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Information content on the carbon cycle
brought by the CARBONES project
Table of content
1
Introduction ____________________________________________________________ 2
2
Information about fossil fuel emissions ______________________________________ 3
3
4
5
6
2.1
Compared products ________________________________________________________ 3
2.2
Horizontal & vertical spatial distributions of CARBONES IER data _______________ 4
2.3
Temporal distributions _____________________________________________________ 5
Information on ocean fluxes from OCVR system _______________________________ 7
3.1
Information about pCO2 spatial and temporal distributions ______________________ 7
3.2
Evaluation of the OCVR ocean flux ___________________________________________ 9
Evaluation of the net surface fluxes from the CCDAS __________________________ 13
4.1
Approach: product used for the evaluation of CARBONES ______________________ 13
4.2
Global annual totals _______________________________________________________ 14
4.3
Long term means _________________________________________________________ 15
4.4
Inter-annual variability ____________________________________________________ 18
4.5
Seasonal flux variations ____________________________________________________ 22
Evaluation of land gross carbon fluxes ______________________________________ 23
5.1
Evaluation at the site level __________________________________________________ 24
5.2
Evaluation at global scale from MTE estimates ________________________________ 25
Evaluation of land carbon stocks___________________________________________ 29
6.1
Product used for the evaluation _____________________________________________ 29
6.2
Results of the comparison __________________________________________________ 29
7
Conclusions and perspectives______________________________________________ 34
8
References _____________________________________________________________ 35
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1 Introduction
The objectives of this deliverable are to analyse the information brought by CARBONES on
the carbon cycle in terms of carbon fluxes and carbon stocks at several spatial and temporal
scales. These concern the study of:

The global net annual carbon balance by apportioning between key regions of the
globe like North America, Europe, North Eurasia, the Tropics and key ocean basins

The inter-annual variability of the sub-continental regions and ocean basins

The trend in the net carbon uptake of the lands in comparison with that of the oceans

The spatial distribution of the forest carbon stocks compared with “observations”
mapped globally (produced by CARBONES)
The analyses were planned initially to take into account the uncertainties of the inverted
fluxes from CARBONES-CCDAS (error propagation) in order to discriminate between robust
signals and other ones.
However, this report will not cover all the above-mentioned objectives, given some delays in
producing the final 20 years carbon flux and stock re-analysis. Moreover, we stress on the fact
that the report mainly evaluates the last version of the CARBONES product (Version V2.0
but also in some cases version V1.0) against other independent flux/stock products. It is
indeed very difficult to present the information content of a given carbon product that
combine observation and models (CARBONES) as “true” information without comparing the
estimated quantities with other independent estimates. It is indeed impossible to establish the
true fluxes at regional to global scales, given that there is no direct measurement of such
quantity. Note also that we consider fossil fuel emissions (WP300) and ocean flux estimates
from ocean pCO2 data (further used as prior in the Carbon Cycle Data Assimilation System)
as CARBONES products. Given that significant progress has been made for these two fluxes,
we discuss the information content of these products with respect to the carbon cycle
(especially the temporal variations).
When making the evaluations of the above-mentioned CARBONES products, the
uncertainties in both CARBONES and other products are not considered yet, because of lack
of time. However, a specific report will analyse independently the estimated errors on the
carbon fluxes derived from the CCDAS. Finally, we stress again that this report:

shows the potential of the evaluation exercise conducted within CARBONES

should not be considered as the most advanced answer to the above scientific
questions but rather a new “contribution” to the overall knowledge.
The outline of the report is as follows:
We describe in section 2) the information content on carbon cycle about fossil fuel emissions.
Then, the ocean products are evaluated in section 3. The net surface fluxes of CARBONES
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are confronted to those derived from direct inversion systems and from global dynamic
ecosystem models (DGVMs) in section 4. In section 5, the CARBONES land carbon gross
fluxes are compared to estimates from machine learning algorithm that uses observations from
water, energy, and carbon fluxes. The CARBONES land carbon stocks are evaluated in
section 6. Finally, conclusions and perspectives are presented in section 7.
2 Information about fossil fuel emissions
2.1
Compared products
CARBONES CCDAS considers new global spatial and temporal resolved CO2 emissions
based on EDGAR v4.2 and time profiles developed by USTUTT (see CARBONES
deliverable D300 for details). The spatial resolution of the CARBONES fossil fuel emissions
product (called hereafter IER data) is at 1°x 1° with an hourly temporal resolution. This
product is derived from annual EDGARv4.2 CO2 emissions by using country, sector, year,
month, day and time zone specific monthly, weekly and daily time profiles. The temporal
variations of IER products are compared to the existing other ones, which are defined as
follows:

The emissions of dioxide from fossil-fuel combustion and cement production reported
in Andres et al. (2012). The spatial resolution of the product is 1°x 1° with a monthly
temporal resolution. This product is called hereafter Andres.

The emissions of fossil fuel CO2 emission inventory at global scale from a
combination of a worldwide point source database and satellite observations of the
global nightlight distribution (Oda and Maksyutov, 2011). The product used for this
exercise is 1°x 1° with a monthly temporal resolution and for only the year 2008. The
product is called ODA.

The emissions from the University of Beijing (hereafter PKU) at global scale are also
considered. PKU uses a 16 sub-national disaggregation method (SDM) applied to
establish a global 0.1°×0.1° geo-referenced inventory of fuel combustion (PKUFUEL) and a corresponding CO2 emission inventory (PKU-CO2) based on 18 upon
64 fuel sub-types for the year 2007 (Rong et al., 2012).

The emissions used in the CarbonTracker model-data fusion system: (see
http://www.esrl.noaa.gov/gmd/ccgg/carbontracker/documentation_ff.html#ct_doc).
The product is based on CDIAC country total using EDGv4.0 spatial distribution and
standard temporal profiles.
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In what follows, we first describe the novel IER products derived for three altitudes, and then
monthly variations of IER data are confronted to Andres, ODA, and PKU fossil fuel emission
products for few regions.
2.2
Horizontal & vertical spatial distributions of CARBONES IER data
Besides spatial and temporal resolved emissions, it is also important to consider the effective
emission height, which significantly influences modelled concentration values (Pregger &
Friedrich 2008). In particular it is crucial to separate:
1. Emission at the surface mainly from transport and residential sectors. These emission
will be emitted in the lowest level of the transport model
2. Emission from power plant that are still injected in the Planetary Boundary Layer
(PBL) but directly mixed within the PBL because the injection is made though “high
chimney”. This corresponds to part of the industrial sector.
3. Emission from aviation above the PBL in mid troposphere.
Most existing global spatially and temporally resolved fossil fuel emission models do not
consider effective emission heights at all or not sufficiently enough. As a consequence, the
application of effective emission heights is a major improvement of the fossil fuel
emission modelling on the global scale. The report describing the CCDAS evolution (D420)
displays the considered effective emission heights based on air quality model from EMEP and
specific assumptions.
JRC, responsible for the EDGAR emissions, delivered within the framework of CARBONES
also the emissions from aviation distinct into “Take-Off and Landing”, “Climb and Descent”
and “Cruise”. Therefore it was also possible to apply emission heights to the subsectors of
aviation, which is also an innovation for the fossil fuel emission modelling. This last
distinction will be crucial for the Carbon Data Assimilation System with respect to the
atmospheric CO2 data.
The results based on the current time profiles for the three emission heights classes are shown
in the Figure 2.1. According to the actual results the highest absolute values occur at the
middle altitude. This is the case in particular because the emissions from the energy industry
as well as the emissions from the industrial combustion are effectively emitted between 92m –
781m. Besides, the hourly shift between the different regions in the current version of the
fossil fuel emissions is studied and corrected (not shown).
A direct evaluation of this vertical splitting of fossil fuel emissions in terms of impact at
atmospheric CO2 stations is only undergoing. Especially we will evaluate the “improvement”
of the simulated concentrations.
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Figure 2.1: Spatial distributions of fossil fuel hourly emissions) for the 16th of January 2008
at 14:00 hours in a 1°x1° resolution in [Mg/h] and at 3 altitude levels: < 92 m (right), between
92 m and781 m (middle), and > 781 m (left).
2.3
Temporal distributions
Figures 2.2 and 2.3 display the temporal variations of IER fossil fluxes together with the
estimates from Andres, ODA, PKU, and Ctracker at global scale and for two continental
regions. This analysis of the temporal variations indicates:

At global scale, IER data compared well with Andres data, with however a larger
seasonal amplitude obtained in the IER-CARBONES product than ODA and Andres
but similar to that of Ctracker. ODA product for 2008 is in good agreement with
Andres but the phase of the seasonal cycle for both product is slightly different than
IER-CARBONES. The PKU yearly data (evenly distributed over the year) for 2007 is
smaller than the other flux estimates given that it does not include “bunker” fuel and
air planes. Note that Andres total flux is also slightly smaller than IER total, given that
it does not include “bunker” fuel. The main differences after correction for differences
in fuel categories included in each product concern the temporal variations. Given the
significant effort brought by IER to construct varying temporal profiles, we believe
that the CARBONES product brings new information to the carbon cycle through the
temporal disaggregation of EDGARv4.2 spatial product.

Over Europe, IER data gives the strongest seasonal amplitude, similar to Ctracker
product and significantly larger that both Andres and ODA product. In this case, given
the detailed analysis and the large collection of temporal profile data made by IER, we
can be confident that such large seasonality is probably more realistic and that the
CARBONES product brings new information. Note that Ctracker provide a slightly
different phase for the seasonal cycle than IER.
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Over Eurasia, IER presents a larger amplitude than ODA and Andres but smaller than
Ctracker that uses similar temporal profile as for Europe. Whether one product is more
realistic that the other one still need to be evaluated against local proxy data.
Overall the IER-CARBONES product provides new information with seasonal amplitude
around 30% to 50% depending on the considered regions and that these variations will be
crucial for the assimilation of atmospheric observations.
Figure 2.2: Temporal variations of the fossil emissions from ODA, Andres, IER (CARBONES), and
PKU products for the globe.
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Figure 2.4: Temporal variations of the fossil emissions from ODA, Andres, IER, and PKU products
for Europe (left), and Eurasia (right).
3 Information on ocean fluxes from OCVR system
The Ocean Carbon Variational Reanalyzer (OCVR) is used to produce a twenty years
global ocean carbon flux reanalyses. OCVR is a neural network framework developed by
CLIMMOD. As input variables, it uses observations from satellites and/or model outputs (as
for instance sea surface temperature, mixed layer depth, wind speed, etc), which control at the
first-order the surface ocean pCO2. Furthermore, a variational data assimilation scheme
incorporates efficiently recent sets of raw pCO2 observations to adjust for the trend and to
take into account extreme events like El Niño. The spatial and temporal resolutions of the
system are adjustable. The system then uses supplied atmospheric CO2 (Globalview product)
concentration to calculate air-sea flux according to a selectable exchange parameterization
(e.g. Liss et Merlivat 1986, Wanninkhof 1992, Takahashi 2009 etc). The system is described
in more details in previous reports (D410 and D420). The fluxes that are produced by OCVR
are further used in the CCDAS as prior ocean fluxes.
3.1
Information about pCO2 spatial and temporal distributions
Figure 3.1.1 displays the spatial variations of pCO2 for three years for the month of January,
from the OCVR system. Results show the spatial variations of pCO2 for the selected monthsyears. The classical spatial pattern is obtained with low pCO2 at high latitudes and high
values in the tropics. However, the new information brought by CARBONES concern the
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inter-annual variability (IAV). Indeed most optimization systems are using so far a
climatology field with no year-to-year flux variations (such at Takahashi et al. products). In
the example below, we see that 2009 has a larger high pCO2 over the tropical Pacific than the
other years.
Figure 3.1.1: Global pCO2sw (in micro-atmosphere) maps from OCVR are shown and for simulations relevant for January
1990, 2000, and 2009.
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As already discussed in the D420 reports, the temporal variations of the OCVR pCO2 data
over the 1989-2009 periods at selected ocean locations outperform the Takahashi standard
climatology estimates (i.e. using a fixed growth rate of pCO2). Over the Equatorial Pacific,
we clearly see an improvement in the OCVR product compared to the “scaled climatology”
with a drawdown of pCO2 during the 1998 El Niño period and with a seasonal cycle much
less pronounced than Takahashi and more in line with the observations.
3.2
Evaluation of the OCVR ocean flux
We now try to evaluate the estimated air-sea fluxes from OCVR. We present here a first
comparison of OCVR product with few other “independent” ones. These are:
1. The result from the Takahashi (2009) flux climatology
2. The results from an ensemble of “Ocean Interior Inversion” from Gruber et al. 2009.
These estimates combine information on DIC measurement in the ocean and ocean
circulation model (ocean inversion)
3. The results from Steinkamp (2012) that were produced at ETH, one partner of
CARBONES. These fluxes correspond to the estimates from an atmospheric inversion
combining the “ocean interior inversion” product and the information content of
atmospheric CO2 data using a classical atmospheric inversion with the transport
models from the TRANSCOM inter-comparison exercise.
4. The results from five Ocean General Circulation Models (OGCM) that were used and
compared within the recent RECCAP synthesis, as part of the Global Carbon Project.
These models comprise different ocean physical models and biogeochemical models
(see table 3.1 below).
Table3.1: List of OGCM used to compare with the CARBONES ocean product.
Model
Ocean model
BGC model
Forcing
Bergen
MICOM (isopycnic)
HAMOCC
NCEP
CSIRO
OGCM
P-based
NCEP
LSCE
NEMO
PISCES
NCEP
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UEA
NEMO
PlankTOM5
NCEP
WHOI
CCSM
BEC
NCEP
The figure 3.2 compares the mean seasonal cycle over the 1990-1999 period from the
estimates1, 2, and 3 (OCVR results are in red) for and ensemble of 11 ocean basin
(TRANSCOM regions). Major features from this analysis are:
 As expected, for most basins the seasonal cycle of OCVR fluxes is close to that of the
Takahashi climatology. The southern ocean and the north Pacific present the largest
deviations from the climatology. Further analysis need to be done to evaluate the level
of improvement brought by OCVR.
 As for the mean fluxes, the OCVR results are generally in good agreement with the
results from the ocean interior inversion, with a mean global sink slightly larger than
Takahashi 2009.
 The large seasonal variations obtained in the Steinkamp 2012 product, from the
atmospheric inversion, are partly incompatible with our OCVR product, especially in
the mid to high latitude basins. Such results, would indicate that the atmospheric CO2
data tend to impact the seasonal ocean fluxes in a way that may be incompatible with
the raw ocean pCO2 surface data. These results will be investigated in a paper under
preparation.
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Figure 3.2: Mean seasonal air-sea flux estimates from OCVAR for the period 1990-1999, for 11 regions, compared with
independent estimates (see legend in the figure).
We now focus on the year-to-year air-sea flux variability. Figure 3.3 compares the annual air-sea flux
for several “latitudinal scale” regions and one specific basin obtained by the OCVR system and the
five OGCM models presented above. The major features of OCVR are:

A smaller global uptake during the period 1998 to 2002 compared to the early 90s and late
2000s, not present in most OGCM (except one).

A pronounced increased of the global ocean uptake after 2002 up to 2009, that is not
present in the OGCM models.

The increased uptake after 2002 is mostly explained by the northern ocean (north of 30°N)
and to a lower extend by the southern ocean (south of 30°S).

The tropical ocean present nearly no trend during the 20 years periods, with a mean release
of carbon around 0.6 PgC/year.

The tropical ocean shows a decrease of the carbon source in 1998 linked to the El-nino
conditions, a feature captured by some OGCM but not all. Such tropical variation in 1998 is
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dominated by the contribution of the tropical Pacific Ocean, mostly affected by El-Nino
conditions.
Overall the main feature brought by the assimilation of surface pCO2 observations into a statistical
ocean model (OCVR) concerns the trend in the ocean uptake after 2002. The mean carbon sink for
the different ocean basins across the 20 years remains similar to what has been estimated by most
approaches. Whether the increased global trend from 1.4 PgC/year in 2002 to 2.6 PgC/year in 2009 is
real or an artefact/bias of the OCVR model is the main question. Possible sources of biases are: i) the
non uniform coverage of the raw pCO2 data over time which could thus bias the neural network
performance towards fitting preferentially a given period and thus introducing a temporal bias; ii) the
non uniform coverage of the raw data over space with more observations over the coastal area at
the end of the period. These crucial points are under investigations and will be discussed in a paper
presenting OCVR system (Kane et al.).
Figure 3.3: Comparison of the air-sea fluxes for different ocean regions estimated by CARBONES OCVR re-analyses (prior
flux used in the CCDAS) with the fluxes from 5 Ocean General Circulation Models used in the RECCAP synthesis.
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4 Evaluation of the optimized surface fluxes from the
CCDAS
4.1
Approach: product used for the evaluation of CARBONES
The CARBONES products are compared to two different types of approaches:

The results from classical recent atmospheric inversions following the synthesis
performed for the RECCAP exercise (Peylin et al., submitted to Biogeocsciences). We
use twelve participating standard atmospheric inversion systems and associated key
attributes are listed in Table 1. Further details on these systems can be found in Peylin
(2012) with a general description given on the TransCom website
(http://transcom.lsce.ipsl.fr).

The results of 8 Dynamic global vegetation models (DGVMs) also compared within
the RECCAP exercise (sitch et al., submitted to Biogeosciences). These data are not
published yet and the figures including their results should not be distributed yet. We
used the results of DGVMs to evaluate the inter-annual variations of CARBONES flux
product only. We considered that these model fluxes do not provide better information
than atmospheric inversions for the mean carbon uptake.
In what follows, we discuss CARBONES land and ocean fluxes in terms of annual totals, long
term means, and inter-annual and seasonal variations with regard to those derived from the
ensemble of standard inversions (and from DGVM for the IAV). The results discussed here
correspond to version V1 of CARBONES and the ocean fluxes are thus those from
OCVR further corrected after the assimilation of atmospheric data. Global patterns of the
results derived from the ensemble of the standard inversions are first given and those from
CARBONES are then confronted. We will only give the main characteristics of this ensemble
of results, with an emphasis on the CARBONES results when they significantly differ from
the envelope of the direct inversion inferences. More detailed discussions on the following
rich information on carbon budget from the standard atmospheric inversions are given in
Peylin et al. (2012). Indeed, the differences due to the different set ups (e.g., fossil emissions,
meteorological forcing used, biomass burning, the inversion systems themselves, etc…) of the
direct systems are not discussed here.
All different “inversion estimates” have used different fossil fuel emissions so that a direct
comparison of the natural fluxes, which should be considered as a residual flux, are biased.
The systems that have used more fossil fuel emissions should have a larger land/ocean carbon
sink to match the atmospheric growth rate. In order to cope with that problem we applied a
correction to all products: we took the total estimated flux (natural + fossil) and then subtract
a common fossil fuel emission (EDGAR v4.2). In the remaining we thus compare the so13
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called “fossil corrected fluxes”. Note that the EDGARv4.2 correspond to the emissions used
by IER to derived the CARBONES fossil fuel product.
Table 4.1: Inversion systems used to compare with CARBONES product with their key attributes.
“MM” denotes monthly mean. IAV indicates inter-annual variations used for meteorological forcing.
4.2
A LSCE1
Inverse System
Lsce_an_v2.1
B LSCE2
Lsce_var_v1.0
C CCAM
D MATCH
E CTRUS
C13_CCAM_LAW
C13_MATCH_Rayner
Carbontracker_US
F CTREU
G
Carbontracker_EU
Jena_s96_v3.3
H
I
J
K
Rigc_Patra
JMA_2010
TRCOM_mean
Nicam_Niwa
RIGC
JMA
TRC
NICAM
No regions
Grid-cell
(96x72)
Grid-cell
(96x72)
146
116
156
156
Grid-cell
(72x48)
64
22
22
40
Obs
MM
# of observing
stations
76
IAV
trans
port
Yes
1988-2008
Raw
128
Yes
Rachel Law
Peter Rayner
Andy Jacobson
Wouter Peters
Wouter Peters
Christian Roedenbeck
1992-2008
1992-2008
2000-2008
MM
MM
Raw
73 CO2, 7 C13
73 CO2, 7 C13
No
No
Yes
2000-2008
1996-2008
Raw
Raw
117
53
Yes
Yes
Prabir Patra
Kazutaka Yamada
Kevin Gurney
Yosuke Niwa
1993-2007
1985-2008
1995-2008
1988-2007
MM
MM
MM
MM
74
Yes
Yes
No
Yes
Contact
Philippe Peylin
Time
Period
1996-2004
Frederic Chevallier
Global annual totals
Figure 4.1 displays for each inversion the posterior estimate of the natural global total fluxes
(land plus ocean), and the global fossil fuel fluxes. The year-to-year variations of the global
total flux (land plus ocean) reflect the variations in global atmospheric CO2 growth rate. As
expected, they are robust across the different inversions, with large fluctuations associated
with the occurrence of El Niño and La Niña conditions. For instance, in 1998, and to a lesser
extent in 1994, the strong El Niño condition led to a small carbon uptake by the land and
ocean ecosystems. Significant differences in prescribed fossil fuel emissions are noteworthy.
The JENA fossil fluxes are larger than other inversions (~0.45 PgC/yr), and consequently that
inversion requires more uptake to match the atmospheric CO2 growth (note that this is taken
care with the “fossil correction” we described above).
CARBONES product compares well with these independent estimates and provides a similar
global picture. First, we should notice that the assimilation of atmospheric data (step 4 of the
sequential approach) does not change too much the ocean fluxes compared to results of the
OCVR model (used as prior in step4). Thus, the monotonous increase of ocean uptake
obtained from CARBONES after 2002 is a new feature, not present in all other inversions. As
noticed above such feature is crucial and need to be confirmed by independent proxy. The
land ocean partition of the global carbon fluxes is directly affected by the results of the OCVR
system and we thus obtain a lower land carbon sink in the early 90s and late 2000s compared
to the other atmospheric inversion systems.
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Figure 4.1: Annual mean posterior flux estimate of the individual inversions and CARBONES (in
black). Shown here are a) natural “fossil corrected” global total carbon exchange, b) fossil fuel
emission, c) natural “fossil corrected” total land, and d) natural total ocean fluxes.
4.3
Long term means
Since the ensemble of the standard inversions has been run for different time periods, a
common time period that allows reducing the inter-comparison timespan when calculating
multi-year means, was selected. Thus, the 2001-2006 period was chosen.
Figure 4.2 displays the total fluxes for the globe and three latitudinal bands as well as the
partition between the land and ocean. From the perspective of the long-term mean, the land
(fossil corrected) and ocean have similar values for total uptake, around -1.5 PgC/yr.
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Figure 4.2: Mean natural fluxes for the period 2001-2006 of the individual inversion posterior fluxes
(exception for LSCE_ana system which is averaged over 2001-2004) and CARBONES (in black). Shown
here are total (first column), natural “fossil corrected” land (second column) and natural ocean (third
column) carbon exchange aggregated over the Globe, the Northern hemisphere (roughly > 25N), the
tropic (roughly 25S-25N) and the southern hemisphere (roughly < 25S). Numbers in parenthesis
represent the mean flux and the standard deviation across all inversions.
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Except for JENA inversion, which has much larger uptake on land and smaller uptake
by the ocean (both compensating), and NICAM which gives the smallest land uptake (~ -0.5
PgC/yr) compensated by the largest ocean sink (~ -2.5 PgC/yr) CARBONES is at the lower
end of the results range. In details, global total flux from CARBONES is slightly lower than
results obtained from most of the direct inversions, which is explained by the lower carbon
uptake of the land either at Northern latitude or in the Tropics. This lower uptake from the
land is compensated by the relatively large uptake of the ocean at global scale. The North and
South oceans contribute to this large uptake, while a lower uptake is found over the tropics.
The large carbon uptake of the ocean, derived from CARBONES CCDAS, can partly be
explained by the large uptake observed from 2002 as shown in Figure 4.3.1b.
Overall CARBONES confirms the results from the inversions that were solving the fluxes at
high resolution (JENA, LSCE, CTRACKER systems) with a nearly neutral tropical land
budget and a very small southern land source.
We briefly investigate the long term mean natural fluxes within continental/basin-scale
subdivisions for a breakdown of the northern hemisphere into three selected continental/basinscale regions: North America, Europe, North Asia (Figure 4.3). These three land regions show
a significant carbon sink, from nearly -0.5 PgGtC/yr over Europe to -1.0 GtCPgC/yr over
North Asia. A large spread among the results from the different inversions is obtained. For
the three selected regions, the standard deviation reaches around 0.5 GtCPgC/yr. CARBONES
results are one of the lowest carbon sink for these regions and especially for North Asia.
However, if we compare only the 5 fives estimates from the left (JENA, LSCE_var and the
two CTRACKER systems), CARBONES results do not appear as outliers. These estimates
come from standard inversions that solve for fluxes either at the resolution of the transport
model or for a large number of regions, which avoid the so-called “aggregation error”
associated to the other estimates that solve for a restricted number of flux-regions. These
technical details are discussed in Peylin et al. 2012.
Overall, CARBONES with the use of FluxNet data and MODIS-NDVI thus decrease the
carbon uptake over North Asia compared to LSCE_var inversion that uses the same
atmospheric transport model. The main changes of the sequential optimization (step 1 and 2),
related to a decrease of the growing season length in ORCHIDEE (through MODIS-DVI data)
and the decrease of the amplitude of the seasonal cycle of NEE for north ecosystems (through
FluxNet data), lead to a stronger reduction of the carbon sink in North Asia compared to
Europe and North America. These results are under investigation and will be published with
the 20 year CARBONES flux reanalysis (Peylin et al., in preparation).
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Figure 4.3: As for Figure 4.2, but for three continental/basin-scale regions: North America,
Europe, and North Asia.
4.4
Inter-annual variability
Figure 4.3 shows the inter-annual variability of land and ocean fluxes for the northern,
tropical and southern aggregates. The results represent annual means with the individual
model long-term means removed (a long-term mean defined over the entirety of the submitted
model timespan). We refer to these as inter-annual carbon exchange anomalies (hereafter
IAV).
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Figure 4.4: Annual mean smoothed average (smoothing window of 3 years) of the individual
participating inversion posterior flux estimates. Shown here are land fluxes for northern, tropics and
south regions as well as for the global total, for land and ocean.
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All the standard inversion systems tend to exhibit greater IAV on land versus ocean
(Figure 4.4), particularly in the tropical latitude band. Within the land aggregates, the tropical
land exhibits the greatest amount of inter-annual variability while in the ocean similar interannual variability is seen in the different ocean basins (note that for the southern ocean the
scale is different than for the two other latitude bands). Note that in the southern ocean a
large part of the IAV is associated with the 1997/1998 time period, in which several model
inversions show large anomalies, though of differing sign.
Overall, CARBONES results are in agreement with the IAV produced by the ensemble of
inversions. In details, CARBONES exhibit a large negative IAV in the Southern land. This
feature mainly arises over South America during the 1997/1998 period, which seems to be
compensated by the large positive IAV obtained in South Asia (not shown). The large
biomass burning fluxes observed during the 1997-1998 period and considered in the
CARBONES optimization may explain these results. Further analysis will be conducted to
evaluate whether other observational evidences support the different flux IAV in
CARBONES for the southern land. As a direct consequence, CARBONES produces a smaller
flux IAV over the tropical land than the other inversions. Such feature is a direct consequence
of the flux optimization with larger IAV in the southern land. The results from version V3
(under analysis) with a correction of the parameters of ORCHIDEE in step 4 (assimilation of
atmospheric CO2 data) will confirm or not if such reduced tropical land IAV can be supported
by the current ORCHIDEE processes.
A second aspect of the evaluation of CARBONES product was performed against the results
of DGVMs that comparable to the one used in our CCDAS. In the set of DGVMs a standard
version of ORCHIDEE was also used following the specific protocol applied to all DGVMs.
Figure 4.5 provides for “big latitudinal bands” and the northern hemisphere split into the three
continental regions the annual flux variations, after subtraction of the mean flux over the 20
years period (i.e., the flux IAV). Major features are:

First the CARBONES product significantly differs from the ORCHIDEE DGVM
version, indicating that the MODIS-NDVI, the FluxNet data, and the atmospheric
data led to significant changes in the model IAV signal.

For the latitudinal breakdown, we obtain IAV signals that are compatible with the
DGVM ones but with slightly lower IAV over the tropics, especially from 1999 to
2004. The tropical positive anomaly en 2005 is significantly larger than the DGVMs.
Over the northern land, CARBONES IAV is slightly smaller that most DGVMs with
a significant increase of the land carbon sink in 2004, remaining the following years.

For the northern breakdown, North America, Europe, and North Asia all show
relatively similar flux IAV. CARBONES provides similar results than the DGVMs
for Europe and North America in terms of phase and amplitude, while over North
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Asia the phase of the IAV varies between all estimates. More in depth analysis are
needed to attribute these IAV to underlying processes.
Overall, if CARBONES does not change radically our knowledge on the land carbon flux
IAV, it will provide new insight on the processes than underline these flux variations and
these processes are likely to differ from the standard DGVM, given that ORCHIDEECARBONES parameters have significantly changed from their original values. This analysis
is however only underway and will be finalized after the duration of the project.
Figure 4.5: Annual mean smoothed average of the CARBONES flux estimates (red) compared to the results from 8
land dynamic global vegetation models (DGVMs). Shown here are land fluxes for northern, tropics and south regions
as well as for the North America, Europe, and North Asia.
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Seasonal flux variations
Figure 4.6 shows the mean seasonal cycle on land and ocean for the latitudinal aggregate
regions. Note that the land and ocean panels use different numerical scales. For this diagnostic
we consider the raw natural fluxes and not the “fossil corrected” fluxes, to avoid any spurious
monthly flux corrections. The global land seasonality is driven by the northern land with close
agreement regarding both the magnitude and phasing of the growing season and dormant
season fluxes. Regarding ocean fluxes, the largest differences between CARBONES and the
other systems are more pronounced over the South hemisphere. Only details relevant for land
fluxes, which largely contribute to the total global fluxes and for the latitudinal aggregate
regions are given hereafter:

The amplitude of the seasonal cycle of land fluxes over the Northern hemisphere is
close to 3 PgC/yr (range needed) and the peak of the growing season is located in July
for all inversion systems, including CARBONES.

Seasonality for the tropical land is quite low and larger differences can occur across
the different systems, including CARBONES. We notice that CARBONES is closer to
the LSCE inversions, which reflect the fact that they use both the ORCHIDEE model,
and that the atmospheric constraint over the tropic is relatively weak. CARBONES
show two peaks: one in March and the other in July, which are not shown by almost
all the other systems. This feature is less prominent in the recent version V2 of
CARBONES and will be analysed at the final meeting.

Seasonality in the Southern Land also shows consistency in terms of phasing though
somewhat less than the northern land. Maximum carbon uptake across the models
spans the February to April time period. The peak of the dormant season carbon
emission varies from June to September depending upon the model. CARBONES
follows the ensemble of the inversion systems, but the maximum of carbon uptake
occur relatively earlier, i.e., around August, while most of the other systems show this
maximum during September to October period. This feature arise from the correction
of the phenology parameters with MODIS-NDVI.
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Figure 4.6: Mean seasonal cycle of the posterior carbon exchange for the individual participating
inversion submissions. Shown here are the natural land (first column) and natural ocean (second
column) carbon exchange aggregated over the Northern hemisphere (> 30N), the tropic (30S-30N) and
the southern hemisphere (< 30S).
5 Evaluation of land gross carbon fluxes
CARBONES gross carbon fluxes are compared to:

GPP and Reco estimates directly derived from the net flux observations at site level
(FLUXNET data) as performed in e.g., Reichstein et al. (2005) by using a flux
partitioning method (e.g., Baldocchi, 2003 and Papale et al., 2006; see the dedicated
website: http://www.fluxnet.ornl.gov)

Global GPP estimates from data‐oriented approach. The data-oriented method used a
Model Tree Ensemble (hereafter MTE) which is a machine learning system builds on
an empirical model. Such a model has been applied to the upscaling of eddy
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covariance measurements (i.e., water, energy and carbon fluxes) from local to
continental scales (Jung et al., 2011).
5.1
Evaluation at the site level
The CARBONES GPP and Reco fluxes are compared to estimates derived directly from
the NEE observations (as performed in Reichstein et al. (2005)). Note that the NEE data were
assimilated in the CCDAS in a sequential approach (see details in the report D420). Although
not independent, these “data-oriented” estimates provide valuable insights on the ORCHIDEE
model performances. Hence, here we evaluate the performances of the model with regard to
the optimization of its process based parameters (step 2 of our sequential assimilation
approach) through different metrics as described in Kuppel et al. (2012): In this comparison
we use the results of a so-called single site optimization to constrain the ORCHIDEE
parameters (SS optimization) and a second approach used in CARBONES that considers the
observations from all sites of a given Plant Functional Type to optimize these parameters (MS
method).
Figure 5.1 shows the seasonal cycle of GPP and Reco at two sites for a 2-year time
period. We observe that in general the optimizations decrease the seasonal amplitude of GPP
and Reco at these sites and as expected are closer to the observations, with a shortening of the
period where GPP is significant. Besides, we observe that the model-data fit is generally
improved both for Reco and GPP, although more significantly for Reco. More details of these
comparisons are given in Kuppel et al. (2012).
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Figure 5.1: Seasonal cycle of GPP and Reco at a) Hainich and b) Harvard Forest sites, smoothed with a
15-day moving average window. The estimations derived from flux-partitioning of NEE (black) are
compared with the prior model (green), the MS optimization (blue) corresponding to CARBONES and
the SS optimization (red, see text). The averaged annual fluxes in gC/m² are given between brackets.
5.2
Evaluation at global scale from MTE estimates
The global GPP estimates from the MTE model are compared to those obtained from
CARBONES project. The comparison is performed over the 1990-2008 period. MTE uses
different datasets to upscale GPP from FLUXNET station data: FAPAR satellite observations,
the SYNMAP land cover map, temperature observations from CRU and precipitation from
GPCC (Jung et al. 2011).
The global spatial patterns of GPP from MTE and CARBONES agree reasonable well. Figure
5.2.1 displays the spatial distributions of both the yearly mean GPPs derived from MTE and
CARBONES together with their differences over the 1990-2008 period at global scale.
Although the two GPP estimates agree reasonably well, differences can be significant in some
areas. We obtain a good agreement in boreal regions, while CARBONES gives higher GPPs
in mainly agricultural used areas. The largest differences between CARBONES and MTE do
occur in South East of Asia in the Tropics, where differences can reach up to 4000 gC/m 2/y at
pixel level. The large differences are partly explained by the fact that MTE does not permit
extreme GPPs values above around 3500 gC/m2/y as can be seen from Figure 5.2.2.
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Differences in mean annual GPP are associated to different climate regions. Mean annual
global GPP over 1990-2008 period from CARBONES are plotted against MTE stratified by
the Koeppen-Geiger climate classes (Figure 5.2.2). The largest differences in mean annual
GPP occur in the Aw climate region (equatorial savannah with dry winter). Also, the
seasonalities of both GPP estimates are well correlated (not shown), except in some arid and
tropical regions where GPP does not show a pronounced seasonal cycle. However, we found
large differences over few areas and during particular seasons, e.g., dry period over savannah
(not shown).
Figure 5.2.1: Yearly mean GPP (gC/m2/y) over 1990-2008 estimated from MTE and CARBONES are
shown. The differences between CARBONES and MTE (CARBONES –MTE) are also given.
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Figure 5.2.2: Mean annual GPP from CARBONES against MTE (1990-2008). The different colours
represent the Koeppen-Geiger climate classes used in the MTE estimates.
GPP from CARBONES has different sensitivities to temperature and precipitation than GPP
from MTE (Figure 5.2.3). The maximum GPP under a specific temperature condition
increases in both approaches with higher mean annual temperatures in case of fully humid
climate regions (Df – purple, Cf – darkgreen, Af – red; upper panel in Figure 5.2.3). In case of
water limited climate regions (ET – blue, BS and BW – yellow, Aw – light red) GPP is clearly
below the GPP of fully humid climate regions and does not show this relationship with
temperature. Nevertheless, CARBONES reaches under all temperature conditions higher
maximum GPP values than MTE. The increase in GPP with increasing temperatures under
non-water limited conditions in boreal climate regions is much stronger for CARBONES than
for MTE (upper limit at purple colors in Figure 5.2.3). GPP increases also with increasing
mean annual precipitation in both approaches (lower panel in Figure 5.2.3). Again, for
CARBONES the increase in GPP with increasing mean annual precipitation is much stronger.
At a certain high amount of annual precipitation, increasing precipitation does not increase
GPP further, i.e. water availability is no longer a limiting factor for GPP. In MTE this point is
reached at ca. 1800 mm of mean annual precipitation whereas in CARBONES this point is
reached already at a lower amount of mean annual precipitation (ca. 1200 mm). In summary,
CARBONES GPP has a higher sensitivity to temperature in boreal regions and a higher
sensitivity to precipitation globally than GPP estimates from MTE.
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Figure 5.2.3: Mean annual GPP from MTE and CARBONES against mean annual temperature (top)
and mean annual total precipitation (bottom). The different colours represent the Koeppen-Geiger
climate classes as in Figure 5.2.2. The black line is a spline fitted to the upper 95% percentile of the
distribution and represents the upper boundary of GPP under different temperature or and precipiation
conditions, respectively.
This comparison of flux estimates from a data-driven approach (MTE) with a model-data
integration approach (CARBONES) should be considered as a first step that will be further
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improved after the end of the project within an article on the Evaluation of CARBONES 20year flux reanalysis. This will provide a more comprehensive and detailed analysis of
CARBONES strengths and weaknesses, although the MTE should not considered as the truth!
A non-exhaustive list of the actions planned can be sum up as follows:
•
Perform the analysis by land cover or plant functional types
•
Include other data-oriented upscaled estimates (TER, LE, H)
•
Perform the estimates from MTE with different forcing data sets
•
Compare CARBONES to other biogeochemical model gross carbon flux estimates
(e.g. LPJ, JSBACH)
6 Evaluation of land carbon stocks
6.1
Product used for the evaluation
We compared CARBONES biomass estimates to the DLO-EFI products, which are
based on FAO data further compiled by ALTERRA, and are available in the database
(http://www.alterra.wur.nl/UK/research/Specialisation+Geo-information/LGN/).
This
database includes data on forest area, growing stock, increment, harvest levels (scattered info
only) and age classes (Europe only), based on historical and recent international assessments
and national forest inventory statistics. For detailed description of these data, consult the
deliverable D300.1 of CARBONES project. Note that DLO-EFI estimates are yearly data at
global scale and cover the 1950-2010 period.
In what follows, inferences on forest above biomass of CARBONES are compared to DLOEFI estimates. For CARBONES, two simulations from ORCHIDEE are considered: i)
modelled biomass by using the default values of the process based parameters of ORCHIDEE
(hereafter ORCHIDEE REFERENCE) and ii) modelled biomass by using optimized
parameters (ORCHIDEE OPTIMIZED) that are constrained by the satellite MODIS NDVI
data and then with satellite and FluxNet data (the first and second steps of our sequential data
assimilation system). Note that we were not able to compare with the version of ORCHIDEE
that has been optimised by all three data streams (V3), given the delay in realisation of the last
step of the sequential approach (step 4). However, given that only few parameters that control
the above biomass were further adjusted in step 4, the comparison below can be considered as
the most relevant one.
6.2
Results of the comparison
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Figure 6.2.1 displays the spatial distributions of the mean annual above ground forest
biomass derived from CARBONES and the estimates from DLO-EFI for year 2005. Overall,
the two CARBONES estimates agree reasonably well with DLO-EFI data in term of global
spatial pattern, but the following differences can be highlighted:

CARBONES tends to produce more biomass over North of Europe, Russia, and over
tropical Asian regions.

CARBONES tends to produce lower biomass in the south part of central Africa.
Qualitatively, differences between the two CARBONES estimates (Figure 6.2.1) appear to be
relatively small. However, as shown in Figure 6.2.2, the estimates from the optimized
ORCHIDEE model represent a better agreement with the DLO-EFI product based on FAO
data compared to the reference simulations. Indeed, a lower bias is obtained between the
optimized ORCHIDEE estimates and DLO-EFI data. This is primarily the result of a reduced
growing season length following the optimisation of the phenology parameters. Figure 6.2.3
shows that the difference in the biomass between the optimized and reference versions of
ORCHIDEE is greatest in northern temperate and boreal regions, though there is a general
reduction in biomass across all forest PFTs.
There is a similar reduction in the positive bias of ORCHIDEE after optimization with both
satellite NDVI and FluxNet data, though it has increased slightly compared to the
optimization using onl satellite NDVI data. This is due to the fact that the value of Vcmax
increases as a result of the optimization with the NEE data, and therefore there is also an
increase in biomass.
The root mean square errors (RMSE) derived from the three CARBONES estimates against
DLO-EFI data remain rather large, and nearly unchanged after optimisation. This
demonstrates that there is potentially a structural error, or lack of process representation, in
the model that cannot be accounted for by parameter optimization. It is likely that disturbance
and forest age need to be properly considered in order to achieve more accurate biomass
estimates.
Note finally that the comparison is done for one year only, as the change in biomass is rather
small across the 20 year period but that further analysis will consider the change between
1990 and 2009.
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DLO - EFI
ORCHIDEE
REFERENCE
ORCHIDEE
OPTIMIZED
Figure 6.2.1: Spatial distributions of mean annual forest above biomass derived from DLO-EFI and
CARBONES for year 2005. For CARBONES, two products are shown: simulations of ORCHIDEE by
using i) the default parameters of the model (REFERENCE) and ii) optimized phenological parameters
of the model when constraining them with the satellite NDVI data (OPTIMIZED).
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Figure 6.2.2: The differences given by the mean bias (top) and the RMSE (bottom) between
DLO-EFI and the three CARBONES estimates are shown. The three CARBONES estimates
are simulations of ORCHIDEE by using i) the default parameters of the model
(REFERENCE) and ii) optimized phenological parameters of the model when constraining
them with the satellite NDVI data (optim. with MODIS) and satellite NDVI and FluxNet NEE
and LE daa (optim. with MODIS + FluxNet).
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Figure 6.2.3: The difference in biomass between the optimized and reference (default
parameters) versions of ORCHIDEE.
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7 Conclusions
We presented in this report a first evaluation of CARBONES products against other
independent products. This comparison was performed to highlight the information content
brought by CARBONES on the carbon budgets at continental to hemispheric scales. These
results should still be considered as preliminary as they will be updated with version V2 of
CARBONES for the final meeting and possibly version V3 (parameter optimizations in step
4) before the end of the project. Note that a possible revision of this report will be considered
before final submission. Overall, CARBONES performs as well as the other products for this
current version (V1.0) and bring new features on the carbon cycle, that need further
investigations:
 Fossil fuel emissions with hourly temporal variations appear to be significantly larger
that those from other products
 Ocean carbon fluxes show new IAV with a pronounced increased ocean uptake after
2002 that still needs to be validated or confirmed with other proxies.
 Net land carbon fluxes follow most standard atmospheric inversion results but with
slight differences: the IAV over the tropic is slightly lower in CARBONES than the
other products; the net land carbon uptake in the northern continental regions is
slightly lower than most inversions, especially for North America; the southern land
IAV tend to be larger than in the other products. All these features will be summarized
in paper focussing on the analysis of the 20-year CARBONES reanalysis.
 A favourable comparison against biomass forest data from FAO statistics appears with
lower biases after the assimilation of MODIS-NDVI and FluxNet data.
As reported in the introduction, this report aims to present the potential of CARBONES
products and should be considered as a framework to analyse the results of a Carbon Cycle
Multi-Data Assimilation System.
The development of the various tools for the evaluation exercise presented in this report
represent a continuing effort and these tools will be used after the duration of the
CARBONES project to further valorised CARBONES products.
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8 References
Andres, R. J., T. A. Boden, F.-M. Breon, P. Ciais, S. Davis, D. Erickson, J. S. Gregg, A.
Jacobson, G. Marland, J. Miller, T. Oda, J. G. J. Olivier, M. R. Raupach, P. Rayner, and K.
Treanton, 2012: A synthesis of carbon dioxide emissions from fossil-fuel combustion,
Biogeosciences, 9, 1845–1871, doi:10.5194/bg-9-1845-2012
Baldocchi, D. D. (2003), Assessing the eddy covariance technique for evaluating carbon
dioxide exchange rates of ecosystems: past, present and future. Global Change Biology, 9,
479–492.
Bush et al., 2010: Bush T., Tsagatakis I., Passant N., Griffin A., Pearson B. (2010): NAEI UK
Emission Mapping Methodology 2007 (URL: http://www.naei.org.uk/reports.php)
Chevallier, F., P. Ciais, T. J. Conway, T. Aalto, B. E. Anderson, P. Bousquet, E. G. Brunke,
L. Ciattaglia, Y. Esaki, M. Fröhlich, A.J. Gomez, A.J. Gomez-Pelaez, L. Haszpra, P.
Krummel, R. Langenfelds, M. Leuenberger, T. Machida, F. Maignan, H. Matsueda, J. A.
Morguí, H. Mukai, T. Nakazawa, P. Peylin, M. Ramonet, L. Rivier, Y. Sawa, M. Schmidt, P.
Steele, S. A. Vay, A. T. Vermeulen, S. Wofsy, D. Worthy, (2010), CO2 surface fluxes at grid
point scale estimated from a global 21-year reanalysis of atmospheric measurements, J.
Geophys. Res., 115, D21307, doi:10.1029/2010JD013887.
Chevallier, F., et al., CO2 surface fluxes at grid point scale estimated from a global 21 year
reanalysis of atmospheric measurements, JOURNAL OF GEOPHYSICAL RESEARCH, Vol.
115, D21307, doi:10.1029/2010JD013887, 2010
EDGAR, 2010:
Emissions Database for
(URL:http://edgar.jrc.ec.europa.eu/methodology.php)
Global
Atmospheric
Research
GLOBALVIEW-CO2: Cooperative Atmospheric Data Integration Project - Carbon Dioxide.
CD-ROM, NOAA ESRL, Boulder, Colorado [Also available on Internet via anonymous FTP
to ftp.cmdl.noaa.gov, Path: ccg/co2/GLOBALVIEW], 2011.
Jung, M., et al. (2011), Global patterns of land‐atmosphere fluxes of carbon dioxide, latent
heat, and sensible heat derived from eddy covariance, satellite, and meteorological
observations, J. Geophys. Res., 116, G00J07, doi:10.1029/2010JG001566.
Kuppel S., P. Peylin, F. Chevallier, C. Bacour, and F. Maignan, 2012: Constraining a
global ecosystem model with multi-site eddy-covariance data, Submitted to Biogeosciences
35
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Oda, T. and Maksyutov, S.: A very high-resolution (1 km×1 km) global fossil fuel CO2
emission inventory derived using a point source database and satellite observations of
nighttime lights, Atmos. Chem. Phys., 11, 543-556, doi:10.5194/acp-11-543-2011, 2011.
Peylin Philippe, Gurney Kevin, Law Rachel, Chevallier Frederic, Jacobson Andy, Niwa
Yosuke, Patra Prabir, Rayner Peter, Roedenbeck Christian, Wouter Peters, Yamada Kazutaka,
2012: Global Atmospheric Carbon Budget: results from an ensemble of atmospheric CO2
inversions, In preparation
Pregger, T., Friedrich, R. 2009: Effective pollutant emission heights for atmospheric transport
modelling based on real‐world information, Environmental Pollution 157 (2009), S. 552‐560.
Rong Wang, Shu Tao, Philippe Ciais, Huizhong Shen, Ye Huang, Han Chen, Guofeng Shen, Bin
Wang, Wei Li, Yanyan Zhang, Yan Lu, Dan Zhu, Yuanchen Chen, Xiaopeng Liu, Wentao Wang,
Xilong Wang, Bengang Li, and Shilong Piao; 2012: High resolution mapping of combustion
processes and implications for CO2 emissions, Submitted to ACP
Takahashi T., Stewart C. Sutherland, Rik Wanninkhof, Colm Sweeney, Richard A. Feely, David W.
Chipman, Burke Hales, Gernot Friederich, Francisco Chavez, Christopher Sabine, Andrew Watson,
Dorothee C.E. Bakker, Ute Schuster, Nicolas Metzl, Hisayuki Yoshikawa-Inoue, Masao Ishii,
Takashi Midorikawa, Yukihiro Nojiri, Arne Körtzinger, Tobias Steinhoff, et al. Corrigendum to
“Climatological mean and decadal change in surface ocean pCO2, and net sea–air CO2 flux over the
global oceans” [Deep Sea Res. II 56 (2009) 554–577].
Tiedtke, M. (1989), A Comprehensive Mass Flux Scheme for Cumulus Parameterization in
Large-Scale Models, Monthly Weather Review, 117, 1779-1800.
Wanninkhof, R., 1992. Relationship between wind speed and gas exchange. J. Geophys. Res.
97, 7373–7382
Zhu C., Byrd R.H., Lu P., Nocedal J. (1995), A Limited Memory Algorithm for Bound
Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing , 16,
5, pp. 1190-1208.
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