ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-1289-2016Estimates of European uptake of CO2 inferred from GOSAT XCO2
retrievals: sensitivity to measurement bias inside and outside EuropeFengL.lfeng@staffmail.ed.ac.ukPalmerP. I.https://orcid.org/0000-0002-1487-0969ParkerR. J.https://orcid.org/0000-0002-0801-0831DeutscherN. M.FeistD. G.https://orcid.org/0000-0002-5890-6687KiviR.https://orcid.org/0000-0001-8828-2759MorinoI.https://orcid.org/0000-0003-2720-1569SussmannR.National Centre for Earth Observation, School of GeoSciences, University
of Edinburgh, Edinburgh, UKNational Centre for Earth Observation, Department of Physics and
Astronomy, University of Leicester, Leicester, UKInstitute of Environmental Physics, University of Bremen, Bremen, GermanyCentre for Atmospheric Chemistry, University of Wollongong, Wollongong, AustraliaMax Planck Institute for Biogeochemistry, Jena, GermanyFMI-Arctic Research Center, Sodankylä, FinlandNational Institute for Environmental Studies (NIES), Tsukuba, JapanInstitute of Meteorology and Climate Research – Atmospheric Environmental
Research KIT/IMK-IFU, Garmisch-Partenkirchen, GermanyL. Feng (lfeng@staffmail.ed.ac.uk)4February20161631289130218December201421January201510December201512January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/1289/2016/acp-16-1289-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/1289/2016/acp-16-1289-2016.pdf
Estimates of the natural CO2 flux over Europe inferred from in situ
measurements of atmospheric CO2 mole fraction have been used previously
to check top-down flux estimates inferred from space-borne dry-air CO2
column (XCO2) retrievals. Several recent studies have shown that
CO2 fluxes inferred from XCO2 data from the Japanese
Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging
Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger
seasonal amplitudes and a more negative annual net CO2 balance than
those inferred from the in situ data. The cause of this elevated European
uptake of CO2 is still unclear, but some recent studies have suggested
that this is a genuine scientific phenomenon. Here, we put forward an
alternative hypothesis and show that realistic levels of bias in GOSAT data
can result in an erroneous estimate of elevated uptake over Europe. We use a
global flux inversion system to examine the relationship between measurement
biases and estimates of CO2 uptake from Europe. We establish a reference
in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate
conventional surface mole fraction observations and XCO2
retrievals from the surface-based Total Carbon Column Observing Network
(TCCON). We use the same EnKF system to assimilate two independent versions
of GOSAT XCO2 data. We find that the GOSAT-inferred European
terrestrial biosphere uptake peaks during the summer, similar to the
reference inversion, but the net annual flux is
1.40 ± 0.19 GtC a-1 compared to a value of
0.58 ± 0.14 GtC a-1 for our control inversion that uses only in
situ data. To reconcile these two estimates, we perform a series of numerical
experiments that assimilate observations with added biases or assimilate
synthetic observations for which part or all of the GOSAT XCO2
data are replaced with model data. We find that for our global flux
inversions, a large portion (60–90 %) of the elevated European uptake
inferred from GOSAT data in 2010 is due to retrievals outside the immediate
European region, while the remainder can largely be explained by a sub-ppm
retrieval bias over Europe. We use a data assimilation approach to estimate
monthly GOSAT XCO2 biases from the joint assimilation of in situ
observations and GOSAT XCO2 retrievals. The inferred biases
represent an estimate of systematic differences between GOSAT
XCO2 retrievals and the inversion system at regional or
sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm
can explain an overestimate of the annual sink of up to 0.20 GtC a-1.
Our results highlight the sensitivity of CO2 flux estimates to regional
observation biases, which have not been fully characterized by the current
observation network. Without further dedicated measurements we cannot prove
or disprove that European ecosystems are taking up a larger-than-expected
amount of CO2. More robust inversion systems are also needed to infer
consistent fluxes from multiple observation types.
Introduction
Observed atmospheric variations of carbon dioxide (CO2) are due to
atmospheric transport and surface flux processes. Using prior knowledge of
the spatial and temporal distribution of these fluxes and atmospheric
transport it is possible to infer (or invert for) the a posteriori estimate
of surface fluxes from atmospheric concentration data. The geographical
scarcity of such observations precludes robust flux estimates for some
regions due to large uncertainties associated with meteorology and a priori
fluxes. Arguably, our knowledge of top-down estimates of regional CO2
fluxes, particularly at tropical and high northern latitudes, has not
significantly improved for over a decade (Gurney et al., 2002; Peylin et al.,
2013), reflecting the difficulty of maintaining a surface measurement
programme over vulnerable and inhospitable ecosystems. Atmospheric transport
model errors compound errors introduced by poor observation coverage,
resulting in significant differences between flux estimates on spatial scales
< O (10 000 km) (e.g. Law et al., 2003; Yuen et al., 2005; Stephens et al., 2007).
The Greenhouse gases Observing SATellite (GOSAT), a space-borne mission
launched in a sun-synchronous orbit in early 2009, was purposefully designed
to measure CO2 columns using short-wave IR wavelengths. Validation of
current XCO2 column retrievals using co-located upward-looking
FTS measurements of the Total Carbon Column Observing Network (TCCON) (Wunch
et al., 2011) shows a standard deviation of 1.6–2.0 ppm (e.g., Parker et
al., 2013). Their global
biases are typically smaller than 0.5 ppm (Oshchepkov et al., 2013). The
disadvantage of using the TCCON is that sites are mainly at northern
extra-tropical latitudes with little or no coverage where our knowledge of
the carbon cycle is weakest. Many surface flux estimation algorithms are
particularly sensitive to systematic errors so that sub-ppm biases can still
significantly change the patterns of regional flux estimates (Chevallier et
al., 2010). This is further complicated by the seasonal coverage of GOSAT
data at high latitudes during winter months when solar zenith angles are too
large to retrieve reliable values for XCO2 (Liu et al., 2014).
Several independent studies have shown that regional flux distributions
inferred from GOSAT XCO2 retrievals are significantly different
from those inferred from in situ data (Basu et al., 2013; Deng et al.,
2014; Chevallier et al.,
2014). In particular, these studies report a larger-than-expected annual net
emission over tropical continents and a larger-than-expected net annual
uptake over Europe. While the GOSAT inversions suffer from larger observation
errors, atmospheric transport errors and issues from the seasonal coverage of
higher latitudes, the in situ inversions are also unreliable over many
regions due to poor coverage and atmospheric transport errors.
Inter-comparisons revealed significant inconsistency in regional flux
estimates inferred from in situ observations by using different inversion
systems, over many regions important for global carbon cycle, including
Europe (Peylin et al., 2013). Consequently, there is an ongoing debate about
whether a recent study that shows a large European uptake of CO2 (Reuter
et al., 2014) reflects a real phenomenon or is an artefact due to
deficiencies both in the observations and in the inverse modelling.
We report the results from a small set of experiments that show systematic
bias can introduce a large difference between European fluxes inferred from
GOSAT and those inferred from in situ data by using a global flux inversion
approach. In the next section we provide an overview of the inverse model
framework used to interpret data from the in situ observation network
(including both the conventional surface observation network and the
relatively new TCCON network), and from the space-based GOSAT
XCO2 data. In Sect. 3, we present results from two groups of
global inversion experiments that characterize the role of systematic bias in
regional flux estimates. Further experiments for quasi-regional flux
inversions are presented in Appendix A. In Sect. 4, we use a modified version
of the inverse model framework to estimate monthly biases by jointly
assimilating all data. We conclude the paper in Sect. 5.
Description and evaluation of control in situ and GOSAT experiments
We use the GEOS-Chem global chemistry transport model to relate surface
fluxes to the observed variations of atmospheric CO2 concentrations
(Feng et al., 2009) at a horizontal resolution of
4∘× 5∘, driven by GEOS-5 meteorological analyses
from the Global Modeling and Assimilation Office Global Circulation Model
based at NASA Goddard Space Flight Centre. We use an Ensemble Kalman Filter
(EnKF) (Feng et al., 2009, 2011) to estimate regional fluxes from in situ or
GOSAT observations for 3 years from 2009–2011, but we focus on 2010 to
minimize error due to spin-up and edge effects. We estimate monthly fluxes on
a spatial distribution that is based on TransCom-3 (Gurney et al., 2002) with
each continental region further divided equally into 12 sub-regions and each
ocean region further divided equally into six sub-regions. As a result, we
estimate fluxes for 199 regions, compared to 144 regions we have used in
previous studies (Feng et al., 2009; Chevallier et al., 2014).
The magnitude and uncertainty of the European annual CO2
biosphere flux (GtC a-1) from 14 global flux inversion experiments.
Except INV_ACOS_INS_DBL_ERR and INV_ACOS_DBL_ERR, the
aggregated European annual uptake of the a priori fluxes is
-0.1 ± 0.52 GtC a-1.
NameDataFlux (GtC a-1)Uncertainty (GtC a-1)INV_TCCONIn situ Flask and TCCON XCO2-0.580.14INV_ACOSACOS XCO2 retrievals-1.400.19INV_UOLUOL XCO2 retrievals-1.40.20INV_ACOS_MOD_ALLModel simulation of ACOS XCO2 by using INV_TCCON posterior fluxes-0.640.19INV_ACOS_MOD_NOEUAs INV_ACOS_MOD_ALL but the real ACOS XCO2 retrievals are assimilated within Europe.-0.880.19INV_UOL_MOD_NOEUAs INV_UOL, but outside the Europe, UOL XCO2 retrievals are replaced with INV_TCCON simulations.-0.670.19INV_ACOS_MOD_ONLYEUAs INV_ACOS, but XCO2 retrievals within EU are replaced by INV_TCCON simulations-1.170.19INV_ACOS_OUT_0.5ppmAs INV_ACOS, but a bias of -0.5 ppm has been added to XCO2 retrievals outside Europe.-0.980.19INV_ACOS_SPR_0.5ppmAs INV_ACOS, but 0.5 ppm bias has been added to the European data in February, March, and April.-1.300.19INV_ACOS_SUM_0.5ppmAs INV_ACOS, but 0.5 ppm bias has been added to the European data in June, July, and August.-1.250.19INV_ACOS_INSACOS XCO2 retrievals and In situ flask and TCCON data-0.620.13INV_UOL_INSUOL XCO2 retrievals and in situ flask and TCCON data-0.670.13INV_ACOS_DBL_ERRACOS XCO2 retrievals, but the a priori uncertainties have been doubled-1.610.27INV_ACOS_INS_DBL_ERRGOSAT ACOS XCO2 retrievals and In situ flask and TCCON databut the a priori flux uncertainties have been doubled-0.670.16
In all global inversion experiments we assume the same set of a priori flux
inventories, including the following: (1) monthly fossil fuel emissions (Oda and Maksyutov,
2011); (2) weekly biomass burning emissions (GFED v3.0) (van der Werf et al.,
2010); (3) monthly oceanic surface CO2 fluxes (Takahashi et al., 2009);
and (4) 3-hourly terrestrial biosphere-atmosphere CO2 exchange (Olsen
and Randerson, 2004). We assume that the a priori uncertainty for each land
sub-region is proportional to a combination of the net biospheric emission
(70 %) at the current month, and its annual variation (30 %). We also
assume that the a priori errors are correlated with each other with a spatial
correlation length of 800 km, and a temporal correlation of 1 month
(Chevallier et al., 2014). We then determine the coefficient for the assumed
a priori uncertainty by scaling the aggregated annual uncertainty over all
133 land sub-regions to 1.9 GtC a-1. In particular, the resulting
annual a priori uncertainty for the European region is about 0.52 GtC a-1,
with the monthly uncertainty varying from 2.0 GtC a-1 for the summer
months to about 0.8 GtC a-1 for winter months, which is generally
larger than the a priori monthly uncertainty used by Deng et al. (2014).
Prior uncertainties over oceans are determined under similar assumption but
with a longer spatial correlation (1500 km), and a smaller aggregated annual
error (0.6 Gt a-1). Our experiments show that doubling the a priori
uncertainty increases the European uptake inferred from GOSAT data by about
0.21 GtC a-1 (from 1.40 to 1.61 GtC a-1), compared to a smaller
increase of 0.09 GtC a-1 for the in situ inversion (from 0.58 to
0.67 GtC a-1).
Our control inversion experiment (INV_TCCON, Table 1 and Fig. 1)
assimilates in situ observations, including the conventional surface
observations at 76 sites (Feng et al., 2011) and, in particular, the total
column XCO2 retrievals from all the TCCON sites of the GGG2014
data set (see Wennberg et al., 2014, and https://tccon-wiki.caltech.edu
for more details) to improve observation constraints. In some studies, TCCON
data were used to evaluate posterior fluxes. However TCCON data have been used
to derive bias corrections for GOSAT XCO2 retrievals (Cogan et
al., 2012), and also the nature of total column measurements means that they
are sensitive to air mass transported from other regions, which complicate
the assessment of European flux estimates.
Monthly a posteriori estimates (GtC) for European
biospheric CO2 fluxes in 2010 using three inversion experiments
(top panel): (1) INV_TCCON (red line), (2) INV_ACOS (green line), and
INV_UOL (blue line). The black line denotes a priori values. The vertical
black lines and grey shading denotes the uncertainties of the corresponding a
priori or a posteriori flux estimates, respectively. Differences in monthly
CO2 uptake (GtC) between INV_TCCON and two GOSAT inversions
(bottom panel): INV_ACOS (green bars) and INV_UOL (blue bars).
We use daytime (09:00 to 15:00 local time) mean TCCON retrievals, with the
observation errors determined by the standard deviation about their daytime
mean. To account for the inter-site biases as well as the model
representation errors, we enlarge the TCCON observation errors by 0.5 ppm.
Including TCCON observations increases the annual net uptake over Europe in
2010 from 0.49 GtC a-1, as inferred from surface observations only, to
0.58 GtC a-1. The increase is mainly due to a larger summer uptake.
TCCON data also reduce the a posteriori uncertainty by about 15 % from
0.16 to 0.14 Gt a-1. However considering the limited spatial
resolution (only 12 sub regions for the whole TransCom European region), and
unquantified model transport and representation errors, we anticipate that
the complete a posteriori uncertainty is larger than the value estimated by
the inversion system itself, as suggested by large inter-model variations
found for in situ inversions (e.g., Peylin et al., 2013).
For the two control GOSAT inversions (Fig. 1), we use two independent data
sets: (1) XCO2 retrievals from JPL ACOS team (v3.3) (Osterman et
al., 2013) (INV_ACOS); and (2) the full-physics XCO2
retrievals (v4.0) from the University of Leicester (Cogan et al., 2012)
(INV_UOL). For both data sets, we assimilate only the H-gain data over
land regions, and apply the bias corrections recommended by the data
providers. We double the reported observation errors, as suggested by the
retrieval groups.
As a performance indicator for our ability to fit fluxes to observed
XCO2 concentrations, we compare a posteriori model
concentrations with GOSAT XCO2 retrievals and show that
INV_ACOS and INV_UOL agree much better than INV_TCCON. For example,
the bias against ACOS XCO2 retrievals is -0.45 ppm for
INV_TCCON and 0.02 ppm for INV_ACOS with a corresponding reduction in
the global standard deviation from 1.69 to 1.57 ppm. However comparison of
GOSAT a posteriori concentrations against independent HIPPO-3 measurements is
worse than INV_TCCON with a positive bias of 0.47 and 0.66 ppm for
INV_ACOS and INV_UOL, respectively, which are mainly caused by the
overestimation of CO2 concentrations (∼ 1.5–2.0 ppm) at low
latitudes (Fig. 2).
HIPPO-3 and GEOS-Chem model atmospheric CO2 mole fractions (ppm)
over the Pacific Ocean below 5 km (black). GEOS-Chem is driven by different
a posteriori flux estimates: (1) INV_TCCON (red), (2) INV_ACOS (blue),
and (3) INV_UOL (green). HIPPO-3 and model CO2 mole fractions are
binned into 5∘ latitude boxes. We calculate the mass-weighted average
over these latitude boxes by assigning each HIPPO-3 and GEOS-Chem model value
a weighting factor according to the observation altitude (air pressure). The
grey envelope (red vertical lines) indicates the one standard deviation of
HIPPO-3 measurements (INV_TCCON model values) within each latitude box.
Results
Figure 1 and Table 1 shows the three inversion experiments, INV_TCCON,
INV_ACOS, and INV_UOL, have similar European uptake values in June 2010
(0.69 GtC for
INV_TCCON and ∼ 0.72 GtC for GOSAT inversions), and are
generally consistent with other GOSAT inversion experiments (e.g., Deng et
al., 2014; Chevallier et al., 2014). But the GOSAT inversions have an annual
net uptake of about 1.40 ± 0.19 GtC a-1 compared to the in situ
inversion of 0.58 ± 0.14 GtC a-1. Figure 1 also shows
significant differences between their monthly flux estimates in early spring
and winter when there is only sparse GOSAT observation coverage, particularly
over northern Europe. Both INV_UOL and INV_ACOS have a cumulative total
of about 0.51 GtC more uptake than INV_TCCON during February–April of
2010, with a further 0.37 GtC uptake accumulated over the following summer
and autumn. This larger uptake is partially cancelled out by larger emissions
(0.17–0.08 GtC) at the end of 2010.
Figure 2 shows that INV_TCCON a posteriori CO2 mole fractions agree
well with the independent HIAPER Pole-to-Pole Observations (HIPPO-3) aircraft
measurements below 5 km over the Pacific Ocean in 2010 (Wofsy et al.,
2011), with a small bias of
0.05 ppm, and a sub-ppm standard deviation of 0.87 ppm. Figure 3 shows
further evaluation of a posteriori CO2 mole fractions using descending
and ascending profile observations over two European airports from the
CONTRAIL experiment (Machida et al., 2008). We calculate monthly mean
CONTRAIL measurements during 2010 using data below 3 km, where there is
greater sensitivity to local surface fluxes. Our current model resolution
precludes small-scale sources (or sinks) so we expect model bias. We find
that INV_TCCON agrees best with CONTRAIL observations, in particular at
the beginning of 2010, partially reflecting the poor GOSAT
XCO2 coverage over Europe during the winter and early spring.
However, we cannot conclude from the slightly degraded agreement with
CONTRAIL (as well as with HIPPO-3) that the European uptake inferred from
GOSAT data is incorrect, because unaccounted small local emissions and/or sinks, and
model transport errors can affect the comparison against aircraft
observations.
Monthly mean observed and model a posteriori model CO2 mole
fractions (ppm) below 3 km above Amsterdam (the top panel) and Moscow (the
bottom panel) airports during 2010, respectively (Machida et al., 2008). The
three sets of a posteriori model concentrations are inferred from three
inversion experiments: INV_TCCON (red line), INV_ACOS (green line), and
INV_UOL (blue line). The broken magenta line represents a model simulation
where the European fluxes from INV_ACOS inversion are replaced by
INV_TCCON estimates.
Figure 3 also presents an additional model simulation forced by a hybrid flux
(denoted by the magenta broken line) where the INV_TCCON a posteriori
fluxes outside Europe are replaced by the results from INV_ACOS. The
resulting CO2 concentrations from these hybrid fluxes are, as expected,
higher than the a posteriori model concentrations for INV_ACOS because of
the larger European emissions (i.e., less uptake) inferred by INV_TCCON.
But they are also systematically higher than the INV_TCCON simulation, in
particular during spring months, despite the same European fluxes being used
to force these two simulations. This suggests an overestimate of CO2
transported into the European region by the GOSAT inversions. Further
comparison of the INV_TCCON simulation and the hybrid run reveals that
systematic differences in the inflow into the European domain can affect the
atmospheric XCO2 gradient across this region. In the
INV_TCCON simulation, the mean XCO2 difference between east
(east of 20∘ E) and west (west of 20∘ E) Europe is
∼ 0.04 ppm for May 2010, which is increased to 0.16 ppm in the hybrid
run (cf. E–W XCO2 gradient of -0.20 ppm for GOSAT ACOS
data).
To understand the differences between the INV_TCCON and GOSAT inversions,
we conducted two groups of sensitivity tests (Table 1 and Fig. 4). First, we
replaced all or part of the GOSAT XCO2 retrievals assimilated in
INV_ACOS with those from a model simulation forced by the a posteriori
fluxes from INV_TCCON. In experiment INV_ACOS_MOD_ALL (Fig. 4),
where we replace all GOSAT data with CO2 concentrations inferred from
INV_TCCON, we reproduce INV_TCCON with small exceptions at the beginning of
2010, reflecting the seasonal variation in GOSAT coverage. In a related
experiment INV_ACOS_MOD_NOEU for which we only replace
XCO2 retrievals outside Europe with the model simulation, the
differences between the GOSAT and in situ inversions are significantly
reduced, particularly over the period with limited observation coverage,
although the actual XCO2 retrievals are still assimilated over
Europe. The simulated GOSAT data outside Europe reduces the estimate of
European uptake from 1.40 to 0.88 GtC a-1. In other words, the GOSAT
observations outside the European region are responsible for about 60 %
(0.52 GtC a-1) of the total enhanced European sink
(0.82 GtC a-1) with the remainder (0.30 GtC a-1) due to
observations taken directly over Europe. The large contribution from GOSAT
retrievals outside Europe has also been confirmed by the high uptake
(1.17 Gt a-1) in a counterpart experiment
(INV_ACOS_MOD_ONLYEU) where only GOSAT retrievals within Europe are
replaced by the model simulations. We show in Appendix B that theoretically
the difference between INV_ACOS and INV_ACOS_MOD_ALL is equal to
the sum of the individual uptake increases in the paired synthetic inversions
of INV_ACOS_MOD_NOEU and INV_ACOS_MOD_ONLYEU.
Monthly European biospheric flux estimates (GtC) from two
groups of sensitivity experiments (top panel, Table 1). Black, green and red
solid lines denote the a priori and the INV_ACOS and INV_TCCON
inversions, respectively. Differences between INV_TCCON inversion and
sensitivity inversions (bottom panel): (1) INV_ACOS_MOD_ALL
(yellow), where all GOSAT retrievals are replaced by the model simulations
forced by INV_TCCON a posteriori fluxes; (2) INV_ACOS (green), where
original GOSAT ACOS retrievals are assimilated; (3) INV_ACOS_NOEU
(blue) where all the GOSAT retrievals outside the European region are
replaced by the INV_TCCON simulations; and
(4) INV_ACOS_MOD_ONLYEU (cyan) where only GOSAT retrievals within
the European region are replaced by the INV_TCCON simulations.
For INV_UOL, when we replace the XCO2 data outside Europe by
the a posteriori INV_TCCON model simulations, European uptake is reduced
to 0.67 GtC a-1 (INV_UOL_MOD_NOEU, Table 1), indicating an
external contribution of nearly 90 % to the enhanced uptake of
0.82 GtC a-1. Together with Fig. 3, these results suggest that GOSAT
inversions result in an overestimated CO2 inflow. This will subsequently
lead to the fitted European flux having to compensate, via mass balance, by
being erroneously low even when un-biased GOSAT XCO2 data are
assimilated over the immediate European region. We find similar effects in
the quasi-regional inversions (Fig. A1 in Appendix A), where only
observations within the European region are assimilated, with flux estimates from
INV_TCCON or from INV_ACOS being used to provide lateral boundary
conditions around Europe.
Second, we crudely demonstrate how regional bias could explain the remaining
discrepancy of up to 0.30 GtC a-1 between GOSAT and in situ inversions
over Europe. In our experiment INV_ACOS_SPR_0.5ppm, we add a bias of
+0.5 ppm to the GOSAT ACOS retrievals within Europe taken in
February-April, inclusively, which effectively reduces the uptake by
0.1 GtC a-1 from 1.40 to 1.30 GtC a-1. Similarly, when the bias
of +0.5 ppm is added to the GOSAT data taken in June–August we find a
larger reduction of 0.15 GtC a-1
(INV_ACOS_SUM_0.5ppm), partially due to a larger a priori
uncertainty and denser GOSAT coverage during the summer. These results
emphasize the importance of characterizing sub-ppm regional bias to avoid
erroneous flux estimates.
Bias estimation
Here we demonstrate a simple approach to quantify systematic bias in
XCO2 retrievals based on a simple on-line bias correction
scheme. We assimilate the GOSAT XCO2 retrievals together with
the surface and TCCON observations in two experiments: INV_ACOS_INS and
INV_UOL_INS (Table 1). We also include monthly GOSAT XCO2
regional biases over 11 TransCom land regions (Gurney et al., 2002) as
parameters to be inferred together with surface fluxes from the joint
assimilation of in situ and satellite observations. To investigate the
spatial pattern of the XCO2 biases within Europe, we split
Europe into West Europe (west of 20∘ E) and East Europe (east of
20∘ E). We assume that a priori for monthly biases is
0.0 ± 0.5 ppm. For simplicity, we have assumed that the a priori errors for
regional XCO2 biases are not correlated. Compared to the
off-line comparisons between GOSAT XCO2 retrieval and model
concentrations, the main advantage of the on-line bias estimation is that the
uncertainties associated with error in flux estimates can be partially taken
into account. However, biases derived by this approach reflect the systematic
difference between the model simulation and GOSAT data over large
(continental) regions, which also contain systematic model errors (such as
the atmospheric transport and representation errors). In addition, the
inversion results are affected by the relative weights assigned to different
data sets, as well as by the relative prior uncertainty assumed for surface
fluxes and for the observation bias. The seasonal variation of the mean
CO2 concentration is an important sign of the underlined biosphere
seasonal cycle. We show in Appendix A that when we inflate the a priori
uncertainty for the assumed observation bias, the observation constraints on
flux estimate will become weaker. Also, the on-line bias correction is only
effective for detecting and correcting bias at specified patterns, which may
increase the sensitivity to other uncharacterized systematic errors. Despite
these weaknesses, a joint data assimilation approach can exploit
complementary constraints from in situ and satellite XCO2 data:
for example there are few GOSAT observations over northern Europe during
autumn and winter months, while Eastern Europe has few in situ observations.
We have also limited the a priori uncertainty for the monthly observation
biases to 0.5 ppm. Figure C1 (Appendix C) shows, for example, the inferred
monthly mean bias for March 2010.
In the joint inversions INV_ACOS_INS and INV_UOL_INS, the annual
European uptake is estimated to be 0.62 and 0.67 GtC a-1, respectively
(Table 1), which is close to the reference value of 0.58 GtC a-1
inferred from the in situ observations. To test the impact of the on-line
bias correction, we set the a priori uncertainty of regional
XCO2 bias to be 0.01 ppm so that on-line bias correction is
effectively turned off. As a result, the annual European uptake for
INV_ACOS_INS is increased by 0.15 GtC to 0.77 GtC a-1, which is
close to INV_ACOS_MOD_NOEU, but about 55 % of the GOSAT only
inversions (1.40 GtC a-1).
Figure 5 shows the estimated monthly biases in ACOS and UOL XCO2
retrievals over East and West Europe during 2010. Monthly biases are
typically smaller than 0.5 ppm over the two regions, but have different
seasonal cycles. Additional experiment shows that after ACOS
XCO2 data over Europe have been corrected for the inferred
biases, the European annual uptake by INV_ACOS is reduced by
0.20 GtC a-1, representing more than half of the contribution from
GOSAT observations within Europe. This result is consistent with our
sensitivity tests. The effect of bias correction is much smaller for
INV_UOL (about 0.07 GtC a-1), because of the different bias
patterns. Differences in GOSAT XCO2 retrievals and their effects
on regional flux estimates have also been investigated in previous studies
(e.g., Takagi et al., 2014).
Estimates of monthly CO2 biases (ppm) in GOSAT ACOS (green) and
UOL (blue) XCO2 retrievals over (top) West (West of
20∘ E) and (bottom) East (East of 20∘ E) Europe. The black
vertical lines represent the uncertainty.
Discussion and conclusions
We used an ensemble Kalman Filter to infer regional CO2 fluxes from
three different CO2 data sets: (1) surface in situ mole fraction
observations and TCCON XCO2 retrievals; (2) GOSAT
XCO2 retrievals from the JPL ACOS team; and (3) GOSAT
XCO2 retrievals from the University of Leicester. Our results,
consistent with previous studies, show that these GOSAT data in a global flux
inversion context result in a significantly larger European uptake than
inferred from in situ data during 2010.
We showed using sensitivity experiments that a large portion (60–90 %)
of the elevated European uptake of CO2 is related to the systematically
higher model CO2 mass being transported into Europe, due to the
assimilation of GOSAT XCO2 data outside the European region. We
find some evidence using aircraft observations over the Pacific that GOSAT a
posteriori fluxes result in higher CO2 concentration over lower
latitudes. But limited observation coverage and unaccounted model errors
prevent us from confidently concluding that GOSAT XCO2 data are
biased high or low. Our global and quasi-regional (Appendix A) flux inversion
experiments show that the main consequence of the elevated CO2 inflow to
the European domain is that the European uptake must increase because of mass
balance, even when GOSAT XCO2 retrievals within the European
domain are not biased. A crude sensitivity test
(INV_ACOS_OUT_0.5ppm) shows that reducing ACOS XCO2
data outside the European region by 0.5 ppm will reduce European annual
uptake from 1.40 to 0.98 GtC a-1. Erroneous interpretation of
XCO2 data can result from analyses if biased boundary conditions
are not addressed. However, as shown in Appendix A, a gross
mis-characterization and correction of bias may weaken observation
constraints, which can also lead to erroneous flux estimates.
We also showed using sensitivity tests that sub-ppm bias can explain the
remaining 0.30 GtC a-1 flux difference between the in situ inversion
and INV_ACOS after accounting for biased boundary conditions. By
simultaneously assimilating the in situ and GOSAT observations to estimate
surface fluxes and monthly XCO2 biases, we infer a monthly
observation bias that is typically less than 0.5 ppm over East and West
Europe, but is able to cause an elevated sink of up to 0.20 GtC a-1.
The inferred monthly biases for UOL XCO2 are also not the same
as the ACOS XCO2 data, particularly over West Europe during the
summer months. This level of sensitivity of regional flux estimate to
time-varying sub-ppm observation bias highlights the challenges we face as a
community when evaluating XCO2 retrievals using current
observation networks.
Flux estimates are sensitive to a priori assumptions, idiosyncrasies of
applied inversion algorithms, and the underlying model atmospheric transport
(Chevallier et al., 2014; Peylin et al., 2013; Reuter et al., 2014). The possible presence of regional
observation biases further complicates the inter-comparisons of flux
estimates based on different inversion approaches, as they may have different
sensitivities to certain observation biases. In our assimilation of ACOS
XCO2 retrievals, we find that doubling the a priori flux error
(INV_ACOS_DBL_ERR) increases the estimated European uptake from 1.40
to 1.61 GtC a-1, consistent with the hypothesis on the increased
vulnerability to the observation biases both within and outside Europe when
using weak a priori constraints. In contrast, doubling the a priori flux
errors only increases the uptake by 0.05 to 0.67 GtC a-1 for the joint
data assimilation (INV_ACOS_INS_DBL_ERR), with very little
changes in the estimated biases (not shown). Examples in Appendix A also
demonstrate different responses to regional and sub-regional biases before
and after an on-line scheme is used to correct the systematic error across
Europe. These differences emphasize the need for a closer examination of the
responses of the inversion systems to the assimilated observations, as well
as to their possible biases, to help understand the inter-model variations in
estimated regional fluxes.
Complicated interactions between observations and the assimilation system
also mean that our present study does not exclude other possible causes for
the elevated European uptake reported by previous research from assimilation
of GOSAT data. Instead, it highlights the adverse effects of possibly
uncharacterized regional biases in current GOSAT XCO2 retrievals
that can attract erroneous interpretation of resulting regional flux
estimates. A more thorough evaluation of the XCO2 retrievals
using independent and sufficiently accurate and/or precise observations is urgently
required to increase the confidence of regional CO2 flux estimates
inferred from space-based observations. Without additional observations, we
cannot rule out either the lower European uptake estimate of around
0.6 GtC a-1 (inferred from the in situ inversion INV_TCCON and the
joint inversion INV_ACOS_INS and INV_UOL_INS) or the higher
European uptake estimate of around 1.40 GtC a-1 (inferred from GOSAT
data). There is also no sufficient reason to believe that the mean value
among these diverse estimates is more reliable, because our study suggests
that small systematic errors can result in significant differences in the
estimated fluxes, and the influences of random errors have also not been
fully quantified. The observational density required to infer flux estimates
over a limited spatial domain such as Europe is crucial. For the time frame
of this analysis, the TCCON network provided good coverage for Europe, North
America, Southeast Asia and Australia and New Zealand. Great efforts were also
taken to reduce inter-station biases. In future the TCCON measurement network
may be supported by smaller, more mobile FTIR instruments, which can be
established, at least on a campaign basis, in tropical and high latitude
locations where observational gaps are greatest.
Our joint data assimilation approach assimilates in situ and space-borne
observations. It also provides estimates of systematic differences between
XCO2 retrievals and the inversion system at
regional/sub-regional scales. However the resulting differences will include
the observation biases and deficiencies in the underlying inversion approach.
To achieve consistent flux estimates inferred from assimilating multiple data
sets using different inversion approaches, we need to better quantify
observation and model errors, and need to better understand the sensitivity
of each inversion system to the assimilated observations as well as to their
possible biases. It is difficult to develop a robust bias correction scheme
before properly characterizing observation biases and the responses by the
inversion system.
Quasi-regional flux inversion
To further study the contributions from XCO2 retrievals within
and outside Europe we have performed quasi-regional flux inversions to infer
the European uptake of CO2 in 2010, based on the same EnKF approach as
the global flux inversions. In contrast to the global experiments (Table 1),
for the quasi-regional inversions we assimilate observations only over
Europe, and assign a small a priori flux uncertainty to any region outside
Europe in order to minimize the influence of observations taken over Europe
on other regions. Consequently, a posteriori flux estimates outside of Europe
are close to their a priori values. We use the a posteriori fluxes from
INV_TCCON as the a priori estimates for 12 sub-regions in Europe, and
assume their uncertainty is two thirds of that we use for the global flux
inversions. This is because the a posteriori estimates from INV_TCCON have
already been refined by in situ data.
The same as Table 1 but for quasi-regional inversions where only
ACOS XCO2 within Europe are assimilated.
NameDescriptionFlux (GtC a-1)Uncertainty (GtC a-1)INV_BD_TCCONOnly ACOS data over Europe are assimilated to infer monthly fluxes over 12 European sub-regions. Fluxes outside the EU are fixed to INV_TCCON inversion.-0.790.18INV_BD_TCCON_BCThe same as INV_BD_TCCON, but monthly bias with an assumed prior uncertainty of 100 ppm are included as additional parameters to be estimated.-0.940.22INV_BD_ACOSThe same as INV_BD_TCCON, but external regional fluxes are fixed to INV_ACOS.-1.580.18INV_BD_ACOS_BCThe same as INV_BD_ACOS, but estimates for monthly observation bias included.-0.960.22
To investigate the influence of lateral boundary conditions on the
quasi-regional flux inversions, we use two different sets of a posteriori
estimates to define fluxes outside Europe: (1) INV_TCCON
(INV_BD_TCCON) and (2) INV_ACOS (INV_BD_ACOS). Figure A1 shows
that INV_BD_ACOS has a higher annual uptake of 1.58 GtC a-1 than
INV_BD_TCCON with an uptake of 0.79 GtC a-1 (Table A1), with
differences larger during the first half of 2010. The estimate for
INV_BD_ACOS is similar to its global inversion counterpart INV_ACOS.
Large differences between INV_BD_ACOS and INV_BD_TCCON highlight
the importance of accurate lateral boundary conditions to a regional European
inversion.
As Fig. 4, but for the comparisons between the quasi-regional
inversions. All the inversion experiments assimilate the same ACOS data set
over Europe, with the a priori for 12 European sub-regions taken from
posterior estimates from INV_TCCON. Fluxes outside Europe are fixed to the
posterior estimates of INV_TCCON (INV_BD_TCCON and
INV_BD_TCCON_BC) or to the estimates of INV_ACOS
(INV_BD_ACOS and INV_BD_ACOS_BC). INV_BD_TCCON_BC and
INV_BD_ACOS_BC also estimate the monthly bias across Europe as an
additional parameter with an assumed a priori uncertainty of 100 ppm
estimated from ACOS data.
We use on-line bias correction schemes to reduce the adverse impacts from
incorrect boundary conditions around Europe. Similar to Reuter et al. (2014),
we estimate monthly observation biases across Europe using our quasi-regional
flux inversion system. Here, we introduce a monthly bias to remove the
systematic difference between model and GOSAT observations across the whole
European region, and assume an associated a priori uncertainty of 100 pm
(Reuter et al., 2014). This is different from our previous bias assumption of
0.5 ppm over East and West Europe for INV_ACOS_INS. Compared to
INV_ACOS_INS, we also do not assimilate any in situ observations as
additional constraints. Figure A1 shows that such a bias correction scheme
(INV_BD_ACOS_BC) successfully reduces European uptake of CO2
during 2010 to 0.96 GtC a-1 from 1.58 GtC a-1 for
INV_BD_ACOS. Table A1 shows that after applying the bias correction
scheme, INV_BD_ACOS_BC and INV_BD_TCCON_BC are consistent
(0.94 GtC a-1 vs. 0.96 GtC a-1) despite different lateral
boundary conditions provided by INV_ACOS and from INV_TCCON. But
INV_BD_TCCON_BC (0.94 GtC a-1) has 0.15 GtC a-1 more
uptake than INV_BD_TCCON (0.79 GtC a-1). We find a similar
difference using UOL data (not shown), which infer an annual uptake of
0.71 GtC a-1 (0.56 GtC a-1) with (without) the on-line bias
correction.
As Fig. 4, but for comparisons of the quasi-regional inversions for
assimilation of synthetic ACOS retrievals against “True” fluxes
(INV_TCCON). All the quasi-regional inversions have assumed the same a
priori fluxes. But INV_REG_BC and INV_REG_BC_1ppm also include
the monthly observation bias across Europe, with a prior uncertainty of
100 pm, as additional parameters to be estimated from the synthetic
observations. In INV_REG_ENKF_1ppm and INV_REG_BC_1ppm,
1 ppm observation bias is added to the (synthetic) observations over a small
south-west strip of Europe during the summer of 2010.
We next examine the effectiveness of the inversion system that uses an
on-line bias correction with large a priori uncertainty. Generally, large a
priori uncertainty for biases will lead to the eventual loss of constraint by
the observed mean CO2 concentration across Europe. The weakened
constraint can be seen by the enlarged a posteriori error (by
0.04 GtC a-1) for INV_BD_TCCON_BC. In additional OSSEs
(Table A2) we find that the loss of such a constraint can result in large
systematic errors in estimated fluxes.
In these OSSEs, we assume the a priori estimates for 12 European sub-regions
to be the same as the a priori used by INV_TCCON. Similar to
INV_BD_TCCON, we set the fluxes outside the European region to be the a
posteriori estimates by INV_TCCON. We assimilate the INV_TCCON model
ACOS XCO2 retrievals over Europe, to test the ability of the
system to recover the “true” European flux (defined by INV_TCCON) from
the assumed a priori that we define as the CASA model. Without the on-line
bias correction, the quasi-regional inversion INV_REG_ENKF reproduces
the truth for most months (Fig. A2), and the associated annual uptake of
0.55 GtC a-1 compared to the true value of 0.58 GtC a-1. If we
also estimate monthly XCO2 bias with a large a priori
uncertainty of 100 ppm (INV_REG_BC), the a posteriori European uptake
is systematically underestimated for almost all months in 2010 (Fig. A2).
Consequently, the a posteriori annual uptake is about 0.38 GtC a-1,
which is 35 % smaller than the true uptake (Table A2). Weakening the
observation constraint also enlarges the a posteriori uncertainty from
0.22 GtC a-1 for INV_REG_ENKF to 0.27 for INV_REG_BC. But
we find that increases in the estimated a posteriori uncertainty (by
0.05 GtC a-1) are smaller than the increase in the systematic
deviation from the true annual uptake (by 0.19 GtC a-1).
More importantly, we find that the derived annual uptake is not linearly
correlated to the assumed true fluxes. In experiment INV_REG_BC_SP
(Table A2) we replace the true fluxes (defined by INV_TCCON) over the
first 3 of 12 European sub-regions, which are at the southern part of Europe
(roughly south of 47∘ N), with values from CASA model. As a result,
the new true fluxes have an annual uptake of about 0.48 GtC a-1 across
Europe, which is about 18 % (0.1 GtC a-1) lower than the original
one defined by INV_TCCON for INV_REG_BC. We then re-generate model
ACOS XCO2 data by running GEOS-Chem driven by the new hybrid
true fluxes. However, after assimilating the new model XCO2
data, INV_REG_BC_SP infers an annual uptake of 0.37 GtC a-1,
which is almost the same as the posterior estimate (0.38 GtC a-1) of
INV_REG_BC, failing to reproduce the 18 % decrease from the true
value of 0.58 GtC a-1 assumed for INV_REG_BC to the
0.48 GtC a-1 assumed for INV_REG_BC_SP. In contrast, the
quasi-inversion without on-line bias correction (INV_REG_ENKF_SP)
well reproduces such a decrease.
The bias correction across Europe can also increase the sensitivity to
sub-regional biases. To illustrate this we added 1 ppm bias to the simulated
observations during June to August of 2010 over south-west Europe between 35
to 42∘ N and 15∘ W to 20∘ E (mostly over Spain and
Italy). Without an on-line bias correction, adding the 1 ppm bias over the
south-west strip leads to a small change (0.01 GtC a-1) in the annual
uptake: a (slightly) reduced uptake in the first half of 2010 is largely
compensated by a slightly enhanced uptake in the second half of 2010.
Conversely, when we use an on-line bias correction with large prior errors
(INV_REG_BC_1ppm), the 1 ppm positive bias increases the uptake by
about 0.24 GtC in June, July and August. This implies that without the
constraint from the mean concentration across the whole European region, the
inversion system is free to interpret the higher concentrations over the
small south-west strip as the signal of more uptakes over other larger parts
of Europe. As a result, the annual uptake changes from an underestimation of
35 % by INV_REG_BC to an overestimation of 15 % by
INV_REG_BC_1ppm (0.65 GtC a-1) (Table A2).
The same as Table A1 but for Observation System Simulation
Experiments, where we assimilate synthetic ACOS XCO2 from model
simulations forced by the assumed “true” fluxes.
NameDescriptionFlux (GtC a-1)Uncertainty (GtC a-1)INV_REG_ENKFSynthetic ACOS data over Europe are assimilated to infer monthly fluxes over 12 European sub-regions, which prior estimates are assumed to be same as INV_ACOS (i.e., CASA model). Here we assume the true fluxes be a posteriori of INV_TCCON inversion.-0.550.22INV_REG_BCThe same as INV_REG_ENKF, but estimates for monthly bias are included as additional parameters.-0.380.25INV_REG_ENKF_1ppmThe same as INV_REG_ENKF, but 1 ppm bias is added to the synthetic observations over a strip at south-west Europe for 3 months from June to August in 2010.-0.540.22INV_REG_BC_1ppmThe same as INV_REG_BC, 1 ppm bias is added to the synthetic observations over a strip at south-west Europe for 3 months from June to August in 2010.-0.650.25INV_REG_ENKF_SPThe same as INV_REG_ENKF, but the “true fluxes” over the first 3 of the 12 European sub-regions are replaced by CASA model values.-0.470.22INV_REG_BC_SPThe same as INV_REG_ENKF_SP, but with on-line bias correction with assumed prior uncertainty of 100 ppm.-0.370.25
In summary, our quasi-regional inversion experiments highlight the
sensitivity of regional flux inversions to the accurate description of the
boundary conditions around the domain. Using an on-line bias correction can
be helpful when the bias has been properly characterized. Over-correcting the
bias can weaken the observation constraints, and possibly increase
sensitivity to other small-scale unknown biases. We have also tested bias
correction schemes using a different inversion algorithm (the Maximum A
Posteriori (MAP) approach, Fraser et al., 2014), and found similar
deficiencies when the a priori uncertainty of the regional observation bias
is assumed to be very large. Our studies cannot prove or disprove Reuter et
al. (2014), but it does highlight previously unrecognized limitation to the
approach. The diversity of results reached under different assumptions
associated with observation biases and emission spatial patterns highlight
the importance of investigating the interaction between
observation and the inversion system for achieving consistent flux estimates
in the future from assimilation of the up-coming observations from OCO-2
satellite as well as from the improved in situ networks.
Additivity of the increased European uptake estimates
In the framework of Kalman Filter data assimilation (Feng et al., 2009),
posterior flux estimates are determined by
fa=ff+Kyobs-Hff,
where ff, fa are the prior and posterior
estimates of monthly regional surface CO2 fluxes, respectively;
yobs represents the GOSAT (real or simulated)
XCO2 retrievals. H is the observation operator for relating
the surface fluxes to the observed GOSAT XCO2, which includes
complicated atmospheric transporting as well as convolving of co-located
model profiles with GOSAT averaging kernels (Feng et al., 2009; Chevallier et
al., 2010). Here, the Kalman gain matrix K is given by
K=BHT[HBHT+R]-1,
where B is the a priori flux error covariance, R is the
observation error covariance, and H is the Jacobian defined by
H=∂H(ff)∂ff.
Although the atmospheric transport is non-linear, the dependence of model
concentrations (such as the column mixing ratios XCO2) on the
surface fluxes is nearly linear if we do not take into account any feedback
of varying CO2 concentrations on atmospheric dynamics (for example,
Chevallier et al., 2010; Baker et al., 2006). As a result, the gain matrix is
eventually independent of actual observation values, but will still be
affected by the location and uncertainty of observations.
As described in the main text, we split the actual (or simulated)
XCO2 observations into two parts: Part A for observations within
Europe; and Part B for observations outside Europe. For the GOSAT inversions
(such as INV_ACOS), we denote the observation vector as
yobs=gAgB.
The corresponding posterior flux estimate is given as
fga=ff+KgAgB-Hff.
In experiment INV_MOD_ALL, we replace the retrieved XCO2
values by the reference model simulation (from INV_TCCON), so that the
observation vector becomes
yobs=mAmB,
and the resulting flux estimates are:
fma=ff+KmAmB-Hff.
The gain matrix in Eq. (B7) is the same as Eq. (B5). Similarly, for
INV_MOD_ONLYEU where GOSAT XCO2 retrievals over Europe are
replaced by model simulations, we have
fmga=ff+KmAgB-Hff.
And for INV_MOD_NOEU where GOSAT XCO2 retrievals outside
Europe are replaced by model simulations, we have
fgma=ff+KgAmB-Hff.
From Eqs. (B5), (B7), (B8), and (B9), we can directly obtain
fga-fma=fmga-fma+fgma-fma.
Equation (B10) demonstrates that elevated European uptake is the sum of the
individual contributions from INV_MOD_NOEU and INV_MOD_ONLYEU. As
discussed in Sect. 3, such additivity has also been found in our inversion
results (Table 1), despite approximations in numerically solving posterior
fluxes (Feng et al., 2009).
Regional and sub-regional systematic errors inferred in joint data
assimilation
In the joint data assimilation, we attempt to estimate and remove systematic
errors at the regional and sub-regional scales from GOSAT XCO2
retrievals. The assimilated XCO2 retrieval can be described as
yc=y-biasm,i,
where y represents GOSAT retrievals before the (extra) bias correction, and
yc is the bias-corrected XCO2 data that we assimilate
in our joint data assimilation experiments. For simplicity, we have assumed
the regional (sub-regional) bias, biasm,i is a
function only of month (m) and geographical region (i).
In the joint data assimilation experiments, we consider bias(m,i) as
part of the state vector that we infer from assimilating in situ and
satellite observations. Figure C1 shows the resulting bias (in ppm) for
March 2010. Like other model and GOSAT inter-comparisons (see for example,
Lindqvist et al., 2015), our results demonstrate a strong spatial dependence
of the derived systematic errors. As discussed in Sect. 4, our results
reflect the mean differences between the inversion system and
XCO2 retrievals at (sub) regional scales, which does not
necessarily suggest that the GOSAT XCO2 bias (as well as the
coverage) within these (sub-) regions is homogeneous.
Inferred regional bias (in ppm) for March 2010 over TransCom regions
and two European (West and North) sub-regions.
L. Feng and P. I. Palmer designed the experiments and wrote the paper,
R. J. Parker provided the GOSAT XCO2 data and comments on the
paper, and N. M. Deutscher, D. G. Feist, R. Kivi, I. Morino, and R. Sussmann
provided access to TCCON XCO2 data and comments on the
paper.
Acknowledgements
Work at the University of Edinburgh was partly funded by the NERC National
Centre for Earth Observation (NCEO). P. I. Palmer gratefully acknowledges
funding from the NCEO and his Royal Society Wolfson Research Merit Award.
Work at the University of Leicester was funded by NCEO and the European Space
Agency Climate Change Initiative (ESA-CCI). The TCCON Network is supported by
NASA's Carbon Cycle Science Program through a grant to the California
Institute of Technology. The TCCON stations from Bialystok, Orleans and
Bremen are supported by the EU projects InGOS and ICOS-INWIRE, and by the
Senate of Bremen. TCCON measurements at Eureka were made by the Canadian
Network for Detection of Atmospheric Composition Change (CANDAC) with
additional support from the Canadian Space Agency. The authors thank the NASA
JPL ACOS team for providing their XCO2 retrievals. We also thank
the CONTRAIL and HIPPO team for their observations used in our validations.
We thank G. J. Collatz and S. R. Kawa for providing NASA Carbon Monitoring
System Land Surface Carbon Flux Products:
http://nacp-files.nacarbon.org/nacp-kawa-01/. We are grateful to
Hartmut Bösch, Chris O'Dell, and Thorsten Warneke for their helpful
comments on the manuscript. Edited by:
M. Heimann
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