Table 1) and the changes in forest carbon (9e; Table 1) .Improved uncertainty estimates require formal uncertainty analyses for the upscaled NEE and changes in forest carbon.Figures

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Full The mass balance approach introduced in Sect. 2 supports internal consistency checks.Stock-based changes in carbon content of the aquatic, terrestrial, product and atmospheric pool obtained from inventories or inversions were confronted with their flux-based equivalents (Fig. 2).This approach is formalized for CO 2 in Eqs.(3) to ( 6) and the balance closure has been reported in Table 3. Balance closure between the stock-based and flux-based estimates is not significantly different from zero mainly because of the wide uncertainty intervals.The current estimates and their uncertainties lack statistical evidence to justify introducing a 'closure gap'.Hence, our estimates for these components were considered consistent.Consistency is expected to further improve if atmospheric transport to adjacent regions would be accounted for (7b, 7d and 7e in Table 1).
However, when the land sink is used as reference, the statistics-based consistency seems not justified for the biological product pool (i.e.all harvested biomass and subsequent products such as food, fodder, wood, paper, . . . ) which shows an inconsistency of 88 Tg C y −1 between the stock-change and flux-based approach.This inconsistency represents about 40 % of the inventory-based change in carbon stock for the region under study (Table 3).This inconsistency may be due to the lack of dense harvest and herbivory observations for grasslands and croplands.The current budget relies on modelled data (see 3b, c and 3b, d in Table 1).Hence, it is expected that the internal consistency of the European C budget could largely improve by informing emissions estimates of biological product pools by measurements.
It should be noted that in this consistency check, two inaccurate fluxes could compensate each other resulting in an apparently high consistency.Consequently, the information content of our balance closure approach is limited as it does not identify which fluxes or stock-change estimates need to be further improved to improve the consistency and accuracy of the net land-atmosphere flux.Introduction

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GHG mitigation of European ecosystems
Sink estimates, based on Eqs. ( 8) to ( 10), show that the European ecosystems and biological product pools were a C-sink between −356 and −201 Tg C y −1 between 2001 and 2005 (Table 4).Individual sink estimates come with large uncertainties ranging between 80 and 330 Tg C y −1 .However, the extreme high and low sink-strengths are in conflict with the inventory-based approach that has a much smaller uncertainty of 80 Tg C y −1 and as such puts a tighter constraint on the estimated sink strength.Applying the Bayesian theorem, a sink of −210 ± 70 Tg C y −1 was found to be consistent with our three independent data sources i.e. atmospheric measurements, observed stock-changes and measured fluxes.
This C-sink in European ecosystems and biological product pools is thought to be mainly driven by changes in atmospheric CO 2 , climate, atmospheric N-deposition, land use (intensity) change and to a minor extent by changes in ozone concentration and diffuse versus direct light flux (Le Qu ér é et al., 2009).Proper understanding of the drivers, their interaction and their contributions to the current sink is a prerequisite to predict whether the current sink-strength will increase, decrease or persist in the future.Spatially explicit sink attribution at the European scale is beyond the capacity of experimental work and can only be achieved by well-validated model-based experiments.For example, model-based experiments could shed a light on the effect of large scale bio-energy production on the current sink-strength.Such modelling-experiments could extent the time period of data-driven studies (for example Hudiburg et al., 2011).However, model-based sink attribution is still in its infancy because currently no single large scale model can deal with all aforementioned factors.At present multiple model-based experiments have been performed with different models.Hence, the observed sink is attributed to just a limited number of drivers, likely overestimating the importance of the drivers the model accounts for.
JULES (Joint UK Land Environment Simulator), a land surface model integrating climate change and [CO 2 ], showed that [CO 2 ] increase had a higher impact on the Introduction

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Full European C-sink than climate (Harrison et al., 2008b).Note that Europe was here defined as continental Europe.Warming was reported to emit C to the atmosphere.This C-source, however, was more than offset by the effect of increasing atmospheric [CO 2 ] resulting in a −114 Tg C y −1 sink between 1980-2005 (Harrison et al., 2008a).This modelling-experiment likely overestimates the effects of climate change and increasing thereafter remained relatively constant with some inter-annual variations related mainly to the interactions of Nr availability with climatic variability (Zaehle et al., 2011).A comparison of BIOME-BGC (Global Biome model -Biogeochemical Cycles), JULES, ORCHIDEE and O-CN suggested a continuous increase in carbon storage from 85 Tg C y −1 in 1980s to 108 Tg C y −1 in 1990s, and to 114 Tg C y −1 in 2000-2007 (Churkina et al., 2010).These estimates are for continental Europe and limited to the terrestrial ecosystems sink.The study identified the effect of rising [CO 2 ] in combination with Nr-deposition and forest re-growth as the important explanatory factors for this net carbon storage.However, the modelling-experiments did not account for changes in the age structure of woody vegetation, a potentially important contributor.Some modelling-experiments zoomed in on a single ecosystem and its specific characteristics.The effect of changes in age structure of forest has been subjected to separate modelling-experiments (Bellassen et al., 2011;Zaehle et al., 2006).For Europe, ORCHIDEE-FM, another branch of ORCHIDEE, integrating climate change, increasing atmospheric [CO 2 ], net forest cover change and changing age structure of forest shows spatial variation in the main drivers.Locally, climate change and changing age structure often determine temporal changes in the forest C-sink, whereas at the continental scale, increasing atmospheric [CO 2 ] drives the increase of the forest sink (Bellassen et al., 2011).A modelling-experiment with a similar capacity but making use of a LPJ (Zaehle et al., 2006) instead of ORCHIDEE-FM (Bellassen et al., 2011)  Contrary to the inventory-based estimates (Table 1), model simulations estimated a small but uncertain CO 2 C-sink in croplands (Ciais et al., 2010b).This sink was attributed mainly to past and current management, and to a minor extent the shrinking areas of arable land consecutive to abandonment during the 20th century (Ciais et al., 2010b).When assessing the effects of rising atmospheric [CO 2 ], changing climate, and agro-technology changes on the carbon balance of European croplands, agrotechnology changes and varieties selection were found to be largely responsible for the sink rather than rising [CO 2 ] and climate change (Gervois et al., 2008).Sink uncertainty for croplands was dominated by unknown historical agro-technology changes (Ciais et al., 2010b;Kutsch et al., 2010;Ceschia et al., 2010) and model structure (Ciais et al., 2010b) with the model potentially missing processes that contribute to the observed Csource i.e. ploughing.Errors in climate forcing played a minor role (Ciais et al., 2010b).
The above mentioned modelling-experiments limited their simulations to CO 2 uptake and emissions.However, the same European ecosystems and biological product pools that were a CO 2 -sink were a source for CH 4 , CO and N 2 O (Table 2).When converted to a common unit i.e.Tg C in CO 2 -eq y −1 the C-sink is most likely offset by the global warming potential of CH 4 and N 2 O.As a consequence, the European ecosystems and biological product pools are a GHG source of 105 ±100 Tg C in CO 2 -eq y −1 to the atmosphere and thus contribute to global warming (Table 4).This finding confirms previous data-driven (Schulze et al., 2009(Schulze et al., , 2010) ) and model-based (Zaehle et al., 2011) studies.
To our best knowledge there are no comprehensive attribution studies of the GHG balance, which for Europe appears to be a source of GHG to the atmosphere (Table 4).However, global GHG-species-specific studies possibly shed some light on the global drivers of N 2 O and CH 4 emissions.Before 1960, agricultural expansion, including livestock production, may have caused globally significant mining of soil nitrogen, fuelling a steady increase in atmospheric nitrous oxide (Davidson, 2009).After 1960, the rate of the increase rose, due to accelerating use of synthetic nitrogen fertilizers.Both agricultural expansion and use of synthetic nitrogen fertilizers are highly relevant Introduction

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Full for Europe.
The emissions of atmospheric methane were investigated by using two atmospheric inversions to quantify the distribution of sources and sinks for the 2006-2008 period, and a process-based model of methane emissions by natural wetland ecosystems (Bousquet et al., 2010).At the global scale, a significant contribution of CH 4 emissions was thought to come from wetlands in Eurasia where annual changes in precipitation where thought to be the underlying driver (Bousquet et al., 2010).However, other studies put forward other drivers i.e. more efficient rice production (Fuu Ming et al., 2011), unlikely to be important for Europe, or changes in petroleum production and use (Aydin et al., 2011).It remains to be quantified how relevant these global drivers are in explaining the European CH 4 emissions.
Integrated studies of the interactions of carbon (i.e.CO 2 , CH 4 , BVOC, CO) and nitrogen (N 2 O) dynamics, land use (intensity) changes and environmental changes (i.e.increasing atmospheric [CO 2 ], climate change, increasing [O 3 ], changes in direct versus diffuse light) are needed to further improve the quantitative understanding of the driving forces of the European land carbon balance.Although such simulations may become available within a couple of years for forest, grasslands or croplands, a single simulation simultaneously accounting for the different ecosystems (including aquatic ecosystems) may not be available within the next 5 yr or so.The major constraints in realizing such simulations are: (a) model development in of support of such simulations and (b) lack of multi-factorial field experiments that can be used to validate such model outcome.

Fossil fuel cost of the C sinks
The carbon sink is often presented as a free service from "nature" to "mankind" and in this section we test whether this statement is justified for the Europe.It has been shown that land management is among the main drivers of the European ecosystembased sink (See Sect. 3.4).It should, however, be recognized that land management requires the input of energy.Hence there is a CO 2 cost to realize the ecosystem-based C-sink.In Table 1 this CO 2 cost is accounted for in the following fluxes: 5a "Burning 2029 Introduction

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Full and product use CO 2 ", 4a "Peat, wood and charcoal burning CO 2 " and 4b "other biofuel burning CO 2 ".In this section we used life cycle analysis (LCA) to estimate how much of the CO 2 emitted through fluxes 4a and 4b and 5a can be allocated to land management.
Based on our LCA assumptions (see Sect. 2.6) we estimated that the CO 2 cost for ecosystem management is 65 Tg C y −1 for cropland, 14 Tg C y −1 for grassland and 10 Tg C y −1 for forest (Table 5).Total emission for land management is thus 89 Tg C y −1 which represents less than 10 % of the total European emissions from fossil fuel burning.However, when the land sink rather than the fossil fuel emissions are used as a reference, emissions due to land management practices (i.e.ploughing, harvesting, fertilizing, etc.) can no longer be ignored.
Current agricultural ecosystems are a source of 21 Tg C y −1 (Table 1) and to create this source another 65 Tg C y −1 is emitted through energy use for land management (including fertilizer and herbicide production and application).Management of grasslands is a small sink: the ecosystems store about 24 Tg C y −1 their management emits about 14 Tg C y −1 .Forest management is doing considerably better: only 0.02 and 0.04 Tg of C are emitted to sequester 1 Tg C in the ecosystem.We hypothise that in addition to fossil fuels consumed to manage the forest C-sink, the sink strength in European forests is an indirect result of high fossil fuel consumption, as has been shown for Austria (Erb et al., 2008;Gingrich et al., 2007).Part of the current forest sink is thought to be a result of society's decreasing dependency on forest biomass resulting in harvest levels well below wood increment.This situation is maintained by the fact that the energy and raw material previously provided by forests has now been substituted by fossil fuel based energy and products.
It should be noted that undisturbed peatlands are observed to be C-sinks (Table 1) but at no management cost as these systems are typically unmanaged (Table 5).The annual C balance of undisturbed peatlands is however highly sensitive for weather conditions.For example summer droughts, which are becoming more frequent, may turn both ombrotrophic and minerotrophic mires into net sources of C (Saarnio et al., 2007).Figures

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Full Rivers, lakes and reservoirs act as C-sinks (Table 1) but the structure of their management cost is somehow different from terrestrial ecosystems as it constitutes mainly of the cost for constructing canals and dams of which we had insufficient information to estimate their cost.
It should be noted that our cost analysis was strictly limited to ecosystem management.Hence, subsequent processing of the food and raw material was not included.Such inclusion is likely to change the outcome of the LCA substantially.The CO 2 cost for food processing in the EU-27 is at least 12 Tg C y the production cost of the wood itself.Also the CO 2 costs for managing the biological product pool are expected to be substantial but not included in this LCA.Given the assumptions and the accounting boundary of this LCA, the results should be considered as indicative rather than final.Nevertheless it clearly demonstrates the point that the European C-sink in ecosystems and biological product pools is not a free service but comes at a considerable CO 2 cost.

Conclusions
This study confirmed that the European land surface (including inland waters and urban areas) is a net source for CO 2 , CO, CH 4 and N 2 O.However, for all ecosystems except croplands, C uptake exceeds C release and us such part of the CO 2 released through fossil fuel burning is removed from the atmosphere and sequestered in both terrestrial and inland aquatic ecosystems.Note that the aquatic systems are estimated to contribute second most to fossil fuel uptake, and thus rank above the European croplands and grassland.Given their surface area, this makes the inland waters a hotspot for C sequestration.Introduction

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Full Owing to its large uncertainty, the additional uptake of 96 to 156 Tg C was not statistically significant but was nevertheless seen as an indicator that the European land surface (including inland waters) takes up more C than the global average.Along the same lines of reasoning, the region under study represents less than 4 % of global photosynthesis but realizes 8 to 18 % of the global C-sink.
If the C cost for ecosystem management is taken into account, the net uptake of ecosystems was estimated to decrease by 45 % but still indicates substantial Csequestration.Also, when the balance is extended from CO 2 towards the main GHGs, C-uptake by terrestrial and aquatic ecosystems is compensated for by emissions of GHGs.As such the European ecosystems are unlikely to contribute to mitigating the effects of climate change.
Until present it appears impossible to estimate independent temporally resolved GHG balances over Europe for 1991-2000 and 2001-2009 due to lack of data.For several of the fluxes, all available data needed to be combined in a single and therefore temporally undefined estimate.We assigned our estimate to the period 2001-2005 but made use of data from other time periods.Hence, we did not succeed in obtaining high temporal consistency as stated in the objectives of this study and therefore temporal patterns in the GHG balance are not supported by this data compilation.Achieving high spatial consistency, another objective of this study, was reasonably well achieved as most data products come with a well defined spatial extent, however, it remains unclear whether all products could be considered representative for the whole spatial domain as often only subregion(s) of the under study were sampled.These inconsistencies are not reflected in the GHG balances for 2001-2005; for five out of six budget components a good agreement was found between the flux-based and inventory based approach.Poor agreement was only observed for the biological product pool.Introduction

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Full Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . 2) was derived from eddycovariance measurements.Following the principle of mass conservation, every individual component flux can be calculated based on the sum of the observed stock change and incoming and outgoing fluxes.Stock changes for fresh water ecosystems (9j), land ecosystems (9a to 9i), biological products (10a, 10b and 11) and non-CO 2 Discussion Paper | Discussion Paper | Discussion Paper | C-gasses (14a and 14a ) have been estimated allowing us to calculate the following component fluxes of Eq. (2): Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Fertilizer and herbicide consumption are respectively based on European Fertilizer Association on fertilizer consumption in the EU between 2006 and 2007 (EFMA, 2007).Wood harvest comes from the FAO (2008).Although the time frames of these data are not exactly in line with Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Further, spatially explicit estimates are none existent for several potential hotspots such as drained peatlands, reservoirs and areas under land-use change, for example, it remains unclear what happens with soil carbon following urbanisation.Assuming that the regions that were inventoried are representative for the spatial domain under study, the European net land-atmosphere flux was estimated at 993 ± 190 Tg C y −1 between 2001 and 2005 (Table 2).A similar approach was used to estimate the net land-atmosphere fluxes for CH 4 , CO and N 2 O.The land surface Discussion Paper | Discussion Paper | Discussion Paper | is a source for CH 4 and CO of respectively 23 ± 5 and 21 ± 5 Tg C y −1 .For N 2 O the land-atmosphere flux is estimated at 125 ± 35 Tg C in CO 2 -eq y −1 or 1 Tg N y −1 .Several of the flux estimates required to estimate the net land-atmosphere flux of CO 2 , CH 4 , CO and N 2 O were temporally unresolved, hence, the interannual variability was not estimated.
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CO 2 ] because it accounted for land-use (intensity) change, N-deposition, increasing atmospheric ozone and diffuse vs. direct light.Another model experiment performed with a version of the LPJ (Lund Potsdam Jena) land surface model) accounting for climate change, increasing atmospheric [CO 2 ] and land cover change, found an important effect of land use change over the EU-15(Zaehle et al., 2007).During the 1990's, 3.3 Tg C y −1 was lost to urbanization, 19.3 Tg C y −1 to agricultural and 14.5 Tg C y −1 to grasslands.Emission due to land cover change were offset by sequestration of −59.1 Tg C y −1 in forest and wood products resulting in a mean annual C-sink of −29 Tg C y −1 (Zaehle et al., 2007).O-CN, a branch of ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) integrating climate change, increasing atmospheric [CO 2 ] and the nitrogen cycle, shows that nitrogen deposition considerably alters the attribution of the C-sink to its drivers.Including nitrogen dynamics limited the global sink-strength by almost 0.4 Pg C y −1 in the N-limited boreal regions, whereas N-deposition was reported to enhance the global terrestrial C-sink by 10 to 20 % (i.e.−0.2 to −0.4 Pg C y −1 ).Given that no N-effect was simulated for tropical regions, interactions with reactive nitrogen (Nr) substantially contribute to the C-sink in the temperate zone (Zaehle et al., 2010).A similar modelling-experiment using a slightly different version of O-CN that also accounts for the effects of land cover change .(Zaehleet al., 2011) resulted in a net forest uptake rate due to Nr deposition of 23.5±8.5 Tg C y −1 (mean and standard deviation of the temporal heterogeneity for the years 1996-2005), In addition, the Nr effect on unmanaged grasslands accounts for a further sink of 2.8 Tg C y −1 .The simulations with O-CN suggest that Nr deposition has played only a minor role in terrestrial C cycling prior to the 1950s, after which the effect increased to the mid 1980s.The effect has Discussion Paper | Discussion Paper | Discussion Paper | found that climate change and increased atmospheric [CO 2 ] resulted in a net increase in the vegetation carbon stock of −57 Tg C y −1 in the 1990s over the EU-25.Afforestation doubled the sink strength by −118 Tg C y −1 .Despite its local importance for determining the carbon balance on the European scale, changes in harvest intensity decreased C-sequestration by 5 Tg C y −1 in forest vegetation and thus had a small impact on the European scale.Both Zaehle et al. (2006) and Bellasen et al. (2011) attributed a modest contribution of changing age structure to the current C-sink and both models were capable of reproducing large-scale forest inventory statistics.Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | If global CO 2 uptake would be uniformly distributed over the globe, the region under study is expected to sequester −45 to −105 Tg C y −1 .Based on three independent approaches we estimated the European C sequestration to amount −210 ± 70 Tg C y −1 .
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | LSCE, Gif sur Yvette, France, now at: Norwegian Institute for Air Research (NILU), Kjeller, Norway).Martin Wattenbach is funded by the MILIEU project as part of the German Excellency Initiative.S önke Zaehle is supported by Marie Curie Reintegration Grant 224775.The publication of this article is financed by CNRS-INSU.Discussion Paper | Discussion Paper | Discussion Paper |