Estimates of the natural CO

Observed atmospheric variations of carbon dioxide (CO

The Greenhouse gases Observing SATellite (GOSAT), a space-borne mission
launched in a sun-synchronous orbit in early 2009, was purposefully designed
to measure CO

Several independent studies have shown that regional flux distributions
inferred from GOSAT X

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
X

We use the GEOS-Chem global chemistry transport model to relate surface
fluxes to the observed variations of atmospheric CO

The magnitude and uncertainty of the European annual CO

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 CO

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 X

Monthly a posteriori estimates (GtC) for European
biospheric CO

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

For the two control GOSAT inversions (Fig. 1), we use two independent data
sets: (1) X

As a performance indicator for our ability to fit fluxes to observed
X

HIPPO-3 and GEOS-Chem model atmospheric CO

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

Figure 2 shows that INV_TCCON a posteriori CO

Monthly mean observed and model a posteriori model CO

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 CO

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 X

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 X

Second, we crudely demonstrate how regional bias could explain the remaining
discrepancy of up to 0.30 GtC a

Here we demonstrate a simple approach to quantify systematic bias in
X

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

Figure 5 shows the estimated monthly biases in ACOS and UOL X

Estimates of monthly CO

We used an ensemble Kalman Filter to infer regional CO

We showed using sensitivity experiments that a large portion (60–90 %)
of the elevated European uptake of CO

We also showed using sensitivity tests that sub-ppm bias can explain the
remaining 0.30 GtC a

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
X

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 X

Our joint data assimilation approach assimilates in situ and space-borne
observations. It also provides estimates of systematic differences between
X

To further study the contributions from X

The same as Table 1 but for quasi-regional inversions where only
ACOS X

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

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 CO

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 CO

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 X

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

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

The same as Table A1 but for Observation System Simulation
Experiments, where we assimilate synthetic ACOS X

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.

In the framework of Kalman Filter data assimilation (Feng et al., 2009),
posterior flux estimates are determined by

As described in the main text, we split the actual (or simulated)
X

In the joint data assimilation, we attempt to estimate and remove systematic
errors at the regional and sub-regional scales from GOSAT X

In the joint data assimilation experiments, we consider

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 X

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 X