Seven years of recent European net terrestrial carbondioxide exchange constrained by atmospheric observations

W. P E T E R S *w , M . C . K R O L *, G . R . V A N D E R W E R F z, S . H O U W E L I N G § , C . D . J O N E S } ,

J . H U G H E S } , K . S C H A E F E R k, K . A . M A S A R I E **, A . R . J A C O B S O N w **, J . B . M I L L E R w **,

C . H . C H O w w , M . R A M O N E T zz, M . S C H M I D T zz, L . C I A T T A G L I A § § , F . A PA D U L A } } ,

D . H E L T A I }} , F . M E I N H A R D T kk, A . G . D I S A R R A ***, S . P I A C E N T I N O ***, D . S F E R L A Z Z O ***,

T . A A L T O w w w , J . H A T A K K A w w w , J . S T R O M zzz § § § , L . H A S Z P R A } } } , H . A . J . M E I J E R kkk,S . VA N D E R L A A N kkk, R . E . M . N E U B E R T kkk, A . J O R D A N ****, X . R O D O w w w w ,

J . - A . M O R G U I w w w w , A . T . V E R M E U L E N zzzz, E . P O PA zzzz, K . R O Z A N S K I § § § § ,

M . Z I M N O C H § § § § , A . C . M A N N I N G } } } } , M . L E U E N B E R G E R kkkk, C . U G L I E T T I kkkk,A . J . D O L M A N z, P. C I A I S zz, M . H E I M A N N **** and P. P. T A N S **

*Department of Meteorology and Air Quality (MAQ), Wageningen University, Droevendaalsesteeg 4, NL-6700 PB, Wageningen,

The Netherlands, wCooperative Institute for Research in Environmental Sciences, University of Colorado, 216 UCB, Boulder, CO

80309-0216, USA, zVU University, Boelelaan 1085, NL-1081 HV, Amsterdam, The Netherlands, §SRON Netherlands Institute for

Space Research, Sorbonnelaan 2, NL-3584 CA Utrecht, The Netherlands, }Met Office Hadley Centre, FitzRoy Road, Exeter EX1

3PB, UK, kNational Snow and Ice Data Center, University of Colorado, 449 UCB, Boulder, CO 80309-0449, USA, **NOAA Earth

System Research Laboratory, 325 Broadway, Boulder, CO 80305-3337, USA, wwNational Institute of Meteorological Research, 45

Gisangcheon-gil, Dongjak-gu, Seoul 156-720, Korea, zzLaboratoire des Sciences du Climat et de l’Environnement, CEA CNRS

UVSQ, 91191 Gif sur Yvette, France, §§Instituto di Scienze dell’Atmosfera e del Clima, via Gobetti 101, I-40129 Bologna, Italy,

}}CESI RICERCA, Environment and Sustainable Development Department, Via Rubattino 54, 20134 Milano, Italy,

kkUmweltbundesamt, Messstelle Schauinsland, Schauinslandweg 2, 79254 Oberried/ Hofsgrund, Germany, ***Ente per le Nuove

Tecnologie, l’Energia e l’Ambiente, Via Anguillarese 301, 00123 S. Maria di Galeria, Italy, wwwFinnish Meteorological Institute, PO

Box 503, FI-00101 Helsinki, Finland, zzzDepartment of Applied Environmental Science, Stockholm University, Svante Arrhenius

vag 8c, SE-106 91, Stockholm, Sweden, §§§Norwegian Polar Institute, Polar Environmental Centre, 9296 Troms�, Norway,

}}}Hungarian Meteorological Service, PO Box 39, H-1675 Budapest, Hungary, kkkUniversity of Groningen, PO Box 72, NL-9700

AB, Groningen, The Netherlands, ****Max-Planck-Institute for Biogeochemistry, Hans-Knoell-Strasse 10, 07745 Jena, Germany,

wwwwCatalan Institute of Climate Sciences (IC3), c/Baldiri i Reixach, 2, 08028 Barcelona, Catalunya, Spain, zzzzEnergy research

Centre of the Netherlands, PO Box 1, NL-1755 ZG Petten, The Netherlands, §§§§AGH University of Science and Technology, Dept

of Env. Physics, al. Mickiewicza 30, 30-059 Krakow, Poland, }}}}School of Environmental Sciences, University of East Anglia,

Norwich NR4 7TJ, UK, kkkkClimate and Environmental Physics, Physics Institute, University of Bern, Switzerland, and Oeschger

Centre for Climate Change Research, University of Bern, Sidlerstrasse 5, 3012 Bern, Switzerland

Abstract

We present an estimate of net ecosystem exchange (NEE) of CO2 in Europe for the years 2001–2007. It is derived with a data assimilation that uses a large set of atmospheric CO2 molefraction observations (� 70 000) to guide relatively simple descriptions of terrestrial andoceanic net exchange, while fossil fuel and fire emissions are prescribed. Weekly terrestrialsources and sinks are optimized (i.e., a flux inversion) for a set of 18 large ecosystems acrossEurope in which prescribed climate, weather, and surface characteristics introduce finer scalegradients. We find that the terrestrial biosphere in Europe absorbed a net average of�165 Tg C yr�1 over the period considered. This uptake is predominantly in non-EU countries,and is found in the northern coniferous (�94 Tg C yr�1) and mixed forests (�30 Tg C yr�1) aswell as the forest/field complexes of eastern Europe (�85 Tg C yr�1). An optimistic uncertaintyestimate derived using three biosphere models suggests the uptake to be in a range of �122 to�258 Tg C yr�1, while a more conservative estimate derived from the a-posteriori covarianceestimates is �165� 437 Tg C yr�1. Note, however, that uncertainties are hard to estimate giventhe nature of the system and are likely to be significantly larger than this. Interannualvariability in NEE includes a reduction in uptake due to the 2003 drought followed by 3 years

Correspondence: Wouter Peters, Department of Meteorology and Air Quality (MAQ), Wageningen University, Droevendaalsesteeg 4, 6708

PB, Wageningen, The Netherlands, e-mail: Wouter.Peters@wur.nl

Global Change Biology (2010) 16, 1317–1337, doi: 10.1111/j.1365-2486.2009.02078.x

r 2009 Blackwell Publishing Ltd 1317

of more than average uptake. The largest anomaly of NEE occurred in 2005 concurrent withincreased seasonal cycles of observed CO2. We speculate these changes to result from thestrong negative phase of the North Atlantic Oscillation in 2005 that lead to favorable summergrowth conditions, and altered horizontal and vertical mixing in the atmosphere. All ourresults are available through http://www.carbontracker.eu

Keywords: atmospheric CO2, carbon exchange, data assimilation

Received 4 April 2009; revised version received 22 July 2009 and accepted 18 August 2009

Introduction

To address important questions that surround the Eur-

opean terrestrial carbon balance the European Union

(EU) funded the CarboEurope program (http://

www.carboeurope.org). One of the goals of this pro-

gram was to assess the carbon balance of the European

continent and its countries, and separate contributions

from fossil fuels and terrestrial carbon exchange to the

annual increase in atmospheric CO2. Several ap-

proaches were included, including accounting methods

(Janssens et al., 2003; Nabuurs & Schelhaas, 2003; Ciais

et al., 2008b), ecosystem measurements (Papale & Va-

lentini, 2003; Luyssaert et al., 2007; Reichstein et al.,

2007b), process modeling (Reichstein et al., 2007a; Vetter

et al., 2008), satellite observations, and atmospheric

trace gas monitoring (Messager et al., 2008). Each has

an important contribution and each has its own

strengths and weaknesses. For instance, accounting

methods are good at tracking merchantable carbon

products such as fuels, crops, and timber, but are less

suited to describe growth of standing biomass and soil

carbon accumulation. Terrestrial carbon process models

are good at combining climate, vegetation health, and

carbon pool dynamics over larger spatiotemporal

scales, but do not include fossil fuels and struggle to

include the recent land-use history. Atmospheric based

estimates integrate over all carbon sources and sinks

and provide constraints on larger totals but cannot

break the information down to specific processes and

regions, partly due to limitations in atmospheric trans-

port modeling accuracy (Lin & Gerbig, 2005; Geels et al.,

2007; Gerbig et al., 2008; Law et al., 2008; Tolk et al., 2008;

Ahmadov et al., 2009). One way to make progress lies in

combining the strengths of each of these methods into

one system, provided their weaknesses can be appro-

priately dealt with.

Prototypes of such systems have been in develop-

ment for some years, and some have been applied

successfully (Pacala et al., 2001; Randerson et al., 2002;

Gerbig et al., 2003; Still et al., 2004; Patra et al., 2005;

Rayner et al., 2005; Crevoisier et al., 2006; Gourdji et al.,

2008; Lokupitiya et al., 2008). For instance, Rayner et al.

(2005) optimized key parameters in the Biosphere

Energy-Transfer-Hydrology Scheme (BETHY) biosphere

model in the first implementation of a carbon cycle data

assimilation system, building on earlier work by Ka-

minski et al. (2002). Direct observational constraints from

atmospheric CO2 and satellite based photosynthetically

active radiation (PAR) were used to limit the range of

possible values of 18 global model parameters, and three

parameters that varied with plant-functional type. The

resulting 2� 2 degree interannual flux fields produced

were consequently analyzed. Zupanski et al. (2007) built

a system that estimates two multiplicative bias correc-

tors for each 1� 1 pixel of the underlying Simple Bio-

sphere (SIB) model, which was driven by analyzed

meteorology and satellite observations. Michalak et al.

(2004) and Gourdji et al. (2008) combined several layers

of geophysical information in a geostatistical framework

to have multiple constraints on carbon exchange. de Wit

& Diepen (2007) focused specifically on crop lands by

combining the World Food Studies (WOFOST) crop

growth model (Diepen et al., 1989; van Ittersum et al.,

2003) with observed meteorology and satellite observed

soil moisture to analyze crop yields across the EU.

Finally, Peters et al. (2007) presented a data assimilation

system for North America that estimated weekly multi-

plicative factors for carbon fluxes across larger ecore-

gions, which in turn were simulated with the Carnegie

Ames Stanford Approach – Global Fire Emissions Data-

base version 2 (CASA GFED2) biosphere model (van der

Werf et al., 2006). Each of these mentioned systems is still

under active development to include more carbon cycle

information, refine their estimation techniques, and

achieve better separation of carbon fluxes in time and

space.

From the point of view of atmospheric inverse mod-

eling the European domain poses some unique chal-

lenges. First of all, it has the smallest land area of the

three major northern hemispheric continents, and the

largest population density. Also, land use in Europe is

much more heterogeneous than in for instance North

America with agricultural lands interspersed with cities

and industrial areas. European forests are relatively

small in size and not at all evenly distributed across

the continent, partly because the geographic distribu-

tion of ecosystems is not limited in either wind direction

1318 W. P E T E R S et al.

r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 1317–1337

by large barriers (e.g., Rocky Mountains of North

America). Finally, nearly every part of the European

terrestrial biosphere is in some way actively managed

by humans to yield crops, timber, and other merchan-

table products. These unique characteristics force in-

verse modeling studies to work on fine spatial scales, to

use more densely distributed atmospheric CO2 con-

straints, and to use appropriate a priori process models

for the mix of ecosystems under the footprint of an

observation site. The data assimilation system pre-

sented in this work is a starting point for the develop-

ment of an inverse system to deal with such complexity.

For this study, we have tailored the Peters et al. (2007)

data assimilation system for CO2 to the European

domain. Most of the changes to the system are related

to improved methodology and are documented in de-

tail at http://carbontracker.noaa.gov as release version

CT2007B. This includes for instance the use of prior

ocean flux estimates from Jacobson et al. (2007) and

updated fossil fuel emissions (J. B. Miller, unpublished

data). Others are implemented specifically for this

paper and will be described here. Notably, they are:

(1) expansion of the observation set with 22 European

sites of which 15 sites sample continuous CO2 mole

fractions, (2) the use of a two-way nested grid over

Europe in the transport model TM5 down to 11� 11, (3)

the use of three different prior flux products to estimate

uncertainty ranges, (4) the further split of the European

domain into two climate zones with their own ecosys-

tem specifications, and (5) the use of seasonal fossil fuel

emissions for Europe. We will discuss these in ‘Materi-

als and methods’.

In this paper, we want to introduce the assimilation

system and assess its realism in deriving spatially

explicit European time-varying fluxes for the years

2001–2007. Research questions are: (1) What fraction

of the European CO2 observation set can we currently

use to extract information on the carbon balance and

what are the research priorities to increase this fraction?

(2) What unique information can large-scale atmo-

spheric CO2 constraints bring to the European carbon

budget? (3) Are our top-down results reliable enough to

complement bottom-up estimates and what will it take

to explicitly merge the information across the different

scales?

Our work complements earlier top-down studies as it

(1) spans some recent years not previously diagnosed,

(2) uses observations not previously available in a CO2

data assimilation exercise, (3) uses a different assimila-

tion technique to target different control parameters,

and (4) uses a different atmospheric transport model

with higher horizontal resolution than previous global

inverse studies that focused on Europe. The resulting

system will be referred to as ‘CarbonTracker Europe’

and is one of several systems applied within the

CarboEurope program that uses atmospheric inverse

constraints. Although the methodology is similar to pre-

vious inverse modeling studies (Bousquet et al., 2000;

Gurney et al., 2002; Gerbig et al., 2003; Law et al., 2003;

Rodenbeck et al., 2003a; Bruhwiler et al., 2005; Patra et al.,

2005; Peylin et al., 2005; Rayner et al., 2005; Baker et al.,

2006; Lokupitiya et al., 2008) this system is among the first

to use the semi-continuous records from European con-

tinental sites. All its results including flux maps and

mixing ratios are made freely available to the research

community through http://www.carbontracker.eu

Materials and methods

Observations

CarbonTracker Europe depends on the availability of

global high precision CO2 observations from a wide

community of experimentalists. We have included data

from seven CarboEurope flask sampling sites, 12

NOAA ESRL flask sites in Europe, and 15 continuous

CO2 records from Europe totaling over 30 000 observa-

tions over the period considered. This was the data set

as available from the CarboEurope atmospheric database

(http://www.ce-atmosphere.lsce.ipsl.fr/database/index_

database.html) on September 25, 2008. A summary of

the European sites is included in Table 1 for reference.

The global volume (including North American contin-

uous data) of assimilated observations is � 70 000. We

will adhere to the GLOBALVIEW (Masarie & Tans,

1995) naming scheme for CO2 monitoring sites with a

three letter site code, an optional four digit sampling

height, a two digit lab code, followed by a C for semi-

continuous and a D for discrete samples.

Assessment of the diurnal cycle of CO2 in the TM5

model (and others) in Patra et al. (2008) indicated that

using the full hourly CO2 record would lead to large

model biases, especially at night. These biases can

partly be avoided by capturing conditions representa-

tive of a larger geographic area. Similar to the procedure

for North American continuous CO2 records, we have

selected a subset of the high frequency data by aver-

aging over a 4 h window during the afternoon (12–16

local solar time) or the night (00–04 local solar time).

Nighttime sampling was found to be beneficial (when

assessing RMS difference between a priori modeled and

observed CO2) for Plateau Rosa (PRS_21C0), Monte

Cimone (CMN_17C0), and Schauinsland (SCH_23C0),

which are all elevated sites. At night, these locations

sample mostly free tropospheric mole fractions repre-

senting background conditions, whereas during the day

local influences from the valley can often be seen. Since

we do not trust our model to simulate such local flows

R E C E N T E U R O P E A N N E T T E R R E S T R I A L C O 2 E X C H A N G E 1319

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Table 1 Summary of the European data assimilated between January 1, 2000 (the spin-up year) and December 31, 2007. The

frequency of semi-continuous data is one per day (when available), while discrete surface data is generally once per week. Flagged

observations denote a model-observation difference that exceeds 3� the model-data-mismatch and are therefore excluded from

assimilation. Model-data-mismatch assigned to each record is indicated asp

R and used to calculate the innovation w2 statistic. The

bias is the average from model forecast minus observations. Laboratory abbreviations refer to the data owners as summarized in the

GLOBALVIEW product (Masarie and Tans, 1995)

Code Name Lat, Lon, Elev Lab N (flagged)p

R Inn w2 Bias

Semi-continuous continental

BIK0300_45C9 Bialystok, Poland 531130N, 23110E, 180.0 m MPI-BGC 434 (6) 3.00 1.09 1 1.19

CBW0200_52C3 Cabauw, the Netherlands 511580N, 41550E, 200.0 m ECN 1821 (24) 7.50 0.68 �0.35

HUN0115_35C3 Hegyhatsal, Hungary 461570N, 161390E, 248.0 m HMS 1996 (85) 3.00 1.38 1 0.11

KAS_53C0 Kasprowy Wierch, Poland 491130N, 191590E, 1987.0 m AGH 1644 (11) 7.50 0.71 1 0.17

LMP_28C9 Lampedusa, Italy 351300N, 121380E, 70.0 m ENEA 660 (11) 3.00 0.86 �0.53

LMU0079_47C3 La Muela, Spain 411350N, 11500E, 611.0 m IC3 364 (2) 3.00 1.23 1 0.89

LUT0060_44C3 Lutjewad, the Netherlands 531210N, 61200E, 60.0 m CIO-RUG 644 (49) 3.00 1.17 �0.35

SCH_23C0 Schauinsland, Germany 471550N, 71550E, 1205.0 m UBA/UHEI- 2482 (31) 3.00 0.87 �0.41

WES_23C0 Westerland, Germany 541560N, 8100E, 12.0 m UBA/UHEI- 1154 (0) 7.50 0.45 �0.46

Semi-continuous mountain

CMN_17C0 Mt. Cimone Station, Italy 441110N, 101420E, 2165.0 m IMS 1841 (6) 3.00 0.64 1 0.56

PRS_21C0 Plateau Rosa, Italy 451560N, 71420E, 3480.0 m CESI RICERCA 2010 (4) 3.00 0.36 1 0.60

PUY_11C0 Puy de Dome, France 451450N, 3100E, 1465.0 m LSCE 1592 (25) 3.00 1.00 1 0.43

Semi-continuous background

MHD_11C0 Mace Head, Ireland 531190N, 91530W, 25.0 m LSCE 2344 (36) 3.00 0.40 1 0.05

PAL_30C0 Pallas, Finland 671580N, 24170E, 560.0 m FMI 2798 (5) 3.00 0.47 1 0.62

ZEP_31C0 Ny-Alesund, Svalbard,

Norway and Sweden

781540N, 111530E, 475.0 m ITM 1231 (0) 2.50 0.32 1 0.83

Discrete surface samples

BGU_11D0 Begur, Spain 411500N, 31200E, 30.0 m IC3 & LSCE 251 (17) 2.50 1.41 1 0.67

BZH_11D0 Portsall, France 481350N, 41400W, 20.0 m LSCE 12 (0) 2.50 0.91 �0.08

FIK_11D0 Finokalia, Greece 351190N, 251400E, 130.0 m LSCE 26 (3) 1.50 2.15 1 0.42

JFJ_49D0 Jungfraujoch, Switzerland 461330N, 71590E, 3580.0 m UBERN 159 (10) 1.50 1.28 �0.06

LMP_28D0 Lampedusa, Italy 351300N, 121380E, 70.0 m ENEA 221 (1) 2.50 0.88 �0.61

LPO_11D0 Ile Grande, France 481350N, 31350E, 20.0 m LSCE 90 (5) 2.50 1.57 �0.12

PDM_11D0 Pic du Midi, France 43140N, 0190E, 2877.0 m LSCE 171 (11) 1.50 0.90 �0.03

AZR_01D0 Terceira Island, Azores,

Portugal

381460N, 271230W, 40.0 m ESRL 262 (3) 1.50 1.05 1 0.53

BAL_01D0 Baltic Sea, Poland 551210N, 171130E, 3.0 m ESRL 650 (0) 7.50 0.33 �0.70

BSC_01D0 Black Sea, Constanta,

Romania

441100N, 281410E, 3.0 m ESRL 299 (4) 7.50 0.97 �3.10

HUN_01D0 Hegyhatsal, Hungary 461570N, 161390E, 248.0 m ESRL 368 (1) 7.50 0.40 1 0.34

ICE_01D0 Storhofdi, Vestmannaeyjar,

Iceland

631200N, 201170W, 118.0 m ESRL 354 (1) 1.50 0.39 1 0.14

IZO_01D0 Tenerife, Canary Islands,

Spain

281180N, 161290W, 2360.0 m ESRL 294 (1) 1.50 1.19 1 1.05

MHD_01D0 Mace Head, Ireland 531190N, 91530W, 25.0 m ESRL 301 (2) 2.50 0.25 1 0.23

OBN_01D0 Obninsk, Russia 55170N, 361360E, 183.0 m ESRL 107 (1) 7.50 0.57 1 1.59

OXK_01D0 Ochsenkopf, Germany 50140N, 111480E, 1193.0 m ESRL 80 (9) 2.50 1.31 1 0.34

PAL_01D0 Pallas-Sammaltunturi,

GAW Station, Finland

671580N, 24170E, 560.0 m ESRL 235 (3) 2.50 0.69 1 0.53

STM_01D0 Ocean Station M, Norway 66100N, 2100E, 0.0 m ESRL 681 (0) 1.50 0.67 1 0.46

ZEP_01D0 Ny-Alesund, Svalbard,

Norway and Sweden

781540N, 111530E, 475.0 m ESRL 447 (1) 1.50 0.82 1 0.78

1320 W. P E T E R S et al.

r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 1317–1337

we chose to exclude these time frames in the assimila-

tion. For the other continuous sites the reverse is true:

our model is expected to better capture the well-mixed

boundary layer (BL) representative of a larger ‘foot-

print’ than the nighttime stable regime and we selected

afternoon hours. Based on its altitude, PUY_11C0

should also be included in the high altitude category

but by mistake was sampled during daytime in this

study. Daytime vs nighttime RMS differences did not

suggest any detrimental impact of this error.

Two continuous sites (Heidelberg and Ochsenkopf)

were excluded from the assimilation as sensitivity tests

indicated that biases in the model simulated CO2 pre-

vented correct assimilation of their information. A

second set of observations was deweighted because

the large spread in model-minus-observed CO2 sug-

gested that our model regularly missed the model-data-

mismatch target. This latter set includes the continuous

data from the Cabauw tower (CBW0200_52C3), Wester-

land (WES_23C0), and Kasprowy Wierch (KAS_53C0)

as well as the discrete samples from BAL_01D0,

BSC_01D0, HUN_01D0, and OBN_01D0. The first two

sites (CBW, WES) are known to be in highly industria-

lized regions and susceptible to strong fossil fuel burn-

ing influences and model representation error. For some

of the other sites there was reason to doubt the repre-

sentivity and/or data quality of parts of the time series.

For KAS_53C0, a change to sampling nighttime data

might have improved the model statistics and will be

considered in future simulations. All continuous sites

were sampled in the model at the level of the intake

above surface level using an interpolation from the

model levels based on each grid box mixing ratio slope

(second moment of the mean concentration carried as a

standard model variable in TM5). Co-located observa-

tions from flask and continuous samples have their

model-data mismatch scaled to prevent double weight-

ing of their information.

Ecoregions

Similar to the procedure in Peters et al. (2007) we

construct optimal simulated CO2 fluxes for the whole

world using:

Fðx; y; tÞ ¼XNeco

r¼1

lecor Fbioðx; y; tÞ þ

XNoce

r¼1

locer Foceðx; y; tÞ

þ FFFðx; y; tÞ þ Ffireðx; y; tÞ;ð1Þ

where Fbio and Foce are 3-hourly, 11� 11 a priori fluxes

for the biosphere and oceans, FFF and Ffire are monthly

1� 1 prescribed fluxes of fossil fuel burning and fire

emissions, while lr are weekly constant multipliers

across large ecoregions r. The advantage of this ap-

proach is that the number of parameters estimated is

relatively small (N 5 30, N 5 228 globally) and that

information from a sparse network is transferred across

the domain in a relatively simple way. In other studies,

this is achieved by prescribing more complex covar-

iance structures between grid boxes ranging from iso-

tropic with distance (Rodenbeck et al., 2003b; Peters

et al., 2005; Carouge et al., 2008) to explicitly data-

derived (Michalak et al., 2004; Gourdji et al., 2008).

Our approach is equivalent to assuming the flux devia-

tions between grid boxes in the same ecoregion to be

fully coupled, which physically suggests that the a priori

models correctly capture the response of the vegetation

in an ecoregion to climatic conditions, and all we need

to do is to estimate the magnitude of that response

through l. The validity of this assumption is easily

questioned, but it is not clear if any other approach

leads to demonstrably better flux estimates hence we

maintain this approach for this study. One known

drawback of this approach is that multiple sites con-

straining different parts of the same ecoregion might

reduce uncertainty over the whole region too rapidly.

Another is that it adjusts the weekly mean flux by

scaling the diurnal amplitude of net ecosystem ex-

change (NEE) whereas especially continental sites

might be specifically sensitive to a subset of the diurnal

NEE signal (day or night). It is likely more appropriate

to separately scale the daytime dominant photosyn-

thetic flux from the nighttime dominant respiration flux

and thus better control the exchange. Because the

relationship between each of the gross fluxes and ob-

served CO2 mixing ratios is much weaker though, we

have not yet successfully implemented a scheme for

this.

The subdivision of the European domain (consisting

of the continental boundaries and ending at 60E) into

ecoregions that can have their fluxes scaled with a

single multiplication factor comes with some special

challenges, as mentioned in ‘Introduction’. Assigning

any 11� 11 grid box to a single category likely already

carries significant aggregation errors, while assuming a

full coupling between areas with the same ecoregion

type, but sometimes thousands of kilometers apart in

the European domain, is also not realistic. On the other

hand, a very fine-grained distinction between differ-

ently managed ecosystems and different countries

would increase the number of unknown parameters

beyond what can be constrained with our observations,

necessitating other assumptions on the covariance be-

tween fluxes. In the near future, it will be feasible to use

a mosaic of land-use types on higher resolution than

11� 11, which would allow us to scale land-use specific

fluxes instead of grid-box specific fluxes. These are

expected to become available to us through CASA

R E C E N T E U R O P E A N N E T T E R R E S T R I A L C O 2 E X C H A N G E 1321

r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 1317–1337

GFED3 and possibly through other specialized carbon

exchange models for instance for peat lands, or crops.

To do more justice to the large heterogeneity of

European land-use and the high density of CO2 obser-

vations, we decided to separate the European domain,

as defined in the TransCom CO2 protocol (Gurney et al.,

2002), into two further categories before assigning each

grid box to an ecosystem type from the Olson ecosystem

database. This initial split follows the definition of the

Koppen-Geiger climate categories (Kottek et al., 2006)

and thereby separates similar plant species growing

under very different climate conditions. We assigned

each 11� 11 grid box in Europe to either climate cate-

gory C (warm temperate, roughly western and southern

Europe), or category D (snow, roughly eastern and

northern Europe). The further division of these two

regions into Olson ecosystems yielded 18 land regions

that were significantly represented over the continent

(41% of the total land area). They are listed for com-

pleteness in Table 2 and visualized in Fig. 3b.

A priori fluxes

The fossil fuel estimates in CarbonTracker Europe are

different from those in CarbonTracker North America,

and are not optimized by the system. Their specification

follows a CO2 flux magnitude for Europe as prescribed

in CarbonTracker 2007B, but has a seasonality per grid-

box that depends on the inventory of the Institute of

Economics and the Rational Use of Energy (IER),

University of Stuttgart (http://carboeurope.ier.uni-stutt

gart.de). Uncertainty on annual mean fossil fuel emis-

sions is typically 5% over Europe (80 Tg C yr�1), which

is one of the factors not accounted for in our inverse

uncertainty estimate. A priori open ocean and fire fluxes

are the same as in CarbonTracker 2007B. Terrestrial

vegetation a priori NEE come in three flavors:

B1: Global 3-hourly fluxes derived from CASA GFED2

monthly means, as in Peters et al. (2007).

B2: as B1, but European domain has hourly fluxes

derived from the SIBCASA land-surface model

(Schaefer et al., 2008).

B3: as B1, but European domain has hourly fluxes

derived from the JULES land-surface model (Essery

et al., 2003; Harrison et al., 2008), also see http://

www.jchmr.org/jules

There are many differences among these three products

concerning physics, biogeochemistry, and input driver

data. Each was started from a steady-state assumption,

Table 2 Ecosystem types considered for terrestrial fluxes. The land-surface characterization is based on Olson et al. (1985) and each

11� 11 gridbox is assigned to a single category based on the locally dominant vegetation type. The European domain is split into two

climate zones corresponding to Koppen-Geiger category C (warm temperate, roughly southern 1 western Europe), or category D

(snow, roughly northern 1 eastern Europe). Percentages indicate the area associated with each category as fraction of the full

European domain. Sensitivity simulation M2 uses the ecoregions as in the ‘Area Total’ column. Terrestrial biosphere fluxes in

T gC yr�1 are from the base simulation and represent the 2001–2007 mean. These fluxes are discussed in ‘Results’

Category Olson Veg. type

% Area in

Koppen C

% Area in

Koppen D Area Total Flux in C Flux in D Flux Total

1 Conifer Forest 2.3 11.7 14.1 �3.2 �91.1 �94.4

2 Broadleaf Forest 1.1 1.4 2.5 �8.6 �2.6 �11.3

3 Mixed Forest 2.6 6.3 8.9 1 16.5 �30.4 �14.0

4 Grass/Shrub 1.8 6.2 8.0 �3.6 �16.5 �20.1

5 Tropical Forest 0.1 0.0 0.1 0.0 1 0.0 0.0

6 Scrub/Woods 2.8 0.0 2.8 �6.2 1 0.0 �6.2

7 Semitundra 0.1 4.8 4.8 0.0 �4.7 �4.7

8 Fields/Woods/Savanna 2.7 3.9 6.6 1 4.1 �21.0 �16.9

9 Northern Taiga 0.1 2.1 2.2 1 0.1 �1.9 �1.9

10 Forest/Field 4.2 7.3 11.5 �7.8 �85.3 �93.0

11 Wetland 0.1 0.6 0.8 �0.1 �1.0 �1.1

12 Deserts 0.0 0.1 0.1 1 0.0 0.0 0.0

13 Shrub/Tree/Succulents 0.0 0.0 0.0 1 0.0 1 0.0 1 0.0

14 Crops 13.3 9.0 22.3 1 96.2 �35.9 1 60.3

15 Conifer Snowy/Coastal 0.0 0.0 0.0 1 0.0 1 0.0 1 0.0

16 Wooded tundra 0.4 1.2 1.6 1 0.6 �1.0 �0.4

17 Mangrove 0.0 0.0 0.0 1 0.0 1 0.0 1 0.0

18 Ice and Polar desert 0.0 0.0 0.0 1 0.0 1 0.0 1 0.0

19 Water 13.1 0.7 13.8 �0.8 �0.3 �1.1

All 44.7 55.3 100.0 1 87.1 �291.9 �204.8

1322 W. P E T E R S et al.

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where annual gross primary productivity (GPP) equals

ecosystem respiration (R), at a point in time well be-

fore January 1, 2000 (JULES was equilibrated in 1850

for instance) and then allowed to run freely. Climate

variations and differences between GPP and R then

introduce small annual uptake or release in the

priors for 2000–2007. Note that we regard the year

2000 as a spin-up year, and do not include it in the flux

analyses.

In the remainder of this paper ‘base’ or ‘optimized’

results are quoted as a combination of the observations,

ecoregions, and fluxes as described in the previous

sections combined with the CASA GFED2 fluxes as in

simulation B1 which performed best when assessing

root-mean-square differences to CO2 observations.

BASE: biosphere as in B1 1 observations as in Table 1

1 ecosystems as in Table 2 and including seasonal fossil

fuels.

The base simulation spans the period 2000–2007,

while the sensitivity tests focus on 2005. An assessment

of the differences in interannual variability (IAV) result-

ing from the three model priors is in progress and will

be presented separate from this work. The range of

values found in the set [B1, B2, B3] in 2005 relative to the

base simulation will be used as an optimistic uncer-

tainty estimate on the long-term mean results while a

more cautious estimate is derived from the error esti-

mates in the covariance matrix.

In this work, carbon exchange from land to atmosphere

will be preceded by a positive (1) sign, and carbon

exchange from atmosphere to land by a negative (�) sign.

As a result, reductions in uptake or increases in release

are denoted as positive (1) anomalies, while increased

uptake or decreased release are negative (�) anomalies. In

the text, we have tried as much as possible to explicitly

mention the direction of a change of flux.

Sensitivity tests

The sensitivity tests for North America in Peters et al.

(2007) suggested a large part of the variations in flux

estimates was due to the implementation of the prior

terrestrial biosphere fluxes, as well as to the discretiza-

tion of the domain into ecoregions. These are expected

to be at least equally, if not more, important in Europe

and are therefore explicitly tested. An advantage of

testing multiple a priori flux fields is that variations

across prior can be included in the uncertainty estimate,

in contrast to simpler approaches that only vary the

uncertainty of the prior itself. In addition to the bio-

sphere priors, the following set of alternate assimila-

tions was performed to investigate sensitivities in the

CarbonTracker Europe system:

M2: Parameters l follow the original terrestrial ecosys-

tem specification without the climate zone split,

this simulation equals B1.

F2: Fossil Fuel emissions contain no seasonality over

Europe.

D2: BL vertical diffusion is implemented with the Yon-

sei University (YSU) mixing scheme (Hong et al.,

2006).

O2: No continuous continental and continuous moun-

tain data is assimilated, only weekly flask samples

and continuous background sites (see Table 1).

O3: No continuous continental data is assimilated, only

weekly flask samples, continuous background sites

and continuous mountain sites (see Table 1).

O4: Continuous observation time series are explicitly

biased with a site dependent annual mean offset

equal to that assessed from a roaming standard

(A. C. Manning et al., ‘Final report on CarboEurope

‘Cucumber’ intercomparison program’, personal

communication).

T2: Simulation from CarbonTracker 2007B without

CarboEurope observations (all site codes from Ta-

ble 1 not ending with 01D0) and no zoom over

Europe.

Note that (a) these were all done using a priori biosphere

fluxes from B1 and (b) all had an ecosystem distribution

without the European climate zone split, like M2. The

total set of nine simulations explores variations over the

main components of the assimilation framework: prior

fluxes, their uncertainty, data availability, ecoregion map-

ping, fossil emissions, and transport. Although this is by

no means a full characterization of the system sensitivity,

our previous experience suggests that the main variations

are represented. Especially the use of three sets of bio-

sphere a priori fluxes, the different BL vertical mixing

scheme, and the assessment of observation biases are an

innovation over previous work. All sensitivity tests have

been performed including the highest transport model

resolution of 11� 11. The results are summarized in Table

3, and discussed in more detail in ‘Results’.

Results

Our results will be presented in the order in which they

address the research questions posed in ‘Introduction’,

with the role of observations first, the flux results next,

and an assessment of the robustness last.

CO2 simulations

Figure 1 summarizes the model-minus-observed bias,

spread, and the data coverage for each of the sites used

in the assimilation. It shows that the coverage of the

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network is not uniform over the years considered. Also,

the distributions of mismatches is often Gaussian when

considered over the full period (see the right-hand-side

panel), but show seasonal mismatches for sites such as

CMN_17C0 indicating that the optimized biosphere

fluxes may not represent peak summer uptake and

winter emissions. This inability to match the amplitude

of seasonal carbon exchange was also found in Carbon-

Tracker North America (Peters et al., 2007) and occurs in

all three biosphere models used (not shown). Such

seasonal mismatches were previously shown to corre-

late with transport model characteristics (Stephens et al.,

2007) but have also been identified as a shortcoming in

terrestrial biosphere models used in carbon cycle in-

verse studies (Yang et al., 2007; Randerson, 2009). On-

going simulations with a priori fluxes that are not

started from a steady state should confirm this in the

near future. The balance between expected uncertainty

Fig. 1 Overview of the European continuous data used per site over the 2000–2007 period. Left panel shows the temporal distribution

of the simulated-minus-observed residuals of CO2 in ppm with red colors for model overestimates and blue colors for model

underestimates. The range on the y-axis is scaled to the distribution of the residuals in the right hand panel where individual values

are in gray vertical lines, the distribution mean and one standard deviation in red, and the simulated total error (model-data-mismatch

plus transported flux uncertainty) in a blue horizontal line. Blue horizontal lines with a width that matches the 1s bounds denote an

innovation w2 of 1.0.

Table 3 Results from a set of sensitivity studies described in the main text. Fluxes are valid for the year 2005 and represent

aggregated annual means over forested areas (cat 1 1 2 1 3 1 8 1 10 from Table 2), crop lands (cat 14), and other areas as well as the

European total. Variance reduction (second-last column) varies with the volume of data constraints, but also with biosphere model

as the uncertainty is proportional to the flux. The percentage deviation from base in simulations B1, B2, and B3 is taken as a measure

of the uncertainty in base

Simulation Forest flux Crop flux Other flux Total flux

%Variance

reduction

%Deviation

from base

Base �422 1 107 �51 �366 61 0

B1/M2 �476 1 124 �71 �423 78 �16

B2 �322 1 78 �46 �291 74 1 21

B3 �541 1 75 �69 �535 79 �46

D2 �473 1 200 �59 �333 78 1 9

F2 �480 1 167 �53 �367 72 0

O2 �404 1 44 �32 �392 50 �7

O3 �534 �50 �33 �618 63 �69

O4 �504 1 182 �53 �375 78 �2

T2 �172 �96 �73 �340 34 1 7

1324 W. P E T E R S et al.

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and achieved skill as given by the innovation measure

is generally good as indicated by the values in Table 1

and the horizontal blue lines in Fig. 1.

As an illustration of the model performance, Fig. 2

shows the observed and simulated CO2 time series at

Schauinsland, Germany (SCH_23C0) for the year 2003.

The original half-hourly observations from the

CarboEurope database are averaged over 4-h intervals

between 00:00 and 00:04 hours local time (UTC 1 1) as

described in the Methods section, and assimilated into

the modeling framework. Modeled mixing ratios are

expected within 3.0 ppm of the averaged data 68% of

the time, a target met in the time series illustrated. Over

the full period considered, Schauinsland modeled-

minus-observed CO2 is �0.87 � 3.72 ppm for the sum-

mer period (June–September) and �0.35 � 2.83 ppm in

winter (November–April). Thirty-one of 2482 data

points (1.2%) were excluded from the assimilation

because the model could not match them to within

9 ppm (3s). Together with the value of 0.87 it suggests

that the Schauinsland observations were satisfactorily

assimilated.

Relatively high rejection rates (4expected 2%) are

found at multiple sites: HUN0115_35C3, LUT0060_

44C3, BGU_11D0, FIK_11D0, JFJ_49D0, PDM_11D0,

and OXK_01D0. For some of these sites the likely reason

is simply the small number of observations available

(o100), but not for others. At HUN, the relatively small

mean bias (1 0.11 ppm) is the result of a large winter

underestimate of mixing ratios (�1.46 � 5.65 ppm) can-

celed by a summer overestimate (1 1.65 � 3.98 ppm).

Especially in winter the model is unable to capture high

CO2 values (4410 ppm) and the distribution of the

residuals departs from the expected Gaussian shape.

The likely reasons for these problems are the same that

make our current framework unsuitable to simulate the

full half-hourly observed CO2 distribution: insufficient

vertical resolution near the surface, inadequate physical

description of dispersion in stable BL, inadequate de-

scriptions of diurnal BL growth, and a lack of mechan-

istic detail in the diurnal cycle of the biosphere models.

This is an important limitation of current global trans-

port models when analyzing continuous time series,

and should be kept in mind in the following discussion

of estimated terrestrial exchange.

Seven-year mean carbon balance

Over the period considered, natural terrestrial carbon

uptake over the European continent averaged

′ ′

Fig. 2 Observed and simulated time series of CO2 at Schauinsland, Germany (SCH_23C0) for the year 2003. The figure illustrates the

reduction of data from semi-continuous half-hourly to daily 4-h nighttime averages, and how the choice of nighttime mixing ratios

avoids the need to simulate a detailed diurnal cycle at this elevated site. The simulations match the data to a satisfactory degree, as

summarized in Table 1.

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�165 Tg C yr�1 consisting of �205 Tg C yr�1 uptake by

the biosphere, and 1 40 Tg C yr�1 release from burning

of biomass. This natural net storage of carbon offsets

10% of the estimated 1 1.63 Pg C yr�1 release from fossil

fuel burning. In our calculations the temperate area of

Europe (Koppen climate C, roughly western 1 southern

Europe, also see Fig. 3) is identified as a net source of

carbon (1 87 Tg C yr�1), while the rest of Europe (Kop-

pen climate D, roughly northern 1 eastern Europe) is a

sink (�292 Tg C yr�1). A possible range for the European

terrestrial uptake, derived from a set of alternate bio-

sphere model priors, is �122 to �258 Tg C yr�1 (1 16%

to �46% from Table 3, also see Table 4). As argued

previously (Peylin et al., 2005; Peters et al., 2007) such an

uncertainty estimate across alternative realizations of

the inverse problem complements the formal Gaussian

uncertainty estimate derived from the covariance ma-

trix (� 438 Tg C yr�1 average per week, not including

the temporal covariance).

Most uptake occurs in the forests of Europe with large

contributions from Conifer Forests (cat 1: �94 Tg C yr�1)

and Forest/Field complexes (cat 10: �93 Tg C yr�1), and

small contributions from Mixed Forests (cat 3: �14 Tg

C yr�1), Field/Wood complexes (cat 8: �17 Tg C yr�1),

and Broadleaf Forests (cat 2: �11 Tg C yr�1). Most of this

forested area is located in Russia, Belarus, and the

Scandinavian countries. Total forest NEE equals

�229 Tg C yr�1, while forest uptake rates in EU27 coun-

tries (i.e., normalized by unit area classified as forest)

are �68 g C m yr�1 in our study.

Crop lands in Europe are a net source of

1 96 Tg C yr�1 in the southwestern sector, or net sink

of �35 Tg C yr�1 in the northeastern sector of Europe.

This contrasting signature of crop lands cannot readily

be explained and contrasts the net carbon sink over

croplands found in Peters et al. (2007). This will be

discussed further in ‘Discussion’. Annual mean uncer-

tainty reduction is sizable for the southwestern domain

(43%) but modest for the northeastern part (15%)

reflecting the predominance of both this ecosystem

and observations in the southwestern sector. In the

better constrained southwestern sector, net crop land

carbon loss rates are 1 62 g C m yr�1.

We have aggregated the 11� 11 optimized carbon

fluxes to country level, which is only meaningful to

make sums across groups of countries. This exercise

shows that the 27 EU countries together are a natural

carbon source of 1 24 Tg C yr�1 with largest net uptake

in Finland, Sweden, and Poland (sum of �34 Tg C yr�1),

and largest release in France, Germany, and the UK

(sum of 1 46 Tg C yr�1). This release is in addition to

the 1 1030 Tg C yr�1 average fossil fuel emissions from

the EU27 countries. Only in Finland, Latvia, and Lithua-

nia does natural uptake exceed fossil CO2 release.

Similar to the bottom-up study of Janssens et al. (2005)

we find more intense forest carbon sinks (g C/per unit

country area) in central and eastern European countries

(Belarus, Slovakia, Latvia, Lithuania) than in the Scan-

dinavian countries (Finland, Sweden, Norway). But the

quantitative agreement on a country-by-country basis

with bottom-up results is generally weak. The release

simulated over most of Western Europe (also see Fig. 3)

mostly results from positive fluxes in crop lands (22% of

area) and mixed forests (2.6% of area), which are the

predominant vegetation types near most of our contin-

uous sites in Koppen region C (CBW, WES, BIK, LUT,

HUN, KAS, SCH). Thus, this inferred release is con-

(a)

(b)

Fig. 3 (Top) 2001–2007 mean terrestrial biosphere carbon flux

over Europe derived with our system. Note that the pattern on

1� 1 is constructed with a detailed a priori biosphere model and

a set of 18 weekly linear scaling factors. Blue colors (� sign)

denote net carbon uptake while red colors (1 sign) denote

carbon release to the atmosphere. (Bottom) Ecoregions across

Europe as used in this study where numbers in the colorbar refer

to entries in Table 2.

1326 W. P E T E R S et al.

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strained by many observations, but dependent on the

sites for which the model skill is generally lower (see

Table 1).

Interannual variations

The IAV in Europe’s terrestrial carbon flux is close to

350 Tg C yr�1 peak-to-peak. This is slightly smaller than

derived by Rodenbeck et al. (2003a) and also smaller

than presented by Baker et al. (2006). Note, however,

that these studies spanned longer time periods (two

decades) including the 1991 Mt Pinatubo eruption and

the strong 1997/1998 ENSO.

Figure 4 shows the cumulative NEE for each of the 7

years considered as well as their mean on a weekly

resolution. Two years stand out as anomalous: 2003 and

2005. The year 2003 is known for the continent wide

heat wave during most of the boreal summer, which

caused a reduction of terrestrial uptake estimated in

a range of 20–500 Tg C yr�1 (Ciais et al., 2005; Vetter

et al., 2008). It can be seen that 2003 started with a

higher-than-average release of carbon in the first 4

months of the year amounting to 100 Tg C. At the start

of the growing season, this positive anomaly was first

strongly diminished by strong uptake in spring (week

22), but then rapidly increased to 140 Tg C at the end of

summer. This summer carbon uptake deficit stayed

nearly constant after the end of the growing season

and the year 2003 ended with 147 Tg C less uptake than

the long term mean. The spatial pattern of the uptake

anomaly in summer (JJA, Fig. 5) strongly resembles the

a priori modeled anomaly from the underlying bio-

sphere model, as expected since posterior fluxes are

created from linear scaling of these fluxes across broad

ecoregions. The a priori modeled anomaly and the

posterior estimated one agree well for summer suggest-

ing that the CASA GFED2 model captured the effect of

the summer drought relatively well. But the annual

total reduction in uptake amounts to only 64 Tg C yr�1

and is thus strongly magnified when using the atmo-

spheric CO2 data as constraints. Spatial patterns of the

2003 anomaly are similar to those presented in Vetter

et al. (2008).

In contrast to 2003, the year 2005 was a very average

year for the first 27 weeks, but then showed very strong

uptake in July and August giving it 202 Tg C extra

uptake relative to the mean (Fig. 4). During this summer

period eastern Europe experienced flooding due to

excessive rainfall, while much of western Europe had

higher than average temperatures and strongly reduced

precipitation. But whereas the Iberian peninsula went into

a massive drought with the worst crop yields in years

(even worse than 2003, see http://ec.europa.eu/eurostat/),

central and eastern Europe profited from these conditions

because enough soil moisture was available to sustain

growth even after the extreme weather had passed. This

forms an important contrast with 2003, and was largely

due to the relatively cool and wet winter of 2005. This, as

we will discuss in ‘Discussion’ was driven by a negative

phase of the North Atlantic Oscillation.

The favorable growth conditions in 2005 were present

in the supplied NDVI and moisture fields for the CASA

GFED2 a priori model, and a positive anomaly in NPP

(more growth) was simulated over all of central and

eastern Europe, while the Iberian peninsula had a

strongly negative NPP anomaly (Fig. 5). Together with

a positive respiration anomaly (more release) and less

release from fires (5 Tg C yr�1) the a priori expected

effect was �84 Tg C yr�1 (extra uptake) averaged over

2005. So the atmospheric data (�202 Tg C yr�1) again

suggest an underestimate of the CASA GFED2 simu-

lated annual anomaly magnitude.

Table 4 Different estimates of the uncertainty on the long-term mean carbon flux estimated in this study. The relative uncertainty

ranges are calculated around a mean natural flux of �205 Tg C yr�1, and have 40 Tg C yr�1 of fire emissions added. The Gaussian

uncertainties are centered on �165 Tg C yr�1

Estimate Type Description

�46% to 1 21% Relative range Min/max deviation from 2005 mean in [B1, B2, B3]

�258 to �122 Tg C yr�1 Range of values The % above applied to the 2001–2007 mean

�69% to 1 21% Relative range Min/max deviation from 2005 mean in [B1, B2, B3, M2, D2, F2, O2, O3, O4, T2]

�305 to –122 Tg C yr�1 Range of values The % above applied to the 2001–2007 mean

�602 to 1 272 Tg C yr�1 Gaussian error, 1s Square root of average 2001–2007 posterior weekly error covariance*

�574 to 1 272 Tg C yr�1 Gaussian error, 1s As above, but including the a-posteriori estimated temporal error covariance

�1132 to 1 802 Tg C yr�1 Gaussian error, 1s As above, but including independently estimated temporal error covariancew�1040 to 1 710 Tg C yr�1 Gaussian error, 2s 2� square root of mean of 2001–2007 estimated weekly error covariance

�839 to 1 509 Tg C yr�1 Gaussian error, 1s Square root of average 2001–2007 prior weekly error covariance

*Derived from the average of the weekly variances, and not from the variance of the annual average which we cannot estimate in our

system with 5-week of lag, also see Peters et al. (2007).

wBased on 30-day linearly decaying temporal error correlations as in Chevallier et al. (2006).

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In contrast to the mostly summer-driven anomaly of

2005, larger than average uptake in 2006 in our results is

driven by lower than average carbon release at the end

of the year when warm weather persisted to late Octo-

ber in much of western Europe. This was followed by

the warmest European winter (2006/2007) on record

and anomalously early Spring greening in 2007

(Maignan et al., 2008). The CASA GFED2 model predicts

the October–November–December 2006 warm weather

influence on respiration to outweigh that on NPP and

thus an increase of carbon release. The assimilated

results however, yield a decrease in carbon release for

the October–November–December period. Whether this

is due to an incorrect temperature sensitivity in the

CASA GFED2 model in winter or whether the problem

lies in the driver data for the biosphere model is under

investigation. In 2004, the positive influence of cool

temperatures and abundant moisture on NEE was

captured in the a priori biosphere model, and subse-

quently magnified in the assimilation. Finally, 2007 saw

a return to more average carbon uptake conditions

mostly due to low winter release followed by low

summer uptake.

One of the most robust feature in our assimilation

results is the larger CO2 uptake in the years following

2003. This feature is not driven by larger a priori uptake

(only 2005 had more than average a priori uptake in all

three models), but by the assimilation of the observa-

tions. Using the full network or one reduced to only

background or only flask sites does not change this

outcome, suggesting that the change is driven by large-

scale changes in the European uptake and not the effect

of a few continental sites. The CO2 signal that causes

this extra uptake is, at least in 2005 and 2006, distin-

guishable in the observations themselves. When analyz-

ing the seasonal cycles of CO2 at the European con-

(a)

(b)

Fig. 4 (Top) Cumulative net ecosystem exchange (NEE) vs. time estimated in our system for each of the individual years

(dashed 1 colored) and for the 2001–2007 mean (bold 1 black). The figure reveals the large uptake in 2005 (purple dashed) and the

effect of the summer drought in 2003 (red dashed). (Bottom) Again, but now with the mean subtracted to see the accumulated anomaly of

C exchange through each year. Small differences between plotted and quoted anomaly magnitudes stem from the discretization of the

365 days yr�1 of calculated flux into 52 weeks yr�1 plotted flux.

1328 W. P E T E R S et al.

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tinuous sites that span several years of our study period

the largest peak-to-peak amplitude anomaly is found

in 2005 for KAS_53C0 (1 4.5 ppm greater than average),

SCH_23C0 (1 4.0 ppm), PUY_21C0 (1 4.6 ppm),

CMN_17C0 (1 2.7 ppm), and PRS_21C0 (1 2.6 ppm), or

in 2006 for HUN0115_35C3 (1 4.0 ppm), with the other

year ranking second at each of these sites. These am-

plitude anomalies are all well outside the analysis

uncertainty, and close to the 2-s of the standard devia-

tion of each time series. In contrast, the background

sites MHD_11C0 (�0.1 ppm), PAL_ 30C0 (1 0.4 ppm),

and ZEP_31C0 (1 0.4 ppm) do not show a significant

change in seasonal cycle those years. This supports the

idea that the increased seasonal cycle is driven by

biospheric exchange over the continent and not simply

advected with westerly winds to Europe. The large

peak-to-peak amplitude detected in the observed CO2

is characterized by deeper than normal boreal summer

minima (especially in 2005) as well as higher than normal

winter maxima (in both early 2005 and early 2006) at

most sites. In our system the increased CO2 mixing ratio

amplitudes, after accounting for transport patterns

through the TM5 model, are translated into increased

CO2 annual mean uptake in 2005 and 2006. Whether this

translation is correct or not depends on several modeling

aspects, as we will discuss in ‘Discussion’.

Robustness and uncertainty

Figure 6 shows the derived annual mean NEE per

ecoregion across the set of sensitivity experiments de-

scribed in ‘Materials and methods’. Note that all fluxes

pertain to the year 2005 only. The integrated uptake

ranges from �291 to �535 Tg C yr�1 with largest uptake

in the simulation that uses the SIBCASA prior biosphere

fluxes (B2), and smallest uptake when using the JULES

biosphere prior (B3). CASA GFED2 (base), JULES (B3),

and SIBCASA (B2) all bring their own 11� 11 flux

patterns depending on driver data and internal model

physics and the posterior CT Europe results are linear

modifications of these patterns across large ecoregions.

The JULES model favors reduced cropland release and

reduced uptake in forest/fields relative to base, while

SIBCASA requires larger sinks in every forested ecor-

egion except broadleaved ones (cat 2), and a smaller

source in croplands. The total European uptake using

Fig. 5 The summer (JJA) mean carbon anomalies in 2003 (top) and 2005 (bottom) in this study. Optimized NEE results from the

assimilation of atmospheric CO2 data using the NEE a priori patterns and magnitudes. NEE a priori is the difference between GPP and R.

The integrated numbers in the top-left corners refer to the 3-month average and not the full year. The panels illustrate how the CASA

GFED2 biosphere model picks up important features in the component fluxes (GPP, R) but the assimilation strongly influences the

resulting optimized NEE. An important driver of these summer differences is the amount of soil moisture which limited the simulated

GPP and respiration in 2003, but not in 2005 due to abundant rainfall in previous seasons. Only Spain and Portugal were strongly

affected by poor growth conditions in both summers.

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SIBCASA as a prior is twice as large as with the other

biosphere models, a difference driven by the amplitude

of the diurnal cycle between the models. In SIBCASA,

for reasons that are still under scrutiny, the diurnal cycle

of CO2 during the growing season (when daily mean

NEE is negative) is almost half that of the other models

necessitating that the amplitude (which is scaled by the

flux multiplication factors) is scaled more strongly to

match the daytime sampled tower data leading to a

larger mean uptake.

Analysis shows that the influence of the choice of

network is large in 2005. When going from a back-

ground-only network (O2) to a network with continu-

ous mountain sites added (O3), the integrated flux

changes from �392 Tg C yr�1 to �618 Tg C yr�1, where-

as adding in the continuous tower sites as well (base)

reduces the uptake back to �390 Tg C yr�1. The moun-

tain top sites (CMN, PRS, PUY) exert a strong pull on

the solution, with PRS and PUY pulling slightly (to

�460 Tg C yr�1), and Monte Cimone pulling strongly

(to �650 Tg C yr�1) when considered separately. Espe-

cially at CMN_17C0, the simulated CO2 bias in 2004 and

2005 unexplainably increases from o0.3 ppm to nearly

1.5 ppm (model too high), while PRS_21CO and

PUY_11C0 are simulated well those years. Part of this

could be because CMN_17C0 is wedged between in-

creased westerlies in western Europe and increased

southerlies in eastern Europe that occurred in summer

2005 when the Azores high briefly intensified. The net

effect is difficult to predict. But we can also speculate

that the larger than average agricultural production in

Italy in 2005 (the highest in the period 2001–2007) has

led to lower observed CO2 values mostly at CMN, and

less so at PRS and PUY. Another effect of increased

southerlies is that more air from tropical Africa comes

to Europe, which is also lower in CO2 mixing ratio. In

both cases CO2 mixing ratios at the mountain top sites

becomes lower than normal which, if not accounted for

in model advection, is translated to extra carbon sinks.

Although the CO2 signal at the mountain sites is real

and also detectable in the observations, the interpreta-

tion by our system can in this case be questioned as

several previous studies have demonstrated the diffi-

culties of simulating anomalous transport patterns in

complex terrain (de Wekker et al., 2005; Aalto et al., 2006;

Perez-Landa et al., 2007; Sun et al., 2007; Ahmadov et al.,

2009). Also, model biases are between 1 1 and 1 2 ppm

for the boreal summer average at the subtropical sites

ASK, IZO, and WIS supporting the possibility that a

large scale model bias is transported to Europe by

anomalous southerlies, and then translated to extra

sinks in the assimilation. Mountain top sites experien-

cing free tropospheric air would be most susceptible to

transport of such large-scale model biases. Note how-

ever, that the sensitivity of the 2005 anomaly to moun-

tain top sites concerns the magnitude (large or very

large), but not at all the presence of an uptake anomaly.

In addition to modifying the integrated annual flux,

changing the configuration of the observation network

shifts the internal pattern of fluxes. When we compare

the simulation with background-only sites (O2) with the

full network (B1/M2) we get the same annual mean for

2005, but a redistribution of fluxes with 100 Tg C yr�1

less release from croplands (cat. 14), 50 Tg C yr�1 less

uptake by fields/forests (cat. 10), 50 Tg C yr�1 less up-

take in grasslands (cat. 4) and field/wood complexes

(cat. 8), and an overall northward shift of the remaining

sink towards coniferous (cat. 1) and mixed boreal

forests (cat. 3). This shift is not necessarily an artifact:

the full network has nearly 26 000 extra daily observa-

tions to derive such finer details from. But not every

region is equally well constrained by these observa-

Fig. 6 Fluxes per ecoregion in 2005 over a range of sensitivity studies discussed in the main text, and summarized in Table 3. Red labels

on the bottom refer to ecoregion area (also see Table 2), the top labels are the annual mean a priori uncertainty and the percentage

uncertainty reduction from the a priori to the a posteriori results in the base simulation.

1330 W. P E T E R S et al.

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tions. The largest estimated error covariance between

European ecoregions is generally between croplands

and forest/field complexes (median correlation coeffi-

cient of �0.17), which are geographically interspersed.

Largest uncertainty reduction is achieved on Forest/

Fields (80%), Croplands (60%), and Conifer Forests

(40%), which also represent the largest surface area

and hence largest atmospheric CO2 signals. Smallest

uncertainty reduction is on grasses/herbs (12%).

The sensitivity to the vertical mixing scheme (D2) is

generally modest, likely because the system uses ob-

servations from well-mixed situations in which the

differences between the schemes are minimal. A large

flux difference occurs during a specific period in July

2005 when uptake shifts from forest/fields to croplands

when using the YSU mixing scheme. The direction of

this shift is not surprising as the correlation (root of

normalized estimated error covariance) between these

ecoregions is close to �0.4 at that time. The cause of this

shift can be related to the difference in simulated CO2 at

a few continental continuous sites. During this period

the simulated afternoon samples at HUN0115_35C3 for

instance are higher by 1–3 ppm when using the YSU

scheme which is not due to daytime mixing, but to

nighttime mixing. The YSU scheme tends to mix CO2

from the stable nighttime PBL more strongly to the free

troposphere causing more respired CO2 to be advected

over the area. Some sites pick up this signal during the

daytime and the model responds by increasing uptake

around that tower and compensating this with fluxes in

nearby regions, or regions coupled through covariance.

In addition to HUN, this effect is also visible at SCH and

LMP. Since these large differences occur only during

short episodes the difference in simulated mixing ratios

is generally normally distributed over a full year. The

effect on the annual mean flux shifts is on the order of

50 Tg C yr�1 which is similar to the effect of using a

coarser ecoregion distribution (M2).

Finally, adding a realistic site dependent bias to the

observations has only a very modest influence on the

fluxes (O4) given the current large model error compo-

nent of several ppm. This confirms the earlier results of

Rodenbeck et al. (2006), where observational biases

were also overshadowed by large model errors.

For completeness, Table 4 lists several estimates of the

uncertainty on the derived long-term mean flux. The

Gaussian error estimates are larger than the derived

ranges of values in the sensitivity experiments. This

partly reflects the limited set of experiments we could

include given computational resources, and partly our

limited ability to estimate annual mean uncertainties in

a system that includes 5 weeks of lag, and no error

propagation model. We nevertheless present these

numbers to indicate that (a) true uncertainty is likely

larger than the range of �46% to 1 21% from our

biosphere sensitivity tests, and (b) the uncertainty esti-

mate itself is uncertain.

Discussion

Our European-wide integrated sink estimate of

�165 Tg C yr�1 including its range can be compared

with the bottom-up derived carbon sink estimate of

�111 � 279 Tg C yr�1 by Janssens et al. (2003) for Eur-

opean forests, croplands, grasslands, and peatlands.

With similar corrections for unaccounted fluxes (lateral

transport, freshwater fluxes, wood products) in each

method as applied in Janssens et al. (2003), these esti-

mates agree to within 50 Tg C yr�1. This, however, does

not mean that bottom-up and top-down agree well. In

addition to large uncertainty on both fluxes making this

small difference somewhat fortuitous, spatial patterns

and ecosystem integrated fluxes can be substantially

different. Our integrated annual forest uptake of

�229 Tg C yr�1 is substantially smaller than the

�363 Tg C sequestration by forests reported in Janssens

et al. (2003) as a result of differences in uptake rates

[�68 g C m�1 forested area year in this study vs.

�124 g C m�1 forested area year in Janssens et al.

(2005)]. One cause of this discrepancy is likely that

our fluxes represent an integral over a coarse 11� 11

area in which ecosystems with lower uptake rates are

also present. Dividing this average by the fraction of

forested area in the 11� 11 box thus likely underesti-

mates the peak uptake in forests. Other complicating

factors for such comparisons are discussed in depth in

Janssens et al. (2003). More recent work (Ciais et al., 2009)

suggests the carbon uptake in European forests to be

smaller than reported in Janssens et al. (2003), falling in a

range between �55 and �105 g C m yr�1 as derived from

forest accounting methods and eddy-covariance observa-

tions. This would bring our top-down derived forest

uptake rates in closer agreement with the independent

bottom-up estimates.

In Peters et al. (2007), we speculated that a large

cropland carbon sink in North America was the result

of a strong growing season uptake signal picked up by

our system, followed by a release of the harvested

products over a different and much larger surface area

which we did not explicitly detect from the atmosphere.

The finding of a net cropland source in this study brings

doubts to that explanation. It is possible that the specific

land-use of Europe (agricultural lands are interspersed

with population) differs from that of North America

(vast expanses of concentrated crop growth in the least

populated areas) enough to make this a real signal. In

that case the 1 60 Tg C yr�1 crop source found in this

study reflects a small net loss of carbon from agricultural

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soils, derived against a background of large gross fluxes

of harvest and consumption in areas that also have large

fossil fuel emissions (1 580 Tg C yr�1). This seems an

unlikely success for our system, also because we have

not included any agricultural product export pathways

shown to be substantial (Ciais et al., 2008a). We further-

more found a sensitivity of the estimated cropland flux

to the diurnal cycle flux amplitude in the a priori model

which suggests that the estimated cropland carbon

balance likely does not reflect soil carbon losses robustly

enough to compare with soil carbon surveys.

Bottom-up estimates of crop land carbon exchange

have indicated arable lands as a consistent source

of carbon to the atmosphere for all countries in

Europe (Janssens et al., 2005). These authors come to

1 300 Tg C yr�1 net loss over arable lands in Europe,

nearly four times as high as our estimate. Uncertainty

on this estimate is also substantial though as the EU15

carbon-sequestration rates derived from crop statistics

and a residue carbon-decomposition model were extra-

polated to the rest of Europe after soil decomposition

measurements from three countries were scaled to the

total domain, and the resulting carbon loss rates cali-

brated to yet four other studies varying in their loss

rates between 1 24 g C m2 yr�1 (Dersch & Bohm, 1997)

and 1 75 g C m2 yr�1 (Sleutel et al., 2003). Our mean

carbon loss rate from crops of 1 63 g C m2 yr�1 in wes-

tern Europe is therefore well within currently accepted

values and driven by more observational data. Again,

more recent work (Ciais et al., 2009) suggests the Jans-

sens et al. (2005) crop flux estimate to have been too

high. Recent observations suggest a soil carbon flux

closer to the lower value of 1 24 g C m2 yr�1 and three

crop specific biosphere models suggest a negligibly

small net source of carbon from croplands, and at least

one model supports a small net carbon sink in crop

lands in central and eastern Europe. These results were

quite sensitive to the prescribed management regime

though. The question whether there really is a net

cropland carbon sink in eastern Europe induced by

known differences in agriculture practices and history

of land-use remains important, but open for now.

Grassland NEE in CarbonTracker Europe range from

1 6 Tg C yr�1 of release to �38 Tg C yr�1 of uptake

across the different simulations, with a best estimate

of �20 Tg C yr�1 of uptake. Comparisons with bottom-

up numbers such as the �99 Tg C yr�1 Net Biome

Production in Ciais et al., 2009 (manuscript in prepara-

tion) are of little help as even the areas of grassland in

both studies differ by a factor of 2.

The 7-year mean carbon flux presented here results

from 3 years of below average uptake (2001–2003)

followed by 3 years of above average uptake (2004–

2006), and 1 year of near average uptake (2007). This

result is robust against the in- or exclusion of the

European continuous data, and much larger than

the increase in fossil fuel emissions (60 Tg C yr�1) over

the second half of the period analyzed. Other inversion

studies (Gurney et al., 2008; Rayner et al., 2008) and

synthesis results (http://www.carboscope.eu) seem to

agree that 2003 was a low uptake year, followed by 2004

and possibly 2005 as a strong uptake year, but the

contrast with the pre-2003 period is not as obvious in

these studies. None of these other studies included 2006

and 2007 though, nor the large number of observations

included here, and a comparison of the 7-year trend is

not feasible at this stage. Although a trend in uptake

over time in our system would be consistent with a drift

of the terrestrial estimate due to incorrect accumulation

of other CO2 (such as through fossil emissions, or

tropical advection), the consistency across different

configurations and subsequent return to average con-

ditions in 2007 supports the interpretation that there

was indeed a strengthening of the carbon sink in

Europe during 2004, 2005, and 2006.

Is there a mechanism that could cause such a compo-

nent of IAV? Previous studies have demonstrated the

influence of the North Atlantic Oscillation (NAO) as

well as the Arctic Oscillation (AO) on European climate

(Hurrell, 1995; Thompson & Wallace, 1998; Slonosky &

Yiou, 2001; Wedgbrow et al., 2002; Schaefer et al., 2005;

Philipp et al., 2007), tracer transport (Li et al., 2002; Chen

et al., 2005), and the carbon cycle (Potter et al., 2003;

Russell & Wallace, 2004; Goetz et al., 2005; Patra et al.,

2005; Kettlewell et al., 2006; Schuster & Watson,

2007; Zhang et al., 2007; Thomas et al., 2008). Positive

phases of NAO bring stronger westerlies to northern

Europe and warmer and wetter than average condi-

tions. Positive phases of the AO strengthen the polar

vortex which inhibits cold air outbreaks in temperate

areas and mostly affects temperatures in Europe. Both

positive winter/spring values of AO and NAO are

associated with earlier snowmelt and an earlier

start of the growing season leading to increased satellite

greenness and larger NPP. The year 2005 started

in a strongly positive NAO phase, and then went

into a prolonged 24-month negative phase (when asses-

sing 3-month running means) for the first time since

2001. Strongly negative phases occurred in MAM of

2005 and in JJA of 2006 while strong NEE anomalies,

responsible for our larger than average annual uptake,

occurred in JJA and OND, respectively, and hence not in

phase with NAO. Also the linear correlation between

the NAO index and NEE anomalies on monthly time

scales over the full period is small (R 5�0.15). If the

negative NAO phase were responsible for the 2005 and

2006 anomalies its effect on NEE is thus likely not linear,

or direct.

1332 W. P E T E R S et al.

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The lack of linear correlation does not exclude a

possible influence of NAO on our results: the mechan-

isms through which temperature and moisture anoma-

lies affect NEE are not as easily predictable as for NPP,

because respiration is also partly temperature con-

trolled on short timescales (Jones et al., 2003; Friedling-

stein et al., 2006). It is even likely that there is a

substantial delay in NEE response to large scale atmo-

spheric conditions, as carbon accumulated through

enhanced NPP is respired in a later season (Russell &

Wallace, 2004), when also soils can still feel earlier spells

of warm/cold and wet/dry conditions. This seems to

have played a role in the 2005 anomaly where higher-

than-average temperature in western Europe (like in

2003) led to increased uptake because over much of

Europe enough soil moisture was available from previous

wet conditions to sustain photosynthesis. In our CASA

GFED2 simulations the moisture limitation on NPP that

played such a large role in 2003 was not present in 2005,

nor in 2006. Abundant precipitation in late winter and

spring, likely induced by the circulation patterns set up

by the negative NAO phase, had provided the needed

moisture for plant growth during summer.

The year 2004 contrasts the 2005 and 2006 situation.

The NAO phase was more positive than negative, the

amplitude of observed CO2 was not larger than normal,

and the cumulative NEE (Fig. 4) resembles the long-

term mean more than any other year. The only differ-

ence is the slightly larger release of carbon before the

start of the growing season, followed by slightly larger

uptake throughout summer and lower release through-

out the next winter, accumulating to a sizable annual total

anomaly (�66 Tg C). Especially the persistence of the

anomaly after May of 2004 causes the anomaly for this

year. This is partly driven by the cool climatic conditions

with May–November temperatures below those of any

other year in our time window. Partly, there could also be

an effect from the recovery from the 2003 drought. Re-

growth of vegetation, increased light availability to plants

previously overshadowed, extra nutrient availability

from last years decay, and a number of other mechanisms

could induce a recoil from 2003. Some of these effects are

(crudely) represented in the CASA GFED2 biosphere

model used here, which for instance carried reduced

carbon and soil moisture pools from 2003 to 2004. A first

analysis using either climatological initial conditions for

2004, or the actual 2004 initial conditions suggests that

these transient effects lead to reduced heterotrophic

respiration (�66 Tg C yr�1) a year after the drought. Be-

cause a similar effect occurs for NPP (�49 Tg C yr�1), the

predicted effect on NEE is that �17 Tg C yr�1 extra up-

take in 2004 was expected due to the poor 2003 growing

conditions. This is close to 25% of the a posteriori NEE

estimate for 2004. More extensive mechanistic analyses of

the 2003–2006 flux variations are currently underway,

and will be presented in a future publication.

What caused the observed increase in CO2 seasonal

cycle amplitude and NEE? Although we can speculate

that the increase in biomass in the years following 2003

drives a larger summer NPP, a larger winter respiration,

and thus a larger NEE and CO2 amplitude from 2004

on, we currently have no evidence to support this

theory. We could test the hypothesis using the CASA

GFED2 model, but such simulations are not straightfor-

ward as this study also suggests that the biosphere

model does not always correctly capture the effect (sign,

and especially magnitude) of climate on NPP and

respiration, which is a prerequisite for more mechan-

istic studies. Analysis of observed surface flux time

series to confirm or disprove our conclusion that this

is a feature in the terrestrial biosphere would be a good

next step to investigate this large-scale change in the

CO2 seasonal cycle. An alternative explanation, where

changes in vertical and horizontal transport due to for

instance the negative NAO phase cause increased sea-

sonal cycles of PBL height and trace gases, was explored

with the TM5 model too. A simulation of 222Rn over

Europe for the same period shows an influence of NAO

on horizontal transport patterns in winter, and also

suggest changes in ECMWF simulated PBL height

amplitudes in 2005 and 2006. This suggests that NEE

is not the only factor playing a role. Further analysis of

coupled transport, land-surface, and biosphere models

for the same period are planned to further attribute the

change in CO2 seasonal cycles to each process.

A valid question to ask in light of our results is

whether the framework presented is adequate for the

purpose of quantitatively analyzing the CO2 balance of

Europe. The seasonal residuals up to 3 ppm in ob-

served-minus-modeled CO2, the large sensitivity to

prior biosphere fluxes, the sometimes unpredictable

influence of particular sites and meteorological events

all suggest one should be cautious in the interpretation

especially at the finer scales. Attempted comparisons

with eddy-covariance observations, regional crop sta-

tistics, CO2 observations in complex terrain, and diurnal

variations in CO2 all show that our framework based on

11� 11 information is not adequate on local up to

country scales, and might already be pushed beyond

its limits in some of the work presented here. Mesoscale

modeling, airborne flux measurements, high resolution

remote sensing, carbon accounting, and upscaling of

local ecosystem observations will be needed to comple-

ment our estimates at those scales for some time.

On the other hand, there is currently no other spa-

tially explicit time varying European carbon balance

available that is consistent to this degree with so many

different pieces of evidence. The reasonably successful

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integration of mechanistic carbon cycle information,

detailed weather patterns, satellite observations, and

of course tens-of-thousands of atmospheric CO2 obser-

vations over the European domain reveal promising

skill at subcontinental scale. At what point beyond the

country scale, but within the continental scale, our

system becomes more reliable than upscaling techni-

ques remains an open question, partly because uncer-

tainties in both methods are poorly defined. The

convergence of our results with the updated numbers

now produced from bottom-up efforts indicate that

further integration of these methods is a viable target

for the next years. Nevertheless, increased resolution of

nearly every component of our framework (fluxes,

land-use maps, transport model, ecoregions) will be

necessary to interpret the available CO2 records in more

detail. In addition, continuation of the existing sites

together with a considerable expansion of the monitor-

ing program is needed to bridge the gap from local to

continental scale. This work suggests that CarbonTrack-

er Europe could play a role in that effort.

Conclusions

We have presented an analysis of net carbon exchange

over Europe over the recent 7 years as diagnosed from

atmospheric CO2 observations and a relatively simple

bottom-up process model. In reference to research ques-

tion 1: analysis of the simulated CO2 patterns and

correspondence to observations suggests that our sys-

tem can adequately simulate a large fraction (480%) of

the available European CO2 data on a daily time scale.

This is only possible due to a large reduction (from

twice per hour to once per day) and selection of data

based on model skill. We also stress that specific con-

ditions related to individual sites and synoptic situa-

tions are sometimes poorly represented and that fluxes

for short time periods and small spatial areas should be

interpreted with much caution as systematic errors in

some seasons remain.

The estimated magnitude and pattern of annual mean

carbon uptake agrees well with other top-down and

bottom-up estimates for the European domain (ques-

tion 3), and confirms the important role of forests in

sequestering carbon in Europe. The coniferous forests in

northern Europe, together with broadleaf forests in

eastern Europe, dominate the total forest sink of

�229 Tg C yr�1 in our system. This sink is nearly 50%

smaller than earlier estimates derived from an interpre-

tation of forest statistical data but quite consistent with

more recent updates of those numbers. The estimate of

crop land fluxes is considerably less robust, but seems

to agree with more recent analyses in that the earlier

published 1 300 Tg C yr�1 of crop land carbon losses is

too large. These results address research questions 2

and 3.

IAV on carbon exchange is strongly climate controlled

with the 2003 drought, and increase of the natural sink

in the following years as most notable features in our

analysis. This increased sink is driven by favorable

growth conditions in each of these years and include,

at least for 2004, an influence of the previous poor

growth year 2003. The presence of favorable conditions

for NPP and respiration is partly captured by the

mechanistic biosphere models, but the atmospheric

data inform strongly on the magnitude of the net

exchange. Getting the sign of such net exchange anoma-

lies correct is relevant for our understanding of carbon

dioxide variations on longer time scales (climate) and

will not be captured easily in bottom-up or accounting

efforts. This is an important and unique feature of the

top-down approach, and thus addresses research ques-

tion 2.

Although many features of our analysis are consistent

with other estimates, and robust across a range of

different realizations of the assimilation system, the

overall utility of the current solution is limited to larger

areas (41000 km), longer time scales (4seasons), and

specific ecosystems. Increased density of atmospheric

CO2 observations significantly helps constrain the solu-

tion and we suggest further expansion of the high

precision monitoring network established under the

EU CarboEurope program as a top priority to improve

assimilation results. In addition, a stronger focus on

modeling of agricultural crops (covering o22% of

Europe’s land area) and their inclusion, including man-

agement influences, as a separate a priori flux from

forests would be desirable. Finally, our experience sug-

gests that the strongly heterogeneous land-use in Eur-

ope warrants higher resolution than the current 1� 1 of

the underlying ecoregion maps, a priori fluxes, and the

transport model used. This answers research questions

1 and 3, and future improvements of our system will

focus on these issues.

Acknowledgements

We wish to thank the many scientists, technicians, and supportstaff involved in producing the high precision CO2 observationsthat are vital to this study, which includes Lucio Fialdini andAndrea Lanza from CESI RICERCA, Italy. We acknowledgeDouglas Worthy of Environment Canada, Thomas Conway andArlyn Andrews of NOAA ESRL, and Britt Stephens of NCAR formaking available the North American CO2 time series used inthis study. We are grateful to Dr Leo Rivier for CarboEurope datamanagement. W. P., J. B. M., and K. S. received support fromNOAA Commerce Award NA07OAR4310115. W. P. was partiallysupported by NWO VIDI Grant 86408012. C. D. J. and J. K. H.were supported by the Joint DECC, Defra and MoD IntegratedClimate Program – DECC/Defra(GA01101), MoD (CBC/2B/

1334 W. P E T E R S et al.

r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 1317–1337

0417_Annex C5). K. R. and M. Z. received partial financialsupport through PBZ-MEiN-3/2/2006 project and through stat-utory funds of AGH University of Science and Technology(project No. 11.11.220.01). A. C. M. is supported by a UKNERC/QUEST Advanced Fellowship (Ref. No. NE/C002504/1). S. H. and SvdL received funding from the Klimaat voorRuimte research program. Funding for the atmospheric stationsin Europe was provided in part from the European Union 6thFramework Program through CarboEurope-IP (Project No.GOCE-CT-2003-505572). BGU sampling is funded by DMAH(Generalitat de Catalunya). We thank three anonymousreviewers for their constructive comments.

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