Post on 30-Apr-2023
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
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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.
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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
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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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