Summary of the Final Report of Research Results1) Title of the proposed research
Estimation of CO2 and CH4 surface fluxes
2) Principal Investigator (PI) and Co-Investigators (Co-Is)
PI:
Dr. Frédéric Chevallier
Co-I:

Dr. Bousquet
Dr. Bréon
Dr. Ciais
3) PI’s affiliation
Laboratoire des Sciences du Climat et de l'Environnement (LSCE)
4) Summary of the Final Report of Research Results
In order to highlight the internal logic of this research within the RA period, papers quoted here are
only those that this proposal lead. Obviously, other papers contributed to the domain and many ofthem are quoted within the proposal papers.
Over the course of this research, we witnessed dramatic improvements brought by XCH4 retrieval
retrievals for flux inversions. We initially showed that their large-scale constraint on the inversion
problem could yield flux maps that are statistically consistent with those inferred from the surface
network, in contrast to SCIAMACHY at the end of its lifetime (Cressot et al. 2014). However, wefound then that the coarse resolution of the transport model used in these results actually masked apoor representation of the stratosphere–troposphere methane gradient in that version of the model,that compensated a large bias in GOSAT CH4 satellite data: improving the stratosphere–troposphere
gradient with a thinner model resolution made surface and satellite inversions inconsistent again andan appropriate bias-correction is needed. At small scale, most methane signals, like those from
Siberian wetlands, may be too small to be well seen by the column retrievals (Berchet et al. 2015).
For CO2, small uncertainties in aerosols, cirrus cloud or surface albedo were known for long toheavily affect the quality of the XCO2 satellite retrievals and to propagate into biases in the fluxes
inverted from them, even when the parasite signal in XCO2 is sub-ppm. Similarly, errors in transportmodelling can also flaw the inversions (Chevallier et al. 2010). Together with the University ofEdinburgh, we built an ensemble of six satellite-based global inversions for year 2010, from threeatmospheric transport models, two independent Bayesian inference algorithms and two versions ofbias-corrected CO2 column GOSAT retrievals. This ensemble showed hemispheric and regional
differences in posterior flux estimates that were beyond theoretical 1 sigma uncertainties and insome regions were judged unrealistic. Given we what was known about the sensitivity of XCO2
inversion to small errors in modelling or retrievals, the spread of the results was not really surprising.
What was confounding is the apparent biases of the ensemble, with, e.g., an excessive sink incontinental Europe and excessive emissions in Northern Africa. In order to understand the cause of
these apparent biases, we analyzed both the data flow and the model-minus-retrieval misfits indetails.
From the theory, we showed that a usual two-step approach to infer the surface fluxes from theGOSAT measured radiances, with CO2 retrievals as an intermediate product, cannot be optimal(Chevallier 2015). This suboptimality corrupts the 4D information flow from the radiancemeasurements to the surface flux estimates. It is amplified by the current retrieval strategy whereprior errors are much larger (by an order of magnitude in terms of variances) than the performance ofprior CO2 simulations used in atmospheric inversions. Indeed, the use of averaging kernels makes
atmospheric inversion insensitive to the choice of a particular retrieval prior CO2 profile if retrievals
are assimilated without any bias correction, but it does not make the retrieval prior error statistics
disappear from the inverse modelling equations. The current strategy likely generates retrieval
averaging kernels that are inappropriate for atmospheric inversions in their default configurations,with a wrong vertical distribution and an excessive weight towards the measured radiances.
Paradoxically, empirical bias correction of the retrievals also contributes to the degradation of the 4D
information flow, because it carries the imprint of the retrieval prior and of the retrieval prior error
statistics. Direct assimilation of the measured radiances would solve the inconsistency, but would
increase the computational burden of atmospheric inversions by several orders of magnitude.
Alternatively, we could adapt the inversion systems to the current retrieval configuration by using
minimal prior information about the surface fluxes, typically a flat prior flux field, but the result
would still over-fit the measured radiances due to the absence of other (compensating) information.
We then compared NASA’s ACOS-GOSAT retrievals b3.4 with a transport model simulation
constrained by surface air-sample measurements in order to find some evidence of retrieval
sub-optimality. Flaws in this transport model and in these inverted surface fluxes necessarily flaw
the simulation in many places over the globe and at various times of the year. We therefore carefully
selected some of the relatively large discontinuities in the XCO2 fields that the simulation unlikely
generated. We found some evidence of retrieval systematic errors over the dark surfaces of thehigh-latitude lands and over African savannahs. We also found some evidence that the high-gain
retrievals over land systematically over-fit the measured radiances, as a consequence of the prioruncertainty overestimation and of an underestimation of the observation uncertainty (as seen by theunderlying radiative transfer model). This over-fit was partially compensated by the bias correction.
An empirical test indicated that halving the retrieval increments without any posterior bias correctionactually cancels the dependency of the statistics of the observation-minus-model misfits to theincrement size and makes the standard deviation systematically better than for the retrieval prior.
Given the diversity of existing satellite retrieval algorithms, our conclusions cannot be easily
extrapolated to other GOSAT retrieval products and even less to XCO2 retrievals from other
instruments, but we note that such mistuning like the one highlighted here is common practice, bothbecause the errors of the retrieval forward model are difficult to characterize and because satellite
retrievals are usually explicitly designed to maximize the observation contribution, at the risk ofover-fitting radiance and forward model noise. A primary consequence of this mistuning is the usual
underestimation of retrieval errors. More importantly, our results show that the mistuning generates
excessive (unphysical) space-time variations of the retrievals up to ~1%. This noise level would not
matter for short-lived species, but for CO2 it is enough to significantly degrade the assimilation of
the retrievals for flux inversion and may explain some of the inconsistency seen between
GOSAT-based top-down results and bottom-up results for CO2 (Chevallier et al. 2014). Therefore,
with the current mistuning, we reiterate previous recommendations to take GOSAT-based CO2
inversion results particularly cautiously. But we also suggest that this situation may dramatically
improve by simply retuning the retrieval schemes.

5) List of publications relating to the proposed research
Berchet, A., I. Pison, F. Chevallier, J.-D. Paris, P. Bousquet, J.-L. Bonne1, M. Yu. Arshinov, B. D.
Belan, C. Cressot, D. K. Davydov, E. J. Dlugokencky, A. V. Fofonov, A. Galanin, J. Lavrič, T.
Machida, R. Parker, M. Sasakawa, R. Spahni, B. D. Stocker, and J. Winderlich, 2015: Natural andanthropogenic methane fluxes in Eurasia: a meso-scale quantification by generalized atmosphericinversion. Biogeosciences, accepted.
Chevallier, F., L. Feng, H. Boesch, P. Palmer, and P. Rayner, 2010: On the impact of transportmodel errors for the estimation of CO2 surface fluxes from GOSAT observations. Geophys. Res.
Lett., 37, L21803, doi:10.1029/2010GL044652
Chevallier, F., and C. W. O'Dell, 2013: Error statistics of Bayesian CO2 flux inversion schemes as
seen from GOSAT. Geophys. Res. Lett., 40, 1252–1256, doi:10.1002/grl.50228.
Chevallier, F., P. I. Palmer, L. Feng, H. Boesch, C. W. O'Dell and P. Bousquet, 2014: Towardrobust and consistent regional CO2 flux estimates from in situ and space-borne measurements ofatmospheric CO2. Geophys. Res. Lett., 41,1065–1070, doi:10.1002/2013GL058772
Chevallier, F., 2015: On the statistical optimality of CO2 atmospheric inversions assimilating CO2
column retrievals. Atmos. Chem. Phys. Discuss., 11889-11923, doi:10.5194/acpd-15-11889-2015
Cressot, C., F. Chevallier, B. Bousquet, C. Crevoisier, E. J. Dlugokencky, A. Fortems-Cheiney, C.

Frankenberg, R. Parker, I. Pison, R. A. Scheepmaker, S. A. Montzka, P. B. Krummel, L. P. Steele,
and R. L. Langenfelds, 2014: On the consistency between global and regional methane emissions
inferred from SCIAMACHY, TANSO-FTS, IASI and surface measurements, Atmos. Chem. Phys.,
14, 577-592, doi:10.5194/acp-14-577-2014
Locatelli, R., P. Bousquet, M. Saunois, F. Chevallier, and C. Cressot, 2015: Sensitivity of therecent methane budget to LMDz sub-grid scale physical parameterizations. Atmos. Chem. Phys., 15,9765-9780, doi:10.5194/acp-15-9765-2015