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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 ............................. 3
2.3
Temporal distributions ......................................................................................................... 6
Information about prior ocean fluxes ............................................................................... 7
3.1
Information about pCO2 spatial and temporal distributions ........................................... 8
3.2
Evaluation of the prior ocean flux...................................................................................... 10
Evaluation of the net surface fluxes from the CCDAS .................................................. 12
4.1
Approach: product used for the evaluation of CARBONES ........................................... 12
4.2
Global annual totals ............................................................................................................ 13
4.3
Long term means ................................................................................................................. 14
4.4
Inter-annual variability....................................................................................................... 17
4.5
Seasonal flux variations ...................................................................................................... 19
Evaluation of land gross carbon fluxes........................................................................... 20
5.1
Evaluation at the site level .................................................................................................. 21
5.2
Evaluation at global scale from MTE estimates ............................................................... 22
Evaluation of land carbon stocks .................................................................................... 24
6.1
Product used for the evaluation ......................................................................................... 24
6.2
Results of the comparison ................................................................................................... 25
7
Conclusions and perspectives .......................................................................................... 28
8
References ........................................................................................................................ 28
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1 Introduction
The objectives of this deliverable are to analyse the information brought by CARBONES on
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 analyses are planned to take into account the uncertainties of the inverted fluxes from
CARBONES CCDAS in order to discriminate between robust signals and other ones.
However, this intermediate report will not cover all the above mentioned objectives.
Moreover, we stress on the fact that the report will mainly evaluate the current version of the
CARBONES product (Version V1.0) against other independent flux/stock products. It is
indeed very difficult to present the information content of a given carbon product
(CARBONES) as “true” information without comparing the estimated quantities with other
independent estimates, given that there is no Truth at regional to global scales. 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 their
information content on the carbon cycle, especially their seasonality. When making the
evaluations of the above mentioned CARBONES products, the uncertainties in both
CARBONES and other products are not considered yet. Finally, we stress on the fact that this
intermediate report aims to show the potential of this evaluation exercise instead of an in deep
discussions of the results.
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
are confronted to those derived from direct inversion systems 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.
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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 1°x 1° with an hourly temporal resolution. This product
is derived from annual EDGAR 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 17 inventory of fuel combustion (PKUFUEL) and a corresponding CO2 emission inventory (PKU-CO2) based 18 upon 64
fuel sub-types for the year 2007 (Rong et al., 2012).
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
emissions data 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.
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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. Table 1 shows the considered effective emission heights based
on air quality model from EMEP and specific assumptions.
Table 1: Derived effective emission heights based on EMEP and own assumptions
EDGAR_42
1A1a
1A1c_2G
1A2
1A3a_1
1A3a_2
1A3a_3
1A3b
1A3c_e
1A3d
1A4
1B2a
2A
2B_3
2C
4C_4D
6C
7A
Sector_Name_EDGAR_42
L (<92m) M(92-781m) H(>781m)
Energy industry
0%
83%
17%
Transformation non-energy use
0%
90%
10%
Combustion in manufaturing industry
0%
94%
6%
Cruise
0%
0%
100%
Climb and descent
0%
70%
30%
Take-Off and Landing
80%
20%
0%
Road transportation
100%
0%
0%
Non-road ground transport
100%
0%
0%
International and domestic shipping
100%
0%
0%
Residential
50%
50%
0%
Oil production and refineries
90%
10%
0%
Non-metallic mineral processes
90%
10%
0%
Chemical processes solvents
90%
10%
0%
Metal processes
90%
10%
0%
Agricultural soils
100%
0%
0%
Solid waste disposal
10%
90%
0%
Fossil fuel fires
100%
0%
0%
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 2.2. 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 will be presented in the next version of that report. Especially we
will evaluate the “improvement” of the simulated concentrations.
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Altitude: <92 m
Altitude: (92 m – 781 m)
Altitude: > 781 m
Figure 2.2: 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 (top), between
92 m and781 m (middle), and > 781 m (bottom)
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Temporal distributions
Figure 2.3 displays the temporal variations of IER fossil fluxes together with the estimates
from Andres, ODA, and PKU at global scale and for four continental regions. First note that
there is a large increase in seasonal amplitude in the IER CARBONES product in 2008 and
2009 compared to the previous year, that looks spurious and that is currently under
investigation. As a first analysis we indicate:
 At global scale, IER data compared well with Andres data, with a stronger seasonal
amplitude obtained in the IER CARBONES product. ODA data for 2008 are in good
agreement with Andres and IER, while the PKU yearly data divided in 12 for year
2007 is larger than the other flux estimates. Note that Andres total flux is slightly
smaller than IER total, given that it does not include “bunker” fuel. The reasons for
larger values in PKU are under investigation.
 Over North America, there is a systematic bias between IER and Andres data, with
IER data being larger (mainly because of the “bunker” fuel difference). IER data
compare well with ODA and PKU.
 Over Europe, IER data give the strongest seasonal amplitude (even without
considering the last 2 years of the record). 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 the other data streams compare between us.
 Over Eurasia, again IER show the largest amplitude, a feature that will be evaluated
against local proxy data to further assess the accuracy of our product.
Note that in this version the global time profiles are extrapolated from European time profiles
and this will change in the next release with an update of the time profiles derived from global
data sets.
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Figure 2.3: Temporal variations of the fossil emissions from ODA, Andres, IER, and PKU products
for global (top left), North America (top right), Europe (bottom left), and Eurasia (bottom right)
3 Information about prior ocean fluxes
The Ocean Carbon Variational Reanalyzer (OCVR) is used to produce a twenty years
(CARBONES project reanalysis period) global ocean carbon dioxide fluxes. 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
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depth, wind speed, etc), which control at the first-order the surface ocean pCO2. Furthermore,
currently a variational data assimilation scheme incorporates efficiently new 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 a previous report (D410). 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 and for January, from the
OCVR system. Results show the spatial variations of pCO2 for the selected months-years.
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 inter-annual
variability (IAV). Indeed most optimization systems are using so far a climatology field with
no year to year flux variations. In the example below, we see that 2009 has a larger high
pCO2 over the tropical Pacific than the other years. A full analysis of the IAV of pCO2 and
air see fluxes will be presented in the next version of the report.
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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The temporal variations of the OCVR pCO2 data over the 1989-2009 periods and for two
selected ocean locations are presented in Figure 3.1.2. The results are compared to Takahashi
standard estimates (i.e. using a fixed growth rate of pCO2). These time series are difficult to
analyse given that they correspond to particular location and that our ocean explanatory
variables are a rather coarse resolution (2 degrees). For the BATS location, OCVR is
producing temporal variations of pCO2 that are comparable to those of Takahashi. However,
over the Equatorial Pacific, we clearly see an improvement in the OCVR product compared to
the “scaled climatology” of Takahashi, 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.
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Figure 3.1.2: JGOFS-BATS (western North Atlantic subtropical gyre), JGOFS-EQPA (Equatorial
Pacific) pCO2sw time series. Raw data in green, climatological year reference corrected by
atmospheric trend in blue (Takahashi et al. 2009) and OCVR simulation in red.
3.2
Evaluation of the prior ocean flux
We now try to evaluate the estimated air-sea fluxes from OCVR. Such effort only started and
we thus present here a preliminary figure comparing OCVR product with other “independent”
ones. These are:
 The result from the Takahashi (2009) flux climatology
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 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)
 The results from Steinkamp (2012) which 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.
The figure hereafter compares the mean seasonal cycle over the 1990-1999 period from these
different estimates (OCVR results are in red) for and ensemble of 11 ocean basin
(TRANSCOM regions). Major features from this first 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, but a more detailed analysis is required
 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 to seasonal ocean fluxes in a way that may be incompatible with
the raw ocean pCO2 surface data. These results will be investigated in the remaining
year of the project.
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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).
4 Evaluation of the net surface fluxes from the CCDAS
4.1
Approach: product used for the evaluation of CARBONES
The CARBONES products are compared to the results from inversions performed in the
RECCAP exercise (Peylin et al., 2012). 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).
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 this
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ensemble of standard inversions. Global patterns of the results derived from the ensemble of
the standard inversions are first given and those from CARBONES are then confronted. For
this intermediate report, we will only give the main characteristics of this ensemble of results,
with an emphasis on the CARBONES results when they significant 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 socalled “fossil corrected fluxes”.
Table 4.1: Participating inversion systems and 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 4.2 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) depicted in Figure 4.2 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,
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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 the “fossil correction” we described above). CARBONES
product compare well with these estimates. However, it is worth noting the monotonous
increase of ocean uptake obtained from CARBONES after 2002. Also, a relatively larger
uptake is obtained from CARBONES in 1992. These results reflect the prior ocean fluxes
from the OCVR system and they need further analysis to estimate to which extend the yearto-year variations from CARBONES are closer to reality.
Figure 4.2: Annual mean posterior flux estimate of the individual participating inversion. 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.
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Figure 4.3.1 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.
Figure 4.3.1: Mean natural fluxes for the period 2001-2006 of the individual participating
inversion posterior fluxes (exception for LSCE_ana system which is averaged over 20012004). 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
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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.
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.
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 4.3.2). 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.
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Figure 4.3.2: As for Figure 4.3.1, but for three continental/basin-scale regions: North
America, Europe, and North Asia.
4.4
Inter-annual variability
Figure 4.4.1 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 4.4.1: 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 4.4.1), particularly in the tropical latitude band. Within the land aggregates, the
tropical land exhibits the greatest amount of inter-annual variability while in the oceans,
similar inter-annual 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 in agreement with this 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 1997/1998 period and considered in the CARBONES optimization may
explained these results. Further analysis will be conducted to evaluate whether other
observational evidences support the different flux IAV in CARBONES for the southern land.
4.5
Seasonal flux variations
Figure 4.5 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. These features will be investigated and discuss in more details in
the next version of the report.
 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
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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.
Figure 4.5: 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
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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
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
FlUXNET data and ERA-Interim temperature, precipitation and Koeppen-Geiger climate
classes to upscale GPP.
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 and at
global scale. Overall, the two GPP estimates agree reasonably well, but differences can be
significant in some areas. We obtain a good agreement in boreal regions, while CARBONES
gives higher GPPs in croplands. 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/m2/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.
Mean annual global GPP over 1990-2008 period from CARBONES are plotted against MTE
by distinguishing the Koeppen-Geiger climate classes used in the MTE approach (Figure
5.2.2). Globally, a relatively good agreement is obtained. Also, the seasonalities of both GPP
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estimates are well correlated (not shown). 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.
This work only started and will be further improved to 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 available in the database
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(http://www.alterra.wur.nl/UK/research/Specialisation+Geo-information/LGN/). This data
base 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 (the first step of our sequential data assimilation system). Note that in the next version of
this report we will compare the optimized version of ORCHIDEE
6.2
Results of the comparison
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 present a larger agreement with the DLO-EFI product based on FAO data
than the reference ones. Indeed, a lower bias is obtained between the optimized ORCHIDEE
estimates and DLO-EFI data. However, the root mean square errors (RMSE) derived from the
two CARBONES estimates against DLO-EFI data remain rather large and nearly unchanged
between the two versions. Note finally that the comparison is done for only one year as the
change in biomass is rather small across the 20 yr 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
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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).
Figure 6.2.2: The differences given by the mean bias (top) and the RMSE (bottom) between
DLO-EFI and the two CARBONES estimates are shown. The two 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 (OPTIMIZED).
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7 Conclusions and perspectives
We have presented in this intermediate report the first evaluation of CARBONES products
against other independent products. These preliminary results are encouraging. Overall,
CARBONES performs as well as the other products for this current version (V1.0). Major
features are:
 Ocean carbon fluxes with new IAV still to be validated
 Fossil fuel emission with hourly temporal variations that are significantly larger that
those from other products
 Net land carbon fluxes that follow most standard atmospheric inversion but with slight
differences that will be investigated
 A favourable comparison against biomass forest data from FAO statistics.
As reported in the introduction, this first report aims to present the potential of the exercise for
the final CARBONES product.
The development of the various tools for this evaluation exercise presented in this report are
continuing efforts until the final version of CARBONES will be released for a full
application.
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